Predicting and Storing Unutilised Irrigation Orders for Environmental Benefit
Matthew James Berrisford
Submitted in total fulfillment of the requirements of the degree of Doctor of Philosophy
May 2008
Department of Civil and Environmental Engineering The University of Melbourne Abstract
Regulation of river systems and the development of irrigated agriculture have contributed to a shift in the seasonality of flow of a large number of river systems. The River Murray, Australia is one such river. The well documented side of a change in seasonality of flow is the decreased incidence of winter and spring flood events; a lesser known impact and a focus of this research is the increased incidence of summer flooding.
One location where the incidence of increased summer flooding is of great importance is the Barmah-Millewa Forest (B-MF) which surrounds the Barmah Choke on the River Murray. The B-MF is located a small distance downstream of Yarrawonga Weir, a major irrigation diversion point for Murray Irrigation Limited and Goulburn-Murray Water. Yarrawonga Weir is located four days water travel time downstream of Lake Hume. Lake Hume is the storage point for these two irrigation authorities.
The process for irrigators to receive water in these two districts is to place an order, water is then released from storage and they receive water four days after the placement of the order. If there is rainfall during the four day lag irrigators can reject or cancel their order; this type of event has previously been termed a ‘rain rejection event’. Rejected water remains in the River Murray continuing to flow downstream, potentially causing unseasonal flooding of the B-MF. Due to the lack of available data on rejection, a substitute data set referred to as unutilised irrigation orders (UIO) has been used throughout this research.
This research firstly seeks to qualify and quantify the link between UIO and rainfall and then between UIO and unseasonal flooding of the B-MF. The research then moves to investigate storing UIO in on-farm or en-route water storages to reduce the incidence and occurrence of unseasonal flooding of the B-MF, using part of the Murray Irrigation Limited system as a study area. The storage assessment is based on the storages ability to capture and reuse UIO.
To undertake the above investigation irrigators were initially surveyed, via an interview questionnaire regarding their order and rejection behaviour. Following this, Multiple
i Linear Regression analysis was used to explore a link between UIO and rainfall and between UIO and unseasonal flood events of the B-MF. To investigate different storage options for UIO the model Options AnalysiS in Irrigation Systems (OASIS) was extended to include a process for placing an order and rejection.
The major findings of this research are that unseasonal flooding of the B-MF can be linked to UIO but there are other more significant factors. UIO were found to be linked to the previous day’s UIO, rainfall and the order volume placed four days prior. From the scenario assessment little difference was found between the performance of on-farm water storages and en-route water storages. Thus, factors other than the ability to capture rejected water and ability to reuse this water for irrigation need to be considered by the irrigation authority when selecting a method for capturing and storing UIO’s. These factors could include other management and operational factors.
ii Declaration
This is to certify that i. the thesis comprises only my original work towards the PhD, ii. due acknowledgement has been made in the text to all other materials used, iii. the thesis is less than 100,000 words in length, exclusive of tables, maps, bibliographies and appendices.
iii Acknowledgements
Throughout this work I have received support from numerous people and organisations. Firstly, I would like to thank Nicolas Roost for volunteering his time and energy to assist with developments and alterations made to the OASIS model. His assistance and enthusiasm with this work was unexpected, invaluable and greatly appreciated.
I have also received considerable support, feedback and guidance from my supervisors Hector Malano and Robert Argent. The staff at Murray Irrigation Limited in particular David Watts have provided invaluable assistance on the operation of the Murray Irrigation supply system.
The Co-operative Research Centre for Irrigation Futures (CRCIF) who provided a scholarship, operating budget and a forum to link with both industry and other researchers.
I would also like to thank my colleagues, family and friends for their friendship and support away from study.
iv Table of Contents
ABSTRACT...... I
TABLE OF CONTENTS ...... V
LIST OF FIGURES...... IX
LIST OF TABLES...... XI
LIST OF TABLES...... XI
LIST OF ACRONYMS AND ABBREVIATIONS ...... XIII
1 INTRODUCTION...... 1
1.1 THE MURRAY DARLING BASIN ...... 1 1.2 MURRAY IRRIGATION LIMITED ...... 4 1.3 IRRIGATION ORDERS ...... 5 1.4 REJECTIONS...... 5 1.5 FLOW CHANGES AND ECOLOGICAL CONSEQUENCES...... 6 1.6 THE RIVER MURRAY ...... 7 1.7 THE BARMAH-MILLEWA FOREST...... 9 1.8 WATER STORAGES...... 11 1.9 RESEARCH QUESTIONS...... 12 1.10 RESEARCH OBJECTIVES ...... 12 1.11 THESIS OUTLINE ...... 12 1.12 ORIGINAL CONTRIBUTIONS ...... 14
2 THE MURRAY IRRIGATION SYSTEM...... 15
2.1 MURRAY IRRIGATION LIMITED ...... 15 2.1.1 AGRICULTURAL LAND USE ...... 16 2.2 CLIMATE DATA...... 19 2.2.1 WATER ALLOCATION...... 21 2.3 OPERATION OF THE MURRAY IRRIGATION SYSTEM ...... 23 2.4 THE STUDY AREA...... 26 2.4.1 CALCULATION OF UIO ...... 28
3 UNUTILISED IRRIGATION ORDERS AND UNSEASONAL FLOODING OF THE BARMAH-MILLEWA FOREST...... 33
3.1 SURVEY QUESTIONNAIRE ...... 35 3.1.1 MIA RESULTS ...... 36 3.1.2 GENERAL RESULTS ...... 37 3.1.3 NON-RICE CROPS RESULTS ...... 40 3.1.4 RICE CROP RESULTS...... 43
v 3.2 REGRESSION ANALYSIS ...... 45 3.3 MULTIPLE LINEAR REGRESSION ...... 47 3.3.1 IDENTIFYING THE BEST MLR MODEL ...... 48 3.4 MLR INVESTIGATION OF FLOWS AT TOCUMWAL...... 50 3.4.1 CALIBRATION AND VALIDATION OF THE MLR MODELS...... 52 3.4.2 MLR MODEL INVESTIGATION...... 54 3.4.3 IDENTIFYING THE BEST MLR MODEL FOR FLOWS AT TOCUMWAL ...... 57 3.5 MLR INVESTIGATION OF UIO ...... 59 3.5.1 MODEL ADEQUACY CHECKS FOR THE BEST MLR MODEL FOR UIO...... 65 3.6 CONCLUSIONS ...... 69
4 IRRIGATION SYSTEM MODELS ...... 71
4.1 MODEL CAPABILITIES FOR THIS RESEARCH ...... 72 4.2 A REVIEW OF IRRIGATION SIMULATION MODELS...... 75 4.2.1 IRRIGATION MAIN SYSTEM OPERATION (IMSOP)...... 75 4.2.2 IRRIGATION NETWORK CONTROL AND ANALYSIS (INCA)...... 76 4.2.3 COMMAND AREA DECISION SUPPORT MODEL (CADSM) ...... 77 4.2.4 SINGH ET AL. (1997) ...... 77 4.2.5 OPTIONS ANALYSIS IN IRRIGATION SYSTEMS (OASIS) ...... 78 4.2.6 TIDDALIK...... 79 4.2.7 MODEL COMPARISON ...... 80 4.3 THE OASIS MODEL...... 82 4.3.1 REPRESENTATION OF THE IRRIGATION SYSTEM ...... 82 4.3.2 REPRESENTATION OF EFFICIENCIES IN OASIS ...... 83 4.3.3 IRRIGATION INFLOWS ...... 84 4.3.4 REPRESENTATION OF STORAGES ...... 85 4.3.5 REPRESENTING FIELDS ...... 87 4.3.6 IRRIGATION SCHEDULE FILES ...... 89 4.3.7 FIELD LEVEL SOIL MOISTURE BALANCE ...... 89 4.3.8 INITIAL CONDITIONS ...... 91 4.4 PREVIOUS APPLICATIONS OF OASIS AND REQUIRED ADAPTATIONS...... 91 4.5 CONCLUSIONS ...... 93
5 APPLICATION OF OASIS TO THE STUDY AREA...... 94
5.1 MODIFICATIONS TO OASIS...... 94 5.1.1 CONVERSION TO A DAILY TIME STEP...... 95 5.1.2 UPDATING OF THE IRRIGATION SCHEDULE FILE ...... 95 5.1.3 CROP DEMAND DRIVEN HEAD INFLOW ...... 96 5.1.4 ORDER AND REJECTION MODULE ...... 96 5.1.5 ORDER DECISION PROCESS ...... 97 5.1.6 REJECTION DECISION PROCESS ...... 101 5.1.7 MLR RESULTS FOR REJECTIONS ...... 104 5.1.8 RESERVOIR MANAGEMENT...... 104 5.1.9 INCLUSION OF REJECTIONS INTO SYSTEM INFLOW ...... 105 5.2 REPRESENTING THE STUDY AREA IN OASIS...... 106 5.2.1 BREAK UP OF IRRIGATION UNITS ...... 106 5.2.2 BREAK UP OF SEGMENTS ...... 108 5.2.3 SUPPLY LINKS...... 111 5.2.4 BREAK UP OF FIELDS...... 111 5.2.5 AGRICULTURAL LAND USE INFORMATION...... 113 5.2.6 IRRIGATION SCHEDULE FILES ...... 115
vi 5.3 CONVEYANCE EFFICIENCY ...... 116 5.3.1 CALIBRATION OF CONVEYANCE EFFICIENCY ...... 119 5.3.2 APPLICATION EFFICIENCY ...... 121 5.4 SENSITIVITY ANALYSIS...... 123 5.4.1 METHOD OF SENSITIVITY ANALYSIS...... 126 5.4.2 SENSITIVITY ANALYSIS RESULTS...... 130 5.4.3 CONCLUSIONS FROM SENSITIVITY ANALYSIS...... 134 5.5 CALIBRATION AT THE SYSTEM SCALE ...... 135 5.5.1 CALIBRATION METHOD...... 135 5.5.2 CALIBRATION FOR SEASON 2000/01...... 138 5.5.3 INFORMATION FOR CROP SCALE PERFORMANCE ASSESSMENT...... 146 5.5.4 PERFORMANCE OF OASIS AT THE CROP LEVEL ...... 148 5.5.5 PERFORMANCE OF OASIS AT THE IU LEVEL...... 151 5.5.6 SUMMARY OF IU MODEL PERFORMANCE ...... 155 5.6 CALIBRATION OF THE MODEL FOR SEASONS OTHER THAN 2000/01...... 155 5.7 PERFORMANCE OF THE ORDER AND REJECTION MODULE ...... 158 5.7.1 ESTABLISHING THE IRRIGATION ORDER MATRIX...... 159 5.7.2 ESTABLISHING THE REJECTION MATRIX ...... 161 5.7.3 ORDER SENSITIVITY ANALYSIS...... 163 5.7.4 CALIBRATION OF THE ORDER MATRIX...... 166 5.7.5 TRIGGER LEVEL FOR PLACING ORDERS ...... 167 5.7.6 BEST METHOD OF PLACING AN ORDER ...... 170 5.7.7 REJECTION MATRIX SENSITIVITY ANALYSIS...... 170 5.7.8 CALIBRATION OF THE REJECTION MATRIX ...... 171 5.8 VALIDATION OF THE MODEL ...... 172 5.9 CONCLUSIONS ...... 174
6 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT ...... 176
6.1 REVIEW OF LITERATURE...... 177 6.1.1 PREVIOUS STORAGE STUDIES IN THE MIA...... 177 6.1.2 OTHER FLOOD PREVENTION STUDIES ...... 182 6.1.3 ON-FARM WATER STORAGES ...... 183 6.1.4 EN-ROUTE STORAGES ...... 185 6.2 EN-ROUTE WATER STORAGE ASSESSMENT...... 186 6.2.1 STORAGE DESIGN...... 187 6.2.2 EVAPORATION CO-EFFICIENT ...... 187 6.2.3 SEEPAGE ...... 188 6.2.4 EN-ROUTE STORAGE LOCATIONS...... 188 6.2.5 INFLOW AND OUTFLOW OPERATING RULES OPTIONS ...... 190 6.3 THE ON-FARM WATER STORAGE ASSESSMENT...... 191 6.3.1 STORAGE DESIGN...... 191 6.3.2 SPLITTING THE STORAGE VOLUME BETWEEN IUS ...... 192 6.3.3 SEEPAGE ...... 193 6.3.4 INFLOW AND OUTFLOW RATES ...... 193 6.4 SCENARIOS FOR ASSESSMENT ...... 193 6.4.1 CLIMATE DATA ...... 194 6.4.2 TARGET WATER DELIVERIES...... 195 6.4.3 SYNTHETIC CROP AREAS...... 197 6.4.4 CALIBRATION OF CROP WATER USE FOR THE SYNTHETIC CROP AREAS...... 201 6.4.5 BULK WATER TRANSPORT AROUND THE BARMAH CHOKE ...... 203 6.4.6 EFFECT OF MODEL UNCERTAINTIES ON THE OPTIONS ASSESSMENT...... 204 6.5 RESULTS – BASE CASE (NO STORAGE)...... 204 6.5.1 VERIFICATION OF THE CROPPING PATTERNS ...... 204
vii 6.5.2 STORAGE VOLUME FOR BASE CASE RESULTS ...... 208 6.5.3 SELECTION OF A STORAGE VOLUME ...... 216 6.6 STORAGE RESULTS ...... 219 6.6.1 STORAGE INFLOWS ...... 220 6.6.2 DESTINATION OF CAPTURED WATER ...... 220 6.6.3 PERCENTAGE OF REJECTIONS CAPTURED ...... 222 6.6.4 PERCENTAGE OF CAPTURED WATER REUSED ...... 224 6.6.5 PERCENTAGE OF REJECTIONS REUSED...... 225 6.7 IMPACT OF STORAGES ON FLOODING OF THE B-MF ...... 226 6.8 CONCLUSIONS ...... 228
7 CONCLUSIONS AND RECOMMENDATIONS FOR FURTHER RESEARCH ....230
7.1 ORDER AND REJECTION BEHAVIOUR...... 230 7.2 UNSEASONAL FLOODING OF THE B-MF AND UIO ...... 231 7.2.1 CAUSES OF UIO IN THE MIA...... 231 7.2.2 CAUSES OF UNSEASONAL FLOODING OF THE B-MF ...... 232 7.3 DEVELOPMENT OF OASIS...... 233 7.4 STORAGE ASSESSMENT ...... 234 7.5 RECOMMENDATIONS FOR FURTHER RESEARCH ...... 235
8 LIST OF REFERENCES ...... 237
APPENDIX A...... 245
APPENDIX B...... 264
APPENDIX C...... 288
APPENDIX D...... 322
APPENDIX E...... 332
viii List of Figures
Figure 1.1: Murray Darling Basin (adapted from www.mdbc.gov.au)...... 1 Figure 1.2: Study Area ...... 4 Figure 1.3: River Murray System (Adapted from http://www.mdbc.gov.au/subs/annual_reports/AR_2003- 04/images/ecological_assets_map.gif)...... 7 Figure 2.1: Irrigation districts in the MIA...... 15 Figure 2.2: Location of rainfall information ...... 19 Figure 2.3: MIL Escape locations ...... 26 Figure 2.4: Study Area ...... 27 Figure 2.5: Available data...... 29 Figure 2.6: Nett Mulwala Canal diversion versus nett study area diversion for season 2000/01...... 31 Figure 3.1: Cropping pattern of respondents in the study area ...... 37 Figure 3.2: Changes in rice area with reduced allocation...... 38 Figure 3.3: Changes in irrigated cereal area with reduced allocation...... 38 Figure 3.4: Changes in winter irrigated pasture area with reduced allocation...... 39 Figure 3.5: Diagram of the River Murray supply to MIL and study area with lag times ...... 50 Figure 3.6: Residual versus MLR predicted flow for validation of model A ...... 55 Figure 3.7: Plot of residual against the explanatory variable Ot-2 for the validation of model A...... 55 Figure 3.8: MLR predicted flow at Tocumwal against observed flow at Tocumwal for seasons 1999/00 and 2000/01 ...... 58 Figure 3.9: MLR predicted flow at Tocumwal against observed flow at Tocumwal for season 2001/02 and 2003/04 ...... 58 Figure 3.10: MLR predicted UIO against observed UIO for seasons 1999/00 and 2000/01...... 65 Figure 3.11: MLR predicted UIO against observed UIO for seasons 2001/02 and 2003/04...... 66 Figure 3.12: Test for Heteroscedasticity ...... 67 Figure 3.13: MLR calibration result using rainfall and orders...... 68 Figure 3.14: MLR validation result using rainfall and orders...... 68 Figure 4.1: Connectivity of parameters in OASIS (Roost 2002 page 19) ...... 82 Figure 4.2: Three level model structure ...... 83 Figure 4.3: Management of OFWS in OASIS ...... 85 Figure 4.4: Example soil and crop GIS layer in IU 1...... 88 Figure 4.5: Two layer soil representation in OASIS (Roost 2002 page 25) ...... 90 Figure 4.6: Reservoir representation of the soil profile (Roost 2002 page 25)...... 91 Figure 5.1: Order matrix for winter irrigated pasture ...... 98 Figure 5.2: Order decision process ...... 100 Figure 5.3: Soil moisture trigger for orders for winter irrigated pasture ...... 101 Figure 5.4: Rejection decision process ...... 102 Figure 5.5: Rejection Matrix for Winter Irrigated Pasture...... 103 Figure 5.6: Allocation algorithm steps...... 105 Figure 5.7: Break up of study area into IU ...... 107 Figure 5.8: Regulators and secondary canals in the study area...... 109 Figure 5.9: Study area represented in OASIS ...... 110
ix Figure 5.11: System efficiency versus nett Mulwala Canal diversion...... 118 Figure 5.12: Annual system efficiency versus nett Mulwala Canal diversion...... 119 Figure 5.13: Validation of overall conveyance efficiency...... 120 Figure 5.14: Correlation between simulated overall conveyance efficiency and study area efficiency (monthly) ...... 121 Figure 5.15: Sensitivity Index for parameters tested in the sensitivity analysis ...... 132 Figure 5.16: Calibration results for level of percent irrigation of lucerne/summer pasture ...... 139 Figure 5.17: Percent irrigation of IP in calibration period 1...... 141 Figure 5.18: ToTStep IC in calibration period 1 ...... 141 Figure 5.19: Time step winter irrigated pasture returns to growing ...... 143 Figure 5.20: Calibration of FromTStep Ipe for period 3...... 143 Figure 5.21: Percentage area of winter pasture irrigated ...... 144 Figure 5.22: Calibrated model performance for season 2000/01...... 145 Figure 5.23: Simulated versus recorded irrigation inflow into the system for season 2000/01...... 145 Figure 5.24: OASIS supply versus recorded supply for IU 1 ...... 151 Figure 5.25: OASIS supply versus recorded supply for IU 6 ...... 152 Figure 5.26: OASIS supply versus recorded supply for IU 11 ...... 153 Figure 5.27: OASIS supply versus recorded supply for IU 14 ...... 154 Figure 5.28: OASIS supply versus recorded supply for IU 21 ...... 155 Figure 5.29: Risk averse order matrix...... 159 Figure 5.30: Risk tolerant order matrix...... 160 Figure 5.31: Risk averse rejection matrix ...... 162 Figure 5.32: Risk tolerant rejection matrix ...... 162 Figure 5.33: Order matrix sensitivity index ...... 165 Figure 5.34: Calibrated orders ...... 168 Figure 5.35: Hydrograph of orders using a soil moisture trigger level...... 169 Figure 5.36: Rejection matrix sensitivity index...... 171 Figure 5.37: Performance of the rejection matrix ...... 172 Figure 5.38: Validation of order model performance (seasons 1999/00 and 2001/02)173 Figure 5.39: Residuals from model validation (seasons 1999/00 and 2001/02)...... 173 Figure 5.40: Residuals from rejection model of seasons 1999/00 and 2001/02 ...... 174 Figure 6.1: Single En-route Storage adapted from GHD (2006) ...... 189 Figure 6.2: Water deliveries versus final water allocation...... 196 Figure 6.3: Relationship between rice area and water allocation...... 199 Figure 6.4: Area of cereals against the area of rice and area of rice and lucerne/summer pasture ...... 200 Figure 6.5: Hydrograph of water deliveries to farms for December to April in season 2001/02...... 206 Figure 6.6: Deliveries for the period December to April versus actual data...... 207 Figure 6.7: Estimated season storage capacities with an unconstrained outflow ...... 211 Figure 6.8: Orders for the 4 cropping patterns with 2003/04 climate...... 212 Figure 6.9: Estimated season storage capacities with outflow constrained to 1,500 ML/day...... 214 Figure 6.10: Destination of captured water...... 221 Figure 6.11: Volume of water captured and reused for each storage measure for all scenarios...... 222 Figure 6.12: Percentage of rejections captured...... 224 Figure 6.13: Percentage of captured water reused ...... 224
x List of Tables
Table 2.1: Crop areas from landholder surveys ...... 17 Table 2.2: Areas sown to rice...... 17 Table 2.3: 2000/01 GIS land use information...... 17 Table 2.4: Irrigation intensities for the irrigation districts in the MIA ...... 18 Table 2.5: Daily rainfall data sites ...... 19 Table 2.6: Rainfall June to July for seasons 1998/99 to 2003/04 ...... 20 Table 2.7: Summer rainfall (December to April for seasons 1998/99 to 2003/04...... 21 Table 3.1: Calibration and validation seasons for each run ...... 52 Table 3.2: Ranking of explanatory variables for each run...... 56 Table 3.3: D-W statistic for each model (A to F) ...... 56 Table 3.4: Rank of validated models...... 57 Table 3.5: t, p and VIF values for the explanatory variables ...... 57 Table 3.6: MLR Models Trialed ...... 62 Table 3.7: t-value, p-value and VIF for the explanatory variables ...... 66 Table 3.8: D-W upper and lower limits for three response variables and 100 observations ...... 67 Table 4.1: Performance of models against the criteria...... 80 Table 4.2: Break up of fields...... 88 Table 5.1: IU properties ...... 107 Table 5.2: Land uses across the study area ...... 111 Table 5.3: Description of soil types ...... 113 Table 5.4: Land use comparison between GIS and landholder surveys for season 2000/01...... 113 Table 5.5: Equivalent land uses between Marshall (2004) and GIS...... 113 Table 5.6: Rice area comparison...... 114 Table 5.7: Monthly overall study area efficiencies...... 117 Table 5.8: System efficiency without season 2002/03...... 118 Table 5.9: Application loss variations for season 2000/01 ...... 122 Table 5.10: Distribution losses against application losses (percent of total seasonal losses)...... 122 Table 5.11: RMSE for season 1999/00 and 2000/01 ...... 123 Table 5.12: Transposing soil properties from Hornbuckle and Christen (1999) to the study area ...... 124 Table 5.13: Sources for crop coefficients and root depths...... 125 Table 5.14: Parameters with sensitivity analysis undertaken...... 131 Table 5.15: Crop irrigation periods (August 1st to December 3rd) ...... 139 Table 5.16: Crop water uses from literature...... 146 Table 5.17: Estimate of crop irrigation application rates using information from MIL ...... 147 Table 5.18: Crop irrigation depth from OASIS (mm) ...... 148 Table 5.19: Crop water balance in OASIS...... 149 Table 5.20: OASIS calibrated levels of crop area irrigated ...... 156 Table 5.21: Validation of crop water usages...... 156 Table 5.22: Results of the order matrix sensitivity analysis ...... 164 Table 5.23: Results of the order matrix calibration...... 167 Table 5.24: Soil moisture trigger levels ...... 170 Table 5.25: Results of the rejection matrix sensitivity analysis...... 170
xi Table 6.1: A review of previous studies into the use of an en-route storage at The Drop ...... 178 Table 6.1 (Continued): A review of previous studies into the use of an en-route storage at The Drop ...... 179 Table 6.2: Climate extremes ...... 195 Table 6.3: Chosen climate season rankings ...... 195 Table 6.4: Target study area deliveries ...... 197 Table 6.5: Land use areas for the study area...... 197 Table 6.6: OASIS calibrated levels of crop area irrigated ...... 198 Table 6.7: Synthetic crop areas and percent of area irrigated...... 203 Table 6.8: Storage capacity with an outflow capacity of 1,500 ML/day ...... 215 Table 6.9: Storage capacity with an unconstrained outflow ...... 215 Table 6.10: Total percentage of rejected water captured ...... 216 Table 6.11: Source of storage(s) inflows ...... 220 Table 6.12: Storage impacts on flooding of the B-MF ...... 227
xii List of Acronyms and Abbreviations
ACT Australian Capital Territory B-MF Barmah-Millewa Forest BID Bojili Irrigation District BIGMOD BIG MODel used by the Murray Darling Basin Commission BoM Bureau of Meteorology CADSM Command Area Decision Support Model CD Coefficient of Determination CIA Coleambally Irrigation Area CSIRO Commonwealth Scientific and Industrial Research Organisation D Mulwala Canal diversion (ML/day)
Dnett nett Mulwala Canal diversion (ML/day) D-W Durbin-Watson DNR Department of Natural Resources Ea Application efficiency in OASIS Ec Conveyance efficiency in OASIS Ed Distribution efficiency in OASIS ERE Edwards River Escape flow (ML/day) ET Evapotranspiration (mm)
ETo Potential Evapotranspiration (mm) FA Factor Analysis FAO Food and Agricultural Organisation of the United Nations FE Finley Escape flow (ML/day) FSL Full Supply Level GAM Generalised Additive Models GIS Graphical Information System GL Gigalitre G-MW Goulburn-Murray Water H the water depth in OFWS in OASIS (m) ha Hectare IC Irrigated Cereals IMSOP Irrigation Main System OPeration
xiii INCA Irrigation Network Canal and Analysis IU Irrigation Unit LS Lawsons Syphon flow (ML/day) LWMP Land and Water Management Plan LWS Local Water Sources
MCadv Orders for Mulwala Canal Irrigators (ML/day)
MCnett nett Mulwala Canal diversion (ML/day) MDB Murray Darling Basin MDBC Murray Darling Basin Commission MIA Murray Irrigation Area (area supplied by Murray Irrigation Limited) MIL Murray Irrigation Limited ML Megalitre MLR Multiple Linear Regression mm millimetre MORE Management Option Rank Equivalence MSM Monthly Simulation Model NSMC Non-Self Mulching Clay NSW New South Wales OASIS Options AnalysiS in Irrigation Systems OFWS On-Farm Water Storage OLS Ordinary Least Squares PCA Principal Components Analysis PRESS PRediction Error Sum of Squares RBE Red Brown Earth RBE-TRBE Red Brown Earth – Traditional Red Brown Earth REALM REal ALlocation Model RMSE Root Mean Square Error RMW River Murray Water SA South Australia
SAnett nett study area diversion of irrigation water (ML/day) SI Sensitivity Index SiC-SMC Silty Clay/Self Mulching Clay SMC Self Mulching Clay SS Sandhills Soils
xiv TAW Total Available Water (%) TRBE Traditional Red Brown Earth TRBE-NSMC Traditional Red Brown Earth – Non-Self Mulching Clay UIO Unutilised irrigation orders
UIOMC Unutilised irrigation orders for the area supplied by the Mulwala Canal (ML/day) V is the storage in OFWS in mm of depth for the area of the IU VIF Variance Inflation Factor WRE Wakool River Escape flow (ML/day) ZIS Zhanghe Irrigation System
xv
1 Introduction
1.1 The Murray Darling Basin
The Murray Darling Basin (MDB) (Figure 1.1) is Australia’s most prominent river basin, encompassing more than 14% of the Australian continent, an area of more than 1 million square kilometres (Global Energy and Water Cycle Experiment 2007). It includes areas in four states (New South Wales (NSW), Queensland, Victoria and South Australia (SA)) and one Territory (Australian Capital Territory (ACT)). Three key Australian rivers are located in the MDB; the River Murray, the Darling River and the Murrumbidgee River.
Figure 1.1: Murray Darling Basin (adapted from www.mdbc.gov.au)
1 INTRODUCTION
The natural annual runoff from the MDB is 23,850 Gigalitres (GL) but nearly 50% of this does not reach the mouth of the River Murray due to the processes of evaporation, seepage and transpiration. This results in a natural (prior to regulation) annual discharge of 12,890 GL/year for the River Murray (Australian Water Resources Council 1987). This is small for rivers of similar length and catchment area throughout the world.
The climate in the MDB consists of high evaporation and periodic and variable precipitation, with the average evaporation being twice the average precipitation across the basin (Cleugh 2007). The climate has important implications for the variability of flows in the River Murray, with Maheshwari et. al. (1995) reporting that between the period of 1894 and 1993 the annual discharge from the River Murray varied between 1,626 GL and 54,168 GL. Due to this variability, measures to regulate the River Murray for navigation of river vessels were set in place in the 1800’s. As the need for river navigation diminished, the community demanded increased security and volumes of water for irrigation, town water supply and recreation. Between the period 1988-89 and 1992-93, the average annual diversion of water from all rivers in the MDB was 10,684 GL. Ninety five percent of this was for irrigated agriculture (Murray-Darling Basin Commission 2006). This has seen the annual discharge of the river drop to around 27% of its original volume and regulation of the River Murray increase to its present level. Regulation of the River Murray and the development of agriculture has seen the MDB become one of Australia's most important agricultural regions, accounting for approximately 50% of the nation's gross value of agricultural production, based on 2000/2001 figures (Bryan and Barvanek 2004). Agriculture, and in particular irrigated agriculture, is the greatest livelihood provider for the 2 million people (approximately 11% of Australia’s population) who live in the MDB (Murray- Darling Basin Commission 2006).
Due to the length of the River Murray, the climate of the region, the catchment area and the considerable demand on the river, it was quickly realised that there was considerable need for an agreement between the three states of NSW, Victoria and SA that used the River Murray. This saw the establishment of the River Murray Commission and then in 1915 the first ‘River Murray Waters Agreement’ between the three state governments and the federal government was signed.
2 INTRODUCTION
Over the next 70 years there were numerous amendments to the River Murray Waters Agreement as community values changed. With the increasing demands on the River Murray Commission, the four governments began discussions in 1985 to develop a new arrangement. These discussions saw the establishment of the Murray Darling Basin Commission (MDBC) in 1987. The prime responsibilities of the MDBC are to: • manage the River Murray and the Menindee Lakes System of the lower Darling River; and • advise the Ministerial Council on matters related to the use of water, land and other environmental resources of the MDB.
In June 1993 the Ministerial Council had sufficient concerns about the sustainability of the increasing development of water uses (particularly irrigation) from the River Murray to launch an Audit of water use in the MDB. The Audit was completed in 1995. In short, the audit found that to prevent further river health problems and to continue to meet the needs of the present water users the River Murray could not sustain further extraction of its water resources.
The Audit’s findings prompted the Ministerial Council to take action on the level of water diversions from the River Murray. The result was what is informally known as ‘the Cap’. ‘The Cap’ was implemented from July 1, 1997 on diversions by NSW, Victoria and SA and from September 2000 for Queensland. In June 2007 discussions were still being held with the ACT (Murray-Darling Basin Commission 2007). The Cap set diversion limits for NSW and Victoria to “The volume of water that would have been diverted under 1993/94 levels of development.” (Murray-Darling Basin Commission 2004). The Cap does not constrain future development but any water used in future developments must be obtained from existing development or obtained by using water more efficiently.
To differentiate between the service responsibilities of the MDBC and its resource management and policy setting functions, an internal business unit (River Murray Water (RMW)) was established on January 1st 1998 for the purposes of bulk water delivery. These services include: • water storage and delivery; • salinity mitigation (operation of salinity mitigation schemes); • navigation;
3 INTRODUCTION
• recreation and tourism; and • others, including hydropower.
There are numerous irrigation districts in the MDB, some of the largest are Coleambally Irrigation Area CIA), Murrumbidgee Irrigation Area, the Murray Irrigation Area (MIA) and the area supplied by Goulburn-Murray Water (G-MW). This research will use part of the MIA as a study area.
1.2 Murray Irrigation Limited
Murray Irrigation Limited (MIL) is the largest private irrigation company in Australia, servicing the Riverine Plains area of NSW, Australia (Figure 1.2). The climate for the Riverine Plains area of NSW is classified as hot and semi arid (Stern, de Hoedt et al. 2000).
Edward River
Edward River Escape Study area
Mulwala Canal
Barmah- Millewa Tocumwal Forest River Murray Lake Hume
Yarrawonga Weir Ovens River
Kiewa River N Yarrawonga Main Canal
0 20 km
Figure 1.2: Study Area
The area contains both dryland and irrigated agriculture. The major irrigated crops in the area are rice, winter irrigated pasture, lucerne/summer pasture and cereals. Irrigation water for the area largely comes from Lake Hume and is delivered to the MIA by RMW. Lake Hume is located approximately four days water travel time upstream of Yarrawonga Weir. MIL is responsible for diverting the water from the River Murray and delivering it to the farm gate as requested by irrigators. Due to the
4 INTRODUCTION four day water travel time, irrigators are required to place orders four days in advance of their required delivery date.
1.3 Irrigation Orders
Across the world there are various irrigation system operation methods. The type of management regime imposed on the irrigation system has direct implications for the level of service the irrigators receive. The Murray Irrigation system is a highly regulated irrigation system with each individual irrigator placing an order for the flow rate of water they would like delivered.
When there are no supply bottle necks, two factors influence the delivery time of ordered water. The first is the travel time between the point of delivery and the head of the irrigation system, and the second is the travel time from the storage point to the head of the irrigation system. As stated above the travel time for water to enter the head of the Murray Irrigation system (Lake Hume to Yarrawonga Weir) is 4 days. Once water enters the head of the irrigation system it can be delivered to any property within 24 hours.
When placing an order 4-days in advance, an irrigator faces a great deal of uncertainty regarding the weather during the lag time. Presently medium term weather forecasts (3 to 5 days) can consist of a rainfall probability divided into increments (<5%, 10%, 20%, 30%, 50%) and a rainfall magnitude again broken into increments (<1mm, 1- 5mm, 5-10mm, 10-20mm, 20-40mm, etc). The forecasts lack accuracy, with 24 hour rainfall predictions having been reported as being ‘good’ at predicting the occurrence of rainfall rather than the location and magnitude of the peak values (McBride and Ebert 1999). If rainfall or other circumstances lead to irrigators no longer needing or wanting their order, MIL allows them to reject their order. These are referred to as rejections. At present orders are not debited from their account.
1.4 Rejections
The mechanisms for the generation of rejections were described above. A small volume of rejections are not a problem for an irrigation authority as these flows can be redistributed to other irrigators meeting their order slightly ahead of the 4-day lag
5 INTRODUCTION between order placement and water delivery. Rejections become a problem when they are a significant percentage (greater than 25%) of the order volume.
The major problem with rejections in the MIL system is that the rejected water remains in the River Murray flowing downstream to the Barmah Choke, often creating flows in excess of the bank full capacity, leading to unseasonal flooding of the Barmah-Millewa Forest (B-MF) (Chong and Ladson 2003).
1.5 Flow changes and ecological consequences
Regulation of rivers for the purposes of distributing water for irrigation, industry and domestic use resulted in significant changes in the seasonality of flow of many rivers across the world. A change in the seasonality of flow refers to a change in the flow patterns of rivers from their natural flow regimes to those imposed upon them by regulation structures and river operators. Regulation of rivers has resulted in the ability to more closely match river flows to meet demand, resulting in a reduction in flows during the natural wetter periods and an increase in flows during the drier periods, when demand for irrigation water and water supply are highest.
The focus of work on the seasonality of flow has largely been on a reduction in winter and spring flows. Less consideration has been given to the impact of the increased flows and flooding during the natural drier periods, which will be discussed in this research.
Perhaps the most highly publicised and significantly regulated river in Australia is the River Murray. The change in seasonality of flow of the River Murray has had a significant impact on the ecosystems of the river, the floodplains and wetlands.
Flows in the River Murray at Tocumwal have changed significantly with regulation. Overall there has been a decreased frequency in flows in the 10,000 to 60,000 megalitre per day (ML/day) range, which is mainly due to the reduced occurrence of winter and spring flooding. Meanwhile, there has been an increased frequency of flows in the 6,000 ML/day to 10,000 ML/day range, mainly due to the increased flows during summer and autumn for irrigation and other uses (Bren 1988).
6 INTRODUCTION
1.6 The River Murray
The once unregulated River Murray is now a highly regulated river, used for bulk water delivery to irrigation areas and many cities and towns along its reach. River Murray Water (RMW), the bulk water distribution arm of the MDBC, is responsible for the operation of the irrigation, domestic and environmental flows in the River Murray.
RMW faces a number of difficulties in meeting the conflicting demands for water along the reaches of the River Murray. The two which have the greatest implications for this research are: • a peak capacity of 8,500 ML/day through the Barmah Choke (Figure 1.3) (Department of Infrastructure Planning and Natural Resources 2004); and • a maximum drawdown rate from Lake Hume of 150 millimetre (mm) per day at Doctors Point (Doctors Point is located approximately 7 kilometres downstream of Lake Hume).
Figure 1.3: River Murray System (Adapted from http://www.mdbc.gov.au/subs/annual_reports/AR_2003-04/images/ecological_assets_map.gif)
The Barmah Choke is a natural constriction of the River Murray created by geological disturbance of the Cadell Tilt block. The River Murray has followed its current path
7 INTRODUCTION for approximately 8,000 years. A drawdown rate of 150mm per day at Doctors equates to around 1,500 ML/day. The maximum drawdown rate is imposed to prevent bank slumping in the River Murray (Maunsell Pty Ltd 1992). This rate is based on empirical estimates and past observations of bank slumping and may perhaps be conservative in comparison to natural drawdown rates (Department of Land and Water Conservation 1996).
The capacity of the Barmah Choke results in difficulties meeting downstream demands without flooding the B-MF. In late summer and early autumn, when downstream demands are at their highest, the major flow requirements are (Figure 1.3) (Murray- Darling Basin Commission 2004): • 7,000 ML/day going to SA, largely supplied from the Darling River or Lake Victoria. The majority of this water is not required to pass through the Barmah Choke. The level of Lake Victoria and the Darling River flows have a significant impact on this; • 1,500 ML/day going to Sunraysia irrigation system; • 4,500 ML/day going to Torrumbarry irrigation system; and • 3,000 ML/day for river transmission losses between the Barmah Choke and Mildura.
Even without any supply to SA, the final three combine to 9,000 ML/day, 500 ML/day higher than the peak capacity of the choke. For this reason water must be passed around the choke via other means. Up to 2,100 ML/day can be passed through the Mulwala Canal to the Edward-Wakool River system, which joins the River Murray downstream of the Barmah Choke. Use of the Mulwala Canal for the transportation of water around the Barmah Choke will be referred to as ‘bulk water transport around the Barmah Choke’ throughout this research.
The above two operational difficulties have their most significant impact on the operation of the River Murray during and immediately after a rain event which causes the two irrigation authorities of MIL and G-MW to rapidly reduce their irrigation diversions. The cause of these rapid demand reductions has previously been termed a ‘rainfall rejection event’. During such events, additional water remains in Lake Mulwala (Lake Mulwala refers to the lake created behind Yarrawonga Weir) and must
8 INTRODUCTION be passed downstream, minimising the impact on the B-MF. Rain rejection flows are managed using the following: • available capacity in the River Murray (up to the capacity in the Barmah Choke); • using available capacity in Edward River Escape to bypass the Barmah Choke; and • available airspace in Yarrawonga Weir to store water upstream of the Barmah Choke.
Flows above these constraints result in unseasonal flooding of the B-MF.
1.7 The Barmah-Millewa Forest
The B-MF surrounds the ‘Barmah Choke’ (Figure 1.4) and is located between Tocumwal, Deniliquin and Echuca in northern Victoria and southern NSW (Ladson 2002). The B-MF is one of the most ecologically significant areas of the River Murray, consisting of approximately 70,000 Hectares (ha) of wetland habitat.
Barmah Choke
Figure 1.4: Location of the B-MF
9 INTRODUCTION
The Barmah Choke is the result of a geological disturbance approximately 25,000 years ago. The disturbance effectively dammed the River Murray and Goulburn River. The River Murray originally turned north (the channel which is now occupied by the Edward River). Sometime between 8,000 and 25,000 years ago the Goulburn River broke out and created a new channel; prior to this time it continued to fill the lake. Approximately 8,000 years ago the River Murray turned south and created the path which it follows today (Thoms, Suter et al. 2000).
The geological disturbance resulted in an area of blocked drainage and the deposition of three alluvial fans known as the Barmah, Gunbower and Wakool fans. These fans are respectively located at the eastern, northern and southern regions of the Cadell Block (Thoms, Suter et al. 2000). The Cadell Block or Cadell Fault runs north/south in a line approximately from Deniliquin to Echuca (Chong 2003). The B-MF consists of two forests, the Barmah Forest (Victoria) and the Millewa Forest (NSW). The Barmah Forest is protected under the Ramsar convention on wetlands (signed in Ramsar, Iran, 1971). The Millewa Forest has been proposed for Ramsar listing (Chong and Ladson 2003). The B-MF has one of the largest existing river red gum (Eucalyptus camaldulensis) populations in the MDB and contains temporary and permanent wetlands, wet meadows and other plant communities (Hillman and Quinn 2002).
This ecosystem has adapted to a pattern of flooding in winter and spring and drying in summer and autumn (Bren 1988). Since regulation of the River Murray there has been a significant reduction in the incidence of winter and spring flooding and a significant increase in the incidence of small summer and autumn flood events. Between December and April the forest naturally flooded 15.5% of days, now under the current regulation conditions this has increased to 36.5% of days (Chong and Ladson 2003). These summer floods have affected the ecosystem of the forest, with 2,400 ha of Moira grass plain being lost since 1930. The loss is divided equally between the encroachment of giant rush (Juncus ingens) and river red gum (Chesterfield 1986). To assist in the prevention of the increased incidence of summer and autumn flooding, one possible method is the use of water storages to temporarily store flows that would otherwise result in flooding of the B-MF.
10 INTRODUCTION
1.8 Water Storages
The generic term water storage covers a broad range of facilities to store water from a number of sources and for a multitude of reasons. These range from, a simple plastic bucket, with water stored from a tap for the purpose of carrying water to irrigate plants, to a large storage reservoir constructed largely from earth and concrete, capturing rainfall runoff for the purpose of public utilisation.
The use of water storages for irrigation water supply includes large reservoirs constructed on rivers to capture rainfall runoff. Farm dams or on-farm water storages (OFWS) refer to small scale reservoirs located on private land. The owner of the land retains the rights to the water in the OFWS. OFWS can be used to capture rainfall runoff, surplus river water, unused allocated water from irrigation schemes and to capture and recycle irrigation tailwater (Lisson, Brennan et al. 2003). Groundwater is also a natural source of irrigation water. This water can be accessed via wells or bores. In this research, the term water storage refers to either medium sized earthen reservoirs located adjacent to the main irrigation canal (referred to as en-route water storages) or OFWS (farm dams) to capture rejected water.
The use of en-route water storages is a relatively new concept, which is gaining popularity in the irrigation areas of NSW, Australia. There have been a number of studies into the use of en-route water storage in the MIA (Foreman 2005; Foreman 2005; GHD Pty Ltd 2006) and Murrumbidgee Irrigation area (URS Pty Ltd 2003; Evans, Wolfenden et al. 2005; Evans, Wolfenden et al. 2005). As the use of en-route water storages is quite new, there are still a number of gaps in literature, including: • comparisons between OFWSs and en-route water storages with respect to: − capturing water; − redistributing water; − losses (namely evaporation and seepage); and − construction and maintenance costs. • cost benefit analysis of basin wide benefits and drawbacks of OFWSs; • system wide service benefits of en-route water storage(s) to the consumers; • the water savings of en route water storage; and • storages (on-farm or en-route) versus total channel control with respect to:
11 INTRODUCTION
− cost; − service improvements to consumers; and − water savings.
1.9 Research Questions
The main questions addressed in this thesis are:
1. What are the main causes of unseasonal flooding in the B-MF?
2. What is the best method to predict UIO in the MIA?
3. What is the best location and storage type (en-route or on-farm) to capture and store UIO in the MIA?
1.10 Research Objectives
The aim of this study is to compare the performance of OFWS to en-route water storages with respect to capturing UIO for environmental benefits. To achieve this, the key research objectives are to: • investigate and identify the main causes of unseasonal flooding of the B-MF; • investigate and identify the main causes of UIO in the MIA; • develop a method to predict UIO for the MIA; and • develop three storage options and compare the performance of these using an irrigation system model.
1.11 Thesis Outline
This research is presented in the following structure. Chapter 1 details the background of the research, research objectives and defines the contribution of this research. This research uses a major proportion of the MIA as the study area. The features of the MIA described in Chapter 2 are; land use, climate, water allocation and operation of the MIL supply system. Chapter 2 also describes how the study area was selected and how UIO were calculated with the available information.
12 INTRODUCTION
Chapter 3 builds on the operation of the irrigation system described in Chapter 2, expanding to incorporate the concepts of orders and rejections or UIO. Described in this section are the present ordering and rejecting procedure(s) in the MIA. Two methods are used to investigate the cause of UIO. These are: • a survey of 66 irrigators; and • Multiple Linear Regression (MLR) to analyse the link between physical parameters and UIO.
Chapter 3 also details the environmental consequences of UIO in terms of unseasonal flooding on the B-MF. Included is a MLR analysis of all parameters that may influence unseasonal flooding of the B-MF.
Having explored orders and UIO in Chapter 3, Chapter 4 describes the process undertaken to choose the most appropriate model for this research. In brief, the process was to undertake a review of literature on available models and identify those that were applicable to this research problem. The applicable models were then compared and evaluated to determine the most appropriate model. From this assessment it was found that the irrigation system model, Options AnalysiS in Irrigation Systems (OASIS) was the most applicable model. Its major advantage was its ability to represent water storages at both the farm and the distribution level. The two major drawbacks of OASIS compared to other models were that it did not operate on a daily time step nor did it have a capacity to be driven by crop water demands. Due to the structure of this model (ease of modification) and the support available in the use of this model it was considered more advantageous than the other models investigated.
Chapter 5 introduces the modifications made to OASIS for application to this problem. These modifications are conversion to a daily time step, converting the driver for OASIS to be crop water demand and the incorporation of an order process and a rejection process. Two different methods for predicting orders and rejections were calibrated and validated. The final version of OASIS is compared to observed data prior to the model being used as part of the scenario assessment in Chapter 6.
Chapter 6 describes three different storage options derived for the capture and temporary storage of UIO in the updated OASIS model. Also included in Chapter 6 is the method used to assess the three storage options. The assessment is based on the
13 INTRODUCTION percentage of UIO captured, the percentage of captured UIO reused and the percentage of total UIO reused. From this analysis the most appropriate method to store UIO in the MIL system was derived.
Chapter 7 provides conclusions from this research and illustrates the limitations of this research and recommends areas for further research. The conclusions are the factors that influence UIO and the factors that cause unseasonal flooding of the B-MF. This chapter also provides the results from the assessment of the performance of the updated irrigation system model and the results from the investigation into the best storage option from those assessed in the MIA.
Data used to undertake the research and additional results which do not form part of the main body of the thesis are presented in the Appendices.
1.12 Original Contributions
The key original contributions of this research are: • a thorough investigation into the factors which contribute to unseasonal flooding of the B-MF and an investigation into the factors which cause rejections and a survey of irrigators to investigate the important factors for them placing an order or rejection; • modifying an irrigation system model to enable it to generate orders and rejections and direct the rejected water into storages (on-farm or en-route); and • using the modified irrigation system model to make a comparison between en- route and on-farm water storages for the capture and temporary storage of rejected water.
14
2 The Murray Irrigation System
This research was undertaken using a section of the Murray Irrigation System as a study area. For this reason it is necessary to describe the individual elements of the system and the links between them that comprise the MIA. The individual elements addressed in this chapter are MIL, agriculture, climate and operation of the MIL irrigation system. The elements and links between agriculture, climate and operation of the irrigation system are explored to select a study area for this research.
2.1 Murray Irrigation Limited
MIL services 2,414 landholdings which vary in size from 5 to 5,200 ha. The total service area is 748,685 ha of which 55% has been developed for irrigation (Marshall 2002). The irrigation area is divided into four districts Wakool, Deniboota, Denimein and Berriquin, (Figure 2.1).
Edward River Denimein Irrigation District
Berriquin Irrigation District Wakool Irrigation District Deniliquin
Deniboota Irrigation District
N Barmah- Millewa 0 20 km Forest Lake Mulwala River Murray
Figure 2.1: Irrigation districts in the MIA
15 The Murray Irrigation System
2.1.1 Agricultural land use
The agricultural land uses in the MIA are important for two reasons. First it is used to generate an irrigation demand during the modelling process undertaken in Chapters 5 and 6. The second reason is to select an appropriate study area (Section 2.4). Prior to using the agricultural land use information it is necessary to provide a brief explanation and examination of the land use information that is available for the MIA.
The majority of agriculture in the region is irrigated agriculture. The irrigated agriculture consists of the annual crops of rice and cereals. Along with these, there is a large dairy industry with winter irrigated pasture and perennial (summer irrigated) pasture and some perennial horticulture.The annual area sown to rice and cereals varies, depending on the annual water allocation for the area. The MIA accounts for 50% of Australia’s rice production and 10% of the milk produced in NSW.
The annual water allocation for the area is highly variable. For this reason the area of annual crops, namely rice and cereals has a high interseasonal variation. To select a study area and accurately model the irrigation demands for the study, annual land use information is required. There are a number of sources for land use information available from MIL, these include: • landholder survey information available annually since at least 1996/97; • satellite imagery (from SPOT Image Corporation, 10m pixel) is used solely to determine the area sown annually to rice, also available since 1996/97; and • a Geographical Information System (GIS) database compiled in 2000/01 for all land uses in the MIA. This was based on the satellite imagery for 2000/01.
The only source that provides information on all land uses over a reasonable time length is the landholder surveys. The surveys are undertaken in July asking landholders to specify the present land use (eg. irrigated cereals, dryland pasture, fallow, etc). Prior to 2006, approximately 10% of landholders were surveyed; in 2006 this was reduced to 6%. The landholders surveyed each year are randomly selected. Some of the difficulties associated with these surveys are that: • only a portion of the supply area is surveyed on an annual basis and this is considered to represent the entire MIA; • the landholders surveyed varies annually; and
16 The Murray Irrigation System
• landholders must make estimates of the portion of their land planted to each crop for that year.
The information obtained from surveys undertaken from 1997/98 to 2003/04 is presented in Table 2.1. This shows the high annual variation in the area of winter cereals and rice, while the area of winter irrigated pasture (used for dairy) remained relatively stable on an annual basis.
Table 2.1: Crop areas from landholder surveys Season Land use 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 Dryland pasture (%) 34 34 24 10 7 5 10 Winter irrigated pasture 20 18 19 16 15 14 16 (%) Winter cereals (%) 21 26 25 32 36 43 41 Rice (%) 6 6 5 0 0 0 0 Rice stubble / fallow 4 2 2 8 5 0.3 2 (%) Lucerne / summer 2 7 6 4 3 3 3 pasture (%) Other crops / fallow 2 1 9 1 1 1 8 (%) Native vegetation (%) 3 4 4 22 17 23 14 Infrastructure / other 7 5 5 11 16 11 6 (%)
The area reported as sown to rice from the satellite imagery is presented in Table 2.2.
Table 2.2: Areas sown to rice Season 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 SPOT (ha) 48,342 55,533 38,416 69,525 55,150 1,545 22,729
The GIS database for the 2000/01 season is shown in Table 2.3. The accuracy and variability surrounding land use information is discussed in Chapter 5 prior to this information being used for the demand modelling undertaken in Chapters 5 and 6.
Table 2.3: 2000/01 GIS land use information Land use % of area Dryland pasture 26 Winter irrigated pasture 21 Winter crop 20 Rice 10 Lucerne/summer pasture 0 Other crops/fallow 1 Native vegetation 8 Infrastructure 14 Total 100
17 The Murray Irrigation System
Due to the size of the MIA and to constrain the size of the study it was seen necessary to select a significant proportion of the MIA but not necessary to use the entire MIA. With the available land use information described above it is necessary to describe how this information was used to select the study area. In selecting the study area it is important to select an area that reliably represents the MIA. To select the study area, two land use criteria were used. These were: • determine if there is significant spatial variability of cropping patterns throughout the MIA (for example, is rice mainly grown in a particular irrigation district?); and • determine if each irrigation district uses approximately the same percentage of the total MIL deliveries on an annual basis.
The only information available to address the first land use criterion was the GIS database for the 2000/01 season. The best information available to assess the second criterion was the irrigation intensity for each district (the total volume of water applied per hectare of the irrigation district), see Table 2.4 (Marshall 2004). Irrigation intensity information was only available from 1999/00 to 2003/04.
Table 2.4: Irrigation intensities for the irrigation districts in the MIA Seasonal irrigation intensities (ML/ha) District 1999/00 2000/01 2001/02 2002/03 2003/04 Berriquin 1.20 2.05 2.15 0.77 1.07 Deniboota 0.30 1.23 1.23 0.31 0.55 Denimein 0.51 1.70 1.59 0.37 0.67 Wakool 0.48 1.53 1.46 0.34 0.84 Region 0.90 1.73 1.74 0.53 0.84
Table 2.4 shows that the irrigation area of Berriquin consistently has the highest irrigation intensity and Deniboota consistently has the lowest irrigation intensity. All four irrigation districts seem to vary consistently from season to season. From this information and viewing the GIS database of crop use for the 2000/01 season it is concluded that the agriculture intensity across the MIA is relatively consistent. This meant that agricultural variation across the MIA was excluded from influencing the selection of the study area.
18 The Murray Irrigation System
2.2 Climate data
Climate information was examined for the same two reasons as agriculture information. The climate data required for the irrigation demand modelling was daily rainfall and potential evapotranspiration (ETo). Ten rainfall stations with varying record lengths were found either in the MIL supply area or just outside the area. The location of the rainfall stations are shown in Figure 2.2.
Edward River
Tullakool Jerilderie Deniliquin MIL Supply Wakool Area Finley
MIL Supply Tocumwal Mulwala Area Corowa Cobram
N Barmah- Millewa 0 20 km Forest Yarrawonga River Murray
Figure 2.2: Location of rainfall information
The record length of each of the ten rainfall stations are shown in Table 2.5.
Table 2.5: Daily rainfall data sites Rainfall Station State Length of Record Irrigation area Cobram Vic 10/1958 to 09/2004 G-MW Corowa NSW 01/1890 to 10/2004 None Deniliquin NSW 01/1858 to 10/2004 MIL Finley NSW 01/1986 to 9/2004 MIL Mulwala NSW 06/1903 to 09/2004 MIL Jerilderie NSW 01/1886 to 07/2004 MIL Tocumwal NSW 08/1897 to 10/2004 None Tullakool NSW 1993 to 2004 MIL Wakool NSW 01/1891 to 08/2004 MIL Yarrawonga Vic 01/1879 to 10/2004 MIL
ETo information was less common for the area but there were records for Finley (1986 to present) and Tullakool (1996 to present). These stations are operated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). Information was also available for Deniliquin (1978 to present) and Wakool (1974 to present) operated by Bureau of Meterology (BoM). The ETo in this area is calculated using the modified Penman Equation (Meyer, Smith et al. 1999). The modified Penman
19 The Murray Irrigation System
Equation is a locally adjusted Penman-Monteith equation. Research has shown that
ETo calculated using the Penman Monteith equation can give values that are as much as 30% below those calculated using the modified Penman Equation (Meyer 1999). The
ETo data available are calculated using the modified Penman equation and these values will be used in this research.
The climate data were also investigated to determine if there were any trends present within the MIL supply area. The investigation of ETo revealed that the average ETo for Finley was 97% of that for Tullakool. This was expected as Tullakool is approximately 135 km west of Finley and has a climate that is reported as being slightly warmer and drier than Finley. A comparison of ETo for Finley (measured by CSIRO) and Deniliquin (measured by BoM) revealed that they are very similar.
The 12 month rainfall from July to June (an irrigation season is from August to May) for the period 1998/99 to 2003/04 from the 10 rainfall stations was investigated to determine if there was a trend of reducing rainfall from East to West (Table 2.6).
Table 2.6: Rainfall June to July for seasons 1998/99 to 2003/04 Season Finley Jerilderie Tocumwal Deniliquin Cobram 1998/99 378.2 391.0 395.7 466.7 408.7 1999/00 479.3 466.4 416.6 391.6 519.0 2000/01 490.5 404.0 412.2 430.8 454.8 2001/02 342.6 351.5 347.1 296.0 350.9 2002/03 278.9 249.8 244.2 185.2 333.3 2003/04 416.3 513.4 487.2 398.0 497.0 Total 2385.8 2376.1 2303.0 2168.3 2563.7 Season Mulwala Yarrawonga Corowa Tullakool Wakool 1998/99 457.2 439.1 597.0 402.4 412.2 1999/00 497.5 522.7 562.1 443.5 452.0 2000/01 513.0 351.1 581.2 355.9 439.0 2001/02 404.7 410.3 474.8 422.5 298.4 2002/03 359.5 343.2 338.5 202.2 205.3 2003/04 465.9 472.8 649.2 382.4 401.8 Total 2697.8 2539.2 3202.8 2208.9 2208.7
An investigation of Table 2.6 and Figure 2.2 shows that there is a general trend of decreasing rainfall from East to West.
The summer rainfall (December to April) for the ten rainfall stations was then investigated to determine if there was a trend of increasing summer rainfall from South to North, Table 2.7. December to April is the period identified as having an increased incidence of unseasonal flooding of the B-MF by Chong and Ladson (2003).
20 The Murray Irrigation System
Table 2.7: Summer rainfall (December to April for seasons 1998/99 to 2003/04 Season Finley Jerilderie Tocumwal Deniliquin Cobram 1998/99 87.7 139.1 103.3 165.0 107.6 1999/00 168.4 169.8 136.3 115.4 198.2 2000/01 167.2 120.0 108.9 100.0 115.0 2001/02 135.5 170.2 134.2 107.0 122.6 2002/03 130.8 106.0 91.4 78.2 114.7 2003/04 94.6 163.0 142.0 79.0 153.2 Total 784.2 868.1 716.1 644.6 811.3 Season Mulwala Yarrawonga Corowa Tullakool Wakool 1998/99 108.3 159.6 107.4 117.9 83.3 1999/00 192.2 190.8 110.0 163.0 176.2 2000/01 134.1 173.8 61.1 97.0 131.6 2001/02 141.4 198.2 165.3 96.7 86.2 2002/03 125.8 120.7 81.2 93.0 73.5 2003/04 143.8 159.4 105.2 84.2 89.2 Total 845.6 1002.5 630.2 651.8 640.0
Table 2.7 shows there was no trend of increasing rainfall from December to April from South to North.
From the above it is concluded that there is a slight trend of warmer drier weather from the east to the west in the MIA. To determine whether this slight trend should influence the selection of the study area it is necessary to consider the nature of rejection events. These are said to be linked to region wide summer rainfall events. Note, from above no trend in rainfall from December to April was revealed for the MIA. For this reason it is concluded that climate variability within the MIA should not influence the selection of the study area.
2.2.1 Water allocation
There are two main concepts in determining the volume of surface water MIL receives annually from Lake Hume: namely ‘water entitlement’ and ‘water allocation’. Water entitlement refers to the maximum volume of water MIL is ever entitled to. Water allocation is the maximum volume of water MIL is entitled to during the current irrigation season (August to May). Water allocation is dependant on the volume of water in storage at the start of the season and the hydrologic and climatic conditions of the season. It is commonly expressed as a percentage of the nominial entitlement.
All entitlements from the MDB were capped from July 1st 1997 (under the Murray Darling Basin Cap) at the 1993/94 levels of development (Murray-Darling Basin Commission 2004). MIL has an entitlement of 1,445 GL, 67% of the NSW River Murray entitlement (Chong 2003). Their allocation can vary between 0% and 100% of
21 The Murray Irrigation System the water entitlement. In August each year an initial allocation for the season is announced, this is reviewed and possibly increased on a weekly basis during the water year. Prior to season 2006/07 the water allocation had never been reduced during an irrigation season. The unprecedented low inflows in season 2006/07 saw the water allocation reduced during the irrigation season. Increases in the water allocation arise from good runoff into Lake Hume (the storage facility for irrigation water used in the MIA).
Since July 2006, irrigators in the MIA have been entitled to 90% of their water entitlement including allocation and carryover (Murray Irrigation Limited 2006). MIL retains the other 10% to account for water distribution losses. Prior to July 2006 MIL restricted property deliveries to 83% of their water entitlement including allocation and carryover, retaining the other 17% to account for distribution losses. Other than water allocation there are a number of other measures that can be used to increase or decrease the water available in a particular year, these are carryover, off-allocation water, temporary water trading, Barmah-Millewa borrow and Snowy advance. These measures are explained in more detail below.
Carryover
Carryover refers to carrying over unused water allocation from one irrigation season to the next. An irrigator in the MIA is allowed to carryover up to 41% of their water entitlement. In any season an irrigator’s allocation may not go above 90% of their entitlement, this includes carryover.
Off-allocation – supplementary water
Off-allocation or supplementary water is water in addition to irrigators annual allocation. There are two sources for supplementary water. The first is water inflowing to the River Murray from unregulated tributary rivers, this generally occurs in late winter and spring. The second source is from water remaining in the MIL canal system at the end of the irrigation season.
Temporary water trading
Temporary water trading into or out of the region can also impact on the annual water availability.
22 The Murray Irrigation System
Barmah-Millewa borrow
Under the operating rules of the River Murray the B-MF has an annual water entitlement used for environmental watering (flooding) of the Forest. It is also possible for MIL to borrow water from the B-MF account during low flow years and repay the water during periods of higher flows.
Snowy system commitment
Snowy Hydro Ltd has an annual commitment of 1,062 GL to River Murray inflows. In the 2004/05 and 2005/06 seasons MIL agreed to a commercial arrangement with Snowy Hydro Ltd and the NSW Department of Natural Resources (DNR) for an advance on future water allocations. These advances were 86,751 ML and 90,879 ML in 2004/05 and 2005/06, respectively. The repayment of this water has been delayed until at least after the 2006/07 irrigation season (Murray Irrigation Limited 2006).
Groundwater
In addition to surface water there are two different licences for groundwater use in the MIL service area; one for deep bores and the other for spear point (shallow) bores. The deep bores are a volumetric licence, which are read and recorded every three months. The spear point bores are currently not metered. There is a moratorium on groundwater use in the irrigation area so no new licences for groundwater are being issued. New bores can be installed to replace existing bores. Groundwater represented 3.3% of irrigation water used in 2001/02 (Marshall 2002). For this reason and due to the flexibility of starting and stopping pumping immediately (no lag period between order and water arrival), groundwater has not been addressed in this study.
2.3 Operation of the Murray Irrigation System
The ‘core business (of MIL) is providing efficient irrigation supply services which maximize water available for productive use’ (Murray Irrigation Limited 2006). This means meeting the water demands of landholders in the MIA. To distribute irrigation water within their supply area MIL has 3,300 km of channels. Flows in the Mulwala Canal (the primary distribution canal in the MIA) and some of the larger secondary canals are controlled by automated regulators. Flows in the smaller channels are controlled by board regulators operated by channel attendants.
23 The Murray Irrigation System
For an individual irrigator to receive irrigation water they must place an order via an interactive telephone water ordering system with MIL. The irrigator is not guaranteed to receive their ordered water until four days after the placement of the order. The status of the order can be reviewed either via the telephone or the internet. The following information is required at the time of the irrigation order: • user number; • Personal Identification Number; • service point (outlet) number; • start date; • flow rate required; and • an advanced finish date is optional.
Each day MIL sums all of the orders in their area and adds a percentage to the orders for losses in their distribution system (approximately 10% during the summer period (Scott Barlow pers. comm.. 24/8/04)). MIL then place a daily water order with State Water for this volume. State Water sums all of the NSW River Murray orders and places an order with RMW for this volume.
MIL allows irrigators to place orders with less than 4-days notice and will meet these demands if water is available. However, they do not guarantee water until four days after the placement of the order. Once the irrigation has commenced, irrigators continue to receive water at their specified rate until they notify MIL of a change in their required rate (increase, decrease or complete stop) or their advanced finish date is reached. All orders (both new orders and changes in orders) for the coming 24 hours must be placed by 6:30 am because this is when the planners in each MIL office (i.e. Deniliquin, Finley, Wakool) process the information.
The information is then collated for the region and the regulators along the Mulwala Canal are initially altered at 7:30 am, to provide the required flows for the coming 24 hours. If there is a large change in flows then altering of regulators can continue for at least 8 hours. For large flow changes, the adjustment of regulators is undertaken in stages. Planners provide instructions for the channel attendants to adjust the non- automated regulators along the spur channels around 7:30 am. The channel attendants spend the day (7:30 am to 4 pm) adjusting their section of the system to meet their irrigators’ needs (Scott Barlow pers. comm.. 24/8/04). An irrigator’s supply will be
24 The Murray Irrigation System stopped, started or adjusted by a channel attendant between 7:30 am and 4 pm depending on its location along the channel (David Watts pers. comm. 9/10/2006).
If for some reason an irrigator wishes to prematurely cancel their order or reduce the flow rate of their order they ring MIL and place either a complete stop to their irrigation or a reduction in their flow rate as required. As the irrigation volumes required to irrigate rice are very dependent on the weather, a change in temperature or a rainfall event usually leads to a change in the requirements of all rice growers. This is also the case to a lesser extent for all of the other crops grown in the MIA. Rainfall events that lead to irrigators as a collective group cancelling their order have been termed ‘rain rejection events’. These have been noted as far back as 1974 (Holmes 1974). Once irrigators have placed their rejection or reduced order with MIL they are required to continue to receive irrigation water until the channel attendant adjusts the delivery to their property (David Watts pers. comm. 9/10/2006).
In the above situation irrigators take a maximum of 33 hours of water after a rejection (if they placed their order cancellation at 7 am and their channel was not attended to until 4 pm the next day), whereas water has been released from Lake Hume at their previous rate for 96 hours in advance. Hence for each irrigator that stopped or reduced their flow rate there is at least 63 hours of their irrigation water flowing down the River Murray that will not be utilized.
In the process of ensuring orders are met, small volumes of excess water travel unused to the end of the irrigation canals. MIL has ‘escapes’ that return excess water back into the river system. In addition to returning excess irrigation water to the river system, escapes can be utilized to distribute environmental flows for DNR. For this reason there are credited and non-credited escapes. Non-credited escapes are used for the sole purpose of releasing excess irrigation water back into the river system. This water is effectively lost to MIL. Credited escapes are used for the dual purpose of distributing environmental flows and releasing excess irrigation water back to the river system. MIL receives credit for environmental flows that travel through their irrigation system and exit via credited escapes. The credited escapes are deducted from MIL’s water account (Mulwala Canal diversion). DNR primarily use the MIL system and escapes to transport water around the Barmah Choke. To do this, DNR request MIL to divert water into the Mulwala Canal at Yarrawonga Weir (upstream of the Barmah Choke).
25 The Murray Irrigation System
This water travels along the Mulwala Canal before being released back into Edward River via Edward River Escape (Figure 2.3). The Edward River joins the River Murray downstream of the Barmah Choke. This method can pass up to 2,100 ML/day around the Barmah Choke.
Denimein Irrigation Edward River District Berriquin Irrigation Finley District Escape Deniliquin Wakool River The Escape Edward Drop River Mulwala Canal Wakool Escape Finley Irrigation District
Deniboota Mulwala Canal Irrigation Diversion point District
N Barmah- Millewa 0 20 km Lake Mulwala Forest
River Murray
Figure 2.3: MIL Escape locations
MIL has 6 credited escapes (Marshall 2004), the three largest (Edward River Escape, Wakool River Escape and Finley Escape) are shown in Figure 2.3. The largest of these is Edward River Escape, with a capacity of 2,400 ML/day. However, the capacity of Edward River Escape has recently been increased (pers. comm.. David Watts, 28/11/2007). As this research analyses flows prior to 2005, the critical value prior to enlargement of 2,400 ML/day will be used in this research.
There are rules regarding the volume of excess irrigation water MIL can release from escapes after December 15th each year. This is to ensure the majority of the escape capacity is available to RMW to pass water around the Barmah Choke. These maximums are set at 300 ML/day for Edward River Escape and 80 ML/day for the two escapes that pass water into the Wakool River (Chong 2003).
2.4 The Study Area
Given the similarities of the irrigation areas with in MIL it was necessary to limit the scope of this study and at the same time maintain the significance of its results; this was achieved by selecting a study area that comprised only part of the MIL supply area. The variation of agricultural land use and climate across the region (Sections 2.1 and
26 The Murray Irrigation System
2.2) was not significant enough to warrant inclusion in selecting the study area. The three main considerations when selecting the study area were to include: • the area known as ‘the Drop’, a 4-metre drop in the floor of the Mulwala Canal, because this is an ideal position to place an en-route storage (Figure 2.3); • the section of the Mulwala Canal used for passing water around the Barmah Choke (Mulwala Canal diversion point to Edward River Escape) also shown in Figure 2.3; and • an area that included at least 50% of the irrigation water delivered by MIL.
The two irrigation districts of Berriquin and Denimein were initially selected for the study because; ‘The Drop’ is located in the irrigation district of Berriquin and the section of the Mulwala Canal that is used for passing water around the Barmah Choke is located in the irrigation districts of Berriquin and Denimein. An investigation was then undertaken using information from Marshall (2002) and Marshall (2003) to determine the approximate percentage of irrigation deliveries that take place in these two irrigation districts. It was found that approximately 70% of the water MIL delivers is to the irrigation districts of Berriquin and Denimein. Hence, these two irrigation districts were assumed to provide a good representation of the MIL supply area (Figure 2.4) and have been selected as the study area for this research.
Denimein Irrigation Edward River District Berriquin Irrigation District Deniliquin The Edward Drop River Mulwala Canal Wakool Escape Finley Irrigation District
Deniboota Mulwala Canal Irrigation Diversion point District
N Barmah- Millewa 0 20 km Lake Mulwala Forest Study Area River Murray
Figure 2.4: Study Area
Climate data for the study area
All of the ETo stations except Finley were located outside of the study area and for this reason Finley ETo will be used throughout this research. For the irrigation demand
27 The Murray Irrigation System
modelling (Chapter 5 and 6) it was considered more appropriate to use rainfall and ETo data from the same location rather than have variable rainfall but consistent ETo across the study area. Finley also had the benefit of being located in approximately the centre of the study area. For these two reasons the data from the weather station at Finley, operated by the CSIRO since 1986, will be used for the irrigation demand modelling in this study. From these data the mean annual winter dominated rainfall is 390mm and the mean annual summer dominated ETo is 1,840mm.
This research also includes an investigation into the factors affecting flooding of the B- MF and UIO (Chapter 3). One of the variables investigated will be rainfall. In these investigations it was considered necessary to use all of the rainfall information available within the study area. The rainfall information from all six rainfall stations located in the study area was therefore used. For this investigation it was also necessary to calculate UIO, as rejections are not recorded by MIL.
2.4.1 Calculation of UIO
Water diverted into the Mulwala Canal can be for irrigation or for bulk water transport around the Barmah Choke. To differentiate between the two uses, MIL provided the following daily data sets (Figure 2.5) for the seasons from 1999/00 to 2003/04:
• orders for Mulwala Canal irrigators (MCadv) (ML/day); • Mulwala Canal diversion (D) (ML/day); • environmental releases through escapes (Edward River Escape (ERE), Finley Escape (FE), Wakool River Escape (WRE)) (ML/day) ; and • water passing through Lawsons Syphon (LS) (ML/day).
It is firstly necessary to explain a little more about the above data sets. Orders for Mulwala Canal irrigators (placed by MIL with State Water) refer to the orders for irrigators along the Mulwala Canal. This is the summation of irrigators’ orders plus the addition by MIL of estimated losses (approximately 10% on average) in the distribution system for each day’s volume of orders. David Watts (pers. comm 11/11/2004) described the order volume MIL place with State Water ‘an indicative’ order for four days time. Note, the Mulwala Canal extends beyond the study area and hence these orders represent irrigators’ orders inside and outside the study area. There is no way to separate the two.
28 The Murray Irrigation System
Edwa rd Finley Escape River (FE) Wakool River Es cape Es cape (WRE) (ERE) Non-study area Study Area
La ws ons Syphon (LS) Mulwala Canal N Diversion (D)
0 20 km
Figure 2.5: Available data
Daily Mulwala Canal diversion refers to all water diverted into the Mulwala Canal (irrigation and bulk water transport around the Barmah Choke). Daily environmental releases through escapes, refers to water returned to the river system. MIL receives credit for this water. Daily volumes passing through Lawsons Syphon refers to water in the MIL canal system that is transported to irrigators outside the study area.
The first calculation was to determine the daily amount of Mulwala Canal diversion that was used for irrigation along the Mulwala Canal (nett Mulwala Canal diversion
(Dnett)), Equation 2.1.
Equation 2.1 Dnett = D − (FE + ERE + WRE )
Where D is Mulwala Canal diversion (ML/day); FE is Finley Escape (ML/day); ERE is Edwards River Escape (ML/day); and WRE Wakool River Escape (ML/day).
The daily volume of water diverted that is used for irrigation of properties in the study area (nett study area diversion (SAnett)) was calculated via Equation 2.2.
Equation 2.2 SAnett = Dnett − LS
29 The Murray Irrigation System
Where LS is Lawsons Syphon (ML/day).
Nett study area diversion and nett Mulwala Canal diversion both consist of deliveries to farm, drainage flows and losses in the distribution system. They can both be calculated with a high degree of confidence as the drainage flows exiting the system are around 7,000 ML/year (David Watts pers. comm.. 7/12/2005). The combined volume is less than 1% of the water entering the system on a yearly basis. So drainage flows will be ignored from here after. Losses in the distribution system were on average 9.1% of nett Mulwala Canal diversion for the period 1999/00 to 2003/04. The exception was the low allocation year of 2002/03 when they reached 19.4% (see Chapter 5 for details).
After obtaining an estimate of the water diverted for irrigation, it is now necessary to explain how the volume of UIO was calculated. To calculate the UIO for the area supplied by the Mulwala Canal (UIOMC), Equation 2.3 was used.
Equation 2.3 UIOMC = MCadv − Dnett
Where
MCadv is the Mulwala Canal advance (ML/day).
UIO is the combination of rejections plus the difference between estimated system losses and actual system losses. If we assume that the estimated system losses (10%) are accounted for by the actual system losses (Chapter 5) (9.1%) and drainage flows (1%), then UIO can be considered to represent rejections.
UIO is not the most ideal data set to use for this research but there is no rejection data kept by MIL. For this reason UIO calculated using Equation 2.3 will be used as a substitute for rejections throughout this research.
To calculate the UIO for the study area the linear ratio between nett Mulwala Canal diversion and nett study area diversion was investigated, see Figure 2.6.
Figure 2.6 shows the ratio of nett study area diversion to nett Muwala Canal diversion to be 0.707 to 1. The ratio (Equation 2.4) will be used to convert the Mulwala Canal orders and Mulwala Canal UIO to study area orders and UIO because of: • the lack of study area specific data; and
30 The Murray Irrigation System
• the high correlation between nett Mulwala Canal diversion (MC) and nett study
area diversion (SAnett).
Equation 2.4 SAnett = 0.707 × MC
8000
y = 0.707x 7000
6000
5000
4000
3000
2000 Nett Study Area Diversion (ML/day)
1000
0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Nett Mulwala Canal Diversion (ML/day)
Figure 2.6: Nett Mulwala Canal diversion versus nett study area diversion for season 2000/01
An investigation into the deliveries to farms was also undertaken to determine the ratio of MIA deliveries to study area deliveries, Figure 2.7. This found the ratio to be 0.609 to 1, less than the ratio for diversions. The difference between the two ratios is considered to be because there is a greater area of channels in the study area to areas outside of the study area and for this reason there are greater losses in the study area compared to areas coutside of the study area.
Chapter 2 has explained the procedure for placing an order and rejection. The land use, climate, order and rejection information available was also described. The land use and climate information was investigated to select a study area for this research. The selected study area is the two irrigation districts of Berriquin and Denimein, which represents an area where approximately 70% of the irrigation deliveries take place. The order and irrigation diversion information was used to calculate UIO for the study area. This information will be used to further analyse the variables that influence UIO in the study area in Chapter 3. The UIO information for the MIA will be used to investigate the variables that effect unseasonal flooding of the B-MF (also in Chapter 3).
31 The Murray Irrigation System
900000
800000 y = 0.609x 700000
600000
500000
400000
300000 Study area deliveries (ML/yr)
200000
100000
0 0 200000 400000 600000 800000 1000000 1200000 1400000 MIA deliveries (ML/yr)
Figure 2.7: MIA deliveries versus study area deliveries
32
3 Unutilised Irrigation Orders and Unseasonal Flooding of the Barmah-Millewa Forest
The previous chapter focused on how the River Murray and the MIL system were linked and it highlighted some of the positives and negatives that the MIL system had on the operation of the River Murray. One of the consequences of the regulation of the River Murray is a significant increase in unseasonal flood events in the B-MF between December and April. Local knowledge suggested that rainfall events are to blame for high levels of UIO in the MIA leading to unseasonal flooding of the B-MF.
Before trying to determine a correlation between rainfall events, UIO in the study area and flood events in the B-MF it is necessary to examine the other flows in the River Murray. Chapter 2 described the River Murray as a finely balanced system, with little capacity to cater for any unexpected changes in the flow without causing flooding of the B-MF. There is a high degree of uncertainty around rainfall predictions, and as a result UIO are a consequence of MIL’s supply system, position in the system and order regulations.
UIO from MIL are not the only possible cause of increased flows in the River Murray after rainfall because there are two unregulated tributary rivers (Kiewa River and Ovens River) which enter the River Murray between Lake Hume and Yarrawonga Weir. Along with this G-MW also divert water from Yarrawonga Weir. The impact of the two tributary river inflows and G-MW’s UIO after rainfall events will be investigated in this chapter.
Marshall (2003) and Marshall (2004) showed that other non-weather factors affect the volume of the orders placed during a season. These are the water allocation for the
33 UIO and Unseasonal Flooding of the B-MF season and the area of each crop planted. The maximum volume of orders in a season is largely based upon the water allocation in that particular season. It follows that the higher the seasonal water allocation and therefore orders, the higher the volume of UIO for the same percentage of orders that become UIO. The impact of water allocation on irrigated crop areas is unknown.
For this reason information from irrigators was sought to determine the affect a reduced water allocation has on the area of each crop type they plant.
The area of each crop planted also has an effect on irrigation demand throughout the irrigation season because each crop has particular periods when it requires irrigation. The irrigation periods for the main crops in the MIA are: • rice from October to February and sometimes into March depending on the climatic conditions of the season; − during the end of November and early December period, the ponded depth of water on rice is increased; and − through February the depth of ponded water is decreased; • winter irrigated pasture during spring, late summer and early autumn; • lucerne/summer pasture mainly from October to March; and • winter cereals in March/April and September/October.
From the above it is therefore inferred that the crop irrigation demands and water allocation would have an impact on the volume of UIO depending on the month and season that rainfall events take place.
The above describes a general pattern of behaviour of irrigators based on information from local people. However, it fails to address the number of irrigators who follow this pattern of behaviour and the percentage of their orders they reject when they follow this pattern of behaviour. In addition to this, the impact of lower water allocations on cropping patterns has not yet been documented. To gain information on these three behavioural responses and also irrigators’ ordering behaviour an interview questionnaire was constructed and undertaken. A more thorough description of the questionnaire is presented in Section 3.1 along with a discussion of the results.
34 UIO and Unseasonal Flooding of the B-MF
Also included in this Chapter is a Multiple Linear Regression (MLR) analysis of the factors that affect the River Murray’s flow at Tocumwal (indicator of flooding of the B- MF). The Chapter then attempts to establish a correlation between UIO and weather events, again using MLR analysis.
3.1 Survey Questionnaire
The interview questionnaire for this research was undertaken in July 2006, in conjunction with the annual MIL landholder survey (described in Chapter 2). Completing the questionnaire was voluntary and the results remained anonymous. The consent of the individuals was given by the fact they completed the questionnaire.
The interview questionnaire was tested at four Land and Water Management Plan (LWMP) working group meetings, where a total of 36 irrigators completed them. During the LWMP meetings feedback from the irrigators was gained and the questionnaire altered to clarify it.
The interviewers for the questionnaire were the same as those used by MIL to undertake their landholder survey. They differed in experience from first time interviewers to those with a few years experience in administering the MIL annual landholder surveys. Their occupations varied and included; non-working wives, former irrigators and local university students. All of the interviewers were local people familiar with irrigation and the region.
The interview questionnaire (Appendix A) consisted of three sections; general, non-rice crops and rice crop. The general section consisted of 5 questions, and all participants were asked to complete this section. The purpose of the general section was to determine some information about the location of their property, their water entitlement, whether they had an OFWS (greater than 10 ML) and the uses of the storage. Along with this irrigators’ behaviour regarding changes in cropping patterns with reduced allocation and critical irrigation periods were included.
The non-rice and rice crop sections both consisted of 8 questions and participants were asked to complete the sections that were relevant to them. The questions in both of these two sections were very similar and aimed at gaining an understanding of the
35 UIO and Unseasonal Flooding of the B-MF behaviour of irrigators with respect to their management approach to ordering and the circumstances that lead them to placing a rejection.
3.1.1 MIA results
The annual MIL landholder survey takes place across the entire MIL service area. As discussed in Chapter 2 the study area comprises two (Berriquin and Denimein) of the four irrigation districts in the MIA. The interview questionnaire took place in conjunction with the MIL landholder survey and as a result, responses were gathered from across the entire MIA. There were 92 responses of which 66 were located in the study area.
To determine whether the information gathered from the landholders outside the study area could be used in this research the annual water licence of irrigators in the two groups was examined. To do this an F-test was conducted. The participants outside the study area had annual water licences which varied from 261 ML to 9,500 ML. The average was 2,009 ML and the median 1,590 ML. Within the study area the annual water licence varied from 3 ML to 4,200 ML, with an average of 1,173 ML and a median of 732 ML. The F-test produced a result of 0.00035, indicating that there was a very high probability that the variance between the two data sets was significantly different. From this it was concluded that the study area consists of properties with smaller water licence entitlements than the other areas of the MIA. For this research only the results from the respondents who are situated in the study area will be investigated. This will avoid any possible impact the size of the water entitlement may have on irrigator’s behaviour.
It should be noted that 31 irrigators completed the rice component in the general questions section, though only 26 completed the rice crop section. There is no way to determine the correct number of rice growers in the study area. It was therefore assumed that the number who completed the rice crop section (26) was more accurate.
Of the 66 study area respondents, 26 used irrigation for rice and 56 used irrigation for non-rice crops. This means that 10 irrigators solely use irrigation on rice, 40 use irrigation for non-rice crops and 16 use irrigation for a combination of rice and non-rice crops, Figure 3.1.
36 UIO and Unseasonal Flooding of the B-MF
45
40
35
30
25
20 Non rice only
15 Number of RespondentsNumber of 10 Rice and non rice 5 Rice only 0 Cropping Pattern
Figure 3.1: Cropping pattern of respondents in the study area
The results from the interview questionnaire will be discussed in the order of the questions. A full discussion of results is presented in Appendix A. Note that during the results section the number of respondents to questions within each section varied. Therefore the total number of respondents to each question has been listed during the discussion of the results.
3.1.2 General results
As previously stated a decreased water allocation in itself will affect the volume of UIO for a given percentage of orders that become UIO. If a link existed between crop types and an increased likelihood of rejections (this is discussed later in this section), then it was necessary to determine if there was a link between water allocation and crop areas.
The first question addressed here is the impact that water allocation has on crop areas. Of the 66 irrigators, 78.8% said that the water allocation affected the area they planted to irrigated crops. All of the crop responses are based on what impact a reduced water allocation would have on each crop area. Rice was the crop area most affected. 93.6% (29 of 31) said they would reduce their rice area, the other two responded that they would keep their rice area constant and decrease other irrigated crop areas, Figure 3.2.
The second most affected crop was irrigated cereals. The responses for irrigated cereals with a reduced water allocation were (Figure 3.3): • 81.8% (27 of 33) would reduce their area; • 12.1% (4 of 33) would keep their area constant; and
37 UIO and Unseasonal Flooding of the B-MF
• 6.1% (2 of 33) would increase their area.
35
30
25 Decrease
20
15
10 Number of RespondentsNumber of Increase Keep constant 5
0 Crop area change
Figure 3.2: Changes in rice area with reduced allocation
30
25 Decrease 20
15
10 Number of RespondentsNumber of 5
Keep constant Increase 0 Crop area change
Figure 3.3: Changes in irrigated cereal area with reduced allocation
Responses for irrigated landuses other than irrigated cereals and rice were not as conclusive. For winter irrigated pasture 24 of 48 (50%) respondents said they would decrease their area, while 20 of 48 (41.7%) said they would keep their area constant, Figure 3.4.
A similar trend to winter irrigated pasture existed for irrigation of lucerne/summer pasture with 15 of 29 (51.7%) responding that they would decrease their area, 9 (31.0%) responding that they would keep their area constant and 5 (17.2%) responding that they would increase their area.
38 UIO and Unseasonal Flooding of the B-MF
30
25
20 Decrease
15
10
Number of RespondentsNumber of Keep constant 5
Increase 0 Crop area change
Figure 3.4: Changes in winter irrigated pasture area with reduced allocation
From the above it is concluded that with a reduced water allocation it is likely the area of irrigated landuse would be reduced. This was particularly evident for rice and irrigated cereals. The effect on winter irrigated pasture and lucerne/summer pasture was not as evident though a trend to reduce these areas was present. An unexpected finding for both irrigated cereals and winter irrigated pasture was that with a reduced allocation less than 50% of irrigators would increase their dryland area of these crops. It was expected that this would be much closer to 100%.
To determine the current level of water recycling on-farms as an input into the chosen model (Chapter 5) and to determine the percentage of properties that presently have OFWS the results from Question 4 were used. The percentage of farms with OFWS greater than 10 ML was 37.9 %, with 24 of the 25 respondents listing recycling as a use of their dam. Of the farms with OFWS used for recycling it was found that 11 out of 26 (42.3%, 26 irrigators completed the rice section) rice farms had OFWS with recycling while 13 out of 40 (32.5%) non-rice farms had OFWS used for recycling. An F-test was carried out to determine if there was a statistical link between growing rice and having an OFWS used for recycling. The result from the F-test was 0.72, indicating that a link between growing rice and having an OFWS used for recycling was unlikely. For this reason 38% of the irrigated area will be listed as having access to recycled water from OFWS in the establishment of the study area in the chosen model (Chapter 5).
39 UIO and Unseasonal Flooding of the B-MF
Question 5 asked irrigators whether there were particular times during the irrigation season they were less likely to consider weather predictions when placing an order. If they answered ‘Yes’ they were asked to specify the time of year and the crop.
It was found that 39 out of 66 (59.1%) irrigators responded ‘Yes’. The most common crop was rice (19 of 31 (61.3%)) for the months of October and January. The second most common crop was irrigated cereals (15 of 33 (45.5%)) for the months of March, September and October.
Only 9 of 48 (18.8%) and 5 of 29 (17.2%) respondents to winter irrigated pasture and lucerne/summer pasture, respectively, answered ‘Yes’. The months for winter irrigated pasture were March, April, September and October and the period between October and March for lucerne/summer pasture. These results have the potential to inform the operation of the rejection matrix (Chapter 5).
3.1.3 Non-rice crops results
Orders
With regards to the factors (e.g. soil moisture) that influence placing an order for non- rice crops the following was found. Only 5 out of 56 (8.9%) irrigators used a soil moisture probe as part of their management approach. This indicates a lack of technology in the approach for placing an order for non-rice crops. The most common management approach for this category was ‘observation of crop and soil’ with 53 out of 56 (94.6%). The second most common response was the climatic conditions since the last irrigation with 45 out of 56 (80.4%). The above three options were all soil moisture related, and every irrigator selected at least one of these options.
There were also two non-soil moisture related categories; using a ‘pre-scheduled time period’ (14 out of 56 (25.0%)) and ‘checking the weather forecast’ (38 out of 56 (67.9%)). From the above it can be concluded that there are two important parameters to be included in the irrigator decision process for non-rice crops (Section 5.1.5). These are the current soil moisture and the weather forecast, used by 100% and 68%, respectively of irrigators.
From the responses the question 6 which focused on the factors affecting the volume of water an irrigator orders, it was found that the most common response was the
40 UIO and Unseasonal Flooding of the B-MF
‘maximum flow rates for paddock and/or infrastructure design’. Forty two out of 56 (75.0%) respondents listed this option. This response was considered as having the fundamental purpose of returning the field to field capacity as quickly as possible. The category of ‘water consumed since last irrigation’ was also considered to fall into the category of returning the field to field capacity. Only 5 out of 56 respondents did not select either of the above two responses as a reason for deciding the volume of water that they ordered. For this reason the model criteria to determine the volume of water ordered in the irrigation decision process for non-irrigated crops (Section 5.1.5) will use the volume required to return the soil to field capacity.
The number of times irrigators order water is important to gauge because it is used in calibrating the chosen model (Chapter 5). Twenty-two out of 56 (39.3%) respondents said that they would place more than 20 orders for non-rice crops in a season. However, there was a broad range of responses as 24 (42.9%) said they would place less than 10 orders while the other 10 (17.9%) would place between 10 and 20 orders over a season.
From the responses to the above three question responses, it can be concluded that: • the majority of irrigators use observation of the soil and crop to determine when to place an order; • the majority of irrigators order their volume based on the capacity of their on- farm infrastructure; and • there is a high degree of variability in the number of orders placed across the season from less than 10 to more than 20.
Rejections
The next area investigated was how often (number of time per season) and why (type of weather events) irrigators place rejections. The investigation consists of two scales of rejection events; events greater than 5% of orders volume and events greater than 50% of the order volume. This information will be used in the calibration of the rejection matrix (Section 5.7.8). One of the best uses of this information was to determine a percentage of irrigators who never place rejections. Thirteen out of 56 (23.2%) stated that they never place rejections and 7 responded that given one or more of the circumstances listed they would in fact reject a small portion of their ordered water.
41 UIO and Unseasonal Flooding of the B-MF
This indicates that the number of irrigators who would never reject some of their order may be as low as 6 out of 56 (10.7%).
Two irrigators who answered that they would reject some of their order, failed to complete the question regarding the circumstances that would lead to this rejection. The responses to circumstances leading to irrigators altering their order were: • 41 out of 48 (85.4%) selected ‘rainfall of greater than 20mm but little change in temperature’; • 20 out of 48 (41.7%) selected ‘cool change with greater than 10mm of rainfall’; • 13 out of 48 (27.1%) selected ‘rainfall of less than 20mm but little change in temperature’; • 2 out of 48 (4.2%) selected ‘cool change with less than 10mm of rainfall’; and • 2 out of 48 (4.2%) selected ‘dry cool change’.
With respect to rejecting greater than 50% of their order, 23 out of 55 (41.8%) irrigators responded that they would never do this. Of these 23, 13 also responded that given one of the circumstances listed in the following question they would in fact reject greater than 50% of their order. This suggests that the number of irrigators who would never reject more than 50% of their order could be as low as 10 out of 55 (18.2%). The main responses to reasons for large irrigation rejections were: • 39 out of 44 (88.6%) selected ‘rainfall of greater than 20mm but little change in temperature’; • 9 out of 44 (20.4%) selected ‘cool change and greater than 10mm of rainfall’; and • 7 out of 44 (15.9%) selected ‘rainfall of less than 20mm but little change in temperature’.
To determine when irrigators reject their orders and to determine if MIL’s rules regarding rejections influenced irrigators’ behaviour, they were questioned on when they place their rejection. MIL’s rule states that unless 36 hours notice is given for a rejection an irrigator must take their first 12 hours of water. Only 2 out of 47 (4.3%) irrigators indicated that they would reject their order 36 hours prior to delivery. From this it can be concluded that MIL’s rejection rules do not force irrigators to give MIL 36 hours notice of a cancellation. Thirty-two out of 47 (68.1%) respondents’ said that
42 UIO and Unseasonal Flooding of the B-MF the time they cancel their order is based on the time the weather event occurs. Eight out of 47 (17.0%) indicated that they reject their order 0 to 36 hours prior to the water arriving. This indicates that they ring up prior to 6:30 am and cancel their order and then their supply is shut down by the channel attendant. This information is used in Chapter 5 to determine when a check is made in the model to place a rejection.
From the above it can be concluded that for non-rice crops 81.8% of irrigators will reject greater than 50% of their irrigation, given a certain weather event. The size of the rainfall event seems to have the greatest influence on irrigators’ behaviour with rainfall of greater than 20mm causing the majority of irrigators to place a rejection. The majority of rejections depend on the time of the weather event, with only 4.3% indicating that they would provide MIL with 36 hours notice.
3.1.4 Rice crop results
Orders
A major difference exists between irrigation of rice and irrigation of non-rice crops during an extended period of dry weather. During these times rice requires a continuous supply of irrigation water, while non-rice crops have wetting (irrigation) and drying (no irrigation) phases. For this reason the options to the question regarding ‘what is your management approach for ordering water’ for rice were limited to ‘depth of the ponding water on bays’ and ‘pre-scheduled daily order’. All of the respondents listed ‘depth of the ponding water on bays’ as their management approach for ordering. This was largely as expected. Of more interest from the interview questionnaire were the responses to the question ‘how do you decide the volume of water ordered’. Twenty-three out of 26 (88.5%) respondents’ replied that the volume of water ordered is influenced by returning the ponding depth of water to an average level. Only 7 out of 26 (26.9%) said that they would check the four or seven day weather forecast when deciding what volume of water to order. This number was lower than expected. This result suggests that the majority of rice irrigators are reactive regarding the depth of water on their ponded bays rather than being proactive.
From the above it can be concluded that there are two parameters that need considering in the irrigators decision process for rice crops (Section 5.1.5), these are the current
43 UIO and Unseasonal Flooding of the B-MF ponding depth (equivalent to soil moisture in non-rice crops) and the forecast weather conditions.
The number of orders placed during the irrigation season will again be used for calibration and validation of OASIS. It was found that 16 out of 26 (61.5%) respondents place more than 20 orders in a season. Only 1 (3.9%) irrigator replied that they placed less than 10 orders in an irrigation season. It is assumed that this person was indicating that he/she ‘changes’ their order less than 10 times during the season. The irrigation of rice is focused around maintaining the depth of ponded water at a certain depth. This leads to a much higher number of orders being placed in comparison to irrigation of non-rice crops.
Rejections
From the questions regarding the number of irrigators placing rejections only 1 out of 26 respondents stated that they would never place a rejection. The two most common responses for this question were: • ‘rainfall of greater than 20mm and little change in temperature’ (21 of 25 (84.0%)); and • 9 out of 25 (36.0%) selected ‘cool change with greater than 10mm’.
The results for the rice section (question 18) were compared to the results for the non- rice crops section (question 9). The result of an F-test was 0.007, indicating that they were statistically different. From this it is concluded that rice irrigators are more likely to place a rejection than non-rice irrigators. This indicated that it was necessary to include rice-specific information in the rice section of the model.
Twenty-four out of 25 (96.0%) rice irrigators stated that they would place a rejection of greater than 50% of an order at some time during the season. This is significantly higher than the results for the non-rice crops irrigation (question 11). The one person who replied that they would never place a rejection greater than 50% of their order also answered that given a rainfall event of greater than 20mm they would in fact place a rejection of more than 50% of their order. The most common responses were ‘rainfall of greater than 20mm but little change in temperature’ (22 out of 25 (88.0%)) and a ‘cool change with greater than 10mm of rainfall’ (9 of 25 (36.0%)). The results for the rice section were compared to the results from the non-rice crops section (question 11).
44 UIO and Unseasonal Flooding of the B-MF
An F-test produced 0.0002, indicating that they are statistically different. From this it is concluded that rice irrigators are more likely to place a rejection of more than 50% of an order than non-rice irrigators. This reinforces the statement above: it is necessary to include rice specific information in the model.
Rice irrigators were also questioned on the circumstances of them placing a rejection. Again the majority of irrigators (20 out of 25 (80.0%)) said they would reduce their order based on the time of the weather event. Only, 3 out of 25 (12.0%) responded that they would cancel 36 hours prior to their time of delivery. Again this information is used in Chapter 5 to determine when a check is made to place a rejection in the model.
From the above it can be concluded that rejections are more common for rice crops than for non-rice crops. The size of the rainfall event again seems to be the factor driving irrigators to place rejections and the time of the weather event seems to dictate when rejections are placed.
The above investigation has gained irrigator responses into their reasons for placing orders and rejections. To gain an independent confirmation of the variables that caused UIO and unseasonal flooding of the BM-F, regression analysis was used.
3.2 Regression Analysis
If one variable (response variable) is thought to be the result of several characteristics (explanatory variables) a multiple regression or multivariate analysis is required to study their relationship. In this research there are two separate cases (UIO and flooding of the B-MF) where the response variable is thought to be the result of several explanatory variables.
The simplest method of multiple regression analysis is MLR, which assumes a linear relationship between the response variable and the explanatory variables. MLR requires no assumptions to be made concerning the distribution of either the response or explanatory variables. Other regression analysis techniques include ridge regression (Draper 1998), weighted least squares regression (Helsel and Hirsch 1992), nonlinear regression (NIST/SEMATECH 2007) and Generalized Additive Models (Der and Everitt 2006). The benefits, drawbacks and uses of these are discussed below.
45 UIO and Unseasonal Flooding of the B-MF
Ridge regression and weighted least squares regression are adaptations of MLR. Ridge regression adds specific additional information to overcome “ill-conditioned” data and should only be used when there is multi-collinearity (see Appendix B for more detail) between variables (Draper 1998). Weighted least squares regression is used when there is heteroscedasticity of data (see Appendix B for more detail) (Helsel and Hirsch 1992).
The major advantage of nonlinear regression is its ability to fit a broad range of functions to the response variable. Nonlinear regression, like MLR uses the method of least squares to estimate the best parameter values. The difference is that nonlinear regression requires iterative optimization procedures to compute the parameter estimates. Iterative optimization procedures unlike linear regression (where the estimates of the parameters can always be calculated analytically) require the user to provide starting values for the unknown parameters. The optimization is quite sensitive to these starting values and may converge to local minima rather than the global minima depending on the starting values inputted into the model (NIST/SEMATECH 2007).
Another category of nonlinear analysis is Generalized Additive Models (GAM). These are again very similar to MLR models but GAM use a scatter plot smoother to explain the behaviour of the explanatory variables. The scatter plot smoother terms often take the form of a low degree polynomial (Der and Everitt 2006).
Both nonlinear and GAM include additional complexity in the model function. For this reason MLR is the chosen method of multiple regression analysis. Prior to applying MLR analysis in this research the concept of multivariate analysis was investigated, primarily the techniques of Principal Component Analysis (PCA) and Factor Analysis (FA). PCA can be applied when there are a large number of interrelated variables. PCA aims to reduce the dimensionality of the data set, without compromising the variation in the data set. This is achieved by transforming the original interrelated variables into a new set of uncorrelated variables (named Principal Components) (Jolliffe 2002). FA is similar to PCA in that the interrelated variables are replaced with a set of new uncorrelated variables. But the aim of FA is to try and explain as much as possible of the interrelationship between variables (Afifi, Clark et al. 2004).
46 UIO and Unseasonal Flooding of the B-MF
The difference between multivariate analysis and regression techniques is that multivariate techniques do not try and explain the behaviour of a response variable by selected explanatory variables as is the case with regression analysis. As this research is primarily focused on investigating the behaviour of a response variable based on a number of explanatory variables in the simplest possible manner the technique of MLR will be initially employed.
MLR has had many applications in irrigation research. The work most relevant to this research is the application of MLR for irrigation demand problems (Yomota and Ndegwa 1995; Pulido-Calvo, Roldan et al. 2003). Pulido-Calvo et al. (2003) applied MLR to demand forecasting in irrigation water distribution systems. Yomota and Ndegwa (1995) applied MLR to determine the actual irrigation water use in irrigation districts in Japan. MLR has not previously been applied to rejections or UIO problems.
MLR has had numerous applications to river flow forecasting (Dawson 1999; Sui 2005; Chetan and Sudheer 2006). These works have focused on rainfall-runoff processes rather than determining the contribution to river flow of regulated and unregulated components as is the case in this research. Of the other techniques investigated ridge regression (Yu and Liong 2007) and PCA (Pandzic and Trninic 1992) were the only two to have had application to river flow research. Yu and Liong (2007) used ridge regression for forecasting of a hydrological time series. Pandzic and Trninic (1992) applied PCA to investigate anomaly fields in river basin discharge and precipitation attributed to global circulation.
3.3 Multiple Linear Regression
MLR is essentially an optimization problem, minimizing the difference between the Ordinary Least Squares (OLS) estimate of the response variable and the observed response variable for each recorded observation used in the optimization. Battaglin and Goolsby (1997) states that explanatory variables need to be significantly correlated (P < 0.1) with the response variables. The MLR model (Montgomery and Runger 2007) is of the form (Equation 3.1).
y = β 0 + β 1x1 + β 2 x2 +Λ + βkxk + ε Equation 3.1
47 UIO and Unseasonal Flooding of the B-MF
Where: y is the response variable;
β j j = 0, 1,…, k are called the regression coefficients; x j j = 1, 2,…, k are called the regression (explanatory) variables; and ε is a random error term.
To simplify the notation in Equation 3.1 the subscript i, referring to i = 1, 2,…, n observations has been omitted.
The OLS method of optimization is given by Equation 3.2 (Helsel and Hirsch 1992). n ∧ (y − y )2 ∑ i i Equation 3.2 i=1
Where th yi is the i observation of the response variable; and
∧ th y i is the MLR calculation of the i response variable.
The computer package Minitab 15 was used to carry out the MLR analysis.
3.3.1 Identifying the best MLR model
With MLR it is possible to have a number of models which fit the data to a similar degree in terms of their correlation (R2) values. To determine which model is most appropriate it is necessary to investigate not only the R2 values but also (Helsel and Hirsch 1992): • the Root Mean Square Error (RMSE); • plots of the residuals to check for heteroscedasticity; • Variance Inflation Factor (VIF) to check for multi-collinearity; • the Durbin-Watson (D-W) statistic to check for autocorrelation; and • the explanatory power of each explanatory variable.
More detail on heteroscedasticity, multi-collinearity and autocorrelation is provided in Appendix B.
The explanatory power of each explanatory variable is important because the addition of any explanatory variable (even an unrelated one) will increase the R2 to some
48 UIO and Unseasonal Flooding of the B-MF degree. The explanatory power of each variable can be checked with each variable’s t- value (or partial F test), p-value and regression coefficient. Most computer packages report either the t-statistic or the result from a partial F test, where t2 = F. Unless all of the explanatory variables in a MLR model pass the tests listed below (Helsel and Hirsch 1992), one of them is not adding explanatory power to the model: • magnitude of the t-values are greater than 2; • p-values are less than 0.1; and • the regression coefficients are significantly greater than zero.
In MLR there are three methods of removing explanatory variables that are not adding power to the model; these are step wise, forward and backwards procedures. The step wise procedure was used in this research.
Apart from the above checks, there are three statistics used to evaluate the overall quality of the model. These are Mallow’s CP, the PRediction Error Sum of Squares (PRESS) statistic and the adjusted R2 (Helsel and Hirsch 1992). Minitab provides both the PRESS and adjusted R2 statistic. Helsel and Hirsch (1992) states that when the number of observations are considerably greater than the degrees of freedom (explanatory variables + 1) then the adjusted R2 statistic can be insensitive. For this reason the PRESS statistic (Equation 3.3), along with the other factors listed above, will be used to determine the most appropriate model. n PRESS = e(i)2 ∑ Equation 3.3 i=1
Where e(i) is the prediction residual.
The prediction residual is defined by Equation 3.4.
∧ e(i) = yi − y(i) Equation 3.4
Where
∧ y(i) is the regression estimate of yi based on a regression equation computed by leaving out the ith observation.
The MLR analysis process described above will now be applied to the response variables of (i) River Murray flows at Tocumwal to assess unseasonal flooding of the
49 UIO and Unseasonal Flooding of the B-MF
B-MF (Section 3.4) and (ii) UIO in the study area (Section 3.5). This analysis will provide independent clarification on the explanatory variables that explain these response variables.
3.4 MLR Investigation of Flows at Tocumwal
The first application of MLR analysis is to assess unseasonal flooding of the B-MF. This investigation will encompass a greater physical area than the investigation into UIO. It will be bounded by the flow of the River Murray at Albury (upstream) and the River Murray flow at Tocumwal (downstream). The response variable will be the River Murray flow at Tocumwal on day (t), (T(t)).
To select the explanatory variables, the River Murray system (Figure 1.3 and Figure 3.5) was investigated. Different time lags for different explanatory variables are necessary to account for different travel times (Figure 3.5).
Edward River
ESC (t-2) Study area
MILOff TCap (t) Barmah- (t-2) A (t-6) Millewa and T (t-1) Forest River Murray Hume Reservoir YMC (t-2) River O (t-2) Murray YWA (t-2) K (t-6)
Figure 3.5: Diagram of the River Murray supply to MIL and study area with lag times
Section 1.6 described how demands downstream of the Barmah Choke were required to be met with water from Lake Hume. For this reason it is necessary to include one of the following explanatory variables:
• Tt-1 = River Murray flow at Tocumwal on day (t-1); or
• At-6 = River Murray flow at Albury on day (t-6).
The irrigation diversions and rejections from MIL and G-MW were also considered to be possible explanatory variables. Unfortunately, no information was available on rejections from G-MW. Also, considered worthy of inclusion was seasonal or monthly
50 UIO and Unseasonal Flooding of the B-MF variability in the irrigation diversions (again this information was only available for the MIL system). The following variables were therefore included:
• UIOt-2 = UIO from MIL on day (t-2);
• Dnett(t-2) = nett Mulwala Canal diversion on day (t-2); • AL = seasonal water allocation; • MD = monthly nett Mulwala Canal diversion; and
• YMCt-2 = Yarrawonga Canal diversion by G-MW on day (t-2);
The two tributary rivers that enter the River Murray between Lake Hume and Yarrawonga Weir could contribute unregulated flows. Hence, the following variables were included:
• Kt-6 = Kiewa River inflow on day (t-6); and
• Ot-2 = Ovens River inflow on day (t-2).
The capacity within the system to pass water around the Barmah Choke was considered to add possible explanatory power to the model. This lead to the inclusion of:
• YWAt-2 = Yarrawonga Weir airspace on day (t-2);
• TCapt = River Murray airspace at Tocumwal on day (t); and
• ESCt-2 = Edward River Escape airspace on day (t-2).
Prior to the MLR analysis being used to investigate the unseasonal flood events, these were identified using the method of Thoms et al. (2000). Using this method a flood event is said to occur when the River Murray flow at Tocumwal exceeds 10,600 ML/day. Around the times of the flood events, the above variables with the addition of rainfall were investigated (Appendix B). The major findings of this investigation were that: • A rainfall event of greater than 20mm occurred prior to each flood event; • UIO from the MIA prior to or during the rain event had a peak of at least: − 3,000 ML/day for December events; and − 4,500 ML/day for events later in the season. • Yarrawonga Main Canal diversions played a role, particularly for December flow events; • during December there was a high contribution from the Ovens and Kiewa Rivers;
51 UIO and Unseasonal Flooding of the B-MF
• for all flood events there was minimal capacity in the River Murray system (Edward River Escape, Yarrawonga Weir and River Murray capacity at Tocumwal) to bypass water around the Barmah Choke; • the only season without a flood event was in the lowest water allocation season (2002/03); and • flood events occurred in the months of December, January and March, which were the three highest ranked months with respect to monthly nett Mulwala Canal diversion.
3.4.1 Calibration and validation of the MLR models
There were five seasons (1999/00 to 2003/04) of UIO data from MIL. Season 2002/03, was not used as it was a very low allocation season (8% allocation for MIL) and there was no unseasonal flooding of the B-MF. To determine the best MLR model for the response variable (flow at Tocumwal on day(t)), the methodology outlined below was followed: i. Undertake a single linear regression analysis to determine the level of
correlation between Tt and Tt-1, using seasons 1999/00 and 2000/01 for calibration. If the level of correlation is high this will lead to autocorrelation problems during the MLR analysis. If a high level of correlation is found, then
Tt-1 will be replaced with At-6. ii. Group various combinations of the 4 seasons of data to produce 6 combinations of seasons (Table 3.1). Allowing 6 MLR runs to produce 6 calibrated and validated MLR models.
Table 3.1: Calibration and validation seasons for each run Run/Model Calibration seasons Validation seasons A 1999/00 and 2000/01 2001/02 and 2003/04 B 2001/02 and 2003/04 1999/00 and 2000/01 C 1999/00 and 2001/02 2000/01 and 2003/04 D 1999/00 and 2003/04 2000/01 and 2001/02 E 2000/01 and 2003/04 1999/00 and 2001/02 F 2000/01 and 2001/02 1999/00 and 2003/04 iii. Determine whether Kt-6 offers any explanatory power to Model A. This is
because data for Kt-6 was only present until the end of the 2000/01 season. If
Kt-6 adds explanatory power to the model then seek additional information on this variable.
52 UIO and Unseasonal Flooding of the B-MF
iv. Perform the step wise procedure on each Run (A to F) to determine the best MLR model. v. Check for multi-collinearity by checking the regression coefficient sign, p-value (greater than 0.1) and VIF (Helsel and Hirsch 1992) of each explanatory variable in each Run (A to F), Equation 3.5. 1 VIF = j 2 Equation 3.5 ()1− R j
Where 2 2 th Rj is the R from a regression of the j explanatory variable on all of the other explanatory variables. vi. Check for autocorrelation in the model using the Durbin-Watson (D-W) statistic test. The D-W statistic (d) is given by Equation 3.6 (Helsel and Hirsch 1992).
2 n ⎛ ∧ ∧ ⎞ ∑⎜ei − ei−1 ⎟ d = i=1 ⎝ ⎠ n ∧ Equation 3.6 2 ∑ ei i=1 Where
ei is the present residual; and
ei−1 is the previous residual. vii. Validate the model for each Run (A to F). To validate the models, the MLR equation was applied to the appropriate validation seasons (those listed in Table 3.1) for each model run and the results compared to the actual data. viii. Check for any curvature or heteroscedasticity in both the calibrated and validated models by plotting the residuals against the MLR predicted values of the model in each Run (A to F). Curvature occurs if the residuals move away from the MLR predicted values with time or with magnitude. ix. If curvature or heteroscedasticity is identified in step (viii) then partial residual plots are required for all explanatory variables in the model. In a partial residual plot using MLR, the partial residual (Equation 3.7) is plotted against the adjusted variable (Equation 3.8) (Helsel and Hirsch 1992).
∧ * e j = y − y( j) Equation 3.7
∧ * x j = x − x( j) Equation 3.8
53 UIO and Unseasonal Flooding of the B-MF
Where
* th e j is the partial residual for the j variable; y is the observed value;
∧ y( j) is the predicted value of y from a regression equation where xj is omitted from the model;
* x j is the adjusted explanatory variable; x is the observed explanatory variable; and
∧ x( j) is the xj predicted from a regression against all other explanatory variables. x. Preprocess the explanatory variable(s) as identified in step (viii) and (ix). xi. Undertake steps (iv) to (ix) with any transformed explanatory variable(s). xii. Rank the 6 runs, on the basis of their validated R2.
3.4.2 MLR model investigation
2 From step (i) above (single linear regression analysis between Tt and Tt-1, an R of 0.99 was produced. For this reason the explanatory variable Tt-1, was excluded from any further analysis and replaced with the explanatory variable At-6.
The results from step (ii) showed that the explanatory variable Kt-6 provided no explanatory power to the model. For this reason and the lack of daily flow data for Kiewa River it was excluded from the other Runs (B to F).
During step (vii) (check for heteroscedasticity) it was found that all the validated models (except Model E), showed non-linear behaviour of the residuals. The non- linear behaviour of the residuals occurred for MLR predicted flows greater than approximately 12,500 ML/day at Tocumwal. Figure 3.6 shows the plot of residuals versus MLR predicted flow at Tocumwal for validation of Model A.
From the plot of residuals against each explanatory variable (step (viii)) it was found that the non-linear behaviour of residuals was linked to inflows greater than 2,500
ML/day from Ot-2, Figure 3.7. This indicates that transformation of the explanatory variable Ot-2 is required. Transformation was completed by taking its square root.
54 UIO and Unseasonal Flooding of the B-MF
Steps (iv) to (viii) of the methodology were then repeated for all but model E. This removed the non-linear performance of the model with high inflow values from Ot-2.
4000
2000
0 4000 9000 14000 19000 24000 29000
-2000 Residual
-4000
-6000
-8000 MLR Predicted Flow at Tocumwal (ML/day)
Figure 3.6: Residual versus MLR predicted flow for validation of model A
6000
4000
2000
0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Residual -2000
-4000
-6000
-8000 Inflow from Ovens River (ML/day)
Figure 3.7: Plot of residual against the explanatory variable Ot-2 for the validation of model A
With all models clear of heteroscedasticity the results from the final 6 MLR runs are presented in Table 3.2. This shows the ranking (in order of importance) of the explanatory variables for each of the 6 models.
55 UIO and Unseasonal Flooding of the B-MF
Table 3.2: Ranking of explanatory variables for each run Variable Rank Run 1 2 3 4 5 6 7
A (Ot−2 ) At-6 TCapt YMCt-2 ESCt-2
B TCapt (Ot−2 ) At-6 YWAt-2 Dnett(t-2) YMCt-2 UIOt-2
C TCapt YMCt-2 (Ot−2 ) UIOt-2
D TCapt (Ot−2 ) At-6 YMCt-2 UIOt-2 YWAt-2 Dnett(t-2)
E Ot-2 At-6 TCapt ESCt-2 YMCt-2
F (Ot−2 ) TCapt At-6 ESCt-2 YMCt-2 YWAt-2
Table 3.2 shows that the explanatory variables of TCapt, At-6, (Ot−2 ) or Ot-2 and
YMCt-2 were present in the best model for all six Runs. TCapt was found to be the most important explanatory variable in three of the models. This is unsurprising as it represents the capacity in the River Murray at Tocumwal to pass water without flooding the B-MF. The high importance of (Ot−2 ) or Ot-2 was unexpected. A possible explanation is that inflows from this river are unregulated. The high importance of At-6 is not surprising as it represents the volume of water in the River Murray to meet demands (both upstream and downstream of Tocumwal). Interestingly,
YMCt-2, rather than either of the MIL irrigation variables was present in all six models.
This was unexpected as the peak YMCt-2 diversion is approximately 30% of the peak
Dnett(t-2) diversion. A possible explanation is that the two irrigation diversion variables are not independent and the fluctations in YMCt-2 are more closesly related to the flow at Tocumwal. The variables of Dnett(t-2) and UIOt-2 were present in 2 and 3 models, respectively. The other variables that were present were ESCt-2 and YWAt-2 which were both present in 3 models.
Step (vi) used the D-W statistic to check for autocorrelation in the models. Table 3.3 shows the dL and dh values for 99% confidence that each model required to be cleared of autocorrelation.
Table 3.3: D-W statistic for each model (A to F)
Run/Model D-W statistic dL (Helsel and Hirsch 1992) dh (Helsel and Hirsch 1992) A 0.20 1.46 1.625 B 0.33 1.441 1.647 C 0.29 1.482 1.604 D 0.42 1.441 1.647 E 0.24 1.46 1.625 F 0.18 1.441 1.647
56 UIO and Unseasonal Flooding of the B-MF
Table 3.3 shows, that all Models (A to F) have a D-W statistic significantly lower than the dL value, indicating all models are free of autocorrelation.
3.4.3 Identifying the best MLR model for Flows at Tocumwal
After the above investigation, the models were ranked using their R2 validation performance (Table 3.4).
Table 3.4: Rank of validated models Rank Run (Calibration seasons) R2 for calibration R2 for validation 1 A (1999/00 and 2000/01) 0.92 0.82 2 F (2000/01 and 2001/02) 0.89 0.82 3 E (2000/01 and 2003/04) 0.92 0.79 4 D (1999/00 and 2003/04) 0.94 0.77 5 B (2001/02 and 2003/04) 0.93 0.74 6 C (1999/00 and 2001/02) 0.90 0.57
Table 3.4 shows that Model A and F had very similar R2 for validation. Model A was ranked ahead of model F due to its slightly better performance during calibration. Equation 3.9 shows the regression equation for Model A.
Tt = 4064 − 0.839×TCapt + 0.242× At−6 − 0.221 Equation 3.9 ×YMCt−2 + 0.552× ESCt−2 +114.7 × (Ot−2) The t-value, p-value and VIF of the explanatory variables in Model A were checked for heteroscedasticity (Table 3.5).
Table 3.5: t, p and VIF values for the explanatory variables Variable t P VIF At-6 13.72 0.00 3.3 YMCt-2 -3.21 0.00 1.4 ESCt-2 5.67 0.00 1.7
(Ot−2 ) 23.74 0.00 1.4
TCapt -13.67 0.00 3.3
Table 3.5 shows that all t-values are above |2|, p-values are less than 0.1 and VIF are below 10. The order of importance of the explanatory variables in Model A was:
i. (Ot−2 )
ii. At-6 iii. TCapt
iv. ESCt-2
v. YMCt-2
57 UIO and Unseasonal Flooding of the B-MF
As mentioned previously the R2 for this model was 0.92 for the two seasons of calibration data (Figure 3.8).
25000
R2 = 0.92
20000
15000
10000
5000 MLR Predicted Flow at Tocumwal (ML/day) Tocumwal at Flow Predicted MLR
0 0 5000 10000 15000 20000 25000 Observed Flow at Tocumwal (ML/day)
Figure 3.8: MLR predicted flow at Tocumwal against observed flow at Tocumwal for seasons 1999/00 and 2000/01
Equation 3.9 was applied to the two seasons of 2001/02 and 2003/04 for validation (Figure 3.9), the R2 was found to be 0.82.
20000
18000 R2 = 0.82 16000
14000
12000
10000
8000
6000
4000
MLR Predicted Flow at Tocumwal (ML/day) Tocumwal at Flow Predicted MLR 2000
0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 Observed Flow at Tocumwal (ML/day)
Figure 3.9: MLR predicted flow at Tocumwal against observed flow at Tocumwal for season 2001/02 and 2003/04
58 UIO and Unseasonal Flooding of the B-MF
From the above investigation it can be concluded that UIO are not the sole or most important variable in explaining the River Murray flow at Tocumwal. The above investigation found UIO to be present in 3 of the 6 models and not present in the best model. The three most important variables were found to be the capacity in the River Murray at Tocumwal, inflow from Ovens River and the River Murray flow at Albury. With the River Murray flow at Tocumwal investigated, the research moves to investigate the variables contributing to UIO.
3.5 MLR Investigation of UIO
The aim of this investigation is to gain an adequate understanding of the variables that affect the daily UIO. The result from this investigation will be one of the methods tested for predicting rejections in Chapter 5.
Prior to the MLR analysis being undertaken the climate variables of ET, daily moisture deficit (rainfall - ETo) and daily rainfall (R) were investigated using time series plots to determine if a visual trend was present. The major finding from the time series plots (Appendix B) were that there appeared to be a lag of 2 to 3 days between reductions in ET or the occurrence of rainfall, and increases in UIO. There was also considerable variation in the daily UIO (up to 1,600 ML/day) independent of rainfall.
A single linear regression analysis was then undertaken between rainfall and UIO because the interview questionnaire and local knowledge suggested that rainfall was a main contributing factor to UIO. From the single linear regression analyses (Appendix B) between R and UIO(t), the highest correlation produced was R2 = 0.46. This was found using the size of the rainfall event determined by identifying the number of consecutive days of rainfall and summing the rainfall for this period. The sum of UIO was determined for the period from the first day with rainfall until three days after the last day on which rain fell for that rain event.
As the time series plot and a single linear regression analysis failed to produce any significant correlations, MLR analysis was used to further explain UIO. The investigation into UIO will be contained to the study area. To determine the time period for the investigation, both the water delivered to properties and the UIO were investigated. The months of August and September often had off-allocation water
59 UIO and Unseasonal Flooding of the B-MF
available to irrigators, making the calculation of UIO difficult during these months. For this reason the time period for each season was taken to be October 1st to April 30th. The investigation used seasons 1999/00 and 2000/01 for calibration and seasons 2001/02 and 2003/04 for validation of the best model.
In determining the explanatory variables to include in the MLR investigation of UIO in the study area, the operation of the Murray Irrigation System (Section 2.3), local knowledge and interview questionnaire responses (Section 3.1) were used.
As stated above the interview questionnaire and local knowledge lead to the inclusion of rainfall. From the interview questionnaire responses, the timing of the rejection varied between during and after the rainfall event. As a consequence of these results, rainfall was tested over different time periods. MIL staff thought it was necessary to test evapotranspiration (ET) as this might offer more explanatory power than rainfall. Even though responses to the interview questionnaire stated that rainfall forecast (RF) was not a contributing factor to UIO it was included in the investigation to test these responses. Irrigators in the MIA continue to receive water at their specified rate until they place a stop or their finish date is reached; there is therefore often 4 days worth of water released from Lake Hume at the time of cancellation. For this reason previous days UIO were included in the investigation. Nineteen different models were tested with different combinations of explanatory variables. The lag time and summation period of the explanatory variables were varied (Table 3.6).
The following methodology was used for this investigation: xiii. Check for autocorrelation between UIO(t) and UIO(t-1) using a single linear regression analysis. xiv. Determine the explanatory power that two explanatory variables offer by undertaking three runs with each combination of the explanatory variables of
t=−3 UIO(t-1); ∑ Rt and AO(t-4). These are the three main variables considered to t=0 explain UIO. xv. Include the three explanatory variables from step (ii) in the MLR analysis. xvi. Add the explanatory variable of rainfall forecast (RF) to step (iii). If RF is not found to add any explanatory power to the model remove it from any further MLR analysis runs.
60 UIO and Unseasonal Flooding of the B-MF xvii. Sum AO over the four days from day t = -1 to day t = -4, to determine if this adds any explanatory power to the model. xviii. Sum the historic UIO over different time periods to determine if this adds any explanatory power to the model. xix. Exchange R for ET to determine if this adds any explanatory power to the model. xx. Replace R with moisture deficit (R less ET) to determine if this adds any explanatory power to the model. xxi. Run the analysis with different single days of R and R summed over different periods.
Run 1 shows that a single linear regression analysis between UIO(t) and UIO(t-1) produced R2 = 0.74 and RMSE = 517. Hence UIO(t) is most widely explained by UIO(t-1).
Run 2 shows that without the explanatory variable of UIO(t-1), the two explanatory variables of AO(t-4) and R summed from day t = 0 to t = -3 produce an R2 = 0.51.
Run 3 shows the explanatory variables of R (summed from t = -3 to t = 0) and UIO(t-1) produced an R2 = 0.81 and RMSE = 446. An inspection of the magnitude of the t-value (12.10) and p-value (0.000) for variable R shows that it is adding additional explanatory power to the model. Run 3 performed better than run 4 [UIO(t-1) and AO(t-4)] which produced an R2 = 0.75. An inspection of the magnitude of the t-value (4.31) and p- value (0.000) for variable AO(t-4) shows that it is adding additional explanatory power to the model.
The combination of the three explanatory variables of UIO(t-1), R (summed from t = -3 to t = 0) and AO(t-4), (run 5) increased the model performance to R2 = 0.83 and RMSE = 418. Again all variables were adding explanatory power to the model.
The inclusion of RF in run 6, improved the R2 performance of the model, but the RMSE was also increased. An inspection of the magnitude of the t-value (0.09) and p- value value (0.93) for variable RF shows it is not adding additional explanatory power to the model. For this reason RF was excluded from any further analyses.
61 UIO and Unseasonal Flooding of the B-MF
Table 3.6: MLR Models Trialed Explanatory Variables Model Quality Response Run Variable t- p- t- p- Variable t- p- Variable t- p- Variable Variable 2 R2 RMSE PRESS 1 value value value value 3 value value 4 value value 1 UIO(t) UIO(t-1) 34.83 0.00 0.74 517 114527874 t=−3 2 UIO(t) ∑ Rt 20.21 0.00 AO(t-4) 7.29 0.00 0.51 715 220284249 t=0 t=−3 3 UIO(t) UIO(t-1) 28.27 0.00 ∑ Rt 12.10 0.00 0.81 446 85712388 t=0 4 UIO(t) UIO(t-1) 35.11 0.00 AO(t-4) 4.31 0.00 0.75 507 110120198 t=−3 5 UIO(t) UIO(t-1) 28.53 0.00 ∑ Rt 14.10 0.00 AO(t-4) 7.71 0.00 0.83 418 76462719 t=0 t=−3 t=−3 6 UIO(t) UIO(t-1) 27.78 0.00 ∑ Rt 14.18 0.00 AO(t-4) 7.23 0.00 ∑ RFt 0.09 0.93 0.84 419 69094131 t=0 t=−1 t=−3 t=−4 7 UIO(t) UIO(t-1) 29.27 0.00 ∑ Rt 13.92 0.00 ∑ AOt 6.92 0.00 0.83 422 76462719 t=0 t=−1 t=−3 t=−3 8 UIO(t) ∑UIOt 19.38 0.00 ∑ Rt 19.76 0.00 AO(t-4) 9.95 0.00 0.74 521 116984738 t=−1 t=0 t=−2 t=−3 9 UIO(t) ∑UIOt 27.79 0.00 ∑ Rt 17.21 0.00 AO(t-4) 8.41 0.00 0.78 479 98916436 t=−1 t=0 t=−3 10 UIO(t) UIO(t-1) 30.11 0.00 ∑ ETt -6.98 0.00 AO(t-4) 7.67 0.00 0.78 480 99268395 t=0 t=−3 11 UIO(t) UIO(t-1) 26.34 0.00 ∑ Rt − ETt 13.97 0.00 AO(t-4) 11.42 0.00 0.83 419 75784355 t=0 12 UIO(t) UIO(t-1) 38.70 0.00 R(t) 10.79 0.00 AO(t-4) 5.54 0.00 0.81 449 87415636 13 UIO(t) UIO(t-1) 34.47 0.00 R(t-1) 9.64 0.00 AO(t-4) 5.62 0.00 0.80 459 92170384 14 UIO(t) UIO(t-1) 30.31 0.00 R(t-2) 5.45 0.00 AO(t-4) 5.03 0.00 0.77 490 103422783 15 UIO(t) UIO(t-1) 30.06 0.00 R(t-3) 1.30 0.20 AO(t-4) 4.45 0.00 0.75 506 110060103
62 UIO and Unseasonal Flooding of the B-MF
Explanatory Variables Model Quality Response Run Variable t- p- t- p- Variable t- p- Variable t- p- Variable Variable 2 R2 RMSE PRESS 1 value value value value 3 value value 4 value value t=−1 16 UIO(t) UIO(t-1) 38.02 0.00 ∑ Rt 13.57 0.00 AO(t-4) 6.44 0.00 0.83 423 77710284 t=0 t=−2 17 UIO(t) UIO(t-1) 34.11 0.00 ∑ Rt 14.44 0.00 AO(t-4) 7.10 0.00 0.83 415 74501477 t=0 t=−2 18 UIO(t) UIO(t-1) 30.57 0.00 ∑ Rt 10.07 0.00 AO(t-4) 6.05 0.00 0.80 456 89792809 t=−1 t=−3 19 UIO(t) UIO(t-1) 25.86 0.00 ∑ Rt 9.30 0.00 AO(t-4) 6.36 0.00 0.80 462 92089229 t=0
63 UIO and Unseasonal Flooding of the B-MF
Run 7 shows that summing AO from day t = -4 to day t = -1 slightly reduces the explanatory power of the model. For this reason AO(t-4) was used for the remainder of the investigation.
An investigation (runs 8 and 9) was undertaken to determine if increasing the number of days of UIO that the model utilized, increased the models performance. A comparison of run 8 to run 5 shows that the inclusion of UIO (summed from t = -3 to t = -1) produced a poorer model, with R2 = 0.74 and RMSE = 520 (run 8) compared to R2 = 0.83 and RMSE = 418 (run 5). This is further verified by a comparison of run 5 (1 days UIO) to run 9 (UIO summed from t = -2 to t = -1) where R2 = 0.78 and RMSE = 479.
The investigation of replacing R with ET (summed from t = -3 to t = 0) (Run 10) found that ET reduced the model performance from R2 = 0.83 and RMSE = 418 (run 5, using R) to R2 = 0.78 and RMSE = 480 (run 10). It is concluded that the four day sum of R is of greater explanatory power in the model than the four day sum of ET.
The investigation of replacing R with the four day sum of moisture deficit (R – ET) (Run 11) produced R2 = 0.83 and RMSE = 419. The performance of this model was very similar to that using the four day sum of R (run 5) (R2 = 0.83 and RMSE = 418). It is concluded that the inclusion of ET in a moisture deficit variable doesn’t add any additional explanatory power to the model and adds additional preprocessing of data, so the inclusion of moisture deficit will not be further investigated.
To determine the length of R summation that provided the most explanatory power, it was varied from one to four days. The initial length was four days (run 5) from t = -3 to t = 0, this produced R2 = 0.83 and RMSE = 481. Reducing this to one day t = 0 (run 12) reduced the models performance to R2 = 0.81 and RMSE = 449. The single day of R was changed to determine if this influenced the models performance. Changing R to day (t - 1) (run 13), day (t – 2) (run 14) and day (t – 3) (run 15) reduced the performance of the model to R2 = 0.80 and RMSE = 459; R2 = 0.77 and RMSE = 490 and R2 = 0.75 and RMSE = 506, respectively. As the one day of R was moved further from the day of the response variable the performance of the model showed a decreasing performance. From this it is concluded that the majority of UIO results from R on the day that the UIO is generated. This fits with the results from the survey
64 UIO and Unseasonal Flooding of the B-MF which showed the majority of respondents reduced or rejected their order based on the time of the weather event.
From the various summations of R (runs 16 to 19), only run 17 with R summed from day t = 0 to t = -2 improved the models performance. The R2 remained at 0.83 but the RMSE was reduced to 415. Table 3.6 shows that run 17 was the best MLR model performance, using the explanatory variables of UIO(t-1), the sum of R from t = 0 to t = -2 and AO(t-4).
3.5.1 Model adequacy checks for the best MLR model for UIO
This section describes the best model, found during the investigation and includes the results from checks carried out for heteroscedasticity, multi-collinearity and autocorrelation on this model.
The best model was found to have the following regression equation (Equation 3.10).
t=−2 Equation 3.10 UIO(t) = −274 + 0.733×UIO(t −1) + 0.0661× AO(t − 4) + 43.4× ∑ Rt t=0 As mentioned previously the R2 for this model was 0.83 for the two seasons of calibration data, Figure 3.10.
6000
5000 R2 = 0.83 4000
3000
2000
1000 Observed UIO (ML/day) 0 -2000 -1000 0 1000 2000 3000 4000 5000 -1000
-2000 MLR Predicted UIO (ML/day)
Figure 3.10: MLR predicted UIO against observed UIO for seasons 1999/00 and 2000/01
65 UIO and Unseasonal Flooding of the B-MF
For validation the model (Equation 3.10) was applied to the two seasons of 2001/02 and 2003/04, obtaining R2 = 0.82, Figure 3.11. This indicates a very good calibration and validation.
6000
5000 R2 = 0.82 4000
3000
2000
1000 Observed UIO (ML/day) 0 -2000 -1000 0 1000 2000 3000 4000 5000 6000 -1000
-2000 MLR Predicted UIO (ML/day)
Figure 3.11: MLR predicted UIO against observed UIO for seasons 2001/02 and 2003/04
Table 3.7 shows the t-value, p-value and VIF values for the explanatory variables included in Equation 3.10.
Table 3.7: t-value, p-value and VIF for the explanatory variables Variable t-value p-value VIF UIO(t-1) 34.11 0.000 1.178 AO(t-4) 7.10 0.000 1.023 t=−2 ∑ Rt 14.44 0.000 1.181 t=0
Table 3.7 shows that all t-values have a magnitude greater than |2|, all of the p-values are less than 0.1, indicating that all three variables play a role in explaining the behaviour of the model. The VIF for all explanatory variables are between 1 and 1.2 which is well below the critical value of 10. The D-W statistic of this model was 1.60. For three response variables and 100 observations the D-W critical statistics are shown in Table 3.8 (Helsel and Hirsch 1992). From Table 3.8 it can be concluded with 95% confidence that autocorrelation is not present in this model. This is due to the D-W statistic being between the lower and upper limit for both the 99% and 97.5% tests.
66 UIO and Unseasonal Flooding of the B-MF
Table 3.8: D-W upper and lower limits for three response variables and 100 observations
Probability dL dU 0.01 1.48 1.60 0.025 1.55 1.67 0.05 1.61 1.74
An investigation into the possibility of heteroscedasticity was then performed. As mentioned previously the best method of investigation is to plot the residual against the predicted value from the MLR model, Figure 3.12.
2500
2000
1500
1000
500
0 Residual -2000 -1000 0 1000 2000 3000 4000 5000 -500
-1000
-1500
-2000 MLR Predicted UIO (ML/day)
Figure 3.12: Test for Heteroscedasticity
Figure 3.12 shows a general trend of an increasing range of residuals with increasing MLR model predicted UIO values. The above is inconclusive with respect to the possibility of heteroscedasticity. This was further investigated, firstly with time series plots of the residual and then with partial residual plots. The time series plots (Appendix B) failed to show any evidence of heteroscedasticity at any particular points during the season. The partial residual plots for all three explanatory variables were all linear (Appendix B) indicating that no heteroscedasticity or curvature of residuals is present. Therefore no data transformation was required.
To incorporate the results from the MLR analysis into the irrigation system model chosen, it is necessary for the equation not to incorporate UIO(t-1). This is because the incorporation of UIO(t-1) into the model, cause the errors in the model to compound with each timestep, in turn causing the performance of the model to diverge with time
67 UIO and Unseasonal Flooding of the B-MF from the observed data. Table 3.6 shows that the best performing model that does not include UIO(t-1) is Equation 3.11 (Run 2).
t=0 UIO = −128 + 58.7× ∑ R(t) + 0.116 × AO(t − 4) Equation 3.11 t=−3
This produced an R2 of 0.504 for calibration (Figure 3.13). When the model was applied to the two seasons of 2001/02 and 2003/04 for validation, an R2 of 0.53 (Figure 3.14) was produced.
3500 R2 = 0.504 3000
2500
2000
1500
1000
500 MLR Predicted UIO (ML/day)
0 -2500 -1500 -500 500 1500 2500 3500 4500 5500 -500 Recorded UIO (ML/day)
Figure 3.13: MLR calibration result using rainfall and orders
5000
4000 R2 = 0.528
3000
2000
1000 MLR Predicted UIO (ML/day)
0 -3000 -2000 -1000 0 1000 2000 3000 4000 5000 6000
-1000 Recorded UIO (ML/day)
Figure 3.14: MLR validation result using rainfall and orders
68 UIO and Unseasonal Flooding of the B-MF
3.6 Conclusions
The aim of this chapter was to determine the variables that cause UIO and unseasonal flood events of the B-MF. This was undertaken using an interview questionnaire of irrigators and a MLR analysis of both UIO and unseasonal flood events of the B-MF.
The main conclusions from the interview questionnaire were that with a rainfall event of greater than 20mm, at least 80% of irrigators would reduce their order by greater than 50%. This equates to at least a 40% rejection of orders. The other major finding was that the majority of irrigators wait until the weather event occurs to reduce or reject their order, rather than acting on forecast weather events.
From the MLR analysis of unseasonal flood events of the B-MF, the best model included the inflow from Ovens River, River Murray flow at Albury, capacity in the River Murray at Tocumwal, Edward River Escape airspace and Yarrawonga Main Canal diversion.
Interestingly, UIO(t-2) was not contained in the best model. To further investigate its possible link to the River Murray flow at Tocumwal, a run was undertaken without the explanatory variables of Dnett(t-2) and YMC(t-2) to determine whether the inclusion of these two variables was leading to the explanatory variable UIO(t-2) being made redundant or whether in fact flooding of the B-MF could not be linked to UIO. The result was that the UIO did add explanatory power to the model in 4 of the 6 calibration data sets. Hence, UIO(t-2) is not without influence on the River Murray flow at Tocumwal. This reinforced the need to further investigate the causes of UIO.
The analysis into UIO found that the parameter which contributed most significantly to UIO was the previous day’s UIO with a single regression analysis between these two parameters producing R2 = 0.74. The addition of rainfall summed from day t = 0 to t = -2 was found to add explanatory power to the model. The order placed four days previously was also found to add explanatory power to the model. This was unsurprising as the volume of UIO is a factor of the order volume. With the inclusion of rainfall, order and UIO from the previous day the MLR model produced R2 = 0.83.
The variable, rainfall forecast was not found to add explanatory power to the model which indicates that irrigators do not rely on rainfall forecasts when canceling orders.
69 UIO and Unseasonal Flooding of the B-MF
The variable of ET, either individually or in the form of moisture deficit was found not to improve the models performance.
This research found a link between rainfall and UIO. Rainfall was found to be the second most important variable with respect to explaining UIO. A link was found between UIO and unseasonal flooding of the B-MF in some seasons contray to conventional belief. UIO were not found to be the sole or most important variable with respect to explaining unseasonal flooding of the B-MF. It can therefore be concluded that there is some degree of correlation between rainfall, UIO and unseasonal flooding of the B-MF.
The above provides substantiated evidence for the need to investigate management options to prevent rain rejection events continuing to contribute to unseasonal flooding of the B-MF. This research investigates the management options of capturing and storing UIO in OFWS or en-route storages. The next step in this investigation is to select an appropriate model to use in this investigation. The selection of an appropriate model for the modelling is the focus of chapter 4.
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4 Irrigation System Models
Chapter 3 explored the factors that contribute to both UIO and unseasonal flooding of the B-MF. The factors found to explain UIO were the previous day’s UIO, order volume and rainfall summed from the present day until two days previous. The remainder of this research is focused on predicting and testing different storage options for UIO, hence it is necessary for the model selected to either represent these factors or be easily updated to represent these factors.
This chapter provides a description of system operation and irrigation simulation models that are used to describe irrigation system processes. Section 4.1 describes the model requirements for this research and develops a set of criteria to assess the capabilities of models for this research. A review of system operation and irrigation simulation models is contained in Section 4.2. Section 4.3 provides a detailed description of the most applicable model for this research and Section 4.4 describes its previous applications.
Some of the problems faced in irrigation water management are optimization based problems such as minimizing water leaked to groundwater via the management of the volume and timing of irrigations at the field level. Another system level optimization problem is the optimal release of water from reservoirs to meet irrigation requirements.
The other major group of problems is managerial problems. That is, assessing how various management problems will affect the performance of the system. An example is how an increase in distribution level storage will affect the performance of the system. All of these problems are better handled by simulation modelling. Optimization problems usually link a simulation model with an optimization algorithm. Depending on the problem, an individual simulation process may be undertaken separately or several simulation processes linked together. Individual simulation processes are: • crop simulation;
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• reservoir release simulation; • canal simulation; • water allocation; • real time simulation of the irrigation distribution system; and • irrigation system simulation for planning.
Crop simulation modelling refers to simulations undertaken to represent crop growth in response to environmental and agronomic factors that are constrained to the field level. They contain a field scale water balance and/or a crop growth module. Reservoir release simulation is primarily concerned with matching reservoir releases to irrigator’s and other water needs. Crop simulation and reservoir release simulation models are often used in conjunction with optimization algorithms to maximize specific objectives. An example of an objective is minimizing the deep drainage.
Canal simulation is used for a variety of problems, including flow volumes and velocities. Water allocation models are used to determine the impact of changes to the river management, on all water users. Real time simulation of the irrigation distribution system is used to assess the performance of the system in real time. Irrigation system simulation for planning is used to determine how various management options will impact on an irrigation system’s performance. Irrigation system simulation for planning is the focus of this research.
4.1 Model Capabilities for this Research
The focus of this research is such that the model selected must be capable of adequately representing the system both at the field level (crop water demand) and at the main system level (main canals). The model requirements for this research are that the model must have or be easily adjustable to include the following characteristics.
I. Representation of multiple water sources, including on-farm recycling Irrigators in the MIA obtain water from the following sources; irrigation canals, OFWS (storing off-allocation water and recycling of tail water) and groundwater. The model needs to be capable of representing all of these water sources.
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II. Time step The temporary storage of UIO will be short term (approximately 7 to 10 days) due to the volume of orders to be stored and the return of irrigation demands after the rejection event. Due to this, a daily time step is required to adequately represent the daily volume of UIO to be stored and accurately assess each storage performance. A smaller time step, e.g. hourly, is not required because the model will only be used to simulate the hydraulic operation of the canal delivery system.
III. Rice, crop and pasture irrigation processes The MIA has significant areas of rice, winter irrigated pasture, lucerne/summer pasture, winter cereals and horticulture from Lake Hume. All of these crops have different irrigation scheduling processes. So the model needs to be capable of representing all the above agricultural crops, each with an individual irrigation scheduling process.
IV. Crop driven water demand MIL supplies water to irrigators upon their demand, using orders to account for the 4 day travel time of water. The main reasons irrigators place orders are to avoid water stressing the crop, climatic conditions since the last irrigation and weather predictions. Ninety five percent of irrigators (Chapter 3) listed avoiding water stress on the crop as a reason for placing an order. Initially, to calibrate the model a crop driven water demand capability is required. This will ensure the most accurate calibration possible.
V. Order/rejection capabilities Once the model is calibrated, the model needs to incorporate an order/rejection capability to provide the most accurate representation of the system possible. This will allow the correct demand and rejection representation of the system.
VI. Lag time between demand for irrigation and water delivery The MIL system operates on a 4-day order process. So the model must be capable of allowing for a lag between order placement and water delivery, 4 days in this case.
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VII. Storage capabilities both at the system and farm level Thirty eight percent (Chapter 3) of MIA irrigators presently have OFWS, mainly used for recycling irrigation water and storing off-allocation water. These need to be represented in the model. The model also needs to have the ability to model storages at the system level. This will allow the scenario assessment to be undertaken placing storages en-route and on-farm to store UIO.
VIII. Adequate canal representation The management objective of this research means that the model is not required to route water through the canal system on an hourly or smaller time step. Hence, the model is not required to provide a hydrodynamic representation of each and every canal in the MIA. However, the model still needs to have adequate representation of the MIL canal system and the constraints and losses associated with water flow in canals. The general modelling method for irrigation system representation is through dividing fractions of the study area into demand nodes. So the model needs to have this capability.
IX. System losses The model needs to include system losses (seepage and evaporation) as these are significant in the overall water balance of the system. Depending on water allocation these can be as high as 24.5% of the inflow into the head of the irrigation system (Marshall 2004).
X. Source code availability or module plug-in capability Ideally the source code of the model will be available to allow alteration of the model to include all of the above requirements that are not present in the original model. Alternatively if the model and sufficient details are available the model could operate on its original code using a module plug-in capability to incorporate requirements that were not originally present in the model.
From a review of literature (Section 4.2) not one occurrence was found of a model that had been developed with the ability to place an order allowing a lag time between placement of an order and the water delivery. So no matter the model chosen this will need to be added to the model. Another requirement for the chosen model will be the ability to place a rejection.
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4.2 A Review of Irrigation Simulation Models
The nature of this research dictates that either a system operation model or an irrigation system planning model is used. System operation models are used to assess and improve the performance of the system by replicating them in near real time. Irrigation system planning models are used to assess the impact management decisions will have on an irrigation system.
Irrigation system models generally incorporate a crop-water simulation module and a form of aggregating these individual field demands at nodes and utilizing a supply network to supply water to nodes. Most of these models have a three scale simulation process. The largest scale is the system, next is the unit or demand node and the smallest is the field. Included at the system scale are the main and secondary canals, characteristics generally include; flow capacity, seepage rates (efficiency) and connectivity to units or demand nodes. The unit scale represents a set of fields and generally an efficiency parameter to characterize the losses in supplying the field requirements from either the main or secondary canal. The characteristics included at the field scale are crop type, soil type, type of irrigation and efficiency of water application at the paddock scale.
The next section includes a review of the following selected system operation and irrigation simulation models: • Irrigation Main System OPeration (IMSOP); • Irrigation Network Control and Analysis (INCA); • Command Area Decision Support Model (CADSM); • the model described in Singh et al. (1997); • Options AnalysiS in Irrigation Systems (OASIS); and • Tiddalik.
4.2.1 Irrigation Main System OPeration (IMSOP)
Irrigation Main System OPeration (IMSOP) is a steady state hydraulic model, which models the main and secondary canals in an irrigation system (Vlotman and Malano 1987). IMSOP has three main uses:
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• compare simulated crop water demand to recorded data to assess the historical performance of an irrigation system; • assess the performance of the system under different operational regimes via simulations; and • to assist system operation in near real time.
IMSOP has the capabilities to model irrigation systems that consist of rice and other upland crops. The main user input information required is meteorological data, crop details, soil details, canal hydraulic data, reservoir details and pump details (George, Malano et al. 2004). A significant drawback of IMSOP is its inability to cater for storage of water at either the farm or distribution level. IMSOP has been applied to the Cu Chi and La Khe irrigation systems in Vietnam and the Thup Salao irrigation system in Thailand.
4.2.2 Irrigation Network Control and Analysis (INCA)
Irrigation Network Control and Analysis (INCA) is an irrigation water management package for medium to large irrigation schemes. INCA is an irrigation system planning and operational tool. INCA has been well utilized, with application on seven countries between 1991 and 1996, on irrigation systems of up to 110,000 ha. INCA uses Management Units to represent a set of fields. Each field has a unique field water balance updated on a daily basis. INCA has the ability to cater for rice and upland crops (Makin and Cornish 1995). The rice demand is calculated including the expected depth of rainfall for the schedule period.
An interesting feature of INCA is the ability to correct the field irrigation requirement and soil moisture balance. There are two methods for rice. The first includes correcting for the difference between expected and actual rainfall. The second also available to upland crops is to adjust field soil moisture balances based on field observations of wetness or field soil moisture measurements.
One of the main drawbacks of INCA is that it is a commercially developed package hence the source code is not freely available.
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4.2.3 Command Area Decision Support Model (CADSM)
Command Area Decision Support Model (CADSM) was developed to estimate aggregated field scale crop demands and study management options. CADSM is different to the aforementioned models in its ability to cater for three types of irrigation scheduling: on-demand, fixed rotation and continuous flow. Another difference of CADSM to all of the other models examined is its ability to generate climatic data from monthly average and generate fields from user inputs that include the portions of each crop and soil type in the area. A restriction of CADSM is that it is capable of only representing 6 different crop types and 6 different soil types (Prajamwong, Merkley et al. 1997). For this research 9 different crop types are required on 8 different soil types.
CADSM does not allow any lag time in the conveyance and distribution system which is one of the reasons Prajamwong (1997) suggested that it performed poorly at predicting daily variations in demand hydrographs. It was found to perform very well on a seasonal basis.
Prajamwong (1997) tested CADSM on two very differently operated irrigation systems. One system was in Utah, USA and the other in Thailand. Both irrigation systems were under 311 ha; less than 1% of the irrigated area for this research. So CADSM’s performance on large irrigation systems has not been tested. This is its largest drawback for application in this research. Along with this the capabilities of CADSM for on farm and en-route water storage were not known.
4.2.4 Singh et al. (1997)
Singh et al. (Singh, Refsgaard et al. 1997) details an example where a complex model was used to determine if water from the Mahanadi Reservoir Irrigation Scheme, a large irrigation project in Central India could be more efficiently used. The model linked MIKE 11 (hydraulic) and MIKE SHE (hydrologic) with an irrigation scheduling model and crop growth module. MIKE 11 is a commercially available one dimensional river simulation model with the ability to model flow over a variety of hydraulic structures and to simulate the operation of gates or head regulators in canals. To use the MIKE 11 model the following information is required: canal cross-sections, head and cross regulators, upstream and downstream boundary conditions and seepage losses.
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To reduce the computational effort the system was simplified and unsteady flow conditions were not modeled. The time step used was a number of hours as a tradeoff between the best possible results and minimization of computational effort. The use of MIKE 11 or a similar dynamic simulation model is not required in this research.
In the example of Singh et al. (1997) the only reservoir represented was at the head of the irrigation system. The ability of this set up to model OFWS is not known. The irrigation schedule module could be operated with either a fixed distribution of water set at the start of the irrigation season or operated with an on-demand irrigation schedule.
Some drawbacks of the model in Singh et al. (1997) are: • it links three models to represent the irrigation system; • one of the three linked models is MIKE 11 which describes the canal system in too greater detail for this research; and • the source code of the model is not freely available.
4.2.5 Options AnalysiS in Irrigation Systems (OASIS)
Options AnalysiS in Irrigation Systems (OASIS) is a simulation tool developed to assess different management and planning options for medium to large-scale irrigation systems (Roost 2002; Roost, Cai et al. 2007). OASIS is largely concerned with accurately describing the water balance within irrigation systems to assist with the decision making processes (Roost and Musy 2005). OASIS operates on a time step of 5 or 10 days with the field level water balance undertaken on a daily basis.
The representation of distribution storages and OFWS in OASIS allows representation of the following: • fraction of storage which is ‘dead storage’; • upper storage limit before spilling; • point at which the storage will be refilled; • the level the reservoir will be filled to; and • the above can be varied for different points throughout the year.
The two major drawbacks of OASIS for application to this research are the 5 or 10 day time step for the allocation of irrigation water throughout the irrigation district and the
78 IRRIGATION SYSTEM MODELS inability of the model to be driven by crop water demands. The OASIS model is driven by a set of ‘Supply Targets’. A ‘Supply Target’ is a demand flow rate (in m3/s) for each time step and each Irrigation Unit (IU). OASIS generates the inflow into the head of the system by summing the IU supply targets and incorporating the losses in the segments. Each ‘Supply Target’ is distributed within each IU based firstly on crop water demand and if this exceeds the inflow then the water is distributed based on a user-defined priority for water supply to crops (for example rice can be given priority over winter irrigated pasture).
The two major alterations OASIS would require for this research are; changing the time step from 5 or 10 days to daily and the incorporation of a crop driven irrigation demand rather than its present capacities to distribute water based on supply targets. These alterations were made easier as the source code for the model was available from the model developer.
4.2.6 Tiddalik
Tiddalik is a node link network model, developed in southern Australia for application to irrigation systems in this area. Tiddalik represents land use, soil properties, irrigation management and irrigation/drainage system as a Cropping Unit with a unique water balance undertaken on each Cropping Unit. Tiddalik was developed for predicting the return flows from a drainage channel network. To do this the crop water demand is calculated using the FAO 56 Methodology (Food and Agriculture Organization of the United Nations) (Allen, Pereira et al. 1998) using locally generated crop coefficients for four different growth stages. Two methods can be used to calculate irrigation. These are accumulated ETc (based on climatic conditions) and soil water deficit. Both methods use a trigger level and a percentage refill (can be greater than 100%) (Hornbuckle, Christen et al. 2005). A refill percentage of greater than 100% accounts for things such as deep drainage, evaporation from the surface water and tail water runoff.
The main advantages of Tiddalik over OASIS are: • it was developed in southern Australia for primary application to irrigation systems in this area; hence it has had previous application to irrigation systems in proximity to the one used for this research;
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• Tiddalik can calculate irrigation demand based on crop water demands; and • it operates on a daily time step.
The primary drawback of Tiddalik is that it has been developed to calculate drainage return flows. It does posses the ability to cater for both OFWS and en-route storages, though these are simple water balances and have not been tested (John Hornbuckle pers. comm.. 27/6/2006). Other drawbacks of Tiddalik are its inability to cater for on- farm water recycling and that it treats water supply exogenously, thus the user needs to specify how much water is allocated in a particular season.
4.2.7 Model comparison
To determine which of the previously listed models was most applicable to this research the models were marked against the criteria list in Section 4.1, Table 4.1. Recall the criteria were: • Representation of multiple water sources, including on-farm recycling (I); • Time step (II); • Rice, crop and pasture irrigation processes (III); • Crop driven water demand (IV); • Order/rejection capabilities (V); • Lag time between demand for irrigation and water delivery (VI); • Storage capabilities both at the system and farm level (VII); • Adequate canal representation (VIII); • System losses (IX); and • Source code availability or module plug-in capability (X).
Table 4.1: Performance of models against the criteria Singh et al. Criteria IMSOP INCA CADSM Tiddalik OASIS (1997) I Unknown Unknown Yes Unknown Unknown Yes II Yes Yes Yes Yes Yes No III Yes Yes Yes Yes Yes Yes IV Yes Yes Yes Yes Yes No V No No No No No No VI No No No No No No VII No Unknown Unknown Unknown Yes Yes VIII Yes Unknown Yes Yes Yes Yes IX Yes Yes Yes Yes Yes Yes X Yes No Unknown No Yes Yes
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INCA and the model in Singh et. al. (1997) were both discounted because their source code is not freely available. Both models also over-represented the flow routing through the system with INCA having an hourly time step and the model of Singh et. al. (1997) using a time step of a number of hours. CADSM was discounted as it lacked testing on a large scale irrigation system, it was also reported as performing poorly with a daily time step and the number of fields was restricted to 54. IMSOP was discounted as it lacks the ability to cater for storages at either the distribution or farm level. This reduced the model selection to OASIS and Tiddalik.
The advantage of OASIS over Tiddalik was its ability to provide a more detailed representation of farm and distribution level storages. This was a key criterion in the selection of the most appropriate model. OASIS has been tested and applied to irrigation schemes with the primary purpose of testing scenarios involving the effect of farm storages on the performance of the system (Roost and Musy 2005).
The primary advantages of Tiddalik were its ability to be driven by crop water demand and having a daily time step. A detailed investigation of OASIS found it to be a very well structured model, meaning the two primary drawbacks could be readily overcome. OASIS has the ability to determine the crop water requirements of a field but does not sum this across the system to determine the inflow into the head of the system as is the need for a crop water demand driven model. The drawback of a non-daily time step was also further investigated and found that from the daily field scale soil moisture water balance the deficit was summed for either 5 or 10 days depending on the user selected time step. Thus, with a small number of alterations OASIS could be converted from a 5 or 10 day time step water distribution model to a daily time step crop water demand driven model.
OASIS’s ability to represent storages over that of Tiddalik and its previous application to scenarios involving storages meant that it was the preferred model. Added to this was OASIS’s ability to cater for recycling of water on-farm. OASIS was chosen as the preferred model for this research for these reasons. A detailed description of OASIS is provided in the following section.
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4.3 The OASIS Model
As previously mentioned OASIS was developed for medium to large scale irrigation schemes to test various management options and their impact on the system’s performance. OASIS is unique in that it incorporates irrigation return flows, recycling and all of the other major components of an irrigation system’s water balance (Roost and Musy 2005). Figure 4.1 provides a schematic description of the major components represented in OASIS and their connectivity to each other.
Figure 4.1: Connectivity of parameters in OASIS (Roost 2002 page 19)
Details of how OASIS represents the major processes are described in the following sections.
4.3.1 Representation of the irrigation system
To represent an irrigation system, OASIS uses a three level modelling setup consisting of field, IU and system levels. The main features of the irrigation system represented at the system scale are reservoirs, supply and drainage segments and downstream supply targets. In OASIS it is necessary to create a new main segment every time a secondary canal branches off a main segment. For example Figure 4.2, shows the first main segment represented as ‘1’, the secondary segment that branched off as ‘11’ and the necessary new main segment as ‘2’.
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IUs represent demand areas within the irrigation system, with each IU located in space. The connectivity of segments to IUs (termed ‘supply links’) is represented along with the assignment of meteorological station data, OFWS, groundwater conditions and hydrological parameters. The demand for an IU is calculated from the summation of the water requirements from the fields within the IU. Fields are not located in space in an IU but are lumped areas that refer to a unique combination of crop type, soil type and irrigation management in an IU. For a detailed description of the parameters that affect each of the above components refer to Appendix C.
Irrigation System
Irrigation Unit Field Irrigation Management
Crop
2 11 Soil 1 set of
Figure 4.2: Three level model structure
4.3.2 Representation of efficiencies in OASIS
Distribution losses in OASIS are accounted for through two means; segment efficiency (referred to as conveyance efficiency (Ec)) and the efficiency of water distribution within IUs (distribution efficiency (Ed)). Conveyance efficiencies are unique inputs for each segment, with (1 – Ec) defining the fraction of water that is lost as seepage in each segment (Roost 2002). Note that OASIS does not include evaporation losses from segments. Conveyance efficiency is represented with three levels for three levels of flow rates; high, medium and low. The user sets a division flow rate for the efficiency to change between each.
Distribution efficiency in OASIS refers to the percentage of water delivered to each IU that is available to be applied to the field. (1 – Ed) represents the fraction of water
83 IRRIGATION SYSTEM MODELS entering the IU that is lost as seepage in the distribution system prior to reaching the fields, again OASIS does not account for evaporation losses. It is a single value across all IUs having no variation with flow rate.
OASIS accounts for application efficiency (the volume of irrigation water applied that is able to be stored in the root zone) with a user-defined application efficiency (Ea) for each type of irrigation undertaken. This concept is not applied to ponded water, where the model assumes an application efficiency of 100%.
Losses through on-farm infrastructure are not accounted for explicitly in OASIS but can be incorporated into either application efficiency or distribution efficiency.
4.3.3 Irrigation inflows
Prior to extension for this research OASIS had two methods of determining water requirements for the IUs. These were fixed targets (m3/s) for each IU and time step, read from input files created by the user. The second method was to calculate targets based on the delivery schedules defined and assigned to the different crops. From this OASIS would calculate a required demand for each IU.
An allocation algorithm using linear programming, searches a matrix representation of the system’s physical constraints (conveyance, allocation and efficiencies) to determine the optimal supply volume to meet all of the demands. In the event that all supply targets can not be met then the supply targets are searched in order of their user-defined priority. If IUs have equitable priorities then supply targets are met equally.
After water supply has been distributed for a time step, OASIS computes the soil water balance of each field on a daily basis. At the end of each time step the simulated daily water fluxes for each field are aggregated and the following water balances are calculated: groundwater system, drainage system and system-level water balances. This process is repeated after each time step in the simulation.
Reporting the results of simulations is an important feature of any model. OASIS produces three output simulation reports ‘Seasonal Outputs’, ‘Time Series Outputs’ and ‘Overview’. The Overview report includes the following information: gross inflow, storage change, net inflow, depletion, outflow and available water. The Time Series Output allows the users to display components of the simulation at either the system or
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IU scale. The Seasonal Outputs report allows the user to review the performance of a crop or IU for each season of the simulation.
4.3.4 Representation of storages
As previously discussed one of the strong reasons for choosing OASIS was its detailed representation of OFWS and distribution level storages. OFWS and distribution level storages (reservoirs in OASIS) each have two input files. One to describe their management and the other to describe their physical properties.
The management input parameters for OFWS are shown in Figure 4.3.
VF = 1
upLimitVF fillToVF
reqThresVF
lowLimitVF
VF = 0
Figure 4.3: Management of OFWS in OASIS
Where VF is the volume full as a fraction between 0 and 1; upLimitVF is the upper limit to which water is held in the OFWS. Any water above this level is assumed to be spilled; fillToVF is the level to which the reservoir is filled from the irrigation system; reqThresVF is the level at which a requirement to fill the reservoir is placed; and lowLimitVF is the limit at which no water is released below.
Note that it is possible to define a different set of OFWS management variables and physical properties for every IU. The physical input parameters are:
85 IRRIGATION SYSTEM MODELS maxstor the maximum storage capacity defined as a millimetre depth per IU area; deadVF dead storage, as a ratio between 0 and 1 of the full volume; seeprate the seepage rate (mm/day); evapCoeff the evaporation coefficient. A coefficient that the ETo is multiplied by to obtain the daily evaporation from the reservoir; and maxOutRate the maximum release rate (mm/day).
Two parameters (shape_K and shape_a) define the shape of the OFWS, Equation 4.1.
Equation 4.1 V = shape _ K × H shape _ a Where V is the storage in mm of depth for the area of the IU (mm); and H is the water depth (m).
The input parameters that define the physical properties of the reservoirs are exactly the same as those for the OFWS, though the parameter names and units are different in some instances, see below for details. maxV the maximum storage capacity (m3); deadV dead storage (m3); seeprate the seepage rate (mm/day); evapCoeff the evaporation coefficient. A coefficient that the ETo is multiplied by to obtain the daily evaporation from the reservoir; and maxOutQ the maximum release rate (m3/s).
Equation 4.1 is also used to define the shape of the reservoir, although the units for V and H are different. In the case of the reservoir, V is in m3 and H is in metre (m).
The management of reservoirs has two options, while there is only one for the management of OFWS’s. These two options are based on either the storage volume of the reservoir (VOL) or the release rate target of the reservoir (REL_RATE). The VOL management option is very similar to the operation of the OFWS requiring all of the variables listed above with the addition of ‘minRelQ’ which is the minimum release
86 IRRIGATION SYSTEM MODELS flow rate in m3/s. Under the ‘VOL’ reservoir management option a single reservoir is filled when the inflow into the irrigation system is greater than the demand from IUs. For this research VOL is the required method of filling an en-route storage. However, this management option does not employ the desired operation of releases from en- route storages for this research. Under this management option releases from the reservoir only occur when IU demands can not be met from inflows into the head of the irrigation system.
The management input file under the REL_RATE operation requires only the following two inputs; the target release rate (targetRelQ) and the target inflow rate (targetInQ) both in m3/s. Under the ‘REL_RATE’ management option the reservoir has a target inflow and outflow rate. Under this management option the inflow into the head of the system is given priority to meet the inflow target of the reservoir above meeting the IU demands. The release rate from the reservoir is set to the target release rate (given there is sufficient water in the reservoir) independent of the downstream demands in the system. Neither the inflow nor the outflow options for this management option resembles the desired operation of the reservoir for this research.
Neither of the two present management types allows the necessary reservoir operation for application to this research, hence this will be updated (see Chapter 5 for details).
4.3.5 Representing fields
As described previously fields are not located in space in an IU but are lumped areas that refer to a unique combination of crop type, soil type and irrigation management in an IU. The first input OASIS uses to create fields is the ‘rota-soil’ input file which represents a certain crop type on a certain soil type in each IU.
An example soil layer and crop layer are shown in Figure 4.4.
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IU 1 TRBE IC(10ha) IP(40ha) IC(30ha)
IC(40ha) IC(20ha) IP(60ha)
NSMC
Figure 4.4: Example soil and crop GIS layer in IU 1
Figure 4.4 shows two paddocks of winter Irrigated Pasture (IP), three paddocks of Irrigated Cereals (IC) and two soil types: Traditional Red Brown Earth (TRBE) and Non-Self Mulching Clay (NSMC). The two paddocks of IP represent two separate fields as they are located on different soil types. The three paddocks of IC represent only two fields. The IC paddock that is located across the two soil types is split at the TRBE/NSMC intersection and the area that is located on each soil type is summed as part of that field.
Table 4.2 describes the example soil and crop system as an input file for OASIS. ‘Rotaaf’ refers to the fraction of the area assigned to the current crop rotation associated with the current soil type in the current zone in the current IU.
Table 4.2: Break up of fields Legend Unit_ID Zone Rota_Name Soil_Name Rotaaf 1 0 IC NSMC 0.6 1 0 IC TRBE 0.4 1 0 IP NSMC 0.6 1 0 IP TRBE 0.4
The second file is used to represent fields is the crop irrigation management file (‘cropirrigmanag’). The crop irrigation management file refers to the irrigation schedule file(s) that is applied to a certain percent of each crop. A separate irrigation schedule file is required for every different type of irrigation management. Note if the same irrigation management is undertaken on two different crops, this is catered for by the same irrigation schedule file.
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4.3.6 Irrigation schedule files
Each irrigation schedule file has as inputs the date from and to that irrigation will take place under that irrigation management. The other major inputs are the criterion and division level that will drive irrigation of the crop under this irrigation management.
Each irrigated crop in OASIS requires at least one irrigation schedule file to define water delivery and water demand parameters (for more information refer to the OASIS User Guide shown in Appendix C). For each crop the parameters relevant to this research are: • fromTStep, toTStep – defines the period that the crop has access to irrigation; • dv_rate – is the target or maximum irrigation rate, note a value of 0 indicates no restriction; • dm_depth – is the demand depth, note a value of 0 indicates that the root zone is returned to field capacity or for rice the maximum ponded depth is returned; • dm_crit_CA – is the criterion used to trigger demand for irrigation water, there are 6 criterions available in OASIS; and • dm_CANAL – is the division value of the dm_crit_CA to trigger demand for irrigation water from the canal.
4.3.7 Field level soil moisture balance
Representation of the soil moisture balance is an important characteristic of all irrigation system models. OASIS represents the soil profile as two layers, topsoil layer (root zone) and subsoil layer, Figure 4.5. The topsoil layer refers to the layer of soil between the soil surface and the bottom of the effective root zone (Zr) at the day in the simulation. The subsoil layer refers to the layer of soil between the bottom of topsoil layer and the largest effective root zone depth (Zswb). This means that as the roots grow deeper into the soil layer the topsoil layer extends and the subsoil layer diminishes.
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Figure 4.5: Two layer soil representation in OASIS (Roost 2002 page 25)
For the soil representation OASIS assumes that the soil characteristics are uniform over the whole profile (0-Zswb). Each of the two defined soil layers function like a reservoir (Figure 4.6), empty at wilting point and full at field capacity.
The daily water balance of the topsoil layer expressed in terms of depletion relative to the field capacity is given by Equation 4.2 (Roost 2002):
D j = D j−1 + ETc, j − Pinf, j − Iinf, j − CR j + SPj + Drg , j Equation 4.2
Where
D j is the depletion at the end of day j (mm);
D j−1 is the depletion at the end of day j – 1 (mm);
ETc, j is the crop ET on day j (mm);
Pinf, j is the infiltrated depth of precipitation on day j (mm);
Iinf, j is the infiltrated depth of irrigation on day j (mm);
CR j is the capillary rise on day j (mm);
SPj is the amount of water percolated to the subsoil on day j (mm); and
Drg, j is the depletion in the root zone (topsoil layer) on day j (mm).
The daily water balance of the subsoil is given by Equation 4.3 (Roost 2002):
Equation 4.3 Ds, j = Ds, j−1 − SPj + DPj
90 IRRIGATION SYSTEM MODELS
Where
Ds, j is the depletion at the end of day j (mm);
Ds, j−1 is the depletion at the end of day j – 1 (mm); and
DPj is the deep percolation on day j (mm).
Figure 4.6: Reservoir representation of the soil profile (Roost 2002 page 25)
4.3.8 Initial conditions
To start a simulation the initial conditions of the irrigation area need to be specified. These include the initial soil moisture conditions, initial storage capacity of both OFWS and distribution level storages. Initial soil moisture conditions affect the volume of irrigation water demanded by crops at the start of the simulation period. The input in OASIS is soil moisture depletion (fraction between the field capacity and the permanent wilting point). Depending on the level of supply from farm and distribution level storages the initial condition of these storages is important information for gaining accurate simulation results.
4.4 Previous Applications of OASIS and Required Adaptations
The OASIS model has been applied to two irrigation districts in China, the Bojili Irrigation District (BID) in the Yellow River Basin (Roost and Musy 2005) and the Zhanghe Irrigation System (ZIS), in central China (Roost, Cai et al. 2007). Application to the BID will be examined first, followed by the application to the ZIS.
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Roost and Musy (2005) provides the following descriptions of the BID. It is 110,000 ha and has scarce and unreliable canal supplies which give priority of supply to upstream users over downstream users. The main crops grown in the area are wheat, maize, orchards and vegetables. The operation of this scheme has led to large volumes of on-farm storage, a high level of shallow aquifer pumping and large reservoirs in the most downstream parts of the irrigation district. OASIS was applied to the BID to assess the impact that increasing the overall efficiency (canal lining and precision land leveling) would have on reducing diversion requirements and making more water available to downstream users. The scenario assessment was relatively easy to test in OASIS as it was a matter of changing two efficiency parameters (conveyance efficiency and application efficiency) (Roost and Musy 2005).
Roost et. al. (2007) provides the following description of the ZIS. It is a 32,500 ha irrigation scheme with the main crops of rice and upland crop. The area has seen a substantial reduction in irrigation supplies from reservoirs over the last few decades, yet the irrigation area remains viable. The application of OASIS to ZIS aimed to explain the contribution ponds (farm dams) play to crop production and yield via the use of scenario assessments that included undertaking simulations with supply from reservoirs and removing ponds from the ZIS.
The major difference between the previous applications of OASIS and the MIA is that in the MIA the irrigators determine the volume of water delivered for irrigation of their crops via the placement of orders. In the previous two applications the volume of water delivered to irrigators was determined by the management authority without any direct input from the irrigators. Hence, in the original version of OASIS the system inflow was a user input, while for application to the MIA it is necessary to update OASIS such that the inflow into the head of the system is firstly driven by crop water demands. OASIS then needs to be extended to cater for orders and rejections. The incorporation of an order and rejection module will be the first use of such a method to predict orders and rejections. This represents a significant contribution of this research. Another adaption required for OASIS is the conversion of the system level water balance to a daily time step rather than the present 5 or 10 day time step. Beyond this is the need to ensure that UIO can be directed into OFWS or en-route water storages and that this water is given priority over external inflows to meet orders. The direction of UIO into
92 IRRIGATION SYSTEM MODELS storages (OFWS or en-route) will allow the testing of different storage options for UIO. This represents a significant contribution of this research.
4.5 Conclusions
This chapter has reviewed a number of irrigation system models finding OASIS (Roost 2002; Roost, Cai et al. 2007) to be the most applicable model for this research. The major advantages of OASIS are its representation of storages (both OFWS and distribution level storages) and the available (no cost) and well structured code. The primary drawbacks of OASIS were; the model was not crop water demand driven and the system level time step was not daily. This chapter then described how OASIS represents the major processes in an irrigation area. Showing, that OASIS uses a three level model structure, representing the irrigation system via the three levels of; system, IU and field. The following parameters are represented at the system level: supply connectivity, constraints and efficiency. An IU represents a set of fields as a demand node located in space and fields represent a certain combination of crop type, soil type and irrigation management regime.
OASIS has previously been applied to two irrigation districts, BID and the ZIS. Both of these were in China. The set up of both of these irrigation districts is such that inflows into the irrigation system are driven by the irrigation authority rather than by irrigator demands as in the MIL system. For this reason it was necessary to update OASIS firstly to a crop water demand driven model and then to incorporate an order and rejection module into it. There was also a need to update the system level time step from a 5 or 10 day time step to a daily time step. With the irrigation simulation model selected, Chapter 5 will focus on the updates made to OASIS, how the study area was represented in OASIS and how the calibration and validation process was undertaken.
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5 Application of OASIS to the Study Area
Chapter 4 reviewed a number of models and found OASIS to be the most suitable model for this research. Chapter 4 also provided a description of the major processes in OASIS and discussed its limitations. The limitations are its system level operation on a user-defined 5 or 10 day time step and its inability to have crop water demands in IUs to drive the supply volume to each IU. There was one major model component missing in all models investigated; an order and rejection module. The development of this module for addition to OASIS is explained in Section 5.1.4.
Chapter 5 describes the processes undertaken to convert the model from the version described in Chapter 4 to a calibrated and validated modified model ready to undertake the options assessment. These processes include: • making updates to OASIS; • establishing the MIL system in OASIS; • undertaking a sensitivity analysis of the MIL system in OASIS; and • testing two different methods of predicting orders and rejections to determine the most appropriate method to use for this research.
5.1 Modifications to OASIS
To meet the model requirements outlined in Chapter 4 it was necessary to complete the following modifications to OASIS: • convert the system level water balance processes from a 5 or 10 day time step to a daily time step; • add a new parameter to the irrigation schedule file to allow a variable maximum ponded depth of water on rice;
94 APPLICATION OF OASIS TO THE CASE STUDY AREA
• convert the supply mode of OASIS to allow crop demand driven inflow into the head of the system; • add an order and rejection module; • create the ability to direct UIO into OFWS or en-route water storages; and • create the ability to prioritize the water from en-route storages to meet orders above water from outside the system.
5.1.1 Conversion to a daily time step
As previously mentioned, it was necessary to convert the system level water balance processes in OASIS to a daily time step. In the original version of OASIS all IU and field water balances, including reservoir, drainage, groundwater and soil water storage are undertaken daily. However, the system level water balance aggregated the field scale water balance fluxes over the 5 or 10 day time step, hence the water allocation calculation for each IU was only made every 5 or 10 days. Prior to converting this process to a daily time step it was necessary to investigate the original purpose for the 5 or 10 day time step for the water allocation calculation. The original purpose was to eliminate the need to handle conveyance time lags in large irrigation systems. As water distribution in the study area takes less than 24 hours it is possible to convert OASIS to a daily time step, without the need to consider time lags in the conveyance system. The conversion was made by modifying the system water balance module in OASIS to reflect the daily timestep.
5.1.2 Updating of the irrigation schedule file
One of the requirements of this research was that the two variables minimum and maximum ponded depth of water on rice be time varying. This is imperative from October until early December when the ponded depth of water on rice is restricted to a depth of between 30 and 50mm. The original version of OASIS was structured such that the minimum depth of ponded water was a time dependent variable but the only limit on the maximum depth of ponded water was that it remained below a height equal to the maximum bund height less the rain reserve depth. To create the above capabilities within OASIS it was necessary to add a new parameter, target surface depth into the irrigation schedule file. The target surface depth parameter creates an upper ponded depth of water for OASIS during irrigation. In the case of rainfall, the depth of
95 APPLICATION OF OASIS TO THE CASE STUDY AREA ponded water is not restricted to the target surface depth unless drainage parameters within OASIS are used to drain the ponded irrigation area.
5.1.3 Crop demand driven head inflow
To replicate the MIL system it is necessary for the inflow into the head of the modeled system to represent the actual inflow into the system by estimating the volume of water ordered (four day advanced orders) and rejected. To incorporate these processes into the model, two steps were taken. The first involved adding another supply option; crop demand driven head inflow. This addition was then extended to include the irrigators’ behaviour in the process of ordering (Section 5.1.5) and rejecting (Section 5.1.6).
As described previously, OASIS has the ability to determine the crop water requirements of a field. It is capable of prioritising the fields to receive water in the case of water scarcity. Originally, the volume of water supplied to each IU for each time step was a model input and not based on the crop water requirements of the fields in each IU. To make the necessary additions to OASIS an additional water supply option ‘AGRO’ which is a crop water demand driven head inflow was added.
The ‘AGRO’ supply option uses the existing field scale water balance code to determine if irrigation is required and to determine the volume of irrigation required for each field. The main addition to the code was to include summing the irrigation volume required for each field in each IU to develop a target supply volume for each IU. The existing process of aggregating the IU demands was utilised to aggregate the crop water demand for the entire modeled system. The existing water distribution processes and the supply deficit situation (when demand is greater than system supply capabilities) were not altered.
5.1.4 Order and rejection module
The order and rejection module contains an order matrix and a rejection matrix used to calculate orders and the rejections. The purpose of the matrix approach for the placement of orders and rejections was to allow and test whether the incorporation of irrigator risk behaviour added any additional benefit to the performance of the irrigation system model. The matrix approach allows a maximum of three risk catergories to be
96 APPLICATION OF OASIS TO THE CASE STUDY AREA represented in OASIS. For this research, two risk categories (risk averse and risk tolerant) have been incorporated from the results of the interview questionnaire.
Chapter 3 describes a process to calculate rejections. The validated result produced R2 = 0.53. To incorporate this result into the model the rejection matrix is replaced with the MLR analysis result, while the orders are placed with the order matrix, this is described in more detail in Section 5.1.7. Since a better performing model is desirable, the rejection matrix was investigated (Section 5.1.6).
Along with testing two methods of calculating rejections, two methods of calculating orders were investigated. These methods were; (i) the order matrix and (ii) a soil moisture trigger approach. Two revised versions of OASIS were created to test the order and rejection options. Prior to describing these two revised versions, it is necessary to mention that the two order placement versions were tested using the order matrix version of OASIS. How this was undertaken is explained in Section 5.1.5.
The first revised version of OASIS used a decision matrix for both ordering and rejecting. The second revised version of OASIS used the ordering matrix and incorporated the results of the MLR analysis to calculate the volume of rejections; a decision making matrix was not used for calculating rejections.
5.1.5 Order decision process
In the order decision process the first check is for the availability of water from Local Water Sources (LWS). LWS includes groundwater and OFWSs, for this study only OFWSs were used. If available, this water is given priority over water from external sources. Water from LWS has no time lag associated with it, so the weather uncertainties an irrigator faces when placing an order is removed. If no water was available from LWS, then the need to place an order was determined using the process shown in Figure 5.2.
Order matrix approach
The decision process (Figure 5.2) is undertaken for each time step that each irrigated field has access to irrigation during the simulation period. The first decision made is whether an order will be placed. This decision requires the forecast moisture deficit, Equation 5.1.
97 APPLICATION OF OASIS TO THE CASE STUDY AREA
t+OAT −1 Equation 5.1 FMD = Kc t × ETo t − Rt − Ot + Pt + ∑ Kc t × XETo t − XRt − Ot + Pt t+1 Where FMD Forecast Moisture Deficit (mm)
Kct the crop coefficient on day t;
ETot the actual reference ETo on day t (mm/day);
XETot the expected reference ETo on day t (mm/day);
Rt the actual rainfall on day t (mm/day);
XRt the expected rainfall on day t (mm/day);
Ot the non-rejected orders on day t, this refers to the efficient fraction of the ordered irrigation depth (mm/day);
Pt the average daily percolation rate from a submerged field on day t, this is only considered for rice (mm/day); and OAT the order advance time (days).
An example of a final order matrix (winter irrigated pasture) is shown in Figure 5.1. The symbols of (a) to (d) are used to illustrate when each risk category (risk tolerant and risk averse) places an order. How the irrigators’ were split between risk tolerant and risk averse is explained in Section 5.7.1.
Symbols (a) risk averse irrigator will place an order; (b) risk averse irrigator will not place an order; (c) risk tolerant irrigator will place an order; and (d) risk tolerant irrigator will not place an order. Forecast Moisture Deficit Medium (< 10.12 High (≥ 10.12mm) Low (≤ 6.16mm) to > 6.16mm) High (≥ 67% (b) (d) (b) (d) (b) (d) TAW) Current Total Medium (> 33 to < Available Water (b) (c) (b) (d) (b) (d) 67% TAW) (TAW) (t) Low (≤ 33% (a) (c) (a) (c) (a) (d) TAW) Figure 5.1: Order matrix for winter irrigated pasture
If an order is required then the second decision is what volume of water to order. The volume of the order is based on the criteria used in the irrigation schedule file. The
98 APPLICATION OF OASIS TO THE CASE STUDY AREA ordering criterion offers the same methods as are present in the original OASIS model (see OASIS operating manual Appendix C). If the order volume is calculated to replenish the root zone or surface storage (rice) to a user-defined level, it is based on the current soil moisture and the forecast moisture deficit assuming no expected rainfall, Equation 5.2. Expected rainfall was excluded from Equation 5.2 because it was assumed that irrigators would irrigate at their ordered rate until the field was returned to field capacity or the target depth (rice).
D + def Equation 5.2 OV = I Where OV is the volume of water to be ordered for the field (mm); D is the soil moisture or surface storage depletion (mm); def is the forecast moisture deficit (Equation 5.1) with no rainfall (mm); and I is the efficiency of irrigation application at the field scale (%).
After the order volume has been calculated it is passed through the following tests to ensure it is greater than or equal to the minimum application (Equation 5.3) and less than or equal to the maximum application specified (Equation 5.4). The minimum and maximum application volumes are user-defined inputs to OASIS.
OV = Max(MinApp,OV ) Equation 5.3
OV = Min(OV , MaxApp) Equation 5.4
Where MinApp is the minimum application volume (mm); and MaxApp is the maximum application volume (mm).
The concept of the order advance time in Figure 5.2 refers, to the period of time between order placement and water delivery to the field. This is 4 days for the MIL system.
Soil moisture trigger approach
The order matrix was also used for the soil moisture trigger approach to ordering. To convert the matrix to the soil moisture trigger only one irrigator behaviour was used.
99 APPLICATION OF OASIS TO THE CASE STUDY AREA
Start
Does the crop have access Is day (t + order Exit No to irrigation on day (t + Yes advance time) within No Exit Decision order advance time) from the crop growing Decision the source of water season
Yes
Calculate the moisture deficit at the end of the Add percolation to the Yes current day and add the forecast moisture deficit Is the soil puddled (rice) moisture deficit to this (Equation 5.1)
No No Order Matrix Exit Decision Irrigator behaviour (Order?) Calculate order Yes volume Order volume (Equation 5.2) Current soil moisture = minimum application
Order volume = Yes maximum application Is order volume ≥ No Is order volume < Yes maximum application minimum application
Order volume = order volume No
Figure 5.2: Order decision process
100 APPLICATION OF OASIS TO THE CASE STUDY AREA
This irrigator behaviour is independent of the forecast moisture deficit and the yes/no decision changes between the high and low soil moisture division (Figure 5.3).
Symbols (a) order will be placed; and (b) order will not be placed.
High (≥ 33% (b) Current TAW TAW) (t) Low (≤ 33% (a) TAW) Figure 5.3: Soil moisture trigger for orders for winter irrigated pasture
5.1.6 Rejection decision process
To determine if a rejection is placed, the rejection decision process (Figure 5.4) is used. This decision process is undertaken for each time step in which each irrigated field has access to irrigation during the simulation period. The decision is undertaken on the day prior to water arriving. This allows for the fact that as explained in Chapter 3, most irrigators place a rejection within 36 hours of the scheduled start time or after the irrigation has commenced.
The two day forecast moisture deficit (Equation 5.5) is calculated to determine the expected moisture deficit at the end of day (t + 1). The calculation is made by calculating the actual moisture deficit at the end of the current day and adding the forecast moisture deficit for the next day to this. That is, a rejection is based on the expected conditions when the order arrives. For rice, 2 days percolation must also be included in this calculation.
t=t+1 Equation 5.5 FMD 2−Day = ∑ Pt − Ot + Kct × ETo t − Rt + Kct+1 × XETo t+1 − XRt+1 t=t The notation for each term in Equation 5.5 is the same as Equation 5.1. An example of a final rejection matrix (winter irrigated pasture) is shown in Figure 5.5. The symbols of (e) to (h) are used to illustrate when a rejection is or is not placed. How the irrigators were split between risk tolerant and risk averse is explained in Section 5.7.2.
101 APPLICATION OF OASIS TO THE CASE STUDY AREA
Start
Calculate the moisture deficit at the end of Exit No Is there an irrigation order Yes the current day and add the 1 day forecast Decision arriving on day (t+1) moisture deficit to this (Equation 5.5)
Irrigator behaviour Add percolation to the Yes Is the soil puddled (rice) moisture deficit
Current soil moisture No Order Rejection Matrix (Reject Order?)
No Yes Rejection volume (Equation 5.6 Exit Decision and Equation 5.7)
Rejection volume = Yes order volume Is rejection volume ≥ No Rejection volume = Order volume = order volume rejection volume order volume - rejection volume
Order volume = 0
Figure 5.4: Rejection decision process
102 APPLICATION OF OASIS TO THE CASE STUDY AREA
Symbols (e) risk averse irrigator will place a rejection; (f) risk averse irrigator will not place a rejection; (g) risk tolerant irrigator will place a rejection; and (h) risk tolerant irrigator will not place a rejection.
Two day forecast moisture deficit Medium (< 3.48 Low (≤ High (≥ 3.48mm) to >1.98mm) 1.98mm) High (≥ 67% (f) (g) (e) (g) (e) (g) TAW) Current TAW Medium (> 33 to (f) (h) (f) (g) (e) (g) (t) < 67% TAW) Low (≤ 33% (f) (h) (f) (h) (f) (h) TAW) Figure 5.5: Rejection Matrix for Winter Irrigated Pasture
If a rejection takes place, the rejected volume is calculated differently for rice and non-rice crops. For non-rice crops it is calculated as the difference between field capacity and the current TAW minus the 2-day forecast moisture deficit, Equation 5.6. The aim is to have the soil moisture level at field capacity at the end of the two day forecast period.
Equation 5.6 RVnon−rice = −RZdeplt − def
Where
RVnon−rice is the rejected volume for non-rice crops (mm);
RZdeplt is the current days root zone soil moisture depletion (mm); and def is the 2-day forecast moisture deficit (mm) from Equation 5.5.
The rejected volume for rice is defined by Equation 5.7. In this case the aim is to have the ponded depth at the target storage depth at the end of the two day forecast period.
Equation 5.7 RVrice = ssc − def − sst arg
Where
RVrice is the rejected volume for rice crops (mm); ssc is the current days depth of ponded water (mm); def is the 2-day forecast moisture deficit (mm) from Equation 5.5; and
103 APPLICATION OF OASIS TO THE CASE STUDY AREA
sst arg is the target storage depth (mm) for day (t).
After the rejected volume has been calculated it is passed through the following tests to ensure it is greater than or equal to zero (Equation 5.8) and less than or equal to the order placed (Equation 5.9).
RV = Max(0, RV ) Equation 5.8
Equation 5.9 RV = Min(RV ,Ordert+1)
Once the rejected volume has been finalized the order arriving on day (t+1) is updated to equal the original volume less the rejected volume, Equation 5.10.
Equation 5.10 Ordert+1 = Ordert+1 − RV
5.1.7 MLR results for rejections
The MLR version of OASIS calculates a system level rejection volume. This is then split evenly amongst the fields which have orders to be delivered on the next day. To incorporate the results from the MLR model, it was necessary to choose the best performing equation that did not include the previous days UIO. From Chapter 3, this was Equation 5.11.
t=0 UIO = −128 + 58.7× ∑ R(t) + 0.116 × AO(t − 4) Equation 5.11 t=−3 5.1.8 Reservoir management
As discussed in Chapter 4, the current structure of reservoir management in OASIS needs modification for this research. The reservoir management module was updated so that it is used as the second source of water (LWS are the first source of water) to meet orders. Another update was that water is only released from the reservoir when there is demand from downstream IUs. The following processes from the original version of OASIS have all been used: • check that the IU is downstream of the reservoir; • check of physical system constraints; and • splitting of available water if demands exceed supply.
104 APPLICATION OF OASIS TO THE CASE STUDY AREA
5.1.9 Inclusion of rejections into system inflow
To enable the rejected water to be directed into the system inflow it was necessary to update OASIS such that the volume of water directed into the irrigation system was the volume of the 4-day orders and not the continuing orders (orders less rejections). The most practical method to achieve this was to set the minimum inflow into the system to the volume of water ordered. The sequence of steps undertaken to implement this are shown in Figure 5.6 and described below.
Calculate Water allocation Day (t) original field algorithm run to calculate advanced system order = ∑ Field orders + conveyance Day (t+1)
Day (t+2)
Day (t+3) Calculate rejected Continuing field field orders orders = Field order day (t) – rejected field orders
Water allocation System Storage Day (t+4) algorithm run to inflow = inflow = calculate system system system order to meet order on inflow - continuing field day (t) system
Figure 5.6: Allocation algorithm steps
To set the minimum inflow to the volume of water ordered, the water allocation algorithm in OASIS is run twice for each time step. The first run calculates the system order required to meet the field orders (the system order includes losses in the system). This advanced system order is stored. On day (t+3) the rejected field orders are calculated. On day (t+4) the water allocation algorithm is run again to calculate the volume of water required to meet the continuing field orders. The system inflow
105 APPLICATION OF OASIS TO THE CASE STUDY AREA on day (t+4) is set to the advanced system order. The difference between the advanced system order for day (t+4) and the system inflow is the storage inflow.
Having completed the necessary alterations to OASIS the next step was to represent the MIA in OASIS to carry out the scenario assessment.
5.2 Representing the Study Area in OASIS
To represent the study area in OASIS a number of key decisions were required: • how best to break up the study area into IUs; • how to break up the canal system into segments; • the level of the canal system that would be represented in OASIS; • how to represent the capacity of the supply links that represented canal supply from segments to IUs; • how to break up the fields in each IU; and • how to determine the area of each land use for each season.
These difficulties are addressed in the remainder of this section.
5.2.1 Break up of Irrigation Units
The first component of the system considered was how to break up the study area into IUs. Ideally, there should be a good level of consistency in the area that each IU represents. Initially, an investigation was undertaken to determine if it was possible to have a system in which each secondary canal (a canal that diverts off the Mulwala Canal) supplied an IU. It was found that the secondary canals ranged in capacities from 15 to 3,200 ML/day. There is therefore a very large variation in the irrigated area that each secondary canal supplied and for this reason this approach was not used.
The second approach trialled was to use the GIS land use layer of the MIA for season 2000/01 to determine the irrigated areas in season 2000/01. A visual estimate was then used to split the area into IUs. The only rule that applied during the splitting of the IUs, was that an IU must represent an area on only one side of the Mulwala Canal. The final division of the 21 IUs is shown in Figure 5.7.
106 APPLICATION OF OASIS TO THE CASE STUDY AREA
Figure 5.7: Break up of study area into IU
The final IUs have the properties shown in Table 5.1, where: • area refers to the total area of the IU; • irrigation refers to the 2000/01 irrigated area; • dryland refers to dryland agriculture and native vegetation areas in season 2000/01; and • bare refers to the difference between the total unit area and the addition of irrigation and dryland agricultural areas. This includes things such as infrastructure and buildings.
Table 5.1: IU properties IU Area (ha) Irrigation (ha) Dryland (ha) Bare (ha) 1 24,477 9,310 14,253 875 2 18,519 5,038 12,434 1,038 3 11,963 4,794 6,813 340 4 11,731 5,747 5,699 266 5 18,037 7,163 9,590 1,279 6 17,811 6,380 10,158 1,258 7 18,219 6,791 10,759 655 8 6,843 3,062 3,712 59 9 23,302 10,426 11,639 1,231 10 12,423 5,349 6,785 273 11 30,611 9,534 18,400 2,599 12 30,111 8,667 18,592 2,806
107 APPLICATION OF OASIS TO THE CASE STUDY AREA
IU Area (ha) Irrigation (ha) Dryland (ha) Bare (ha) 13 22,927 9,884 11,670 1,352 14 13,073 7,650 5,219 182 15 16,656 6,712 8,712 1,219 16 9,823 5,685 3,757 370 17 29,686 9,391 17,243 3,023 18 16,873 6,860 8,386 1,618 19 15,695 7,293 6,740 1,631 20 21,554 6,035 12,791 2,720 21 33,378 8,177 19,456 5,706
Maximum 33,378 10,426 19,456 5,706 Minimum 6,843 3,062 3,712 59 Average 19,224 7,140 10,610 1,452 Median 18,037 6,860 10,158 1,231
5.2.2 Break up of segments
The study area consists of a large number of canals and it was not necessary to represent all of these in OASIS. It was however necessary to adequately represent the supply to the IUs. Figure 5.8 shows the regulators and canals that branch off the Mulwala Canal.
Initially, only secondary canals with capacities greater than 300 ML/day were represented as secondary segments in OASIS. An investigation of this system representation revealed that the majority of water from Blighty canal (capacity 450 ML/day) flows into Mayrung canal (capacity 750 ML/day). For this reason Blighty canal was merged with Mayrung canal. The final representation of the system included the following secondary canals: • Berriquin Canal; • Finley Canal; • Tuppal Canal; • Mayrung Canal; and • Moulamein Canal.
The Denimein regulator was considered sufficiently close (2,440 m) to Lawsons Syphon and Edward River Escape to represent Lawsons Syphon and Edward River Escape at this point.
In Figure 5.9, the Mulwala Canal is represented by the segments 1 to 5, the main secondary canals by segments 11, 12, 21, 31, 41, 51. It was also necessary to use a
108 APPLICATION OF OASIS TO THE CASE STUDY AREA
Figure 5.8: Regulators and secondary canals in the study area
109 APPLICATION OF OASIS TO THE CASE STUDY AREA
Moulamein Blighty Canal Berrigan Canal Canal Coree Canal 111 12 Finley Canal
51 James 21 Edward River 41 Regulator Denimein Cowans Dawes Escape Regulator Regulator Regulator 4 6 5 3 11 2 7 31
Lawsons 1 Berrigan Canal Syphon Tuppal Canal The Drop Regulator Legend
Segment Regulator
Entry and exit flows
Figure 5.9: Study area represented in OASIS
110 APPLICATION OF OASIS TO THE CASE STUDY AREA tertiary segment on one occasion (segment 111) due to the capacity of Berriquin Canal. Segments 6 and 7 represent flows going to Edward River Escape and Lawsons Syphon, respectively. These segments represent flows exiting the study area.
5.2.3 Supply links
In OASIS, supply links provide a link between IUs and segments. To accurately represent the study area in OASIS, it was necessary to consider the supply capacity of each supply link. To do this, the point where a canal entered an IU was found in the GIS description of the system and the capacity of that canal obtained from the MIL canal data base. All of the canal capacities that entered an IU were summed and all of the canals that exited an IU were subtracted to determine the supply capacity of each of the supply links represented in OASIS.
The final representation of the study area is shown in Figure 5.10.
5.2.4 Break up of fields
The next step in representing the MIL system in OASIS was to break up the area in each IU into fields. The land use information was obtained from the GIS representation of season 2000/01, Table 5.2.
Table 5.2: Land uses across the study area Land use Study area (%) Land use Study area (%) Dryland pasture 25.6 Irrigated perennial horticulture 0.2 Irrigated pasture 20.9 Irrigated seasonal horticulture 1.0 Dryland cereals 15.1 Irrigated farm forestry 0 Irrigated cereals 5.1 Inactive 7.6 Rice 9.8 Storage dam 0.1 Irrigated treelot 0.1 Other 14.4 Irrigated vineyard 0
It should be noted that irrigated vineyard, irrigated farm forestry, and irrigated treelot are all very small areas. Representing 0.1% of the study area, and therefore they have been excluded from any further use in the study.
The soil information was obtained from a GIS layer provided by MIL (Demelza Brand pers. comm.. 4/10/2005). The initial soil maps were created by the Council for Scientific and Industrial Research (field operations division of soils, now the CSIRO). The map for the Wakool area was undertaken in 1940-41, Berriquin 1943, Deniboota 1946-48 and Denimein 1954-55. Table 5.3 lists the soils present in the study area.
111 APPLICATION OF OASIS TO THE CASE STUDY AREA
12
11 15 111 12 21 17 10
51 13 21 41 9 19 18 16 14 20 8 4 6 5 3 11 2 7 31 3 7
5 4 2 1 Legend
Segment 6 Supply Link 1 Irrigation Unit
Entry and exit flows
Figure 5.10: System layout
112 APPLICATION OF OASIS TO THE CASE STUDY AREA
Table 5.3: Description of soil types Soil Description Soil Description Red Brown Earth – Traditional Red RBE-TRBE SMC Self Mulching Clay Brown Earth Traditional Red Brown Earth – Non- TRBE-NSMC SS Sandhills Soil Self Mulching Clay SiC-SMC Silty Clay – Self Mulching Clay RBE Red Brown Earth NSMC Non-Self Mulching Clay TRBE Traditional Red Brown Earth
The soil and 2000/01 land use GIS layers were used to find the area in each IU that a soil-crop combination represented. This is described as a fraction of that particular land use in that particular IU. For this study, initially the entire area of each crop was considered to have the same irrigation management applied to it. That is, the initial number of fields in the study represented the number of combinations of crop type on soil type in an IU. All soil-crop combinations are present in the study area, except irrigated cereals on SiC-SMC.
5.2.5 Agricultural land use information
GIS land use information was available for season 2000/01 (Chapter 2). For the other seasons landholder survey information was used (Marshall 2004).
To determine how accurate the crop areas from the landholder survey were, a comparison was made between the GIS information and the landholder survey information for season 2000/01, (Table 5.4) using the assumptions listed in Table 5.5.
Table 5.4: Land use comparison between GIS and landholder surveys for season 2000/01 Land use 2000/01 GIS (% of area) Marshall (2004) (% of area) Dryland pasture 26 10 Winter irrigated pasture 21 16 Irrigated cereals 20 32 Rice 10 8 Lucerne/summer pasture 0 4 Other crops/fallow 1 1 Native vegetation 8 22 Infrastructure 14 11 Total 100 100
Table 5.5: Equivalent land uses between Marshall (2004) and GIS Land use GIS Marshall (2004) Dryland pasture Dryland pasture Dryland pasture Winter irrigated pasture Irrigated pasture Winter irrigated pasture Dryland cereals Dryland cereals Winter crop Irrigated cereals Irrigated cereals Winter crop Rice Rice Rice Lucerne/summer pasture Not listed Lucerne/summer pasture Irrigated seasonal horticulture and Other crops/fallow Other crops/fallow irrigated perennial horticulture
113 APPLICATION OF OASIS TO THE CASE STUDY AREA
Land use GIS Marshall (2004) Native vegetation Inactive Native vegetation Infrastructure Other Infrastructure Irrigated treelot Irrigated treelot Not listed Irrigated vineyard Irrigated vineyard Not listed Irrigated farm forestry Irrigated farm forestry Not listed Storage dam Storage dam Not listed
A concerning aspect of Table 5.4 is the large variation in the categories of dryland pasture, irrigated cereals, and native vegetation. A difficulty with the GIS data is the lack of discrimination between lucerne/summer pasture and winter irrigated pasture as described by Marshall (2004). Marshall (2004) presented this as the third biggest water user behind rice and winter irrigated pasture. The representation of lucerne/summer pasture in the GIS layer was thought to be included in the area of winter irrigated pasture. This will be increased to 4% of the study area as per the landholder survey information and the area of winter irrigated pasture reduced to 17% of the study area for season 2000/01. For the purpose of this research it is assumed that the results from the GIS information are more accurate for season 2000/01 than those from the landholder survey.
The inaccuracies in the information from the landholder survey are best illustrated by an investigation of the area said to be sown to rice. As mentioned in Section 2.1, MIL quantifies the area sown to rice across the MIA with the use of SPOT satellite data. Table 5.6 contains the areas from SPOT for the study area and the percentage areas from the landholder survey (converted into hectares).
Table 5.6: Rice area comparison 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 Landholder surveys (ha) 59,895 52,408 59,895 37,434 2,246 14,974 SPOT (ha) 55,533 38,416 69,525 55,150 1,545 22,729
Table 5.6 shows that the area said to be sown to rice from the landholder surveys varies between 145% (2002/03) and 66% (2003/04) of that identified as sown to rice via SPOT. The difference is thought to be the result of inaccuracies in the landholder survey information. The SPOT information regarding the rice area is considered more accurate. For that reason it will be used in this research.
A difficulty with the landholder survey information is that it did not differentiate between irrigated and dryland cereal crops. Using the GIS information for season 2000/01, it was found that 25% of the area planted to cereals was listed as irrigated. As
114 APPLICATION OF OASIS TO THE CASE STUDY AREA there was no other information, 25% of the area specified as winter crops in Marshall (2004) was said to be irrigated throughout this research.
5.2.6 Irrigation schedule files
To represent the study area in OASIS each crop had its own irrigation schedule file. The main inputs for the irrigation schedule files are; the target or maximum irrigation rate, demand depth, irrigation scheduling criteria and the irrigation trigger level, point when a new irrigation is triggerred.
For all crops no restriction on the target or maximum irrigation rate was imposed. For non-rice crops, the demand depth was assumed to be that of returning the root zone to field capacity (using the results from the irrigator survey Chapter 3). For rice the demand depth was to return the depth of ponded water to the maximum ponded depth for that time in the season.
The irrigation scheduling criteria and irrigation trigger levels for winter irrigated pasture, irrigated cereals and lucerne/summer pasture are discussed below.
Winter irrigated pasture
Goulburn-Murray Water (Undated) advise irrigators to use accumulated evaporation less effective rainfall in the range of 50 to 65mm, to trigger irrigation of winter irrigated pasture. The direct option of accumulated evaporation less effective rainfall was not available as an irrigation demand criterion in OASIS. OASIS has an option which is very similar; this option uses water depletion (mm) relative to field capacity in the root zone to trigger irrigations.
Irrigated cereals
Three methods to trigger irrigation of cereals were found in the literature. These were percentage soil moisture deficit, weekly or fortnightly irrigation and accumulated evaporation. From Chapter 3, the most common response to the management approach for ordering for non-rice crops was ‘observation of crop and soil’. Based on the above the irrigation demand criterion of water depletion in the root zone as a fraction of the water between the field capacity and the permanent wilting point was selected to be used to trigger irrigation of cereal crops.
115 APPLICATION OF OASIS TO THE CASE STUDY AREA
Lucerne/summer pasture
Bethune (2004) states that irrigation of lucerne/summer pasture should be triggered when the accumulated evaporation reaches the range of 50 to 80mm. A method to represent this in OASIS is to use the ‘DEPL’ criterion to trigger irrigation.
For crops where a range of values was given as a trigger for irrigation this range will be tested in the sensitivity analysis (Section 5.4). Also, for winter irrigated pasture and irrigated cereals the parameters of fromTStep and toTStep were variable and will be included in the sensitivity analysis (Section 5.4).
Prior to undertaking the sensitivity analysis it was necessary to investigate the conveyance efficiency in the Murray Irrigation System.
5.3 Conveyance Efficiency
To represent the efficiency of the study area in OASIS, the first step was to investigate the available data. The only method available to estimate system level efficiency was, to calculate the percentage of nett Mulwala Canal diversion that was recorded as supply. Supply data is collected weekly and the data were available for each season 1999/00 to 2003/04. That is, average supply efficiency could be calculated on a weekly time step.
There was one decision to make in the representation of efficiency in OASIS and one investigation to undertake. Chapter 4 described how OASIS used two efficiency variables to represent efficiency in the irrigation system, but the efficiency information for the Murray Irrigation System was only a single value. For this reason it was important to decide how to use the two efficiency variables in OASIS to represent a single efficiency in the study area. For this it was necessary to determine if there was any relationship between efficiency and flow rate in the study area. Firstly, the method to be used to represent efficiency in OASIS will be discussed and then the investigation into a relationship between efficiency and flow rate will be presented.
To represent the average study area efficiency in OASIS there are two methods: i. let conveyance efficiency (Ec) (Section 4.3.2) equal 100% and set distribution efficiency (Ed) (Section 4.3.2) to the average study area efficiency; or
116 APPLICATION OF OASIS TO THE CASE STUDY AREA
ii. use the three levels of Ec for each segment and let Ed equal 100%.
The advantage of (i) is that it would allow the actual study area efficiency to be used as a direct input into the model. The disadvantage of (i) is that it unrealistically assigns the same efficiency of water irrespective of how far the water travels in canals in the study area. This is in contrast to (ii) where the efficiency is determined, based on the distance the water travels in canals in the study area. To achieve this, the model would need to be calibrated to determine the Ec values in OASIS that produced the same average study area efficiency. This would ensure that the supply losses to IU 1 were significantly less than those to IU 21.
For the investigation into the relationship between flow rate and system efficiency,
nett Mulwala Canal diversion firstly the weekly average system efficiency ( ) values deliveries to farms were investigated. This found that 25% of weeks had system efficiency greater than 100%. For this reason the time length was extended to monthly. Despite this change, 4 of the 44 months had system efficiency greater than 100%. To further investigate the monthly characteristics of this data, the minimum, maximum, average and median were determined for each month, August to April using data from the period September 1999 to April 2004 (Table 5.7).
Table 5.7: Monthly overall study area efficiencies Month Min Max Average Median Aug 5.58% 54.84% 34.53% 38.85% Sep 67.62% 300.26% 127.04% 88.26% Oct 55.52% 107.76% 88.41% 90.38% Nov 37.90% 90.76% 76.18% 86.17% Dec 31.91% 98.42% 75.46% 93.90% Jan 40.85% 97.50% 79.08% 84.87% Feb 57.34% 96.15% 83.41% 87.48% Mar 79.38% 95.69% 88.06% 86.49% Apr 82.26% 103.28% 91.15% 89.43%
Table 5.7 shows a maximum system efficiency of 300.26% in September 2003. The minimum system efficiency was 5.58% in August 2003. It is thought that these anomalies are possibly due to supply measurements for August being recorded in the month of September. The system efficiency of August and September 2003 have been removed from any further analysis as they are considered to be outliers.
117 APPLICATION OF OASIS TO THE CASE STUDY AREA
Due to the extremely low system efficiency values which occurred in season 2002/03 as a result of the 8% allocation that MIL irrigators received, Table 5.7 was recalculated without season 2002/03 and August and September 2003 (Table 5.8). Table 5.8 represents seasons with water allocation between 29% and 86%.
Table 5.8: System efficiency without season 2002/03 Month Min Max Average Median Aug 26.2% 51.5% 38.9% 38.9% Sep 81.0% 98.1% 89.1% 88.3% Oct 85.5% 107.8% 96.6% 96.6% Nov 79.7% 90.8% 85.8% 86.3% Dec 55.5% 98.4% 86.3% 95.7% Jan 84.0% 97.5% 88.6% 86.5% Feb 85.0% 96.2% 89.9% 89.3% Mar 79.4% 95.7% 89.0% 90.5% Apr 82.3% 103.3% 92.1% 91.5%
Table 5.8 shows that the average system efficiency from November to March is between 85.8% and 89.9%. System efficiency values are low in August (maximum of 51.5%). This has two possible explanations; the wetting up, and initial filling of canals, and/or irregular recording of supply data during this traditionally low irrigation month.
To investigate if a relationship existed between flow rate and system efficiency, nett Mulwala Canal diversion was plotted against system efficiency for the monthly data (Figure 5.11).
120.00% Recorded Data 90.9% Efficiency Poly. (Recorded Data) 100.00%
80.00% y = -2E-11x2 + 6E-06x + 0.5245 R2 = 0.1983 60.00%
40.00% Study area efficiency (%) Study
20.00%
0.00% 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000 Nett Mulwala Canal diversion (ML/month)
Figure 5.11: System efficiency versus nett Mulwala Canal diversion
118 APPLICATION OF OASIS TO THE CASE STUDY AREA
Figure 5.11 shows that no relationship exists. It also shows that the average system efficiency is 90.9%. As no relationship was present, the possibility of an annual trend was investigated, Figure 5.12. The following data points were ignored: season 2002/03, September 2003 and August in each season. August was ignored because: • the scenario assessment will concentrate on the period December to April; and • the low system efficiency value for August would distort the actual system efficiency for the scenario assessment period.
96.0%
94.0% y = 2E-07x + 0.7452 R2 = 0.7463 92.0%
90.0%
88.0%
86.0% Study area efficiency (%) Study area 84.0%
82.0%
80.0% 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 Nett Mulwala Canal Diversion (ML/yr)
Figure 5.12: Annual system efficiency versus nett Mulwala Canal diversion
Figure 5.12 shows that there is some correlation (R2 = 0.75) between system efficiency and nett Mulwala Canal diversion. There are only four seasons (four points) of data to consider. The lack of data at the annual time step and the lack of correlation at the monthly time step, means that the three separate levels of Ec in OASIS will not be used. Instead, a single value of Ec will be used for all flow rates in the system. Figure 5.11 showed the average study area efficiency to be 90.9%.
5.3.1 Calibration of conveyance efficiency
With a study area efficiency determined, the next step was to calibrate the model (varying Ec) to achieve an overall efficiency of 90.9%. For the calibration, OASIS was run in the Supply Target mode using the recorded supply data as the supply targets for each IU. This meant that the modeled irrigation deliveries were the same as the
119 APPLICATION OF OASIS TO THE CASE STUDY AREA recorded irrigation deliveries for each IU. The calibration used season 2000/01, as this season had the most accurate land use information. The calibration process found that an Ec value of 0.986 for each segment produced an overall efficiency of 91.1%. Chapter 4 showed, that within OASIS conveyance efficiencies are unique inputs for each segment, with (1 – Ec) defining the fraction of water that is lost as seepage in each segment (Roost 2002). Hence the volume of water traveling in each segment and their conveyance efficiencies will affect the overall system level conveyance efficiency.
As validation, seasons 1999/00, 2001/02 and 2003/04, were used, producing overall conveyance efficiencies of 88.6%, 89.1%, and 86.0%, respectively. The variation of overall conveyance efficiency between seasons is due to different flow requirements at the different IUs during different seasons. Figure 5.13 shows a monthly comparison between the validated model conveyance efficiency and recorded efficiency for the study area.
120.00% Recorded OASIS
100.00%
80.00%
60.00%
40.00% Conveyance Efficiency (%)
20.00%
0.00% Jul-99 Jan-00 Aug-00 Feb-01 Sep-01 Apr-02 Oct-02 May-03 Nov-03 Month Figure 5.13: Validation of overall conveyance efficiency
The two data sets were then compared (Figure 5.14) to gain an appreciation of the correlation between the data sets.
Figure 5.14 shows there is little correlation between the two data sets with R2 = 0.13. The RMSE between the two sets is quite small (41.5), while the Coefficient of Determination (CD) is 10.1, indicating that the simulated overall conveyance efficiency
120 APPLICATION OF OASIS TO THE CASE STUDY AREA does not have the same variation as the recorded data. The small R2 value and large CD value are due to the variable nature of the study area efficiency, which was found to be independent of flow rate. For this reason the conveyance efficiency was calibrated to the average value of recorded supply efficiency. An Ec value of 0.986 for each segment for all flow levels reproduced an adequate level of supply efficiency for all seasons; hence this value will be used.
110%
105%
100%
R2 = 0.1318 95%
90%
85% Recorded study area efficiency (%) 80%
75% 75% 80% 85% 90% 95% 100% Simulated overall conveyance efficiency (%)
Figure 5.14: Correlation between simulated overall conveyance efficiency and study area efficiency (monthly)
5.3.2 Application efficiency
To determine if application efficiency played an important role in losses in the irrigation system two items were investigated, the range of application efficiency in literature and the volume of water applied to each crop during the irrigation season. The investigation of application efficiency values in literature found a range of 0.55 to 0.9, Aqualinc (2006) and Clemmens (2000). To investigate the volume of water applied to each crop, MIL estimates from the crop specified at the time of ordering were investigated for season 2000/01. As rice is assumed to have an application efficiency of 100% in the model, only non-rice crops were used to calculate the maximum and minimum application losses for season 2000/01 in the study area, Table 5.9.
121 APPLICATION OF OASIS TO THE CASE STUDY AREA
Table 5.9: Application loss variations for season 2000/01 MIL estimate of irrigation Losses (ML) Losses (ML) Crop supplied for each crop (ML) with Ea = 0.55 with Ea = 0.9 Rice 435,540 (58.9%) - - Winter irrigated pasture 152,000 (20.6%) 68,400 15,200 Lucerne/summer pasture 88,660 (12.0%) 39,900 8,870 Irrigated cereal 63,330 (8.6%) 28,900 6,330 Total 739,530 136,800 30,400
The above process was then used to calculate the range of application losses for seasons 1999/00 to 2003/04. The application losses were compared to the distribution losses (calculated as the difference between nett Mulwala diversion and recorded supply) in each season to gain an understanding of the importance of application efficiency in overall efficiency losses, Table 5.10.
Table 5.10: Distribution losses against application losses (percent of total seasonal losses) Application loss range Season Distribution losses (ML/yr) (ML/yr) % of total losses 1999/00 54,302 16,488 to 74,197 23.3% to 57.7% 2000/01 54,053 30,399 to 136,755 36.0% to 71.7% 2001/02 82,472 37,504 to 168,767 31.3% to 67.2% 2002/03 192,704 26,761 to 120,423 12.2% to 38.5% 2003/04 89,436 24,354 to 109,592 21.4% to 55.1%
Table 5.10 shows that application losses vary between 12.2% of total losses (application efficiency of 0.9 in season 2002/03) and 71.7% (application efficiency of 0.55 in season 2000/01), meaning that application and distribution losses are of approximately equal importance in calibrating the model.
To calibrate the value of application efficiency for the study area, groundwater data from 687 boreholes across the study area were used. MIL has taken readings in August and March annually between March 1997 and March 2003. To determine the average groundwater level in each IU the non-zero readings from the boreholes in each IU were averaged. The August readings are used to set the groundwater level in each IU for the start of each simulation in OASIS. OASIS was then run in Supply Target mode ensuring that the simulated volume of water entering each IU matched the recorded volume of water entering each IU. The groundwater output from the final time step in March from OASIS was compared to the average recorded groundwater level in each IU. The comparison used the RMSE and R2 (Table 5.11) between the simulated groundwater level and the recorded groundwater data in each IU. This was completed for 5 values of Ea (0.55, 0.6, 0.7, 0.8 and 0.9) for seasons 1999/00 and 2000/01.
122 APPLICATION OF OASIS TO THE CASE STUDY AREA
Table 5.11: RMSE for season 1999/00 and 2000/01 Ea 0.9 Ea 0.8 Ea 0.7 Ea 0.6 Ea 0.55 Season RMSE R2 RMSE R2 RMSE R2 RMSE R2 RMSE R2 1999/00 37.14 0.984 34.89 0.985 33.29 0.986 31.75 0.987 30.97 0.987 2000/01 27.74 0.987 27.97 0.987 30.33 0.987 33.73 0.986 36.06 0.985
Table 5.11 shows that for season 1999/00, an Ea of 0.55 produced the smallest RMSE. Season 2000/01 was the complete reverse of the trend, with an Ea of 0.55 producing the largest RMSE and an Ea value of 0.9 produced the lowest RMSE. Also shown in Table 5.11 is that the R2 is very close to 1 for all values of Ea tested. A slight trend is evident that reflects the performance of the RMSE in each season, with the highest R2 values coming with an Ea value of 0.55 and 0.6 in 1999/00 and with 0.7, 0.8 and 0.9 in season 2000/01, although they are not significantly different.
From the above no evidence exists to support the selection of one particular value for application efficiency. A value of 0.7 was chosen for Ea because it is very close to the average RMSE for the two seasons. An Ea of 0.7 is also very close to the centre of the range listed by Aqualinc (2006) and Clemmens (2000). Ea will also be included in the sensitivity analysis to determine how sensitivite the model is to Ea.
5.4 Sensitivity Analysis
Prior to describing the sensitivity analysis it is necessary to discuss the most important parameters for this research. An important parameter is described as one whose uncertainty causes a high degree of output variability (Hamby 1994). OASIS incorporates a large number of parameters, the most important for this research, are: • Soil parameters for 8 different soil types. The inputs for each soil type consist of 6 general parameters and 4 parameters for each different soil layer within the soil types. Generally there are at least 3 soil layers within each soil type. This equates to approximately 144 soil parameters for the study. • Crop parameters for 7 different crop types. The parameters for each crop consist of; 2 general parameters and the crop co-efficient and root zone depth are specified for critical dates (approximately 5 per crop) during each crop’s growing season. This equates to 84 crop parameters for the study. • Irrigation scheduling parameters for 5 irrigated crops. There are at least 12 input parameters for each irrigated crop. If any of the irrigation scheduling parameters change during the irrigation season (for example, irrigation of rice is
123 APPLICATION OF OASIS TO THE CASE STUDY AREA
initiated with different water depths at different points in the irrigation season) it is necessary to specify another set of 12 input parameters for this period in the irrigation season. This equates to at least 60 input parameters for irrigation scheduling. • Application efficiency (as noted previously this will be included in the sensitivity analysis). • 4 initial field condition parameters for each irrigated crops. This equates to 20 initial field condition parameters. • Percentage of capacity full for the OFWS is in each IU. This level will be assumed constant across the 21 IUs, hence this equates to a single input parameter.
The above list describes the most important parameters for this research. This is not however an exhaustive list of input parameters, for a comprehensive list of parameters, refer to the OASIS operating manual (Appendix C). For the parameters described above, the first source of information was the literature. Where no literature was present a sensitivity analysis was undertaken to determine the importance of the parameter.
Input parameters from literature
Parameters where information was available from literature were soil parameters and crop coefficients. The soil parameter information was available for soil properties in the Murrumbidgee Irrigation Area and CIA (Hornbuckle and Christen 1999). Local knowledge, (Lindsay Evans, pers. comm.. 17/10/2005) was used to convert the soil types in Hornbuckle and Christen (1999) to those present in the study area, Table 5.12.
Table 5.12: Transposing soil properties from Hornbuckle and Christen (1999) to the study area Soil from Hornbuckle and Christen (1999) Study area soil Sandmount sand and Danberry sand Sandhills Soils (SS) Cobram loam and Birganbigal loam Red-Brown Earths (RBE) Mundiwa, Morago, Marah and Tuppal Clay Traditional Red-Brown Earths (TRBE) Riverina and Billabong Clay Non-Self Mulching Clay (NSMC) Wunnamurra Clay Self Mulching Clay (SMC)
There were three soil types present in the study area that are not listed in Table 5.12, Red Brown Earth – Traditional Red Brown Earth (RBE-TRBE), TRBE – Non-Self Mulching Clay (TRBE-NSMC) and Silty Clay/Self Mulching Clay (SiC-SMC). RBE– TRBE and TRBE–NSMC were considered to have properties at the mid point between
124 APPLICATION OF OASIS TO THE CASE STUDY AREA the properties of the two soils that were present in Hornbuckle and Christen (1999). SiC-SMC was assumed to lie between RBE and SMC.
Locally calibrated crop coefficients were used where possible as the ETo data provided, was calculated using the locally adjusted Penman-Monteith equation. If local crop coefficient data was not available these crop coefficients were adjusted based on the
method of calculating reference ETo used.
The root depth of crops, where available were obtained from local literature. Otherwise the method of Borg and Grimes (1986) was used, Equation 5.12.
⎡ ⎛ DAP ⎞⎤ Equation 5.12 RD = RDm ⎢0.5 + 0.5sin⎜3.03 −1.47⎟⎥ ⎣ ⎝ DTM ⎠⎦ Where RD is the root depth (cm);
RDm is the maximum root depth (cm); DAP is the number of days the current day is after planting (days); and DTM is the days to maturity (days).
Root depth reaches its maximum at the end of the development stage in all vegetation. This information was taken from Allen et. al. (1998) for each crop.
The source of each crop coefficient and root depth information is shown in Table 5.13.
Table 5.13: Sources for crop coefficients and root depths Crop Source of crop coefficient Source of root depth Dryland pasture Sinclair Knight Merz Pty Ltd (2003) 1 Bethune (2004) Winter irrigated pasture Sinclair Knight Merz Pty Ltd (2003) 1 See Section 5.4.1 Dryland cereals Wheat (Meyer, Smith et al. 1999) (Incerti and O'Leary 1990) Irrigated cereals Wheat (Meyer, Smith et al. 1999) (Incerti and O'Leary 1990) (Fukai and Inthapan 1988; Rice (Meyer, Smith et al. 1999) Kusnarta, Tisdall et al. 2004) Crop Source of crop coefficient Source of root depth (Blaikie, Martin et al. 1988; Lucerne/summer pasture (Bethune and Wang 2004)2 Ridley and Simpson 1994) Irrigated seasonal Tomatoe (Allen, Pereira et al. 1998) 2 (Borg and Grimes 1986) horticulture Native vegetation As per dryland pasture As per dryland pasture Notes : 1. The use of crop factors (using Class A Pan Evaporation) as listed in Sinclair Knight Merz Pty Ltd (2003) is possible using the relationship between Epan and ETo as Epan = 1.0 ETo (Humphreys, Meyer et al. 1994). This is using the locally calibrated Penman Monteith method of estimating reference evapotranspiration for Griffith, NSW. 2. The crop coefficient (Allen, Pereira et al. 1998; Bethune and Wang 2004) is for the Penman Monteith method of calculating ETo, so it has been adjusted for the locally calibrated Penman Monteith method of estimating reference evapotranspiration as used in this research.
125 APPLICATION OF OASIS TO THE CASE STUDY AREA
Prior to undertaking a sensitivity analysis, the most appropriate method to use for the sensitivity analysis was investigated.
5.4.1 Method of sensitivity analysis
The simplest method of sensitivity analysis is to assess the impact that each individual parameter has on the output variability by varying the parameters one at a time (Hamby 1994). A more recent sensitivity method applicable to management decision modelling is the Management Option Rank Equivalence (MORE) (Ravilico, Dandy et al. 2007). MORE is used to assess the sensitivity of management decisions to variability in input variables and parameters. MORE uses the evolutionary algorithm technique, of Genetic Algorithms to search the search space to locate the Rank-Equivalence Boundary, which is the surface in parameter space where the management options become equal in rank.
One of the difficulties with the sensitivity analysis to be undertaken on OASIS for this research is the interconnected nature of the parameters surrounding crops. This is discussed further in Section 5.5. Along with this there are also periods during the irrigation season when irrigation of particular crops is more important than others, hence it was seen necessary to have user input into the sensitivity and calibration process. For these reasons the individual sensitivity analysis method described by Hamby (1994) was implemented for this research.
To assess the performance of each model run, the RMSE between modeled and actual inflow into the study area was used in most instances. The Sensitivity Index (SI) (Hamby 1994) was used to assess the impact the variability in each parameter had on the model. The SI was used to rank the parameters from most sensitive to least sensitive. The SI calculates the output percent difference when a parameter is varied across its entire range, Equation 5.13.
D − D SI = max min Equation 5.13 Dmax
Dmax and Dmin represent the maximum and minimum model output-values, respectively as a result of varying the parameter over its entire range.
The parameters where no literature information was present were included in the sensitivity analysis. The first step in the sensitivity analysis was to choose an
126 APPLICATION OF OASIS TO THE CASE STUDY AREA appropriate value for each parameter. Each parameter was then varied individually (globally, i.e. each occurrence of the variable was changed) within its feasible range of values, while holding the other parameters constant. This was to determine how each parameter affected the models performance. The aim of the sensitivity analysis was to replicate the recorded inflow hydrograph for the study area as accurately as possible. The parameter sensitivity analysis was undertaken using the period 1st August 2000 to 30th April 2001 as season 2000/01 contained the most accurate land use information.
Prior to outlining the parameters included in the sensitivity analysis it is necessary to examine the parameters related to irrigation volume applied to each crop. These are: • crop area; • non-optimal irrigation of crop areas as either: • percentage of the crop area irrigated at the optimum level; or • the level of deficit irrigation applied across the crop area; or • a combination of the two; • crop growth times; • times of irrigation; and • irrigation trigger levels.
‘Non optimal irrigation of crop areas’ can occur for all crops except rice and horticulture. Three methods were outlined above as a means of capturing ‘non optimal irrigation of crop areas’, for this research the method of ‘percentage of the crop area irrigated at the optimal level’ has been used. This method was chosen as it is the simplest method to use in the OASIS model. That is, with less water a smaller percentage of the crop is irrigated to the optimum irrigation level. Note, if the level of deficit irrigation is 0%, the crop is irrigated to meet crop water demands, while if the level of deficit irrigation is set to 100%, there is no irrigation of the crop.
The land use information (crop area) was assumed to be accurate as it came from the GIS land use layer for season 2000/01, as discussed in Section 5.2.5. Land use information for seasons other than 2000/01 and crops other than rice are still considered to be approximations. For seasons other than 2000/01 the variability between the reported and actual land use and the non optimal irrigation of crop areas are intertwined, with no discernible method to separate the two. For this reason crop areas
127 APPLICATION OF OASIS TO THE CASE STUDY AREA have been assumed accurate in all seasons and the percentage of the crop area irrigated at the optimum level assumed uncertain for all crops except rice and irrigated horticulture. Variability still surrounds the area of irrigated horticulture, for seasons other than 2000/01. The variability of the area of irrigated horticulture (Area of H) will be tested for the range 66% to 145% as per Section 5.2.5.
With the above assumption the parameters which required a sensitivity analysis are discussed below. These are broken into three groups; initial parameters, general parameters and crop specific parameters for winter irrigated pasture, irrigated cereals and lucerne/summer pasture.
Initial parameters
The two initial condition parameters to be tested are: • initial level of soil moisture depletion (maxDeplF); and • initial percentage of capacity full of OFWS (FStorVF).
General parameters
The only general parameter to be tested is application efficiency (Ea). Application efficiency is included for reasons discussed in Section 5.3.2.
Winter irrigated pasture
The parameters related to winter irrigated pasture to be tested are: • root zone depth (IP-RZ); • date when winter irrigated pasture returns to growing again in late summer (Time step IP); • soil moisture depletion level to trigger irrigation (DEPL for IP); and • percentage of the crop area irrigated at the optimum level (Percent irrigation of IP).
Information from literature was available for the first three parameters listed above. Bethune (2004) states that typically dairy pasture in the MDB consists of white clover and rye grass where the typical root depth does not extend beyond 0.6 metre below the surface. Guobin and Kemp (1992) stated that 80% of the roots of a white clover and phalaris mixture were within 0.2 metre of the surface. Therefore the sensitivity analysis included the range of winter irrigated pasture root zone from 0.2 to 0.6 metre.
128 APPLICATION OF OASIS TO THE CASE STUDY AREA
The date when winter irrigated pasture returns to growing in late summer was investigated. Initially, the 8th of February for northern Victoria (Sinclair Knight Merz Pty Ltd 2003) was used, the tested range was from February 1st to the 22nd.
The trigger for irrigation of winter irrigated pasture was 50 to 65mm of accumulated evaporation less effective rainfall (Goulburn-Murray Water Undated). Water depletion in the root zone is not a direct representation of accumulated evaporation. When water is freely available in the soil profile, the soil water can be expected to be depleted at a level of crop co-efficient multiplied by ETo, as the level of soil water is reduced the soil water is depleted at less than the rate of crop co-efficient multiplied by ETo. To gain an estimate of the soil water level to test for this research it was assumed that the soil water was depleted at the maximum level. Using this relationship, the maximum crop co-efficient for winter irrigated pasture is 0.8. This leads to a soil water depletion (DEPL) range of 40 to 52mm. Hence applying this relationship means that a conservative estimate of soil water depletion to trigger an irrigation is created. In addition to testing the range for DEPL to trigger irrigation, the effect of deficit irrigation needs to be tested.
Irrigated cereals
The parameters related to irrigated cereals to be tested are: • date when the irrigation finishes (ToTStep); • soil moisture depletion level to trigger irrigation (REL_DEPL for IC); and • percentage of the cereal area irrigated at the optimum level (Percent irrigation of IC).
Information was available for the first two parameters above. The completion date of the irrigation period that will be tested is between September 30th and November 30th. This is based on results from the irrigator survey (Chapter 3). The range of soil moisture deficit tested will be 40% to 90% (Steiner, Smith et al. 1985).
Lucerne/summer pasture
The parameters related to lucerne/summer pasture to be tested are: • soil moisture depletion level to trigger irrigation (DEPL for Ipe); and
129 APPLICATION OF OASIS TO THE CASE STUDY AREA
• percentage of the lucerne/summer pasture area irrigated at the optimum level (Percentage irrigation of Ipe).
Information was available for the first parameter above. Bethune and Wang (2004) state that irrigation of lucerne/summer pasture should occur when the accumulated evaporation reaches the range of 50 to 80mm. The irrigation criteria ‘DEPL’ is used in OASIS, hence it was necessary to convert the accumulated evaporation into a soil moisture depletion. Again a conservative estimate is gained by assuming that the soil water is depleted at the maximum level for the range of accumulated evaporation. The crop coefficient for lucerne/summer pasture is 0.7 throughout the season; this means the test range for ‘DEPL’ is 35 to 56mm.
Table 5.14 displays the parameters used to conduct a sensitivity analysis, a description of them, their initial values and each parameter’s entire range of values tested.
5.4.2 Sensitivity analysis results
The SI was used to compare the sensitivity of the model to the parameters listed in Figure 5.15. Figure 5.15 shows the SI of each parameter tested in the sensitivity analysis. Figure 5.15 has been ranked from most sensitive (left) to least sensitive (right). Each parameter in the sensitivity analysis is discussed below.
Application efficiency of flood irrigation (Ea) (SI = 0.27)
The performance of the model increased with increasing values of Ea. A value of 55% for Ea increased the RMSE by 19%, while a value of 90% decreased the RMSE by 13.2%.
Percent irrigation of lucerne/summer pasture (Percent irrigation of Ipe) (SI = 0.22)
There was a continued reduction in the RMSE as the level of percent irrigation of lucerne/summer pasture was increased from 0% (optimal irrigation) to 100% (no irrigation). This model performance could be caused by a number of factors including; • an over estimation of water applied to all crops except rice and horticulture. The water applied to rice and horticulture is considered to be the optimal crop water requirement otherwise the crop will suffer large production losses; or • an overestimation of when and how much water is applied via irrigation to lucerne/summer pasture.
130 APPLICATION OF OASIS TO THE CASE STUDY AREA
Table 5.14: Parameters with sensitivity analysis undertaken Variable Description Range Initial value Best value maxDeplF Initial level of soil moisture depletion 0– 0.65 0.65 0 FStorVF Initial percentage of capacity full of OFWS 0.2-1.0 0.2 1.0 Ea Application Efficiency 0.55 – 0.9 0.7 0.9 ToTStep IC Date when irrigation finishes for irrigated cereals 30/9 – 30/11 20/10 31/10 IP-RZ Root zone depth of winter irrigated pasture 0.2 – 0.6m 0.3m 0.6m Time step IP Date when winter irrigated pasture starts to grow again in late summer 1/2 – 22/2 8/2 22/2 Percent irrigation of IP Percentage of the winter irrigated pasture area irrigated at the optimum level 0% - 100% 0% 50% DEPL for IP Soil moisture depletion level to trigger irrigation of winter irrigated pasture 40 – 52mm 50mm 52mm Percent irrigation of IC Percentage of the crop area irrigated at the optimum level 0% - 100% 0% 100% REL_DEPL for IC Relative soil moisture depletion level to trigger irrigation of Irrigated Cereals 0.6 to 0.1 0.25 0.5 Area of H Area of Horticulture irrigated 66% - 145% 100% 66% Percent irrigation of Ipe Percentage of the irrigated perennial pasture area irrigated at the optimum level 0% - 100% 0% 100% DEPL for Ipe Soil moisture depletion level required to trigger irrigation of irrigated perennial pasture 35 to 56mm 50mm 56mm
131 APPLICATION OF OASIS TO THE CASE STUDY AREA
0.30
0.25
0.20
0.15
0.10 Sensitivity Index Sensitivity
0.05
0.00
P F H e Ea I pl IC IC p IP e f r I r rVF o o IP-RZ o o n of xD a of n St io Step re o L f L f F t T A ti ation of Ipe ma o a g ga T Timestep IP EP DEP irri rrig D t i t irri n nt REL_DEPL for IC rce rce Percen Pe Pe Parameter
Figure 5.15: Sensitivity Index for parameters tested in the sensitivity analysis
Percent irrigation of winter irrigated pasture (Percent irrigation of IP) (SI ≈ 0.20)
The best performance occurred with the level of irrigation set to 50% of the optimal level of irrigation. As the level of percent irrigation of winter irrigated pasture was either increased or decreased from this value there was a continuing reduction in the performance of the model.
Maximum depleted fraction of fields at the start of the simulation (maxDeplF) (SI = 0.15)
A trend existed between decreasing the initial soil moisture depletion of fields and decreasing RMSE. The best performance of the model occurred when the initial soil moisture depletion was set to 0.
The final time step of cereal irrigation (ToTStep IC) (SI = 0.13)
Increasing the period of cereal irrigation from October 20th to November 30th produced an 11.7% increase in the RMSE. Setting crop irrigation to finish on October 31st produced a small decrease in the RMSE.
132 APPLICATION OF OASIS TO THE CASE STUDY AREA
Time step when winter irrigated pasture returns to growing in late summer (Time step IP) (SI = 0.09)
From the investigation into the date when winter irrigated pasture returns to growing in late summer, it was found that using a date of the 22nd of February for the return to growth of winter irrigated pasture produced a 7.3% reduction in the RMSE (the initial date was the 8th of February). An increase in the RMSE occurred when a return to growth of winter irrigated pasture of February 1st was tested.
Area of irrigated horticulture (Area of H) (SI = 0.05)
There was a correlation between improved model performance and reducing the area of irrigated horticulture.
Percent irrigation of irrigated cereals (Percent irrigation of IC) (SI = 0.04)
As the level of percent irrigation of irrigated cereals was increased from 0% to 100% there was a continued reduction in the RMSE. The best performance occurred with the level of percent irrigation set to 100% with a 4.2% reduction in the RMSE.
Level of relative depletion of soil moisture used to trigger irrigation of irrigated cereals (REL_DEPL for IC) (SI = 0.04)
The best performance of the model occurred with a relative depletion of 0.5 while the worst performance occurred using a relative depletion of 0.6.
Level of soil moisture depletion used to trigger irrigation of lucerne/summer pasture (DEPL for Ipe) (SI = 0.03)
The performance of the model increased as the level of soil moisture depletion was increased to trigger irrigation of lucerne/summer pasture.
Root zone depth of winter irrigated pasture (IP-RZ) (SI ≈ 0.02)
The performance of the model increased with increasing winter irrigated pasture root zone depth. Using a root zone depth of 0.6 metre decreased the RMSE by 0.39%. Setting the root zone depth to 0.2 metre increased the RMSE by 1.45%. The root zone depth of winter irrigated pasture has only a small influence on the overall performance of the model.
133 APPLICATION OF OASIS TO THE CASE STUDY AREA
Level of soil moisture depletion used to trigger irrigation of winter irrigated pasture (DEPL for IP) (SI = 0.01)
Varying the level of soil moisture depletion (40mm to 52mm) used to trigger irrigation of winter irrigated pasture had a small impact on the RMSE. It was noted that this parameter caused a high degree of variability in the inflow hydrograph during autumn. It was also noted that the inflow hydrograph was constantly overestimating the volume of irrigation entering the system during late March and April.
Initial OFWS level (FStorVF) (SI = 0.0004)
The initial OFWS level had negligible impact on the model performance.
5.4.3 Conclusions from sensitivity analysis
From the parameter sensitivity analysis the parameters that will be included in the calibration are those that meet the following three criteria: • no literature information is available; • a sensitivity index of greater than 0.04; and • non-initial parameters.
The parameters which meet these three criteria were Percent irrigation of Ipe, Percent irrigation of IP, Percent irrigation of IC, Area of H, ToTStep IC and Time step IP (for a description of these parameters refer to Table 5.14). Hence these parameters will be adjusted during calibration of the model.
The other parameters tested in the sensitivity analysis will be set to their best performing value from the sensitivity analysis. A discussion of these parameters is presented below.
The initial parameters of; initial OFWS level and maxDeplF were set to their best performing values, 1 and 0, respectively. The parameters which had a SI less than 0.04 were set to their best performing values these parameters and best performing values were; IP-RZ (0.6 metre), DEPL for Ipe (56mm), DEPL for IP (52mm) and REL_DEPL for IC (0.5). Ea was returned to its original value for reasons stated in Section 5.3.2.
134 APPLICATION OF OASIS TO THE CASE STUDY AREA
5.5 Calibration at the System Scale
There are a number of ways the model could be calibrated. Calibration data is available on drainage flows, piezometer data (groundwater levels), recorded supply data and recorded system inflows. All of these measures are discussed below.
For calibration of the model, daily data is desired because the nature of rain rejection events is that they occur over a period of 2 to 7 days. Theoretically, the best data available for this study are daily system flows, daily volumes of orders and daily volumes of rejections. Daily 4-day orders and daily system flows are recorded though daily rejections are not recorded. For this reason other calibration measures were investigated.
Investigation of piezometer data found that it was only available on a six monthly basis from February 1988 until March 2003. For this reason, it was discarded as an option for calibration of the model.
An investigation of the drainage flows from the MIL system found them to be estimated at 7,000 ML/yr (pers. comm.. David Watts, 6/9/2005). This equates to less than 1% of the total inflow into the system. Given the small drainage flows relative to inflows, use of this data for calibration of the model would be highly inaccurate.
An investigation of the supply data, found that it is only available on a weekly time step. A weekly time step is inadequate for this research because of the nature of rejection events. For this reason supply data will not be used for calibration of the model.
This left recorded inflows at the Mulwala Canal diversion (top of the irrigation system) as the only data that met the requirement of a daily time step. For this reason, these are the data used for calibration of the model.
5.5.1 Calibration method
Prior to outlining the calibration method used it is necessary to discuss some of the difficulties with calibration and validation of the model. The MIA system (described in Chapter 2) has a highly variable annual water allocation, for this reason the area of annual crops (namely rice and cereals) varies significantly on an annual basis. The
135 APPLICATION OF OASIS TO THE CASE STUDY AREA other irrigated land uses; winter irrigated pasture, lucerne/summer pasture and horticulture have relatively constant land use areas on an annual basis. Marshall (2004) showed that the irrigation depth applied to the crops of winter irrigated pasture, lucerne/summer pasture and irrigated cereals have a high annual variation. Obviously, the climate in each season affects the volume of water applied to these crops, though the water allocation also plays a significant role in determining the volume of water applied to these crops. The sensitivity analysis showed the percentage irrigation of the lucerne/summer pasture crop area and the percentage irrigation of the winter irrigated pasture crop area to be the parameters that have the second and third most impact on the performance of the model. That is the performance of the model is significantly affected by the irrigated percentage area of these two crops. Three methods were considered to calibrate the model: • use a broad range of water allocation seasons (at least four seasons) for calibration and another four seasons for validation of the model; • for a broad range of water allocation seasons use a part of each individual season for calibration and the remaining part of the season for validation; and • calibrate the model for a broad range of water allocation seasons and use literature and crop water use estimates provided by MIL to validate the model.
To determine which of the above three methods is most appropriate to use for this research it is necessary to investigate the available data and the OASIS model. The available data consists of 5 seasons of recorded data, with water allocations that vary between 8% and 86%. Due to the lack of available data the first method listed above can not be used.
To determine if the second method listed above could be used it is necessary to investigate the periods during the irrigation season when each crop is irrigated. From the sensitivity analysis, the possible crop irrigation periods are: • irrigation of cereals from the 1st of August until a date between September 30th and November 30th; • irrigation of rice from October 1st to February 28th; • irrigation of horticulture from 23rd of October until the 31st of March;
136 APPLICATION OF OASIS TO THE CASE STUDY AREA
• irrigation of winter pasture from the 1st of August until 1st of November and from between February 1st and 22nd and the end of the simulation period (April 30th); and • irrigation of lucerne/summer pasture throughout the year.
From the above there are three distinct periods, these are: • from the start of the simulation period to the 30th of November (last possible date for irrigation of cereals), during this period there is irrigation of all of the above crops at different times; • during the months of December and January there is irrigation of rice, horticulture and lucerne/summer pasture; and • from the 1st of February until the end of the simulation period there is irrigation of all crops except cereals.
Each of the above periods are considered to have different preferences of crops, hence it is considered there are three periods within each irrigation season to calibrate. If the original time step (5-days) of OASIS is used to calibrate the model, then there is insufficient data to both calibrate and validate the model for each period of the irrigation season. For this reason information from MIL and literature has been used to validate the model. That is, calibration of the model was undertaken at the system level for each season. The model’s performance was then validated at the crop and IU level (spatial distribution) for each season. Literature and crop irrigation estimated from MIL were used to validate the model at the crop level and the delivery records were used to validate the spatial distribution of water over time.
Calibration and validation for season 2000/01 (shown below) was undertaken first as it had the most accurate land use information. The parameters calibrated in season 2000/01 were percentage irrigation of winter irrigated pasture (Percent irrigation of IP), percentage irrigation of irrigated perennial pasture (Percent irrigation of Ipe), percentage irrigation of irrigated cereals (Percent irrigation of IC), the timestep on which irrigation of irrigated cereals finishes (ToTstep IC) and the timestep on which winter irrigated pasture returns to growing in late summer (Timestep IP). As the variation in irrigation levels between seasons is considered to come from the percentage area of each crop that is irrigated, the calibrated values for TStep IC and Time step IP were then used for the four remaining seasons. Hence the only parameters calibrated
137 APPLICATION OF OASIS TO THE CASE STUDY AREA for seasons other than 2000/01 were Percent irrigation of Ipe, Percent irrigation of IP, and Percent irrigation of IC. The results for these four seasons are shown in Section 5.6.
5.5.2 Calibration for season 2000/01
This section shows the calibration and validation process for season 2000/01. As season 2000/01 has a GIS land use layer, the calibration does not include the area of irrigated horticulture (Area of H).
The main parameters for which calibration was required were: Percent irrigation of IP, Percent irrigation of IC, Percent irrigation of Ipe, ToTStep IC and Time step IP.
The calibration took place using the three periods listed in Section 5.5.1. The first period was for Percent irrigation of IC in late winter through spring; August 1st to December 3rd. December 3rd was chosen as the end of the first simulation because it was the completion of the last time step of supply data (supply data is recorded weekly) that could include irrigation of cereals. Recall supply data will be used to validate the performance of OASIS at the IU scale.
The third period (which was identified secondly) was for Percent irrigation of IP in late summer through until the end of April; February 1st until April 30th. The third period started on the 1st of February as it was the first time step that could include irrigation of winter irrigated pasture.
The second period was the time between the first and third period, during this time there was only irrigation of rice, horticulture and lucerne/summer pasture.
The second and third periods are most critical for this research as unseasonal flooding of the B-MF is of most importance for the months of December to April. RMSE between the predicted system inflow and the recorded system inflow was used as the calibration measure for each period. The periods and the irrigated crops are listed in Table 5.15. Period 2 was investigated first.
138 APPLICATION OF OASIS TO THE CASE STUDY AREA
Table 5.15: Crop irrigation periods (August 1st to December 3rd) Period Crop Irrigation period Winter irrigated pasture 1st August to 2nd November Period 1: Irrigated cereals 1st August to (30th September to 30th November) 1st August to 3rd Lucerne/summer pasture 1st August to 3rd December December Irrigated horticulture 23rd October to 3rd December Rice 1st October to 3rd December Period 2: Lucerne/summer pasture 4th December to 31st January 4th December to Irrigated horticulture 4th December to 31st January 31st January Rice 4th December to 31st January Winter irrigated pasture (1st to 22nd February) to 30th April Period 3: Lucerne/summer pasture (1st February to 1st March) to 30th April 1st February to 30th April Irrigated horticulture 1st February to 31st March Rice 1st February to 28th February
Period 2
The second simulation period from December 4th to January 31st was investigated first as the least number of crops were irrigated during this period. The three irrigated crops were rice, horticulture and lucerne/summer pasture. All three crops are irrigated throughout the simulation period (December 4th to January 31st). As data on actual deficit irrigation practice for each crop is not available, it was necessary to assume for calibration purposes that one or all three crops was not irrigated to their optimum level. Because of their higher value, growers give priority to rice and horticulture and provide irrigation at their optimum level. For this reason the non-optimum irrigation was assumed to occur on lucerne/summer pasture. The results from the calibration of Percent irrigation of Ipe are shown in Figure 5.16.
60 R2 = 0.89 50
40
30
RMSE 20
10
0 0% 20% 40% 60% 80% 100% Crop area (Ipe) irrigated
Figure 5.16: Calibration results for level of percent irrigation of lucerne/summer pasture
Figure 5.16 shows a near linear trend between decreasing the crop area irrigated and an improvement in the model performance. The best model performance (RMSE = 34.41)
139 APPLICATION OF OASIS TO THE CASE STUDY AREA occurred with Percent irrigation of Ipe set to 0%, that is no irrigation of lucerne/summer pasture. From this it can be concluded that the area listed as lucerne/summer pasture does not receive irrigation all year round as was expected and in fact the best performance of the model occurs when there is no irrigation during the period December 4th to January 31st. This finding is in contrast to the information received from the landholder survey (Chapter 3) where October to March was listed as the critical period for irrigation of lucerne/summer pasture. This result is very difficult to explain as Section 5.5.4 shows that the irrigation depth applied to rice and horticulture is within the expected range. A possible explanation is that in reality all three crops are not irrigated to their optimum level through this period. As there is no evidence to support another result the percentage area of lucerne/summer pasture irrigated was set to 0% for the period December 4th to January 31st.
Period 1
The next simulation period undertaken was from August 1st to December 3rd; the irrigated crops and their irrigation period were given in Table 5.15. Again if all crops were considered to be irrigated at their optimum level this lead to a large overestimation of irrigation for the period. For this reason it was necessary to investigate which crops should not be irrigated at their optimum. From Section 5.5 the irrigation parameters for irrigated horticulture and rice were fixed. This left the crops of; lucerne/summer pasture, winter irrigated pasture and irrigated cereals.
There are normally smaller levels of irrigation during the months of August and September, for this reason the crops which were more likely to be irrigated in October and November of this period were given preference over the other crops.
From the interview questionnaire results (Chapter 3) the period from September to November was listed as the critical period for irrigation of cereals and the period October to March was listed as the critical period for irrigation of lucerne/summer pasture. Winter irrigated pasture is beginning to die off for the summer period from the 21st of September (Sinclair Knight Merz Pty Ltd 2003) for this reason irrigation is preferred for cereals and lucerne/summer pasture over winter irrigated pasture. This meant setting the percent area of cereals irrigated (Percent irrigation of IC) and percent area of lucerne/summer pasture irrigated (Percent irrigation of IPe) to 100%. The percent irrigation of winter irrigated pasture (Percent irrigation of IP) was calibrated
140 APPLICATION OF OASIS TO THE CASE STUDY AREA first, with the time step of irrigation of irrigated cereals (ToTStep IC) set to October 31st, Figure 5.17. This shows that Percent irrigation of IP has a very small impact on the performance of the model for calibration of period 2. The best model performance occurred with Percent irrigation of IP set to 50%.
259
258
257
RMSE 256
255
254 0% 20% 40% 60% 80% 100% Crop area (IP) irrigated
Figure 5.17: Percent irrigation of IP in calibration period 1
The second parameter calibrated was the timestep on which irrigation of irrigated cereals finished (ToTStep IC), Figure 5.18.
295 290 285 280 275 270 RMSE 265 260 255 250 30/09 10/10 20/10 30/10 09/11 19/11 29/11 Date
Figure 5.18: ToTStep IC in calibration period 1
Figure 5.18 shows that ToTStep IC had a greater impact on the model’s performance. The best performance of the model occurred with this parameter set to the 31st of October. Either side of this value saw the model’s performance decrease. The best model performance occurred with Percent irrigation of IP set to 50% and ToTStep IC set to the 31st of October, producing a RMSE of 254.44.
141 APPLICATION OF OASIS TO THE CASE STUDY AREA
Period 3
The third simulation period was from February 1st to April 30th. The irrigated crops and their irrigation periods were given in Table 5.15. Again if all crops were considered to be irrigated at their optimum level this led to a large overestimation of irrigation for the period. For this reason it was necessary to investigate which crops should not be irrigated at their optimum. The irrigation parameters for irrigated horticulture and rice were fixed, again leaving lucerne/summer pasture, winter irrigated pasture and irrigated cereals.
From the interview questionnaire, March and April were listed as critical months for irrigation of winter irrigated pasture, February and March were critical months for irrigation of lucerne/summer pasture, while March and April were considered critical months for pre-watering of cereals. As lucerne/summer pasture was found to have 100% of the area irrigated in Period 1, it was set to 100%. Winter irrigated pasture was considered to be the next crop irrigated as autumn is very important to re-initiate the growth of this crop. If 100% of the area of winter irrigated pasture was found to be irrigated the percentage area of cereals prewatered will be investigated.
Recall in Period 2 it was found that the best performance of the model occurred when lucerne/summer pasture was assumed not to be irrigated during the period from December 4th to January 31st. For this reason part of the work in this section was to determine when irrigation of lucerne/summer pasture returned (FromTStep Ipe). The period tested was from February 1st to March 1st. Initially, FromTStep Ipe was set to February 1st and the crop area irrigated set to the best performing value from Period 1 (50%). Along with this it was necessary to calibrate the time step when winter irrigated pasture returns to growing in late summer (Time step IP).
To start the calibration the remaining parameters were set to the best parameter values from the previous two calibration periods, this produced a RMSE of 178.44. A very good correlation (R2 = 0.88) was found between moving Time step IP towards February 1st and the model performance, Figure 5.19. The most obvious exception is in early February where the 1st and 8th of February produce very similar RMSE of approximately 130, while the 4th of February produced a RMSE of approximately 140. An investigation of the CD values for these three runs showed an improved
142 APPLICATION OF OASIS TO THE CASE STUDY AREA performance as Time step IP was moved towards the 1st of February. The CD’s were 1.02 (8/2), 1.01 (4/2) and 1.01 (1/2). Time step IP was therefore set to February 1st.
180 R2 = 0.8796 170
160
150
RMSE 140
130
120 1/02 6/02 11/02 16/02 21/02 26/02 Timestep IP
Figure 5.19: Time step winter irrigated pasture returns to growing
From the investigation into FromTStep Ipe it was found that as this date was initially moved to later in February a reduction in the model performance occurred (Figure 5.20). The worst model performance occurred with FromTStep Ipe set to February 16th, after this date there is an improvement in the model performance with the best model performance occurring with the start date set to March 1st. The RMSE for the best model performance was 100.59.
150
140
130
120
RMSE 110
100
90 1/2 11/2 21/2 FromTStep Ipe
Figure 5.20: Calibration of FromTStep Ipe for period 3
The final parameter investigated for period 3 was the percentage area of winter irrigated pasture that is irrigated Percent irrigation of IP, Figure 5.21. This parameter was investigated for areas between 10 and 90%.
143 APPLICATION OF OASIS TO THE CASE STUDY AREA
120.0
116.0
112.0
108.0
RMSE 104.0
100.0
96.0 0 20406080100 Percentage of winter pasture irrigated
Figure 5.21: Percentage area of winter pasture irrigated
Figure 5.21 shows that the best model performance occurred with Percent irrigation of IP set to 50%. This is coincidently the same percentage as was found for Period 1.
In summary the best performance of the model for period 1 occurred with FromTStep Ipe set to March 1st and Time step IP set to February 1st and Percent irrigation of IP set to 50% producing a RMSE of 100.59.
Irrigation season 2000/01 result
The calibrated parameters were then combined and a simulation undertaken from August 1st to April 30th (Figure 5.22). For this simulation the RMSE was 177.89, R2 was 0.81 (Figure 5.23) and CD was 0.89. The supply of irrigation water to IUs was 709,276 ML against the recorded supply of 748,089. Losses in OASIS were 72,831 ML against the recorded losses of 53,821 ML.
From the above it can be concluded that the best performance of the model occurred with: • Percent irrigation of IC set to 100%; • ToTStep IC set to October 31st; • REL_DEPL for IC set to 50%; • Percent irrigation of IP set to 50%; • Time step IP set to February 1st; • Percent irrigation of Ipe to 100% for all months except December, January and February where it is set to 0%; and
144 APPLICATION OF OASIS TO THE CASE STUDY AREA
• All other parameters were set to their best performing values found in the sensitivity analysis (Table 5.14).
90
Actual Irrigation 80 Model calibration
70
60
/s) 50 3
40 Flow (m
30
20
10
0 1/8/2000 5/9/2000 10/10/2000 14/11/2000 19/12/2000 23/1/2001 27/2/2001 3/4/2001 Date
Figure 5.22: Calibrated model performance for season 2000/01
90
80 R2 = 0.81 70
60 /day) 3
50
40
30 Simulated Flow (m Simulated Flow
20
10
0 -10 0 10 20 30 40 50 60 70 80 90 Recorded Flow (m3/day)
Figure 5.23: Simulated versus recorded irrigation inflow into the system for season 2000/01
145 APPLICATION OF OASIS TO THE CASE STUDY AREA
5.5.3 Information for crop scale performance assessment
The next step was to validate the performance of the calibrated model at the crop level. There were two sources of data for determining the performance of the model at the crop level. The first was, MIL estimates of water use based on the crop specification of irrigators during their ordering procedures. The second source was literature on soil water balances performed at the paddock scale for crops in northern Victoria and southern NSW.
Literature
Table 5.16 provides information on crop water uses from literature. Discussions regarding each of the four major irrigation crops are undertaken below.
Table 5.16: Crop water uses from literature Irrigation Rain ET Location Soil Crop Source (mm) (mm) (mm) 656 to 846 543 1,241 Goulburn Lucerne/summer (1999) (1999) (1999) (Bethune and Tatura, Vic clay loam pasture 637 to 590 1,256 Wang 2004) 827 (2000) (2000) (2000) Deniliquin, Noorong (Humphreys Rice 1,230 90 1,180 NSW clay loam 1997) Deniliquin, Neimur (Humphreys Rice 1,330 130 1,230 NSW clay 1997) Griffith, Hanwood (Meyer, Dunin Wheat 306 200 598 NSW loam et al. 1987) Griffith, Hanwood (Meyer, Dunin Wheat 602 24 643 NSW loam et al. 1987) Griffith, Hanwood (Meyer, Dunin Wheat 628 24 645 NSW loam et al. 1987)
Lucerne/summer pasture
Bethune and Wang (2004) determined lucerne/summer pasture water usage averaged over a 20 year period at Finley by the use of the standardized FAO Penman-Monteith equation as 980mm for 100% effective rainfall and 1,030mm for 80% effective rainfall. Table 5.16 shows results for studies undertaken at Tatura in northern Victoria where the depth of irrigation applied was between 827 and 846mm for the two years if water stress conditions were to be avoided. ET was estimated at 1,241 and 1,256mm for the two seasons in the plots where water stress was avoided. This equates well with
146 APPLICATION OF OASIS TO THE CASE STUDY AREA
Humphreys et. al. (2003) who estimates lucerne/summer pasture in the southern MDB uses between 1,200 and 1,250mm of water annually.
Rice
Table 5.16 shows the results from two studies on rice by Humphreys (1997). The depth of irrigation applied was between 1,230 and 1,330mm and via a simple water balance the seepage varied between 140 and 230mm in the two studies (Humphreys 1997).
Irrigated cereals
Table 5.16 shows that Meyer et. al. (1987) estimates water usage of wheat to be between 598 and 645mm for Griffith, this equates well with estimates by Humphreys et. al. (2003) of 650mm for areas of the southern MDB.
Winter irrigated pasture
Bethune (2001) states that the first application of irrigation to winter irrigated pasture is 200 to 300mm on cracking soils and 100 to 150mm on duplex soils. Goulburn-Murray Water (Undated) provides the following recommendations for irrigation volumes applied to winter irrigated pasture: initial irrigation of 100 to 150mm, follow up irrigation of 30 to 40mm and any subsequent irrigation of 50mm.
MIL
Information from Marshall (2004) was used to calculate study area irrigation volumes for winter irrigated pasture, lucerne/summer pasture, rice and cereals. This was then converted to an application rate by dividing the volume by the crop area. For all crops except rice the land use was obtained using the GIS layer for season 2000/01 and the landholder survey for the other seasons. Rice information was also obtained from SPOT for all seasons. The resulting application rates are shown in Table 5.17. For more information on these calculations refer to Appendix D.
Table 5.17: Estimate of crop irrigation application rates using information from MIL Season 1999/00 2000/01 2001/02 2002/03 2003/04 Rice (ML/ha) 9.89 11.08 11.79 12.94 10.56 Winter Irrigated Pasture (ML/ha) 1.13 2.10 2.70 2.03 1.88 Lucerne/summer pasture (ML/ha) 2.29 5.19 7.71 11.53 14.79 Cereals (ML/ha) 0.69 2.92 2.31 2.06 1.20
147 APPLICATION OF OASIS TO THE CASE STUDY AREA
5.5.4 Performance of OASIS at the crop level
The performance of OASIS at the crop level for season 2000/01 is investigated in this section. This was completed by comparing, the irrigation depth applied to each irrigated crop in each IU where that crop was present, to the MIL estimate of crop water usage and literature estimates of crop water usage.
The irrigation depths of each crop in each IU are shown in Table 5.18. The rainfall during the crop growing period is shown in brackets in the average row at the bottom of the table.
Table 5.18: Crop irrigation depth from OASIS (mm) Irrigated Winter Irrigated Lucerne/summer IU cereals irrigated horticulture Rice (mm) pasture (mm) (mm) pasture (mm) (mm) 1 181.1 169.3 533.5 1094.0 1178.8 2 181.7 178.5 532.5 1146.7 1158.4 3 190.5 171.2 560.6 - 1107.9 4 183.2 166.2 567.5 1085.3 1116.4 5 143.7 148.2 555.9 1127.4 1073.2 6 145.5 149.4 528.3 1146.7 1066.0 7 187.9 152.7 509.1 1067.7 1143.3 8 179.8 193.7 540.9 - 1181.6 9 142.8 163.3 577.4 1132.2 1081.7 10 155.7 178.5 619.8 - 1111.7 11 169.3 185.7 546.2 844.7 1139.5 12 136.8 195.9 561.4 1040.5 1080.2 13 138.5 173.9 592.4 1036.2 1084.9 14 184.0 167.4 542.6 1020.3 1169.2 15 148.2 164.9 551.0 812.0 1101.1 16 150.7 140.3 474.8 - 1054.4 17 156. 5 163.4 482.4 743.5 1043.1 18 148.1 139.2 528.1 1039.2 1053.6 19 144.7 143.6 530.2 1104.5 1048.9 20 152.9 145.0 511.6 1166.4 1040.2 21 143.4 148.2 456.9 843.0 1012.9 Average 154 (269) 162 (432) 535 (432) 969 (247) 1084 (247)
Table 5.18 shows that the average volume of irrigation applied to rice (1,084mm) is very close to that estimated from MIL data of 1,108mm (Table 5.17). The total water applied (rain plus irrigation) for rice at the IU scale in the OASIS simulation varied between 1,260mm (IU 21) and 1,423mm (IU 1). This compared well to Humphreys (1997) where it was reported that between 1,320 and 1,460mm of water (rain plus irrigation) was applied to rice.
148 APPLICATION OF OASIS TO THE CASE STUDY AREA
A simple water balance (irrigation + rainfall - ET) was used to estimate the nett percolation (nett percolation B in Table 5.19) from each crop. This was compared to the nett percolation output from OASIS (nett percolation A in Table 5.19).
Table 5.19: Crop water balance in OASIS Winter Lucerne/su Irrigated Irrigated Crop irrigated mmer Rice cereals horticulture pasture pasture Irrigation (mm) 154 162 535 969 1,084 Rainfall (mm) 269 432 432 247 247 ET (mm) 453 395 745 897 1,281 Nett percolation A (mm) 62 197 182 271 74 Nett percolation B (mm) -30 199 222 319 50
Table 5.19 shows the nett percolation for rice (via the simple water balance equation) to be 50mm which is less than the range of 140 to 230mm in Humphreys (1997). While OASIS estimates the net percolation to be 74mm; this is with the assumption in OASIS that the application efficiency for rice is 100%. The above difference could be due to this assumption being conservative, particularly during the initial flooding of the rice fields onto dry soil. A number of sources on the topic confirm that the deep drainage under rice is generally quite low. Beecher et. al. (2002) reports that from 154 sites in 29 different rice fields on Traditional Red Brown Earth, Red Brown Earth, Self Mulching Clay and Non-Self Mulching Clay the deep drainage ranged from 40 to 1,800mm, but the median of the sites was 59mm and 75% of the sites had deep drainage of less than 180mm. In addition to this Humphreys and Barr (1998) reported values of 50mm to 60mm on Non-Self Mulching Clay with no gypsum application and Slavich et. al. (1992) reported 90mm on Red Brown Earth with a watertable of less than 2 metre below the surface. So considering the strict rice growing requirements in the MIA the nett percolation is within the range found in the literature.
Table 5.19 shows the average water application to irrigated cereals to be 423mm of which 154mm was from irrigation. This equates closely to Whitfield and Smith (1989) and Humphreys et.al. (2003) where values between 378 and 550mm are given for wheat grown at Tatura in northern Victoria. A range of 310 to 560mm is given for ET by Whitfield and Smith (1989) with no nett percolation assumed. The ET from the simulations of 453mm fits within this range. That is, the results from the simulations for irrigated cereals are within the ranges set by literature for northern Victoria. A further investigation of Table 5.17 shows that season 2000/01 has the highest estimated
149 APPLICATION OF OASIS TO THE CASE STUDY AREA depth of irrigation on irrigated cereals so it is thought that for season 2000/01 MIL has overestimated the volume of irrigation applied to cereals.
The underestimation of irrigation applied to irrigated cereal crops was not further investigated because: • MIL may have overestimated the volume of irrigation applied to cereals; • the depth of water applied to the cereals crops, is well within the range outlined in literature; • the irrigation of cereals takes place between April and October which is outside the storage assessment period (December to April); • the irrigation of all other crops is within the range outlined in literature; and • the system inflow hydrograph replicates the actual hydrograph to an R2 of 0.81.
Table 5.19 shows the irrigation depth of winter irrigated pasture is 162mm. This is lower than the 210mm estimated by MIL. A difference of 48mm equates to approximately one follow up irrigation. From Figure 5.22 this may have occurred in spring when there is a lag between the actual inflow hydrograph and the OASIS modeled hydrograph.
Table 5.19 shows the irrigation depth for lucerne/summer pasture to be 535mm. This is very similar to that estimated by MIL of 519mm. This is less than the amount of 637 to 846mm (Bethune and Wang 2004). Recall that the best model calibration had irrigation of lucerne/summer pasture during the periods of August to November and March to April, hence the irrigation depth of 537mm for 6 months of irrigation rather than 9 months as in Bethune and Wang (2004) seems suitable.
Table 5.19 shows the water applied (20% rainfall) to seasonal horticulture to be 1,216mm. This compares well with Clark et. al. (1999) where the highest recorded water application (only 5% rainfall) was approximately 1,150mm. Due to the small area of irrigated seasonal horticulture and the small overestimation of water applied to seasonal horticulture, this was not further investigated.
The above shows that the calibrated model performs very well for season 2000/01 when assessed at the crop level with the exception of irrigated cereals where the
150 APPLICATION OF OASIS TO THE CASE STUDY AREA irrigation depth was possibly underestimated. With the crop assessment completed it was then necessary to assess the performance of the model at the IU level.
5.5.5 Performance of OASIS at the IU level
Five IUs were selected to give a spatial spread of IUs to assess the performance of the model at the IU scale. The selected IUs were 1, 6, 11, 14 and 21. OASIS simulated supply ranged from 66% to 132% of the recorded supply to the IU, with an average of 95% and a median of 92%. There are a number of possible reasons for the range of simulated supply to recorded supply ratio. Differences could be due to the assumption that each supply only accounts for delivered water to land that is located in the same IU. The majority of the difference is thought to be caused by the assumption that all irrigated cereals, lucerne/summer pasture and winter irrigated pasture fields are all irrigated using the same criteria (trigger level for irrigation, applied volume, application efficiency) and winter irrigated pasture is irrigated to the same non-optimal level. In the model a single criterion is used to best represent the scatter of irrigator behaviour with respect to ordering and level of non-optimal irrigation of winter irrigated pasture.
Figure 5.24 shows the simulated supply versus the recorded supply for IU 1.
6.00 OASIS Supply Recorded Supply
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Figure 5.24: OASIS supply versus recorded supply for IU 1
151 APPLICATION OF OASIS TO THE CASE STUDY AREA
Figure 5.24 shows an over estimation of water supply in spring, late summer and early autumn. This is likely to be linked to an under estimation of the level of non-optimal irrigation of winter irrigated pasture during these times. The under estimation of irrigation during summer is interesting as at the start of rice irrigation the simulated flow estimates the recorded flow quite well, though for the majority of the rice irrigation season thereafter the flow is approximately half of the recorded volume. One possible explanation is that there are significant areas of rice located just inside IU 2 along the border between IU 1 and IU 2. IU 2 has an oversupply of irrigation during the summer period; hence supplies located in IU 1 may supply rice which is located in IU 2. Another possible explanation is that there is lucerne/summer pasture located in IU 1, which was irrigated. Over the whole season the simulation supplied 91% of the total water recorded as entering IU 1, with an R2 of 0.45.
Figure 5.25 shows the simulated supply versus the recorded supply for IU 6.
6.00 OASIS Supply Recorded Supply
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3.00 Flow (m
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Figure 5.25: OASIS supply versus recorded supply for IU 6
For IU 6 (shown in Figure 5.25), across the season the simulated volume of supply is 98% of the recorded volume, with an R2 of 0.53. The exception is spring where there is a distinct under estimation of supply. There are two possible causes for the under estimation of supply during spring. The first is that the criteria used to initialize irrigation of cereals for this IU is too low. The second is that there is a higher
152 APPLICATION OF OASIS TO THE CASE STUDY AREA percentage of winter pasture irrigated in this IU than the average of 50%. Given there is a slight over estimation of supply in late summer/early autumn for irrigation of pasture the under estimation of supply in spring is more likely to be caused by the first reason.
Figure 5.26 shows the simulated supply versus the recorded supply for IU 11.
OASIS Supply 6.00 Recorded Supply
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4.00 /s) 3
3.00 Flow (m
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0.00 Aug-00 Sep-00 Nov-00 Dec-00 Feb-01 Apr-01 Date
Figure 5.26: OASIS supply versus recorded supply for IU 11
Figure 5.26 shows that the simulated supply in IU 11 is over estimated throughout the simulation period. The total simulated supply volume was 132% of the recorded supply volume, with an R2 of 0.72. With respect to the supply volume IU 11 was the worst performing IU investigated but with respect to the correlation (R2) the IU was the best performing. The reason for the significant over supply of irrigation water was further investigated. It was found that there are a number of supplies located just on the IU 11 side of the IU 10-IU 11 border. A check of IU 10 shows that during summer and autumn there is an under estimation of simulated supply which accounts for some of the over supply of irrigation into IU 11. IU 10 and IU 11, respectively had 77% and 132% of the recorded volume simulated as entering the IU.
The hydrograph of inflow for IU 11 shows that irrigation in spring appears to estimate the total volume quite well, though the recorded supply has a much more consistent inflow than the simulated supply which has large spikes.
153 APPLICATION OF OASIS TO THE CASE STUDY AREA
Figure 5.27 shows the simulated supply versus the recorded supply for IU 14.
OASIS Supply 6.00 Recorded Supply
5.00
4.00 /s) 3
3.00 Flow (m Flow
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0.00 Aug-00 Sep-00 Nov-00 Dec-00 Feb-01 Apr-01 Date
Figure 5.27: OASIS supply versus recorded supply for IU 14
Figure 5.27 shows that the simulated supply in IU 14 is underestimated during summer and slightly overestimated for early autumn. OASIS only estimates 83% of the recorded supply volume for the simulation period, with an R2 of 0.57.
For this IU the neighboring IU either had a simulated supply that estimated the recorded supply or had an under estimation of supply. A possible explanation is that the soil properties listed for Red Brown Earth may be under representative of the volume of water percolating under ponded conditions. It may also be possible that IU 13 actually receives simulated supply that was recorded as entering IU 14, and IU 12 receives simulated supply that was recorded as entering IU 13. It was found that IU 12 has an over simulated supply of water so the above is a likely combination of events that has resulted in OASIS under estimating the supply of irrigation to IU 14 during summer.
Figure 5.28 shows the simulated supply versus the recorded supply for IU 21.
Figure 5.28 shows that the simulated irrigation supply is under estimated during spring. The simulation estimates the other periods well, though the R2 is 0.43. There are two possible causes for the under estimation of supply during spring. The first is that the
154 APPLICATION OF OASIS TO THE CASE STUDY AREA criteria used to initialize irrigation of cereal crops for this IU is too low. The second is that there is a higher level of winter pasture irrigated in this IU than the average of 50%.
OASIS Supply 6.00 Recorded Supply
5.00
4.00 /s) 3
3.00 Flow (m
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0.00 Aug-00 Sep-00 Nov-00 Dec-00 Feb-01 Apr-01 Date
Figure 5.28: OASIS supply versus recorded supply for IU 21
5.5.6 Summary of IU model performance
From the above it can be concluded that the calibrated model performs well at replicating the overall system level and crop demands, with the exception of the demands for irrigated cereal. The inspection of the models performance at the IU level showed it to be much more variable. For the purposes of this research the performance of the model at the system level is the most important as the underlying purpose of the model is to test storage options for the temporary storage of rejected irrigation orders. The assessment tests both en-route and OFWS, though the criteria used to compare the options is system level performance.
5.6 Calibration of the model for seasons other than 2000/01
The calibration process as described in Section 5.5.2 for season 2000/01 was applied to the other 4 seasons to determine the percentage irrigated areas of rice, winter irrigated pasture, lucerne/summer pasture, irrigated cereals and horticulture. Table 5.20 shows
155 APPLICATION OF OASIS TO THE CASE STUDY AREA the resulting irrigated areas and the performance of the model. Hydrograph and scatter plots for the 4 seasons other than 2000/01 are shown in Appendix D.
Table 5.20: OASIS calibrated levels of crop area irrigated Season 1999/00 2000/01 2001/02 2002/03 2003/04 Final Allocation (%) 29 78 86 8 45 Allocation 1st October (%) 12 52 33 8 25 Area of rice irrigation (%) 100 100 100 100 100 Area of winter Autumn (%) 50 50 100 20 40 pasture irrigated Spring (%) 50 50 35 0 0 (%) Area of December 4st to 0 0 0 0 0 lucerne/summer February 28th (%) pasture irrigated Other (%) 0 100 50 0 0 (%) Area of irrigated cereals irrigated (%) 100 100 100 45 50 Area of horticulture irrigated (%) 100 100 100 0 100 System performance (R2) 0.51 0.79 0.79 0.29 0.55
Table 5.20 shows season 2001/02 to perform well (R2 = 0.79) when the water allocation was 86%, this is a similar level of both performance and water allocation to season 2000/01. The performance of the model seems to show some relationship to water allocation with seasons 1999/00 and 2003/04 performing similarly when the water allocation was 29% and 45%, respectively. Table 5.20 shows the worst performance of the model occurred in season 2002/03 when the water allocation was only 8%. The above clearly shows the difficulty of replicating the system; particularly during low water allocation seasons. The poorer performance with low water allocations is considered to be due to farmers planting and irrigation application decisions being significantly affected by water allocation. Information on this behavioural adjustment is not available.
The crop water usages are shown in Table 5.21. These were calculated as the irrigation volume applied to each crop divided by the total crop area.
Table 5.21: Validation of crop water usages Season 1999/00 2001/02 2002/03 2003/04 OASIS Output 8.74 12.47 12.18 11.66 Rice (ML/ha) MIL Estimate 9.89 11.79 12.94 10.56 Winter irrigated OASIS Output 1.06 2.31 0.63 1.92 pasture (ML/ha) MIL Estimate 1.13 2.70 2.03 1.88 Lucerne/summer OASIS Output 0 7.03 0 0 pasture (ML/ha) MIL Estimate 2.29 7.71 11.53 14.79 Irrigated cereals OASIS Output 1.44 3.24 1.58 1.04 (ML/ha) MIL Estimate 0.69 2.31 2.06 1.20
156 APPLICATION OF OASIS TO THE CASE STUDY AREA
The individual crops are discussed below.
Rice
Table 5.20 shows that the total rice area is irrigated in every season. Table 5.21 shows that the modeled irrigation depth (OASIS output) is generally quite close to the MIL estimate of rice water usage for each season.
Winter irrigated pasture
Table 5.20 shows that in no season is 100% of the area of winter pasture irrigated throughout its growing season. Only once was 100% of the area of winter irrigated pasture found to be irrigated during autumn (season 2001/02). There are two explanations for this; first 100% of winter irrigated pasture area is very rarely irrigated or the area said to be sown to winter irrigated pasture is over estimated. Table 5.21 shows that for all seasons except season 2002/03 the modeled irrigation depth for winter irrigated pasture compares well with the depth estimated by MIL.
Irrigated cereals
Table 5.21 shows that the performance of irrigated cereals is poorer than either of rice or winter irrigated pasture. A comparision of the irrigated depth between the calibrated model and the MIL estimate for the 1999/00 and 2001/02 seasons shows that the calibrated model over estimates the irrigation depth. For the season 2002/03 there is an underestimation of irrigation applied to irrigated cereals, while season 2003/04 performs relatively well. Due to the good overall performance of the system and the irrigation of cereals being outside the storage assessment period (December to April) this crop was not further investigated.
Lucerne/summer pasture
Table 5.20 shows that the only season other than 2000/01 when the calibrated model included any irrigation of lucerne/summer pasture was 2001/02. For each season the best performance of the model occurred with no irrigation of lucerne/summer pasture during the period December 1st to February 28th. The reasons for this were discussed in Section 5.5.2.
157 APPLICATION OF OASIS TO THE CASE STUDY AREA
5.7 Performance of the Order and Rejection Module
With the performance of the crop water demand version of OASIS established, it was necessary to extend the model and determine the most appropriate method of predicting advanced orders and rejected orders. This section describes the incorporation of the order and rejection module used to test the matrix approach to placing orders and rejections. It also tests the soil moisture trigger approach to order placement.
Both of the matrix approaches require forecast moisture deficit information (Sections 5.1.5 and 5.1.6). There are three data sets used in the forecast moisture deficit calculation: • orders that will arrive in the next three days; • forecast rainfall data; and • forecast evaporation data.
The forecast rainfall data was incomplete. Gaps in the data were filled using rainfall forecast data generated using the first-order Markov chain to generate the occurrence of rainfall based on the occurrence of rainfall on the previous day. A gamma probability distribution is used to determine the volume of rainfall if a wet day was generated with the first-order Markov chain (Bruhn and Fry 1980; Kuchar 2004). More information is provided in Appendix D. The BoM do not archive forecast evaporation data hence it was necessary to generate synthetic forecast evaporation data. Forecast evaporation data was generated from a MLR equation that included the three terms of ETo, rainfall and rainfall forecast. For more information see Appendix D.
To establish the order and rejection decision matrices there were a number of things to consider: • how to split irrigators’ behaviour between risk averse and risk tolerant; • under what combination of soil moisture and forecast moisture deficit conditions would each type of risk behaviour place an order or a rejection; • how to split the divisions for current soil moisture; and • how to split the divisions for forecast moisture deficit.
158 APPLICATION OF OASIS TO THE CASE STUDY AREA
5.7.1 Establishing the irrigation order matrix
Splitting irrigator behaviour
To split the irrigator behaviour between risk averse and risk tolerant, information from the irrigator survey was used (Chapter 3). Question 6 was used for non-rice crops and question 15 used for rice crops. A risk tolerant irrigator was one who considered the forecast moisture deficit in their decision. This meant that 68% of non-rice crops irrigators were considered risk tolerant (selected option (e) in question 6) and 27% of rice crop irrigators are risk tolerant (selected option (c) in question 15) for the placement of an order.
Placement of an order
The next decision was under what combination of soil moisture and forecast moisture deficit conditions would each “type of irrigator” place an order (Section 5.1.5). To do this, it is necessary to consider each risk behaviour type separately. Risk averse irrigators do not consider forecast moisture when placing an order. This makes their decision a simple soil moisture decision, independent of the forecast moisture deficit. To incorporate this into the model it was necessary to set the division between low and medium soil moisture (Figure 5.29) equal to that which would trigger a risk averse irrigator to place an order.
Forecast moisture deficit Medium (Y < Def High (Def ≥ X) Low (Def ≤ Y) < X) moisture Current High (SM ≥ A) No No No soil soil (t) Medium (B < SM No No No < A) Low (SM ≤ B) Yes Yes Yes Figure 5.29: Risk averse order matrix
From the above, risk tolerant irrigators place emphasis on the forecast moisture deficit, hence in the case of low soil moisture and high and medium forecast moisture deficit, a risk tolerant irrigator would place an order as did the risk averse irrigators. The two different decisions between risk averse and risk tolerant irrigators come when the soil moisture is low and the forecast moisture deficit is low. It was thought that the risk tolerant irrigator would NOT place an order but would wait. The other difference was when the soil moisture was medium and the forecast moisture deficit was high, it was
159 APPLICATION OF OASIS TO THE CASE STUDY AREA assumed that the risk tolerant irrigator would act on the forecast moisture deficit by placing an order to prevent stressing the crop, Figure 5.30.
Figure 5.30 shows that the key divisions for the risk averse order matrix are: • the division between high and medium current soil moisture; and • both of the divisions for forecast moisture deficit.
Forecast moisture deficit Medium (Y < Def High (Def ≥ X) Low (Def ≤ Y) < X) moisture Current High (SM ≥ A) No No No soil soil (t) Medium (B < SM Yes No No < A) Low (SM ≤ B) Yes Yes No Figure 5.30: Risk tolerant order matrix
The division between high and medium current soil moisture needs to be a value that differentiates between placing an order or not when the forecast moisture deficit is high.
The division between low and medium forecast moisture deficit needs to identify a forecast weather event that causes a risk tolerant irrigator to withhold from placing an order when the current soil moisture was low. The division between high and medium forecast moisture deficit needs to identify a forecast moisture deficit that would cause a risk tolerant irrigator to place an order when the current soil moisture was medium.
Splitting divisions for current soil moisture
There are two division levels in the current soil moisture that need determining for each crop, low to medium and medium to high. From the above, the low and medium division needs to represent the current soil moisture level which would trigger an order from risk averse irrigators. This level was determined in Section 5.4, when OASIS was run in crop irrigation demand mode (on demand water). Note this level was for irrigation on the current day and hence it will be used as the lower soil moisture limit. As irrigators in the MIA are familiar with the process of placing an order, they will place an order prior to their soil moisture level reaching the division for irrigation with on demand water.
There was no available data on how to initially set the division between medium and high current soil moisture. The initial values were therefore set half way between the
160 APPLICATION OF OASIS TO THE CASE STUDY AREA division between low and medium current soil moisture and the field capacity of the soil (non-ponded crops) or the maximum ponded depth (rice crop). A sensitivity analysis will be conducted on both divisions for current soil moisture to determine the best model performance.
Splitting divisions for forecast moisture deficit
Due to the behaviour of risk averse irrigators, the divisions for forecast moisture deficit are only important for risk tolerant irrigators. The medium to low forecast moisture deficit division needs to represent the forecast moisture deficit that a risk tolerant irrigator with low soil moisture would be triggered into NOT placing an order. This needs to be a significant rainfall event. This was initially set to -5mm for all crops, indicating at least 5mm of rainfall. A range of 0 to -15mm will be tested for all crops in the sensitivity analysis. The high to medium forecast moisture deficit division needs to represent the forecast moisture deficit that a risk tolerant irrigator with medium soil moisture would be triggered into placing an order. This needs to be a period of significant dry weather hence this was initially set to 15mm. A range of 10 to 25mm will be tested for all crops in the sensitivity analysis.
5.7.2 Establishing the rejection matrix
Splitting irrigator behaviour
In the case of rejection, a risk averse irrigator was considered to be someone who is risk averse when it comes to ensuring that their crop receives adequate water when it is needed. The percentage of risk averse irrigators in this context was a direct translation of those who answered (d) to question 3 for non-rice crops and 21 for rice crops. The question posed was ‘How long prior to the delivery of your irrigation order do you reduce (this is any reduction) your irrigation order?’ and the answer was ‘Varies depending on the time of the weather event that initiates the reduction in order volume’. The results from this were that 68% of non-rice crops and 80% of rice crop irrigators are considered to be risk averse irrigators with respect to rejection.
Placement of a rejection
To determine under what combination of soil moisture and forecast moisture deficit conditions each irrigator behaviour type would place a rejection, it is necessary to
161 APPLICATION OF OASIS TO THE CASE STUDY AREA consider each risk behaviour type separately. The definition of a risk averse irrigator, means they place no emphasis on the forecast moisture deficit. This is represented in the rejection matrix by the use of a soil moisture trigger level to decide when to place a rejection, see Figure 5.31.
Forecast moisture deficit Medium (J < Def High (Def ≥ I) Low (Def ≤ J) < I) moisture Current High (SM ≥ C) Yes Yes Yes soil soil (t) Medium (D < SM No No No < C) Low (SM ≤ D) No No No Figure 5.31: Risk averse rejection matrix
This means that for risk averse irrigators a rejection would be placed in all cases when the soil moisture was considered to be ‘high’ independent of the forecast moisture deficit.
Risk tolerant irrigators were considered to place emphasis on the forecast moisture deficit, hence in the case of ‘low’ (< - 15mm) forecast moisture deficit, risk tolerant irrigators would place a rejection, independent of the current soil moisture. Another difference in the matrix decision between risk averse and risk tolerant irrigators was with medium soil moisture and medium forecast moisture deficit (between -5 and - 15mm) where a risk tolerant irrigator was considered to place a rejection while a risk averse irrigator would not. The risk tolerant rejection matrix is shown in Figure 5.32.
Forecast moisture deficit Medium (J < Def High (Def ≥ I) Low (Def ≤ J) < I) moisture Current High (SM ≥ C) Yes Yes Yes soil soil (t) Medium (D < SM No Yes Yes < C) Low (SM ≤ D) No No Yes Figure 5.32: Risk tolerant rejection matrix
Splitting divisions for current soil moisture
There are two division levels in the soil moisture that need determining, low to medium and medium to high. From the above, the behaviour of risk averse irrigators makes it necessary for the division between high and medium to represent the soil moisture level which would trigger a rejection from these irrigators. This value was set very close to field capacity for non-rice crops and the target ponded depth for rice crop.
162 APPLICATION OF OASIS TO THE CASE STUDY AREA
Selection of the division value between medium and low soil moisture, should be to represent the value at which a risk tolerant irrigator will not consider placing a rejection, when the forecast moisture deficit is categorized as medium. This is the only parameter in the rejection matrix for which a sensitivity analysis will be undertaken.
Splitting divisions for forecast moisture deficit
There are two forecast moisture deficit division levels that need determining, low to medium and medium to high. From Question 12 (non-rice crops section) and Question 20 (rice crop section) it was found that 76% and 81% of irrigators, respectively would place a rejection if there was greater than 20mm of rain. For this reason the low to medium division for forecast moisture deficit was set to -15mm (conservatively allowing 5mm of ET), (noting that moisture deficit refers to ET less rainfall).
From Question 12 (non-rice crops section) and Question 20 (rice crop section) it was found that 36% and 52% of irrigators, respectively, would place a rejection if there was either, greater than 10mm of rainfall and a cool change or less than 20mm of rainfall and little change in temperature. This is a significant percentage of irrigators hence this weather event was also tried to be captured by setting the medium to high forecast moisture deficit division to -5mm (again conservatively allowing 5mm of ET).
5.7.3 Order sensitivity analysis
It was necessary to undertake a sensitivity analysis on the parameters in the order matrix. The method used for the sensitivity analysis was described in Section 5.4.1. Again season 2000/01 was used for the sensitivity analysis and calibration of parameters. If the model is found to perform well during the calibration, then validation will take place using seasons 2001/02 and 2003/04. The results for the order module are presented below.
A sensitivity analysis was undertaken for all four divisions in each of the five irrigation schedule files. The parameters, range of values, initial values and the best performing values are listed in Table 5.22. The SI was used to rank these parameters from most to least sensitive, see Figure 5.33. Figure 5.33 shows that the division between medium and high soil moisture used for lucerne/summer pasture irrigation is the most sensitive parameter. There are only five parameters that affect the model performance by more than 2.1%.
163 APPLICATION OF OASIS TO THE CASE STUDY AREA
Table 5.22: Results of the order matrix sensitivity analysis Crop Variable Description Value range Initial Value Best Value 30 and 160 to 40 Rice Rice_Ord_SM_L Division between low and medium soil moisture 30 and 160 30 and 160 and 175 40 and 175 to 50 Rice Rice_Ord_SM_H Division between medium and high soil moisture 40 and 175 40 and 175 and 190 Rice Rice_Ord_Def_L Division between low and medium forecast moisture deficit 0 to -15 -5 -15 Rice Rice_Ord_Def_H Division between medium and high forecast moisture deficit 10 to 25 15 25 Cereal Crop_Ord_SM_L Division between low and medium soil moisture 0.5 to 0.3 0.5 0.3 Cereal Crop_Ord_SM_H Division between medium and high soil moisture 0.4 to 0.25 0.25 0.25 Cereal Crop_Ord_Def_L Division between low and medium forecast moisture deficit 0 to -15 -5 -5 Cereal Crop_Ord_Def_H Division between medium and high forecast moisture deficit 10 to 25 15 10 Horticulture Hort_Ord_SM_L Division between low and medium soil moisture 0.25 to 0.15 0.25 0.15 Horticulture Hort_Ord_SM_H Division between medium and high soil moisture 0.15 to 0.1 0.15 0.1 Horticulture Hort_Ord_Def_L Division between low and medium forecast moisture deficit 0 to -15 -5 -10 Horticulture Hort_Ord_Def_H Division between medium and high forecast moisture deficit 10 to 25 15 20 Irrigated Pasture Other_Ord_SM_L Division between low and medium soil moisture 0.75 to 0.4 0.75 0.4 Irrigated Pasture Other_Ord_SM_H Division between medium and high soil moisture 0.6 to 0.2 0.35 0.4 Irrigated Pasture Other_Ord_Def_L Division between low and medium forecast moisture deficit 0 to -15 -5 -10 Irrigated Pasture Other_Ord_Def_H Division between medium and high forecast moisture deficit 10 to 25 15 10 Lucerne/summer TandIPe_Ord_SM_L Division between low and medium soil moisture 0.75 to 0.4 0.75 0.7 pasture Lucerne/summer TandIPe_Ord_SM_L Division between medium and high soil moisture 0.75 to 0.35 0.35 0.75 pasture SM_H Lucerne/summer TandIPe_Ord_SM_L Division between low and medium forecast moisture deficit 0 to -15 -5 0 pasture Def_L Lucerne/summer TandIPe_Ord_SM_L Division between medium and high forecast moisture deficit 10 to 25 15 20 pasture Def_H
164 APPLICATION OF OASIS TO THE CASE STUDY AREA
7.00
6.00
5.00
4.00
3.00
2.00
1.00 PercentRMSE in change acrossparameter range 0.00
H L L L H H H L H L H _L f_ f_ f_L f_ _L f_ f_ f_ e M_ ef_ e ef_L e e e SM def_H d SM_ d d d SM SM_ d d _ _ _S _SM_L _SM_ _ _ d_ rd rd rd rd_d rd rd r rd_ Ord_ O Ord_ Ord_ O Ord Ord_ O Ord_ O O O ______Ord_de_ _ _ _Ord_SM_H e_ r_ rt _Ord_SM_Hp ce_O er_ er_ rt o e IPe IP IPe i the h h o ic ice ice Crop Crop_Ordd R Hort_Ord_deH H R Cro Hort R R nd Other_O O Ot Ot Crop_Ord_SM_H a and andIPe T Tan T T Parameter
Figure 5.33: Order matrix sensitivity index
165 APPLICATION OF OASIS TO THE CASE STUDY AREA
The most significant result from the sensitivity analysis was that the best performance of the model occurred when the divisions between low and medium soil moisture and medium and high soil moisture were set to very similar values. This indicates that there may be no benefit in splitting the irrigators into risk averse and risk tolerant catergories. For this reason it was decided to test whether using a simple soil moisture trigger to place an order generated a better model performance than the use of the order matrix.
The following sections describe the calibration results and performance of the calibrated model for the two tested methods for predicting orders.
5.7.4 Calibration of the order matrix
This section presents the results from the calibration of the order matrix and assesses the performance of this version of the model. The order matrix had more than 20 parameters on which the sensitivity analysis was undertaken. For this reason only parameters that had a SI of greater than 0.01 were used for calibration of the model.
The calibration process was as follows: • set each parameter to its best performing value from the sensitivity analysis; • select the parameter with the highest SI and vary this parameter across its range and determine the best performing value; • set this parameter to its best performing value and vary the second highest ranked SI parameter and set it to its best performing value; and • repeat the above process until all parameters with a SI of greater than 0.01 have been set to their best performing values.
Calibration of the order matrix produced the following values for the irrigation decision parameters, see Table 5.23.
The best performance of the model produced a RMSE of 53.62 and R2 of 0.50 between the observed orders and the OASIS simulated orders for season 2000/01. A hydrograph of the orders is shown in Figure 5.34.
166 APPLICATION OF OASIS TO THE CASE STUDY AREA
Figure 5.34 shows that the order matrix method of predicting orders is capable of predicting the general trends in orders to an acceptable level. It also shows that there is a high level of variation in the orders from day to day. This is particularly evident in autumn. A possible explanation for this behaviour is the lumped nature of fields in the model.
Table 5.23: Results of the order matrix calibration Calibrated Calibrated Crop Parameter Crop Parameter Value Value Rice Rice_Ord_SM_L 30-160 Horticulture Hort_Ord_Def_L -5 Rice Rice_Ord_SM_H 40-175 Horticulture Hort_Ord_Def_H 10 Winter irrigated Rice Rice_Ord_Def_L -5 Other_Ord_SM_L 0.4 pasture Winter irrigated Rice Rice_Ord_Def_H 15 Other_Ord_SM_H 0.35 pasture Winter irrigated Cereal Crop_Ord_SM_L 0.3 Other_Ord_Def_L -10 pasture Winter irrigated Cereal Crop_Ord_SM_H 0.25 Other_Ord_Def_H 10 pasture Lucerne/summer Cereal Crop_Ord_Def_L -5 TandIPe_Ord_SM_L 0.75 pasture Lucerne/summer Cereal Crop_Ord_Def_H 10 TandIPe_Ord_SM_H 0.75 pasture Lucerne/summer Horticulture Hort_Ord_SM_L 0.25 TandIPe_Ord_Def_L -5 pasture Lucerne/summer Horticulture Hort_Ord_SM_H 0.15 TandIPe_Ord_Def_H 20 pasture
5.7.5 Trigger level for placing orders
The initial values used for the trigger level to place an order were the values that were found to perform the best for the low to medium division in the sensitivity analysis. The ranges of values in the sensitivity analysis were then tested to determine the best model performance, using a soil moisture trigger to initiate an order. The best performance of the model for season 2000/01 is shown in Figure 5.35, this produced a RMSE of 58.62 and R2 of 0.51 between the observed orders and the OASIS simulated orders.
The best performance of the model was found to occur with the following soil moisture trigger levels, see Table 5.24.
167 APPLICATION OF OASIS TO THE CASE STUDY AREA
10000 Actual Advance 9000 OASIS Order 4 per. Mov. Avg. (OASIS Order) 8000
7000
6000
5000
4000
Orders (ML/day) 3000
2000
1000
0 7/31/2000 9/19/2000 11/8/2000 12/28/2000 2/16/2001 4/7/2001 -1000 Date
Figure 5.34: Calibrated orders
168 APPLICATION OF OASIS TO THE CASE STUDY AREA
Actual Advance 9000 OASIS Order 4 per. Mov. Avg. (OASIS Order)
8000
7000
6000
5000
4000 Orders (ML/day) 3000
2000
1000
0 7/31/2000 9/19/2000 11/8/2000 12/28/2000 2/16/2001 4/7/2001 -1000 Date
Figure 5.35: Hydrograph of orders using a soil moisture trigger level
169 APPLICATION OF OASIS TO THE CASE STUDY AREA
Table 5.24: Soil moisture trigger levels Crop Irrigation Schedule Trigger Mode Soil moisture trigger level Irrigated cereals Relative soil moisture depletion 0.3 Irrigated pasture Relative soil moisture depletion 0.4 1/10 to 30/11 30mm Rice Surface depth (ponded depth) 1/12 to 31/1 160mm 1/2 to 28/2 30mm Irrigated horticulture Relative soil moisture depletion 0.25 Lucerne/summer pasture Relative soil moisture depletion 0.75
5.7.6 Best method of placing an order
A comparison of Figure 5.35 to Figure 5.34 shows that the soil moisture trigger approach produces slightly less daily variation in orders. This is most evident in autumn. The general shape of the hydrographs for the two figures is very similar. The RMSE was slightly smaller for the matrix approach 53.62 versus 58.62 (soil moisture trigger). The correlation (R2) was slightly better for the soil moisture trigger approach 0.51 versus 0.50 (matrix approach). The above differences are insignificant, hence there appears to be no improvement in the use of an irrigation decision matrix approach compared to a soil moisture trigger level. For this reason it is considered that the increased difficulty in incorporating the weather forecasts to an irrigation decision matrix approach, adds insufficient improvement in the generation of orders, to warrant its use. For this reason the soil moisture trigger approach will be used to generate orders for the scenario assessment.
5.7.7 Rejection matrix sensitivity analysis
As previously described there were only five parameters to include in the rejection matrix sensitivity analysis. These were the division between low and medium soil moisture for each of the five irrigation schedule files (one per irrigated crop). The tested ranges, initial values and best performing values are shown in Table 5.25.
Table 5.25: Results of the rejection matrix sensitivity analysis Crop Parameter Value range Initial value Best value 40 to 45 and 185 to Rice Rice-rej_SM_L 40 and 185 45 and 185 190 Irrigated cereal Crop-rej_SM_L 0.1 to 0.4 0.25 0.20 Horticulture Hort-rej_SM_L 0.1 to 0.4 0.25 0.30 Winter irrigated pasture Other-rej_SM_L 0.1 to 0.4 0.25 0.15 Lucerne/summer TandIPe-rej_SM_L 0.1 to 0.4 0.25 0.10 pasture
The results of the sensitivity index are shown in Figure 5.36. This shows that the parameter Crop-rej_SM_L caused the most variation in the model performance. All of
170 APPLICATION OF OASIS TO THE CASE STUDY AREA the variables caused less than a 2.5% change in model performance. This was expected as the majority of irrigators are risk averse when placing a rejection.
2.50
2.00
1.50
1.00
0.50 Percent change in RMSE across parameter rangeI Percent change
0.00 Crop-rej_SM_L Other-rej_SM_L Rice-rej_SM_L TandIPe-rej_SM_L Hort-rej_SM_L Parameter
Figure 5.36: Rejection matrix sensitivity index
5.7.8 Calibration of the rejection matrix
The same process was followed for calibration of the rejection matrix as with calibration of the irrigation order matrix. In each case, the best performing value from the sensitivity analysis was found to improve the performance of the model. The calibrated model is shown in Figure 5.37.
Figure 5.37 shows the calibrated model performing very poorly. This poor performance, resulted in the MLR analysis of UIO (Chapter 3) being incorporated into a second version of OASIS. In this version the rejection matrix was replaced with the MLR equation, to calculate rejections.
As described in Section 5.1.7, the MLR equation used in the analysis was Equation 5.11. Chapter 3 showed this produced an R2 of 0.504 for calibration. Due the significantly better performance of the MLR equation method of generating rejections this method will be used to generate rejections for the scenario assessment undertaken in Chapter 6.
171 APPLICATION OF OASIS TO THE CASE STUDY AREA
3500
3000
2500
R2 = 0.094 2000
1500
1000 Recorded rejected orders (ML/day) orders rejected Recorded 500
0 0 1000 2000 3000 4000 5000 6000 Rejected orders using the matrix approach (ML/day)
Figure 5.37: Performance of the rejection matrix
5.8 Validation of the Model
To validate the order and rejection method it was considered most appropriate to use two seasons; a low and a high water allocation season. For this reason seasons 2001/02 and 1999/00 were selected. Validation of the soil moisture trigger method of predicting orders produced R2 = 0.38 (Figure 5.38) this is slightly worse than the R2 = 0.51 during calibration.
To use the above model in the scenario assessment it was necessary to know; firstly is there any skewness in the model result and secondly what is the variability in the model at predicting orders. To observe this, the residuals between the recorded and modeled orders (Figure 5.39) were investigated.
Figure 5.39 shows that the mean and standard deviation of the residuals (recorded minus modeled) is -280 and 1,878, respectively. This is a Coefficient of Variation of 6.7. Along with this the median was calculated to be -209 and the skewness to be -0.45. That is, the model slightly over estimates the volume of orders placed. An investigation of the standard deviation of the model shows that there is a very large degree of variability in the model in predicting orders. For 95% confidence (+/- 2 standard deviations) that range of residuals is approximately 7,600 ML/day, which is
172 APPLICATION OF OASIS TO THE CASE STUDY AREA very close to the entire range between minimum and maximum daily volumes of orders for the study area.
12000
R2 = 0.38
10000
8000
6000
4000
ModelledAdvanced Orders (ML/day) 2000
0 0 1000 2000 3000 4000 5000 6000 Recorded Advanced Orders (ML/day)
Figure 5.38: Validation of order model performance (seasons 1999/00 and 2001/02)
Histogram of Residuals Normal
80 Mean -280.1 StDev 1878 70 N 516
60
50
40
Fr e que ncy 30
20
10
0 -8000 -6000 -4000 -2000 0 2000 4000 Residuals
Figure 5.39: Residuals from model validation (seasons 1999/00 and 2001/02)
173 APPLICATION OF OASIS TO THE CASE STUDY AREA
Chapter 3 showed that the MLR approach for predicting rejections produced an R2 of 0.53 for validation. Again the residuals of this model were investigated, see Figure 5.40.
90 Mean -33.50 StDev 440.5 80 N 516
70
60
50
Frequency 40
30
20
10
0 -1200 -800 -400 0 400 800 1200 1600 Residuals
Figure 5.40: Residuals from rejection model of seasons 1999/00 and 2001/02
Figure 5.40 shows that the mean and standard deviation of the residuals were -33.5 and 440.5, respectively. This is a Coefficient of Variation of 13.1. Along with this the median was calculated as -39.0 and the skewness as 0.61. An investigation of the standard deviation of the model shows that there is still a high degree of variability in predicting rejections, although the range of variation estimated is less than the variation in orders. The 95% confidence interval for the level of rejections is +/- 881 ML/day.
The impact of the high level of variability in the model outputs on the assessment of the various storage options is discussed in Chapter 6.
5.9 Conclusions
Chapter 5 initially described the key changes undertaken to the OASIS model. One of the necessary changes to the model was the addition of an order and rejection module. This work provides the first testing of a method to predict both orders and rejections. Testing of two different methods to predict orders found that a simple soil moisture trigger approach and a more complicated matrix approach performed equally poor. Of
174 APPLICATION OF OASIS TO THE CASE STUDY AREA the two methods tested to predict rejections, the results from a MLR analysis performed considerably better than a matrix approach.
This chapter also outlined the detailed sensitivity analysis undertaken firstly on the OASIS model and then on the order and rejection matrices. From the sensitivity analysis it was found that the model was most sensitive to application efficiency. Unfortunately, no local information was available on application efficiency. The calibration method tested for application efficiency failed to offer any firm evidence for the selection of an appropriate value. The level of non-optimal irrigation applied to crops other than rice and horticulture was also found to significantly influence the models performance. For this reason a three period calibration process was undertaken to try and produce the best possible performance of the model. With respect to irrigation applied, the model performed very well, with an R2 of 0.81 for calibration. At the crop level the model was also found to perform well (Table 5.19). An investigation of the performance of the model at the IU level showed that there was considerable variability, in the spatial representation of irrigation deliveries.
Validation of the model with respect to predicting orders and rejections saw the model produce an R2 of 0.36 and 0.53, respectively. An examination of the residuals showed that there was a high level of variability in the performance of the model. This variability needs to be considered in the results of the scenario assessment. The updated, calibrated and validated model will now be applied to the study area to determine the most appropriate storage option for UIO, Chapter 6.
175
6 On-farm and En-route Storage Assessment
The focus of this research is an assessment of OFWS and en-route water storage to capture and temporarily store UIO. The previous chapters have provided the background to the problem and described the changes and testing of the OASIS model so that it could be used for the storage assessment.
Chapter 3 investigated the main causes of unseasonal flooding of the B-MF. It was found here that UIO do play some role in unseasonal flood events of the B-MF. All of the alterations made to OASIS were described in Chapter 5. Chapter 5 also described the sensitivity analysis, calibration and validation of the altered OASIS model applied to the MIL system.
Chapter 6 uses the validated OASIS model to test alternative storage options for the capture and temporary storage of UIO in the study area. To undertake this assessment it was necessary to develop a broad range of climate and water allocation scenarios under which to assess the performance of the different storage options. The two factors that influence the water used in a particular season are the climate and water allocation. The research is focused on testing different storage options. In this chapter, each combination of climate, water allocation and storage option is referred to as a scenario.
Prior to establishing the scenario assessments, it is necessary to further explore previous investigations that have been undertaken to store rejected water. Previous investigations assist in selecting the key factors that influence the design and operation of the storage. These are the: • the storage size; • the storage inflow and outflow capacity; • the inflow and outflow operating rules; and • the size of the rain rejection event to be captured.
176 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
6.1 Review of Literature
The literature review has been broken into four sections: • previous storage studies in the MIA (Section 6.1.1); • previous studies that have investigated the use of other measures to prevent unseasonal flooding of the B-MF (Section 6.1.2); • studies into the uses, benefits and drawbacks of OFWS (Section 6.1.3) and en- route water storages (Section 6.1.4).
6.1.1 Previous storage studies in the MIA
There have been a number of studies into the use of storages within the MIA (Booth Associates Pty Ltd 1994; Foreman 2005; Foreman 2005; GHD Pty Ltd 2006; Sinclair Knight Merz Pty Ltd 2006) (Table 6.1). The studies have had two approaches, looking at the issues of: • solely capturing rejected water; or • the dual function of capturing rejected water and providing on demand irrigation scheduling.
Table 6.1 shows that Booth Associates Pty Ltd (1994) solely looked at an en-route storage to improve on-demand supply and for this reason it will not be considered. Further, it is noted from Table 6.1 that GHD Pty Ltd (2006) based their sizing on the work carried out by Foreman (2005a and 2005b). Note, that the studies of Foreman (2005a) and Foreman (2005b) were not independent studies. There have therefore been only two independent investigations that have considered the size of the en-route storage (Foreman 2005; Foreman 2005; Sinclair Knight Merz Pty Ltd 2006). Table 6.1 also shows there are only two investigations that have included information on dimensions and shape (Booth Associates Pty Ltd 1994; GHD Pty Ltd 2006). The relevant areas of the previous investigations are explored in more detail below.
Storage volume and design
It is necessary to explore the storage volume of Foreman (2005) and Sinclair Knight Merz Pty Ltd (2006) to determine if either of these two results could be used in this research. Both of these studies used the outputs from the MDBC Monthly Simulation Model (MSM) BIGMOD model (Haisman 2000; Sinclair Knight Merz Pty Ltd 2006)
177 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Table 6.1: A review of previous studies into the use of an en-route storage at The Drop Sinclair Knight Booth Foreman Foreman Foreman GHD Pty Ltd GHD Pty Ltd GHD Pty Ltd Merz Pty Ltd Study Associate (2005a) (2005b) (2005b) (2006) – 6 GL (2006) – 11 GL (2006) – 16 GL (2006b) – 30 (1994) GL On demand Rain rejection Rain rejection Rain rejection Reason for Rain rejection Rain rejection Rain rejection Rain rejection supply and on demand and on demand and on demand investigation storage storage storage storage scheduling supply supply supply Improved forecast and combinations of reduced Current operating range Current Current Current Current Scenario conditions of Lake conditions conditions conditions conditions Mulwala and reduced River Murray channel capacity Output from Output from Output from Output from MDBC MDBC MDBC MDBC Based on the Based on the Based on the BIGMOD Modelling BIGMOD1 BIGMOD BIGMOD work of work of work of model into
process model into model into model into (Foreman 2005; (Foreman 2005; (Foreman 2005; REALM2 spreadsheet spreadsheet spreadsheet Foreman 2005) Foreman 2005) Foreman 2005) model for model model model demands from the storage 15 GL considered Total inadequate and Capacity 6 GL 6 GL 6 GL 6 GL 11 GL 16 GL 30 GL a 30 GL (GL) storage recommended
178 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Table 6.1 (Continued): A review of previous studies into the use of an en-route storage at The Drop Sinclair Knight Booth Foreman Foreman Foreman GHD Pty Ltd GHD Pty Ltd GHD Pty Ltd Merz Pty Ltd Study Associate (2005a) (2005b) (2005b) (2006) – 6 GL (2006) – 11 GL (2006) – 16 GL (2006b) – 30 (1994) GL Different Different 15 GL storage: options one 11 options one 16 Capacity Dead 3 GL GL storage or GL storage or Break Semi-active 1.5 6 GL for RMW cells 6 GL two cells 6 GL down GL and 10.5 RMW and 5 RMW and 10 fully active GL for MIL GL for MIL Side slopes Shape 4H:1V Inlet 9 GL/day 9 GL/day 6 GL/day 6 GL/day 6 GL/day 6 GL/day Capacity Gravity at 2 Gravity at 2 Gravity at 2 Outlet GL/day and GL/day and GL/day and 3 GL/day 3 GL/day 2 GL/day Capacity pumping at 350 pumping at 350 pumping at 350 ML/day ML/day ML/day Annual Water 4.8 GL 1.9 to 4.5 GL Saving Notes:
1. BIGMOD refers to the BIG MODel used by the MDBC to simulate flows in the River Murray.
2. REALM is the REal ALlocation Model used to allocate flows in the River Murray
179 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT as an input into another model: a spreadsheet model (Foreman 2005; Foreman 2005) and the REALM model (Sinclair Knight Merz Pty Ltd 2006). As the name suggests MSM represents the flows in the River Murray on a monthly time step. The monthly flows in the River Murray are converted to daily flows by BIGMOD, which uses the outputs from MSM along with historical data and/or weather data to convert these monthly flows into daily flows (Haisman 2000; Sinclair Knight Merz Pty Ltd 2006). MSM-BIGMOD represents irrigation demands by numerical relations with climate variables (Sinclair Knight Merz Pty Ltd 2006).
The work of Sinclair Knight Merz Pty Ltd (2006) did not involve a detailed assessment of the storage size required but instead undertook a preliminary investigation into the benefits of a 30 GL storage. The preliminary option by Sinclair Knight Merz Pty Ltd (2006) of a 30 GL storage (22 GL active storage of which 10 GL would be used for capturing rain rejection flows and 8 GL dead storage) included no justification for the need to have 10 GL of capacity for capturing rain rejection flows. The use of the 30 GL en-route storage option was later discarded by Sinclair Knight Merz Pty Ltd (2006) in the final options assessment. For this reason it will not be addressed any further in this analysis.
The main storage sizing work was completed by Foreman (2005), who found that 6 GL of storage was required in the MIA. Hence it was explored in greater detail. Upon further investigation this work was found to be based on an unrealistic assumption, which was that ‘the storage would be able to release at capacity after one day of inflows’ (Ben Dyer pers. comm.., 21/12/2006). In reality there are two factors which influence the release rate. These are the irrigation demand from irrigators downstream of the storage and the available capacity in Edward River Escape.
Under the current operation of the MIL system, the first four days of a rain rejection event MIL will have demands pre-released from Lake Hume which will either need to be stored (i.e. rejected water) or distributed to irrigators to meet their non-cancelled orders. There will therefore be no demand from irrigators for water stored in the storage for the first four days of operation from the start of the rain rejection event. Hence, the release rate will only be constrained by the available capacity of the Edward River Escape. The available capacity in the Edward River Escape is likely to be very close to zero as one day after the commencement of a rain rejection event as it will be
180 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT utilized by RMW for transport of water around the Barmah Choke. This was highlighted in Chapter 3.
If the assumption of Foreman (2005) was correct then to ensure the storage is able to release at capacity after one day of inflows, the Edward River Escape would need to be increased to 4,400 ML/day (2,400 ML/day for RMW and 2,000 ML/day for release from the storage). If this did occur it would lead to flooding of the Werai forest (Ben Dyer pers. comm.., 21/12/2006). That is the problem of unseasonal flooding would simply have been moved from the B-MF to the Werai Forest. From the above it is concluded that neither of the above investigations provide adequate justification for the selected storage volume used in the investigations. For this reason this research will include an investigation into the selection of an appropriate storage volume.
Even though the work of Foreman (2005) is considered to have been based on an unrealistic assumption, it has been used by GHD Pty Ltd (2006) to assess a number of different detailed storage scenarios for MIL. The work by GHD Pty Ltd (2006) was the only work to include information on storage shape, evaporation co-efficients and seepage. These are considered to be accurate for the MIL supply area; hence they will be used in this research. These details are shown in Section 6.2.
Orders and rejections
None of the previous studies has made any attempt to understand and replicate the order and rejection process as part of the assessment. This study includes these factors which represent a key contribution of this research.
Inlet and outlet capacities
Work on inlet and outlet capacities has been undertaken by GHD Pty Ltd (2006) and Foreman (2005). GHD Pty Ltd (2006) assumed an inlet capacity of 6 GL/day and an outlet capacity of 2 GL/day. Foreman (2005) tested inlet capacities of 9 and 6 GL/day and outlet capacities of 3 and 2 GL/day. No quantification of the inlet and outlet capacities is provided by either GHD Pty Ltd (2006) or Foreman (2005), hence this investigation will also assess the inflow and outflow design requirements.
181 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
On-farm water storages
GHD Pty Ltd (2006) and Sinclair Knight Merz Pty Ltd (2006) both discussed the possible introduction of an order debit or polluter pays scheme. The introduction of such a scheme would provide an enormous incentive for irrigators to have OFWS to capture their UIO, rather than pay for this water and forego it to downstream users during a rain rejection event. Neither study investigated how OFWS could be used to capture UIO. Sinclair Knight Merz Pty Ltd (2006b) listed the possible drawbacks of using OFWS’s as: • the water table depth (2 metre in many areas) will constrain storage depth, leading to greater loss of agricultural land and greater evaporation losses from the storage; • soil type, drainage and salinity issues; and • the discharge of lower quality recycled water to capture better quality rain rejection water.
This will be the first investigation into the use of OFWS to capture UIO in the MIA.
6.1.2 Other flood prevention studies
Prior to this research there had been two significant studies of other methods to prevent unseasonal flooding of the B-MF (Chong and Ladson 2003; Sinclair Knight Merz Pty Ltd 2006). These two studies are discussed and compared below.
Chong and Ladson (2003) investigated two methods, these were: • reducing the operating Full Supply Level (FSL) of Yarrawonga Weir; and • reducing the maximum flow through the Barmah Choke during non-flood periods.
Chong and Ladson (2003) identified the period from December 1st 1980 to April 30th 2000 to have the current level of irrigation development, and therefore undertook their investigation on the 20 years of actual data. Interestingly, this period encompasses periods both before and after the establishment of the Cap on River Murray extractions. Chong and Ladson (2003) found that to reduce flooding of the B-MF to pre-regulation levels it was necessary to increase the airspace at Yarrawonga Weir by 9,100 ML (equivalent to reducing the operating FSL of the weir by 200mm) or reducing the
182 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT maximum flow at Tocumwal to 9,600 ML/day. Chong and Ladson (2003) placed an economic value of $60 per ML of water prevented from flooding the B-MF. Using this assessment method it was reported that the option of increasing the airspace at Yarrawonga Weir would have an economic benefit of $1.4M while reducing the flow at Tocumwal would have a net cost of $2.5M.
Sinclair Knight Merz Pty Ltd (2006b) investigated: • lowering the operating FSL of Yarrawonga Weir between 100 and 1,000mm; • increasing the escape capacity to Edward River; and • adding an additional Wakool River Escape.
Sinclair Knight Merz Pty Ltd (2006) undertook their investigation on the 66 seasons from May 1st 1934 to April 30th 2000, using the MDBC models of MSM (Monthly Simulation Model) and BIGMOD. That is Sinclair Knight Merz Pty Ltd (2006) used a combination of simulated and actual data for their investigation rather than purely actual data as Chong and Ladson (2003) used. The work by Sinclair Knight Merz Pty Ltd (2006) found that the option of lowering the operating level of Yarrawonga Weir by 200mm was the most preferred method. The next four best options were lowering the operating level of Yarrawonga Weir by amounts of 300mm, 400mm, 100mm and 500mm. The first option that involved something other than lowering the operating level of Yarrawonga Weir was a combined option of lowering the operating level of Yarrawonga Weir by 200mm and increasing escape capacities.
Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006) used different methods of analysis and assessment but both found that lowering the operating FSL of Yarrawonga Weir by approximately 200mm, creating an additional 9 GL of storage was the most preferred method to reduce flooding of the B-MF. The main information that is relevant to this research is the storage volume obtained by both Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006) of approximately 9 GL. This storage volume will be used as a guide to the findings of this research.
6.1.3 On-farm water storages
One of the purposes of this research is the use of OFWS to benefit the irrigation distribution system. The main focus of literature involving OFWS is on the individual dam unit or individual farm. No reviews on the use of OFWS to capture rejected water
183 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT were found in literature. The most relevant study is Lisson et al. (2003), where the main advantages of OFWS are listed as: • a useful means of storing additional water by capturing: − rainfall runoff; − irrigation tailwater; − unused allocated water from irrigation schemes (rainfall rejection flows are one source of these); − surplus river water during high flow periods (‘out of allocation water’); and • a management option for water quality both on the farm and as it leaves the farm.
The main difference between the above work and the aim of this research is that the above work is focused on the individual property, while the aim of this research is to use OFWSs to generate system level benefits. There have been some examples of regional incentives implemented to entice irrigators to assist with the management of system level problems with on-farm measures. In the Shepparton Irrigation Region (close to the MIA) Smith and Maheshwari (2002) reported on the benefits of incentives provided to irrigators to construct OFWS to capture high drainage flows after rainfall events. Some of the benefits included reduced inflows into the River Murray and greater flexibility in irrigation, by allowing on-demand irrigation as opposed to the traditional 4-day ordering. Singh and Christen (2001) also cover drainage flows, reporting that new horticulture ventures in the Murrumbidgee Irrigation Area must construct evaporation basins due to the high salinity of subsurface drainage flows.
It was also necessary to investigate the literature to determine the appropriate storage size to use on each individual property. It was shown in Chapter 3 that 38% of properties in the study area have an OFWS of greater than 10 ML. The main uses of OFWS were for capturing irrigation tailwater and off-allocation water. Marshall (2002) recommends a storage size of 4 ML for every 100 ha of irrigable land for OFWS in the MIA. Section 6.3 discusses the design of OFWS used in this research.
From the above it is clear that this research will provide the first investigation into the use of OFWS for the capture and temporary storage of rejected water in an irrigation system.
184 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
6.1.4 En-route storages
En-route storages in an irrigation area refer to storages placed adjacent to a main irrigation canal and connected via an inlet and outlet channel to this canal. Minimal literature is available on the use of en-route storages to assist with improving service to consumers or to capture surplus orders. One exception has been in the Murrumbidgee Irrigation Area where several investigations have been undertaken to determine the best location to place an en-route storage to capture rainfall, rejected water and catchment runoff (URS Pty Ltd 2003; Evans, Wolfenden et al. 2005; Evans, Wolfenden et al. 2005; Wolfenden, Evans et al. 2005).
In the Murrumbidgee Irrigation Area, URS Pty Ltd (2003) proposed the use of a 4.2 GL en-route storage along Mirrool Creek Floodway. Evans et. al. (2005) investigated several pre-feasibility options for storages between 50 and 200 GL at various locations. The objectives of these studies are similar to those of the studies to reduce unseasonal flooding of the B-MF. In both cases they investigated options to reduce the impact of high summer flows, particularly during and shortly after rainfall events and continuing to meet the needs of irrigators and other stakeholders both in the vicinity and downstream of the investigation area.
An area that can provide some insight into the use of en-route storages is the optimal management of reservoirs (Raman and Chandramouli 1996; Russell and Campbell 1996; Stevens, Stephenson et al. 1998; Ghahraman and Sepaskhah 2002; Mousavi, Mahdizadeh et al. 2004). Reservoirs are similar to en-route storages in that they are both concerned with matching the release of water to meet the optimal crop irrigation requirements. Some of the differences are that reservoirs normally have more than one demand to meet, where as en-route storages will normally only have a single irrigation demand to meet. Another difference is the target water to capture and the time length of the water to be stored. Reservoirs are designed to capture rainfall runoff and store water for periods of up to or greater than 12 months where as en-route storages are designed to capture surplus irrigation water and store water for a short period of time; one to two weeks.
In summary, the literature regarding OFWS and en-route water storages describes the main costs associated with on-farm/en-route storages as:
185 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
• the water lost through seepage and evaporation; • construction of the storage; • operation and maintenance of the storage; and • loss of useable land with the construction of the storage.
The main benefits associated with on-farm/en-route storages described in literature are: • on demand watering, increasing the productivity of crops; • capturing of water that would otherwise be lost to the irrigation system; and • a benefit restricted to OFWSs is the increased consumer control over the application rate, because irrigators are not reliant on the service provider for the flow (application) rate.
The major gaps in literature are in the following areas: • benefits of OFWSs versus en-route water storage to capture rejections or rain rejection flows; • the environmental benefits of capturing water that would otherwise be lost to the irrigation system; and • system wide service benefits of en-route water storage(s) to the consumers and water savings.
Having described the present limitations of available literature, it is necessary to illustrate how this research will build on the current knowledge, what features of the previous studies this research will utilize and the limitations of this research.
6.2 En-route water storage assessment
The en-route water storage assessment will involve assessing the performance of an en- route water storage placed at two different locations (The Drop and Burns Regulator, Figure 5.8). As previously stated, the key factors that influence the size of the storage are: • the storage design; • the inflow and outflow operating rules; and • the nominated size of the rain rejection event to be captured.
186 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
6.2.1 Storage design
The design of storages is largely driven by; soil type, depth to bedrock, depth to groundwater, minimizing the volume of cut and fill required and the method of inflow and outflow for the storage. The optimum storage design is not part of this research, hence it is considered appropriate to use previous studies in the MIA to determine a storage design. The study by GHD Pty Ltd (2006) is the only study that has undertaken a detailed storage design, for this reason the design in GHD Pty Ltd (2006) of a storage depth of 4 metre and side slopes 4 horizontal to 1 vertical will be used.
Allowing for dead storage is also a fundamental process of storage design. Sedimentation is a major cause of a loss of useable storage volume, while the outflow mechanism (gravity or pump) also plays a significant role with a storage located in the MIA due to the flat terrain in the majority of the areas. The exception being the Drop.
GHD Pty Ltd (2006) stated that due to the design of their storage (which included pump out capacity) the dead storage would be very small. In contrast, Sinclair Knight Merz Pty Ltd (2006) estimated that a 30 GL storage at the Drop would have a dead storage of 8 GL (27% of the total volume). Kahsay (2003) discusses four irrigation storage reservoirs in Ethiopia as having between 2 and 27% of their total storage as dead storage.
Pump-out capabilities for the en-route storage will be ignored for this research. For this reason the dead storage volume of Sinclair Knight Merz Pty Ltd (2006) is most applicable to this research. For the en-route storage, this will be assumed to be 25% of the total storage volume.
6.2.2 Evaporation co-efficient
From the previous studies, only one included estimates of evaporation losses, this was GHD Pty Ltd (2006). GHD Pty Ltd (2006) provided no method of calculating the evaporation losses. For this reason the method of Brutsaert (1982) will be used. The evaporation from a storage (Ews) is given by Equation 6.1:
Equation 6.1 Ews = E pan × K pan
187 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Where
Epan is the pan evaporation; and
Kpan is the pan co-efficient.
In Equation 6.1 it is possible to replace Epan with the locally calibrated Penman
Monteith method of estimating ETo used in this research, as the ratio between Epan and the locally calibrated Penman Monteith method of estimating ETo, (Griffith, NSW) was
1.0 (Humphreys, Meyer et al. 1994). No information on Kpan for the local area is available. In general Kpan is between 0.6 and 0.9 (Brutsaert 1982). Due to the lack of local information 0.75 (the medium of Brutsaert (1982)) will be used for Kpan to model both the en-route water storages and the OFWS.
6.2.3 Seepage
Of the previous studies in the MIA, GHD Pty Ltd (2006) was also the only one to include seepage. GHD Pty Ltd (2006) provided estimates of seepage losses from both lined and unlined storages. For the purposes of this research the en-route storage will be assumed to be lined. GHD Pty Ltd (2006) estimates that a lined storage would have annual seepage losses of 55mm. OASIS requires a daily seepage value in mm/day, that is a seepage loss of 55mm is assumed to occur evenly over the irrigation season (August to May), this equates to approximately 0.2mm/day.
6.2.4 En-route storage locations
A key decision with the en-route storage options was where to position these storages. This is discussed below.
First en-route storage location
A number of studies (Booth Associates Pty Ltd 1994; Foreman 2005; Foreman 2005; GHD Pty Ltd 2006; Sinclair Knight Merz Pty Ltd 2006) have investigated the benefits of an en-route storage located at the Drop. The Drop provides a very attractive location for the single en-route storage because the bed of the Mulwala Canal drops 4 metre at this point and this allows for gravity inflow and outflow from the storage to occur (GHD Pty Ltd 2006). The Drop is also upstream of approximately two thirds of irrigation properties that MIL supplies. It therefore offers the ability to redistribute
188 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT flows to approximately two-thirds of properties once orders recommence after a rain event. Figure 6.1 shows the position of a single en-route storage located at the Drop.
The one major drawback of the Drop is that, it is located just downstream of the diversion to the largest secondary canal, Berrigan Canal (capacity 3,200 ML/day), see Figure 6.1. A position only one or two kilometers further upstream would enable water to be redistributed to nearly the entire MIA. The other drawback of the Drop is that there is a hydroelectric plant located at the outlet so when water is redistributed into storage rather than flowing down the Mulwala Canal there would be a loss of revenue from the hydroelectric power plant. No investigation has been undertaken to determine the impact that placing an en-route storage at the Drop would have on revenue from the hydroelectric plant and this will not form part of this study.
Mulwala Canal Inlet
Berrigan Canal
Hydroelectric Plant Outlet
Figure 6.1: Single En-route Storage adapted from GHD (2006)
For a single storage a large area of land would be required and there is no position available along the Mulwala Canal unless private land is purchased. GHD Pty Ltd (2006) estimated the cost of agricultural land acquisition at $2000/ha and of seven different storage shapes and sizes for a Drop storage, GHD Pty Ltd (2006) estimated that there would be inundation of between 3 and 5 properties. Some of the options would inundate houses and other buildings.
In order to compare the difference between the Drop and a position upstream of Berrigan Canal, a second en-route storage positioned upstream of the Drop was included in the options assessment.
189 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Location of the second single en-route storage option
The major decision when positioning the en-route storage in the second assessment was where exactly upstream of Berrigan Canal to place it. There are four regulators between the Mulwala Canal diversion point and the Drop regulator. All four locations are relatively flat and none have a significant availability of land adjacent to them. Due to the equivalence of all four regulators, the second regulator (Burns) from the Mulwala offtake was chosen as the position for the second en-route storage.
A possible drawback of an en-route storage located at Burns Regulator is the possible lack of gravity outflow for low storage volumes. A detailed design investigation is not part of this research; hence the availability of gravity outflow has not been further investigated. To allow a direct comparison between the two en-route storage options within the bounds of this research gravity inflow and outflow is assumed for both en- route storage locations.
6.2.5 Inflow and outflow operating rules options
Two of the key operational rules for the en-route storage options are when to allow inflows into the en-route storage and when to allow releases from the storage. The impact of the operational rules on the storage volume required is unknown, for this reason this research will include a short investigation into this area. If water is stored in en-route storages then water can be released to meet two demands; (i) downstream demands outside of the MIA, and (ii) MIL orders. For this assessment there is no information on the downstream demands outside of the MIA during the scenario assessment. From Section 2.3 up to 2,100 ML/day can be passed around the Barmah Choke via the Mulwala Canal to meet downstream demands, that is downstream demands could be a significant demand for releases from the storage. Due to the lack of available information on these demands during the scenario assessment they have been excluded from this analysis. The exclusion of these demands means that the en- route storages will empty less quickly than if water was released for downstream demands. The exclusion of these demands allows an accurate comparison between the en-route storage option and the on-farm water storage option.
All rejected water will flow into the storage as long as there is storage capacity available in the reservoirs.
190 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
6.3 The On-Farm Water Storage Assessment
The third option to be assessed is the use of OFWS to capture rejected water. This section outlines and discusses the main questions that arise when establishing the OFWS in the OASIS model of the study area. The design requirements addressed are: • storage design; • how to split the study area storage volume between the IUs in OASIS; • evaporation; • seepage; and • inflow and outflow constraints.
6.3.1 Storage design
Storage design refers to the physical properties of the OFWS. The OASIS model requires the following inputs to describe the storage: • dead storage; • maximum storage; • shape including depth; and • maximum outflow.
The design of OFWSs is very specific to individual farms with all aspects of the design varying from farm to farm with the constraints on the property such as available land, soil type, size of the property. For this reason information from local sources was initially sought and used in conjunction with information from literature.
To allow a direct comparison between the OFWS option and the en-route storage option the dead storage for the OFWS’s was set to that used in the en-route storage design, 25% (see Section 6.2.1). The design of OFWS is described in Addison et. al. (2003). Here side slopes of 3:1 are recommended and the depth of the dams used in example calculations was 6 metre. Sinclair Knight Merz Pty Ltd (2006b) reports that in a significant portion of the MIL supply area the groundwater is within 2 metre of the surface hence a storage depth of greater than 2 metre is difficult in some places. The main impact of the dam design is on the seepage and evaporation losses from the OFWS. The purpose of the OFWS for this research is for the temporary storage of water; hence the sensitivity of the model to OFWS designs is expected to be minimal.
191 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
A design depth between 2 and 6 metre must be chosen, and in the absence of any other information the mid-point of 4 metre will be used. To describe the shape as an input into OASIS, the OFWS will be assumed to be a square shape and to have a base width of 40 metre, leading to a full storage capacity of 11.40 ML for each OFWS.
6.3.2 Splitting the storage volume between IUs
Once the storage volume has been decided it is necessary to split the storage volume amongst the 21 IUs in OASIS to model the OFWS option. The simplest method would be to split the volume evenly amongst the IUs, though this is not practical as the area in each IU varies considerably across the study area as does the area of irrigated land in each IU. For this reason it is necessary to explore options to determine the most appropriate method of splitting the storage volume amongst the IUs. There were a number of options considered, these are: • based on GIS information depicting where the existing storages are located; • proportional split based on the total area of each IU; and • proportional split based on the irrigated area of each IU.
The GIS information from season 2000/01 provided information on the location of existing OFWS’s. A significant number of these have been installed as part of LWMP incentives. These provide financial assistance to irrigators to install OFWS to capture and recycle irrigation tailwater and rainfall runoff. Hence, this method of splitting the storage volume amongst the IUs has merit as it perhaps indicates the likelihood of each individual irrigator constructing a storage to capture their rejected water. A drawback of this method is that when it came to splitting the IUs for modelling in OASIS there was no information on property boundaries. The IU in which an OFWS is located does not necessarily correlate with the IU in which the irrigated area of that property is located. For this reason a more generic approach was required.
The advantage of option (ii) is that it is the simplest of the above methods. Its major drawback is that there is no relationship between the irrigated area in each IU and the storage volume in that IU because the ratio of total area to irrigated area in each IU is not consistent.
Option (iii) provides the most realistic method of splitting the storage volume amongst the IUs. This method is not without drawbacks as the percentage of irrigated area in
192 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT each IU varies from year to year. The percentage irrigated crop areas was based on the ratio of irrigated area to total area for season 2000/01, this percentage of irrigated area in each IU for this season will be used across all of the water allocation seasons.
6.3.3 Seepage
GHD Pty Ltd (2006) stated that the optimistic estimate of soil permeability in the area is 10-8 m/s, which equates to 0.86 mm/day. For the purposes of this research seepage of 0.86 mm/day will be used for OFWS.
6.3.4 Inflow and outflow rates
To ensure an accurate comparison of en-route and OFWS options, no inflow constraint will be placed on inflows to the OFWS. The outflow constraints placed on the OFWS will be equivalent (ratio of outflow rate to total storage capacity) to the outflow rate of the en-route storage.
6.4 Scenarios for assessment
To undertake the scenario assessment it was necessary to make sure that the assessment captured a variety of different water allocation (and the resulting cropping pattern) and climatic conditions. To undertake this assessment it was necessary to address the following criteria: • choose climate seasons to capture the broadest possible range of conditions; • establish the range of water allocation necessary and establish the different cropping patterns (area and percentage of optimal irrigation of each crop) for these water allocations; and • generate target water deliveries for the different water allocation seasons.
To determine the range of water allocations to use, it was necessary to investigate the historic time series of water allocations for property owners in the area. It was found that for the seasons 1999/00 to 2006/07 the allocations had been; 29%, 78%, 86%, 8%, 45%, 56% and 0%. For this reason a broad range of water allocation seasons was chosen 80%, 60%, 40%, 20% and 0%. Note that, a 0% water allocation effectively results in zero irrigation deliveries and hence no irrigation rejections. Each different water allocation results in a different cropping pattern of annual crops and different
193 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT percentage areas of winter pasture and lucerne/summer pasture that are irrigated. For this reason it was necessary to generate different cropping patterns for each of the 5 different water allocations. This is addressed in Section 6.4.3. Part of this process meant knowing the target water use in each of the different water allocation seasons and this is addressed in Section 6.4.3. For the 40% and 80% allocation seasons information on water use and cropping patterns from seasons with similar water allocations will be used. The 40% allocation will use information from season 2003/04 (45% allocation) and the 80% allocation will use information from season 2000/01 (78% allocation).
As previously mentioned 25 different climate-water allocation combinations were considered to be appropriate. Weather data was available from 1986 for Finley. From these data a 9-year climate time series was chosen. This generated 45 different climate- water allocation combinations for the 4 different storage options (the fourth option is no storage). This created 180 different scenarios for the scenario assessment. Selection of the climate seasons is shown in Section 6.4.1.
The scenario assessment focuses on the period December to April in each irrigation season as this was identified by Chong and Ladson (2003) as the period when unseasonal flooding of the B-MF had increased above pre-regulation levels.
6.4.1 Climate data
The available climate data from the CSIRO station in Finley was daily rainfall and ETo for the period 1/1/1986 till 31/7/2004. This comprises 18 irrigation seasons. In selecting the seasons it was considered most appropriate to select each of the following extremes: • maximum rainfall; • minimum rainfall;
• maximum ETo; and
• minimum ETo.
To determine the seasons in which these extremes occurred, the seasons were ranked from maximum (1) to minimum (18), in terms of ETo, rainfall and moisture deficit (ETo - rainfall) for the period December to April, these are shown in Table 6.2.
194 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Table 6.2: Climate extremes Rainfall (mm) ETo (mm) ETo – Rainfall (mm) Maximum 264 (1988/89) 1,409 (2002/03) 1,278 (2002/03) Minimum 50 (1990/91) 940 (1992/93) 736 (1992/93) Average 136 1,157 1,021
For this research the four climate extremes were selected because they were considered to cover the widest range of climatic conditions that could occur during a simulated season. Note that the four climate extremes also included the the maximum and minimum moisture deficit seasons. The four climate extremes represented the seasons of 1988/89, 1990/91, 1992/93 and 2002/03. Seasons 1992/93 and 2002/03, were the minimum and maximum moisture deficit (ETo – rainfall) seasons, respectively. To complete the 9-year climate time series it was necessary to chose five more irrigation seasons. To ensure a variety of conditions were captured the selection sought to chose a warm dry, warm wet, cool dry, cool wet and an average season. The nine chosen climate seasons are presented in Table 6.3 along with their ETo, rainfall and moisture deficit rankings.
Table 6.3: Chosen climate season rankings Season ETo Rainfall ETo – Rainfall Comment 1988/89 10 1 17 Maximum rainfall 1990/91 4 18 3 Minimum rainfall 1992/93 18 2 18 Minimum ETo 1996/97 12 17 11 Cool dry season 1999/00 11 4 13 Ave ETo wet year 2000/01 3 6 9 Warm, wet year 2001/02 6 8 6 Average year 2002/03 1 9 1 Maximum ETo 2003/04 2 15 2 Warm, dry year
An examination of the moisture deficit (fourth column) in Table 6.3 shows that the two wettest (17, 18) and three driest seasons (1 to 3) have been selected along with the seasons ranked 6, 9, 11 and 13. Again this appears to provide a good representation of a range of climatic conditions for the scenario assessment.
6.4.2 Target water deliveries
As there was no historical information on the water deliveries that have occurred with 20% and 60% water allocation it was necessary to estimate the water use that would be expected based on information from the other seasons. A longer data set of water delivery data was available for the entire MIL system as opposed to the study area. For
195 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT this reason the MIL system data set was used to establish an expected water delivery for the MIL supply area and then converted to a study area water delivery volume.
Chapter 2 showed that water allocation alone is not the sole factor for determining the water deliveries in a particular season. Some of the other factors that affect the water delivered to farms are: • carryover of water from season to season, up to 200 GL; • snowy advance (water borrowed from Snowy Hydro) 90 GL in 2005/06; • temporary water trading; • off-allocation water; not too important but useful for irrigated cereals or storing water on-farm; and • Barmah-Millewa borrow; water borrowed from the B-MF environmental allocation and repaid in higher allocation seasons.
The effect of the above borrowing and repaying of water on the irrigation deliveries each season is largely unexplored and is outside the bounds of this research. For the purpose of establishing water deliveries for the 20% and 60% allocation years a linear relationship between water deliveries and water allocation was investigated, Figure 6.2.
1400000 y = 11899x + 258714 R2 = 0.92 1200000
1000000
800000
600000
Final Allocation
MIL Deliveries to MIL Deliveries to farm (ML) Linear (Final Allocation) 400000
200000
0 0 102030405060708090100 Allocation (%)
Figure 6.2: Water deliveries versus final water allocation
196 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Figure 6.2 shows that, as expected the water deliveries to farms shows a strong correlation to the final water allocation in the irrigation season. The strong correlation independent of the inclusion of the above methods of borrowing and repaying water was perhaps a little surprising but indicates that borrowing and repaying of water has minimal impact on the overall volume of water MIL delivers.
To estimate the MIL deliveries for the final water allocations of 20% and 60% the relationship in Figure 6.2 was used. To convert the MIL deliveries to study area deliveries the average ratio of study area deliveries to MIL deliveries of 60.9% was used (Section 2.4.1). The results are shown in Table 6.4. This shows that for the 20% and 60% allocation years the study area delivery targets are 302,600 and 592,200 ML, respectively.
Table 6.4: Target study area deliveries Allocation (%) MIL deliveries (ML) Study area deliveries (ML) 80 Use season 2000/01 60 972,100 592,200 40 Use season 2003/04 20 496,700 302,600
6.4.3 Synthetic crop areas
The next step in the process was to generate synthetic crop areas and levels of optimum irrigation applied to these crop areas for the 20% and 60% water allocation seasons. As described in Chapter 5, the land use for the study area is presented in Table 6.5 along with the final water allocation and the water allocation on October 1st (the decision date for the planting of rice).
Table 6.5: Land use areas for the study area Season 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 Final allocation 29 78 86 8 45 42 56 (%) Allocation 1st 12 52 33 8 25 17 26 October (%) Rice (ha) 23,041 39,326 31,354 628.6 11,215 (17,863) (41,387) Study area (MIA) (38,416) (69,525) (55,150) (1,545) (22,729) Winter irrigated 76,705 64,594 75,898 56,520 64,594 - - pasture (ha) Lucerne/summer 24,223 16,148 12,111 12,111 12,111 - - pasture (ha) Irrigated cereals 25,232 20,509 36,334 43,399 41,380 - - (ha) Irrigated 4,036 4,036 4,036 4,036 4,036 - - horticulture (ha)
197 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
For the crop areas presented in Table 6.5, when calibration of the model was undertaken (Chapter 5) the percentage of the crop area irrigated was found to vary between crops and seasons. The percentage area irrigated of each crop in each seaon is shown in Table 6.6. Each crop is discussed in more detail below.
Table 6.6: OASIS calibrated levels of crop area irrigated Season 1999/00 2000/01 2001/02 2002/03 2003/04 Final Allocation (%) 29 78 86 8 45 Allocation 1st October (%) 12 52 33 8 25 Area of rice irrigation (%) 100 100 100 100 100 Area of winter Autumn (%) 50 50 100 20 40 pasture irrigated Spring (%) 50 50 35 0 0 (%) Area of December 4st to 0 0 0 0 0 lucerne/summer February 28th (%) pasture irrigated Other (%) 0 100 50 0 0 (%) Area of irrigated cereals irrigated (%) 100 100 100 45 50 Area of horticulture irrigated (%) 100 100 100 0 100
Synthetic area of Rice
For the generation of data the first crop investigated was rice. Rice is always irrigated at the optimum level. To generate an area of rice for the 20% and 60% allocation seasons, a possible relationship between the area planted to rice and both the final water allocation and the water allocation on the 1st of October (the decision date for rice planting) was investigated. The rice areas (for seasons 1999/00 to 2005/06) for the MIA were used because it had the largest amount of available data, Figure 6.3.
The aim of this investigation was to determine the area of rice for the study area for the 20% and 60% final water allocations. For this purpose the relationship between final water allocation and rice area in the MIA (Figure 6.3) will be scaled to a rice area for the study area. For this it was necessary to investigate the ratio of rice in the study area to the MIA. This found that the ratio of rice area in the study area to the MIA varied between 49.3% (2003/04) and 60% (1999/00). To convert the MIA rice area to an area of rice in the study area the average for the five seasons of data (56.3%) was used.
198 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
80000 y = 1339.2x + 2132.9 y = 741.29x - 1203.8 2 R2 = 0.73 R = 0.75 70000
60000
50000
40000
Final Allocation Rice area (ha) Rice area 30000 Rice decision date (1/10) Linear (Rice decision date (1/10)) Linear (Final Allocation) 20000
10000
0 0 102030405060708090100 Allocation (%)
Figure 6.3: Relationship between rice area and water allocation
Synthetic area of irrigated horticulture
Table 6.5 showed that the area of irrigated horticulture was constant at 4,036 ha and irrigated at 100% for all seasons except 2002/03 when the allocation was 8%. The best calibration results found irrigated horticulture not to be irrigated in 2002/03, although in reality the crop would have been irrigated at a sufficient level to keep plants alive. Appling this information to the 60% final water allocation case the area of irrigated horticulture was set to 4,000 ha and irrigated at 100%. The 20% final water allocation case was less straight forward. The area was set to 4,000 ha as the horticulture is specified as perennial horticulture by MIL. The level of irrigation that was applied to this crop was unclear because the synthetic allocation of 20% is between the 8% allocation (season 2002/03) with no area irrigated and 29% allocation (season 1999/00) with 100% of the area irrigated. As 20% allocation is closer to 29% than to 8% it was assumed that 100% of the area of irrigated horticulture was irrigated.
Synthetic area of lucerne/summer pasture
Table 6.5 shows that the area of lucerne/summer pasture has been constant at 12,111 ha for the last three years. Prior to this plateau the annual area of the crop had been showing a reducing trend. For these reasons, the area of lucerne/summer pasture will
199 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT be set to 12,000 ha for both the 20% and 60% allocation seasons. The level of irrigation of lucerne/summer pasture in Table 6.5 shows some relationship to water allocation. All of the seasons except 2001/02 (86% allocation) and 2000/01 (78% allocation) were found to have no irrigation of lucerne/summer pasture (the reality is that its irrigation was captured in the irrigation of winter pasture). For this reason the level of irrigation of lucerne/summer pasture for the 20% allocation year was set to zero. Setting the level of irrigation for the 60% allocation year was less straight forward and calibration (Section 6.4.4) was used.
Synthetic area of irrigated cereals
The area of irrigated cereals for the study area appeared to have an inverse relationship to the area of rice. This was initially investigated and an R2 = 0.66 was obtained, Figure 6.4. From Table 6.5 it was then noted that if lucerne/summer pasture was added to the area of rice for the study area and plotted against the area of cereals for the study area the relationship improved further (R2 = 0.84), Figure 6.4.
50000
45000 y = -0.5187x + 52278 R2 = 0.84 40000
35000 y = -0.5265x + 44486 R2 = 0.66 30000
25000
20000 Areaof cereals (ha)
15000 Area of rice and lucerne/summer pasture Area of rice 10000
5000
0 0 10000 20000 30000 40000 50000 60000 Area of rice and Area of rice and lucerne/summer pasture (ha)
Figure 6.4: Area of cereals against the area of rice and area of rice and lucerne/summer pasture
200 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
The relationship in Figure 6.4 was used to calculate the area of irrigated cereals in both the 20% and 60% allocations. The result was 42,000 ha in the 20% allocation season and 33,400 ha in the 60% allocation season.
Synthetic area of winter irrigated pasture
Table 6.5 shows that the area of winter irrigated pasture is relatively consistent from year to year. For this reason the area of winter irrigated pasture will be set to the average area (67,700 ha) for the five seasons.
In summary the variables still to be decided are; the level of irrigation of irrigated cereals and winter irrigated pasture for both the 20% and 60% allocation seasons and the level of irrigation of lucerne/summer pasture for the 60% allocation season. These variables will be calculated via calibration of water used in these two seasons.
6.4.4 Calibration of crop water use for the synthetic crop areas
It is necessary to calibrate the water used for the synthetic crop area and water allocation years. The first question that must be addressed is what climate season(s) should be used for this purpose. It was considered most appropriate to use a single season that had average rainfall and ET, as the synthetic crop areas are to be run with a variety of climate seasons. Recall from Section 6.4.1 that season 2001/02 best represented an average season; therefore it will be used for calibration of water used for both the 20% and 60% allocation seasons.
20% allocation season
The target water delivery for the 20% water allocation season was 302,600 ML (Table 6.4). It was then necessary to establish how this volume was distributed between the 4 irrigated crops. Section 6.4.3 showed that no irrigation would be applied to lucerne/summer pasture in the 20% allocation season. Rice and irrigated seasonal horticulture were the first crops investigated. These crops have 100% of the area irrigated and using the crop areas from Section 6.4.3 this equated to deliveries of 99,500 ML for rice and 42,350 ML for horticulture.
Irrigated cereal was investigated next. Table 5.20 shows that for water allocations of 8% and 45%, the area of irrigated cereals that was irrigated was 45% and 50%,
201 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT respectively. For this reason irrigated cereal was set to 50% of the area being irrigated, equating to 83,200 ML of irrigation deliveries.
This left 77,550 ML for irrigation of winter irrigated pasture. This equates to 35% of the crop area being irrigated during late summer and autumn. This compares well with the results presented in Section 5.6, which show that 20% of the area was irrigated in the 8% allocation season and 40% of the area was irrigated in the 45% allocation season. This level of irrigation for winter irrigated pasture is also in agreement with findings presented in Section 6.4.3 where it was shown that for the 8% allocation season, 20% of the area of winter irrigated pasture was irrigated and for the 29% allocation season, 50% of the crop area was irrigated. In both these seasons the irrigation occurred only in late summer and autumn.
60% allocation season
The water delivery target for the 60% allocation season was 592,200 ML (Table 6.4). It was then necessary to establish how this was to be split between the 5 irrigated crops. Again rice and irrigated seasonal horticulture were the first crops investigated. Using the crop areas from Section 6.4.3, irrigation deliveries of 314,400 ML (rice) and 42,500 ML (irrigated horticulture), were obtained. For reasons stated above the area of cereals irrigated was set to 50%, which equates to 67,850 ML of deliveries.
The discussion above implies that there was 167,450 ML of water for irrigation of 12,000 ha of lucerne/summer pasture and 67,700 ha of winter irrigated pasture. This was found to be an insufficient volume of water for irrigation of these two crops. From previous examples 100% of the area of these crops is rarely if ever irrigated. The irrigated area of lucerne/summer pasture and winter irrigated pasture were then calibrated. This found that 35% of the area of lucerne/summer pasture was irrigated throughout the season excluding the period December 4th to February 28th and 61% of the area of winter pasture was irrigated during late summer and autumn. The level of irrigation of lucerne/summer pasture agrees with the findings in Table 6.6 where 100% and 50% of the area were shown to be irrigated with water allocations of 78% and 86%, respectively, while there was 0% in the other years. With respect to the area of winter pasture irrigated, Chapter 5 showed the areas irrigated in autumn for the 78% and 86% allocation season to be 50% and 100%, respectively. Chapter 5 also showed that in the
202 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
45% allocation season 42% of the area of winter pasture was irrigated in late summer and autumn. 61% seems to fit well with the above values.
The calibrated crop areas and levels of irrigation are presented in Table 6.7.
Table 6.7: Synthetic crop areas and percent of area irrigated Winter Rice area Lucerne/summer Irrigated Irrigated irrigated Allocation (ha) (% pasture area (ha) cereals area horticulture pasture area (%) area (% area (ha) (% area area (ha) (% (ha) (% area irrigated) irrigated) irrigated) area irrigated) irrigated) 80 Use season 2000/01 GIS data base on land use, except rice which is obtained from SPOT 12,000 (35% all 24,360 67,700 (61% year, except 4th 60 33,350 (50%) 4,000 (100%) (100%) in autumn) December to 28th February) Use season 2003/04 landholder survey land use, except rice which is obtained from 40 SPOT 7,670 67,700 (35% 20 12,000 (0%) 42,000 (50%) 4,000 (100%) (100%) in autumn)
6.4.5 Bulk water transport around the Barmah Choke
It was recognized in the setup of the scenario assessment that there is no information on bulk water transport around the Barmah Choke (Section 2.4) for nearly all of the cropping pattern and climate season combinations. For this reason it was necessary to determine if bulk water transport around the Barmah Choke could be removed from the scenario assessment without influencing the results. An appropriate method of determining the impact on the use of the Mulwala Canal for bulk water transport was the impact this had on MIL’s ability to meet orders within the 4-day advance.
Murray Irrigation Limited (2006) reports that for seasons 2004/05 (42% allocation) and 2005/06 (56% allocation) that 99.3% and 99.9% of orders were met within 4 days of order placement. Prior to these two seasons there had been no reporting of MIL’s performance in meeting irrigation requirements within the 4-day advance time. Neither of these two seasons are high allocation years but the very high level of performance with respect to meeting orders within the 4-day advance means that supply capacity in the MIL system is rarely a problem. This means that for the purpose of this assessment bulk water transport around the Barmah Choke can be ignored, without significantly impacting on the results.
203 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
6.4.6 Effect of model uncertainties on the options assessment
With the options assessment described above it is necessary to revisit the high levels of model uncertainties shown in Chapter 5 and discuss how these may affect the options assessment in this Chapter. It was discussed in Section 6.4 that the two most important parameters for the volume of orders placed are climate and crop areas irrigated. It is shown in Section 3.5 that the most important parameters for rejection are orders and rainfall volume. Section 6.4 showed that the model is run with each storage option with the same combinations of climate and crop areas irrigated. For this reason it is concluded that there would be minimal difference between the orders and rejections between each climate – cropping pattern combination when the model is run with the different storage options.
For the above reason the uncertainty in the model is considered to have minimal impact on the performance of the different storage options tested in the option assessment. Hence, the uncertainty in the model will not be considered as a factor in the comparison of storage options.
6.5 Results – Base Case (No Storage)
With the 4 water allocation cropping patterns calibrated above, Section 6.5 presents: • further checks on the 4 water allocation cropping patterns in Section 6.5.1; • results from the base case simulations used to investigate the volume of storage required for the scenario assessment, in Section 6.5.2; and • the storage volume selection for the scenario assessment in Section 6.5.3.
6.5.1 Verification of the cropping patterns
To verify the water deliveries for each combination of cropping pattern (Table 6.7) and irrigation season (Table 6.3) to be used in the scenario assessment, two checks were undertaken. The first check was a comparison of the water deliveries hydrograph and total irrigation deliveries between each cropping pattern in each season. The second check was to compare the modelled water deliveries to the recorded water deliveries for the available data (seasons 1999/00 to 2003/04). Both checks were undertaken for the period December to April as this is the period idenfied for the storage assessment.
204 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
The first check involved plotting the water deliveries hydrograph from OASIS for each cropping pattern for each of the 9 climate seasons to ensure that each cropping pattern compared well to the other cropping patterns. For example during the months of December and January when rice is the main irrigated crop, the magnitude of deliveries were checked to ensure they were in the order of 80%, 60%, 40%, 20% allocation cropping patterns. Figure 6.5 shows the results for the period December to April for season 2001/02.
Figure 6.5 shows a good even separation between the water delivered to farms for each of the different cropping patterns particularly during rice irrigation through December, January and early February. Through late February, March and April there is a lot more variability in the water delivered for all of the cropping patterns. This is due to the fluctuating nature of water deliveries to meet the demands of winter irrigated pasture. There is also a less obvious difference in the daily water delivered to farms during these months with the exception of the 20% allocation season. This is because the area of winter irrigated pasture is very similar between the 40%, 60% and 80% allocation cropping patterns.
The total deliveries for each of the 4 water allocation cropping patterns for the period December to April for the 9 simulated seasons are shown in Figure 6.6. Note a 0% allocation would result in 0 deliveries to farm. The recorded delivery data for the final 5 of these seasons have been plotted to assist in determining whether the level of model deliveries is appropriate for the synthetic cropping patterns.
For seasons 2000/01 and 2003/04 (Figure 6.6) where the cropping pattern and climate information were exactly the same between the modeled and actual, deliveries were nearly identical. Season 2002/03 (8% allocation) performs as expected with recorded supply being less than the synthetic 20% allocation cropping pattern. These are all anticipated results, below is a discussion of some of the other results.
In season 2001/02 when the allocation was 86%, the recorded deliveries for the period December to April are lower than the modelled deliveries for the synthetic 80% allocation cropping pattern. This is because the actual cropping pattern consisted of a much higher area of cereal than the synthetic 80% allocation cropping pattern. This resulted in more deliveries occurring during spring (hence not captured in this
205 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
10000 80% 60% 9000 40% 20% 8000
7000
6000
5000
4000
3000 Water delivered to farms (ML/day)
2000
1000
0 1-Dec 21-Dec 10-Jan 30-Jan 19-Feb 11-Mar 31-Mar 20-Apr Date
Figure 6.5: Hydrograph of water deliveries to farms for December to April in season 2001/02
206 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
80% Allocation 800000 60% Allocation 45% Allocation 78% Allocation 40% Allocation 8% Allocation 700000 20% Allocation Actual deliveries 29% Allocation 86% Allocation
600000
500000
400000
Deliveries (ML) 300000
200000
100000
0 88-89 90-91 92-93 96-97 99-00 00-01 01-02 02-03 03-04 Season
Figure 6.6: Deliveries for the period December to April versus actual data
207 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT analysis). An investigation of the rice area shows that the area in the 80% allocation cropping pattern is 8,000 ha higher than in season 2001/02. This is the largest source of difference between the actual deliveries and the 80% allocation deliveries.
A comparison of the recorded rice areas and water deliveries between the seasons of 1999/00 and 2003/04 shows season 1999/00 had a final allocation of 29% and a rice area of 23,041 ha and season 2003/04 had a final allocation of 45% and 11,215 ha of rice. The recorded supplies for these two seasons were 317,999 ML for 1999/00 and 286,228 ML for 2003/04. Even though season 1999/00 had a 16% lower final allocation than season 2003/04 it had 11,826 ha more rice and 31,771 ML more irrigation water delivered. Using the relationship in Figure 6.3, the area planted to rice in 1999/00 and 2003/04 are equivalent to 53% and 32% allocation seasons, respectively. An explanation for the ambiguity between the rice area and water deliveries for the two seasons is that season 1999/00 was the first season with a relatively low water allocation, and irrigators were probably still trying to maintain historic cropping and water use patterns. Irrigators possibly also had a high level of carryover water available to irrigate in 1999/00. Season 2003/04 occurred after the lowest water allocation season up to that point in time (2002/03) and hence a conservative approach with respect to the area of rice sown probably occurred. It is also likely that in 2003/04 most irrigators had minimal water available from carryover as they would have used their allocation in 2002/03. The above seems to indicate that the previous season’s water allocation has some effect on the present season’s rice crop areas. This theory has not been further explored in this research but it is recommended for further investigation.
With the water deliveries to farms verified for all of the 4 water allocation cropping patterns it is now necessary, using these results to investigate and determine a storage volume to use for the scenario assessment.
6.5.2 Storage volume for base case results
The cropping patterns verified in Section 6.5.1, were simulated for the climate seasons in Table 6.3 using the present MIL system (no storage) to produce the ‘base case simulations’. The IU level orders and rejection outputs were used to assist in determining
208 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT the volume of storage that will be used for testing the alternative storage methods, simulated with the OASIS model.
To assist in the selection of a storage volume, the IU level orders and rejections were aggregated and converted to daily system inflow volumes. The conversion occurred by using the average conveyance efficiency (90.9%) for the system (Chapter 5). The reason these volumes were converted to daily system inflows was because both en-route storage options are close to the head of the irrigation system, meaning that there would be minimal conveyance losses before rejected water was directed into the storage.
An estimate of the storage volume that would be required to capture each rainfall rejection event can be described by the inflow (Equation 6.2), outflow (Equation 6.3) and storage update (Equation 6.4) equations.
Inflow = RO(t) Equation 6.2
Outflow = Min[AO(t −1),Capout , S(t −1)] Equation 6.3
S(t) = S(t −1) + Inflow − Outflow Equation 6.4
Where RO(t) is the rejection on the current day (ML/day); AO(t-1) is the previous days orders (ML/day); Capout is the outflow capacity of the storage (ML/day); S(t-1) is the storage required on the previous day (ML); and S(t) is the storage required on the current day (ML).
Equation 6.3 assumes that when there is water in the storage it will be used to meet irrigators’ orders with only a one day lag between order placement and water delivery.
To gain an estimate of the storage volume, the first analysis that was undertaken was to determine the maximum storage volume required for each cropping pattern in each simulation season, assuming an unconstrained outflow capacity, Figure 6.7. The two most curious results occurred for the 80% allocation cropping pattern in seasons 1996/97
209 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT and 2003/04, where the storage volume required is significantly less than that required for the 60% and 40% allocation cropping patterns.
There are two plausible explanations for this; the method of calculating the rejection; or the outflow capacity of the storage. These are addressed individually below.
Calculating rejection
In Chapter 5 the method of calculating rejections is outlined. This method is to determine a system level volume (Equation 6.5) and then split the volume evenly between all of the fields that had an order for deliver on day (t).
t=0 RO(t) = Min[AO(t − 4), Max[0,−128 + 58.7× ∑ R(t) + 0.116× AO(t − 4)]] Equation 6.5 t=−3 Where RO(t) is the rejection on the current day; AO(t – 4) is the order placed on day (t – 4), i.e. for delivery on the current day; and R(t) is the rainfall on day (t).
Equation 6.5 shows that the rejection on the current day consist of three components;
t=0 residual component (-128); rainfall component ( 58.7 × ∑ R(t) ); and order component t=−3 ( 0.116× AO(t − 4) ). It is important to examine the impact that each of these components has on the total rejection at different times.
The peak order volume is approximately 8,000 ML/day. Hence, the peak contribution of the order component is approximately 928 ML/day. When the residual (-128) is added to this, the result is that the rainfall component will contribute all but a maximum of 800 ML/day to the rejected volume. The rejections can reach a maximum of approximately 4,500 ML/day, meaning in the case of very high levels of rejections; the rainfall component contributes the majority of the calculated volume of rejections. The rainfall component is independent of the water allocation which means very similar volumes of rejections can occur for the different water allocation seasons.
210 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
12000 80% 60% 40% 10000 20%
8000
6000
Storage Required (ML) 4000
2000
0 1988-89 1990-91 1992-93 1996-97 1999-00 2000-01 2001-02 2002-03 2003-04 Season
Figure 6.7: Estimated season storage capacities with an unconstrained outflow
211 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Impact of storage outflow capacity
With the method of calculating rejections described above the impact of the storage outflow capacity is now explored. The 4 cropping patterns are considered for the 2003/04 climate season and with the outflow capacity unconstrained this allows the storage to be emptied based on the orders placed. The orders for the four cropping patterns for season 2003/04 are shown in Figure 6.8.
10000 20% allocation 19th to 22nd December 9000 rain event 40% allocation 60% allocation 8000 80% allocation
7000
6000
5000
4000
3000 Advanced Orders (ML/day) Orders Advanced
2000
1000
0 12/1/03 12/21/03 1/10/04 1/30/04 2/19/04 3/10/04 3/30/04 4/19/04 Date
Figure 6.8: Orders for the 4 cropping patterns with 2003/04 climate
Figure 6.8 shows that during and immediately after the rain event from the 19th to the 22nd of December the orders for the 80% allocation cropping pattern remained at about 3,000 ML/day. If the storage outflow capacity is unconstrained then rejections are redistributed one day after occurring. While, in the 60% and 40% allocation cropping patterns after the rainfall event the orders drop to 700 ML/day. That is, there is not the same demand for rejected water one day after inflows with 40% and 60% allocations as there is with the 80% allocation cropping pattern.
212 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
The effect of the outflow capacity is further illustrated by investigating the 80% allocation cropping pattern for season 2003/04 in Figure 6.9. The investigation shows that when the outflow capacity is constrained to 1,500 ML, approximately 11,000 ML of storage is required, while with an unconstrained capacity 5,500 ML of storage is required.
The effect of the outflow capacity has been illustrated in isolation, it is necessary to further investigate whether the outflow capacity for the storage has a significant overall impact on the performance of the storage. Along with this it is necessary to translate the seasonal maximum storage volume requirements shown in Figure 6.7 to answer such questions as: If a storage of 7,000 ML was implemented, what is the percentage of seasons in which all of the rejected water would be captured?; or alternatively what storage volume is required to capture all rejected water in 60% of seasons?
To address the above it is necessary to make an assumption regarding the water allocation in each season and to decide upon the range of outflow capacities to test and the inflow capacity to use. With respect to water allocation it is very difficult to determine the likely occurrence of each water allocation as there is a limited amount of historical information for the system. In the absence of this information and for the purposes of this research it is assumed that all allocation seasons (80%, 60%, 40%, 20% and 0%) will occur with equal frequency.
Inflow capacity
The results from the modelling show that the maximum volume of rejections on a single day was 4,473 ML/day. When this result is scaled to the MIA, a result of 6,326 ML/day is obtained. This again compares very well with the work undertaken by Foreman (2005b) and GHD Pty Ltd (2006) where an inflow capacity of 6,000 ML/day was used. For the purpose of this modelling an inflow capacity of 4,500 ML/day will be assumed to ensure that all rejected water can be captured in the en-route storage. This also allows a direct comparison between the OFWS and en-route storage options. The inflow capacity into OFWS was assumed to be unrestricted.
213 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
14000 80% 60% 12000 40% 20%
10000
8000
6000 Storage Required (ML) 4000
2000
0 1988-89 1990-91 1992-93 1996-97 1999-00 2000-01 2001-02 2002-03 2003-04 Season
Figure 6.9: Estimated season storage capacities with outflow constrained to 1,500 ML/day
214 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Outflow capacity
The previous studies of Foreman (2005b) and GHD Pty Ltd (2006) used an outflow capacity of 2,000 ML/day for gravity outflow and 350 ML/day as the pumping outflow rate. This research has been undertaken modelling 70.7% of the MIA, where as the work of Foreman (2005b) and GHD Pty Ltd (2006) used the entire MIA. Hence, a 1,500 ML/day outflow capacity for this research is approximately equivalent to an outflow capacity of 2,000 ML/day for the MIA. For this reason this research tested an outflow capacity of 1,500 ML/day and an unconstrained outflow capacity.
The base case (present set up of the MIL system) scenarios of the nine climate seasons and the 5 water allocations were used to test the number of scenarios that would exceed different storage capacities for the two different outflow capacities. The results when the outflow capacity was restricted to 1,500 ML/day are shown in Table 6.8 and the results when the outflow was unrestricted are shown in Table 6.9.
Table 6.8: Storage capacity with an outflow capacity of 1,500 ML/day Number of scenarios that exceed the storage capacity (out of 9) Storage Total out 80% 60% 40% 20% 0% capacity (ML) of 45 (%) 8000 4 5 3 0 0 27% 7000 5 8 4 0 0 38% 6000 8 8 4 2 0 49% 5000 8 8 7 2 0 56% 4000 8 8 8 6 0 67%
Table 6.9: Storage capacity with an unconstrained outflow Number of seasons that exceed the storage capacity (out of 9) Storage Total out 80% 60% 40% 20% 0% capacity (ML) of 45 (%) 8000 3 5 3 0 0 24% 7000 4 8 3 0 0 33% 6000 6 8 4 2 0 44% 5000 7 8 7 2 0 53% 4000 7 8 8 6 0 64%
Table 6.8 and Table 6.9 show that the outflow capacity of the storage has a small overall impact on the performance of the storage. This is in contrast to the isolated effect highlighted previously in this section. For this reason the smaller outflow capacity is recommended to minimize cost.
To assist with storage requirements for the irrigation authority, another assessment method is to determine the percentage of rejected water captured in each season. This
215 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT assessment method is shown for the base case results using an outflow capacity of 1,500 ML/day and a broader range of storage volumes (1,000 to 10,000 ML), Table 6.10.
Table 6.10: Total percentage of rejected water captured Percentage of rejected water captured Storage Capacity 80% 60% 40% 20% 0% Total (%) (ML) 1,000 76.0% 73.3% 67.0% 69.7% 100% 72.8% 2,000 84.9% 81.4% 77.0% 83.3% 100% 82.2% 3,000 89.0% 85.5% 84.3% 90.1% 100% 87.2% 4,000 92.0% 88.4% 88.7% 95.3% 100% 90.7% 5,000 94.2% 91.4% 91.8% 98.6% 100% 93.4% 6,000 95.8% 93.8% 95.1% 98.6% 100% 95.4% 7,000 97.5% 95.8% 96.9% 100.0% 100% 97.2% 8,000 98.1% 97.4% 98.3% 100.0% 100% 98.2% 9,000 98.7% 98.5% 98.8% 100.0% 100% 98.8% 10,000 99.2% 98.5% 99.6% 100.0% 100% 99.2%
Table 6.10 shows that even a small storage of 1,000 ML will capture 72.8% of rejected water in the study area. This is because each day has a small volume of rejections. To capture 90% of rejected water approximately 4,000 ML of storage is required for the study area. The above information should prove to be very useful for the irrigation authority to assist with cost-benefit analysis. It does not provide any information on the environmental impact this would have on unseasonal flooding of the B-MF. A cost- benefit analysis is outside the bounds of this research but it is recommended for further investigation. The above has provided two sets of information to determine the appropriate storage volume to test in the scenario assessment. First it is necessary to decide upon the appropriate method to use for the selection of the storage volume.
6.5.3 Selection of a storage volume
Prior to selecting a storage volume to test, it is necessary to reiterate that the purpose of this research is to demonstrate a method to test storage volumes using the OASIS model as a tool and a portion of the MIA as the study area.
There are three ways to select the appropriate storage volume to undertake the assessment of different storage options: • results from Chapter 3; • results from previous investigations in the area; or • results from Section 6.5.2 and levels of pre-regulation flooding of the B-MF.
216 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Chapter 3 showed that flooding of the B-MF was most strongly linked to Ovens River inflow, River Murray flow at Albury, capacity in the River Murray at Tocumwal, Edwards River Escape capacity and Yarrawonga Main Canal offtake. That is UIO were not included in the best MLR model and therefore these results provide no basis to estimate a storage volume to use in Chapter 6. Because of this, previous investigations into the use of storage (en-route or Yarrawonga Weir) to reduce unseasonal flooding of the B-MF were investigated.
As discussed in Section 6.1.1 the most recent work into this area (GHD Pty Ltd 2006) used a storage volume of 6 GL for the storage of surplus River Murray flows. As previously discussed this work was based on work by Foreman (2005a) who used a very optimistic assumption of being able to discharge from the storage at the maximum rate, one day after the commencement of inflows into the storage.
Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006b) estimated 9.1 and 9 GL respectively, of storage was required at Yarrawonga Weir to reduce the unseasonal flooding of the B-MF to pre-regulation levels. The major difference between these two works (Chong and Ladson 2003; Sinclair Knight Merz Pty Ltd 2006) and this research is that they were looking at flows in the River Murray. This research is focused on the irrigation authority of MIL and managing its influence on flooding of the B-MF. Chapter 3 found that UIO from the MIA were not a significant contributor to flooding of the B-MF and for this reason it was difficult to select an appropriate storage volume to test the performance of the two storage options.
A possible method of determining a storage volume is with input from MIL. For this it is necessary to investigate the main reason MIL is interested in capturing their UIO. George Warne stated that a significant reason MIL was investigating the use of storages to capture UIO was the future possibility that MIL could be charged (in monetary terms) for the environmental cost of UIO (pers. comm.. 12/6/2004). That is, the cost of unseasonal flooding that UIO were thought to create. The above is in contrast to the findings presented in Chapter 3, where it was shown that rejected water is not a significant contributor to unseasonal flooding of the B-MF.
After dialogue (George Warne pers. comm.. 25/6/2006) it was seen necessary to still investigate the possibility of capturing all rejected irrigation orders within the MIL
217 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT system in case the future direction of policy was still to charge MIL for the environmental damage of their rejected orders. To investigate this it was seen necessary to apply the approach of either, Chong and Ladson (2003) or Sinclair Knight Merz Pty Ltd (2006b) to the MIL system.
Before determining the method to apply it was necessary to determine which approach (i.e. Chong and Ladson (2003) or Sinclair Knight Merz Pty Ltd (2006b)) should be used. There are two key criteria to address; the first is the time period identified for the assessment and the second is the approach used to determine the pre-regulation levels of unseasonal flooding of the B-MF. The methods of Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006b) are discussed briefly below and in more detail in Appendix E.
To identify the period for unseasonal flooding of the B-MF; Sinclair Knight Merz Pty Ltd (2006b) used a qualitative method to identify the period January to April. Chong and Ladson (2003) used a quantitative approach and identified the period from December to April to have had an increase in flooding due to regulation of the River Murray. More weight is placed on the quantitative method used by Chong and Ladson (2003) than the qualitative method used by Sinclair Knight Merz Pty Ltd (2006b). For this reason the period December to April (inclusive) will be used to assess options in this investigation.
Investigation of the approach used to identify pre-regulation B-MF flooding events revealed that Sinclair Knight Merz Pty Ltd (2006b) determined the percentage of dry seasons during their assessment period. They found that during the time series May 1934 to April 2000, 71% of seasons would have been dry seasons prior to regulation. Chong and Ladson (2003) used the time series from December 1st 1980 to April 30th 2000 as they considered this period to have the current level of irrigation development and hence undertook their investigation on these 20 years of actual data. Their main identification methods were: • number of events per season; • event duration; and • total surplus flow volume per season.
218 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Selecting the criteria to use was more difficult. There was no way to transfer the findings of Chong and Ladson (2003) to this research. For this reason it was necessary to use the findings that 71% of years are dry years Sinclair Knight Merz Pty Ltd (2006b). It must be noted that, Sinclair Knight Merz Pty Ltd (2006) used the period January to April, while this investigation is using the period (December to April, inclusive) identified by Chong and Ladson (2003). Based on the conservative assumption already made this subtle difference is assumed to be insignificant in the overall selection of the storage volume. It must also be noted that a key contribution of this research is the comparison between en-route and OFWS for the capture and storage of UIO and not on the selection of an appropriate storage volume to use.
Using the assessment approach identified above and information provided in Table 6.8, a storage volume of 7,500 ML was obtained to capture all UIO in 70% of seasons for the period December to April.
6.6 Storage Results
The above selected storage volume was implemented into each of the three scenarios described in Sections 6.2 and 6.3 and simulations undertaken for each water allocation cropping pattern described in Section 6.4. A detailed description of the results from each water allocation and climate season combination for all three storage options is presented in Appendix E. The information in Appendix E is; total rejections, rejections captured, non-captured rejections, reused rejected water, seepage, evaporation, rainfall, volume remaining on April 30th and the volume on November 30th. The information from Appendix E is summaried in the following subsections: • storage inflows; • destination of captured water; • percentage of rejected orders captured; • percentage of captured water reused; and • percentage of total rejections reused.
The setup of each subsection is to compare the performance of the three different storage options for each criterion.
219 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
To accurately compare the results from the OFWS option, to those of the two en-route storage options, it was necessary to convert the results for inflows into and releases from the OFWS, to the equivalent volume of the en-route storage. This was done by dividing the volume by 0.909 (the system level efficiency obtained in Chapter 5).
6.6.1 Storage inflows
There are three main sources of inflows to the storage(s). These are: • the volume in the storage at the start of the analysis period (30th November); • captured rejections; • rainfall on the storage(s); and • OFWS could have on-farm recycling of irrigation water. This has been ignored in this study.
The contributions of the three inflow sources into the three storage options are shown in Table 6.11. Percentages in Table 6.11 do not always add to 100% due to rounding.
Table 6.11: Source of storage(s) inflows Storage option Inflow source The Drop Burns Regulator OFWS Rejections (% of total) 98.1 98.6 98.4 Volume on 30th of November (% of total) 1.3 0.8 1.2 Rainfall on storages (% of total) 0.7 0.7 0.4
Table 6.11 shows that the major contribution of water for reuse is from captured rejections; being 99% for the OFWS and 98% for both of the en-route storage options. Hence, for operational or further modelling studies in the MIA any water that is added to the storage via rainfall could be ignored. Interestingly, there was a slightly greater contribution of water from rainfall for both of the en-route storage options. OASIS only includes contributions of rainfall if the rain falls on water in the storage. For small volumes of water in storage (this is generally the case at the start of a rain event) the water surface area in the en-route storages is greater than OFWS, hence there is a greater contribution of rainfall to en-route storages than OFWS.
6.6.2 Destination of captured water
There are four destinations for water captured in storages: • water remaining in the storage(s) at the end of the analysis period (30th April); • seepage;
220 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
• evaporation; and • use for irrigation.
Of the four destinations listed above seepage and evaporation are losses and use for irrigation is a beneficial use of the captured water. Water remaining at the end of the analysis period is difficult to classify as this water will eventually be split into seepage, evaporation or use for irrigation. The official end of the irrigation season is the 31st of May, hence the majority of water remaining in storage(s) on the 30th April should be reused prior to the end of the irrigation season. If not then most of this water would be available for reuse at the beginning of the next irrigation season (1st August), as the months of June and July generally have a positive moisture deficit (rainfall less potential evaporation).
To allow for maintenance it is recommended that the en-route storage be empty during the non-irrigation period of June 1st to July 31st. To try and ensure that this occurs, a cut-off date for inflows into the storage is required. Selection of an appropriate cutoff date is outside the bounds of this research.
The next criterion investigated was the destination of captured water, Figure 6.10. This shows that the major destination of captured water is for reuse during the analysis period; 94% for the OFWS option and 92% for both of the en-route storage options.
Reused Rejected Orders Seepage Evaporation Volume remaining on April 30th 100%
90%
80%
70%
60%
50%
40%
30% Percentage of captured water 20%
10%
0% The Drop Burns Regulator OFWS Storage Option Figure 6.10: Destination of captured water
221 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
An investigation of the results in Appendix E shows both en-route storage options capture and reuse a greater volume of water, though they are slightly less efficient at reusing the captured water for irrigation. These results are summarized in Figure 6.11 where the total volume of captured and reused water is shown for all scenarios.
1850000
1800000
Burns Regulator 1750000 The Drop OFWS
1700000 1823305 1807141 1650000 Volume of Water (ML) 1724918 1712258
1600000 1684368 1653861
1550000 Capture Reuse Storage Assessment Measure Figure 6.11: Volume of water captured and reused for each storage measure for all scenarios
There are slightly higher evaporation losses from the en-route storages than the OFWS, this difference seems to account for the difference between the reuse of rejections between the en-route and OFWS options. Again, the greater evaporation losses from the en-route storage options compared to the OFWS option is due to the larger surface area of the en-route storages with low storage volumes. The seepage losses for either storage option are negligible and hence for further storage modelling in this area on these soil types the seepage losses from the storages could be ignored. All three storage options have 4% of captured water remaining on the 30th of April and for reasons described above the majority of this water will be reused for irrigation.
6.6.3 Percentage of rejections captured
There are two criteria used to measure the ability of the three storage options to capture rejections; the number of simulated seasons in which all of the rejections during the analysis period were captured; and the percentage of total rejections captured for the entire range of simulations undertaken.
222 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Using the first criterion from above to analyse the results in Appendix E it was found that the OFWS option was not capable of capturing all of the rejections in any of the 36 simulated scenarios (those seasons with water allocation greater than zero). Both of the en-route storage options performed better than the OFWS option. The storage at the Drop, capturing all of the rejections in 13 (36%) seasons and the storage at Burns Regulator performed best, capturing all of the rejections in 19 (53%) seasons.
The poorer performance of the OFWS option in comparison to either of the two en- route storages is due to the need to capture the rejections in the IU that they origninate from. This is perhaps best illustrated with an example.
Consider a situation with 1,050 ML of storage available across the study area, with each IU having 50 ML of storage available. The rejections are 20 ML in each IU except IU 1 which has a rejection volume of 100 ML (generating total rejected orders across the study area of 500 ML). The en-route storage options will be able to capture all of the rejections but the OFWS option will not due to the volume of rejections in IU 1 exceeding the storage capacity in this IU.
A comparison of the en-route storage options shows a slightly better performance of the storage at Burns Regulator to the storage at the Drop.
The results using the second criterion (Figure 6.12) showed the same ranking of the storage options as for criterion 1. It was found that for the entire range of simulations the OFWS option captured 89% of rejections, while the storage located at the Drop captured 96.5% of rejections and the storage at Burns Regulator captured 97.4% of the rejections.
Figure 6.12 shows, the two en-route storage options captured between 5 and 12% more rejections than the OFWS option. This is due, as discussed earlier to the lack of flexibility with the OFWS. There is no discrenable difference between the performances of the two en-route storage options. The difference between the performance of the two en-route storage options and the OFWS option is worth noting.
223 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Burns Regulator The Drop OFWS 100%
80%
60% 99% 99% 100% 97% 97% 96% 96% 96% 91% 91% 87% 40% 87%
20% Percentage of Rejected Orders Captured Orders Rejected of Percentage
0% 80% 60% 40% 20% Allocation
Figure 6.12: Percentage of rejections captured
6.6.4 Percentage of captured water reused
To assess the performance of the storage options with respect to the reuse of captured water, the criterion used was the percentage of rejections reused, Figure 6.13.
Burns Regulator The Drop OFWS 100%
80%
60% 98% 97% 96% 96% 95% 95% 94% 91% 91% 90% 90% 40% 88%
20%
Percentage of Captured Rejected Orders Reused 0% 80% 60% 40% 20% Allocation
Figure 6.13: Percentage of captured water reused
224 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
Over the complete set of simulations it was found that 95.9%, 93.9% and 93.2% of captured water was reused with OFWS, the Drop en-route storage and Burns Regulator en-route storage, respectively. Interestingly, the ranking for the reuse of captured water was the reverse of the storages ability to capture rejections (Section 6.6.3). This is because the OFWS option captures a smaller volume of rejections whilst the demand for this water is constant between all three storage options.
One of the contributing factors to the slightly better performance of the OFWS compared to the two en-route storage options is due to less water being lost to evaporation in the OFWS option. Over the set of simulations the OFWS option lost 38,450 ML of water to evaporation compared to 79,550 ML and 79,500 ML for the Drop and Burns Regulator options, respectively.
Figure 6.13 shows that the performance of the two en-route storage options reduces with reducing water allocation. This is not surprising as the volume of water lost to evaporation has little variation for each water allocation set of scenarios. The consistent water loss to evaporation is largely due to 25% of the storage volume being dead storage. This translates to a higher percentage of captured water being lost to evaporation when lower volumes of rejections are captured. Also, the percentage of captured water remaining in the storage is greater for low allocation seasons. These two factors cause the reduction in the percentage of captured water reused in the two en-route storage options with reducing water allocation.
The percentage of captured water that was reused with the OFWS option was also higher in the two higher allocation seasons than the two low allocation seasons. Again, the OFWS option showed a trend between increases in the percentage of captured water lost to evaporation and reductions in water allocation. Due to the smaller volumes of water lost this had less impact on the percentage of water reused. The volume of water remaining on the 30th of April had a larger impact on the volume of captured water reused for the OFWS option.
6.6.5 Percentage of rejections reused
With respect to the percentage of the rejections reused, the en-route storage options performed better than the OFWS option. The en-route storage at Burns Regulator was found to reuse 90.8% of the rejections and the Drop storage was found to reuse 90.6%
225 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT of total rejections. The OFWS option was found to reuse 85.6% of the rejections. It is concluded that the two en-route storage options perform better than the OFWS using this criteria.
6.7 Impact of Storages on Flooding of the B-MF
As part of this research it is necessary to assess the impact each storage option will have on unseasonal flooding of the B-MF. To undertake this assessment, flooding of the B-MF was investigated for the five seasons of 1999/00 to 2003/04. It was only possible to investigate these five seasons of data as there was no UIO data for MIL prior to 1999/00. The method used to identify flood events in the B-MF was that described by Thoms et. al. (2000) of River Murray flows in excess of 10,600 ML/day at Tocumwal. The analysis period used was that described by Chong and Ladson (2003) 1st December to 30th April. This investigation found that for the five seasons (607 days) there were 94 days (15.5% of the time) when the River Murray flows exceeded 10,600 ML/day at Tocumwal, Table 6.12. Note that season 2000/01 had more than half of these days, with 55 days (36.4% of the time).
Chong and Ladson (2003) reported that prior to regulation of the River Murray the level of B-MF flooding during the period December to April, inclusive was 15.5% of the time. Hence, the five seasons included in this analysis had an average level of unseasonal flooding that was similar to pre-regulation levels. To determine whether these seasons provide a good representation of the climatic conditions it is necessary to investigate Table 6.3. Table 6.3 shows that of the five seasons used only one season (1999/00) had lower than average rainfall from the 18 seasons investigated in Section 6.4.1. One season (2003/04) had lower than average evaporation. The above results in 4 of the 5 seasons having higher than average moisture deficits, hence it can be concluded that these 5 seasons were on average drier than the seasons included in the 20 years of data that Chong and Ladson used in their investigation. This is thought to be a significant contributor to the lower level of unseasonal flooding during these 5 seasons.
To investigate the impact that each storage option would have on flooding of the B-MF the OASIS model was run using the calibrated cropping information (Section 5.6) for each season 1999/00 to 2003/04 and for all three storage options, using the 7,500 ML
226 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT of storage outlined in Section 6.5.3. To determine the impact that each storage option would have on flooding of the B-MF, Equation 6.6 was used to equate the flows in the River Murray with each storage option.
RM new = RM − Rcap Equation 6.6
Where
RMnew is the equated flow in the River Murray (ML/day); RM is the original flow in the River Murray (ML/day); and
Rcap is the daily volume of rejected orders captured in storage (ML/day).
The results of this analysis are shown in Table 6.12.
Table 6.12: Storage impacts on flooding of the B-MF Number of days with B-MF flooded for the period 1st December to 30th
April (% of days) En-route storage En-route storage Season No Storage OFWS (The Drop) (Burns Regulator) 1999-00 6 (3.95%) 5 (3.3%) 5 (3.3%) 6 (4.0%) 2000-01 55 (36.4%) 44 (29.1%) 44 (29.1%) 45 (29.8%) 2001-02 7 (4.6%) 6 (4.0%) 6 (4.0%) 7 (4.6%) 2002-03 15 (9.9%) 13 (8.6%) 13 (8.6%) 13 (8.6%) 2003-04 11 (7.2%) 7 (4.6%) 7 (4.6%) 6 (4.0%) Total 94 (15.5%) 75 (12.4%) 75 (12.4%) 77 (12.7%)
Table 6.12 shows that both of the en-route storage options have the same impact on flooding of the B-MF, reducing the incidence of flooding by an average of 3.1% of the time over the five seasons. OFWS performed slightly worse; on average reducing the incidence of B-MF flooding by an average of 2.8%. Under no storage option would any of the five seasons have been a dry season as described in Section 6.5.3, where Sinclair Knight Merz Pty Ltd (2006b) stated that prior to regulation 71% of seasons were dry between January and April, inclusive. Due to the small number of seaons for which this investigation can take place it is difficult to form any strong conclusions as to the impact that 7,500 ML of storage would have on the frequency of unseasonal flooding of the B-MF. It can be concluded that 7,500 ML of storage either in the form of en-route storage or OFWS would reduce the frequency of unseasonal flooding of the B-MF to some degree.
227 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
6.8 Conclusions
The results of this Chapter have demonstrated a method to select an appropriate storage volume in the MIA and OASIS as a tool to undertake simulations of different storage options (on-farm and en-route). The storage options were compared using the percentage of rejections captured, the percentage of captured water reused and the percentage of rejections reused.
Comparing the two locations for the single en-route storage, it was found that with all of the assessment methods the performance of the two en-route storage options was nearly identical. That is, no additional benefit was found in placing a storage upstream of Berrigan Canal (Burns Regulator) as opposed to the major topographic advantage (4 metre surface elevation change) at the Drop. For this reason, the Drop is the most appropriate position for a single en-route storage in the MIA. The 4 metre surface elevation change means that gravity inflows and outflows to and from the storage can be maximized.
From the storage options assessment it was found that OFWS offer less flexibility for capturing rejections, as the available volume needs to be located at the source of the rejection. OFWS out performed the two en-route storage options by approximately 2% using the criterion of percentage of captured water reused, although the percentage of the rejections reused was approximately 5% better using either of the en-route storage options compared to the OFWS. OFWSs are less efficient at capturing rejections when looking at the percentage of rejections captured per ML of storage. At the same time they allow greater flexibility for the operation of on-farm irrigation.
Due to the strong performance of the OFWS option, there is no doubt that they have a role in capturing rejections. Due to the relatively similar performance between the en- route and OFWS options the best storage option will be based on the policy selected by MIL. If their policy is for irrigators to take personal responsibility for their rejections then OFWS offer the best management option. If MIL decide to take responsibility for the rejection then either the en-route storage at the Drop or OFWS offer equally good management solutions.
228 ON-FARM AND EN-ROUTE STORAGE ASSESSMENT
It is concluded that a 7.5 GL storage located at the Drop would capture around 96.5% of rejections and capture all rejections in 36% of seasons. It was found that 94% of the captured water would be reused during the period December to April (inclusive) and the majority of the water remaining on April 30th (4% of captured) would also be reused for irrigation. For 7.5 GL of OFWS an average of approximately 89% of rejections would be captured and in no season (except zero allocation seasons) would all rejections be captured. It was found that 96% of the captured water would be reused during the period December to April (inclusive) and the majority of the water remaining on April 30th (4% of captured) would also be reused for irrigation.
From the results of the study it can be concluded that for future storage studies in the MIA it is possible to ignore the contribution of rainfall to the storage. It was also found that with the soil properties used in this investigation the percentage of seepage losses was less than 1% of the water captured in all storage options. For this reason it is recommended that seepage losses also be ignored from future storage studies in the MIA.
It was found that 7.5 GL of storage would reduce unseasonal flooding of the B-MF by around 3% using the seasons 1999/00 to 2003/04 for the investigation. These seasons actually had the same average level of unseasonal flooding of the B-MF as pre- regulation periods. For this reason it was difficult to make any broad conclusions about the impact that 7.5 GL of storage would have on unseasonal flooding of the B-MF, other than reduce the percent of time that the B-MF is flooded between December and April.
229
7 Conclusions and Recommendations for Further Research
UIO in the MIL region contribute to unseasonal flood events of the B-MF. They are partly linked to rainfall events in the MIA, though they were also found to have a strong dependency on the previous days UIO. To assist in the reduction of unseasonal flood events of the B-MF it is necessary to manage UIO. Mangement options include OFWS and en-route water storage.
The main areas investigated in this research have been: • irrigators’ order and rejection behavior; • the causes of UIO in the MIA; • the causes of unseasonal flooding of the B-MF; • incorporation of order and rejection processes into an irrigation system model; and • comparison of OFWS to en-route water storage as a mechanism for capturing and temporarily storing UIO.
The sections in this Chapter expand on the above main areas of research and also detail the areas identified as requiring further research.
7.1 Order and Rejection Behaviour
Chapter 3 reports on an interview questionnaire of 66 irrigators, 39% of whom grow rice. The main conclusion from the interview questionnaire with respect to rejections was that with a rainfall event of greater than 20mm, at least 80% of irrigators would reduce their order by greater than 50%. This equates to at least a 40% rejection of the
230 CONCLUSIONS AND RECOMMENDATIONS volume of orders across the study area. Order cancellations were found to be placed after the occurrence of a rainfall event rather than irrigators acting on weather forecasts.
The rejection of irrigation water was found to be more common for rice crops than for non-rice crops.
The main findings regarding the placement of orders were divided into two sections: those for rice and those for non-rice crops. For rice crops the main influencing factor was the depth of ponded water on the rice crop for both the time and volume of the order placed. The weather forecast was found to influence only approximately, 25% of respondents. This indicates two things; firstly a reactive approach to the placement of orders and secondly a lack of confidence in the weather forecast.
For non-rice crops the majority (95%) of irrigators use a visual check (observation of crop and soil) as the main method of determining when to place an order and only 9% included a scientific approach (using a soil moisture probe). A much higher percentage of non-rice crops irrigators (approximately two-thirds) said that the weather forecast influenced their order decision.
Thus it is concluded that irrigators have different order and rejection behaviours between rice and non-rice crops. Thus OASIS was changed to incorporate separate order and rejection criteria for rice and non-rice crops. The other main finding from the interview questionnaire was that irrigation rejections occur after a rainfall event, rather than based on the rainfall forecast. As a result of this finding the timing of a check for rejections in OASIS was one day prior to the order arriving.
Having gained irrigator responses into their reasons for placing orders and rejections a quantitative approach was used to try and identify a relationship between rainfall and UIO.
7.2 Unseasonal flooding of the B-MF and UIO
7.2.1 Causes of UIO in the MIA
From a single regression analysis that considered the previous day’s UIO, rainfall and the orders placed on day (t–4), it was found that the previous day’s UIO was the most significant contributor to the current day’s UIO, R2 = 0.74. A single linear regression
231 CONCLUSIONS AND RECOMMENDATIONS analysis between rainfall and UIO did not produce a correlation above R2 = 0.46. From the MLR analysis it was found that the three parameters of the previous day’s UIO, rainfall summed from day t = 0 to t = –2 and the order placed on day (t–4) provided the most explanatory power, R2 = 0.83. The variables of rainfall forecast and ET (either individually or in the form of moisture deficit) were found to add no explanatory power to the model.
The best performing MLR analysis run without UIO(t–1) included the variables of rainfall summed from day(0) to day(t–3) and the order placed on day(t–4). This produced a model with a correlation during calibration and validation of R2 = 0.51 and R2 = 0.53, respectively.
The inclusion of rainfall in the above clearly shows for the first time that rainfall is a significant factor in UIO. To determine the possible consequences for forest flooding it was then necessary to test if UIO was a significant component of unseasonal flooding.
7.2.2 Causes of unseasonal flooding of the B-MF
A MLR analysis was undertaken to investigate the causes of unseasonal flooding of the B-MF. This investigation found the best model for predicting the River Murray flow at Tocumwal included the following explanatory variables: • River Murray airspace at Tocumwal; • Edward River Escape airspace; • River Murray flow at Albury; • diversion into Yarrawonga Canal by G-MW; and • Ovens River inflow.
The inclusion of River Murray flow at Tocumwal and Edward River Escape capacity indicates that the capacity available to pass water either through or around the Barmah Choke influences whether or not a large rainfall event causes flooding of the B-MF.
The above indicates that the UIO from MIL are not the sole cause of unseasonal flooding of the B-MF nor were they found to be in the top five contributing factors. The inclusion of diversion into Yarrawonga Canal by G-MW and the exclusion of UIO from MIL, indicates that MIL is not even the most important irrigation district in contributing to unseasonal flooding of the B-MF.
232 CONCLUSIONS AND RECOMMENDATIONS
As the best MLR model did not include UIO from MIL, an investigation was undertaken excluding the response variables of diversions into Mulwala Canal by MIL and diversion into Yarrawonga Canal by G-MW. In this investigation it was found, that UIO were an important variable in 4 of the 6 calibration data sets. Hence, in some seasons flooding of the B-MF can be linked to UIO. A link between UIO and rainfall was found to exist. Hence, it can be concluded that rain rejection events do contribute to unseasonal flooding of the B-MF, but are not the sole factor causing unseasonal flooding of the B-MF.
It was found that 7.5 GL of storage would reduce unseasonal flooding of the B-MF for the seasons 1999-00 to 2003-04 by around 3%. As the number of seasons in this investigation was small it was difficult to draw any conclusions regarding the impact that 7.5 GL of storage would have on unseasonal flooding of the B-MF other than to reduce it.
7.3 Development of OASIS
Management options assessed as part of this research were the capture and storage of UIO in OFWS or en-route storages. To undertake these assessments it was firstly necessary to choose an appropriate model and then update the model.
From the assessment of relevant models, OASIS was found to be the model most suitable to this research. Its major benefit was in its detail in representing storages at both the system and farm level. Of all the models examined none were found to include order and rejection capabilities, hence it was necessary to incorporate these two functions into OASIS. The other significant alteration made to OASIS was updating of the system level water balance to a daily time step.
Two methods were trialed for predicting UIO: these were the incorporation of a MLR equation and irrigation rejection matrix approach. The most effective method found for predicting UIO was the incorporation of the MLR equation. The poor performance of the irrigation rejection matrix was thought to be due to the data available on rejections, referred to as UIO throughout this research.
To predict orders the irrigation order matrix and soil moisture trigger approach were tested. It was found that the addition of both forecast moisture deficit and irrigators
233 CONCLUSIONS AND RECOMMENDATIONS risk behaviour with the order matrix did not add any additional predictive capabilities to the model. Thus, a soil moisture trigger approach is the best predictor of orders.
7.4 Storage assessment
The base case (no storage) simulations were used to estimate the volume of storage required to capture UIO in 70% of seasons, finding that approximately 7.5 GL of storage would be required. Using this storage volume it was found that the OFWS option did not capture all UIO in any season, while the en-route storage options located at the Drop and Burns Regulator were found to capture all UIO in 36% and 53% of seasons, respectively.
There was little difference in the percentage of total UIO captured between the two en- route storage options. The Drop storage was found to capture 96.5% of UIO and the storage located at Burns Regulator was found to capture 97.4%. The OFWS option performed slightly worse, but it still captured 89% of UIO.
For all of the storage options the majority of the captured water was found to be reused for irrigation. The two en-route water storages reused a slightly smaller percentage of the water they captured (93.9% for the Drop storage and 93.2% for the storage located at Burns regulator) than the OFWS option which reused 95.9% of the water captured.
Perhaps a more important statistic is the percentage of total UIO that each storage option would reuse. The en-route storages performed very similarly reusing 90.8% (Burns Regulator) and 90.6% (The Drop) of the total UIO. The OFWS option was only able to reuse 85.6% of the total UIO.
OFWS performed slightly worse than the en-route storage options with regards to capturing UIO. This is because OFWS offer less flexibility for capturing UIO as the available volume needs to be at the location of the UIO as opposed to being available adjacent to the main canal. Though this storage option resulted in approximately 2% more of the captured water being reused for irrigation. There is no doubt that OFWSs have a role in capturing UIO if the direction of policy is for irrigators to personally take responsibility for their UIO. OFWSs are less efficient at capturing UIO with regard to the percentage of UIO captured per ML of storage. At the same time they allow greater flexibility with regards to operation of on-farm irrigation.
234 CONCLUSIONS AND RECOMMENDATIONS
There was minimal difference between the performances of the two en-route storage options. The Drop has a 4 metre drop in the ground surface maximizing gravity inflows and outflow from the en-route storage. For this reason it is the recommended location for an en-route storage in the MIA.
7.5 Recommendations for Further Research
This research has identified the following areas as requiring further research. The first area identified is the need to investigate the effect of policy options as a means of reducing the volume of UIO. A possible option is to shift from delivery debiting to order debiting. This would result in irrigators having to take ownership of their water and as a result there would be a much greater use of OFWS to capture and temporarily store UIO.
It is recommended that more specific data is collected on rejections so that these can be further explored. A useful data set would be the volume of rejections placed on each day and the date the order was due to be delivered. It is considered that the non- specific rejection data has contributed to the poor performance of the model with respect to predicting rejections.
With the assessment of UIO and unseasonal flooding of the B-MF it is recommended that other statistical techniques such as Artifical Neural Networks (ANNs), Generalised additive models and symbolic regression be explored. It is considered that these statistical techniques may add some additional explanatory power that MLR could not.
For the storage assessment it is recommended that the affect of climate variability on the model performance be further investigated. This modeling was undertaken applying the weather data from Finley to the entire study area. Additional weather information from within the study area could be incorporated to determine if this improves the performance of the model.
An area that requires further investigation is a comparison between storages and Total Channel Control for the capture and storage of rejected water. The assessment measures recommended are cost versus the volume of rejected water to be captured and also the ease with which this captured water can be redistributed to irrigators.
235 CONCLUSIONS AND RECOMMENDATIONS
Finally, decisions are required on the level of unseasonal flooding of the B-MF that is acceptable. A practical yet difficult method is to place an economic value on the reduction in unseasonal flooding of the B-MF. This could then be used in conjuction with storage size and reduction in unseasonal B-MF flood incidence versus cost, to recommend a storage size for the MIA.
236 LIST OF REFERENCES 8 List of References
Addison, J. S., R. J. Law, et al. (2003). Dam design for pastoral stock water supplies. Kalgoorlie, Department of Agriculture and Government of Western Australia.
Afifi, A., V. A. Clark, et al. (2004). Computer-Aided Multivariate Analysis. New York, Chapman & Hall/CRC.
Allen, R. G., L. S. Pereira, et al. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. Rome, Italy.
Aqualinc (2006). Irrigation Efficiency Gaps - Review and Stock Take. Hamilton, Aqualinc Research Limited.
Australian Water Resources Council (1987). 1985 Review of Australia's Water Resources and Water Use. Canberra, Department of Primary Industries and Energy.
Battaglin, W. A. and D. A. Goolsby (1997). "Statistical modeling of agricultural chemical occurrence in midwestern rivers." Journal of Hydrology 196(1): 1-25.
Beecher, H. G., I. H. Hume, et al. (2002). "Improved method for assessing rice soil suitability to restrict recharge." Australian Journal of Experimental Agriculture 42: 297- 307.
Bethune, M. (2001). Recharge review. Attachment 3 in Wood (2001) "Improved Irrigation Practices for Forage Production, Module 1: Flood Irrigation Research Review". Final Report. Tatura, Department of Natural Resources and Environment.
Bethune, M. (2004). "Towards effective control of deep drainage under border-check irrigated pasture in the Murray-Darling Basin: a review." Australian Journal of Agricultural Research 55: 485-494.
Bethune, M. and Q. J. Wang (2004). "A lysimeter study of the water balance of border- check irrigated perennial pasture." Australian Journal of Experimental Agriculture 44: 151-162.
Blaikie, S. J., F. M. Martin, et al. (1988). "A basis for improved soil and water management for irrigated pastures in northern Victoria." Australian Journal of Experimental Agriculture 28(315-319).
Booth Associates Pty Ltd (1994). Hydraulic Modelling and Economic Analysis of Proposed Enroute Storage at The Drop. Griffith, Booth Associates Pty Ltd.
237 LIST OF REFERENCES
Borg, H. and D. W. Grimes (1986). "Depth Development of Roots with Time: An Empirical Description." Transactions of the ASAE 29(1): 194-197.
Bren, L. (1988). "Flooding characteristics of a riparian red gum forest." Australian Forestry 51: 57-62.
Bren, L., I. O'Neill, et al. (1987). "Flooding of the Barmah Forest and its relation to flow in the Murray-Edward River System." Australian Forest Research 17: 127-144.
Bruhn, J. A. and W. E. Fry (1980). "Simulation of Daily Data Using Theoretical Probability Distributions." Journal of Applied Meteorology 19(9): 1029-1036.
Brutsaert, W. H. (1982). Evaporation into the atmosphere. Pordrecht, Holland, D. Reidel Publishing Co.
Bryan, B. and S. Barvanek (2004). Quantifying and Valuing Land Use Changes for Integrated Catchment Management and Evaluation in the Murray Darling Basin, 1996/7 - 2000/01. Canberra, CSIRO Land and Water Client Report.
Chesterfield, E. A. (1986). "Changes in the vegetation of the river red gum forest at Barmah, Victoria." Australian Forestry 49: 4-15.
Chetan, M. and K. P. Sudheer (2006). "A hybrid linear-neural model for river flow forecasting." Water Resources Research 42(4).
Chong, J. (2003). Analysis and Management of Unseasonal Surplus Flows in the Barmah-Millewa Forest. Melbourne, Cooperative Research Centre for Catchment Hydrology.
Chong, J. and A. R. Ladson (2003). "Analysis and Management of Unseasonal Flooding in the Barmah-Millewa Forest, Australia." River Research and Application 19: 161-180.
Clark, M., W. R. Horwath, et al. (1999). "Nitrogren, weeds and water as yield-limiting factors in conventional, low-input, and organic tomato systems." Agriculture, Ecosystems and Environment 73: 257-270.
Clemmens, A. J. (2000). Measuring and improving irrigation systems performance at the field level. Irrigation Association of Australia Conference, Melbourne.
Cleugh, H. (2007). "Murray-Darling Basin (MDB) Water Budget Project." Retrieved 25/7, 2007.
Dawson, C. W. (1999). "A comparison of artificial neural networks used for river flow forecasting." Hydrology and Earth System Sciences 3(4): 529-540.
Department of Infrastructure Planning and Natural Resources (2004). Water Sharing Plan for the Murray and Lower Darling Regulated Rivers Water Sources. Sydney, Department of Infrastructure, Planning and Natural Resources.
238 LIST OF REFERENCES
Department of Land and Water Conservation (1996). Unseasonal Surplus Flow Management (DRAFT ONLY). Sydney, Department of Land and Water Conservation.
Der, G. and B. S. Everitt (2006). Statistical Analysis of Medical data using SAS. New York, Chapman & Hall/CRC.
Draper, N. R. (1998). Applied Regression Analysis. New York, Wiley-Interscience Publication.
Evans, M., J. Wolfenden, et al. (2005). Murrumbidgee En-Route Storages Project - Workshop 2 Report. Wagga Wagga, NSW, Department of Infrastructure, Planning and Natural Resources.
Evans, M., J. Wolfenden, et al. (2005). Murrumbidgee En-Route Storages Project - Workshop 3 Report. Wagga Wagga, NSW, Department of Infrastructure, Planning and Natural Resources.
Foreman, J. (2005). Further Spreadsheet Modelling of the Proposed Drop Storage. Canberra, Murray Darling Basin Commission.
Foreman, J. (2005). Spreadsheet Modelling of the Proposed Drop Storage. Canberra, Murray Darling Basin Commission.
Fukai, S. and P. Inthapan (1988). "Growth and yield of rice cultivars under sprinkler irrigation in south-eastern Queensland. 3. Water extraction and plant water relations - comparison with maize and grain sorghum." Australian Journal of Experimental Agriculture 28: 249-52.
George, B. A., H. M. Malano, et al. (2004). "Using Modelling to Improve Operational Performance in the Cu Chi Irrigation System, Vietnam." Irrigation and Drainage 53(3): 237-249.
Ghahraman, B. and A.-R. Sepaskhah (2002). "Optimal allocation of water from a single purpose reservoir to an irrigation project with pre-determined multiple cropping patterns." Irrigation Science 21(3): 127-137.
GHD Pty Ltd (2006). Engineering and Economic Assessment of the Proposed Storage at "The Drop"; Milestone 3 Report - Draft Feasibility Assessment. Canberra, GHD Pty Ltd.
Global Energy and Water Cycle Experiment. (2007). "Murray-Darling Basin (MDB) Water Budget Project." Retrieved 5/10/2007, from http://www.gewex.org/mdb.html.
Goulburn-Murray Water (Undated). Irrigator Handbook. Tatura, Rodney Printers.
Guobin, L. and D. R. Kemp (1992). "Water Stress Affects the Productivity, Growth Components, Competitiveness and Water Relations of Phalaris and White Clover Growing in a Mixed Pasture." Australian Journal of Agricultural Research 43: 659-672.
239 LIST OF REFERENCES
Haisman, B. (2000, 2000). "Hume and Dartmouth Dams, Murray Darling Basin, Australia." Dams and Development: A New Framework for Decision-Making Retrieved 12/10/2007, 2007.
Hamby, D. M. (1994). "A review of techniques for parameter sensitivity analysis of environmental models." Environmental Monitoring and Assessment 32: 135-154.
Helsel, D. R. and R. M. Hirsch (1992). Statistical Methods in Water Resources. New York, Elsevier.
Hillman, T. J. and G. P. Quinn (2002). "Temporal Changes in Macroinvertebrate assemblages following experimental flooding in permanent and temporary wetlands in an Australian Floodplain forest." River Research and Application 18: 137-154.
Holmes, J. W. (1974). Future management and planning. In The Murray Waters: Man, Nature and a River System. Sydney, Angus and Robertson.
Hornbuckle, J. and E. W. Christen (1999). Physical Properties of Soils in the Murrumbidgee and Coleambally Irrigation Areas. Griffith, CSIRO: 175.
Hornbuckle, J., E. W. Christen, et al. (2005). Tiddalik: An Irrigation Area Model for Predicting and Managing Drainage Return Flows. Australian National Committee on Irrigation and Drainage, Mildura.
Humphreys, E. (1997). Rice paddock water balance in the Murray Valley. Griffith, CSIRO.
Humphreys, E. and H. D. Barr (1998). Constraints to Rice Establishment and Yield in the Western Murray Valley. RIRDC Final Report Project CSI-7A. Griffith, CSIRO.
Humphreys, E., M. Edraki, et al. (2003). Deep drainage & crop water use for irrigated annual crops & pastures in Australia - a review of determinations in fields & lysimeters. Griffith, CSIRO.
Humphreys, E., W. S. Meyer, et al. (1994). "Estimation of evapotranspiration from rice in southern New South Wales: a review." Australian Journal of Experimental Agriculture 34(7): 1069-78.
Incerti, M. and G. J. O'Leary (1990). "Rooting depth of wheat in the Victorian Mallee." Australian Journal of Agricultural Research 30: 817-824.
Jolliffe, I. T. (2002). Principal Component Analysis. New York, Springer.
Kahsay, L. (2003). Design and Performance of Community-based Irrigation in Tigray. Workshop for Research Project for Community-based Irrigation Management in Ethiopia, Addis Ababa, Ethiopia.
Kuchar, L. (2004). "Using WGENK to generate synthetic daily weather data for modelling of agricultural processes." Mathematics and Computers in Simulation 65: 69-75.
240 LIST OF REFERENCES
Kusnarta, I. G. M., J. Tisdall, et al. (2004). Rice root distribution under various systems of soil management on rainfed Vertisols in Southern Lombok, Eastern Indonesia. 4th International Crop Science Congress, Brisbane.
Ladson, A. (2002). Floods and the Barmah-Millewa Forest. Victoria National Parks Association, Briefing note 5th December 2002.
Lisson, S. N., L. E. Brennan, et al. (2003). "DAM EA$Y - software for assessing the costs and benefits of on-farm water storage based production systems." Agricultural Systems 76: 19-38.
Maheshwari, B. L., K. F. Walker, et al. (1995). "Effects of regulation on the flow regime of the River Murray, Australia." Regulated Rivers: Research & Management 10(1): 15-38.
Makin, I. W. and G. A. Cornish (1995). Irrigation scheduling at system level: An analysis of practical applications of the INCA software. Proceedings of the ICID/FAO Workshop on Irrigation scheduling, Rome, Italy, Food and agriculture organization of the united nations.
Marshall, A. (2002). 2001/2002 Environment Report. A. Marshall. Deniliquin, Murray Irrigation Limited.
Marshall, A. (2003). Compliance and Environment Report 2002/2003. Deniliquin, Murray Irrigation Limited.
Marshall, A. (2004). Murray Irrigation Environment Report 2004. Deniliquin, Murray Irrigation Limited.
Maunsell Pty Ltd (1992). Barmah-Millewa Forests Water Management Plan. Canberra, Murray Darling Basin Commision.
McBride, J. L. and E. E. Ebert (1999). "Verification of Quantitative Precipitation Forecasts from Operational Numerical Weather Prediction Models over Australia." Weather and Forecasting 15(1): 103-121.
Meyer, W. S. (1999). Standard reference evaporation calculation for inland, south eastern Australia. Adelaide, CSIRO.
Meyer, W. S., F. X. Dunin, et al. (1987). "Characterizing water use by irrigated wheat at Griffith, New South Wales." Australian Journal of Soil Research 25: 499-515.
Meyer, W. S., D. J. Smith, et al. (1999). Estimating reference evaporation and crop evapotranspiration from weather data and crop coefficients. Adelaide, CSIRO.
Montgomery, D. C. and G. C. Runger (2007). Applied Statistics and Probability for Engineers. New York, John Wiley & Sons, Inc.
241 LIST OF REFERENCES
Mousavi, S. J., K. Mahdizadeh, et al. (2004). "A stochastic dynamic programming model with fuzzy storage states for reservoir operations." Advances in Water Resources 27: 1105-1110.
Murray-Darling Basin Commission (2004). The Cap. Canberra, Murray Darling Basin Commission.
Murray-Darling Basin Commission. (2004, Unspecified). "Fact Sheet 6: The Barmah Choke." Retrieved 29/10/2004, 2004, from http://www.mdbc.gov.au/river_murray/river_murray_system/barmah/barmah_choke.ht m.
Murray-Darling Basin Commission. (2006, 29th October 2006). "Basin Statistics." Retrieved 5/10/2007, 2007, from http://www.mdbc.gov.au/about/basin_statistics.
Murray-Darling Basin Commission. (2006). "Water Use within the Murray-Darling Basin." Retrieved 25/7, 2007, from http://www.mdbc.gov.au/nrm/water_issues/water_use.
Murray-Darling Basin Commission (2007). Murray-Darling Basin Commission - June 2007, E-letter No 67. Canberra, Murray-Darling Basin Commission.
Murray Irrigation Limited (2006). Murray Irrigation Limited Annual Report 2006. Deniliquin, Murray Irrigation Limited.
NIST/SEMATECH (2007). e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/.
Pandzic, K. and D. Trninic (1992). "Principal component analysis of a river basin discharge and precipitation anomaly fields associated with the global circulation." Journal of Hydrology 134(1-4): 343-360.
Prajamwong, S., G. P. Merkley, et al. (1997). "Decision Support Model for Irrigation and Water Management." Journal of Irrigation and Drainage Engineering 123(2): 106- 113.
Pulido-Calvo, I., J. Roldan, et al. (2003). "Demand Forecasting for Irrigation Water Distribution Systems." Journal of Irrigation and Drainage Engineering 129(6): 422-431.
Raman, H. and V. Chandramouli (1996). "Deriving a General Operating Policy for Reservoirs Using Neural Network." Journal of Water Resources Planning and Management 122(5): 342-347.
Ravilico, J. K., G. C. Dandy, et al. (2007). MORE Sensitivity Analysis of the MSM- BIGMOD River Murray Flow and Salinity Model. International congress on modelling and simulation, Christchurch, New Zealand.
Ridley, A. M. and R. J. Simpson (1994). "Seasonal Development of Roots under Perennial and Annual Grass Pastures." Australian Journal of Agricultural Research 45: 1077-1087.
242 LIST OF REFERENCES
Roost, N. (2002). Strategic Options Analysis in Surface Irrigation Systems: Integrated Modelling for Efficient, Productive and Equitable Water Use. PRÉSENTÉE À LA FACULTÉ ENVIRONNEMENT NATUREL, ARCHITECTURAL ET CONSTRUIT. Geneva, ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE.
Roost, N., X. L. Cai, et al. (2007). "An assessment of distributed, small-scale storage in the Zhanghe Irrigation System, China." Unpublished.
Roost, N. and A. Musy (2005). "Planning Irrigation Interventions in a Basin Context: Development and Application of the OASIS Simulation Model." Unpublished.
Russell, S. O. and P. F. Campbell (1996). "Reservoir Operating Rules with Fuzzy Programming." Journal of Water Resources Planning and Management 122(3): 165- 170.
Sinclair Knight Merz Pty Ltd (2003). GSM Additional PRIDE Demands. Melbourne, Sinclair Knight Merz Pty Ltd.
Sinclair Knight Merz Pty Ltd (2006). Assessment of Victorian Demands in the River Murray and Future Supply Options. Melbourne, Sinclair Knight Merz Pty Ltd.
Sinclair Knight Merz Pty Ltd (2006). Improved Management of Rainfall Rejections Upstream of the Barmah Choke. Melbourne, Sinclair Knight Merz Pty Ltd.
Singh, J. and E. W. Christen (2001). "Evaporation Basins: Opportunities for Cost Minimisation in siting, design and construction." Irrigation and Drainage 50: 19-29.
Singh, R., J. C. Refsgaard, et al. (1997). "Hydraulic-hydrological simulations of canal- command for irrigation water management." Irrigation and Drainage Systems 11: 185- 213.
Slavich, P. G., G. H. Petterson, et al. (1992). The effect of gypsum on deep drainage from clay soils used for rice. Sodic soils: the next battle for land managers: national conference and workshop, Adelaide, Cooperative Research Centre for Soil and Land Management.
Smith, A. and B. L. Maheshwari (2002). "Options for alternative irrigation water supplies in the Murray-Darling Basin, Australia: a case study of the Shepparton Irrigation Region." Agricultural water management 56: 41-55.
Steiner, J. L., R. C. G. Smith, et al. (1985). "Water use, foliage temperature and yield of irrigated wheat in south-eastern Australia." Australian Journal of Agricultural Research 36: 1-11.
Stern, H., G. C. de Hoedt, et al. (2000). Objective Classification of Australian Climates. Australian Meteorlogical Magazine. 49: 87-96.
Stevens, E. G., D. Stephenson, et al. (1998). "Management of a Reservoir for Drought." Water SA 24(4): 287-292.
243 LIST OF REFERENCES
Sui, J. (2005). "Estimation of Design Flood Hydrograph for an Ungauged Watershed." Water Resources Management 19: 813-830.
Thoms, M., P. Suter, et al. (2000). Report of River Murray Scientific Panel on Environmental Flows. Canberra, Murray Darling Basin Commission.
URS Pty Ltd (2003). Proposed Barren Box Storage, Reconstructed Wetland and En- Route Storages. Sydney, URS Pty Ltd.
Vlotman, W. F. and H. M. Malano (1987). Mircro-computer Steady State Modeling of Irrigation Delivery Systems. Logan, International Irrigation Center, Department of Agricultural and Irrigation Engineering, Utah State University.
Whitfield, D. M. and C. J. Smith (1989). "Effects of irrigation and nitrogen on growth, light interception and efficiency of light conversion in wheat." Field Crops Research 20: 279-295.
Wolfenden, J., M. Evans, et al. (2005). Murrumbidgee En-Route Storgaes Projects - Workshop 1 Report. Wagga Wagga, NSW, Department of Infrastructure, Planning and Natural Resources.
Yomota, A. and G. M. Ndegwa (1995). "Water use for upland irrigation in a humid region of Japan." Agricultural Water Management 28: 185-200.
Yu, X. and S. Y. Liong (2007). "Forecasting of hydrologic time series with ridge regression in feature space." Journal of Hydrology 332: 290-302.
244
Appendix A
A.1 Interview Questionnaire
A copy of the interview questionnaire in presented below.
245 APPENDIX A
Plain English Statement
This research is part of the project ‘Feasibility of Storages in Irrigation Systems to Capture Rain Rejection Flows’ for Doctorate studies by Matthew Berrisford at The University of Melbourne, in the Department of Civil and Environmental Engineering. The supervisors for this research are Associate Professor Hector Malano and Dr Robert Argent.
This interview is being undertaken on the behalf of Matthew Berrisford to gain an understanding of how you, the irrigator, place your irrigation orders and the circumstances that lead to the cancellation of some or all of your irrigation orders. Information is being gathered on your ordering decisions to better understand your actions and hence enable the most accurate research possible.
The findings from this research will be incorporated into the use of storages (on-farm or en-route) to capture and Storage rain rejection flows (irrigation orders rejected by irrigators due to rainfall between the time the order was placed and the irrigation water arriving). The research is using the MIL system as a study area and the outcomes of the research will hopefully enable the irrigation water currently lost to rain rejection flows to be captured and Storaged and hence improve the efficiency of the MIL system.
Due to the focus on the Murray Irrigation Limited area your anonymity is guaranteed as this survey is separate to the LWMP survey undertaken by MIL and any identification of participants is destroyed upon the completion of this survey. Your consent to participating in this research is granted through the verbal agreement you make with the interviewer.
Involvement in this research is voluntary and if at any time you feel you wish to withdraw consent of any unprocessed data previously supplied please contact Matthew Berrisford.
Once the information has been gathered the initial surveys will be kept securely in the department for at least five years after publication of the results, as stated in the University of Melbourne Code of Conduct for Research, after this time the questionnaire will be destroyed using a Shredder machine and the computer files will be Storaged on CD which will also be destroyed with scissors after this time. The raw data obtained from the surveys will only be accessible by the researches outlined in this statement.
The results from the research will be made public through conference presentations, thesis dissertation and journal articles. A small report showing the findings of the research will also be made available to all of the persons who receive this document.
246 APPENDIX A
Contact Details
Mr Matthew Berrisford, PhD Candidate, Dept. of Civil and Environmental Engineering, The University of Melbourne, Victoria, 3010 Australia. Tel. 03 83444709 Mobile 0428 910130 Fax: 03 83446868 email: [email protected]
247 APPENDIX A
GENERAL QUESTIONS SECTION
This section is to be completed by everybody who undertakes irrigation on their property. The aim of the survey is to gain an understanding of the pattern of irrigation behaviour so please complete the survey with a number of previous years in mind.
1. Land and Water Management Plan (LWMP) area your holding(s) is located in? …………………………………………………………………………………………. 2. Annual water licence entitlement (ML)? ………………………………………………………………………………………….
3. Do your irrigated crop areas vary with water allocation?
Yes No
3. (a) If Yes, describe the changes in your crops by selecting the appropriate crops and changes in area below, with a decreased allocation. Increase Decrease Constant Rice Winter irrigated pasture Lucerne/summer pasture Dryland pasture Prewatering of winter crops Dryland Crops Other Crops Fallow
248 APPENDIX A
4. If you have a farm dam (turkey’s nest) greater than 10 mega litres what is this used for?
Recycling tail water Stock and domestic water supply Storing surplus irrigation orders Storing supplementary water
5. Are there critical growth stages in the crop cycle (eg germination, winter growth stage) that mean you are particularly conservative about including the weather predictions in your ordering processes?
Yes No If yes please specify the crop and month in which irrigation application is extremely critical.
Crop…………………………………………Month...... …………..…………………. Crop…………………………………………Month...... ………………………………. Crop…………………………………………Month...... ………………………………. Crop…………………………………………Month...... ……………………………….
249 APPENDIX A
NON-RICE CROPS SECTION
This Section refers to all irrigated crops or pastures OTHER THAN RICE. If you use all your irrigation for rice do NOT complete this section, move on to the RICE section starting on page 8.
6. What is your management approach for ordering water, (select as many of the items below as appropriate)?
Use of a soil moisture probe Pre-scheduled time period, eg. 14 days Observation of crop and soil Climatic conditions since last irrigation order Checking the weather forecast Other, please specify ………………………………………………………………..
7. How do you decide the volume of water ordered (select as many of the items below as appropriate)?
Application of a minimum depth Water consumed since last application Checking the four or seven day weather forecast Entitlement remaining for the season Maximum flow rates for paddock design or infrastructure Other, please specify ………………………………………………………………..
250 APPENDIX A
8. How many times a season would you place an irrigation order?
Less than 5 5 to 10 10 to 15 15 to 20 More than 20
9. How many times a season would you reduce your irrigation order by even a small amount after the initial order (this is any reduction be that 5% up to a complete cancellation)?
Never Once Twice Three times Four or more
10. What circumstances would lead to a reduction in your irrigation order (this is any reduction be that 5% up to a complete cancellation) (select as many of the below as appropriate)?
Dry cool change Cool change with less than 10mm of rainfall Cool change with greater than 10mm of rainfall Rainfall of less than 20mm but little change in temperature Rainfall of greater than 20mm but little change in temperature
251 APPENDIX A
11. How many times a season would you reduce your irrigation order by more than 50% (this is any reduction greater than 50% up to a complete cancellation)?
Never Once Twice Three times Four or more
12. What circumstances would lead you to reduce your irrigation order by more than 50% (this is any reduction greater than 50% up to a complete cancellation) (select as many of the below as appropriate)?
Dry cool change Cool change with less than 10mm of rainfall Cool change with greater than 10mm of rainfall Rainfall of less than 20mm but little change in temperature Rainfall of greater than 20mm but little change in temperature Other, please specify …………………………………………………………………….
13. How long prior to the delivery of your irrigation order do you reduce (this is any reduction) your irrigation order? Once the irrigation has commenced 0 to 36 hours prior Greater than 36 hours prior Varies depending on time of the weather event that initiates the reduction in order volume
252 APPENDIX A
RICE SECTION
This section refers to irrigation practices purely for RICE. If you do not grow rice then do NOT complete this section.
14. What is your management approach for ordering water?
Depth of the ponding water on bays Pre-scheduled daily order Other, please specify ………………………………………………………………………..
15. How do you decide the volume of water ordered (select as many of the below as appropriate)?
Application of a minimum depth Increase the ponding depth to the average level Checking the four or seven day weather forecast Entitlement remaining for the season Other, please specify ………………………………………………………………..
16. How many times a season would you place an irrigation order?
Less than 5 5 to 10 10 to 15 15 to 20 More than 20
253 APPENDIX A
17. How many times a season would you reduce your irrigation order by even a small amount after the initial order (this is any reduction be that 5% or a complete cancellation)?
Never Once Twice Three times Four or more
18. What circumstances would lead to a reduction in your irrigation order (this is any reduction be that 5% or a complete cancellation) (select as many of the below as appropriate)?
Dry cool change Cool change with less than 10mm of rainfall Cool change with greater than 10mm of rainfall Rainfall of less than 20mm but little change in temperature Rainfall of greater than 20mm but little change in temperature Other, please specify …………………………………………………………………….
19. How many times a season would you reduce your irrigation order by more than 50% (this is any reduction greater than 50% up to a complete cancellation)?
Never Once Twice Three times Four or more
254 APPENDIX A
20. What circumstances would lead you to reduce your irrigation order by more than 50% (this is any reduction greater than 50% up to a complete cancellation) (select as many of the below as appropriate)?
Dry cool change Cool change with less than 10mm of rainfall Cool change with greater than 10mm of rainfall Rainfall of less than 20mm but little change in temperature Rainfall of greater than 20mm but little change in temperature Other, please specify …………………………………………………………………….
21. How long prior to the delivery of your irrigation order do you reduce (this is any reduction be that 5% up to a complete cancellation) your irrigation order? Once the irrigation has commenced 0 to 36 hours prior Greater than 36 hours prior Varies depending on time of the weather event that initiates the reduction in order volume
255 APPENDIX A
A.2 Interview Questionnaire Results
The responses to question 6 (management approach to water ordering) in the interview questionnaire are shown in Figure A-1.
60
50
40
30
20 Number of responses
10
0 abcde Option selected
Figure A-1: Responses to Question 6 in interview questionnaire
The responses to question 7 (volume of water ordered) in the interview questionnaire are shown in Figure A-2.
45
40
35
30
25
20
15 Number of responses 10
5
0 abcde Option selected
Figure A-2: Responses to Question 7 in interview questionnaire
256 APPENDIX A
The responses to question 8 (number of times per season an irrigation order is placed) in the interview questionnaire are shown in Figure A-3.
25
20
15
10 Number of responses
5
0 abcde Option selected
Figure A-3: Responses to Question 8 in interview questionnaire
The responses to question 9 (number of times per season a rejection is placed) in the interview questionnaire are shown in Figure A-4.
16
14
12
10
8
6 Number of responses 4
2
0 abcde Option selected Figure A-4: Responses to Question 9 in interview questionnaire
The responses to question 10 (circumstances that lead to a rejection being placed) in the interview questionnaire are shown in Figure A-5.
257 APPENDIX A
45
40
35
30
25
20
15 Number of responses 10
5
0 abcde Option selected
Figure A-5: Responses to Question 10 in interview questionnaire
The responses to question 11 (number of times per season a rejection of greater than 50% of the order is placed) in the interview questionnaire are shown in Figure A-6.
25
20
15
10 Number of responses
5
0 abcde Option Selected
Figure A-6: Responses to Question 11 in interview questionnaire
The responses to question 12 (circumstances that lead to a rejection of greater than 50% of the order) in the interview questionnaire are shown in Figure A-7.
258 APPENDIX A
45
40
35
30
25
20
15 Number of responses 10
5
0 abcde Option selected
Figure A-7: Responses to Question 12 in interview questionnaire
The responses to question 13 (time prior to the delivery of the irrigation order that the rejection is placed) in the interview questionnaire are shown in Figure A-8.
35
30
25
20
15
Number of responses 10
5
0 abcd Option selected
Figure A-8: Responses to Question 13 in interview questionnaire
The responses to question 14 (management approach for ordering water for rice) in the interview questionnaire are shown in Figure A-9.
259 APPENDIX A
30
25
20
15
10 Number of responses
5
0 ab Option selected
Figure A-9: Responses to Question 14 in interview questionnaire
The responses to question 15 (decision method for the volume of water ordered for rice) in the interview questionnaire are shown in Figure A-10.
25
20
15
10 Number of responses
5
0 abcd Option selected
Figure A-10: Responses to Question 15 in interview questionnaire
The responses to question 16 (number of times per season that an irrigation order is placed for rice) in the interview questionnaire are shown in Figure A-11.
260 APPENDIX A
18
16
14
12
10
8
6 Number of responses
4
2
0 abcde Option selected Figure A-11: Responses to Question 16 in interview questionnaire
The responses to question 17 (number of times per season that a rejection is placed for rice) in the interview questionnaire are shown in Figure A-12.
25
20
15
10 Number of responses
5
0 abcde Option selected
Figure A-12: Responses to Question 17 in interview questionnaire
The responses to question 18 (the circumstances that lead to a rejection being placed for rice) in the interview questionnaire are shown in Figure A-13.
261 APPENDIX A
25
20
15
10 Number of responses
5
0 abcde Option selected
Figure A-13: Responses to Question 18 in interview questionnaire
The responses to question 19 (number of times per season that a rejection of greater than 50% of the order is placed for rice) in the interview questionnaire are shown in Figure A-14.
12
10
8
6
4 Number of responses
2
0 abcde Option selected
Figure A-14: Responses to Question 19 in interview questionnaire
The responses to question 20 (circumstances that lead to a rejection of greater than 50% of the order volume for rice) in the interview questionnaire are shown in Figure A-15.
262 APPENDIX A
25
20
15
10 Number of responses
5
0 abcde Option selected
Figure A-15: Responses to Question 20 in interview questionnaire
The responses to question 21 (time prior to delivery of irrigation order than a rejection is placed) in the interview questionnaire are shown in Figure A-16.
25
20
15
10 Number of responses
5
0 abcd Option selected
Figure A-16: Responses to Question 21 in interview questionnaire
263 APPENDIX B Appendix B
B.1 Single Parameter Investigation of Unseasonal B-MF Floods
This section identifies unseasonal B-MF flood events and then individually investigates all of the possible factors that could contribute to these events to try and identify links between these factors and unseasonal flooding of the B-MF. Prior to this, it is necessary to identify all of the possible factors that could contribute to unseasonal flooding of the B-MF and the data available to undertake this investigation.
The factors thought to possibly contribute to unseasonal flooding of the B-MF are: • rainfall; • UIO; • G-MW irrigation diversions from Yarrawonga weir; • tributary inflows; • flood prevention measures which include: airspace at Yarrawonga Weir; capacity in the River Murray at Tocumwal; and capacity in Edward River Escape. • rainfall forecasts; • seasonal water allocation; and • monthly nett Mulwala Canal diversion.
Explicit information was available on all of these factors with the exception of UIO, for this reason it was necessary to calculate UIOs following the procedure described below.
B.1.1 Single parameter investigation of B-MF flood events
This section discusses the individual parameter investigation to determine the role each of the aforementioned parameters play in causing B-MF flood events between December and April. The analysis will use data from seasons 1998/99 to 2003/04.
264 APPENDIX B
Firstly it was necessary to identify flood events of the B-MF. The method of Thoms et. al. (2000) was used to identify flood events of the B-MF for reasons outlined in Chapter 2. Using this method a flood event is said to occur when flows in the River Murray at Tocumwal exceed 10,600 ML/day. B-MF flood events identified between December and April in the seasons 1998/99 to 2003/04 are shown in Table B.1. Here, ‘Peak excess flow’ refers to the amount by which the peak flow at Tocumwal was in excess of 10,600 ML/day and ‘Total excess volume’ refers to the total volume of the flow that was greater than 10,600 ML/day for the duration of the flood event.
Table B.1: December to April B-MF flood events from 1998 to 2004 Peak excess flow Total excess Start date Finish date Duration (days) (ML/day) volume (ML) 17/12/1998 01/01/1999 16 301 2,490 07/01/1999 14/01/1999 8 130 462 29/01/1999 30/01/1999 2 8 12 20/03/1999 29/03/1999 10 4,327 24,738 29/12/1999 04/01/2000 7 3,928 14,964 29/01/20011 04/02/20011 7 4,1141 16,3181 19/02/2001 19/02/2001 1 287 287 23/02/2001 26/03/2001 32 726 15,906 28/03/2002 04/04/2002 8 4,586 23,664 01/01/2003 02/01/2003 2 169 268 13/11/2003 25/01/2004 74 1,123 67,651 19/12/20032 02/01/20042 152 6,1492 32,2332 29/01/2004 17/02/2004 20 311 3,172 21/02/2004 10/03/2004 19 457 3,369 13/03/2004 18/03/2004 6 93 342 Notes: 1: This event started with the flow (11,997 ML/day at Tocumwal) receding from a controlled flood. 2: This event started when the flow at Tocumwal had been 10,911 ML/day for the three previous days.
An investigation of Table B.1 shows that there are 3 types of flood events, these are: • medium duration (15 days or less), high peak excess floods (greater than 2,000 ML/day), shown in bold above; • medium to long duration (greater than 7 days in duration), small peak excess floods (less than 2,000 ML/day); and • short duration (7 days or less), small peak excess floods (less than 2,000 ML/day).
The flood events that are of particular interest are those that are linked to a sudden high level of UIO. From an investigation of UIO it was found that a sudden high level of UIO corresponded to events that were medium duration, high peak excess floods, shown in bold in Table B.1.
265 APPENDIX B
In the following sections the possible causes of flooding in the B-MF listed above are investigated prior to and during the B-MF flood events that were of a medium duration and a high peak excess flow.
Rainfall
For the above identified flood events, the average rainfall of the six rainfall stations across the study area was investigated to determine the extent of the rainfall that fell to induce flooding of the B-MF, Table B.2.
Table B.2: Rainfall and medium duration, high peak excess B-MF floods Start date Finish date Duration (days) Rainfall (mm) 18/03/1999 22/03/1999 5 24.2 24/12/1999 29/12/1999 6 47.0 24/01/2001 27/01/2001 4 28.5 26/03/2002 27/03/2002 2 40.3 18/12/2003 22/12/2003 5 63.8
Table B.2 shows that the rainfall depth varied considerably but all events were in excess of 20mm. The lowest rainfall depth which produced a flood event was 24.2mm over 5 days in March 1999 and the largest event was 63.8mm over 5 days in December 2003. All of the events seem to be the result of a cool weather change and rainfall as the duration of rain events is 4 to 6 days with the exception of the March 2002 event which lasted only two days. An investigation of the temperature records for Finley revealed that the March 2002 event was also associated with a cool change as the temperature dropped 13°C from March 25th to 26th. It is concluded that to induce a medium duration, high peak excess flood in the B-MF, greater than 20mm of rainfall is required in less than 6 days in association with a cool change.
UIO
For each flood event the UIO for the study area were examined from the start of the rain event until two days prior to the subsidence of the flood event. In Table B.3 the ‘Peak’ and ‘Total’ refer to the peak UIO and total UIO. The ‘First 4 days’ column refers to the volume of UIO for the first 4 days after the start of the rain event. This volume was calculated to allow a comparison between the volume of UIO from the study area and the volume of UIO from the G-MW area. The start date, finish date and duration refer to the B-MF flood event.
266 APPENDIX B
Table B.3: UIO for medium duration, high peak excess B-MF floods Duration Peak First 4 days Start date Finish date Total (ML) (days) (ML/day) (ML) 20/03/1999 29/03/1999 10 No data No data No data 29/12/1999 04/01/2000 7 3,067 3,805 15,940 29/01/20011 04/02/20011 61 4,7131 11,2301 26,6871 28/03/2002 04/04/2002 8 5,093 17,741 23,908 19/12/20032 02/01/20042 152 2,9852 4,3912 13,2342 Notes: 1: This event started with the flow (11,997 ML/day at Tocumwal) receding from a controlled flood. 2: This event started when the flow at Tocumwal had been 10,911 ML/day for the three previous days.
There is only a small amount of data available for medium duration, high peak excess floods but there appears to be a difference in the peak, first 4 days and total volumes for flood events that occurred in December compared to other months. The following was observed regarding UIO: • in December, flood events had peak UIO around 3,000 ML/day; and • later in the season, flood events had peak UIO greater than 4,500 ML/day.
It is interesting to compare the total volume of UIO for the flood event to the ‘Total excess volume’ column in Table B.1 for the four events where there is data on UIO. For the two events 29/12/1999 to 04/01/2000 and 28/03/2002 to 04/04/2002 the volume of UIO is approximately equal to the total excess volume at Tocumwal. For the event 29/01/2001 to 04/02/2001 the volume of UIO is around 10,000 ML greater than total excess volume at Tocumwal indicating that a significant volume of the UIO must have been stored by Yarrawonga Weir airspace or passed around the Barmah Choke in airspace in the River Murray and Edward River Escape. In the event from 19/12/2003 to 02/01/2004 the total excess volume at Tocumwal (32,233 ML) greatly exceeded the total volume of UIO (13,324 ML) this seems to indicate that there was minimal airspace in Yarrawonga Weir, the River Murray at Tocumwal and Edward River Escape but also there was considerable inflow from the tributary rivers and possibly a high level of UIO from G-MW during this event.
G-MW UIO
Data available from G-MW only consists of actual diversion from Yarrawonga Weir on a daily basis. The ideal data from G-MW would be four day order volumes and actual diversion from Yarrawonga Weir on a daily basis, as the difference between the two data sets should be the volume of UIO on a daily basis. As this is not available, an estimate of daily UIO will be extracted from the actual diversions from Yarrawonga
267 APPENDIX B
Weir. From an analysis of the data there was generally a steady diversion by G-MW prior to the start of the rain event. Using the philosophy that had the rain event not occurred the diversion would have remained at this steady rate, the UIO for the first 4 days after the commencement of the rain event could be extracted by determining the difference between the diversion on the day prior to the rain event and the actual diversion for the first four days after the commencement of the rain event. This only allows the first four days of UIO to be summed, rather than the sum of the UIO across the length of the B-MF flood event. For this reason to determine the significance of the G-MW UIO to UIO from the study area, a comparison was made between the first 4 days of UIO from G-MW and the study area, Table B.4. The ‘Minimum’ column in Table B.4 refers to the minimum diversion by G-MW during the duration of the flood event in the B-MF.
Table B.4: G-MW rejections for medium duration, high peak excess B-MF floods G-MW diversion on Duration Minimum First 4 days Start date Finish date the day prior to the (days) (ML/day) (ML) rain event (ML/day) 20/03/1999 29/03/1999 10 3,039 696 4,608 29/12/1999 04/01/2000 7 3,106 301 6,762 29/01/2001 04/02/2001 6 3,106 448 4,504 28/03/2002 04/04/2002 8 3,106 570 7,334 19/12/2003 02/01/2004 15 2,485 148 3,615
Comparing Table B.4 to Table B.3 shows that for the 29/01/2001 to 04/02/2001 and 28/03/2002 to 04/04/2002 events the volume of UIO from the study area is considerably larger than the volume of UIO from G-MW. For the two events which commenced in December, the volume of UIO from G-MW was 3,000 ML greater (29/12/1999 to 04/01/2000 event) and 800 ML less (19/12/2003 to 02/01/2004 event) than the volume of UIO from the study area.
From the above it can be concluded that G-MW UIO play a role in medium duration, high peak excess B-MF floods, particularly during December.
Tributary inflows
There are two rivers with significant inflows that join the River Murray between Lake Hume and Yarrawonga Weir these are Ovens River and Kiewa River. Ovens River (annual discharge of 1,620 GL (Murray-Darling Basin Commission 2004)) enters the River Murray just upstream of Yarrawonga Weir, while Kiewa River (annual discharge
268 APPENDIX B of 705 GL (Murray-Darling Basin Commission 2004)) enters the River Murray just downstream of Lake Hume (Figure 2.6).
It is most realistic to investigate the increase in inflows from Ovens and Kiewa River using a method similar to that used for investigating UIO. The method used for investigating UIO used the change in the volume of UIO from the start of the rain event until two days prior to the subsidence of the flood event. Using this method for the Ovens and Kiewa Rivers requires determining the inflow from the Ovens River on the day prior to the commencement of rainfall and determining the change in inflow for each day until two days prior to the subsidence of the flood event. Due to the travel time from the Kiewa River is was necessary to determine the inflow from Kiewa River five days prior to the commencement of rainfall and determine the change in inflow for each day until six days prior to the subsidence of the flood event, Table B.5.
Table B.5: Tributary inflows for medium duration, high peak excess B-MF floods Event Peak (ML/day) First 4 days (ML) Total inflow (ML) March 1999 386 48 1,984 December 1999 2,168 -3,943 -3,117 January 2001 1,187 -352 1,904 March 2002 -166 -1,512 -2,621 December 2003 6,670 -1,668 41,276
Using the above method the following was found regarding inflows from Ovens and Kiewa Rivers. Three of the five events had no significant increase in contribution of flow from Ovens River. The event in late December 1999 showed an increase in Ovens River inflow of up to nearly 3,000 ML/day and the event in December 2003 showed an increase of nearly 7,000 ML/day.
For the March 1999, December 2003 and March 2002 events there was no significant change of inflow from Kiewa River. For the events in late January 2001 and December 2003, the contribution from Kiewa River increased a peak 832 and 1,690 ML/day, respectively.
There is only minimal data but no trend is obvious from Table B.5 and the inflows from the tributary rivers seem to be highly variable particularly for the December 1999 event and to a lesser extent the January 2001 event. For the December 1999, January 2001 and December 2003 events there was a reasonable level of peak contribution from the rivers but for the first four days there was a decrease in contributing flow from these rivers at Tocumwal. This is due to the lag time between rainfall in these catchments
269 APPENDIX B and the flow reaching Tocumwal. It is difficult to draw any definitive conclusions regarding the extent that tributary inflows contribute to flooding of the B-MF, though the high peak contribution in December should be noted.
Flood prevention measures
As mentioned previously there are three options to try and prevent flooding of the B- MF, these are; • Yarrawonga Weir airspace; • River Murray airspace at Tocumwal; and • Edward River Escape airspace.
These three options will be listed individually below and then discussed together as a whole measure for flood prevention.
The Yarrawonga Weir airspace was investigated for the day prior to the start of the rain event, Table B.6. Note that once the airspace has been used it is no longer available.
Table B.6: Yarrawonga Weir airspace prior to medium duration, high peak excess B-MF floods Date Yarrawonga Weir airspace (ML) 17/03/1999 12,712 23/12/1999 6,054 23/01/2001 9,406 25/03/2002 12,107 17/12/2003 18,626
The River Murray airspace at Tocumwal was investigated for the day after the start of the rain event (this corresponds to the day prior to the rain event at Yarrawonga Weir due to the two day travel time between Yarrawonga Weir and Tocumwal for water in the River Murray), Table B.7. Note that the airspace is a daily volume, assuming the downstream demands remain constant throughout the rainfall event.
Table B.7: River Murray airspace at Tocumwal prior to medium duration, high peak excess B-MF floods Date River Murray airspace at Tocumwal (ML/day) 19/03/1999 3 25/12/1999 1,494 25/01/2001 0 27/03/2002 418 19/12/2003 0
270 APPENDIX B
Edward River Escape airspace was investigated for the day prior to the start of the rain event, Table B.8. Note that the airspace is a daily volume, assuming the downstream demands remain constant throughout the rainfall event.
Table B.8: Edward River Escape airspace prior to medium duration, high peak excess B-MF floods Date Edward River Escape airspace (ML/day) 17/03/1999 500 23/12/1999 1,445 23/01/2001 660 25/03/2002 0 17/12/2003 85
Table B.8 shows that the lowest Yarrawonga Weir airspace was just over 6,000 ML for the December 1999 flood event. Interestingly, this was the only event which had significant airspace (nearly 3,000 ML/day) to pass rejections around the Barmah Choke via the River Murray and Edward River Escape.
Yarrawonga Weir airspace for the December 2003 flood event was over 18,500 ML on the day prior to the commencement of the rain event. Though there was no airspace to pass water around the Barmah Choke in either the River Murray or Edward River Escape.
The other three events had between 9,000 and 13,000 ML of Yarrawonga Weir airspace on the day prior to the rain event. The January 2001 event had no River Murray airspace at Tocumwal and only 660 ML/day of Edward River Escape airspace. In the March 1999 event there was 3 ML/day of River Murray airspace at Tocumwal and 500 ML/day of Edward River Escape airspace. The March 2002 event had no Edward River Escape airspace and only 418 ML/day of River Murray airspace at Tocumwal.
It can be concluded that for only one of the medium duration, high peak excess B-MF floods was there significant airspace to pass water around the Barmah Choke without flooding the B-MF. In all cases there was a considerable Yarrawonga Weir airspace to store water. It is thought that all three flood prevention measures play a significant role in preventing flooding of the B-MF.
Seasonal water deliveries
As previously stated the seasonal water allocation may affect the volume of orders being placed throughout the season, which in turn affects the volume of UIO for the
271 APPENDIX B same percentage of UIO. For this reason the water deliveries in each of the seasons 1998/99 to 2003/04 was investigated to determine if it affected the years in which medium duration, high peak excess B-MF floods occurred.
It was found that during the 2002/03 season when there wasn’t a medium duration, high peak excess flood in the B-MF, the water delivered to farms across the MIA was 399,740 ML. The next two lowest water deliveries were in 2003/04 and 1999/00 with 658,608 and 675,155 ML, respectively. Both 2003/04 and 1999/00 had a medium duration, high peak excess flood in December. The other three seasons 1998/1999, 2000/01 and 2001/02 had deliveries of 1,167,755, 1,295,437 and 1,239,536 ML, respectively and the medium duration, high peak excess B-MF floods all occurred in either January or March.
The above supports the prediction that the seasonal water allocation affects flood events in the B-MF. This is most likely due to the fact that for the same percentage of UIO there is a much lower volume of UIO in a low water allocation season. Interestingly, the two seasons with medium water deliveries, flood events occurred in December when the volume of UIO required to induce a flood event is thought to be lower. The above fails to separate the effect of MIL’s UIO from those demands of other users. As during low allocation years it is also likely that the River Murray is not transporting as much water downstream of the Barmah Choke for other users and hence there is more capacity in the River Murray to pass water downstream of the Barmah Choke without exceeding the bank full capacity.
Monthly nett Mulwala Canal diversion
To determine if there was a link between nett Mulwala Canal diversion and medium duration, high peak excess B-MF floods. The average monthly nett Mulwala Canal diversion for the months of December to April for the seasons of 1999/00 to 2003/04 was investigated, Figure B-1.
Figure B-1 shows that the three months with the highest average nett Mulwala Canal diversion are January, March and December. These were also the only months to have recorded medium duration, high peak excess B-MF floods. The demand in December and January is primarily for rice and the demand in March is primarily for irrigation of winter irrigated pasture, lucerne/summer pasture and prewatering of cereal crops. This
272 APPENDIX B supports the prediction that flood events are more likely in the months with the highest irrigation demands.
6000
5000
4000
3000
2000
1000
Average Nett Mulwala Diversion (ML/day) Diversion Mulwala Nett Average 0 December January February March April Month
Figure B-1: Average Mulwala Canal diversion (ML/day) for season 1999/00 to 2003/04
B.2 Multiple Linear Regression Analysis
The three checks for model appropriateness with MLR are heteroscedasticity, multi- collinearity and autocorrelation; these are discussed in the following sections.
B.2.1 Heteroscedasticity
The OLS optimization method makes the assumption that the error term has a constant variance. This is referred to as homoscedastic data. If the data has a varying error term, it is referred to as heteroscedastic data. Heteroscedasticity is often a problem in time series or cross sectional data. If the data is found to be heteroscedastic, optimization techniques that do not involve the use of OLS should be used. In cases where it is not known whether the data is heteroscedastic, OLS regression is initially
∧ used. The residuals ( ei ) are then plotted against y i to determine whether non-constant variance is observed. The residual ( ei ) is given by Equation B.1.
∧ ei = yi − y i Equation B.1
To investigate whether heteroscedasticity is present, the residuals should be plotted against the predicted value or in a time series to identify curvature or heteroscedasticity. If heteroscedasticity or curvature is identified, plots of the residual against explanatory variables included in the model should be conducted. To avoid the possibility of other
273 APPENDIX B explanatory variables influencing the residuals, partial residual plots are required with a MLR model (Helsel and Hirsch 1992).
B.2.2 Multi-collinearity
In MLR it is important for the explanatory variables to be independent of each other. If one or more of the explanatory variables is closely related to another explanatory variable the result can be multi-collinearity. Some of the symptoms of multi- collinearity are that the regression coefficients may be unrealistic in sign or slope coefficients are unstable (Helsel and Hirsch 1992). Multi-collinearity can be tested for with the Variance Inflation Factor (VIF), ideally VIFj = 0. Serious problems are indicated if VIFj is greater than 10 (Helsel and Hirsch 1992).
B.2.3 Autocorrelation
Autocorrelation refers to the situation where the present residual ( ei ) is highly correlated with the previous residual ( ei−1 ). This is a common problem in a distributed lag model where the predictors of yt include xt±m , where m might take on some value besides zero. Autocorrelation can be tested by using the Durbin-Watson (D-W) statistic test, specifically with lag 1 autocorrelation. The D-W statistic test, tests the null hypothesis of no first-order autocorrelation against the alternative hypothesis of positive first-order autocorrelation.
The D-W statistic refers to an upper (du) and lower (dl) limit for the significance limits of the computed d, this is available in many texts. The following exists with respect to the D-W statistic.
If d > du then do not reject the hypothesis that first order autocorrelation is
present.
If d < dl then reject the hypothesis that first order autocorrelation is present.
If du >d > dl then the test is inconclusive.
B.3 Single Regression Analysis of UIO
Initially a link between UIO and rainfall was investigated. The link has been discussed (Holmes 1974; Bren, O'Neill et al. 1987; Maunsell Pty Ltd 1992) but never quantified.
274 APPENDIX B
This section seeks to establish a link between rainfall and UIO. To do this, firstly an individual parameter investigation is undertaken and then a single regression analysis is undertaken. Firstly it is necessary to explain single regression analysis.
B.3.1 Description of single regression analysis
One of the most widely used methods to initially test for a correlation between two continuous variables is to use a scatter plot, plotting one variable against the other. To measure the strength of the association between the two variables, the correlation coefficient is used. The correlation coefficient varies between -1 and 1, with the closer the magnitude of the correlation coefficient being to 1 the higher the correlation between the two variables. A positive correlation coefficient indicates that an increase in one variable results in an increase in the other variable, while a negative correlation coefficient indicates that the increase in one variable results in the decrease in the other variable (Helsel and Hirsch 1992). A correlation coefficient close to 0 indicates that the two variables are not associated and the change in one variable is caused by a strong association to another variable(s) or is a result of chance.
The most commonly used correlation coefficient is Pearson’s r. This technique is used to determine the strength of the linear relationship between two variables. Pearson’s r assumes that the data follows a bivariate normal distribution (Helsel and Hirsch 1992). Other non-linear correlation coefficients are Kendall’s Tau and Spearman’s Rho both of these methods are more resilient to outliers than Pearson’s r.
B.3.2 Single variable investigation of UIO
The individual parameter investigation for UIO in the study area involved initially plotting (in a time series) daily ETo, daily moisture deficit (rainfall - ETo) and daily rainfall along with daily UIO to try and determine if a trend existed between weather events and high volumes of UIO. The benefit of time series charts over scatter plots are that they allow checks for trends to be made that may have a time lag.
The first parameter plotted against daily UIO was daily ETo. Daily ETo was chosen first as it provides information across all types of weather events. Figure B-2 shows a plot of daily ETo and daily UIO for December 2000 to April 2001.
275 APPENDIX B
Unutilised irrigation orders Reference ET 14.0 5000 12.0 4000 10.0 3000 8.0
2000 6.0 (ML/day) 1000 4.0 Reference ET Reference (mm) 0
Unutilised irrigation orders irrigation orders Unutilised 2.0
-1000 0.0 2/9/2001 3/9/2001 4/6/2001 12/1/2000 1/12/2001 1/26/2001 2/23/2001 3/23/2001 4/20/2001 12/15/2000 12/29/2000 Date
Figure B-2: Daily UIO and daily ETo for December 2000 to April 2001
Figure B-2 shows that significant (greater than the average daily variation) drops in ETo usually correspond to increases in UIO. There appears to be variations (between -400 and 1,600 ML/day) in UIO independent of ETo. Note negative UIO are created when nett Mulwala Canal diversions are greater than the orders, this occurs at times when there is a sudden increase in temperature and water is available in the River Murray for MIL to divert without affecting downstream orders.
It appears that there is a time lag (2 to 3 days) between drops in ETo and increases in daily UIO. It also appears that a drop in ETo lasts for only a few days where as the increase in UIO appears to last for a longer period, around a week or more in some cases. Due to the high daily variability of ETo, it is difficult to determine the difference between a dry cool change and a cool change with rainfall. For this reason daily moisture deficit (ETo – rainfall) was plotted along with daily UIO for December 2000 to April 2001, Figure B-3.
Figure B-3 shows that troughs in moisture deficit correspond to spikes in UIO. The majority of negative moisture deficit values (rainfall - ETo) correspond to UIO of greater than 2,000 ML/day. Moisture deficit provides a better means of establishing a possible link between high volumes of UIO and weather events. There does not appear to be a direct correlation between moisture deficit and the volume of UIO. This is particularly evident in the period from January 20th to February 20th where there are 4 troughs in moisture deficit that indicate 4 rainfall events of various sizes. The first moisture deficit trough (to -5) induces an increase in UIO to approximately 4,500
276 APPENDIX B
ML/day, while the second trough (to -17) only translates to an increase in UIO to approximately 1,800 ML/day. The third trough induces no increase in UIO, perhaps because it occurs shortly after the second trough, while the fourth trough (to -1) induces an increase in UIO to approximately 2,500 ML/day. It appears that a significant increase in UIO is more likely after a period of sustained dry weather.
Unutilised Irrigation Orders Moisture Deficit 5000 15
4000 10 5 3000 0 2000 -5 1000 -10 Moisture Deficit (mm) 0 -15
Unutilised irrigation orders (ML/day) irrigation Unutilised -1000 -20 2/9/2001 3/9/2001 4/6/2001 12/1/2000 1/12/2001 1/26/2001 2/23/2001 3/23/2001 4/20/2001 12/15/2000 12/29/2000 Date Figure B-3: Daily UIO and daily moisture deficit for December 2000 to April 2001
From Figure B-3 the association between weather events and high levels of UIO appears to be closely linked to rainfall. For this reason rainfall alone was investigated to determine if a correlation existed between it and high levels of UIO. The time series plot of daily UIO and daily rainfall for December 2000 to April 2001 (Figure B-4) showed a correlation between large volumes of UIO and the occurrence of rainfall.
5000 25.0
4000 20.0
3000 15.0 2000 10.0
1000 Rainfall (mm)
5.0 0 Unutilised irrigation orders (ML/day) orders irrigation Unutilised -1000 0.0 2/9/2001 3/9/2001 4/6/2001 12/1/2000 1/12/2001 1/26/2001 2/23/2001 3/23/2001 4/20/2001 12/15/2000 12/29/2000 Date Figure B-4: Daily UIO and daily rainfall for December 2000 to April 2001
277 APPENDIX B
Figure B-4 shows that there is considerable variation in the daily UIO (up to 1,600 ML/day) independent of rainfall. There does not appear to be a trend between daily rainfall volume and the volume of UIO, as the one day with rainfall greater than 20mm only induced 1,800 ML of UIO where as four other rainfall events with daily rainfall volumes up to 10mm induce larger volumes of UIO. From the time series analysis a possible trend was identified between rainfall and UIO. To determine the extent of this trend scatter plots were used along with filtering of the rainfall and UIO data.
B.3.3 Single regression analysis of UIO
The first single regression analysis between rainfall and UIO was using daily rainfall (day t) and the four day sum of UIO between day (t) and day (t+4), as rainfall can affect orders that will arrive up to four days after the rain fell, Figure B-5. The time period of the data used for all scatter plots in this section was the months of December to April for the seasons 1999/00 to 2003/04.
20000
R2 = 0.2029
15000
10000 orders (ML/day)
5000
Unutilised irrigation Unutilised 0
-5000 0 5 10 15 20 25 30 35 Rainfall (mm/day)
Figure B-5: Scatter plot of the 4 day sum of UIO and daily rainfall for December to April for seasons 1999/00 to 2003/04
Figure B-5 shows a small correlation (R2 = 0.20). Interestingly the volume of the four day sum of UIO varies between -2,000 and 15,000 ML independent of rainfall. There is not a direct relationship between the daily rainfall value and the volume of UIO. This is thought to be because of the other parameters affecting the volume of orders. Due to the variability of order volumes between seasons and at different times within
278 APPENDIX B seasons, an investigation was carried out using the percentage of orders that were UIO, Figure B-6. Again the 4 day sum of orders and 4 day sum of UIO was used.
120% R2 = 0.2147 100%
80%
60%
40%
20%
Orders (%) 0% 0 5 10 15 20 25 30 35 -20%
-40%
-60% Advanced Orders that are Unutilised Irrigation
-80% Rainfall (mm/day) Figure B-6: Scatter plot of the percentage of orders that are UIO and daily rainfall for December to April for seasons 1999/00 to 2003/04
Figure B-6 produced a correlation of R2 = 0.21 which is very similar to Figure B-5. Figure B-6 shows that the percent of orders that are unutilised is between -70% and 80% independent of rainfall. An interesting and unexpected result was that even when greater than 10mm of rainfall occurred on a particular day the percentage of orders being UIO varied between 20 and 90%. Though if the result from Figure B-6 is considered which indicated that rainfall in the previous two weeks can affect the volume of UIO that will result from a rainfall event, then this is not an unexpected outcome.
The possibility of recent rainfall affecting UIO was further investigated by filtering the data such that the analysis only captured the four day sum of UIO for days when there was greater than 1mm of rainfall and there had been no significant rainfall (<1mm) on any of the seven previous days, Figure B-7.
Figure B-7 shows an even poorer correlation than the previous two plots. The negative volumes of UIO for rainfall events between 1 and 3mm, was an unexpected result. The variation in the 4 day sum of UIO between -1,000 to 12,000 ML is possibility because this filter selects the first day of a rainfall event which may only have a small rainfall
279 APPENDIX B volume with the majority of rainfall falling on the second or third day of the rain event which is not included in this filtered data.
20000
15000
R2 = 0.1408
10000 (ML) 5000
0 0 5 10 15 20 25 Four day sum of Untulised Irrigation Orders
-5000 Rainfall (mm/day)
Figure B-7: Scatter plot of the 4 day sum of UIO and daily rainfall for December to April for seasons 1999/00 to 2003/04, for no significant rainfall 7 days prior to rainfall
Due to no significant correlation in Figure B-7 the four day sum was converted to a percentage of the orders, Figure B-8.
100%
80% R2 = 0.314
60%
40% Orders (%) Orders
20%
0% 0 5 10 15 20 25 Advanced Orders that are Unutilised Irrigation Irrigation Unutilised are that Orders Advanced -20% Rainfall (mm/day)
Figure B-8: Scatter plot of the percentage of orders that are UIO and daily rainfall for December to April for seasons 1999/00 to 2003/04, for no significant rainfall 7 days prior to rainfall
Figure B-8 shows the best correlation of the analyses so far with, R2 = 0.31. A closer inspection of Figure B-8 shows that rainfall of between 1 and 5mm resulted in between -14% and 63% of the four day sum of orders being UIO. Only rainfall of greater than 15mm on a day resulted in a clear increase in UIO from 50% up to 90%. The two
280 APPENDIX B events captured with greater than 20mm on one day support the survey results with at least 50% of the orders being rejected.
To try and better capture the size of the rainfall event, the previously identified days for Figure B-7 and Figure B-8 were revisited and the size of the rainfall event determined by identifying the number of consecutive days on which rain fell and summing the rainfall for this period. The sum of UIO was determined for the period from the first day with rainfall until three days after the last day on which rain fell for that rain event, Figure B-9.
25000
R2 = 0.3575 20000
15000
10000
5000
0
Sum of Untulised Irrigation Orders (ML) Orders Irrigation Untulised of Sum 0 10203040506070
-5000 Rainfall (mm)
Figure B-9: Scatter plot of the sum of UIO and depth of the rain event for December to April for seasons 1999/00 to 2003/04, with no significant rainfall 7 days prior to rain event
Figure B-9 shows a correlation of R2 = 0.36. Again as the actual volume of orders prior to the rain event dictates the maximum sum of UIO, this is thought to explain some of the considerable variation in the volume of UIO during the rain event. A plot using the percentage of orders that were UIO is shown in Figure B-10.
Figure B-10 provides a reasonable correlation of R2 = 0.46. Interestingly of the 12 rain events that were greater than 20mm, only 5 had greater than a 50% rejection of orders, 4 others were in the 40 to 50% range. As approximately 80% of irrigators responded that given certain circumstances (rainfall greater than 20mm the most common response) they would reject more than 50% of their order, which corresponds to at least 40% of the total orders. The above plot seems to support this finding with 9 out of 12 rainfall events with greater than 20mm resulting in a rejection of greater than 50% of the order. There were only 5 events in the 10 to 20mm range and the unutilised
281 APPENDIX B percentage for these was between 15 and 39%. If this is compared to the 36% of irrigators who said that they would reject 50% of their order (which equates to a total rejection in excess of 18%) if a cool change with greater than 10mm occurred, then there is a reasonable level of correlation between the two data sets.
80% R2 = 0.4643 70%
60%
50%
40%
30%
Orders (%) Orders 20%
10%
0% 0 10203040506070 -10% Advanced Orders that are Unutilised Irrigation Irrigation Unutilised are that Orders Advanced -20% Rainfall (mm)
Figure B-10: Scatter plot of the percentage of orders that are UIO and depth of the rain event for December to April for seasons 1999/00 to 2003/04, with no significant rainfall 7 days prior to the rain event
From the above it can be concluded that there is some degree of correlation between rainfall events and the percentage of orders that are unutilised. Figure B-10 provided the best correlation (R2 = 0.46) between rainfall and unutilised percentage of orders. This was found when only rainfall events after at least 7 dry days were analysed. Even in Figure B-11 there was a high degree of variability in the percent of orders that were UIO.
One finding from the above analysis was that the results from the irrigator survey largely correlated with the results from this analysis. This analysis found that if a rain event of greater than 20mm occurs then at least 40% of the total orders would be unutilised, this correlated with the survey results of at least 40% of the total orders being rejected. If a rain event in the 10 to 20mm range occurred then between 15 and 39% of orders would be unutilised which correlated reasonably well with the results from the survey which found that at least 18% of orders would be rejected if a cool change with 10mm of rain occurred.
282 APPENDIX B
6000 R2 = 0.4224 5000
4000
3000
2000
1000
0 0 10203040506070 Unutilised Irrigation Orders (ML/day) -1000
-2000 Sum of rainfall (t-4 to t) in mm
Figure B-11: Scatter plot of the daily UIO and the sum of rainfall for day (t-4 to t) for December to April for seasons 1999 to 2004
The above involves investigating the effect of a single day’s rainfall on all of the orders currently traveling in the River Murray. The reverse approach was taken such that the rainfall on any of the four days travel time (day t - 4 to day t) was investigated to determine its impact on UIO on day t. Again the months of December to April for seasons 1999/00 to 2003/04 were used, Figure B-12.
Figure B-12 shows a correlation of R2 = 0.42, with some degree of correlation between UIO and rainfall during their travel time. There is still a significant level of variance between points. Again to test if some of this variance was due to the variability of order volumes between seasons and at different times within seasons, an investigation was carried out using the percentage of orders that were UIO, Figure B-12.
Figure B-12 produced a correlation (R2 = 0.40) very similar to Figure B-11 (R2 = 0.42). Rainfall only explains a small percentage of the total variability but a general trend existing between rainfall during the travel time of orders and the percentage of these being unutilised. Filtering of the data to capture events that occurred after a period of dry weather failed to produce any improvement in the results.
283 APPENDIX B
150.00% R2 = 0.3963
100.00%
50.00%
0.00% 0 10203040506070
-50.00% Irrigation Orders (ML/day)
-100.00% Percent of Advanced Orders that are Unutilised -150.00% Sum of rainfall (t-4 to t) in mm
Figure B-12: Scatter plot of the daily percentage of orders that were UIO and the sum of rainfall for day (t-4 to t) for December to April for seasons 1999 to 2004
B.4 Model Adequacy Checks for MLR Anaylsis UIO Model
B.4.1 Time series plots
Figure B-13 and Figure B-14 show the time series plot of the residuals for the two seasons used for calibration (1999/00 and season 2000/01). These were the two seasons used for calibration of the model. From the two figures there is no trend in the residuals with time.
1500
1000
500
0 10/1/1999 11/20/1999 1/9/2000 2/28/2000 4/18/2000
Residual (ML/day) Residual -500
-1000
-1500 Date
Figure B-13: Time series residual plot for season 1999/00
284 APPENDIX B
2500
2000
1500
1000
500
0 10/1/2000 11/20/2000 1/9/2001 2/28/2001 4/19/2001 Residual (ML/day) Residual -500
-1000
-1500
-2000 Date
Figure B-14: Time series residual plot for season 2000/01
The two seasons (2001/02 and 2003/04) used for validation were then checked for trends. These are shown in Figure B-15 and Figure B-16.
1500
1000
500
0 10/1/2001 11/20/2001 1/9/2002 2/28/2002 4/19/2002
-500
-1000 Residual (ML/day) Residual
-1500
-2000
-2500 Date
Figure A-15: Time series residual plot for season 2001/02
285 APPENDIX B
1000
500
0 10/1/2003 11/20/2003 1/9/2004 2/28/2004 4/18/2004
-500
Residual (ML/day) Residual -1000
-1500
-2000 Date
Figure B-16: Time series residual plot for season 2003/04
A.6.2 Partial residual plots
The second check for heteroscedasticity was the partial residual plots. These were checked for all three variables; rainfall in Figure B-17, orders in Figure B-18 and UIO(t-1) in Figure B-19. These three figures show that the partial residual is linear, indicating that there is no heteroscedasticity present.
50.00
40.00 R2 = 1
30.00
20.00
10.00 Rainfall (mm)
0.00 -1000 -500 0 500 1000 1500 2000
-10.00
-20.00 Partial residual (ML/day)
Figure B-17: Partial residual plot for rainfall for season 2000/01
286 APPENDIX B
5000.0
4000.0
3000.0
2000.0
1000.0
0.0 -400 -300 -200 -100 0 100 200 300 400 -1000.0
-2000.0 Advanced Order (t-4) (ML/day) (t-4) Order Advanced -3000.0
-4000.0 R2 = 1 -5000.0 Partial residual (ML/day)
Figure B-18: Partial residual plot for order (t-4) for season 2000/01
3000.0
2000.0
1000.0
0.0 -2000 -1000 0 1000 2000 3000 4000
-1000.0
-2000.0 UIO (t-1) (ML/day) UIO
-3000.0 R2 = 1 -4000.0
-5000.0 Partial residual (ML/day)
Figure B-19: Partial residual plot for UIO(t-1) for season 2000/01
287
Appendix C
C.1 OASIS Operating Manual
The following is the OASIS operating manual used for this research. It was obtained from Nicolas Roost. Some sections of the document were incomplete. At present there is no official publication of an operating manual for OASIS, hence the following document has been included to assist the reader with the operation of OASIS.
288 APPENDIX C
OASIS (Options AnalysiS in Irrigation Systems) Basic User Guide
1 Model Overview OASIS is a planning model for medium to large-scale canal irrigation systems. It was designed to capture the impacts of a range of structural (hardware) and managerial (software) interventions on water use, depletion and productivity in irrigated agriculture. OASIS is based on water balance and includes a strong management component. It is primarily innovative because it: 1. captures irrigation return flows – the model includes a drainage and a groundwater components; 2. integrates recycling of water (through conjunctive groundwater use and drainage water reuse); 3. also factors in the influence of non-irrigated areas such as natural vegetation and fallow lands. OASIS was built with the flexibility required to accommodate a diversity of real-world conditions. It can for example be used to analyse strategic options in systems with or without conjunctive use, surface storage, rice, rainfed areas, etc. 1.1 Key Model Components In OASIS, an irrigation system is made up of three key components (Figure 1): Description Characteristics inflow and outflow capacity conveyance efficiency min. inflow rate for allocation main elements of the inflow hydrograph (head segments irrigation/drainage network segments) / outflow requirements (tail segments); side flows dead storage max. storage reservoirs V-H relationship management (based on target release rates or storage ranges) soils and land use distribution, drainage and GW systems basic spatial subdivisions of the local water sources Irrigation Units (IUs) system water management (irrigation/delivery scheduling, conjunctive use,…)
289 APPENDIX C
Segment (irrigation)
Supply link
Irrigation Unit (IU) 7 Drainage link Reservoir
8 FQ
Segment (drainage)
Figure 1: Sample system layout (fragment of a larger system with 2 reservoirs and 14 IUs), also showing linkages between segments and units.
Each IU includes the following elements: Description Characteristics landscape areas with specific 1) soils and land use; 2) surface and GW zones systems; and 3) sources, uses and internal flow paths of water. Each IU can be subdivided into 1-5 zones1 land use comprised of 3 types of crop rotations associated to a% areas: irrigated, rainfed (incl. natural of each type of area soils and land use (by zone) vegetation) and bare soil (permanent soil types associated to a% of fallow) each crop rotation area the drainage system collects rainfall runoff and irrigation return flows; it has a certain storage capacity and interacts distribution, drainage and GW with the GW the GW system captures systems (by zone) seepage and percolation fluxes; it is of uniform depth within each zone/unit. GW flows between adjacent zones/units can be simulated dead storage farm-level storage is made up of a max. storage number of small reservoirs (ponds) V-H relationship that generally supply individual management based on target farm and distribution-level farms; distribution-level storage storage ranges storage (by zone) comprises 1 or more reservoirs, the note: physical characteristics and command area of which covers a management are defined for a significant portion of the zone/unit lumped storage (obtained by area aggregating individual reservoirs at each level) fields elementary, 1-D land use units used soil profile
1 More than 1 zone will be considered within a IU only when areas can be identified that show strong contrasts in: 1) topography and/or land use (e.g. large patch of forest); 2) groundwater conditions; 3) availability and distribution of local water sources (e.g. pond storage).
290 APPENDIX C
to represent the various crop combinations of soils, land use and irrigation method and irrigation management found in each management unit/zone; fields are not located in (irrigation/delivery schedule) space
At the lowest level, each field is made up of: Description Characteristics multi-layered different possible levels of description (e.g. soil soil profile moisture at wilting point, field capacity and saturation vs. soil hydraulic functions) simple or more detailed (rice crop only) modeling of ET and yield response application efficiency irrigation method differentiated by soil type combination of delivery and demand parameters irrigation management specific scheduling conditions for each water source 1.2 Key Model Processes The model processes are best described with reference to the 3 aforementioned levels (system, IU and field).
System level
At the system level, available water supplies – including water released or diverted at the head of the system, reservoir storage and return flows into segments from the previous time step – are allocated to satisfy the water requirements of IUs2. Outflow requirements (e.g. for a downstream wetland) can also be taken into account. The water allocation process, based on linear programming, considers the physical constraints of segments as well as the management rules of reservoirs. In conditions of scarce supplies, priorities – assigned to IUs and downstream requirements – are used to control how deficits are shared. The process is associated with a time step of typically 5-10 days to eliminate the need to model conveyance time lags (the model focuses on the volumes conveyed and allocated on each time step rather than the daily flowrates in the canal system). Important note about time steps: The water allocation time step defines the basic simulation time step of the model. As OASIS assigns a constant number of time steps to each month, the last time step of each month is of variable length (e.g. 8-11 days in the case of a 10-day time step).
IU level
2 In OASIS, IU requirements are defined as fixed targets or calculated based on the delivery schedules selected within IUs.
291 APPENDIX C
The main output of the system-level water allocation process is a supply flow (assumed constant throughout a time step) to each IU. Within IUs, the main system supply is disaggregated, on a daily basis, based on the selected crop irrigation/delivery schedules. When supply is scarce, priorities are used to share deficits among crops. OASIS considers, in addition to canal supply, 4 local sources of water: 1) groundwater; 2) surface storage in drainage canals; 3) surface storage in distribution-level reservoirs3; and 4) surface storage in farm-level reservoirs. Daily withdrawal and allocation of water from these local sources is based on crop irrigation schedules and actual main system supplies. A set of management parameters are also taken into account to control in what order and to what extent local water sources are used. On a daily basis, the model simulates the soil water balance of each field and the storage variation in the surface reservoirs and the shallow aquifer of each IU (or zone).
Field level
Fields are the place where the soil-crop-water interactions are simulated. Soil water balance provides the basis for assessing crop ET and yields, as well as groundwater recharge. For the specific case of rice, response functions – based on the outputs of a physiological model such as ORYZA2000 – can be included to simulate ET and yield more realistically. 1.3 Data requirements
2 Using the Model The OASIS application was structured around the concepts of project and simulation. A project stores a set of input files defining a specific study area. Each simulation associated with a project introduces a number of variations in the input data (e.g. to represent alternative management options) or the model settings (e.g. the year taken into account for climate data). The application’s Graphical User Interface (GUI) was designed to provide a simple platform for managing projects and simulations. It gives access to all the input and output data of the model. This section provides short guidance on the basic workings of the OASIS application. 2.1 Graphical User Interface (GUI)
Main window
File menu: offers commands for creating a new project, opening an existing project or saving the current one Edit button: opens the Project window for editing the active project
3 Distribution-level reservoirs are typically connected to secondary or tertiary canals and have a command area that covers a significant portion of the IU area.
292 APPENDIX C
Project tree: allows the user to navigate in the input files of the active project; a double-click will open a file for editing New Simulation button: opens the Simulation window for creating a new simulation Active Simulations list: shows all the active simulations of the current project; a double-click on a simulation item will open the Simulation window (with the options defined for that specific simulation); a selected simulation item can be deleted by pressing the DEL key. Display Outputs button: displays the output data for all the active and complete simulations
Project window
Project Data Directory tree: allows the user to select the project directory (the location where the data file structure required by the model can be found) Project Files list: allows the user to select the project files; a pick list displays the files available in the selected directory Important note about input files: Files such as those storing the climate data, the inflow hydrograph of head segments and the operating rules of main reservoirs do not need to be explicitly associated with the project. These files – all with an extension different from .dbf – are ‘implicitly’ associated with the project by being placed in specific sub-directories of the project data directory.
Simulation window
Definition of simulation options: Description Meteo. Data Year year of climate data taken into account for simulations year of flow data taken into account for simulations (applies to the Flow Data Year head inflow, external flow and outflow requirement files) Simulation Start first simulation time step (format:
rainfall exceedance probability taken into account for computing theoretical agronomic water requirements; value only used for the rainExceedProb specific supply option where allocation to IUs is based on agronomic targets reference ET exceedance probability taken into account for computing theoretical agronomic water requirements; value only EToExceedProb used for the specific supply option where allocation to IUs is based on agronomic targets maximum thickness (m) of a soil compartment4; value used by the maxSoilCompartThick model to divide defined soil profile layers into compartments indicates whether supply deficits are shared equally (TRUE) or not equitFieldAlloc (FALSE) among the fields of the same zone/IU that grow the same crop and that are to be irrigated at the same time (on the same day)
Run button: runs the current simulation (using the input data stored in the project files and the selected simulation options)
4 A compartment is a basic volume for soil water balance. It has uniform properties.
293 APPENDIX C
OK button: closes the window and adds the current simulation to the list of active simulations of the project; if a complete run was performed, the outputs of the current simulation will be available for display
Simulation log window
During the model development, a constant attention was given to the handling of exceptions caused by erroneous input data. The simulation log window gives detailed feedback on errors. It also produces information and warning messages that provide critical feedback on the input data and the processes simulated
Output window
The output window provides access to the time-series and seasonal outputs of all the active simulations for which a run was completed. It allows the user to graphically compare outputs for these simulations.
2.2 Data Preparation
2.3 Project and Configuration Files For running simulations, OASIS requires a few files in addition to those used to store input and output data. Although none has a .txt extension, they are all text files.
Project (.prj) file
A project file is used to store the information relevant to a specific project, including its name, data directory and input data files. Project files are created by the Save Project As command of the File menu on the main window.
Initialisation (.ini) file
The .ini file stores key information used by the OASIS application. [Model] section: the simulation time step can be modified by setting a new value to the TStepLength variable [FileDir] section: can be used to customize the specific directory structure required by the application; note: all paths are defined with reference to the data root directory (as defined for each project) [FileExt] section: can be used to customize the extensions assigned to the various file types handled by the model [Add] section: the defaultProj variable can be used to define a default project (.prj) file (which will automatically be opened when the application starts); note: the path must be defined with reference to the location of the application (.exe) file
294 APPENDIX C
‘options.sim’ file
This file needs to be found in the same directory as the application (.exe) file. It stores default simulation options which are displayed by the simulation window when a new simulation is created.
3 Guide to the Model Input and Output Data Running simulations with OASIS requires a fairly large number of input data, which are all stored in (dBase) tables. The aim of this section is to provide guidance on the various tables and the data they store. 3.1 Input Tables – Designation and Purpose
purpose SYSTEM Layout segments definition of the characteristics of segments definition of IUs, including their irrigated, rainfed and units bare soil areas; also definition of the number of zones each IU should be divided into reservoirs definition of the characteristics of reservoirs Flow Links segment-segment definition of (flow) connections among segments definition of (flow) connections between reservoirs res.-segment and segments supply definition of supply links (between segments and IUs) definition of seepage links (segment seepage into IUs; seepage reservoir seepage into IUs or segments) definition of drainage links (IU drainage into drainage segments or other IUs) Environment meteo. assignment of a meteorological station to each IU and stations reservoir assignment files storing the climate data of meteorological Meteo. Files stations Head Inflow files storing inflow hydrographs for head segments Files Ext. Flow files storing side flow hydrographs for segments
Files collecting flows from outside of the system Outflow Req. files storing outflow requirement hydrographs for tail
Files segments Water
Management selection of an option for simulating supply to IUs; selection of a priority criterion for sharing supply supply manag. deficits among IUs; also definition of a default priority score for requirements from IUs, reservoirs and downstream areas definition of supply targets for IUs; note: only supply targets required when the fixed targets supply option was selected definition of supply shares for IUs; note: only supply shares required when the fixed shares supply option was selected definition of supply priorities for requirements from supply IUs, reservoirs and downstream areas; note: only
priorities required when the fixed priority option was selected and default priorities are not sufficient Res. Manag. files defining the operational management of
Files reservoirs Crop Files files defining crop characteristics Soil Files files defining soil characteristics IRRIGATION
UNITS Layout
295 APPENDIX C
definition of how the various flows into each IU are ext.-zone partitioned among its zones definition of drainage connections among zones zone-zone within each IU Surface and
GW Systems definition of the characteristics (fraction of seepage distribution and operational spills) of the distribution system of each zone/IU definition of the physical characteristics (including surf. drainage storage and reuse capacity) of the (surface) drainage system of each zone/IU definition of the physical characteristics (including groundwater recession flow and pumping capacity) of the shallow aquifer of each zone/IU storage – definition of the physical characteristics of the
distr. distribution-level storage of each zone/IU definition of the physical characteristics of the storage – farm (lumped) farm-level storage of each zone/IU definition of the water flow paths within each zone/IU (e.g. fraction of the irrigation return flows hydrology captured by farm-level storage); also runoff coefficients for cropped and fallow conditions Land use definition of crop rotations (1-3 crops over a crop rotations maximum period of 1 year) definition of the irrigated land use of each zone/IU land use – (assignment of fractions of the nominal irrigated area irrig. of each zone/IU to crop rotations) definition of the rainfed land use of each zone/IU land use – (assignment of fractions of the nominal rainfed area rainfed of each zone/IU to crop rotations) rotations-soils definition of soil-crop associations Irrigation irrig. methods definition of the characteristics of irrigation methods irrig. definition of a theoretical application efficiency for
efficiencies each combination of irrigation method and soil type crop irrig. definition of crop-irrigation method associations methods Irrig. Sched. files defining irrigation/delivery schedules (to be
Files assigned to irrigated crop rotations) crop irrig. definition of crop rotations-irrigation schedules
manag. associations crop water definition of the access to local water sources
sources (differentiated by crop rotation) Water
Management crop supply definition of a priority criterion for sharing supply
manag. deficits among crops definition of a priority score for each crop type; note: crop priorities only required when the fixed priority option was selected water source definition of a priority order among local water
priorities sources storage definition of the operational management of manag. – distribution-level storage distr. storage definition of the operational management of farm-
manag. – farm level storage GW and definition of division groundwater levels for pumping
drain. manag. and drainage outflow control parameters MISCELLA-
NEOUSs definition of water requirements for non-agricultural non-ag. purposes (e.g. when municipal use of water is requirements significant within the modelled area) definition of statistical meteorological data (reference stat. meteo. ET and effective rainfall for different exceedance
data probability levels) for computing crop irrigation requirements Initial
Conditions reservoir definition of the initial volume of storage in each
storage main reservoir GW and local definition of the initial groundwater level and surface
296 APPENDIX C surf. storage storage (in drains and local reservoirs) in each zone/IU definition of the initial soil moisture conditions soil moisture (differentiated by crop rotation) for each zone/IU
297 APPENDIX C
3.2 Input Tables – Parameter Description
parameter unit description remark(s) SYSTEM
Layout value not used for segments (.dbf) length (m) segment length modelling used to discriminate indicates whether the segment belongs to a between irrigation and other drainage drainage stream (TRUE) or not (FALSE) types of inflows/outflows in the evaluation of indicators conveyance efficiency; 1-Ec_… defines the Ec_highQ, fraction of the water conveyed by the Ec_medQ, (-) segment that is lost as seepage for high, Ec_lowQ (see Figure 8.6) medium and low flowrates, respectively highQthres, (m3/s) high and low flowrate divisions5 lowQthres division inflow rate below which no allocThres (m3/s) allocation is possible slope of the allocation/inflow rate allocSlope (-) (see Figure 8.7) relationship maxInQ, (m3/s) inflow and outflow capacity of the segment maxOutQ value not used for units (.dbf) area (ha) total unit area modelling natural vegetation should be irrigA, rainA, (nominal) irrigated area, rainfed cultivation (ha) included in the rainfed bareA area and bare soil (permanent fallow) area cultivation area defines an initial approximation of the number of field objects to be created; this number is target-value for the area represented by each fieldA (ha) then adjusted based on the field object created in the unit different combinations of soils, crops and irrigation management to be represented nb_zones nb. of topographic/landscape zones in the 1 ≤ nb_zones ≤ 5
5 The flowrate compared to the division values is actually the average segment inflow and outflow rate.
298 APPENDIX C
unit reservoirs (.dbf) deadV (m3) dead (inactive) storage volume (see Figure 8.8) all storage above maxV is maximum storage volume (storage at maxV (m3) assumed to be evacuated emergency spillway level) (spilled) within 1 time step ‘aperture’ coefficient; also storage (m3) for V = shape_K * H shape_a shape_K a depth of 1 m where: shape_a ‘concavity’ coefficient V = storage (m3) H = water depth (m) seepRate (mm/day) average seepage rate Eres = evapCoeff * ETo
where: ratio of the reservoir evaporation rate to the evapCoeff (-) Eres = reservoir evaporation reference ET rate (mm/day) ETo = ref. ET rate (mm/day) set to 0 if not relevant (release then limited by the maxOutQ (m3/s) water release capacity conveyance capacity of the downstream segments) Flow Links downCS1_ID, segment-segment IDs of the segments (up to 4) immediately circular connections are not downCS2_ID, downstream of the current segment allowed (.dbf) … inCS1_ID, IDs of the segments (up to 4) immediately res.-segment (.dbf) inCS2_ID, upstream of the current reservoir … relCS1_ID, IDs of the segments (up to 4) collecting the relCS2_ID, reservoir water releases … can be one of the segments collecting releases; warning: ID of the (unique) segment collecting the conveyance capacity should spillCS_ID reservoir spills be large (assumption that excess storage is released within 1 time step) supply (.dbf) toUnit_ID ID of the supply-recipient unit maxQ (m3/s) capacity of the supply link use 1 for links to be used in priority to supply the unit; 2 priorLevel priority level (1, 2 or 3) of the supply link can only be used when there is at least another link with value 1; 3 can only be used
299 APPENDIX C
when there are other links with values 1 and 2 type of the seepage-producing object; CS seepage (.dbf) obj_type for segment and RES for reservoir IDorName ID of the segment or name of the reservoir toObj_type=CS is only possible when obj_type=RES (seepage type of the seepage-recipient object; IU for from segments can only be toObj_type unit and CS for segment intercepted by units; seepage from reservoirs can be intercepted by units and/or segments) toObj_ID ID of the seepage-recipient unit or segment fraction of the seepage produced in the seepF (-) segment or reservoir that is captured by the unit or segment type of the drainage-recipient object; IU for drainage (.dbf) toObj_type unit and CS for segment toObj_ID ID of the unit or segment fraction of the surface drainage flow surfF (-) produced in the current unit that is captured by the recipient object fraction of the superficial GW flow supGWF (-) produced in the current unit that is captured by the recipient object deep GW flows can only be fraction of the deep GW flow produced in captured by segments (non- deepGWF (-) the current unit that is captured by the zero value only allowed recipient object when toObj_type=CS) Environment meteo. stations type of object; IU for unit and RES for obj_type assignment (.dbf) reservoir enter 0 for any unit or -ALL- for any reservoir that is not IDorName ID of the unit or name of the reservoir otherwise specified in the table name of the meteorological station assigned stat_name to the current object naming convention: Meteo. File (.met)
dayOfYear chronological order ETo (mm) reference ET for the current day rain (mm) rainfall for the current day climate variables other than
300 APPENDIX C
ETo and rain are not read by the model Head Inflow File naming convention:
(.efl)
(.dbf) 312) supply targets targets apply to units only (.dbf) format:
301 APPENDIX C
type of object; IU for unit and CS for obj_type segment ID ID of the unit or segment format:
(.rem) Further descriptions p. 312) naming convention:
UNITS Layout data required only for the ext. - zones (.dbf) units containing more than 1 zone 1-N if N zones in the toZone zone no. current unit irrigF_min, min. and max. shares of the unit irrigation (-) irrigF_max supply captured by the current zone fraction of the unit seepage inflow captured seepF (-) by the current zone fraction of the unit surface drainage inflow surfDrainF (-) captured by the current zone
302 APPENDIX C
fraction of the unit sub-surface drainage GWdrainF (-) inflow captured by the current zone data required only for the zone - zone (.dbf) units containing more than 1 zone 1-N if N zones in the zone source zone no. current unit toZone destination zone no. fraction of the surface drainage flow surfF (-) produced in the first zone that is captured by the second fraction of the superficial GW flow supGWF (-) produced in the first zone that is captured by the second Surface and
GW systems distribution (.dbf) zone zone no. distribution efficiency; 1-Ed defines the fraction of the main system supply entering Ed (-) the distribution system that is lost as seepage can be used to reproduce conditions where irrigation fraction of the distributed water that spills to spillF (-) takes place on only a the drains portion of the time where water is delivered it is assumed that no distribution efficiency for supply from local Ed_LWS (-) operational spills occur for water sources supply from local sources drainage (.dbf) zone zone no. all values inmm are expressed with reference to buffer storage capacity in the drainage maxStor (mm) the total modelled area (sum system of the irrigated, rainfed and bare areas) reference level for simulating groundwater GW drainage occurs refLevel (m) drainage; 0 at soil surface and defined whenever the GW level in positively upward the zone is higher than refLevel. The drainage rate is given by:
GW_drain = 1000 * (GWZ– gamma (day) drainage resistance refLevel) / gamma
where: GW_drain = GW drainage
303 APPENDIX C
(mm/day) GWZ = GW level (m) pumping capacity associated with drainage maxPumRate (mm/day) water reuse maxOutRate (mm/day) drainage outflow capacity represents the time needed to reach the next timeLag (day) drainage outflow time lag downstream zone or the unit boundary groundwater zone zone no. (.dbf) Sy (-) specific yield of the aquifer empirical factor for simulating groundwater GW recession flow occurs Arec recession flow whenever the GW level in empirical factor for simulating groundwater the zone is higher than Brec recession flow refLevel. The flow rate is given by:
GW_rec = 1000 * Arec * reference level for simulating groundwater (GWZ – refLevel)^Brec refLevel recession flow; 0 at soil surface and defined positively upward where: GW_rec = GW recession flow (mm/day) GWZ = GW level (m) maxRecRate (mm/day) maximum possible recession flow rate used to capture the influence of a regional average rate of inflow from (+) / outflow to system (e.g. a semi- regionRate (mm/day) (-) a regional aquifer system confining aquifer lying below the study area) on the local, shallow aquifer partition between shallow and deep flows; when not fraction of the recession flow that enters a deepFlowF (-) captured by segments, deep deeper aquifer system flows are accounted for as system outflow represents the time needed time lag of the shallow component of the to reach the next timeLag_s (day) recession flow downstream zone or the unit boundary represents the time needed time lag of the deep component of the to reach the segment timeLag_d (day) recession flow capturing the flow or the system boundary
304 APPENDIX C
maxPumRate (mm/day) groundwater pumping capacity physical characteristics must be defined for a single, lumped distribution-level storage – distr. reservoir; all values inmm
(.dbf) are expressed with reference to the total modelled area (sum of the irrigated, rainfed and bare areas zone zone no. fraction of the maximum storage capacity deadVF (-) that is inactive all storage above maxStor is maxStor (mm) maximum storage capacity assumed to be evacuated (spilled) within 1 day ‘aperture’ coefficient; also storage (mm) for V = shape_K * H shape_a shape_K a depth of 1 m where: shape_a ‘concavity’ coefficient V = storage (mm) H = water depth (m) seepRate (mm/day) average seepage rate Eres = evapCoeff * ETo
where: ratio of the reservoir evaporation rate to the evapCoeff Eres = reservoir evaporation reference ET rate (mm/day) ETo = ref. ET rate (mm/day) maxOutRate (mm/day) water release capacity physical characteristics must be defined for a single, lumped farm-level storage – farm reservoir; all values inmm
(.dbf) are expressed with reference to the total modelled area (sum of the irrigated, rainfed and bare areas zone zone no. fraction of the maximum storage capacity deadVF (-) that is inactive all storage above maxStor is maxStor (mm) maximum storage capacity assumed to be evacuated (spilled) within 1 day ‘aperture’ coefficient; also storage (mm) for V = shape_K * H shape_a shape_K a depth of 1 m
305 APPENDIX C
where: shape_a ‘concavity’ coefficient V = storage (mm) H = water depth (m) seepRate (mm/day) average seepage rate Eres = evapCoeff * ETo
where: ratio of the reservoir evaporation rate to the evapCoeff Eres = reservoir evaporation reference ET rate (mm/day) ETo = ref. ET rate (mm/day) maxOutRate (mm/day) water release capacity (see Figure 8.9 for an illustration of some of the hydrology (.dbf) parameters introduced below) zone zone no. fraction of the (surface) drainage flow from ups_FStorF (-) upstream zones/units that is intercepted by the farm reservoir fraction of the (surface) drainage flow from ups_DStorF (-) upstream zones/units that is intercepted by the distribution-level reservoir fraction of the rainfall runoff on non- rainfall runoff on the run_FStorF (-) irrigated areas that is intercepted by the irrigated area is accounted farm reservoir for as irrigation return flow fraction of the rainfall runoff on non- run_DStorF (-) irrigated areas that is intercepted by the distribution-level reservoir irrigation return flows include distribution spills and runoff and seepage (lowland rice) of fields in fraction of the irrigation return flows that the irrigated area; without topoLossF (-) cannot be recovered in the current zone pumps, only a fraction of these return flows will be recoverable locally, the rest becoming available to downstream zones/units fraction of the irrigation return flows IRF_FStorF (-) (locally recoverable part) that is intercepted by the farm reservoir fraction of the irrigation return flows IRF_DStorF (-) (locally recoverable part) that is intercepted by the distribution-level reservoir GW_FStorF (-) fraction of the groundwater drainage that is
306 APPENDIX C
intercepted by the farm reservoir fraction of the groundwater drainage that is GW_DStorF (-) intercepted by the distribution-level reservoir the same fraction of the fraction of the farm storage capacity that spills from the farm storInterF (-) lies within the distribution-level reservoir reservoir is assumed to be catchment captured by the distribution- level reservoir runoff coefficient for cropped land/natural runF_crop (-) vegetation runF_bare (-) runoff coefficient for bare soil Land use crop rotations each rotation can include 1- 3 crops and must cover a (.dbf) period no longer than 1 year a natural vegetation indicates whether the current rotation ‘rotation’ will obviously isNatVeget corresponds to natural vegetation (TRUE) or have a single ‘crop’ (e.g. not (FALSE) forest) crop1_name,…, name of the crops grown in the current
crop3_name rotation land use – irrig. rota_name name of the crop rotation (.dbf) fraction of the (nominal) irrigated area of the entries for each unit irrigAF (-) the current unit assigned to the current crop must sum to 1 rotation fraction of the area where the current crop prop_zone1, the fractions over all zones (-) rotation is grown found in each zone of the prop_zone2,… must sum to 1 current unit nil for bare soil (entry required to define how land use – rainfed permanent fallow areas are rota_name name of the crop rotation (.dbf) distributed among zones if the current unit contains more than 1 zone) fraction of the (nominal) rainfed cultivation the entries for each unit rainAF (-) area of the current unit assigned to the must sum to 1; no value current crop rotation needed for the nil rotation fraction of the area where the current crop prop_zone1, the fractions over all zones (-) rotation is grown found in each zone of the prop_zone2,… must sum to 1 current unit rotations-soils zone zone no. (.dbf) rota_name name of the crop rotation nil for bare soil (entry
307 APPENDIX C
required to define what soil types permanent fallow areas are associated with) soil_name name of the soil type fraction of the area assigned to the current no differentiation between rotaAF (-) crop rotation associated with the current soil irrigated and rainfed areas type in the current zone Irrigation irrig. methods bndHeight (mm) height of the field dikes for basins only (.dbf) buffer depth (below the top of the dikes) rainReserv (mm) for basins only maintained for capturing rainfall minApplic (mm) minimum possible application depth maxApplic (mm) maximum possible application depth average fraction of the water applied that must be 0 for basins runF (-) runs off the field (bndHeight > 0) irrig. efficiencies soil_name name of the soil type (.dbf) theoretical application efficiency, defining the application efficiency the maximum fraction of the applied concept is not applied for Ea (-) irrigation water that can be stored in the root puddled soils (Ea set to 1 by zone (the actual fraction depends on the soil the model) moisture deficit at time of application) crop irrig. crop_name name of the crop methods (.dbf) meth_name name of the irrigation method Irrig. Sched. File (see Further descriptions p.
(.sch) 312) crop irrig. manag. zone zone no. (.dbf) rota_name name of the crop rotation sched_name name of the irrigation/delivery schedule fraction of the area with the current crop rotation in the current zone of the current areaF (-) unit assigned to the current irrigation/delivery schedule crop water zone zone no. sources (.dbf) rota_name name of the crop rotation sched_name name of the irrigation/delivery schedule fraction of the area with the current crop areaF rotation in the current zone of the current unit
308 APPENDIX C
access to distribution-level reservoir (TRUE D_RES or FALSE) access to farm-level reservoir (TRUE or F_RES FALSE) DRAIN access to drainage water (TRUE or FALSE) GW access to groundwater (TRUE or FALSE) Water
Management crop supply priority criterion for sharing supply deficits management priorCrit among crops; FIXED for fixed priorities (.dbf) minimum fraction of the relative water set to 1 for a fully equitable equitF (-) supply (ratio of supply to demand) that must allocation among crops be satisfied for all crops crop priorities only required when the fixed
(.dbf) priority option was selected crop_name name of the crop priority score (0-100; 100 for the highest crops without an entry in the prior priority) of the requirements associated with table get a default score of the current crop 50 water source format:
309 APPENDIX C
requirement is expressed to refill the satisfied with water from reservoir the main supply system and when crop requirements are fully satisfied storage manag. – zone zone no. farm (.dbf) format:
NEOUS non-ag. format:
310 APPENDIX C
(typically 5-10 days) values are defined inmm effRain_10,… , effective rainfall for an exceedance (mm) over each time step effRain_90 probability of 10-90% (typically 5-10 days) Initial
Conditions reservoir storage name name of the reservoir (.dbf) fraction of the maximum storage that is VF (-) available at the start of a simulation GW and local surf. storage zone zone no. (.dbf) watertable depth in the current zone at the GWZ (m) start of a simulation fraction of the distribution-level storage DStorVF (-) capacity that is available at the start of a simulation in the current zone fraction of the farm-level storage capacity FStorVF (-) that is available at the start of a simulation in the current zone fraction of the drainage storage capacity that drainVF (-) is available at the start of a simulation in the current zone soil moisture zone zone no. (.dbf) nil for bare soil (entry required for the zones with rota_name name of the crop rotation some permanent fallow land) minimum fraction of the water available between either the saturated water content minDeplF (-) (for puddled soils) or the field capacity and the permanent wilting point that is depleted values assumed uniformly at the start of a simulation distributed between the maximum fraction of the water available defined min. and max. between either the saturated water content maxDeplF (-) (for puddled soils) or the field capacity and the permanent wilting point that is depleted at the start of a simulation minPondZ (mm) minimum initial ponded water depth applies to lowland rice only (puddled soil conditions); values assumed uniformly maxPondZ (mm) maximum initial ponded water depth distributed between the defined min. and max.
311 APPENDIX C
Further descriptions
supply manag. (.dbf) Allocation of main system supply to units can either be based on targets (associated with priorities) or shares. supOpt – defines the option for sharing supply among units FTarget: fixed targets (in m3/s) are read in input files FShare: fixed shares (-) are read in input files AREA: allocation (share-based) is proportional to the irrigated area AGRO: targets are calculated, which correspond to the defined land use and some theoretical agronomic water requirements (considering probabilistic values of reference ET and rainfall and efficiencies at different levels in the system) ROTA: targets are calculated based on the delivery schedules defined and assigned to the different crops priorCrit – defines the priority criterion for sharing supply deficits among units FIXED: fixed priorities are read in input files ECO: priorities are calculated based on the economic value of production in each unit STRESS: priorities are calculated based on the average level of water stress in each unit SENSIT: priorities are calculated based on a combination of the 2 above criteria dUnitPrior (0-100; 100 for highest priority) – default priority score for unit requirements dResPrior (0-100; 100 for highest priority) – default priority score for reservoir requirements dDownPrior (0-100; 100 for highest priority) – default priority score for requirements from downstream areas
Res. Manag. File (.rem) Reservoirs can be operated following either storage volume targets or release rate targets (reproduction of observed conditions). TStep – time step no. (format:
312 APPENDIX C
targetInQ – target rate of inflow (m3/s) to the reservoir for the current time step; only required when targetType='REL_RATE' lowLimitVF – lower limit (-) (defined as a fraction of the maximum storage) of the target storage range for the current time step; no water can be released below this limit; only required when targetType='VOL' upLimitVF – upper limit (-) (defined as a fraction of the maximum storage) of the target storage range for the current time step; storage above this limit is spilled; only required when targetType='VOL' reqThresVF – division storage (-) (defined as a fraction of the maximum storage) below which a requirement is expressed to refill the reservoir; only required when targetType='VOL' minRelQ – minimum release rate (m3/s) allowed for the current time step; only required when targetType='VOL' maxRelQ – maximum release rate (m3/s) allowed for the current time step; only required when targetType='VOL'
Irrig. Sched. File (.sch) In OASIS, one or more irrigation/delivery schedule is associated with each irrigated crop rotation. Irrigation/delivery schedules involve a combination of water delivery and water demand elements. Delivery parameters are used to control access, in timing and amount, to main system (canal) supplies6. Demand parameters control how available main system supplies (the part given access to) are actually used and when and how the available local water sources are solicited. fromTStep, toTStep – period identified by its first and last time step no. (format:
6 Other sources of supply (internal to IUs) are assumed flexible enough to be managed in a purely demand-oriented fashion (e.g. no formal timing restrictions)
313 APPENDIX C
dm_D_RES – division value of the selected demand criterion to solicit the distribution-level reservoir (after considering supply from the canal and provided there is access to this alternative source) dm_F_RES – division value of the selected demand criterion to solicit the farm-level reservoir (after considering supply from the canal and provided there is access to this alternative source) dm_drain – division value of the selected demand criterion to solicit the drainage storage (after considering supply from the canal and provided there is access to this alternative source) dm_GW – division value of the selected demand criterion to solicit the groundwater (after considering supply from the canal and provided there is access to this alternative source)
Crop File (.crp)
Soil File (.sol)
314 APPENDIX C
3.3 Output Data Most of the indicators evaluated by OASIS are based on water accounting. The following table introduces water accounting components at the field, IU and system levels.
field IU system inflow surface rainfall (rain) rainfall (rain) rainfall (rain) irrigation (irrig) main system supply (canal_in) irrigation inflow (irrig_in)7 upstream drainage (drain_in) upstream drainage (drain_in) groundwater uptake sub-surface groundwater inflow (GW_in) groundwater inflow (GW_in) (GW_uptake)8 storage surface storage change reservoir storage change reservoir storage change surface change (dStor_surf) (dStor_res) (dStor_res) drainage storage change drainage storage change
(dStor_drain) (dStor_drain) soil moisture storage change soil moisture storage change soil moisture storage change sub-surface (dStor_moist) (dStor_moist)9 (dStor_moist) groundwater storage change groundwater storage change
(dStor_GW) (dStor_GW) crop evapotranspiration crop evapotranspiration crop evapotranspiration depletion process (ET_crop) (ET_crop) (ET_crop) evapotranspiration from evapotranspiration from non- evapotranspiration from non- non-process non-crop vegetation crop vegetation (ET_non_crop) crop vegetation (ET_non_crop) (ET_non_crop)10 evaporation from bare soils and evaporation from bare soils and bare soil evaporation (E)11 reservoirs (E) reservoirs (E) non-agricultural water use non-agricultural water use outflow committed - (NA_use) (NA_use) irrigation outflow (irrig_out) uncommitted runoff (run) drainage outflow (drain_out) drainage outflow (drain_out) seepage (seep) groundwater outflow (GW_out) groundwater outflow (GW_out) deep percolation (perco) gross inflow rain + canal_in + drain_in + rain + irrig_in + drain_in + rain + irrig + GW_uptake (gross_in) GW_in GW_in gross_in – (dStor_res + gross_in – (dStor_res + net inflow gross_in – (dStor_surf + dStor_drain + dStor_moist + dStor_drain + dStor_moist + (net_in) dStor_moist) dStor_GW) dStor_GW) available net_in net_in – NA_use net_in – (NA_use + irrig_out) water (avail)
Field indicators
yield (kg/ha) water productivity: PW_ET (kg/m3) = prod / ET_crop, where prod is the crop production (kg) PW_I+R (kg/m3) = prod / (irrig + rain)
7 Inflow/outflow through an irrigation segment is taken as irrigation inflow/outflow; inflow/outflow through a drainage segment is taken as drainage inflow/outflow. 8 Includes capillary rise and direct uptake by roots. 9 Here, includes the surface storage on fields. 10 Concerns the fields associated with non-crop (natural) vegetation. 11 Concerns the permanent fallow fields or the temporary fallow periods for cultivated fields (no distinction is made between evaporation and transpiration during a cultivation period).
315 APPENDIX C
net percolation: netPerco (mm) = perco – CR, where CR stands for capillary rise average depth to the watertable during the crop growing season: avgGWZ (m)
IU indicators
depleted fractions of gross inflow and available water: DF_gross (-) = depl_tot / (gross_in – NA_use), where the total depletion depl_tot is calculated as depl_tot = E + ET_crop + ET_non_crop DF_avail (-) = depl_tot / avail process fraction of depleted water: PF_depl (-) = ET_crop / depl_tot gross value of production: GVP (1000 $) water productivity: PW_gross ($/m3) = GVP / (gross_in – NA_use) PW_avail ($/m3) = GVP / avail PW_depl ($/m3) = GVP / depl_tot reuse intensity: reuseInt (-) = LWS_tot / (LWS_tot + canal_ in), where LWS_tot represents the total amount of water used from local water sources drainage fraction: drainF (-) = (drain_out + GW_out) / (gross_in – NA_use)
System indicators
depleted fractions of gross inflow and available water: DF_gross (-) = depl_tot / (gross_in – NA_use), where the total depletion depl_tot is calculated as depl_tot = E + ET_crop + ET_non_crop DF_avail (-) = depl_tot / avail process fraction of depleted water: PF_depl (-) = ET_crop / depl_tot gross value of production: GVP (1000 $) water productivity: PW_gross ($/m3) = GVP / (gross_in – NA_use) PW_avail ($/m3) = GVP / avail PW_depl ($/m3) = GVP / depl_tot reuse intensity: reuseInt (-) = LWS_tot / (LWS_tot + irrig_ in – irrig_out), where LWS_tot represents the total amount of water used from local water sources drainage fraction: drainF (-) = (drain_out + GW_out) / (gross_in – irrig_out – NA_use)
Segment outputs
head inflow: Qin (m3/s)
316 APPENDIX C
side inflow (including external flow and return flows from units): Qin_add (m3/s) seepage: Qseep (m3/s) allocation: Qalloc (m3/s) outflow requirement (tail segments only): Qreq_out (m3/s) tail outflow: Qout (m3/s)
Reservoir outputs
catchment inflow (through the upstream segments): Vin (m3) direct rainfall (rain falling on the reservoir surface): Vrain (m3) evaporation loss: V_E (m3) seepage: Vseep (m3) spillage: Vspill (m3) release: Vrel (m3) storage: V (m3) water spread area: A (m2)
317 APPENDIX C
FIGURES
conveyance efficiency (%)
Ec_highQ Ec_medQ
Ec_lowQ
avg. segment flowrate (m3/s) lowQthres highQthres Figure 2: Conveyance effieicy of a segment over different intervals of flowrate; note: seepage (%) given by 1 – Ec (%)
segment allocation (m3/s)
conveyance and allocation domain of the segment total allocation capacity
allocSlope segment inflow rate (m3/s) allocThres maxInQ Figure 3: Typical conveyance and allocation domain of a segment
emergency maxV (m3) spillway level
active storage
deadV (m3) dead storage
Figure 4: Conceptualisation of a reservoir
318 APPENDIX C irrig. return flows (surf. flows and seepage from irrigated fields; distribution canals spills)
spill ftopo_loss 1-f topo_loss farm res.
ff_stor fstor_inter 1-fstor_inter withdrawal
fd_stor
distr. res. spill
1-(ff_stor+fd_stor) withdrawal
drain. stor. withdrawal
drainage outflow Figure 5: Water flow paths within a zone/IU; case of the irrigation return flows
319 APPENDIX C
FIGURES
conveyance efficiency (%)
Ec_highQ Ec_medQ
Ec_lowQ
avg. segment flowrate (m3/s) lowQthres highQthres Figure 6: Conveyance efficiency of a segment over different intervals of flowrate; note: seepage (%) given by 1 – Ec (%)
segment allocation (m3/s)
conveyance and allocation domain of the segment total allocation capacity
allocSlope segment inflow rate (m3/s) allocThres maxInQ Figure 7: Typical conveyance and allocation domain of a segment
emergency maxV (m3) spillway level
active storage
deadV (m3) dead storage
Figure 8: Conceptualisation of a reservoir
320 APPENDIX C irrig. return flows (surf. flows and seepage from irrigated fields; distribution canals spills)
spill ftopo_loss 1-f topo_loss farm res.
ff_stor fstor_inter 1-fstor_inter withdrawal
fd_stor
distr. res. spill
1-(ff_stor+fd_stor) withdrawal
drain. stor. withdrawal
drainage outflow Figure 9: Water flow paths within a zone/IU; case of the irrigation return flows
321 APPENDIX D Appendix D
D.1 MIL Irrigation Application Rates
Marshall (2004) gave the estimates of rice, winter irrigated pasture, lucerne/summer pasture and cereals water use in each season for the MIA (Table D.1).
Table D.1: Crop water uses estimated by MIL for the MIA Season 1999/00 2000/01 2001/02 2002/03 2003/04 Rice (ML) 380,000 770,000 650,000 20,000 240,000 Winter irrigated pasture (ML) 125,000 240,000 263,000 160,000 195,000 Lucerne/summer pasture (ML) 80,000 140,000 150,000 65,000 95,000 Irrigated cereals (ML) 25,000 100,000 135,000 125,000 80,000 Total (ML) 610,000 1,250,000 1,198,000 370,000 610,000
To convert the MIA crop water usage to a study area crop water usage the total crop water usage was multiplied by the ratio of study area water usage to total MIA water usage. The results are shown in Table D.2. Note this assumes an even distribution of each crop across the entire MIL supply area.
As an example the water usage of winter irrigated pasture in season 2000/01 is shown below.
240,000 Winter Irrigated Pasture (2000/01) = ×748,089 = 143,633ML 1,250,000
Table D.2: Crop water usage for the study area (ML) 1999/00 2000/01 2001/02 2002/03 2003/04 Rice (ML) 263,426 460,823 404,603 14,320 149,450 Winter Irrigated Pasture (ML) 86,653 143,633 163,709 114,561 121,428 Lucerne/summer pasture (ML) 55,458 83,786 93,370 46,540 59,157 Cereals (ML) 17,331 59,847 84,033 89,501 49,817 Total (ML) 422,868 748,089 745,715 264,922 379,853
The data from Table D.2 was used along with crop areas to determine the average volume of water applied to each crop across the study area. The crop areas for the 2000/01 season were obtained from GIS information (Table 5.4) (except for lucerne/summer pasture which was taken from the landholder survey information). The
322 APPENDIX D remaining crop areas (excluding rice; its area was taken from SPOT data for the study area) in the remaining seasons were obtained by using the percentage area of each crop from the landholder survey information (Table 2.1) and multiplying by the study area. The resulting ML/ha irrigation uses are shown in Table D.3.
Table D.3: Crop water usage Season 1999/00 2000/01 2001/02 2002/03 2003/04 Rice (ML/ha) 11.43 11.72 12.90 22.78 13.33 Winter irrigated pasture (ML/ha) 1.13 2.10 2.70 2.03 1.88 Lucerne/summer pasture (ML/ha) 2.29 5.19 7.71 11.53 14.79 Irrigated cereals (ML/ha) 0.69 2.92 2.31 2.06 1.20 Notes: 1. Lucerne/summer pasture calculated from landholder survey crop area as, lucerne/summer pasture is not represented in the GIS information.
Table D.3 shows higher than expected ML/ha water uses for rice particularly in season 2002/03 and also in 2001/02 and 2003/04. With the exception of 1999/00 the estimated application of irrigation is between 1.88 and 2.70 ML/ha for winter irrigated pasture and 1.20 and 2.92 ML/ha for irrigated cereals. The crop water usage for lucerne/summer pasture in seasons 2002/03 and 2003/04 are higher than expected. Lucerne/summer pasture has been found to have annual water usage of 1,200 to 1,250mm in the southern MDB (Humphreys, Edraki et al. 2003). The annual average rainfall in Finley is 390mm, so if 100% effective rainfall is assumed then the average irrigation application for lucerne/summer pasture to avoid water stress is around 800mm (8 ML/ha). Only one of the five irrigation applications for lucerne/summer pasture is around 8 ML/ha. From this it can be assumed there are significant inaccuracies in the landholder survey information regarding the area of lucerne/summer pasture and/or the estimation of water going to lucerne/summer pasture by MIL.
The problem with rice water usage (Table D.3) was investigated. It is considered that the assumption of even distribution of each crop across the MIL supply area may be causing the problem with rice water usage. For this reason the rice water usage was recalculated. This recalculation involved calculating the average ML/ha irrigation of rice for the whole MIA. This was completed by using the MIL estimate of irrigation water utilized by rice and dividing by the rice area from SPOT satellite data for the whole MIA, the results are presented in Table D.4.
323 APPENDIX D
Table D.4: Average water usage of rice Season 1999/00 2000/01 2001/02 2002/03 2003/04 Rice (ML/ha) 9.89 11.08 11.79 12.94 10.56
Table D.5 shows that the recalculation of rice water usage compares well with the estimates of rice water usage provided in literature for the area. For this reason Table D.4 was combined with the other crops water usages in Table D.2 to provide crop water usages for the study area, Table D.5.
Table D.5: Re-equated water use in the study area (ML) 1999/00 2000/01 2001/02 2002/03 2003/04 Rice (ML) 227,915 435,541 369,539 8,142 118,421 Winter irrigated pasture (ML) 86,653 143,633 163,709 114,561 121,428 Lucerne/summer pasture (ML) 55,458 83,786 93,370 46,540 59,157 Cereals (ML) 17,331 59,847 84,033 89,501 49,817 Total (ML) 387,357 722,808 710,651 258,744 348,824
D.2 Calibration of the OASIS Model
The results from calibration of the OASIS model are shown for the seasons 1999/00 (Figure D.1 and D.2), 2001/02 (Figure D.3 and D.4), 2002/03 (Figure D.5 and D.6) and 2003/04 (Figure D.7 and D.8) below.
60
50
R2 = 0.51
40 /s) 3
30
20 Simulated Flow (m
10
0 -10 0 10 20 30 40 50 Recorded Flow (m3/s)
Figure D.1: Simulated versus recorded irrigation inflow into the system for season 1999/00
324 APPENDIX D
60
Actual irrigation Model calibration 50
40 /s) 3 30 Flow (m Flow
20
10
0 9/1/1999 10/21/1999 12/10/1999 1/29/2000 3/19/2000 Date
Figure D.2: Calibrated model performance for season 1999/00
80 R2 = 0.79
70
60 /s) 3 50
40
Simulated Flow (m 30
20
10
0 0 1020304050607080 Recorded Flow (m3/s)
Figure D.3: Simulated versus recorded irrigation inflow into the system for season 2001/02
325 APPENDIX D
80 Actual irrigation Model calibration
70
60
50 /s) 3
40 Flow (m
30
20
10
0 8/1/2001 9/20/2001 11/9/2001 12/29/2001 2/17/2002 4/8/2002 Date
Figure D.4: Calibrated model performance for season 2001/02
70
60
50
R2 = 0.29 /s) 3 40
30 Simulated Flow (m
20
10
0 0 10203040506070 3 Recorded Flow (m /s) Figure D.5: Simulated versus recorded irrigation inflow into the system for season 2002/03
326 APPENDIX D
70 Actual irrigation Model calibration 60
50
40 /s) 3
Flow (m 30
20
10
0 8/1/2002 9/20/2002 11/9/2002 12/29/2002 2/17/2003 4/8/2003 Date
Figure D.6: Calibrated model performance for season 2002/03
50
45 R2 = 0.55 40
35 /s) 3 30
25
20 Simulated Flow (m 15
10
5
0 -10 0 10 20 30 40 50 Recorded Flow (m3/s)
Figure D.7: Simulated versus recorded irrigation inflow into the system for season 2003/04
327 APPENDIX D
50 Actual irrigation 45 Model calibration
40
35
30 /s) 3 25
Flow (m Flow 20
15
10
5
0 8/1/2003 9/20/2003 11/9/2003 12/29/2003 2/17/2004 4/7/2004 Date
Figure D.8: Calibrated model performance for season 2003/04
D.3 Rainfall Forecasts
Throughout the seasons (1999/00 to 2001/02 and 2003/04) of forecast rainfall data provided by the BoM there were 191 days with missing forecast data. As the rainfall forecast information is an input data set for the order matrix approach it was necessary to generate synthetic data to fill the missing dates. Prior to generating synthetic data it was necessary to investigate the accuracy of the rainfall forecasts. This was completed by using a single linear regression analysis to determine the level of correlation between forecast rainfall and actual rainfall for the days with forecast rainfall data. From this analysis it was found that the level of correlation (R2) between the 24, 48 and 72 hour forecasts and the actual recorded rainfall were: • 24hr R2 = 0.29; • 48hr R2 = 0.17; and • 72hr R2 = 0.12.
The above indicates that there is minimal correlation between the rainfall forecast and the actual rainfall. For this reason it was decided that the rainfall forecast data be randomly generated.
328 APPENDIX D
For this research the first-order Markov chain will be based on a historical analysis between the occurrence of actual rainfall and the probability that this was predicted by the rainfall forecasts. A gamma probability distribution will then be used to generate the rainfall volume for the rainfall forecast.
Even though the correlation between the forecast rainfall and the actual rainfall was poor, it was noted from an investigation of the scatter plots that when the actual rainfall was zero the forecast rainfall was generally small and often zero. For this reason it was considered necessary to separate the data points with zero actual rainfall recorded from the other data points. It was also considered necessary to separate small rainfall events from larger rainfall events, for this reason the data was broken into three categories based on the actual rainfall: • zero (no) rainfall; • less than 5mm of rainfall (small rainfall days); and • greater than or equal to 5mm of rainfall (large rainfall days).
The next step was to identify whether a ‘dry’ (no rainfall forecast) or ‘wet’ (rainfall forecast) day was forecast for each of the three classifications of actual rainfall events. Table D.6 shows the percent of time that dry days were forecast for each of the three classifications of rainfall events.
Table D.6: Percent of time dry day forecast for three classifications of rainfall events Actual Rainfall Event (mm) 24 hour 48 hour 72 hour 0 38.0% 37.4% 36.2% > 0 to < 5 14.8% 12.8% 12.8% ≥ 5 10.8% 4.6% 10.8%
Table D.6 shows that between 36.2% and 38% of the time that there was no rainfall recorded this was predicted by the 24 to 72 hour rainfall forecasts. It is interesting to note that when between 0 and 5mm of rainfall was recorded, no rainfall was predicted between 12.8% and 14.8% of the time from the rainfall forecasts and when greater than or equal to 5mm of rainfall was recorded no rainfall was forecast between 4.6% and 10.8% of the time.
As previously mentioned a gamma probability is used to determine the volume of rainfall predicted if a wet day is forecast (Bruhn and Fry 1980; Kuchar 2004).
329 APPENDIX D
A gamma probability distribution is used to determine the volume of rainfall if a wet day was generated with the first-order Markov chain.
The next step was to determine the shape and scale values for the non-zero rainfall forecast-values. Minitab was used to determine the shape and scale values that provided the best fit for the gamma distribution, Table D.7.
TableD.7: Gamma probability values 24 hour 48 hour 72 hour Rainfall (mm) Shape Scale Shape Scale Shape Scale 0 0.311 3.694 0.323 4.825 0.312 5.938 > 0 to < 5 0.522 6.318 0.528 7.495 0.460 10.81 ≥ 5 0.625 12.54 0.497 16.14 0.536 14.92
The generation of the synthetic rainfall forecast data was undertaken using the following steps: • determine the category of the actual rainfall (no rainfall, small rainfall event or large rainfall event); • generate a random number; • for the category of actual rainfall does the random number fall into the occurrence of rainfall category or the non-occurrence of rainfall category; • if there is an occurrence of rainfall then generate another random number; and • from the second random number and the gamma distribution of rainfall for the category of rainfall determine the forecast rainfall volume.
D.4 ET Forecast
As no forecast evaporation data is available to be used as an input into the model, it was necessary to generate synthetic evaporation forecast data. There were a number of options investigated these are: • use of recorded evaporation data, ie forecast (t + 1) = recorded (t + 1); • assume that current conditions will continue as the forecast, ie forecast (t + 1) = recorded (t); and
• use MLR analysis to determine if a correlation can be found between ETo, rainfall and rainfall forecast.
The first option was quickly discarded as using the recorded ETo as the forecast ETo would lead to a very unrealistic performance of the model. An investigation was
330 APPENDIX D undertaken into the second option and the following correlations were found between the current day’s ETo and the recorded ETo of one, two and three days advance. • 24hr R2 = 0.739; • 48hr R2 = 0.629; and • 72hr R2 = 0.592.
The major draw back with this method was that it did not use the forecast weather conditions. For this reason a MLR analysis was undertaken using the variables of the present day’s ETo, present day’s rainfall and rainfall forecast, these results provided slightly improved R2 values but the major advantage was in the inclusion of a forecast weather variable. The correlations between the current day’s ETo and the recorded ETo of one, two and three days advance were: • 24hr R2 = 0.745; • 48hr R2 = 0.640; and • 72hr R2 = 0.611.
331 APPENDIX E
Appendix E
E.1 Methods of Assessing Impacts on the B-MF
There have been two methods used to assess modelling scenarios to reduce unseasonal flooding of the B-MF, these are Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006). A comparison of these two methods is discussed below.
The work of both Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006) used the method described by Thoms et. al. (2000) (flow at Tocumwal of greater than 10,600 ML/day) to identify a flood event.
The first significant difference between the two approaches was in the separation time between identification of a separate flood event. Chong and Ladson (2003) used a separation time of one day to identify two separate flood events. Sinclair Knight Merz Pty Ltd (2006) required at least seven days between flood events. This is best represented with an example. If the flow at Tocumwal was 10,900, 10,500 and 10,700 ML/day on three consecutive days Chong and Ladson (2003) would identify this as two separate flood events while Sinclair Knight Merz Pty Ltd (2006) would identify this as only one flood event. The method of Sinclair Knight Merz Pty Ltd (2006), is considered to be superior to that used by Chong and Ladson (2003), because it allows time for the flood waters to subside before identifying another increase in the flow at Tocumwal of greater than 10,600 ML/day as a separate flood event.
The next difference between the methods of Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006) was the annual period used for the investigation. Chong and Ladson (2003) carried out an investigation to identify the months in which unseasonal flooding of the B-MF had increased due to regulation of the River Murray.
332 APPENDIX E
They identified the period between December and April (inclusive) in each irrigation season. Sinclair Knight Merz Pty Ltd (2006) used a qualitative method identifying the period of January to March as the optimum drying period for the wetland. They then selected the annual period from January to April (inclusive) for their investigation. Sinclair Knight Merz Pty Ltd (2006) argue that if a flood event occurs in December then the wetland still has the period from January to April to dry out.
With regards to modelling irrigation water use the approach taken by Sinclair Knight Merz Pty Ltd (2006) and Chong and Ladson (2003) was different. Chong and Ladson (2003) identified the period from December 1st 1980 to April 30th 2000 to have the current level of irrigation development and hence undertook their investigation on these 20 years of actual data. Sinclair Knight Merz Pty Ltd (2006) undertook their investigation on the 66 seasons from May 1st 1934 to April 30th 2000, using the MDBC models of MSM (Monthly Simulation Model) and BIGMOD. MSM was used to simulate the River Murray system run on a monthly time step. Output from MSM, historical data and/or weather data was then used as an input into BIGMOD. That is Sinclair Knight Merz Pty Ltd (2006) used a combination of simulated and actual data for their investigation rather than purely actual data as Chong and Ladson (2003) used.
The method to assess the performance of options used by Chong and Ladson (2003) and Sinclair Knight Merz Pty Ltd (2006) were also fundamentally different. Chong and Ladson (2003) focused on reducing the annual number of days between December 1st to April 30th in which the B-MF was flooded to that of pre-regulation levels. Other methods used by Chong and Ladson (2003) were: • number of events per season; • event duration; and • total surplus flow volume per season.
In contrast Sinclair Knight Merz Pty Ltd (2006) used a multi-criteria approach with 70% of the weighting being on a cost effectiveness ratio. The cost was represented as the net present-value of the capital and annual costs of the option, assuming a discount rate of 4% and a project life of 30 years. The benefit was determined as the change in the number of dry years per decade that the option provided. The remaining 30% of the emphasis of Sinclair Knight Merz Pty Ltd (2006) was divided as follows:
333 APPENDIX E
• 10% was placed on the impact of the option on Lake Mulwala aesthetics and tourism based on the drawdown level of Lake Mulwala; • 10% was placed on the reduced flooding of Werai Forest (one of the consequences of some options is an increased flooding of Werai Forest. Transferring the problem of unseasonal flooding from one location to another; and • 10% was placed on the reduced evaporative losses in the B-MF.
The final significant difference between the two approaches was on the economic value of water saved from flooding the B-MF. Chong and Ladson (2003) placed a value of $60/ML (average farm gross margin of foregone irrigated agriculture) for every ML prevented from flooding the B-MF while Sinclair Knight Merz Pty Ltd (2006) placed a value of $1,000/ML for every ML of saved water (all of the water flooding the B-MF is not lost, approximately two-third of the water returns to the River Murray and is utilized by downstream users (GHD Pty Ltd 2006)). GHD Pty Ltd (2006) state that the value of saved water is $1,500/ML.
E.2 Tables of simulated results for each climate year/crop allocation
The results of each of the allocation season and climate season are shown in Table E.1 to Table E.36.
334 APPENDIX E
Table E.1: 80% allocation and 1988-89 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 91,124 89,132 91,983 ML 84,533 85,899 83,647 Rejections captured % of total 92.77% 96.37% 90.94% ML 6,591 3,233 8,336 Non-captured rejections % of total 7.23% 3.63% 9.06% ML 78,801 79,039 81,662 Reused rejected water % of captured 93.22% 92.01% 97.63% ML 76 75 205 Seepage % of captured 0.09% 0.09% 0.25% rejected orders ML 1,996 1,988 1,042 Evaporation % of captured 2.36% 2.31% 1.25% rejected orders ML 662 652 479 Rain % of total inflows 0.78% 0.75% 0.57% ML 4,780 5,918 1,585 Volume remaining April % of captured 30th 5.65% 6.89% 1.89% rejected orders Volume on November ML 456 469 368 30th
Table E.2: 80% allocation and 1990-91 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 29,263 25,210 29,395 Rejections captured ML 29,263 25,210 27,484 % of total 100.00% 100.00% 93.50% Non-captured rejections ML 0 0 1,911 % of total 0.00% 0.00% 6.50% Reused rejected water ML 27,004 21,870 25,995 % of captured 92.28% 86.75% 94.58% Seepage ML 75 75 131 % of captured 0.26% 0.30% 0.48% rejected orders Evaporation ML 2,299 2,298 880 % of captured 7.86% 9.12% 3.20% rejected orders Rain ML 125 125 56 % of total inflows 0.43% 0.49% 0.20% Volume remaining April ML 176 1,216 594 30th % of captured 0.60% 4.82% 2.16% rejected orders Volume on November ML 165 124 60 30th
335 APPENDIX E
Table E.3: 80% allocation and 1992-93 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 79,897 77,095 80,849 Rejections captured ML 77,741 75,592 73,210 % of total 97.30% 98.05% 90.55% Non-captured rejections ML 2,156 1,503 7,639 % of total 2.70% 1.95% 9.45% Reused rejected water ML 76,443 74,473 72,424 % of captured 98.33% 98.52% 98.93% Seepage ML 76 76 183 % of captured 0.10% 0.10% 0.25% rejected orders Evaporation ML 1,764 1,761 947 % of captured 2.27% 2.33% 1.29% rejected orders Rain ML 508 508 314 % of total inflows 0.65% 0.67% 0.43% Volume remaining April ML 142 0 533 30th % of captured 0.18% 0.00% 0.73% rejected orders Volume on November ML 176 211 563 30th
Table E.4: 80% allocation and 1996-97 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 73,444 73,488 73,094 Rejections captured ML 72,312 73,020 69,863 % of total 98.46% 99.36% 95.58% Non-captured rejections ML 1,132 468 3,231 % of total 1.54% 0.64% 4.42% Reused rejected water ML 71,146 71,867 69,475 % of captured 98.39% 98.42% 99.44% Seepage ML 75 75 148 % of captured 0.10% 0.10% 0.21% rejected orders Evaporation ML 2,091 2,091 955 % of captured 2.89% 2.86% 1.37% rejected orders Rain ML 183 183 104 % of total inflows 0.25% 0.25% 0.15% Volume remaining April ML 0 0 149 30th % of captured 0.00% 0.00% 0.21% rejected orders Volume on November ML 811 829 760 30th
336 APPENDIX E
Table E.5: 80% allocation and 1999-00 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 88,647 88,026 89,219 Rejections captured ML 87,084 86,785 79,966 % of total 98.24% 98.59% 89.63% Non-captured rejections ML 1,563 1,241 9,253 % of total 1.76% 1.41% 10.37% Reused rejected water ML 86,890 86,980 79,370 % of captured 99.78% 100.22% 99.25% Seepage ML 76 76 190 % of captured 0.09% 0.09% 0.24% rejected orders Evaporation ML 2,113 2,113 1,149 % of captured 2.43% 2.43% 1.44% rejected orders Rain ML 421 421 261 % of total inflows 0.48% 0.48% 0.33% Volume remaining April ML 1,358 1,420 126 30th % of captured 1.56% 1.64% 0.16% rejected orders Volume on November ML 2,934 3,383 608 30th
Table E.6: 80% allocation and 2000-01 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 90,572 85,764 87,550 Rejections captured ML 87,116 85,266 79,874 % of total 96.18% 99.42% 91.23% Non-captured rejections ML 3,456 498 7,676 % of total 3.82% 0.58% 8.77% Reused rejected water ML 78,126 76,369 72,677 % of captured 89.68% 89.57% 90.99% Seepage ML 76 76 176 % of captured 0.09% 0.09% 0.22% rejected orders Evaporation ML 2,314 2,314 1,270 % of captured 2.66% 2.71% 1.59% rejected orders Rain ML 419 419 288 % of total inflows 0.48% 0.49% 0.36% Volume remaining April ML 7,475 7,442 6,357 30th % of captured 8.58% 8.73% 7.96% rejected orders Volume on November ML 455 514 318 30th
337 APPENDIX E
Table E.7: 80% allocation and 2001-02 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 85,987 84,148 84,079 Rejections captured ML 77,902 79,207 75,775 % of total 90.60% 94.13% 90.12% Non-captured rejections ML 8,085 4,941 8,304 % of total 9.40% 5.87% 9.88% Reused rejected water ML 76,192 77,602 74,884 % of captured 97.80% 97.97% 98.82% Seepage ML 75 75 169 % of captured 0.10% 0.09% 0.22% rejected orders Evaporation ML 2,254 2,271 1,131 % of captured 2.89% 2.87% 1.49% rejected orders Rain ML 339 338 219 % of total inflows 0.43% 0.42% 0.29% Volume remaining April ML 0 40 102 30th % of captured 0.00% 0.05% 0.13% rejected orders Volume on November ML 280 407 292 30th
Table E.8: 80% allocation and 2002-03 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 98,109 96,681 96,772 Rejections captured ML 96,475 93,946 89,254 % of total 98.33% 97.17% 92.23% Non-captured rejections ML 1,634 2,735 7,518 % of total 1.67% 2.83% 7.77% Reused rejected water ML 88,875 86,134 85,184 % of captured 92.12% 91.68% 95.44% Seepage ML 76 75 170 % of captured 0.08% 0.08% 0.19% rejected orders Evaporation ML 2,641 2,641 1,303 % of captured 2.74% 2.81% 1.46% rejected orders Rain ML 327 327 215 % of total inflows 0.34% 0.35% 0.24% Volume remaining April ML 5,867 6,181 3,497 30th % of captured 6.08% 6.58% 3.92% rejected orders Volume on November ML 657 758 685 30th
338 APPENDIX E
Table E.9: 80% allocation and 2003-04 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 88,924 89,550 89,060 Rejections captured ML 85,511 84,143 81,546 % of total 96.16% 93.96% 91.56% Non-captured rejections ML 3,413 5,407 7,514 % of total 3.84% 6.04% 8.44% Reused rejected water ML 86,350 83,939 81,058 % of captured 100.98% 99.76% 99.40% Seepage ML 76 76 151 % of captured 0.09% 0.09% 0.19% rejected orders Evaporation ML 2,417 2,416 1,111 % of captured 2.83% 2.87% 1.36% rejected orders Rain ML 237 237 162 % of total inflows 0.28% 0.28% 0.20% Volume remaining April ML 0 12 31 30th % of captured 0.00% 0.01% 0.04% rejected orders Volume on November ML 3,089 2,063 643 30th
Table E.10: 60% allocation and 1988-89 climate En-route En-route Storage Criteria Units Storage OFWS (Burns (The Drop) Regulator) Total rejections ML 73,120 71,373 74,122 Rejections captured ML 67,102 67,852 59,600 % of total 91.77% 95.07% 80.41% Non-captured rejections ML 6,018 3,521 14,522 % of total 8.23% 4.93% 19.59% Reused rejected water ML 61,663 61,791 55,467 % of captured 91.89% 91.07% 93.07% Seepage ML 76 76 261 % of captured 0.11% 0.11% 0.44% rejected orders Evaporation ML 1,996 1,995 1,434 % of captured 2.97% 2.94% 2.41% rejected orders Rain ML 663 662 570 % of total inflows 0.98% 0.97% 0.95% Volume remaining April ML 4,249 4,913 3,778 30th % of captured 6.33% 7.24% 6.34% rejected orders Volume on November ML 220 261 770 30th
339 APPENDIX E
Table E.11: 60% allocation and 1990-91 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 56,501 56,413 56,166 Rejections captured ML 56,501 56,413 54,074 % of total 100.00% 100.00% 96.28% Non-captured rejections ML 0 0 2,092 % of total 0.00% 0.00% 3.72% Reused rejected water ML 54,535 54,556 51,679 % of captured 96.52% 96.71% 95.57% Seepage ML 75 75 151 % of captured 0.13% 0.13% 0.28% rejected orders Evaporation ML 2,300 2,300 1,020 % of captured 4.07% 4.08% 1.89% rejected orders Rain ML 125 126 63 % of total inflows 0.22% 0.22% 0.12% Volume remaining April ML 0 0 1,717 30th % of captured 0.00% 0.00% 3.18% rejected orders Volume on November ML 283 393 430 30th
Table E.12: 60% allocation and 1992-93 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 62,111 60,936 64,421 Rejections captured ML 61,808 60,936 58,178 % of total 99.51% 100.00% 90.31% Non-captured rejections ML 303 0 6,243 % of total 0.49% 0.00% 9.69% Reused rejected water ML 60,432 59,755 59,247 % of captured 97.77% 98.06% 101.84% Seepage ML 76 76 245 % of captured 0.12% 0.12% 0.42% rejected orders Evaporation ML 1,762 1,760 1,336 % of captured 2.85% 2.89% 2.30% rejected orders Rain ML 508 507 401 % of total inflows 0.82% 0.83% 0.68% Volume remaining April ML 126 0 928 30th % of captured 0.20% 0.00% 1.60% rejected orders Volume on November ML 80 141 3,177 30th
340 APPENDIX E
Table E.13: 60% allocation and 1996-97 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 55,113 55,352 54,391 Rejections captured ML 53,346 53,360 49,379 % of total 96.79% 96.40% 90.79% Non-captured rejections ML 1,767 1,992 5,012 % of total 3.21% 3.60% 9.21% Reused rejected water ML 51,768 51,798 49,178 % of captured 97.04% 97.07% 99.59% Seepage ML 75 75 162 % of captured 0.14% 0.14% 0.33% rejected orders Evaporation ML 2,091 2,090 1,039 % of captured 3.92% 3.92% 2.10% rejected orders Rain ML 183 183 122 % of total inflows 0.34% 0.34% 0.25% Volume remaining April ML 0 50 66 30th % of captured 0.00% 0.09% 0.13% rejected orders Volume on November ML 404 470 944 30th
Table E.14: 60% allocation and 1999-00 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 71,214 71,184 71,811 Rejections captured ML 68,757 68,683 63,147 % of total 96.55% 96.49% 87.93% Non-captured rejections ML 2,457 2,501 8,664 % of total 3.45% 3.51% 12.07% Reused rejected water ML 67,968 67,607 65,389 % of captured 98.85% 98.43% 103.55% Seepage ML 76 76 209 % of captured 0.11% 0.11% 0.33% rejected orders Evaporation ML 2,111 2,091 1,277 % of captured 3.07% 3.04% 2.02% rejected orders Rain ML 420 420 275 % of total inflows 0.61% 0.61% 0.43% Volume remaining April ML 88 194 801 30th % of captured 0.13% 0.28% 1.27% rejected orders Volume on November ML 1,067 866 4,254 30th
341 APPENDIX E
Table E.15: 60% allocation and 2000-01 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 64,223 62,930 64,513 Rejections captured ML 63,750 61,904 56,631 % of total 99.26% 98.37% 87.78% Non-captured rejections ML 473 1,026 7,882 % of total 0.74% 1.63% 12.22% Reused rejected water ML 54,498 52,867 50,031 % of captured 85.49% 85.40% 88.35% Seepage ML 76 76 194 % of captured 0.12% 0.12% 0.34% rejected orders Evaporation ML 2,313 2,314 1,315 % of captured 3.63% 3.74% 2.32% rejected orders Rain ML 419 420 362 % of total inflows 0.65% 0.67% 0.64% Volume remaining April ML 7,475 7,212 6,364 30th % of captured 11.73% 11.65% 11.24% rejected orders Volume on November ML 192 145 911 30th
Table E.16: 60% allocation and 2001-02 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 66,303 66,161 68,850 Rejections captured ML 61,031 61,407 58,557 % of total 92.05% 92.81% 85.05% Non-captured rejections ML 5,272 4,754 10,293 % of total 7.95% 7.19% 14.95% Reused rejected water ML 58,981 59,435 57,186 % of captured 96.64% 96.79% 97.66% Seepage ML 75 75 188 % of captured 0.12% 0.12% 0.32% rejected orders Evaporation ML 2,271 2,271 1,250 % of captured 3.72% 3.70% 2.13% rejected orders Rain ML 338 338 251 % of total inflows 0.55% 0.55% 0.43% Volume remaining April ML 44 46 844 30th % of captured 0.07% 0.07% 1.44% rejected orders Volume on November ML 4 82 660 30th
342 APPENDIX E
Table E.17: 60% allocation and 2002-03 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 76,418 77,374 76,809 Rejections captured ML 73,110 73,586 63,667 % of total 95.67% 95.10% 82.89% Non-captured rejections ML 3,308 3,788 13,142 % of total 4.33% 4.90% 17.11% Reused rejected water ML 64,866 65,736 61,903 % of captured 88.72% 89.33% 97.23% Seepage ML 76 76 188 % of captured 0.10% 0.10% 0.30% rejected orders Evaporation ML 2,640 2,640 1,423 % of captured 3.61% 3.59% 2.24% rejected orders Rain ML 327 327 241 % of total inflows 0.45% 0.44% 0.38% Volume remaining April ML 6,229 5,818 5,176 30th % of captured 8.52% 7.91% 8.13% rejected orders Volume on November ML 374 357 4,782 30th
Table E.18: 60% allocation and 2003-04 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 69,967 69,929 70,970 Rejections captured ML 64,010 63,208 60,707 % of total 91.49% 90.39% 85.54% Non-captured rejections ML 5,957 6,721 10,263 % of total 8.51% 9.61% 14.46% Reused rejected water ML 64,017 61,556 61,303 % of captured 100.01% 97.39% 100.98% Seepage ML 76 76 162 % of captured 0.12% 0.12% 0.27% rejected orders Evaporation ML 2,416 2,416 1,204 % of captured 3.77% 3.82% 1.98% rejected orders Rain ML 236 236 185 % of total inflows 0.37% 0.37% 0.30% Volume remaining April ML 0 0 377 30th % of captured 0.00% 0.00% 0.62% rejected orders Volume on November ML 2,248 590 2,154 30th
343 APPENDIX E
Table E.19: 40% allocation and 1988-89 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 45,713 41,911 47,865 Rejections captured ML 40,886 41,364 41,224 % of total 89.44% 98.69% 86.13% Non-captured rejections ML 4,827 547 6,641 % of total 10.56% 1.31% 13.87% Reused rejected water ML 34,448 35,255 39,305 % of captured 84.25% 85.23% 95.34% Seepage ML 76 76 187 % of captured 0.19% 0.18% 0.45% rejected orders Evaporation ML 1,998 1,993 946 % of captured 4.89% 4.82% 2.29% rejected orders Rain ML 663 661 434 % of total inflows 1.60% 1.57% 1.04% Volume remaining April ML 5,003 4,722 1,220 30th % of captured 12.24% 11.42% 2.96% rejected orders Volume on November ML 0 21 0 30th
Table E.20: 40% allocation and 1990-91 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 29,263 30,042 29,395 Rejections captured ML 29,263 30,042 27,518 % of total 100.00% 100.00% 93.61% Non-captured rejections ML 0 0 1,877 % of total 0.00% 0.00% 6.39% Reused rejected water ML 27,004 27,907 26,030 % of captured 92.28% 92.89% 94.59% Seepage ML 75 75 130 % of captured 0.26% 0.25% 0.47% rejected orders Evaporation ML 2,299 2,299 880 % of captured 7.86% 7.65% 3.20% rejected orders Rain ML 125 125 56 % of total inflows 0.43% 0.41% 0.20% Volume remaining April ML 176 0 594 30th % of captured 0.60% 0.00% 2.16% rejected orders Volume on November ML 165 113 60 30th
344 APPENDIX E
Table E.21: 40% allocation and 1992-93 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 43,677 40,728 42,835 Rejections captured ML 43,677 40,728 40,533 % of total 100.00% 100.00% 94.63% Non-captured rejections ML 0 0 2,302 % of total 0.00% 0.00% 5.37% Reused rejected water ML 42,422 39,385 39,669 % of captured 97.13% 96.70% 97.87% Seepage ML 76 75 152 % of captured 0.17% 0.18% 0.38% rejected orders Evaporation ML 1,762 1,758 785 % of captured 4.03% 4.32% 1.94% rejected orders Rain ML 508 507 265 % of total inflows 1.15% 1.23% 0.65% Volume remaining April ML 0 81 280 30th % of captured 0.00% 0.20% 0.69% rejected orders Volume on November ML 69 64 88 30th
Table E.22: 40% allocation and 1996-97 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 29,149 29,098 28,756 Rejections captured ML 29,149 29,098 25,659 % of total 100.00% 100.00% 89.23% Non-captured rejections ML 0 0 3,097 % of total 0.00% 0.00% 10.77% Reused rejected water ML 27,260 27,267 24,800 % of captured 93.52% 93.71% 96.65% Seepage ML 75 75 132 % of captured 0.26% 0.26% 0.51% rejected orders Evaporation ML 2,089 2,089 840 % of captured 7.17% 7.18% 3.27% rejected orders Rain ML 183 183 103 % of total inflows 0.62% 0.62% 0.40% Volume remaining April ML 14 9 112 30th % of captured 0.05% 0.03% 0.44% rejected orders Volume on November ML 106 159 122 30th
345 APPENDIX E
Table E.23: 40% allocation and 1999-00 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 43,668 44,075 44,951 Rejections captured ML 43,668 44,075 41,282 % of total 100.00% 100.00% 91.84% Non-captured rejections ML 0 0 3,669 % of total 0.00% 0.00% 8.16% Reused rejected water ML 41,990 41,724 40,069 % of captured 96.16% 94.67% 97.06% Seepage ML 76 76 161 % of captured 0.17% 0.17% 0.39% rejected orders Evaporation ML 2,110 2,109 973 % of captured 4.83% 4.79% 2.36% rejected orders Rain ML 420 420 211 % of total inflows 0.95% 0.94% 0.51% Volume remaining April ML 607 566 290 30th % of captured 1.39% 1.28% 0.70% rejected orders Volume on November ML 693 0 0 30th
Table E.24: 40% allocation and 2000-01 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 37,217 37,237 38,110 Rejections captured ML 36,435 37,237 33,194 % of total 97.90% 100.00% 87.10% Non-captured rejections ML 782 0 4,916 % of total 2.10% 0.00% 12.90% Reused rejected water ML 29,085 29,422 28,153 % of captured 79.83% 79.01% 84.81% Seepage ML 75 75 154 % of captured 0.21% 0.20% 0.46% rejected orders Evaporation ML 2,312 2,311 1,045 % of captured 6.35% 6.21% 3.15% rejected orders Rain ML 419 419 258 % of total inflows 1.14% 1.11% 0.77% Volume remaining April ML 5,382 5,766 4,100 30th % of captured 14.77% 15.48% 12.35% rejected orders Volume on November ML 0 0 0 30th
346 APPENDIX E
Table E.25: 40% allocation and 2001-02 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 33,164 32,511 32,623 Rejections captured ML 30,530 30,401 28,774 % of total 92.06% 93.51% 88.20% Non-captured rejections ML 2,634 2,110 3,849 % of total 7.94% 6.49% 11.80% Reused rejected water ML 28,619 28,313 27,714 % of captured 93.74% 93.13% 96.32% Seepage ML 75 75 145 % of captured 0.25% 0.25% 0.50% rejected orders Evaporation ML 2,268 2,268 959 % of captured 7.43% 7.46% 3.33% rejected orders Rain ML 338 338 162 % of total inflows 1.09% 1.10% 0.56% Volume remaining April ML 0 44 118 30th % of captured 0.00% 0.14% 0.41% rejected orders Volume on November ML 94 0 0 30th
Table E.26: 40% allocation and 2002-03 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 44,704 45,889 47,035 Rejections captured ML 43,012 44,120 32,147 % of total 96.22% 96.15% 68.35% Non-captured rejections ML 1,692 1,769 14,888 % of total 3.78% 3.85% 31.65% Reused rejected water ML 34,969 36,570 23,152 % of captured 81.30% 82.89% 72.02% Seepage ML 75 75 231 % of captured 0.17% 0.17% 0.72% rejected orders Evaporation ML 2,638 2,638 1,583 % of captured 6.13% 5.98% 4.92% rejected orders Rain ML 327 327 287 % of total inflows 0.75% 0.74% 0.88% Volume remaining April ML 5,796 5,272 7,500 30th % of captured 13.48% 11.95% 23.33% rejected orders Volume on November ML 141 108 32 30th
347 APPENDIX E
Table E.27: 40% allocation and 2003-04 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 35,969 35,209 37,810 Rejections captured ML 35,228 35,209 32,836 % of total 97.94% 100.00% 86.84% Non-captured rejections ML 741 0 4,974 % of total 2.06% 0.00% 13.16% Reused rejected water ML 35,164 33,281 26,035 % of captured 99.82% 94.52% 79.29% Seepage ML 76 76 191 % of captured 0.22% 0.22% 0.58% rejected orders Evaporation ML 2,414 2,413 1,248 % of captured 6.85% 6.85% 3.80% rejected orders Rain ML 236 236 152 % of total inflows 0.67% 0.67% 0.46% Volume remaining April ML 149 154 5,993 30th % of captured 0.42% 0.44% 18.25% rejected orders Volume on November ML 2,339 479 479 30th
Table E.28: 20% allocation and 1988-89 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 31,899 30,047 33,198 Rejections captured ML 30,527 30,047 31,088 % of total 95.70% 100.00% 93.64% Non-captured rejections ML 1,372 0 2,110 % of total 4.30% 0.00% 6.36% Reused rejected water ML 26,567 26,392 29,588 % of captured 87.03% 87.84% 95.17% Seepage ML 76 75 160 % of captured 0.25% 0.25% 0.51% rejected orders Evaporation ML 1,995 1,992 855 % of captured 6.54% 6.63% 2.75% rejected orders Rain ML 661 660 348 % of total inflows 2.12% 2.15% 1.11% Volume remaining April ML 2,508 2,192 833 30th % of captured 8.22% 7.30% 2.68% rejected orders Volume on November ML 0 0 0 30th
348 APPENDIX E
Table E.29: 20% allocation and 1990-91 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 16,629 16,962 17,924 Rejections captured ML 16,629 16,962 15,945 % of total 100.00% 100.00% 88.96% Non-captured rejections ML 0 0 1,979 % of total 0.00% 0.00% 11.04% Reused rejected water ML 13,994 14,646 14,567 % of captured 84.15% 86.35% 91.36% Seepage ML 75 75 119 % of captured 0.45% 0.44% 0.75% rejected orders Evaporation ML 2,297 2,297 827 % of captured 13.81% 13.54% 5.19% rejected orders Rain ML 125 125 51 % of total inflows 0.75% 0.73% 0.32% Volume remaining April ML 336 0 483 30th % of captured 2.02% 0.00% 3.03% rejected orders Volume on November ML 0 0 0 30th
Table E.30: 20% allocation and 1992-93 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 32,268 27,943 32,045 Rejections captured ML 32,268 27,943 30,742 % of total 100.00% 100.00% 95.93% Non-captured rejections ML 0 0 1,303 % of total 0.00% 0.00% 4.07% Reused rejected water ML 31,993 26,664 30,267 % of captured 99.15% 95.42% 98.45% Seepage ML 76 75 141 % of captured 0.24% 0.27% 0.46% rejected orders Evaporation ML 1,761 1,758 741 % of captured 5.46% 6.29% 2.41% rejected orders Rain ML 507 507 241 % of total inflows 1.55% 1.78% 0.78% Volume remaining April ML 0 0 160 30th % of captured 0.00% 0.00% 0.52% rejected orders Volume on November ML 1,051 33 326 30th
349 APPENDIX E
Table E.31: 20% allocation and 1996-97 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 16,338 15,870 15,304 Rejections captured ML 16,338 15,870 13,633 % of total 100.00% 100.00% 89.08% Non-captured rejections ML 0 0 1,671 % of total 0.00% 0.00% 10.92% Reused rejected water ML 14,415 14,011 12,818 % of captured 88.23% 88.29% 94.02% Seepage ML 75 75 121 % of captured 0.46% 0.47% 0.89% rejected orders Evaporation ML 2,087 2,087 774 % of captured 12.77% 13.15% 5.68% rejected orders Rain ML 183 183 80 % of total inflows 1.11% 1.14% 0.58% Volume remaining April ML 0 0 0 30th % of captured 0.00% 0.00% 0.00% rejected orders Volume on November ML 0 63 0 30th
Table E.32: 20% allocation and 1999-00 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 31,081 33,169 30,943 Rejections captured ML 31,081 33,169 28,801 % of total 100.00% 100.00% 93.08% Non-captured rejections ML 0 0 2,142 % of total 0.00% 0.00% 6.92% Reused rejected water ML 30,552 30,374 27,918 % of captured 98.30% 91.57% 96.93% Seepage ML 76 76 144 % of captured 0.24% 0.23% 0.50% rejected orders Evaporation ML 2,109 2,108 880 % of captured 6.79% 6.36% 3.06% rejected orders Rain ML 420 420 188 % of total inflows 1.33% 1.25% 0.65% Volume remaining April ML 1,858 2,073 102 30th % of captured 5.98% 6.25% 0.35% rejected orders Volume on November ML 3,094 1,041 55 30th
350 APPENDIX E
Table E.33: 20% allocation and 2000-01 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 19,096 19,840 21,130 Rejections captured ML 19,096 19,840 18,480 % of total 100.00% 100.00% 87.46% Non-captured rejections ML 0 0 2,650 % of total 0.00% 0.00% 12.54% Reused rejected water ML 16,415 14,759 16,912 % of captured 85.96% 74.39% 91.52% Seepage ML 75 75 135 % of captured 0.39% 0.38% 0.73% rejected orders Evaporation ML 2,308 2,308 948 % of captured 12.09% 11.63% 5.13% rejected orders Rain ML 418 418 235 % of total inflows 2.14% 2.06% 1.26% Volume remaining April ML 2,063 3,116 1,019 30th % of captured 10.80% 15.71% 5.51% rejected orders Volume on November ML 1,078 0 299 30th
Table E.34: 20% allocation and 2001-02 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 28,065 27,736 27,871 Rejections captured ML 28,059 27,736 24,880 % of total 99.98% 100.00% 89.27% Non-captured rejections ML 6 0 2,991 % of total 0.02% 0.00% 10.73% Reused rejected water ML 26,015 25,716 23,934 % of captured 92.72% 92.72% 96.20% Seepage ML 75 75 142 % of captured 0.27% 0.27% 0.57% rejected orders Evaporation ML 2,269 2,269 954 % of captured 8.09% 8.18% 3.83% rejected orders Rain ML 338 338 158 % of total inflows 1.19% 1.20% 0.63% Volume remaining April ML 0 0 8 30th % of captured 0.00% 0.00% 0.03% rejected orders Volume on November ML 0 0 0 30th
351 APPENDIX E
Table E.35: 20% allocation and 2002-03 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 28,564 26,566 44,081 Rejections captured ML 28,564 26,566 39,356 % of total 100.00% 100.00% 89.28% Non-captured rejections ML 0 0 4,725 % of total 0.00% 0.00% 10.72% Reused rejected water ML 21,857 21,033 35,193 % of captured 76.52% 79.17% 89.42% Seepage ML 75 75 154 % of captured 0.26% 0.28% 0.39% rejected orders Evaporation ML 2,636 2,617 1,162 % of captured 9.23% 9.85% 2.95% rejected orders Rain ML 327 327 204 % of total inflows 1.13% 1.22% 0.52% Volume remaining April ML 4,362 3,183 3,083 30th % of captured 15.27% 11.98% 7.83% rejected orders Volume on November ML 41 0 32 30th
Table E.36: 20% allocation and 2003-04 climate En-route En-route storage Criteria Units storage OFWS (Burns (The Drop) Regulator) Total rejections ML 21,543 20,317 37,239 Rejections captured ML 21,543 20,317 34,267 % of total 100.00% 100.00% 92.02% Non-captured rejections ML 0 0 2,972 % of total 0.00% 0.00% 7.98% Reused rejected water ML 20,934 18,275 33,625 % of captured 97.17% 89.95% 98.13% Seepage ML 76 76 131 % of captured 0.35% 0.37% 0.38% rejected orders Evaporation ML 2,413 2,412 960 % of captured 11.20% 11.87% 2.80% rejected orders Rain ML 236 236 135 % of total inflows 1.08% 1.15% 0.39% Volume remaining April ML 27 87 165 30th % of captured 0.13% 0.43% 0.48% rejected orders Volume on November ML 1,670 297 479 30th
352