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MODELING OF FLOODING RESPONSE AND ECOLOGICAL ENGINEERING IN AN AGRICULTURAL REGION OF CENTRAL

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By Randall J.F. Bruins, M.A.

*****

The Ohio State University 1997

Dissertation Committee:

Professor William J. Mitsch, Adviser Approved by Professor David A. Culver A ^ Professor Frederick J. Hitzhusen Adviser Professor Mohan K. Wali Environmental Sciences Graduate Program UMI Number: 9813229

UMI Microform 9813229 Copyright 1998, by UMI Company. All rights reserved.

This microform edition is protected against unauthorized copying under Title 17, United States Code.

UMI 300 North Zeeb Road Ann Arbor, MI 48103 ABSTRACT

Ecological engineering techniques are widely used in Chinese to reduce wastes and improve efficiency, but rarely to address crop loss associated with flooding. In the Jianghan-Dongting Plain of the middle River basin in central China, large areas that were formerly shallow lakes and marshes are now empoldered for the cultivation of , fish and other crops. These areas are economically productive but regularly experience crop damage due to rainfall amounts exceeding the removal capacity of pumps and drainage canals.

A investigation gathered existing data on landforms, hydrologie management, agricultural and aquacultural practices, production economics, and recent flooding events at two scales (Honghu Flood Diversion Area, 2800 km", and Xiaogang , 24 km*) within the lacustrine plain. A dynamic, pseudo-spatially distributed model was developed to simulate flooding and crop damage at these two scales. Simulated canal water elevations were calibrated at the farm scale for a 19-day flooding episode in 1996 and at the area scale for the 4-month rainy season over the years 1987 - 1994. Rice damage indices were derived as a function of time and area of inundation in excess of 1-day tolerance limits. Damage indices simulated at the farm scale for 1980 -1994 were comparable in pattern to flooding-year yield reductions observed at the county level over the same period.

The model was used to simulate selected engineering strategies for reducing flooding loss. Increased pumping capacity at the farm level reduced internal flood levels and crop damage, but this benefit was reduced if the strategy was implemented areawide. Converting some low-position area to flood-tolerant crops such as lotus (Nelumho niicifera) or stem (ZizcmialatifoUa) tended to increase internal flooding levels and damage to low position rice. These adverse effects were reduced or eliminated if dikes around flood-tolerant cropping areas were raised to provide passive water storage capacity, or if conversion were selectively implemented at the very lowest field elevations, or both. Economic evaluation of wide scale crop conversion is complicated by inelasticity of demand for lotus and wild rice stem in the absence of regional marketing or transportation structures.

ui To the people of China

IV ACKNOWLEDGMENTS

I am grateful to many people without whose support this work could not have been completed. I thank my adviser. Professor William Mitsch, for providing a stimulating environment for learning and for imparting a systems perspective. I thank my Chinese adviser. Professor Cai Shuming, for welcoming my family to Wuhan and for opening many doors for me in China, and I thank my collaborator and friend, Mr. Chen Shijian, for many hours spent with me explaining the agricultural systems of the Jianghan Plain. 1 thank Dr. Yi Chaolu, Mr. Yang Handong, and Mr. Wu Yijin for assisting me with translation, travel, and data collection, for helpful scientific discussions, and for teaching me to play majiang. I thank Director Wang Susong and the managers of Xiaogang Farm for their warm welcome, openness and patience during many visits and hours of discussion, and Vice Manager Zhou Zonglin and members of the Honghu City Planning

Department for providing needed information. I am grateful to Dr. Xinyuan Wu, for his help in arranging my period of residence in China, and to Dr. Nairaing Wang, for every imaginable kind of technical and language assistance. I also thank the members of my dissertation committee. Professors Fred Hitzhusen, Mohan Wali, and David Culver, for their careful review and encouragement of this work.

Dr. Terry Harvey and Mr. Steve Lutkenhoff of the U.S. EPA’s National Center for Environmental Assessment in Cincinnati made a long-term commitment to my educational goals, Ms. Nancy Bauer handled the details, and the rest of the NCEA group was supportive during my long absence. I am indebted to all of them. Rnally. I am especially grateful to my wife. Connie King Bruins, for her enduring support amidst many sacrifices, and to my daughters. Emily and Adrienne, for participating with me in these adventures.

VI VITA

June 19, 1955 ...... Born - Elgin, Illinois 1978 ...... B.A. Zoology, Miami University, Oxford, Ohio 1980 ...... M.A. Zoology, Miami University, Oxford, Ohio 1980 - Present ...... Environmental Scientist, U.S. EPA National Center for Environmental Assessment, Cincinnati, Ohio 1991 - 1993 ...... Regional Scientist, U.S. EPA Region 10, Seattle, Washington 1994 - 1996 ...... Program, The Ohio State University (under U.S. EPA Long-Term Training Agreement)

PUBLICATIONS

Wang, N., RJ.F. Bruins, W.J. Mitsch and W.T. Acton. 1997. Water budgets of the two Olentangy River experimental wetlands, 1994- 1996. pp. 55-84 in: W J. Mitsch, ed. Olentangy River Wetland Research Park at The Ohio State University, Annual Report, 1996. Wetlands Program, The Ohio State University, Columbus, OH. Naim, R.W., N. Wang, RJ.F. Bruins and W.J. Mitsch. 1996. Hydrological budgets of the Olentangy River wetlands, pp. 69-82 in: W J. Mitsch, ed. Olentangy River Wetland Research Park at The Ohio State University, Annual Report, 1995. Wetlands Program, The Ohio State University, Columbus, OH. Levin, A., D.B. Fratt, A. Leonard, RJ.F. Bruins and L. Fradkin. 1991. Comparative analysis of health risk assessments for municipal waste combustors. J. Air Waste Manag. Assoc. 41:20-31/. Bruins, RJ.F., N.E. Kowal, C. Sonich-MuUin and S.D. Lutkenhoff. 1990. The development of risk assessment methodologies for use in regulation of sewage sludge disposal. In: Proceedings of the 12th United States/ Conference on Sewage Treatment Technology, EPA/600/9-90/036. U.S. Environmental Protection Agency, Cincinnati, OH.

v ii Fradkin, L., C.P. Gerba, S.M. Goyal, P. Scarpino, RJ.F. Bruins and J.F. S tara. 1989. Municipal wastewater sludge: The potential public health impacts of common pathogens. J. Environ. Health 51:148-152. Bruins, R.J.F., L. Fradkin, J.F. Stara, W.B. Peirano and V. Molak. 1988. Analytical methods necessary to implement risk-based criteria for chemicals in municipal sludge. In: Chemical and Biological Characterization of Sludges, Sediments, Dredge Spoils and Drilling Muds. ASTM STP976, Amer. Soc. Test Mater., Philadelplua, PA. Rubin, A.B., E.D. Lomnitz, L. Fradkin, R.J.F. Bruins and J.F. Stara. 1988. U.S. sludge management guidelines explained. In: Chemical and Biological Characterization of Sludges, Sediments, Dredge Spoils and Drilling Muds. ASTM STP 976, Amer. Soc. Test. Mater., Philadelphia, PA. Fradkin, L. R.J.F. Bruins, S.D. Lutkenhoff, et al. 1987. Screening methodology for assessing the potential impacts from municipal sludge incinerators. J. Air Fallut. Contr. Assoc. 37:395-399.

Stara, J.F., RJ.F. Bruins, M.L. Dourson, L.S. Erdreich, R.C. Hertzberg, P R. Durkin and W.E. Pepelko. 1987. Risk assessment is a developing science: Approaches to improve evaluation of single chemicals and chemical mixtures. In: Methods for Assessing the Effects of Mixtures of Chemicals, V.B. Vouk et al., eds. J. Wiley and Sons, Ltd. Sussex, U.K.

Stara, J.F., R.C. Hertzberg, RJ.F. Bruins, M L. Dourson, P.R. Durkin, L.S. Erdreich and W.E. Pepelko. 1985. Approaches to risk assessment of chemical mixtures, pp 71-73 In: Chemical Safety and Compliance, F. Homberger and J.P. Marquis, eds. Karger, Basel, Switzerland. Flickinger, A.L., RJ.F. Bruins, R.W. Winner and J.H. Skillings. 1982. Filtration and phototactic behavior as indices of phototactic copper stress in Daphniamagna Straus. Arch. Environ. Contam. Toxicol. 11:457-463.

FIELDS OF STUDY

Major Field: Environmental Science

viu TABLE OF CONTENTS

page Abstract...... ii

Dedication ...... Iv

Acknowledgments...... v Vita...... vii

List of Tables...... xii List of Figures...... xiv

Chapters

L Introduction...... 1

1.1 Ecological Engineering and Sustainability ...... l 1.2 Hypothesis and Objectives of Study ...... 2 1.3 Limitations of Study Scope ...... 6 1.4 Document Organization...... 6 2. Literature Review...... 8

2.1 Traditional Food Production in Depressionai Landscapes in China ...... 8 2.2 Ecological Engineering of Wetland Regions in China ...... 12 23 Models for Regional Integration of Ecology and Economics ...... 16 23.1 Partial Integration Approaches ...... 16 2 3 2 Attempts at Complete Integration ...... 19 2 3 3 Bounding and Spatial Scale ...... 20

3. Methods ...... 23 3.1 Site Description and Field Investigation Methods ...... 23 3.1.1 Jianghan-Dongting Plain Wetland Environment ...... 23 3.1.2 Field Investigation Methods ...... 28 3.13 System Boundaries for Modeling ...... 32 3.2 Hydrologie Model Development Methods ...... 33 33 Sensitivity Analysis and Calibration ...... 38 3.4 Scenario Simulations...... 39 ix 4. Results Of Reid Investigation ...... 42 4.1 Xiaogang Farm Land Use and Hydrology ...... 42 4.1.1 Landform and Land Uses ...... 42 4.1.2 ...... 48 4.13 Agriculture...... 50 4.1.4 ...... 51 4.1.5 ...... 51 4.1.6 Water Management Schedules and Tolerances ...... 52 4.1.7 Hydrology ...... 57 4.2 Honghu Rood Diversion Area Land Use and Hydrology ...... 62 4.2.1 Land Use ...... 62 4.2.2 Precipitation/Evaporation ...... 65 4.23 Canal Hydrograph Data ...... 65 43 Recent Rood Events ...... 71 4.4 Ecological Engineering Strategies for Reducing Hooding Damage ...... 78 4.5 Limitations of Data and Strategy for Integrated Ecologie^ Economic Modeling ...... 81 4.5.1 System Bounding ...... 82 43.2 Costs and Benefits of Implementing Engineering Strategies ...... 82 43.3 Approach for Model Development ...... 84 5. Results Of Model Development And Calibration ...... 85

5.1 Conceptual Model Development ...... 85 5.2 Farm-Scale Model ...... 91 5.2.1 Type, Area and Elevation of Landform/Land Use Categories ...... 91 5.2.2 Surface and Water Storage {WaterJ)...... 95 5.23 Precipitation and Evapotranspiration {RainJ)...... 95 5.2.4 Vertical Seepage {SeepJ)...... 97 5.23 and Drainage {IrrigateJ, DrainJ)...... 97 5.2.6 Inner Canal Elevation-Volume Relationship {Canal_a, Canal_b, and Canal_c) ...... 98 5.2.7 Inner Canal Storage {Irmer_ccmals) ...... 100 5.2.8 Drainage And Rooding {DrainJ and Flood_elevJinal) ...... 100 5.2.9 Outflow To Outer Canals {Farm Jlow_out) ...... I ll 5.2.10 Damage Due To Rooding {Damage_areaJ,Max_damage_areaJand C ropJossJ)...... 112 53 Honghu Rood Diversion Area-Scale Model ...... 114 5.3.1 Outer Canals (Sihu General Canal) And Honghu Lake...... 114 53.2 Rows To/From Production Areas {Farms_c^ve. Jjelow) ...... 116 5 3 3 Surface Inflow To Honghu Lake{HFDAJlnflows) ...... 118 53.4 Row From Honghu Lake To Lower HFDA {Xiaogang_gate) ...... 119 5 3 3 Outflow From K D A To Rivers{HFDAjOutflows) ...... 119 5.4 Sensitivity Analysis and Calibration ...... 121 5.4.1 Xiaogang Farm Scale ...... 121 5.4.2 Honghu Rood Diversion Area Scale ...... 140 6. Simulation Of Ecological Engineering Alternatives For Improving Rooding Resistance...... 154 6.1 Introduction...... 154 6.2 Simulation Results...... 160 6.2.1 Baseline Simulations...... 160 6.2.2Test Strategy I: Increase Pump Number from 7 to 10...... 162 6.23 Test Strategy 2: Deepen Canals and Ditches by 20% ...... 169 6.2.4 Test Strategy 3a: Change LUE 2 to Two-crop Rice ...... 169 6.2.5 Test Strategy 3b: Change LUE2toTatami : Rice Rotation ...... 170 6.2.6 Test Strategy 3c: Change LUE 2 to Lotus ...... 170 6.2.7 Test Strategy 3d: Change LUE 2 to Wild Rice Stem ...... 171 6.2.8 Test Strategies 4a and 4b: Change Test Crop To Lotus Or Wild Rice Stem and Use as a Passive Storage Reservoir ...... 171 6.2.9 Test Strategies 5a and 5b: Change Test Crop To Lotus Or Wild Rice Stem And Selectively Cultivate At The Lowest Positions ...... 172 6.2.10 Test Strategies 44-5a And 4+5b: Change Test Crop To Lotus Or Wild Rice Stem, Selectively Cultivate At The Lowest Positions And Use As A Passive Storage Reservoir ...... 172 63 Discussion...... 173 63.1 Traditional Engineering Approaches That Maintain Current Land Area Devoted To Rice Production ...... 173 63.2 Ecological Engineering Approaches That Restructure The Biological Components Of The Lowland Agricultural System ...... 176 6.4 Recommendations for Further Research ...... 179 6.4.1 Modeling Hydrology and Crop Damage ...... 179 6.4.2 Economic Policy...... 180 6.43 Other Ecosystem Services ...... 182 6.4.4 Sustainability...... 182 List of References...... 183 Appendices

A. STELLA Model Equations...... 190

XI LIST OF TABLES

Table Page 2.1. Stages of growth and generalized water management for paddy rice ...... 9 3.1. Engineering strategies, including ecological engineering strategies, tested in model simulations...... 41

4 .1. Types, locations and years of data obtained for study of land use and hydrology in the lower Four lakes Region, Hubei, China ...... 43 4.2. Xiaogang Farm landform categories and associated uses ...... 44

43. 1996 input and output prices associated with selected Xiaogang Farm land use practices, according to written information provided by Xiaogang farm managers ...... 45 4.4. Net annual financial returns to land and labor of selected Xiaogang Farm land use practices for 1996, according to written information provided by Xiaogang farm managers ...... 46

4.5. Water management during stages of rice cultivation in the Four Lakes Region. Durations are approximate and change with field conditions ...... 53 4.6. Estimated return frequencies for 1-day, 3-day and 10-day precipitation totals for Honghu, 1957-1994; and 1996 extreme rainfall event ...... 70

4.7. Reported pumping capacities of Honghu Flood Diversion Area flood control gates ...... 75

5.1. Summary of reported information on Xiaogang Farm land areas devoted to various uses, and calculations or assumptions used to set estimated areas for land use elements in the farm-scale model ...... 94

5.2. Data used for calculation of inner canal and field ditch system volume at various water elevations...... 99

5.3. Sensitivity of canal/ditch system volume at 21.2 m water elevation to changes in canal configuration parameters. Sensitivity statistic S = (Ax/x)/( Ap/p) where x = volume, p = parameter and Ap/p = ± 10% ...... 102

XU 5.4. Final input values for calibration runs: (a) Farm Scale Model parameters (listed only where different from 1996 values); (b) Honghu Flood Diversion Area Scale Model parameters (HFDA-Scale runs only) ...... 126

5.5. Sensitivity analysis of farm scale model, where baseline is simulation of June 19 - July 18,1%1, with LUE 2 set to two-crop rice (late rice was not affected). Sensitivity statistic is (Ax/x)/( Ap/p), and (Ap/p) = ± 10%, except as noted ...... 141

5.6. Sensitivity analysis of Honghu Flood Diversion Area scale model, where baseline is simulation of June 19 - July 18,1991, with LUE 2 set to two-crop rice (late rice was not affected). Sensitivity statistic is (Ax/x)/( Ap/p), and (Ap/p) = ± 10%, except as noted ...... 153 6.1. Experimental runs for Xiaogang Farm, Farm-Scale model: (a) Switch settings used for test strategies; and (b) input values. Only input values that varied are shown; others are as shown for 1996 run in Table 5.4. Values shown are those for 1980 - 1994 runs. 1996 runs differed in state variable initial conditions because run period differed; those values are not shown ...... 156

6.2. Experimental runs for HFDA, Farm- and HFDA-Scale models: (a) Switch settings used for test strategies; and (b) input values. Only input values that varied are shown; others are as shown for 1996 run in Table 5.4...... 158

63. Baseline runs for determining effects of implementing engineering strategies on peak canal water elevations and crop loss indices for (a) Xiaogang Farm (at the farm scale) and (b) the Honghu Flood Diversion Area (at the farm and area scales)...... 161

6.4. Effects of implementing engineering strategies, including ecological engineering strategies, at the scale of Xiaogang Farm. For canal elevations and LUE 1 crop loss indices, percent change with respect to baseline is reported; negative values indicate peak canal water elevation or crop loss index was lowered as a result of the strategy; positive values indicate increases. For LUE 2 index values are not compared with baseline since LUE 2 crop varied among strategies ...... 163 6-5. Inter-scale comparison of effects of implementing engineering strategies, including ecological engineering strategies, at the scale of Xiaogang Farm. For canal elevations and LUE 1 crop loss indices, percent change with respect to baseline is reported; negative values indicate peak canal water elevation or crop loss index was lowered as a result of the strategy; positive values indicate increases. For LUE 2 index values are not compared with baseline since LUE 2 crop varied among strategies ...... 166

6.6. A ranking of strategies for flood protection that maintain land areas presently in lowland rice production, based on simulation results...... 174 6.7. A ranking of strategies for flood resistance that rely on changing some biological components of the lowland production system; based on simulation results...... 175

xui LIST OF FIGURES

Figure Page

1.1. Situation of the Jianghan-Dongting Plain in the Middle Yangtze River Basin, China. The Four Lakes (Sihu) Region is shaded ...... 4

3.1. The Honghu Flood Diversion Area, located within the Four Lakes Region, is bounded by a major regional dike and includes Honghu Lake and Xiaogang Farm...... 30

3.2. Xiaogang Farm, located near Honghu Lake, is divided by inner canals which are drained through two pumping stations to the Sihu General Canal ...... 31 33. Symbols used in model diagrams; (a) conceptual model symbols, based on Odum's energy language (Odum and Odum, 1994); (b) STELLA model symbols (HPS, 1996) ...... 35

4.1. Xiaogang farm annual production area (a) and yield (b) of crops and fish, 1990 - 1995, based on statistical data provided by Xiaogang Farm. Because of , sum of production areas exceeds total land area ...... 47 4.2. Water management for Four Lakes Region cultivation of (a) mid-season rice and (b) two-crop rice: normal minimum and maximum water depths, and depths at which damage occurs after 1 day or 3 days of inundation. Approximate transplant (T) and harvest (H) periods are indicated ...... 54

43 . Water management for Four Lakes Region cultivation of (a) tatami, (b) root lotus and (c) wild rice stem: normal minimum and maximum water depths, and depths at which damage occurs after 1 day of inundation. Approximate transplant (T) and harvest (H) periods are indicated ...... 55 4.4. Water management for Xiaogang Farm aquaculture including (a) managed lake, (b) circle-style pond and (c) intensive pond: normal minimum and maximum water depths, and depths at which production loss occurs after 1 day of inundation. Approximate stocking (S) and harvest (H) periods are indicated ...... 58

4.5. Schematic of Xiaogang Farm canal and field ditch dimensions for (a) normal conditions and (b) flooded conditions, and water volume calculation approach ...... 60

4.6. Comparison of (a) Honghu City and (b) Xiaogang Farm land use as proportion of total productive area, based on Hutei Agricultural Statistics

XIV Yearbooks, and Xiaogang Farm agricultural records, respectively, for 1990- 1992, and 1994 ...... 63

4.7. Long-term increasing trend in crop yields for (a) Honghu City and (b) Jianli County, based on Hubei Statistical Bureau data, 1949 - 1992. Impacts of Yangtze River dike breaches ( 1954, 1969) and recent heavy rainfall years (1980, 1983, 1991) are evident ...... 64

4.8. Comparison of monthly rainfall totals (mm) for Xiaogang and Honghu, 1978 - 1994, and Jianli 1978 - 1992 ...... 66

4.9. Lognormal plot of frequency of 1-day, 3-day and 10-day rainfall amounts ...... 68

4.10. Crop water use: (a) évapotranspiration coefficients for rice specific to Four Lakes Region; (b) monthly mean pan evaporation for Honghu City, 1980 - 1994 (columns show grand means; error bars show year-to-year range) ...... 69 4.11. Water elevations at three water control gates of the Sihu General Canal during 1991, a high rainfall year. See Rgure 3.1 for gate locations ...... 72

4.12. Water elevations at three water control gates of the Sihu General Canal during 1992, a low rainfall year. See Figure 3.1 for gate locations ...... 73 4.13. Daily water elevations (m a.s.l.) above and below Xiaogang Gate, and annual duration and volume of gate operation, 1991 - 1 9 ^ ...... 74 4.14. Percent decline in early, mid and late season rice yields during recent heavy rainfall years (when compared to immediately preceding and following years) for Honghu City (solid bars) and Jianli County (shaded bars), based on Hubei Statistical Bureau data...... 76

4.15. Xiaogang Farm hydrologie system response to extreme rainfall event in July, 1996: h i^ external water levels (Sihu General Canal) forced a temporary reduction of pumping and prolonged the internal flooding ...... 79

5.1. Conceptual model of Jianghan-Dongting Plain hydrology and production: original condition before cultural modification. Symbols are as defined in Figure 33a...... 86

5.2. Conceptual model of Jianghan-Dongting Plain hydrology and production: landform/land-use category scale, with imposed, crop- or pond-specific water management schedule. Symbols are as defined in Figure 33a ...... 87

5.3. Conceptual model of Jianghan-Dongting Plain hydrology and production: farm scale, with imposed, farm-level water management schedule. Symbols are as defined in Figure 3.3a ...... 89 5.4. Profile view of Xiaogang Farm conceptual model, showing relative areas, ground elevations and typical and flooded water depths of different landform/land-use categories ...... 90

XV 5.5. Conceptual model of the Jianghan-Dongting Plain hydrology and production: Honghu Flood diversion Area scale, with imposed, area level water management schedule. Symbols are as defined in Figure 33a ...... 92 5.6. Storage and flows for land use element (LUE) 1. LUE I is one of seven LUEs draining to the storage lnner_canals; the latter provides irrigation to all LUEs and excess water in Inner_canals flows out of the model. Symbols are as defined in Figure 3 3 b ...... 96 5.7. Sensitivity analysis of relationship of canal and field ditch volume to water elevation: effect of a ± 10% change in eissumed canal depth is ± 15% volume at 21.2 m elevation...... 101 5.8. Profile view of Xiaogang Farm conceptual model, showing only the low- position fields (LU& 1 & 2), lake (LUE 3) and canals, and illustrating computational approach for flooding elevation, (a) Canal is above criterion level; flood level is computed from shape M. (b) A portion of LUE 1 is also above typical maximum; flood level is computed from shapes M + N. (c) Flood level is computed from shapes M, N, P + Q ...... 104 5.9. The variables flood_elev_l, flood_elev_2,..., flood_eIev_5 are calculated from iteratively defined quadratic variables (e.g., canal_a, a_plus_l, etc.). Each is used to derive a test variable (flood?_l, flood?_2, etc) to determine whether a given land use element is flooded, at each dt. Symbols are as defined in Figure 33b ...... 108 5.10. The test variables flood?_l, flood?_2, etc. are used to determine which value (flood_elev_l, flood_elev_2,..., flood_elev_5) should be accepted as the actual flood elevation (flood_elev_flnal). The latter determines volume in the canal system (canal_vol_final) and is compared with a design level (criteria_canal_elev) to determine amount of exchange with the outside (exchange_flow). Symbols are as defined in Figure 3 3 b ...... 109

5.11. Flood_elev_final defines the volume of water (e.g., flood_vol_l) assigned to each land use element (LUE 1 is shown) in the flood redistribution process. If the assigned amount exceeds the actual amount (Water_l), drainage (drainage_l) is negative; i.e., excess water flows in. Symtxsls are as defined in Figure 3 3 b ...... 110 5.12. The Honghu Flood Diversion Area (HFDA) model adds two state variables, Honghu_lake and outer_canal. Flows include surface inflow to the HFDA (HFDA_Inflows), net precipitation to the lake (Rain_Honghu) and flows to or from farm areas (farms_above, farras_below). The latter is divided into five delayed flows (split_0, split_l, etc.) to desynchronize storm flow peaks entering the outer canal. Symbols are as defined in Figure 3 3 b ...... 115 5.13. Honghu Flood Diversion Area model computations: (a) computation of water elevation in the outer canal, based on a trapezoidal channel; (b) computation of surface inflow rate based on Manning equation for charmel flow. Symbols are as defined in Figure 3 3 b ...... 117

XVI 5.14. Honghu Flood Diversion Area model computations: (a) computation of HFDA irrigation demand and flow through Xiaogang Gate, based on elevation criteria for Honghu Lake and the Sihu General Canal; (b) computation of HFDA outflow through Xintankou, Gate, according to elevation criteria for the outer canal and pump capacity or weir flow. Symbols are as defined in Figure 3 3 b ...... 120 5.15. Simulation of Xiaogang Farm inner canal water elevation for flood event of July 14 - Aug 7,1996, before (a) and after (b) model calibration. Recorded water elevations are shown for comparison ...... 122 5.16. The relationship between water elevations inside Xintankou and below Xiaogang gates, respectively, was used to estimate 1980 missing data for water elevations below Xiaogang: (a) regression model; (b) estimation of 1980 elevations below Xiaogang. The relationship is strongest at high water elevations...... 125

5.17. Simulation of Xiaogang farm iimer canal elevation ( I) during May - August for years 1980 - 1986. Outer canal elevation (2), a forcing function, is also shown. At outer canal elevations above 243 m a.s.l., pumps were assumed to be shut down and the period of internal flooding was extended ...... 129 5.18. Simulated timing and area of Xiaogang Farm flooding damage (i.e., in excess of I-day tolerance depth), with respect to periods of crop growth, during May - August for years 1980 - 1986, in Land Use Elements I (a, mid-season rice) and 2 (b, two-crop rice). Periods of transplant (T) and harvest (H) for each crop are shown. Year of each flood event is indicated; gray arrows indicate years that were not described as flooding years ...... 131 5.19. Simulated peak flood elevation and crop damage, farm-scale model, 1980 - 86: (a) internal flood elevation and fraction of crop area damaged; (b) crop loss index, computed as area under the damage area-time curve, normalized to total crop area ...... 132

5.20. Simulation of Xiaogang Farm inner canal elevation (I) during May - August for years 1987 - 94. Outer canal elevation (2), a forcing function, is also shown. At outer canal elevations above 243 m a.s.l., pumps were assumed to be shut down and the period of internal flooding was extended ...... 135 5.21. Simulated timing and area of Xiaogang Farm flooding damage (i.e., in excess of I-day tolerance depth), with respect to periods of crop growth, during May - August for years 1987 - 1994, in Land Use Elements I (a, mid-season rice) and 2 (b, c, mid-season rice : tatami ; late rice, 2-year rotation). Periods of transplant (T) and harvest (H) for each crop are shown. Year of each flood event is indicated; gray arrows indicate years that were not described as flooding years ...... 137

5.22. Simulated flood elevation and crop damage, farm-scale model, 1987 - 94: (a) inner canal peak elevation and fraction of crop area damaged; (b) crop loss index, computed as area under the damage area-time curve, normalized by area planted ...... 139

XVII 5.23. 1987 water elevations and water flows in the Honghu Flood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison ...... 143 5.24. 1988 water elevations and water flows in the Honghu Flood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison ...... 144

5.25. 1989 water elevations and water flows in the Honghu Flood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison ...... 145 5.26. 1990 water elevations and water flows in the Honghu Rood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison ...... 146 5.27. 1991 water elevations and water flows in the Honghu Rood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated...... 147

5.28. 1992 water elevations and water flows in the Honghu Rood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison ...... 148 5.29. 1993 water elevations and water flows in the Honghu Rood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison ...... 149

5.30. 1994 water elevations and water flows in the Honghu Rood Diversion Area- Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison ...... 150 5.31. Simulated flood elevation and crop damage, HFDA-scale model, 1987 - 94: (a) iimer canal peak elevation and fraction of crop area damaged; (b) outer canal peak elevation and crop loss index, computed as area under the damage- area curve, normalized by area planted ...... 152

xvin CHAPTER 1

INTRODUCTION

l.l. Ecological Engineering and Sustainability

Sustainability of ecological and socioeconomic systems has been called "arguably, the central environmental issue facing us” (Levin, 1993). Patterns of economic development which ignore the life-support, productive and other functions of ecological systems (de Groot, 1994) have accelerated natural resource depletion and environmental damage (Hall, 1992). Natural-system impacts such as soil loss, eutrophication and habitat destruction result in economic loss because of lost production and because 'free' ecosystem services often must be replaced with costly, fossil energy-based substitutes (Hueting,

1991, Repetto, 1992; Azarand Holmberg, 1995). These economic losses may stimulate further ecological impact (i.e., through -depleting agricultural extensivication or intensification), resulting in a self-reinforcing cycle. Ecological engineering, also termed ecotechnology, has been broadly defined as "the design of sustainable ecosystems that integrate human society with its natural environment for the benefit of both” (Mitsch, 1993,1994, 1996) and the number and variety of its applications has been growing in Western countries and in China. Western approaches to ecological engineering are characterized by reliance upon self-design (rather than imposed design) and solar energy input (rather than technological energy input), and conservation of ecosystems and species (as opposed to their replacement or loss) (Mitsch I and Jorgensen. 1989: Mitsch, 1993. 1996. 1997). Chinese approaches, while otten not relying on self-design (since many of their systems are highly structured and closely managed), emphasize species symbiosis, efficiency, materials cycling and minimization of

wastes (Ma and Yan, 1989; Mitsch. 1991. 1995; Mitsch et al., 1993; Yan et al.. 1993). Although emphases differ, many of the techniques applied (such as hydrologie modification, nutrient recycling, ecosystem rehabilitation, enhancing biodiversity and

sustainable harvesting) are common to both (Mitsch, 1996). However, if it is to help us solve the serious problems facing us. ecological engineering must do more than design new tools. The above definition suggests that the ‘sustainability* and ‘benefit* of ecologically engineered systems should be verified. These terms imply that a ‘bottom line* of overall effectiveness should be examined, such as may be determined through modeling and economic analysis. Ecological engineering therefore relies in part on two related interdisciplinary fields. Ecological modeling enables the simulation of ecological outcomes of various management options, allowing critical variables to be identified and ranges of uncertainty to be quantified (Jdrgensen, 1989). Ecological economics, described as “the science of sustainability,” views human systems as components of, not as separate from, ecological systems, and seeks analytical and institutional frameworks that encompass both economic forces and biophysical realities (Costanza et al., 1991).

1.2. Hypothesis and Obj ectives of Study

This dissertation presents a case study to evaluate the general hypothesis that ecological engineering can improve regional ecological and economic sustainability. The goal is to examine, through ecological modeling, the interactions between an ecosystem process (flood peak attenuation by wetlands) and an economic activity (food production) in the Jianghan-Dongting (JH-DT) Plain, located in the middle Yangtze River valley of central China (Rgure l.l).

Changes presently occurring in China have important implications for global sustainability (Ryan and Ravin, 1995; Meadows, 1995, Brown, 1995; Harris, 1996). Rising affluence is increasing the demand for meat, and therefore for grain. Until the 1950s, was increased through extensivication, i.e., the conversion of marginal

land; subsequently, grain yields were increased through intensification, or the introduction of new crop varieties, and increased irrigation and use. In the future, however, further productivity increases are expected to be modest, and increasing industrial value of land will tend to reduce cropland area. Increasing income and population and level or falling grain production are projected to cause a large grain shortfall and to strain world grain markets. Therefore, the application of new analytical approaches to environmental and resource management in China is critical.

An important part of China’s agricultural productivity lies within the JH-DT Plain.

It is an area of climate, shallow lakes, diked agriculture, elevated river beds, frequent flooding, and increasing environmental degradation (Cai and Zhang, 1991 ; Cai and Yao, 1992; Cai and Xia, 1993). Both grain productivity and habitat character depend on the dynamic interaction of land and water. The seasonally flooded depressionai wetlands

(emergent marshes and shallow lakes) that originally typified this region have in large measure been empoldered to create artificial wetland systems— i.e., fish ponds and paddy fields— protected by means of dikes, drainage canals and pumps. Yet they are still occasionally subject to inimdation by rainfall in excess of drainage capacity, resulting in economic loss. Thus, the system for land protection and drainage has diminished, but has not eliminated, the need for a flood storage function; the artificial wetland system that currently exists plays a flood storage role only at some economic cost. 111° E I 114°E

Scale (km)

Yangtze R s Three Gorges Project

Wuhan D a b i e ^ ^^^Yichang Mountains

Qing R. 30° N 30° N °"9hu J i u II n y Poyang Mountains r Lak

Dongting Lake Yuan R. Nanchang Xiang R. Gan R 111° E Changsha 1114® E 117° E

Figure l.l. Situation of the Jianghan-Dongting Plain in the Middle Yangtze River Basin, China. The Four l^kes Region is shaded. This flooding problem may be addressed by conventional engineering— i.e.. by augmenting the protection and drainage system. Indeed, flood control works have been in the process of development for thousands of years, and progress since the 1950s has been rapid and is still ongoing. However, drainage improvements are costly, and they create a downstream externality by further decreasing water storage and increasing pressure on large waterways. The problem can also be addressed by ecological engineering. The latter approach would adjust the artificial wetland systems themselves to improve their ability to play a flood storage role without economic loss. One means would be to raise crops with wide tolerances for inundation, instead of rice which has only a narrow tolerance. Another would be to return some areas currently used for agricultural production to lake/wetland status. These measures would be expected to produce benefits within the economically engineered system and also reduce the downstream externality. Thus, more benefits should be captured as the scale of the analysis increases.

Therefore, the more specific hypotheses of this study are as follows: (a) Shifting land use in empoldered areas of the JH-DT Plain toward productive uses with greater water storage capacity and inundation tolerance will produce net economic benefit, and

(b) This benefit will become more pronounced as the spatial scale of evaluation increases.

These hypotheses cannot be evaluated by static computation of system storage capacity. Storages and flows constantly change according to meteorological factors and cropping system needs, resulting in nonlinear hydrologie responses. Therefore, this dissertation undertakes the following objectives: (a) Develop a computer simulation model of regional land use (focusing on agriculture and aquaculture), hydrology, and flooding damage for an area within the JH-DT wetland plain. (b) Use the model to evaluate and compare the physical efficacy of both conventional and ecotechnological approaches for reducing flooding loss. 5 (c) Incorporate economic valuation into the model, to evaluate efficacy in economic terms.

1.3. Limitations of Study Scope

This dissertation addresses only the type of flooding that occurs when local or regional precipitation exceeds drainage capacity, requiring excess water to be retained within production areas. It does not address the more famous and catastrophic form of flooding that occurs when a of the Yangtze River is breached. The latter event has not occurred in the study area since 1969, whereas local inundation due to excess precipitation is a more frequent problem. The likelihood of an extreme, regional event remains high because of the elevated state of the Y angtze River bed, and because of regional land use patterns that accelerate runoff. For example, such a breach was said to have been narrowly averted in the Four Lakes Region during the heavy rains of July, 1996. Several of the engineering approaches evaluated in this dissertation would lead to the detaining of more water within production areas, thus potentially relieving pressure on the Yangtze River. However, a study of Yangtze River hydrology and levee conditions such as would be needed to assess this potential benefit was outside the scope of this investigation. The Three Gorges Project, currently under construction about 50 km upstream of the JH-DT Plain, is expected to control Yangtze River flood peaks and minimize the danger of a catastrophic breach, albeit at a very high social, economic and ecological cost

1.4. Document Organization

Development of an ecological model is a systematic process involving the following steps (modified from Jorgensen, 1986): 1. Definition of modeling objectives 2. System description 3. Data collection

4. Conceptual model development 5. Mathematical model development 6. Computer model development

7. Model verification (testing for correct mathematics and logic) 8. Sensitivity analysis (systematic variation of parameters to identify those most critical to model accuracy) 9. Model calibration (adjustment of model parameters to achieve correct performance according to known information) 10. Model validation (testing against a new data set not used in model development). The first step, definition of objectives, has been discussed in this first chapter, and Chapter 2 evaluates the recent literature relevant to those objectives. In Chapter 3, a description of the study site is presented, and then the methods used for carrying out steps 3 - 9 are summarized. Chapter 4 presents the data collected during a field investigation of the study site. Chapters presents conceptual and simulation models that were develojjed to represent that system. Chapter 6 presents the results of hydrologie modeling, discusses the economic implications of these findings and presents conclusions and recommendations, including a description of research required to complete the 10th step, model validation. CHAPTER 2

LITERATURE REVIEW

This chapter reviews the recent literature regarding the traditional practices and problems of lowland agriculture in China, applications of ecological engineering that are being made in those areas, and new methods for the integrated modeling of ecology and economics.

2.1. Traditional Food Production in Depressional Landscapes in China

Water management is critical in assuring high yields of rice (Oryzasaiiva L.). Irrigated-paddy cultivation of rice ensures not only an adequate water supply for plant transpiration needs, but also improves , crop nutrition and soil conservation (van de Goor, 1980). Water is carefully managed during each stage of crop growth (Table

2.1). Paddy farming is practiced on lands at any elevation having a sufficient supply of water, but is most common in lowlands, especially in association with the major rivers that flow east and south from the Himalayan highlands. Moorman and van Breemen ( 1978) describe six landform categories of lowland rice cultivation areas: inland valleys, alluvial fans/piedmont plains, meander floodplains, lacustrine floodplains, marine floodplains and alluvial terraces. Lacustrine floodplains typically occur in major river valleys, have a saucer shaped landform, and have a perennial or intermittent lake in the lowest part.

8 Stage" Description'’ W ater management" Puddling/Soil preparation Successive steps of preparing flooded Deep soil including plowing, flailing, smoothing, mixing Transplant Hand transplant of seedlings Shallow

Rooting/T um-green Recovery/rooting of transplanted Deep seedlings

Tillering/Stooling Proliferation of tillers or shoots Shallow

End of Tillering/ Stooling Drainage to halt excessive tillering and Drain oxygenate soil

Young panicle Panicle differentiation, meiosis Deep formation/Booting

Heading Panicle emergence and flowering Deep Maturing/Ripening - Starch deposition in endosperm Intermittent irrigation Milky stage Maturing/Ripening - Hardening of grain Drain in preparation Yellow stage for harvest

Harvest Drain

'’Grist (1986); Koga (1992); Shijian Chen (pens, comm.) 'Koga(1992)

Table 2.1. Stages of growth and generalized water management for paddy rice. Among these landforms. Moorman and van Breemen ( 1978) characterize three types of physiographic-hydrologic landscape positions. Pluvial rice lands are on upland, sloped terrain and are watered only by rainfall. Under natural conditions their soils are

freely drained, but when leveled and diked (i.e., pluvial-anthraqiiic) they take on some of the characteristics of phreatic rice lands. Phreatic rice lands are partially fed by ground water. They are located on foot slopes, above flood level, where ground water is

seasonally high due to interflow from adjacent highlands; their soils are partially gleyed. Fluxial rice lands receive surface flow; they are poorly drained, depressional areas that are

inundated during part of the growing season, yet may be phreatic or pluvial in character during other parts of the year.

In fluxial rice lands, flooding is a common cause of crop damage during periods of unusually heavy rain (Reddy et al., 1991). varieties are capable of rapid stem elongation and/or flotation, and may survive submergence of up to 10 days and water depths of up to 5 m (Jackson et al., 1982). Brady ( 1977) estimated that areas suited to deepwater rice comprise 25 - 30% of the world’s rice lands. However, deepwater varieties are not as highly yielding as lowland varieties, and their cultivation is still subject to high risk of partial loss due to erratic flooding (Grist, 1986). The more highly yielding semidwarf varieties, which are grown at water levels of 0 - 10 cm, vary in their tolerance to submergence; efforts to transfer submergence tolerance into high yield varieties are progressing, but the most tolerant lines tend to have low yield potential (Mackill et al., 1993). Flood resistance of rice varies with timing as well as variety. Many varieties can survive a period of inundation as long as a small portion of the plant remains above water level. Crops submerged at tillering or flowering tend to suffer severe damage (Grist, 1986). The degree of damage also increases with depth, duration, turbidity and flow of water, and it varies inversely with plant nutritional status (Reddy et al., 1991).

10 Empoldering is a method of achieving water control by enclosing a tract of low - lying land to prevent inundation from outside. The interior area is supplied by irrigation and drainage canals, and unless adjacent to a tidal system, pumps are required for internal water control. Water control systems can shift fluxial rice lands to phreatic-anthraquic. allowing high-yielding varieties to be grown, but these crops are susceptible to control system failures (Moorman and van Breemen, 1978; Grist. 1986). Protection by

empoldering has been an important method for increasing rice acreage and yield in the fluxial rice lands of the middle Yangtze lacustrine floodplains in China, but periodic crop

loss due to inundation continues to plague these areas (Cai and Yao, 1992; Cai and Xia, 1993).

Little information is available in the Western literature on alternative paddy or pond crops grown in China that may tolerate deeper water. Yamaguchi ( 1990) published a brief, generalized description of Chinese/Taiwanese cropping practices for root lotus and wild rice stem. The East Indian lotus, Nelumbo nucifera Gaertn., is an important traditional crop in China. It is grown mainly for the starchy storage rhizome, but the young shoots, immature leaves, mature seeds and carpels are also eaten. Rhizome segments are planted in the mud of a prepared paddy with 6 cm water depth. With growth, water level is raised to about 30 cm and maintained at this level. Flowers emerge in July, and their removal may improve rhizome yield. Rhizome development begins in early August; rhizomes grow at a depth of 30 cm below the soil surface and are fully developed by late September. Harvest follows planting by 120 days in warm climates and 150-180 days in cold climates. At harvest, water is drained and the rhizomes are dug by hand. Rhizomes are 60 - 120 cm in length and 6 - 9 cm in diameter; yields vary from 3.5 - 4.5 t/ha (Yamaguchi, 1990). Wild rice stem, Zizanialatifolia Turcz (Z.aquatica L.) is another traditional Chinese and Indo-Chinese crop, also known as water bamboo. The species is closely related to the North American natives Z. aquatica, Z. palustris and Z. texana. Unlike the large-seeded Z.

11 palustris. which is cultivated for grain in North America (Oelke. 1991 ). Z. latlfoUa grows symbiotically with the fungus Ustilagoesculenta which causes the stem to enlarge and evidently prevents floral initiation. Stems are harvested before the fungus enters the spore- producing (black smut) phase; when sheathing leaves are removed, the tender, enlarged stem is sliced and eaten raw or cooked (Yamaguchi, 1990). Field cultivation of wild rice stem is similar to that of paddy rice, except that propagation is asexual. Cultivation time varies regionally; transplanting is in January - March (subtropical) or March - April (temperate). Tillers from the previous year are retained and nursery cultivated before transplanting to a prepared paddy. Initial water depth of 6 cm is soon raised to 10 - 15 cm; 15 cm is optimal but 20 cm is used in hot weather.

Three varieties are grown in China; green stem variety is small and early-maturing ( 150 days after transplanting) and white variety and red or pink variety are larger and later maturing ( 170 days). Plants grow 1.2 - 2.4 m in height (Yamaguchi, 1990).

In addition to paddy cultivation, both N. nucifera and Z. latifolia are commonly found in shallow lakes of the JH-DT plain (Cai and Xia, 1993). Other locally grown deepwater crops include water chestnut, Trapanatans, but information on its Chinese culture was not available.

2.2. Ecological Engineering of Wetland Regions in China

Many studies have explored the use of ecological engineering approaches to improve production in wetland agricultural areas of China, but few of these have dealt with the problem of crop damage from inundation of depressional lands. Several farm or regional level studies have analyzed productive systems and their interconnected components, but the relationship between land use, production and hydrology usually has not been quantitatively analyzed.

12 For example. Yan and coworkers (Ma and Yan. 1989; Yan and Yao. 1989; Yan and Ma. 1991; Yan et al., 1993; Mitsch et al.. 1993; Yan and Zhang, 1992. 1994) and Mitsch ( 1991, 1993, 1995) have discussed the design of multi-species, interlocking production

systems that recycle materials, reduce pollutant release and promote overall efficiency. ■ Ecological' enhancements to agricultural production may take the form of inserting a new species into a production food chain (e.g., using earthworms or maggots to process wastes

and increase protein content, before feeding the wastes to ), linking two production systems (e.g., mulberry cultivation and fish production) so that each uses the other’s waste stream, or combining species to optimize space utilization while minimizing niche overlap (e.g.. carp ). They have also used water hyacinth. Eichhomia

crassipes, to remove metals and harness excess nutrients from aquatic systems. Chung ( 1989. 1993, 1994) has described the planting of marsh cordgrass, Spartina spp., as a

method for preventing coastline erosion and for creating vegetated, productive land areas from coastal mud flats.

Guo and Bradshaw ( 1993) evaluated the flow of nutrients and conducted field trials to improve productive structure in a farm in Jiangsu Province. Through substitution of waste materials for purchased feed or fertilizer, cultivation of plants {Justicia americana) in river channels, and deepening of fish ponds, yields could be maintained or increased while purchased inputs were minimized. Landscape position and hydrologie aspects were not discussed, however.

Hsiung and Xue ( 1993) evaluated a marshland region in northern Jiangsu Province to compare a range of land uses—including natural marshland, agriculture, aquaculture, silviculture (Pond cypress. Taxodium ascendens), agro-silviculture, silvo-aquaculture, and agro-si 1 vo-aquaculture—for optimal overall benefit. Each land use was scored against 12 metrics, which included ecological measures (e.g., accumulated biomass, protection of biological resources), economic measures (e.g., production value, output/input ratio,

13 product variety), and a social measure ( manpower utilization). Each metric was weighted "objectively" (weighting methods were not well described) according to the overall objective of sustainable development, and weighted values were summed to give an integrated evaluation value for each land use. Natural marshland scored very low by this method; agriculture and silviculture received moderate scores. Aquaculture and the mixed systems all scored highly, with the agro-silvo-aquaculture system scoring the highest. One of the metrics included in Hsiung and Xue’s scoring matrix was tolerance to flooding, determined according to the time interval between flood-destruction events for each production system. However, the matrix scores for each system were not reported, and the specific system properties conferring the flood tolerance were not brought out. X. Ma et al. ( 1993) qualitatively described an integrated, paddy-reed-fish production system developed as an alternative to the prevalent practice of wetland draining in Heilongjiang Province. The reed-marsh component was shown to be capable of serving

as a regional streamflow regulation reservoir, protecting downstream areas from both flooding and drought. Water-quality and biodiversity benefits were also cited, but no quantitative analysis was provided. Shuming Cai and coworkers at the Department of Land Use and Environment.

Institute of Geodesy and Geophysics in Wuhan, have studied the history of lake and wetland conversion to agriculture and aquaculture in the Four Lakes Area of the Jianghan- Dongting Plain (Figure 1.1). They documented the pattern and rate of land use conversion in the vicinity of Honghu Lake (Yu et al., 1993). They suggested that the resulting loss of water storage capacity has led to increases in regional flooding (Cai and Yao, 1992), and they provided rough calculations of the current deficit of regional discharge pumping capacity (Cai and Zhang, 1991). Cai and coworkers have also described new production models that tailor land use to micro landform, to maximize productivity while minimizing flooding damage (Cai and

14 Zhang, 1991; Cai and Xia, 1993; Chen et al., unpub.). For example, Xinxingyuan. a lacustrine, depressional formation {oryuan rian) of about 51.2 km" total area located in Jianli County, is a dish-shaped area with an elevation difference of about 7 m between edge and center. Chen et al (unpub.) have identified six kinds of micro landforms lying in concentric rings within this formation: Higher plain with and yards elevated by natural river (the courtyard circle, 28.8 - 30.2 m above sea level), high plain used as dry fields (the dry land circle, 27.0 - 28.0 m); gently sloping plain (the good paddy field circle, 26.0 - 27.0 m); waterlogging low plain (the lowland circle, 25.0 - 26.0 m) and surface water (the depression or lake circle. 24.0 - 25.0 m).

The initial development of most of the low-lying areas of Xinxingyuan was carried out in a rectangular pattern with a single land use (rice cultivation); this structure resulted in frequent flooding and crop loss. An improved development model, based on the concentric rings of micro landform, was then implemented as follows (Chen et al., unpub.):

- Courtyard Zone (both courtyard circle and dry land circle); Diversification is developed with vegetable planting and animal husbandry as the main land use, to provide greatest benefit to .

- Main Crop Zone with High Benefit (gently sloping plain): Rice, wheat and rape, etc. are planted in this zone to provide basic food to the farmers. Such rotation models as "rape-rice-rice", "rape-soybean-rice" in one year and "rice -rice-wheat- cotton-soybean" in two years are used. Grain yield reaches 15,000 kg/ha in one year. - Wetland Plant Zone (low land zone); Root and seed lotus, wild rice stem and water chestnut are the main crop in this zone to adapt to the condition of flooding and waterlogging. As water is controlled in the field for a long time under this model, fish and other water products are also raised with the plants. The economic output of the model is also very high; 30,000 - 50,000 Yuan per ha, or 3 - 5 times that of one-crop rice. - Fish Raising Zone (open lake and closed pond from low land); Four species of local fish combined with special, high benefit species are raised in one pond. The Xinxingyuan model is thus a landform-sensitive development approach, in which wetland crops other than rice play a key role. This model has been carried out to varying degrees at other locations, such as Xiaogang Farm, near the northeast comer of

15 Honghu Lake. A more detailed analysis of this model is needed, however, in order to better quantify the magnitude and variability of the benefits claimed, and to determine how these benefits would respond to further refinement of the model, during both moderate and adverse rainfall years, and under various economic conditions.

2.3. Models for Regional Integration of Ecology and Economics

Many of the problems that are of interest to ecological modelers deal with anthropogenic impacts, which have their origin in socioeconomic processes. To better address these problems, ecological modelers have increasingly sought to extend their analyses into the economic sphere; similarly, economists have sought to incorporate ecologically relevant factors into their analyses. Studies in which joint analysis of ecological and economic phenomena is attempted for a particular geographic location are generally itvmQd regional or integrated assessments. In some cases the term ecological economic modeling is applied, but not all regional or integrated assessments model both the ecology and the economics. All of these approaches must deal with the fundamental dissimilarity of biophysically-based and welfare-based analytic frameworks (Shogren and

Nowell, 1992). Therefore, few of them are truly integrated to the point of successfully capturing the dynamic feedback that occurs between the two spheres. This section will discuss the limitations that are apparent in studies that incorporate both ecological and economic factors, whether those studies claim the term "integrated” or not.

2.3.1. Partial Integration Approaches

Many studies are primarily either ecological or economic in methodology but have sought to be anchored in the other framework as well. Models that examine ecosystem responses to a set of conditions or management alternatives, and then place economic values on the modeled productive flows, have been described as production jiinctions; these constitute a special case of cost-benefit analysis (Hanley and Spash, 1993). For

16 example. Higgins et al. ( 1997a. b) used plant population, fire and hydrologie models to estimate discounted net values of economic flows (i.e.. wildflower harv esting, fuelwood

harvesting or ecotourism) from the South African lowland and mountain fynbos ecosystems, respectively. An ongoing study (Islam et al., 1997) is examining the economic consequences of alternative floodplain management strategies (i.e.. levee heights and lock and dam operations) for the La Grange Reach of the Illinois River. For each strategy, a hydrologie model feeds into wetland vegetation models, the results of which predict habitat suitability for fish and wildlife. The per-acre benefits of taking these, together with estimates of crop losses due to flooding, constitute the outputs of the model, but the full modeling system is not yet implemented. In each of the above cases, the economic valuation component is an add-on to the écologie components, and the resulting economic conditions do not feed back to define subsequent scenarios to be ecologically evaluated. Therefore, within the modeling system itself true integration is not achieved.

However, if modeling were conducted iteratively with scenario redesign, a kind of integration could be established. A subset of the production function approach comprises studies employing safe minimum standards. These studies often focus primarily on economic analysis, but they employ a constraint that is meant to ensure that the results will be ecologically benign. The constraint is usually exogenously determined, and thus real integration is again not achieved. Habitat evaluation methods are often used in defining a management scenario which is then economically evaluated. Schaumbergeret al. ( 1992) evaluated the economic costs (e.g.. Federal and other revenue and wage loss) and benefits (recreational and intrinsic values) of withholding from timber harvest areas that had been separately designated as critical habitat units for the Northern Spotted Owl. Watts et al. ( 1997) similarly measured the economic outcomes from irrigation, hydropower, water supply and recreational changes that would result from implementing critical habitat designations for

17 endangered species in the Colorado River and its major tributaries (in seven U.S. States) and a 160 mile stretch of the Virgin River (in three States).

In other cases the safe minimum standard can be met by a variety of methods that vary in cost and efficacy, and modeling is used to optimize achievement of the standard. Braden et al. ( 1989) tied a habitat suitability index to an agricultural management model that

cost-optimizes sediment reduction, and as a result they were able to determine least-cost measures for protecting salmonid spawning habitat in a 93-ha sub-watershed in Michigan. Lakshriminarayan et al. ( 1996) used the Comprehensive Environmental Economic Policy Evaluation System (CEEPES) to conduct an 18-state evaluation of optimized costs of

achieving given, health-based water concentrations of the herbicide atrazine. But in other cases where ecological impacts of soil loss were modeled, no clear target or standard was determined, and tons of soil loss was used as a proxy for ecosystem damage (e.g.. Lupi, 1988).

In another variation of the safe minimum standard approach, the attainment of some equilibrium condition within the ecological model is used as an optimization constraint; for example, van den Bergh (1995) used non-reduction of a state variable, fish stocks, as a

basis for one of several scenarios evaluated in a production function-type model of economic development in the Greek Sporades Islands. V. Costanza and Neuman (1997) examined agro-silvo-pastoral practices in degraded areas of the subhumid Chaco region

(Bolivia, , Paraguay and Argentina). Their model solved for initial conditions which would attain, over long time horizons, values for forest, forage and biomass that were within preset bounds; net discounted present value was the optimized indicator of system performance. The latter study entailed a higher degree of integration because, at times throughout the modeled period, simulated management control actions could occur in response to changing ecological conditions, but a decision support system based on the

18 method is still under development, and particular recommendations for ecosystem management were not made.

2.3.2. Attempts at Complete Integration

To achieve true integration, ecological economic models should enable dynamic interaction of economic and ecological phenomena. In most models, fundamental dissimilarities hamper the inclusion of both types of phenomena within a modeled system.

One approach for complete integration has postulated that energy, in one or another form, underlies both systems and can be used to unify their analysis. The problem of dissimilar energy types, i.e., solar energy in ecosystems and fossil energy in most economic systems, is not overcome by most types of energy analysis. In embodied energy analysis using the linear programming techniques of input-output analysis, solar energy that is embodied in economic products is ignored when the ‘cultural’ energy is being evaluated (Brown and Herendeen, 1996). In other approaches the two are assumed interconvertible on either a kilocalorie or money value basis (e.g.. Costanza, 1980; Costanza et al., 1989). Berg et al.,

( 1996) used such an approach to evaluate two alternative approaches (cage vs. pond) to developing aquaculture in the 5356 km* Lake Kariba, Zimbabwe. These methods have been criticized as postulating an energy theory of value’ that ignores other forms of scarcity, e.g. of minerals (Heuttner, 1976; Shabman and Batie, 1988). H.T. Odum and co-workers (e.g., Odum, 1988; 1996) have developed a more comprehensive theoretical framework which, by encompassing geologic time scales, postulates an embodied-solar-energy (or“emergy”) basis for ecosystem work, fossil fuel- based work and the use of ‘nonrenewable’ natural resources (within the emergy framework, renewable and nonrenewable resources are not distinguished). The optimizing or sustainability principle that emerges is the ‘maximum power principle,’ which states that those systems that utilize the most emergy will displace other systems. Many emergy analyses are static, balance-sheet approaches showing whether the net emergy of a system

19 such as a region or nation is enhanced or diminished under a given policy (e.g.. Huang et al., 1995). However, emergy simulation models can also be developed (e.g.. Odum and Arding, 1991) and may include not only the typical ecological model components (e.g., nutrients, food web nodes, mortality, harvesting pressure) but also tidal and rainfall energies, fuel and electricity consumption, goods and services, markets and investments.

The emergy approach, like other approaches that value natural capital (e.g..

Repetto, 1992), is particularly useful for exposing policies that diminish the "real wealth’ embodied in natural resources for the sake of gaining money income. However, the unifying concept that is the strength of emergy analysis is also its greatest weakness, because it assumes that economic agents should act to maximize emergy and thus fails to

recognize other dimensions of scarcity. Additionally, its system of valuation is based on events occurring over time scales that may not be particularly relevant to present human endeavor. Another approach to more complete integration retains the traditional distinction between ecological and economic metrics but seeks to improve connections between ecological and economic model components, making feedbacks more automatic and iterative. R. Costanza and coworkers (Costanza et al., 1995; Reyes et al., 1996) are developing a model of the Patuxent River watershed that focuses on information exchange between ecological and economic models. Watershed land uses drive hydrologie and ecological models of stream and river reaches; these, and modeled management policies, drive land use changes which in turn redefine model inputs. The modeling system is complex, however, and the modeling goals have not yet been achieved.

2.3.3. Bounding and Spatial Scale

Integrated regional models usually attempt to establish meaningful system boundaries. The boundaries of watersheds (Lupi, 1988; Braden et al., 1989; Reyes et al.

1996), water bodies (Berg et al., 1996; Watts et al., 1997) or islands (Huang et al., 1995;

20 van den Bergh et al.. 1995) are usually physically meaningful. Distinctive terrestrial ecosystems may also be modeled (e.g., Higgins et al., 1997 a, b; V. Costanza and

Neuman, 1997). From an economic standpoint, if the region is not exceedingly large, it greatly simplifies the analysis to treat the bounded region as a price-taker. That is, all inputs and outputs are assumed to (at least potentially) cross the system boundaries at an exogenous market-determined price; profits or net present values can thus be determined. This approach was followed for watersheds as large as the Patuxent (i.e., 166,000 km*. Reyes et al., 1996), although not for the Colorado River (Watt et al.. 1997) or for atrazine- using states (Lakshriminarayan et al., 1996).

Within the system boundaries, especially where the bounded system is large and heterogeneous, it is common to establish subunits for spatially distributed modeling. Ecologically, these should be relatively homogeneous, and the scale should be relevant to the scale of economic decisionmaking. For example, the Patuxent model (Reyes et al., 1996) divides the river and streams into 5896 spatial cells for ecological modeling.

Agricultural models typically recognize land management units (LMUs) of uniform soil, slope, rotation and ownership (Lupi, 1988; Braden et ai., 1989). Often spatial units are determined by political boimdaries and statistical aggregation (e.g. by state in Watt et al, 1997). On the other hand, analyses may be spatially lumped for any of several reasons.

The area may be considered relatively homogeneous (e.g., Higgins et al., 1997b), detailed information may be lacking, or it may be necessary to limit computational complexity (e.g.,

V. Costanza and Neuman, 1997). In summary, interest in integrated ecological economic modeling is growing rapidly as modelers develop new tools for addressing problems of environmental degradation. However, unlike in the well-established fields of cost-benefit analysis and environmental impact assessment, there is not yet an accepted standard of practice; methods vary widely.

21 and most suffer from integrational or informational deficiencies. The most promising approaches are not yet fully implemented because of their complexity.

Many workers have applied these tools in developing regions, because of their obvious relevance to natural resource management decisions, but it is not clear whether the results are yet being used by decisionmakers. Examples are still lacking of the use of

integrated modeling tools in China. Furthermore, while examples exist of economic evaluations of a given ecological engineering method (e.g.. Baker et al., 1991), integrated evaluations of regionally implemented ecological engineering strategies have not yet been

carried out. Many studies deal with agricultural sedimentation impacts on waterbodies, but

few deal with the impacts of hydrologie modification of the landscape. One study (Islam et al., 1997) plans to evaluate upland crop losses due to flooding; in that proposed model, however, once a crop is inundated its total economic loss is assumed. This approach

obviously cannot be used for lowland crops that are maintained wet and must regularly withstand some additional inundation.

The present study, which examines the interaction of economic production, system hydrologie response, and crop loss in a wetland region in China, confronts the same problems of integration method, bounding, scale and information limitation that are actively being addressed by many researchers. It also adds the feature of a regional-scale focus on ecological engineering strategies.

22 CHAPTERS

METHODS

3.1. Site Description and Field Investigation Methods

3.1.1. Jianghan-Dongting Plain Wetland Environment

The Jianghan-Dongting (JH-DT) Plain, located in the middle Yangtze River Basin (Figure 1.1) and renowned as the "land of rice and fish” (Cai and Zhang, 1991 ), is an area characterized by low relief, many shallow lakes, and a honeycomb of natural and manmade

waterways interconnected with the Yangtze River. Increased sediment and nutrient inflows

from deforestation and agriculture in the watershed, and construction of river and canal dikes, confining sediments within the waterways, have caused progressive bed elevation. Agricultural encroachment on the lakes through the building of dikes has decreased the natural flood storage capacity of the region. These changes have lead to increased flooding, decline of lake and wetland biotic resources, and economic loss (Cai and Zhang,

1991; Cai and Yao, 1992; Cai and Xia, 1993), in the face of a still-growing population in the region. The JH-DT Plain occupies an area of 50,000 km" north and south of the Yangtze

River in Hubei and Hunan provinces in central China. The area is low-lying and is characterized by deep alluvial deposits (Cai and Yi, 1991), many smaller rivers (Cai and Xia. 1993) and numerous large and shallow lakes formed by meanderings of the Y angtze

23 River (Hutchinson, 1957). The Han. Xiang, and Yuan Rivers are major tributaries which enter the Yangtze River in the JH-DT Plain area.

The area straddles the 30^ N parallel and is transitional in climate between temperate and subtropical. Monsoon precipitation traverses the Yangtze River basin moving from southeast to northwest, influencing in turn the various tributaries of the JH-DT Plain from April through September (Cai and Yao, 1992). Peak flow from each drainage therefore occurs at different times in a normal year, but flooding of the Yangtze River occurs when peaks overlap. During the past 2000 years. 214 floods occurred, averaging once per decade. The two greatest occurred in I860 and 1870, with discharges >l 10,000 m^s ‘ and area flooded as great as 30,000 km' (Cai and Yao, 1992).

Hydrology and sediment deposition in the area are therefore dynamic. For example, a paleolimnological study of Honghu Lake, the largest lake in Hubei Province

(presently 355 km'; 135 m average depth), indicates that the lake was formed less than 3000 YBP between natural levees of the Yangtze and Dongjing rivers. Since its formation, the lake has undergone at least two major cycles of expansion and contraction, attributable in part to changes in sea level and precipitation (Cai and Yi, 1991). Corresponding to these fluctuations, Honghu sediment layers alternate between lacustrine (gray-blue to brownish- yellow clays) and wetland (gray-black clays with up to 12% organic matter). Historical records corroborate that Honghu has alternated between an expansive lake and a reed marsh

(Cai and Yi, 1991). Lakes presently constitute 5% of the surface area of the JH-DT Plain (Cai and Yao. 1992), although this percentage recently was much higher, as will be further explained below. The network of natural levees, developed along the large and small rivers by frequent flooding, encloses many dish-shaped depressional areas ranging from 1 to over 50 km' in area. These formations typically have centers 2 -4m lower than the edges, and depressional lakes were originally formed at the center of these. Lake bank areas that have

24 been protected by dikes and converted to cultivation are termed yuan tian. Yuan rian development in this region is so extensive and distinctive it is described as a "major ecological feature" of the JH-DT Plain (Cai and Xia, 1993). Wetlands in the region are also extensive: most of these are under some form of cultivation or . Wetlands are associated with lakes (including submerged vegetation zones, emergent vegetation zones, and wet areas within yuan tian), rivers, and manmade structures such as ditches, aquaculture ponds and reservoirs. In areas too wet for other crops, wetland crops such as lotus root {Nelumbo nucifera) and wild rice stem {) are cultivated, and in reed {Phragmitesaustralis) marshes cattle are grazed (Cai and Xia. 1993).

Lake and Wetland Encroachment for Agriculture

Cultural influences in the JH-DT Plain have contributed to the seriousness of flooding and have caused other environmental problems as well. The plain has long been an important area of cereal production, and arable land has been expanded through "reclamation" of land from wetlands and lakes—that is, through the use of dikes to create yuan dan. Yuan tian development progressed during successive dynasties from the Han-

Jing (ca. 250- 380 AD) to the Qing ( 1780 - 1911 AD), causing a gradual reduction in lake area (Cai and Zhang, 1991). Following establishment of the People’s Republic of China

(PRC), a new wave of encroachment occurred, reaching a peak in the 1970s. Arable land area in the JH-DT Plain was increased by about one third, at the expense of lakes (Cai and

Zhang, 1991). Regional population has roughly doubled; population density was put at around 320 km * in 1991 (Cai and Zhang, 1991).

From the early 1950s to the 1980s, lake area in the Jianghan Plain (north of the Yangtze River) decreased from 4708 to 2657 km* (i.e., a 43.6% decrease). In the “Four Lakes” region (a subset of the of the Jianghan Plain), lakes decreased in number from 128 to 38, and in total area from 2034 to 844 km* (58.5%) (Cai and Xia, 1993). Honghu,

25 largest of the "Four Lakes,” decreased from 724 to 355 km’ (51%), while two of the other four major lakes, Bailuhu and Sanhu. have nearly disappeared (Cai and Xia, 1993; Yu et al., 1993). Total lake area in the Dongting Plain (south of the Yangtze River) decreased from 4010 to 2243 km’ (44.1%). Dongting Lake itself diminished from 2085 to 1308 km’ (37%), prompting Cai and Zhang (1991) to comment, "If the rate continues on, the whole Dongting Lake will no longer exist by the middle part of the next century, degrading into a Dongting plain with exuberant overgrowths of reeds, dotted with depressions, and a vast expanse of yuan tian."

A remote sensing and GIS study of Honghu Lake (Yu et al., 1993) determined that the area of open water decreased from 604 km’ in 1953 to 235 km’ in 1967. This loss corresponded to increases of 101 km’ in emergent macrophyte-dominated area and 50 km’ in "swamp” (not defined, but presumed to mean marshy areas no longer permanently inundated); the remainder of the loss (ca. 200 km’) was to agricultural encroachment. After 1967, the loss of open water has been slower, with an estimated 218 km’ remaining in

1988. During the period 1967 to 1977, ca. 110 km’ of emergent macrophytes and "swamp” were converted to 55 km’ of enclosed ponds and 55 km’ of new . Since 1977 the total lake area (open water plus emergent macrophytes) has remained static, since the lake is now almost totally enclosed by dikes, but the possibility of further loss of open water area remains a concern (Yu et al., 1993). Because they are interconnected to the river system by natural or man-made chatmels, lakes play an important role in temporarily storing peak flows during the monsoon season. Until recently, Honghu Lake, which is under hydrologie control, was drained to low levels (i.e., < 0,5 m) during the dry (winter) season. This primary use conflicted with other uses— fisheries and net-pen aquaculture, navigation, water supply, and irrigation (Cai and Xia, 1993)— and has been discontinued.

26 Flooding and AgriculturaL Production Loss

Deforestation and cultivation of steep slopes in the Yangtze River watershed have increased sediment loadings to rivers and lakes of the JH-DT Plain. The resulting deposition has contributed to lake shrinkage and limited the discharge capacity of the Yangtze and other rivers (Cai and Yao, 1992).

Agricultural areas of the JH-DT Plain suffer frequent inundation and/or waterlogging. Since the founding of the PRC, flooding has occurred in the area north of the Yangtze River in I of every 3 years, with an average inundation of 1270 km* and waterlogged area of 2000 km^; south of the Yangtze River the flooding frequency was I in 4 years, and average area of inundation or waterlogging was 553 km* (Cai and Zhang, 1991).

As a result of these inundations, soil fertility is said to be decreasing; organic matter and nutrient declines and gleization of soil are cited (Cai and Zhang, 1991). While overall productivity data are not given in the available reports, economic gains are said to be low, as yuan tian construction has evidently proceeded past the point of diminishing returns (Cai and Xia, 1993).

Decreases in Aquatic Resources

The decline in aquatic resources includes a loss of lake number and area, as described above, accompanied by a loss in lacustrine wetlands. Some areas not directly converted to farmland are used to produce crops such as lotus root and wild rice stem, and about 400 km* of wetlands in the “Four Lakes” area are said to be in their original state (Cai and Xia, 1993). Fish harvest from the lakes has dropped precipitously over the past 30 years; combined harvest from the north and south areas fell from ca. 67,000 Mg in 1957 to

11,000 Mg in 1982. Numbers of species in these areas declined by 20 - 30% over the

27 same period, and population structure shifted to a prevalence of young-of-year (Cai and Zhang. 1991). In Honghu alone, species numbers fell from >90 in the 1950s to 74 in 1964, and to 54 in 1982, and yield fell from 15,000 Mg to 2200 Mg (J. Yan, pers. commun, to W.J. Mitsch). Data are not available on the extent to which the proliferation of aquaculture ponds has offset these production losses.

3.1.2. Field Investigation Methods

Data collection for this study was carried out through a collaborative agreement between the Wetlands Program of the School of Natural Resources, The Ohio State University, and the Department of Land Use and Environment, Institute of Geodesy and Geophysics of the Chinese Academy of Science in Wuhan. A three-month period of residence at the Institute was arranged for April - June, 1996, with a two-week follow-up visit in October, 1996. During these visits. Institute publications and maps were made available, and translation assistance was provided. General information on local crop cultivation practices and hydrologie systems was obtained through discussions with Institute staff. Recent records on local conditions, such as meteorology, hydrology and agriculture records, were not always readily available. In some instances. Institute staff had relationships with individuals in provincial or city-level bureaus and could obtain these records. In other cases, personal visits to remote locations were required. Many of the original records and detailed maps could not be taken out of China. Within the JH-DT Plain, most of the Institute’s previous study had focused on the Four Lakes Region (Figure l.l). It became apparent, however, that the Four Lakes Region was too large for complete coverage by this study, because data would have to be collected from several city/county-level administrations. Time was not available for all of the visits required, and visits would not necessarily yield the desired information. Therefore, study was focused on lower third of the Four Lakes Region, i.e., the Honghu

28 Rood Diversion Area (HRDA), and within that area Xiaogang State Farm ( Rgure 3.1 and Rgure 3.2).

The study area was visited on three occasions in 1996: April 9-11, June 12 - 14. and October 19 - 20. Institute staff served as guides and translators. On each occasion, meetings were held with Xiaogang Farm administrators to discuss hydrologie structure and

production, and production areas were toured. On the latter two visits, farm personnel also provided written answers to questions dealing with production methods, economy and hydrology. Visits were also made to administrative offices in Honghu City* and Jianli

County (see Figure 3.1), and to the flood control gates at Futianshi, Xiaogang, Xinti and Xintankou. Fish production and flood control areas in Honghu Lake were also toured by boat.

Xiaogang State Farm occupies an area of 24 km’ in the Four Lakes (Sihu) Region, approximately 10 km north of Honghu City at the northeastern comer of Honghu Lake (Figure 3.2). Until 1958, the farm was a part of the seasonally flooded marsh that surrounded Honghu Lake. Conversion works were undertaken in 1958 - 1959. While most of the surrounding areas are under Honghu City administration, with a comparatively low level of central planning and control, Xiaogang Farm is one of a few state-run areas remaining. Although individual farmers within the farm operate as independent economic agents, buying inputs and selling products according to the market, the farm management designs and constructs production areas (fields, dikes, ponds), operates processing industries, and maintains records. The farm is completely surrounded by rivers, which have now been converted to canals (here termed “outer canals”), through deepening, straightening, and augmentation of the natural levees. The outer canal dikes rise to approximately 25 m elevation, whereas the

‘Unlike Jianli County, the rural region administered by the city of Honghu is referred to collectively as ‘ Honghu City.’ It is termed a ‘ city’ because of the main city’s population size and importance. 29 Futianshi Gate Xiaogang Gate Xintankou Gate

Honghu Lake Honahu 5Wf;S*ï»WÎ!» u> O Xaiogang Farm /'Honghu Flood Xinti Gate Diversion Area City or County Center Major Regional Dikes Major Regional Canals ^ ^ ' Rivers O Pumping Gates Scale (km) B Nonpumping Gates

Figure 3.1. The Honghu Flood Diversion Area, located within the Four Lakes Region, is bounded by a major regional dike and includes Honghu Lake and Xiaogang Farm maWnewiaPanal / Xiaogang Flood Gate

Ponds Honghu

w I

Residential, Commercial 0 Pumping Station

Outer Canals 0 0.5 1.0 1.5 1 I I I —I— Inner Canals Scale (km) Farm Boundary

Figure 3.2. Xiaogang Farm, located near Honghu Lake, is divided by inner canals which are drained through two pumping stations to the Sihu General Canal elevation of the fields within the farm is as low as 21 m; thus during flooding season the outer canal water level may run as much as 4 m above field level. A network of drainage

canals (“inner canals.” see Figure 3.2) has been constructed within the farm, supposedly designed for a 10-year return period storm. The two largest north-south canals drain to the outer canals through two water control gates at the northern margin. These two gates are equipped with a combined total of seven fixed pumps that are used to discharge water when outer canal water levels rise above the desired inner water level of 21.2 m. This system of

protective dikes, drainage canals and pumps prevents seasonal inundation in most years, enabling the development of agriculture and aquaculture within the farm.

Separate canals at higher elevations are used for irrigation; water is lifted to the irrigation canals from the inner drainage canals (or occasionally from the outer canals) by small gasoline (3 - 24 horsepower) or electric (7.5 - 20 kW) pumps, and small pumps are used to raise or lower water levels in ponds as needed. Over 95% of the cultivated areas within the farm are served by irrigation. Farm population in 1996 was given as 9954. comprising 2850 families and 3514 workers.

The Honghu Flood Diversion Area (HFDA), which measures approximately 2800 km', is surrounded by a levee and can be flooded if necessary to relieve flooding danger to cities such as Wuhan. The Sihu General Canal (SGC) is the primary conduit for surface flow into and out of the region. A total of 11 pumping stations for drainage of this area exist (Figure 3.1), the largest being the one located on the SGC at Xintankou. The Xinti gate connecting Honghu Lake to the Yangtze River plays little role in drainage because it lacks pumps. It could be used instead to flood Honghu Lake or the HFDA, but such uses are evidently rare.

3.1.3. System Boundaries for Modeling

Based on these visits, it was determined that a detailed model could be developed for Xiaogang Farm. The farm is a distinctly bounded system, being surrounded by canals

32 (Figure 3.2). Once external water levels rise (i.e.. May or June through October), surface inflow to the farm becomes negligible, and outflow occurs only through pumps of know n

capacity. Except for a small area at the southwest edge of the farm, which is under Honghu City administration, the area of farm administration and record-keeping matches the area bounded. Land-use, hydrologie and economic data for the farm were considered sufficient for model development.

It was also considered desirable to model a larger scale, in order to capture regional- scale effects not seen at the level of the farm (such as the flood-storage capacity of Honghu Lake). This poses a more difficult problem, since physical boundaries at the larger scale are not as well defined, and administrative boundaries also do not correspond nearly as well. For example, the Honghu Flood Diversion Area (HFDA) is also a physically defined entity (Figure 3.1). Although outflow rates for the 11 pumping gates are not available, their total pumping capacity is available, giving an upper bound for outflow when the

Yangtze River is high (roughly mid-June through August at Xintankou). However, at least three points of inflow exist, and their flow rates are not available. Furthermore, while most of the HEDA is within Honghu City administration, a significant portion (all the area west of Honghu Lake) is within Jianli County administration. Both administrations also include large areas outside the HFDA, and thus their land use and production records are not directly applicable (only those for Honghu City were actually obtained). Nonetheless, an attempt is made herein to take an analysis that is largely based on Xiaogang Farm and modify it, using available data, to give suggestive results at the scale of the HFDA, Therefore, data and analyses were developed at two scales: Xiaogang Farm and HFDA.

3,2, Hydrologie Model Development Methods

All of the hydrologie records obtained gave water levels as elevations in meters above sea level (m a.s.l.), where sea level was according to the Wusong datum, an old but

33 commonly used standard. Land elevations were usually according to the Hiiari'^hai datum, considered the official Chinese standard. The two standards differ by 1.894 m. This amount was subtracted from all Wusong elevations, so that all elevations in this analysis are Hiuinghai values.

Conceptual models were developed to aid in understanding system relationships and to form a basis for mathematical model development; symbols used are adapted from

Odum and Odum ( 1994) as defined in Rgure 33a. Mathematical and computer model development proceeded simultaneously using STELLA software (HPS. 1996). Based on user-defined storages and flows, STELLA compiles difference equations that can be dynamically simulated using numerical methods. Symbols in model diagrams produced using STELLA are defined in Figure 33b. This section presents a summary of modeling methods; a detailed model description is presented in Chapter 5.

Hydrologie models developed in STELLA included state variables representing water volumes, and flows representing water flows into the model, between state variables, and out of the model. Water was conserved within the model. All units based on length, including elevations, areas and volumes, were converted to meters (m, m* and m', respectively), and units of time were converted to days. A time step of 0.1 d and a 2nd- order Runge-Kutta integration algorithm yielded stable results with minimum computation time. Model development and simulation were carried out interchangeably using Macintosh (Power PC processor) and DOS-based (486 processor) computers.

Model development proceeded in two phases, corresponding to the two areal scales modeled. A model at the scale of a single farm such as Xiaogang Farm ("Farm-scale model’) was developed first and parametrized to represent current ( 1996) conditions at Xiaogang Farm. The Farm-scale, Xiaogang Farm model used as inputs daily information on net precipitation at one location (Honghu City), and SGC water elevation adjacent to

34 a. Conceptual Model Symbols

Interaction or control of Forcing function; Z> flow external control Switching action; control of flow Storage or state variable n Denotes predominantly natural control Producer; transforms inputs to outputs _ n Denotes predominantly ^ Flow of water. L-f” anthropogenic control nutrients or biomass p Denotes pumping Control of an capability interaction or switcfi

b. STELLA Model Symbols

Storage or state Converter; contains a variable O parameter or expression

Contains a graphical Flow function, usually a time- u variable parameter

Information flow; ^ Denotes flow origination computational connection or termination outside model

Figure 33. Symbols used in model diagrams: (a) conceptual model symbols, based on Odum's energy language (Odum and Odum, 1994); (b) STELLA model symbols (HPS. 1996)

35 Xiaogang Farm; as outputs, the model simulated internal water levels, flooding damage to crops, and water exchanges between Xiaogang Farm and SGC.

Seven different categories of landform and land use (termed ‘land use elements.' or LUEs) within the farm were established, having areas and elevations inferred from production records, information from farm-manager interviews and limited mapped elevation data. A state variable was established representing the combined volume of root-

zone soil water and surface water for each LUE. This method constituted a pseudo spatially-distributed approach in that areas of similar landform and use from around the

farm were lumped within each LUE, although their specific locations were not determined. Net precipitation, irrigation and drainage flows for each LUE were determined based on

daily precipitation and pan evaporation records and minimum and maximum water depth limits from land use-specific water management schedules. The volume of water in the farm drainage system, including in-farm canals and drainage ditches, was represented by

an eighth state variable; this component exchanged water with the outside (i.e., the SGC) according to flow rules that were sensitive to internal and external water elevations (i.e..

head), optimal internal water elevation, and the limitations of the pumping system. The latter included not only the size and number of drainage pumps, but also a requirement that

these be shut down at extreme SGC water elevations.

Computed overflows from the in-farm drainage system (i.e., volumes in excess of pump-out capacity) were assigned to the lower-elevation LUEs according to the assumed system geometry. That is, drainage of excess water from the lower LUEs was disallowed if computed drainage system elevation would be too high to allow such flows, and drainage flows for these low areas could in fact reverse, causing flood inflows to the lower LUEs. if drainage from higher LUEs exceeded drainage system capacity.

The critical outputs of the model for determining flood impacts were as follows; (1) elevation of the inner canals; (2) maximum area flooded in excess of crop I -day

36 tolerance limits; and (3) an index of crop loss that incorporated area and duration of damaging flooding (see Chapter 5 for definitions). This latter index served as a qualitative

indicator of economic loss; it was not possible to relate the index to quantitative yield reductions.

The Farm-scale, Xiaogang Farm model was calibrated for a 25-day period in 1996. the only period for which daily internal water level data for Xiaogang Farm were available.

Next, the Farm-scale model was tested using as inputs net precipitation and SGC water level records for the years 1980 - 1994. When the years 1980 - 1987 were tested several parameters were reset to represent Xiaogang Farm conditions in the early 1980s. These tests allowed a limited, further degree of calibration since certain of those years were

described as flooding years, although detailed information on internal flooding levels and crop damage was unavailable.

The Farm-scale model parameters were again adjusted (making minor modifications to account for proportional landform-lemd use differences) to represent a farm with

conditions more typical of the HFDA, thus creating a Farm-scale, HFDA model. The latter model was then nested within a model of HFDA-wide hydrology to create an Area-scale. HFDA model. Honghu Lake and the section of the SGC between Honghu Lake and

Xintankou were modeled as storages in the Area-scale, HFDA model, and their water

levels were simulated by the model. Daily inputs for the model included net precipitation records for two locations (Honghu City and Jianli County) and water level data from outside the area (at Futianshi and Xintankou); the latter were used to simulate the HFDA's net inflows and outflows. Water inputs and outputs from the Farm-scale, HFDA model were multiplied by area ratios to simulate water consumption and drainage over larger areas; delay functions were used to desynchronize these exchanges in order to simulate the larger scale. The model was calibrated using daily water level records for Honghu Lake and the SGC for 1987 - 1994.

37 The Farm-scale model was used to investigate effects of within-farm structural changes (based on conventional or ecological engineering) on within-farm flood peaks and crop damage. In the Farm model, the SGC water level was a forcing function—i.e., an input—and could not respond to changes within the system. In the Area-scale model, however, SGC water level was based on a state variable (SGC water volume) which could respond to changes made within the Farm-scale model. Thus, the Farm-scale model was used to investigate only farm-level impacts of structural changes made within the farm, whereas the Area-scale model was used to investigate area-level impacts of more widely implemented changes.

3.3. Sensitivity Analysis and Calibration

A parameter-by-parameter sensitivity analysis of critical model outputs was conducted to examine model performance prior to calibration. A sensitivity index S was calculated as recommended by J0rgensen ( 1986) as follows:

S = (Ax/x)/(Ap/p) (3-1 ) where x = the selected output variable

p = the selected parameter, and

(Ap/p) =+10%.

However, certain modifications to this procedure were required. Many parameters, and also the output variable Max_flood_level, are elevations above sea level, which vary from 19 - 25 m. A 10% variation of these parameters was in most cases outside the range of physically plausible variation and therefore not suitable for sensitivity analysis. Therefore, a fixed value of 2 m was judged as the maximum possible range of most elevation parameters and output variables, and Equation 3-2 was modified as follows:

38 s = (Ax/r)/( Ap/r) (3-2)

where x = an output variable expressed as an elevation

p = a parameter expressed as an elevation, and

r = 2 m

Thus, Ap = + 0.2 m for ail elevation parameters.

Proportional variation was also not meaningful for the quadratic parameters. Canal_a, Canal_b and Canal_c, which describe canal elevation-volume relationship. These were instead rederived based on a +10% variation in assumed canal depth. Forcing functions that were a graphical function of time, such as precipitation, pan evaporation, and water management schedules were varied by introducing a constant multiplier that could be set at unity for baseline conditions and 0.9 or 1.1 for sensitivity runs. Seepage rate, which had been set at 0 m/day, was adjusted by + 0.003 m/day for sensitivity runs.

3.4. Scenario Simulations

A baseline scenario was simulated for the flooding period (i.e.. May - August) for each of the following models:

(a) Farm-scale, Xiaogang Farm (b) Farm-scale, HFDA (c) Area-scale, HFDA

These scenarios were simulated for all years in which damaging flooding was found to have occurred and for which sufficient input data were available, and peak flood elevations and indices of crop damage were determined. For the Farm-scale, Xiaogang Farm simulations these years included 1980, 1981, 1983, 1987, 1988, 1991 and a 25-day period in 1996. For the Farm-scale and Area-scale HFDA simulations, these included only

39 1987, 1988 and 1991 (and, of these, only in 1991 was the flooding substantial). Based on these results, it was concluded that the model satisfactorily represents hydrologie responses at the farm and HFDA scales and can be used to investigate the impacts on flooding of various engineering strategies. Engineering scenarios, including ecological engineering scenarios (Table 3.1), were then simulated for the same years, and the results were expressed as a percentage of the appropriate baseline. Because the years for which

simulations could be run were few, and because of important yearly differences in flood timing and severity, summary statistics across years were not used. To draw conclusions about the impacts of implementing a given engineering

measure at the level of Xiaogang Farm, results of simulations for the Farm-scale, Xiaogang Farm model were used. For impacts of implementation over the entire HFDA, effects of a strategy at the Farm-scale, HFDA were compared with those at the Area-scale, HFDA. If the Area-scale simulations showed greater or lesser benefits (i.e., in terms of reduced flood

peak or crop damage) than the Farm-scale, it was Inferred that there was an effect of scale that was not determinable at the farm level alone.

4 0 Strategy Identifier Number, and Crop

Engineering Strategy Mid­ Two- Tatami: Lotus Wild season rice crop rice rice, 2-yr rice rotation stem

Increase pump number from 1 7 to 10

Deepen inner canals and 2 ditches by 20% Change LUE 2 crop 3a 3b 3c 3d Change LUE 2 crop and use 4a 4b as passive storage reservoir Change LUE 2 crop and shift 5a 5b to lowest position land Change LUE 2 crop, use as 4+5a 4i-5b passive storage reservoir and shift to lowest position land

Table 3.1. Engineering strategies, including ecological engineering strategies, tested in model simulations.

41 CHAPTER 4

RESULTS OF HELD INVESTIGATION

A field investigation of the study area during 1996 resulted in the collection of

annual records of local land use and hydrology covering various locations and years (Table 4.1 ). Information describing specific land use practices was also obtained through discussions with managers at Xiaogang Farm and from records that they provided (Table 4.2). For selected practices, this included input and output prices and values (Tables 4.3,

4.4) and production yields (Figure 4.1). Information on hydrologie systems and recent

(post-1980) flood events, and on potential measures for mitigation of future floods, was also obtained.

4.1. Xiaogang Farm Land Use and Hydrology

4.1.1. Landform and Land Uses

When Xiaogang Farm was first converted to agriculture, an area of ~2 km" was originally left as open lake, surrounded by a high (24.6 m a.s.l.) dike. Overtime, this lake area has been gradually converted to ponds, separated by lower dikes (22.6 m a.s.l.), so that now only 80 ha remains as lake. Land within the farm is flat and generally dish­ shaped, with the lowest ground elevations (20.5-21 m a.s.l.) occurring in the lake / pond area, and the highest (25 m a.s.l.) at the outer edges on the river levees. Soils range from

42 Data Availability by Year, 1978 - 1996 initial Data Type, Location Year 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

Agricultural Pnxluction/Land Use Xiaogang Farm XXXX X X Honghu City XXXX CroD Yield Honghu City and Jianli County 4 9 - XX X XX X X X X XXXXXX Dailv Rainfall Honghu City 57- X X XXX X XX X XXXXXXX X X* Jianli County 71 - X X X X X X X X X X X X XXX Monthly Rainfall Xiaogang Farm XXXXXXXX X XXX X X X X X X X" Dailv Pan Evanoration Honghu City 57- XX XXXXXXXXXXXXXXX à Dailv Water Elevations Futianshi Gate, above/below X X XX X XX X Xiaogang Gate, above/below X X X X X X X X X X X X X X X* Xintankou Gale, insidc/outside XXXXXXX X XX Xiaogang Farm, inner canals X" Annual flow duration and volume Xiaogang Gate X X XXX Hcxxl Damage Information Xiaogang Farm X' X' X' Honghu City ______X' * * July 14- August 8 only Areas and percentages of crop loss *■ Through October, 1996 Dates and areas of inundation within HFDA ‘ Very limited description of type of damage

Table 4.1. Types, locations and years of data obtained for study of land use and hydrology in the lower Four Lakes Region, Hubei, China Lake / Ponds Dry Fields Managed lake Vegetables Circle-style, mixed fish culture Fruit trees (pear, orange) Intensive, mixed fish culture T ree nursery Intensive, breeding/fry production Hemp : Rape Intensive, freshwater crab Cotton : Wheat. Rape or Vegetable Lotus Various Summer : Winter combinations: Water chestnut Summer Winter Sesame Broad bean Peanut Pea Low-Position Paddy Fields Sweet potato Rape Mid-rice : Winter fallow Maize Mid-rice : Tatami : Late rice (2-yr) Sorghum Lotus Soybean Wild rice stem

Other Dry Lands Hiah-Position Paddy Fields Dike and Roadside Agroforestry Mid-rice : Tatami : Late rice (2-yr) Canopy Understory Mid-rice : Rape Metasequoia Palm (fiber) Early rice : Late rice : Rape Larch Rape Watermelon : Late rice Broad bean Factories: Rice paper Tatami mat Brick Residential Commercial

Table 4.2. Xiaogang Farm landform categories and associated land uses.

44 Price Input or Output Category Unit (Yuan-*) Land Use Seed or fry Rice seed 0.5 kg 9 All rice rotations Tatami propagules 0.5 kg 0 Tatami (not purchased) Lotus tuber 0.5 kg 1 Lotus Fish fingerlings 0.5 kg 3.5 Controlled lake Fish fingerlings 0.5 kg 5 Circle-style pond Fish fingerlings 0.5 kg 4 Intensive pond Feed Grain, fish feed ton 1600 Intensive pond Grass ton 0 Ponds, grown on-site Fertilizer Superphosphate ton 400 Every land use Ammon, carb. ton 550 Every land use Urea ton 2150 Every land use Organic ton 2000 Tatami Organic ton 1300 Root lotus Rape oil cake ton 1320 Watermelon Pesticide Tetrachlorvinphos ton 4500 All rice rotations Methamidophos ton 22,000 Lotus CaO ton 200 Ponds Other (unidentified) ton 13000 Lake Work Human (unpaid) day 0 All land uses Human (hired, unskilled) day 20 Controlled lake (net harvest) Animal power day 30 Virtually all land uses Machine, 24 hp, 5 years unit-yr 1000 Low-position rice Machine, 10 hp, 5 years unit-yr 800 Tatami, high-position rice, lotus Machine, 10 hp, 5 years unit-yr 2200 Lake Fuel ton 2200 All land uses, for pump & hand tractor Electricity kwh 0.5 Virtually all land uses, for pumping Product Rice 0.5 kg 0.75 Rice straw 0-5 kg 0.025 Tatami, fresh 0.5 kg 1.2 Lotus root 0.5 kg 0.8 Lotus seed 0.5 kg 7.2 Rape seed 0.5 kg I Watermelon 0.5 kg 03 Fresh fish 0.5 kg 23 *1996 exchange rate: 8.1 Yuan = 1 U.S. Dollar

Table 4.3. 1996 input and output prices associated with selected Xiaogang Farm land use practices, according to written information provided by Xiaogang Farm managers. All prices reportedly were market-determined.

45 Tatami Mid­ Late Rice : Water- Circle- Intensive season Mid-Rice Root Rape : melon : Control­ style Pond, plus Rice (2-yr avg) Lotus Mid-Rice Late Rice led Lake Pond Grass 1996 Approximate Area (ha) 400 67 40 267 33 80 133 381 Inputs (Yuan/ha)" Land (rent & tax) 1,913 1,913 1,913 1,913 1,913 __h 2,700 2700' Seed (or fish fry) 338 338 4,500 350 347 131 3,000 2,655 853 1,906 1,219 840 2,541 293 1,819 1,641 Pesticides 34 84 50 34 41 111 60 42 Pumping 682 671 273 398 564 110 297 282 Animal power^ 900 1350 900 1350 1350 0 0 252 Hired Labor 0 0 0 0 0 281 0 0 Total 4,718 6,261 8,854 4,883 6,755 646 7,876 7,572 Outputs (Yuan/ha) Product 9,000 36,563 36,000 12,525 32,063 27760 20,700 26,565 Byproduct 300 309 0 338 319 0 0 0 Total 9:300 30^75 36:000 î%§63 32:381 2,760 20,700 Unpaid Labor (Pcrson-dlays/ha)‘ "7T W 300 105 103 3 3 42 Net R e tu r n s______L and (V uan/ha) 30,611 37ÏÏ43 ~7WB 35:623 3 1 W 13,823 18,993 61 292 90 76 244 94 570 452 ' 1996 exchange rale: 8.1 Yuan = 1 U.S. Dollar 'Animai power valued at 30 yuan/day il hired; most larmcrs ow n or share " Rent/lax nol reported 'Family latxir is commonly used ‘ Rent/lax nol reported; assume similar to circle-style pond

Table 4.4. Net annual financial returns to land and labor of selected Xiaogang Farm land use practices for 1996, according to written information provided by Xiaogang Farm managers. i — 0— Cotton -&— Oil products -X - - - Tatami straw ; ! - Vegetable # Fish - 5 — Grain a. 450 1450

£ I 400 : 1400 g 350 I 1 1350 II 300 - 1300 O 250 - - 1250 -c

200 -- + 1200 II -X- i l 150 -- + 1150 3 < 0 CO .O— - —. À 1100 T3 O 50 1050

1000 19901991 1992 1993 1994 1995 b.

o 7 + ' -28

X . . X -24 a 5 I ÎI -22 } 4 -- -20 <0 - - 18 i CB T3 € -- 16 2 - > -- 14 TJ O

— — “O' — —

1990 1991 1992 1993 1994 1995

Rgure 4.1. Xiaogang Farm annual production area (a) and yield (b) of crops and fish, 1990 - 1995, based on statistical data provided by Xiaogang Farm. Because of multiple cropping, sum of areas exceeds total land area.

47 poorly-drained clays in the low-lying areas to well-drained sandy clays on the natural levees. Cultivated areas are categorized as low-position paddy fields (-21 - 22 m a.s.l.). high-position paddy fields (22 - 23 m a.s.l.) and dry-crop fields (>23 m a.s.l.). Each landform category is suited to particular land uses (Table 4.2); areas actually devoted to each use vary annually according to agricultural prices (Figure 4. la).

4.1.2. Aquaculture

Most of the ponds maintain deep water levels (> 2 m) and practice a multi-species fish cultivation technique that has been shown to efficiently utilize several food and spatial niches within the water column. The standard practice in Xiaogang Farm is to raise four carp species (grass, Ctenopharyngodon idella', silver, Hypophthalmichtys molirrLx: bighead, Arystichthyes nobilis and black, Mylopharyngodonpiceus) which variously rely on grass/vegetation, phytoplankton, zooplankton, benthos and added grain (Yan and Yao, 1989; Yao, 1993; Hagiwara and Mitsch, 1994). Other fish species of higher economic quality may be included as well. A few ponds are used for specialty products such as turtles or freshwater crabs, but these are not included in this analysis. Unless otherwise specified, fertilization always includes single-superphosphate (Ca(H2PO j2 +

CaSO_; 2HnO), ammonium carbonate (NH4HCO3) and urea ((NH,),CO).

Managed Lake

The remaining lake area is managed for aquaculture, although both degree of management and yield in the lake are much less than in the ponds. Water level is managed at I.O - 13 m depth. Fingerlings (four carp species plus common carp, Cyprinuscarpio) are stocked annually in March, fertilizer is added, and the pond is harvested in late

November through early December by lowering the water level (to 03 - 0.6 m depth) and seining. Yield is about 0.6 ton/ha.

48 Circle-Style Ponds

These are large (8 ha) ponds dug in the fashion of a square doughnut, where an

outer, excavated channel (13 m below grade) forms a square-shaped ring surrounding an inner, unexcavated area. The entire area is surrounded by a dike (13 m above grade). Water level is managed so that the inner area is not flooded during the winter, and ryegrass is cultivated; grass-to-water area ratio is 2:3. Fingerlings (four carp species) are stocked

into the outer channel during December through March, and grass is harvested and placed in the channel as feed. In mid-April the water level is raised a few centimeters to allow the

fish to feed directly over the grassy area. In mid-May, water level is lowered again to allow the grass to regrow; then in early June water level is raised a full I m above the center

area (depth in the outer channels = 23 m) to allow feeding and to increase total water volume during the hot summer months. Ponds are drained for harvest around November,

and grass area is plowed and reseeded. Ponds are disinfected with lime prior to refilling and stocking. Both grass and water areas are fertilized during the growing season. Total

area of these ponds, including grassy center areas, is 130 ha and yield is about 4.5 ton/ha.

Intensive ponds

These are smaller (<1 ha), conventionally shaped ponds, excavated 1.3 m below

grade and diked 1.3 m above grade. Since grass grown on the dikes is not submerged, a

summer sorghum : winter ryegrass rotation is used; grass-to-water area ratio is 3:7. Management is like that for the circle-style ponds, but feed is 30% grain (wheat bran, maize and wheat grain) and 70% grass, and mechanical aeration is practiced during summer.

Total area of intensive ponds is 270 ha, or 380 ha when grassy dikes are included; yield per water area is 7.5 - 9 ton/ha.

49 Wetland plants

A small number of ponds are used for cultivation of wetland plants such as lotus or water chestnut.

4.1.3. Agriculture

Crops are selected according to landform/gleization conditions. Water level in paddy fields is carefully managed. Paddy field preparation involves flooding two weeks prior to transplant and plowing by or hand tractor. All paddy crops are transplanted as seedlings (rice) or root cuttings (lotus, tatami). All crops are irrigated when needed, and all are fertilized with superphosphate, ammonium carbonate and urea. All rice rotations use the pesticide Tetrachlorvinphos, and lotus cultivation uses Methamidophos.

Low-position paddy fields

These fields have poorly-drained clay soils, with water table depth ranging from <0.3 - 0.5 m during dry periods and up to the surface during the rainy season, and gleization of these soils is described as “moderate” to “serious.”* Soil moisture content prevents the winter cultivation of dry crops such as rape, and delays spring warming so that only a single rice crop can be grown; thus the typical rotation is mid-season rice : winter fallow. However, tatami (Juncus sp.) is a winter crop that tolerates moisture well; through a joint-venture with a Japanese firm, tatami is grown, processed on-farm into straw mats (using imported machinery) and exported. Tatami seedlings are transplanted in mid-late October, following a mid-season rice crop. Tatami is harvested the following summer in time for a late-season rice crop; thus, middle-season rice : tatami : late-season rice : winter fallow is practiced as a two-year rotation. The amount of tatami grown must

Local characterize gleization as “serious,” “moderate,” “low” or “none" according to a variety of soil changes and degree of reductions.

50 be regulated according to anticipated mat production capacity. Low-position area is also suited to cultivation of wetland crops such as lotus.

High-position paddy fields

These fields are similar to the low fields except that water table is lower (03 - 0.6 m) and degree of gleization less (“moderate” or “low”). Two rice crops can be grown, although this rather intensive rotation is not typical in Xiaogang Farm, where land-to- population ratio is comparatively high. More common is middle-season rice in rotation with a winter crop such as rape. Watermelon, an early summer crop, is also grown in rotation with late rice, but only in areas of low gleization. Rice : tatami : rice rotation is practiced here as well.

Dry fields

Well-drained soils are used for various fiber and oil crops, root crops, legumes and grains; these are irrigated as needed and grown in various summer crop : winter crop rotations (see Table 4.2).

4.1.4. Animal Husbandry

Animal husbandry currently is not well developed at Xiaogang Farm. Individual farmers typically have draft animals, swine, and poultry, and these are important in the consumption of crop residuals and production of organic wastes, but no large, organized husbandry operation exists.

4.1.5. Agroforestry

Ail dikes and roadsides are stabilized by trees, which are harvested for wood. The poplar {Populus sp.) that were initially planted were killed by a severe flood in 1969. These were initially replaced by larch {Lorex sp.), many of which remain, but the wood is

51 of poor quality. Metasequoia is now most commonly planted. In the understory , a small fiber-producing palm is planted, or dry crops (e.g., broad bean, rape) are cultivated.

4.1.6. Water Management Schedules and Tolerances

Detailed information was obtained from a local agricultural specialist (Chen Shijian. personal communication) concerning cropping and water management schedules for early,

middle-season and late-season rice in the Four Lakes Region. This information included minimum and maximum water levels typically maintained in the field and those which, if maintained for 3 days or I day, respectively, cause flood damage (Table 4.5). These data were used to develop a typical field scenario for each crop type, using a typical

transplanting date. Minimum levels were those below which irrigation would be used; maxima were those above which water would be drained.

These scenarios were further modified to be generally representative of the whole farm, where different fields may be transplanted at different times (Figure 4.2). Minimum

and maximum water levels were transformed using a moving average to account for fields planted ahead of and after the typical field. For middle-season rice, a 2 1-day averaging

period (± 10 days)was used, and for early and late rice, a 7-day period. The low damage

limits which characterize the periods of transplanting and harvest were simply extended, to cover earlier transplanting and later harvest.

For paddy crops other than rice, including lotus, tatami, and wild rice stem, detailed water management and damage information was not available, so approximate information was obtained through discussion with local farm managers (Figure 43). These crops may be managed at deeper levels than rice (e.g., <0.5 m), although actual maximum management levels may be lower than potential maxima if field dikes are built to a low level or are in poor condition. Paddy field dike height often does not exceed 0.2 - 03 m.

However, these crops are sometimes cultivated in ponds (especially poorly maintained ponds that are not well suited to fish culture) or in old canals. These crops can also 52 Water Depth (mmi Date/ Typical 3-Day 1-Day Stage' Season Duration Range Damage Damage Soil Preparation Early 14 d Middle 14 d Late <5d Transplant Early 25Apr - 5Jun Middle 15May - 7Jun Late Before lAug Turn Green Early 7d 0 -3 0 50 70 Middle 7d 0 -3 0 60 80 Late 5d 0 -5 0 50 70 Stooling/Tillering, Initial Early 13 d 0 -3 0 Middle 23d 0 -3 0 Late 13 d 0 - 50/70 Stooling/Tillering, Final Early 7d Drain Middle 7d Drain Late 7d Drain Booting Early 30 d 10 - 70/80 190 250 Middle 35 d 10-90 270 330 Late 30 d 10 - 70/80 190 250 Heading Early 8 - lOd 10- 100 200 260 Middle 8 - lOd 10 - 120 240 350 Late 8- lOd 10- 100 200 260 Maturing - Grain in Milk Early 15 d 0 -4 0 Middle 15 d 10-60 Late 15 d 0 -4 0 Maturing - Y allow Early 5d Drain 50 100 Middle 7d Drain 50 100 Late 7 - lOd Drain 50 100 Harvest Early lOJul - 25Jul Drain 50 100 Middle Drain 50 100 Late Drain 50 100 Total Early 80 - 87d Middle 100 - 105 d Late 85 - 90d Stages are explained in Table 2.1.

Table 4.5. Water management during stages of rice cultivation in the Four Lakes Region. Durations are approximate and change with field conditions.

53 ■t - T\pical minimum water level 3- 1-day damage limit ■2^ Typical maximum water level ,4” 3-davdamaec limit a. Mid-Season Rice 0.36-j

S s S' 0.12- IS-i

- 0 .12-1------b. Two-Crop Rice

S 6 S' 0.12- I

- 0.12 91.00 151.75 212.50 273.25 334.00 Julian date Apr. May June July Aug. Sept. Oct. N’o \ .

Figure 4.2. Water management for Four Lakes Region cultivation of (a) mid-season rice and (b) two-crop rice: normal minimium and maximum water depths, and depths at which damage occurs after 1 day or 3 days of inundation. Approximate transplant (T) and harvest (H) periods are indicated.

54 -1 - Typical miiumum water level I-dav damage limit - 2^ Typical maximum water le\ el a. Tatami 1.60n

I

0.00 b. Root Lotus 1.601

:

0.00 i ' 1 —2' 0.00 182.50 273.75 365.00

Julian date

Jan. Mar. Apr. Jun. Jul. Sept. Oct. - Dec. Continued on next page

Figure 43. Water management for Four Lakes Region cultivation of (a) tatami, (b) root lotus and (c) wild rice stem: normal minimium and maximum water depths, and depths at which damage occurs after I day of inundation. Approximate transplant (T) and harvest (H) periods are indicated.

55 Figure 43 (continued).

-1 — Typical minimum water level 1-day damage limit —2^ Typical maximum water level c. Wild Rice Stem

------. // S H •Ô S' 0.80 / I J' J 1 J h ...... i i 1n ...... I ------2-----

------2----- 0.00 -I"-- ■' -1------0.00 9125 182.50 273.75 365.00 Julian date Jan. - Mar. Apr. - Jun. Jul. Sept. Oct. Dec.

56 withstand much higher levels of inundation during flooding (e.g.. I -2 m). but damage can

• I result if the rate of depth increase exceeds 0.2 m d .

Water management schedules and damage limits were also developed for lake and ponds (Figure 4.4), based on discussion with farm managers. The damage limits in this case corresponded to dike height; when exceeded, fish may escape. During flooding in

July. 1996, water levels were equal to pond dike height for 4 days, and 0.12 m above pond dike height for 1 day. Production loss over the farm was estimated as 20 - 30%, where about half that amount was due to escape during flooding and half to other weather effects (cold, clouds).

4.1.7. Hydrology

During most of the growing season, water level in the inner canals is maintained at approximately 21.2 m a.s.l. (Figure 4.5a). This water level is a compromise between the goals of maintaining adequate drainage of low-lying fields and minimizing the expense of irrigating higher fields. This level exceeds ground elevation in many low fields (~2l m), and since the field drains lack gate controls, backflow to the fields cannot be prevented.

Field berms of 0.25 - 03 m can be used to maintain a higher water level in fields than in the canals, as may be needed for, e.g., rice or lotus cultivation, but they are not effective for protecting low fields when inner canal water level rises. Outflow from the inner canals to the outer canal is through seven conduits of 1.2 m diameter with a bottom elevation of 19.6 m a.s.l. and an additional conduit of 3.5 m diameter with its bottom at 18.1 m. Whenever outer canal level exceeds 21.2 m a.s.l., these outer gates are closed and the fixed pumps are used to maintain inner water level. However, rainfall in excess of pumping capacity will cause canal levels and levels in low fields to rise simultaneously (Figure 4.5b).

57 - 1 - Tvpical minimum water level W av damaee limit a. Managed lake —2^ Typicai maximum w ater level 3.001

?

0.004------b. Circle-Style Pond .001 ? pq

0.00 0.00 182.50 273.75 365.00 Julian date Jan. Mar. Apr. Jun. Jul. Sept. OcL - Dec. Continued on next page

Figure 4.4. Water management for Xiaogang Farm aquaculture including (a) managed lake, (b) circle-style pond and (c) intensive pond: normal minimium and maximum water depths, and depths at which production loss occurs after I day of inundation. Approximate stocking (S) and harvest (H) periods are indicated.

58 Figure 4.4 (continued).

-1 - Typical minimum water le\e! 1-day damage limit -2 '' Typical maximum water level

c. Intensive Pond

3.00n

■S I I« 1.50-

.00 0.00 182.50 273.75 365.00 Julian date

Jan. - Mar. Apr. Jun. Jul. Sept. Oct. - Dec.

5 9 23 m - Large inner canal Small inner canal Field berm Field ditch 22 m - \ I I 21 m - j n LJ 2 0 m - 2m 1 m

19m - a. Normal conditions for low field s

b. Flooded conditions for low field

^ Water In the drainage system ' Water ‘in the fields'

Figure 4.5. Schematic of Xiaogang Farm canal and field ditch dimensions for (a) normal conditions and (b) flooded conditions, and water volume calculation approach. The two fiKed pumping gates together house a total of seven fitted pumps, each of 155 kW. or 2.2 rn's capacitv. for a total discharge capacity of 1085 kW. or 15.4 m's .

Three more pumps of the same capacity are planned, for a total of 10. How e\ er. in-farm pumping capacity is not always the limiting factor for in-farm flooding. When the Sihu General Canal (’outercanal" or SGC) water level reaches a dangerous level, the government limits pumping into the canal. For example, during the extreme rainfall event of 1996 (see Section 43.), the farm was ordered to pump at half power for 5 days, and then to cease pumping for 4 days, during which time internal water levels were 223 - 22.6 m a.s.l.. or roughly 1.5 m above the level of the low fields.

Canal lengths were determined from a 1:10.000 map of Xiaogang Farm, and approximate width and depth dimensions were obtained from observations and interview s w ith farm managers. The largest canals, those spaced at 1000 m intervals, are dug to bottom elevations that vary among canals from 18.1 -19.6 m a.s.1.. bottom widths vary from 4 - 8 m, and side slopes are 3:1. Smaller canals, those spaced at intervals of 200 - 500 m, are dug 03 - 0.4 m below grade (with dikes 03 - 0.4 m above grade) and are of 2 m bottom width and 1.5:1 side slope. Mapped locations of large and small inner canals are shown in Figure 23. Smaller ditches within the fields were said to be spaced at 200, 1(X) or 50 m and are about 1 m in width. The ditches were not mapped and are not shown in

Figure 3.1 ; the various spacings would result in estimates ranging from 40 to 340 linear meters of ditches per ha of field area, in addition to canals. Relative sizes of canals and ditches for a low-position field are pictured in Figure 4.5. Precipitation data for Xiaogang Farm were obtained only as monthly summaries. This information is presented in the context of regional precipitation data in Section 4.2.2.

6 1 4.2. Honghu Flood Diversion Area Land Use and Hydrology

4.2.1. Land Use

Land use information for the HFDA was not directly available. Agricultural statistics, including various types of production acreage data, were obtained for Honghu

City (Figure 4.6). Biases in official Chinese agricultural statistics have been recognized. According to Du ( 1995), land taxation provides an incentive to under-report area planted. When 1989 State Statistical Bureau acreage statistics were checked against the more physically based National Land General Census, 39% under-reporting was found for

China as a whole, and 44% for Hubei, although the problem was less pronounced in plain areas than in complex terrain (Du, 1995).

Because absolute acreages are probably inaccurate, relative acreages for Honghu

City were used to draw comparisons with Xiaogang Farm. Each reported a similar ratio of aquacultural (-20%) to cultivated (-80%) areas. (Lotus may be cultivated either in ponds or in paddy fields, and is shown here as a separate category.) Within cultivated lands,

Honghu City has a much larger proportion of dry fields, as compared to paddy fields, than does Xiaogang Farm, and most of the paddy fields produce two to three crops per year, whereas most of those on Xiaogang Farm produce just one crop per year. This comparison is consistent with the status of Xiaogang Farm as low-lying land, recently developed from marsh/lake bed, whereas Honghu City includes both low-lying and relatively high-position land.

Yield data may also be subject to reporting biases because of incentives to under­ report total output and to over-report damage from natural disasters (Du, 1995). Crop yields for Honghu City and Jianli County show a long-term rising trend, but flooding impacts are evident (Figure 4.7). Rice, cotton and wheat yields for these two areas are similar, although mid-season rice yield in Jianli County is higher.

62 a. Honghu City Aquaculture 20% Cultivated Land Intensive Ponds 78% 8 %

Lotus Dry Reids 29%

Paddy Reids 3 crops/yr 49% 5%

1 crop/yr 2 crops/yr 20% 25%

b. Xiaogang Farm Dry Reids Aquaculture Intensive Ponds 9 % 21 % 14%

Cultivated Land V 78% Lotus

Paddy Reids 3 crops/yr 0% 68 %

1 crop/yr 2 crops/yr 42% 26%

Figure 4.6. Comparison of (a) Honghu City and (b) Xiaogang Farm land use as proportion of total productive area, based on Hubei Agricultural Statistics Yearbooks, and Xiaogang Farm agricultural records, respectively, for 1990-1992, and 1994.

63 6 T a. Honghu City 1991 — - • Early Rice 5 -- 1983 — B— Mid Rice - - A - - - Late Rice 1980 4 -- — #— Cotton — — Wheat

< I ! ! I I- I- I I I --t I t + I ; i- ( I )- J T-co»nr>-o)r-co mi^o) T-comi^o>T- ■^tomminmcocDcocDCDi^r^r^r^r^ooflocoaacoo)

b. Jianli County

1991

AA a /\^ A .

2 ’Z'2'fih:®!rÇ2‘oriO>»-comp^<»T-coior^OiT- ^iomuîioin

Figure 4.7. Long-term increasing trend in crop yields for (a) Honghu City and (b) Jianli County, based on Hubei Statistical Bureau data, 1949 - 1992. Impacts of Yangtze River dike breaches ( 1954, 1969) and recent heavy rainfall years ( 1980, 1983, 1991) are evident.

6 4 4.2.2. FYecipitation/Evaporation

Daily meteorological records in computerized form were obtained for Honghu and

Jianli meteorological stations, located near the respective city centers (see Figure 3.1). For Honghu. daily precipitation and pan evaporation records from 1957 - 1994 were obtained, as well as daily precipitation records for the period July 14 - August 7, 1996, when a major flood occurred. For Jianli, daily precipitation records from 1971 - 1992 were obtained, and for Xiaogang Farm, monthly precipitation records from 1978 - October, 1996 were obtained.

Since it was necessary to characterize precipitation for two areas, Xiaogang Farm and HFDA, based on these data, monthly values from 1978 - 1994, for Xiaogang Farm.

Honghu and Jianli were compared (Figure 4.8). Monthly totals for the respective locations clearly are highly correlated, but major differences, often representing localized storms, appear at least annually, especially during the rainy months of May - August

The frequency of high-precipitation events likely to cause local inundation was determined for the Flonghu data set since it represented the longest record (38 years).

Return frequencies for 1-day, 3-day and 10-day precipitation totals were estimated based on assumption of lognormal distribution (Figure 4.9, Table 4.6). Pan evaporation data for Honghu City showed less year-to-year variation than did precipitation (Figure 4.10b). Evapotranspiration coefficients are used in conjunction with pan evaporation data to estimate daily water requirements for crops. Locally applicable coefficients for rice exceed unity in June and July; i.e., daily requirement exceeds pan evaporation (Figure 4.10a).

4.2.3. Canal Hydrographs

Photocopies of water level elevation records were obtained for the following water control gates on the SGC (see Figure 3.1 for locations): Futianshi, 1987-1994; Xiaogang,

65 Xiaogang Honghu Jianli âr A A o o o

400 j— ----- r 1978 1979 300 I-

200 ^ t « A 100 « 0 § 1980 1981 300

+ ' 200 & $-

100 $ / o - A 4' $ i 0

1982 1983 300 i + 200 A A. ° o A - A $ ^ 2 O 100 O

0 ■*

1984 1985 300

200 A 100 A » . ■é ■'a' -—0:—;---- ;--- JFMAMJJASONDJFMAMJJASOND Continued on next page

Figure 4.8. Comparison of monthly rainfall totals (mm) for Xiaogang and Honghu, 1978 - 1994, and Jianli, 1978 - 1992.

66 Figure 4.8 (continued).

Xiaogang Honghu Jianli A- A -A 1986 1987

300 1988 1989 O- o 200

100

300 1990 1991

200

100

300 - 1992 1993

200

100

300 1994

200

100

JFMAMJJASONDJFMAMJJASOND

67 3-dav 1.8 y = 7.6287x- 16.618 R* = 0.949 1-dav 1.6 y = 9 .7143x-20.171 R=‘ = 0.985

1.4

S' 1.2 § I ' 10-dav y = 7.0591x- 16.174 R" = 0.9408 0.8 •i

N 0.6

0.4

0.2

0 4 2,1 2.2 2,3 2.4 2,5 2 6 log rainfall (mm)

Figure 4.9. lx)gnornu)l pioi of I’reiiucncy of 1-, and 10-day rainfall anuninls. 1.8 a. Rice Evapotranspiration Coefficient

1.6 - g -X Early rice 1.4 - - 43- - Middle rice A Late rice S i 1.2 3c

1.0

0.8

0.6 —

10 - b. Pan Evaporation

Figure 4.10. Crop water use: (a) évapotranspiration coefficients for rice specific to Four Lakes Region; (b) monthly mean pan evaporation for Honghu City, 1980 - 1994 (columns show grand means; error bars show year-to-year range).

6 9 Total Precipitation (mm) Return Period ( years) I-day 3-dav 10-dav

10 162 222 297

20 176 248 334

40 190 272 371 July 14 - August 7, 1996 250 370 494

Table 4.6. Estimated return frequencies for I-day, 3-day and lO-day precipitation totals for Honghu, 1957-1994; and 1996 extreme rainfall event.

1981 - 1994; and Xintankou, 1980, 1983, and 1987-1994. Daily readings were recorded to the nearest 0.01 m above sea level, except that Futianshi values were not recorded during the low-water months of November - March. Water flows by gravity from west to east through the Futianshi and Xiaogang gates; thus “above” and “below” water levels are west and east of the gate, respectively. During the low-rainfall season (i.e., November - March), water flows by gravity through the Xintankou gate to the Yangtze River. During the high rainfall period (especially June - July), the Xintankou gate is closed as the Yangtze

River stage rises as much as 3 m above the canal water level, and canal water is expelled to the Yangtze River by pumping. Therefore the terms “inside” (toward SGC) and “outside”

(toward the Yangtze River) are applied for this gate.

70 For each gate, water level readings above (or inside) and below (or outside) the gate were obtained. These data were entered into a spreadsheet and graphed, and then obviously erroneous values were corrected or deleted; corrected data for two representative years are presented (Figures 4.1 1. 4.12). During the low-rainfall season (i.e., November - March), water elevation gradient between the respective gates is pronounced, but during the rainy season (especially June - August) the elevation gradient virtually disappears. At these times, SGC outflow is limited to Xintankou pumping capacity.

The “Xiaogang, above” water elevation is determined by the level of Honghu Lake, which communicates directly with the SGC, and therefore it fluctuates less rapidly than the other canal readings. The “Xiaogang, below” data set (Figure 4.13) gives the level of the outer canal at Xiaogang Farm and thus is useful for evaluating flood events at the farm. While the Xintankou gate is the largest pumping gate for expelling water from the HFDA, a total of 11 such gates exist—two in Jianli County and the rest in Honghu City, as shown in Figure 3.2. Eight are effluent to the Yangtze River and three to the Dongjing

River. Reported pumping capacities of these gates, proceeding counterclockwise from southwest to north are given in Table 4.7.

An important nonpumping gate (Xinti Gate, design flow 800 m^ s ') connects Honghu Lake to the Yangtze River and can be used to drain Honghu Lake. It is not usually useful for controlling high water levels in the HFDA, however, because of concurrent high Yangtze River levels.

4.3. Recent Flood Events

Floods occurred in the study area in 1980, 1983 and 1991 because of rainfall in excess of drainage capacity. In 1980,8980 ha of the portion of Honghu City lying within

71 &

4 4A \ AT (ZW

Above Futianshi Above Xiaogang (Honghu Lake) Inside Xintankou 0 - - Outside Xintankou (Yangtze River)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 4. ! 1. Water elevations at three water control gates of the Sihu General Canal during 1991. a high rainlall year. See Figure 3.1 for gate locations. O • I I I "I A H LU \ Above Futianshi Above Xiaogang (Honghu Lake) Inside Xintankou - -G - - Outside Xintankou (Yangtze River)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 4.12. Water elevations at three water control gales of (he Sihu (îeneral ('anal during 1992, a low rainfall year. See Figure 3 .1 for gate locations. Above 24 a. 1991

22

'—I G ate O peration 20 I 1204 days

24 b. 1992

j G ate O peration j 133 days 20 1 2 . 7 1 X 1 0 ® m 3

24 c. 1993

22

G ate O peration 1 8 7 d a y s 20 2 . 5 9 X 1 0 ®

24 d. 1994 23 22 G ate O peration 1 5 4 d a y s 20 7 . 4 2 X 10®m^

J F M M J JA S 0 N DA

Figure 4.13. Daily water elevations (m a.s.l.) above and below Xiaogang Gate, and duration and volume of gate operation, 1991 - 1994.

74 Gate Name Capacity, cn^ s '

Yaninshan (Jianli County) 88 Luoshan (Jianli County) 128 Shimatou 18 Longkou 15 Gaoqiao 9 Dasha 30 Yanwo 15 Xintankou 220 Hanyangkou (exits to Dongjing River) 15 Nantougou (exits to Dongjing River) 80 Yaerhe (exits to Donsiins River) __ 9 Total capacity 627

Table 4.7. Reported pumping capacities of Honghu Flood Diversion Area flood control gates.

the HFDA was reported as inundated; the inundation began as early as August 11 in some areas and as late as August 15 in other areas, and typically lasted about 0.5 months in each area (similardata for Honghu City or the HFDA were not available for the other flooding years). This report is consistent with the 40 - 50% yield reductions in Honghu and Jianli for late rice (Figure 4.14), which is vulnerable during early/mid August (see Figure 4.2) and 15 - 30% reductions for mid-season rice, which is less vulnerable then. However, the reported impacts on early rice in 1980 indicate that some areas may have flooded earlier as well.

75 1980 1983 1991

Early Mid Late Early Mid Late Early Mid Late

- 10%

- 20%

re -30% s(C I

-40%

-50%

-60%

Figure 4.14. Percent decline in early, mid and late season rice yields during recent heavy rainfall years (when compared to immediately preceding and following years) for Honghu City (solid bars) and Jianli County (shaded bars), based on Hubei Statistical Bureau data.

7 6 Anecdotal information on the 1980 and other floods was provided by Xiaogang Farm managers. In 1980. 260 mm of rain was said to have fallen within 2 hours. At that time, a large area of the farm was used for two-crop rice; early rice was reportedly in the

booting/heading stage (i.e.. mid- or late June) and was heavily damaged with many areas having no or low yield, whereas middle-season rice was said to be unaffected. This report cannot be verified from other data, however. The official rainfall monthly record for the

farm was <200 mm for June, 1980, and no 1-day event exceeding 63 mm was recorded

during June only 10 km away at Honghu. If late rather than early rice was actually the crop affected, the Xiaogang Farm flooding event would be placed into mid or late August, in better agreement with other reported flooding. During that period, 1-, 3- and 10-day

maxima recorded at Honghu were only 98, 166 and 207 mm, respectively, and total monthly rainfall at Xiaogang was only 233 mm in August. High rainfall events were recorded at Honghu in mid-July in both 1980 and 1981, either of which could have

affected early or mid-season rice. Either highly localized storms or translation problems

could account for the discrepant report. Canal data for 1980, available only for Xintankou, confirmed that the SGC (Xintankou, inside) peaked at that location at 24.54 m a.s.l. on August 14.

Farm managers reported that in 1983, the entire month of June was rainy and outer

canals were very high. Pumping was not permitted, and early rice was reportedly

damaged. This report agrees well with the monthly rainfall totals for June, 1983, which were approximately 450 mm for both Honghu and Xiaogang, and for the stronger impacts on early and mid-season rice compared with late rice (Figure 4.14). Outer canal level

(Xiaogang, below) rose sharply during late June, peaking above 24.5 m a.s.l. on July 6 , 12 and 16. Farm managers reported that in July of 1991, the middle reach of the Yangtze River was very high and pumping was halted. The tatami harvest was large but of very poor

77 quality because of inability to dry the crop. Other crop damage was not specifically mentioned. This report corresponded well with high monthly rainfall totals reported for July, 1991 and with a July 13 peak of 24.76 m a.s.l. in outer canal elevation (Figure 4.13). As would be expected based on July tolerance limits, early rice was more affected than mid-season rice. The low yield of late rice, which must be transplanted by August I, may indicate delayed transplanting or a prolonged effect of soil waterlogging. The year 1996 was one of serious flooding throughout south and central China. Heavy rains in late July (Figure 4.15; see also Table 4.6) caused serious flooding within Xiaogang Farm, with a peak depth of 1.5 m above normal inner canal levels. Elevated outer canal levels forced a pumping shutdown, which prolonged the inundation period to about 17 days. Of the Xiaogang Farm middle-season rice crop, about 130 ha was a total loss; 150 ha had sharply reduced yield (about 50 - 60%) and another 400 ha suffered some loss (10 - 20%). Some dry fields also experienced a total crop loss, including 20 ha of watermelon, 7 ha of cotton and 7 ha of sesame. Wetland crops reportedly suffered no flooding damage; the seed lotus crop was reduced because of low sunshine but not because of flooding.

4.4. Ecological Engineering Strategies for Reducing Flooding Damage

Historically, flood damage to crops has been reduced by a strategy of protection (through construction of dikes) and drainage (at first by canal construction and more recently by pumping), enabling low-position land to be used. Some low-lying areas initially developed for rice production have been converted to ponds because of the problems of flooding and a perennially high water table. In Xiaogang Farm, pond area has expanded recently, but mostly by conversion of remaining lake areas, rather than conversion of paddy areas. However, large paddy areas previously used for two-crop rice

7 8 250 - A- - -A

A'

Pump operation: 200 norma power 50% power 0% power normal power

150 E E Peak internal flooding depth = 1.5 m % c <3 OC Normal Internal elevation = 21.2 m 100

r 1 Honghu Rainfall — A - - Sihu General Canal — ♦— Inside Xiaogang Farm

3 3 3 3 33 3 3 3 3 3 3 3 3 3 3 O) O) ~5 •3 3 3

Figure 4.15. Xiagang Farm hydrologie system response to an extreme rainfall event in July, 1996: high external water levels (Sihu General Canal) forced a temporary reduction of pumping and prolonged the internal flooding. have been converted to one-crop rice because of the susceptibility of early rice to flood damage. Some areas have also been devoted to wetland crops other than rice.

Xiaogang Farm managers were questioned on their strategies for further improving the farm’s resistance to flood damage. Their primary reliance is on the drainage system. Plans are already made to increase pumping capacity by adding three more fixed pumps to the existing seven. They also feel that the inner canals should be deepened, to improve storage capacity and drainage speed. In spite of the recurrent problem of pumping shutdowns because of high external levels, they generally express less interest in other methods for storing water within the farm. For example, a high (24.6 m) dike exists that surrounds many of the ponds and the remaining lake area. Before the ponds were built in the 1980s, a pump had been used to pump water from the fields into this contained area. The managers mentioned an existing proposal for restoring a system for pumping water into this internal, diked area. However, most of them oppose the idea because (a) if the ponds become flooded (i.e., by flood levels above 22.6 m), fish would be lost and (b) pumping water within the system seems pointless if it will have to be pumped again later to the outside. They feel that any additional pumping capacity should be installed only for the latter purpose.

When farm managers were asked about increasing the area of wetland plants in order to increase water storage area, thereby protecting other crops, they raised several issues. First, the idea of actively pumping water into such a storage area was not supported for the reason just stated; pumping should remove water, not just redistribute it.

Passive storage was viewed more favorably; dikes around such areas could be raised so as to passively withhold water, taking pressure off the canals and pumps. The larger objection, however, was that the local market for non-rice wetland crops is limited, and no regional system exists for the collection, processing and transportation to distant urban (or international) markets of these perishable crops. Prices for commodities

80 such as lotus root, wild rice stem and water chestnut, (in spite of being relatively high) are therefore seasonally unstable, limiting the areas that farmers are willing to plant. An additional objection had to do with government policies that discourage the conversion of rice production areas to other uses, in order to maintain rice production levels. Other problems concerned farmers’ lack of familiarity with these crops, and the fact that some of these crops (e.g., root lotus) are very labor-intensive.

These objections may not be insurmountable, however. The organization of local distribution centers for agricultural commodities and the extraregional marketing of specialty products have been increasing undercurrent policies of economic liberalization (Gamaut and Ma, 1996). Education and the profit motive can overcome the other problems as well, if feasibility and net benefit can be demonstrated.

The micro-landform distribution of any cropping changes is also critically important, as elevation changes of <1 m affect fitness for particular crops (Chen et al., unpub). Flood resistance improvement strategies that may be evaluated by hydrologie modeling should therefore include the following: 1. increasing pumping capacity (as is already planned);

2. deepening of inner drainage canals (as has been proposed); 3. conversion of existing rice production areas to other wetland crops; 4. engineering of wetland cropping areas to increase passive water storage capacity; and

5. focusing cropping changes at the lowest land elevations.

4.5. Limitations of Data and Strategy for Integrated Ecological Economic Modeling

Given the data that have been presented, the prospects for developing an integrated ecological economic model may now be considered. This section will discuss several

81 problems connected with the bounding of the system, and the difficulty of economically evaluating the ecosystem service flows of the study area.

4.5.1.System Bounding

Both Xiaogang Farm and the HFDA are reasonably bounded from a standpoint of water flows. There is some difficulty in distributing the area of Xiaogang Farm into areas

that are homogeneous with respect to use and elevation because a detailed map of this information was not obtained. This information can be inferred, however, from the production statistics provided (Table 4.4; Figure 4.1). Inferences can also be made for HFDA land areas (Figure 4.6) but these are somewhat more indirect.

For most of the inputs and outputs (fertilizers, rice, etc.), Xiaogang Farm and the HFDA are price takers. For the wetland crops, however, which are of particular interest, prices vary according to amount and timing of local production. This is true even at the

scale of Xiaogang Farm, where the local market is limited. Full economic evaluation would therefore require evaluation of price elasticity and the potential for developing new

markets.

4.5.2. Costs and Benefits of Implementing Engineering Strategies

The costs of implementing the strategies mentioned have primarily to do with either changes in flood control works, such as dikes and pumps, or changes in crop choices.

Information on the former was not obtained. Cost information on the latter was available (Table 4.4), but its usefulness is limited by the problem of price instability due to demand inelasticity. Determination of benefits depends on the ability to estimate beneficial changes in ecosystem services and to place a value on them. The services typically recognized for wetland ecosystems are discussed below.

82 Rood Peak Attenuation

Sufficient information is available to enable modeling of system hydrologie response to engineering changes. The value of these changes would be expressed in terms of reduced defensive expenditures and increased production. Information on pump and dike construction and maintenance was not collected. Electric power consumption by flood control pumps can be modeled, however.

Productive Capacity

Wetlands can be among the most highly productive of ecosystems (Mitsch and

Gosselink, 1993). Since the primary economic value of the study region is its agricultural production capacity, perhaps the most important weakness of the data set is the paucity of data obtained on actual flood damage to crops (Table 4.1 ). Crop inundation tolerances (Figures 4.2 - 4.4) can be used to derive indices of damage, but these indices cannot be directly converted to loss values.

Water Quality Improvement

It was not determined whether fertilizer use is carefully calibrated to crop needs, but it is reasonable to assume that fertilizer use causes nutrient outflows to exceed inflows. To determine nutrient budgets for these wetlands, however, measurements of nutrient levels in water flowing into and out of system boundaries would be required, and this information was not available.

Habitat for Biodiversity

Paddy fields were observed to be teeming with aquatic insects, small fish and amphibians. No information was found surveying the diversity of these various cropping or aquaculture systems with respect to native flora and fauna. The impact of pesticide use also could not be evaluated.

83 4.5.3. Approach for Model Development

It was determined that a model could be developed that would simulate flood peak attenuation. Indices of crop damage could serve as a proxy for loss of productive capacity, and could be used in qualitative estimation of benefits, even though the actual amount of crop loss could not be determined. Some of the costs of implementation could be estimated, subject to the limitation of price elasticity problems. The model can be implemented at two scales, where the Xiaogang Farm scale model is based on relatively detailed information and that for the HFDA is much less detailed. This approach is carried out in the following chapters. Chapter 5 develops a model of hydrology and crop damage in Xiaogang Farm and the HFDA; Chapter 6 uses the model to test the results of the above engineering strategies and Chapter? qualitatively discusses the costs and benefits of these respective approaches.

84 CHAPTERS

RESULTS OF MODEL DEVELOPMENT AND CALIBRATION

5 .1. Conceptual Model Development

Figure 5.1 shows the original condition of the JH-DT Plain with respect to hydrology and production. Before cultural modification of the landscape, natural levees formed the geographic boundaries of the lake/wetland plain, and seasonal flooding by

rivers was a normal event. Rivers played a direct role in supplying both water and

nutrients for production. Flood storage capacity within the system was relatively large. Export of produced biomass occurred with surface outflows, mediated by falling river levels.

Three conceptual model diagrams, nested in scale, are used to show the state of the present system. The first (Figure 5.2) is at the scale of one or several fields or ponds in a given landform/land-use category. The boundary is now a low field or pond dike, and surface inflows and outflows are via irrigation and drainage canals. Water storage is controlled, to a large extent, according to a crop- or pond-specific water management schedule, but at times the drainage system is overwhelmed and the system may be flooded by rainfall or by surface inflow. Because of isolation from the rivers, the primary source of nutrients is now purchased inputs. Production that is not lost to flooding is exported to the human economy.

85 Rainfall Evap. Rivers

Flood storage Rivers Seasonal flooding Drainage V—

Nutrient Nutrient Export

Export Solar Production

Figure 5.1. Conceptual model of Jianghan-Dongling Plain hydrology and production: original condition before cultund modification. Symbols are as defined in Figure 3.3a. Evapo­ Drainage Rainfall ration System

irrigation , Water

Surface Drain and Soil Water é e e p / Water Flood 0^1 Mgmt. V~n_r

Field or Fertilizer Pond Harvest Production

Flood Loss

Figure 5.2. Conceptual model of Jianghan-Dongling Plain hydrology and production: landform/land-use category scale, with imposed, crop- or pond-specific water management schedule. Symbols are as defined in Figure 3..Ja. Figure 53 is at the scale of Xiaogang Farm (see Figure 3.2), containing several different landform/land-use categories. At this scale, the external boundary is a high dike, which includes the apparatus for maintaining internal water levels. The inner canals are now within, and controlled by, the system. These canals provide a limited amount of water storage, but no other water storage body exists. Exchanges with the outside are mediated by outer canal levels, which vary seasonally.

This farm scale is pictured in simplified profile view to conceptualize the distribution of water (Figure 5.4). In this view, areas of a given landform, although in reality spatially distributed across the farm, are aggregated to show relative proportion and landscape position. The lowest areas, at left, remain as lake or have been converted to ponds. The lake and pond areas are surrounded by a high dike, but the lake itself is separated from the ponds by a lower dike. Ponds are excavated below grade and surrounded by elevated grassy dikes; unexcavated areas represent the centers of the circle- style ponds. Low-position paddy fields constitute the largest areal proportion, followed by high-position paddies and dry fields, respectively. Homes and commercial areas tend to be located in the most protected positions along the natural levees. The diagonal horizon indicates that, although individual fields are flat, they are distributed along an elevation gradient even within a landform category. This gradient, although slight, is important because it determines the areal extent of flooding. While canals traverse all areas, they are pictured next to the low-position paddy fields because the latter are most directly connected with them and are most readily flooded. Although the large canals have high dikes, they are continuous with small canals which lack high dikes or gate controls and backflow directly into low fields. Excess water from drainage of all areas enters the canals, and when canal levels rise above the regulated level, flooding proceeds from lowest areas to higher areas.

88 Rainfall Evapo­ ration

Outer ©iàitpigÿiiës Canals

Irrigation Irrigation Water Inner Canals

Drainage Drair age Production Flooding Water Mgmt.

S eep age

Figure 5.3. Conceptual model ofof Jianghan-DonglingJianghan Dongting Plain Plain hydrologyhydrol and production; farm scale, with imposed, farm-level water management schedule. Symbols are as defined in Figure 3.3a. 25 I Dike I I Ground level

2 4 - I I T ypical water level C a n a ls Flooding water level

23

£ 2 2 c o

4 21 111

20

19-* Ho^ Shops Ponds and Grass Lake Low Paddies High Paddies Roads ------Relative Area ------

Figure 5.4. Profile view of Xiaogang Farm conceptual model, showing relative areas, ground elevations and typical and flooded water depths of different landform/land-use categories. The third conceptual diagram. Figure 5.5, is at the scale of the HFDA (see Figure 3.1). At this scale, the boundary is the higher, Yangtze River dike, and the outer canal (i.e., the Sihu General Canal) is now within the system. Unlike at the farm scale, there is a major surface inflow (at Futianshi and some smaller inflow points) and Honghu Lake plays an important role in regional flood storage. Outflow is via the major pumping gate at Xintankou (and other, smaller pumping gates), to the Yangtze River. This scale includes many farming areas of the size of Xiaogang Farm.

The interactions of the three scales are apparent in the diagram. Actions taken at the farm scale, such as land use changes to increase storage or the installation of more pumping capacity, help determine the amount of pressure on the storage and drainage system at the HFDA scale. These effects may have reciprocal repercussions in the form of flood loss at the land use scale.

5.2. Farm-Scale Model

5.2.1. Type. Area and Elevation of Landform/Land Use Categories

For modeling at the farm scale, a baseline scenario was developed based on areas and elevations of Xiaogang Farm, as pictured in Figure 5.4. Of the total number of land uses (see Table 4.2), a few may be considered major uses; for these, area data were obtained and these uses were further grouped to obtain seven categories for baseline modeling. These are listed in order of their tendency to flood;

1. low-position paddy fields (one-crop rice) 2. low-position paddy fields (middle-season rice : tatami : late rice [ 2-year])

3. lake (non-intensive carp polyculture) 4. high-position paddy fields (rape : middle-season rice rotation) 5. ponds and grass (intensive carp polyculture)

91 Rainfall Evapo­ ration Surface Inflow

Honghu Lake Farms Rivers

Water Inner Canals Irrigation Outer Production Canals Drainage

Water Mgmt.

Figure 5.5. Conceptual model of Jianghan-Dongling Plain hydrology and production: Honghu Flood Diversion Area scale, with imposed, area-level water management schedule. Symbols are as defined in Figure .1.3a. 6 . “dry” fields (unspecified irrigated crop combination), and 7. other, unirrigated dry areas.

Except for the lake and pond areas, which are relatively static year-to-year, areas for these categories were not directly known and had to be deduced (Table 5 .1 ) based on farm agricultural statistics (see Figure 4.1) and written responses provided by farm managers (see Table 4.4). Elevations for lakes, ponds and low-position fields were determined from

discussions with farm managers and from a farm map bearing a few elevation indicators. The low extent of low-position fields was estimated by map elevations to be around 2 1.0

m; the fact that canal level was ordinarily maintained at 21.2 m suggested that rice cultivation would be impractical below the latter elevation. The upper extent of low fields was initially estimated at 22.0 m based on map elevations and discussions. However, although some rice areas were wiped out in the July, 1996 event, most rice areas suffered only incipient (10-20 %) damage from a 1-day flood level of 22.7 m and 5-day level of 22.5 (see Figure 4.15). When these levels were evaluated against 1-day and 3-day inundation limits of about 033 and 0.27 m, respectively (see Figure 4.2, booting stage), an upper extent of around 22.5 m for low position land was inferred. Other damaged crops included watermelon, which is rotated with late rice in high-position paddies, and cotton and sesame which are grown in non-paddy dry fields. It was inferred that these cropping areas must extend to at least as low as 22.5 m to have been so affected.

For each of these categories, a model element (hereafter termed "land use element’ or LUE) was developed, with a defined land area {Land_areaJ)' and lower and upper elevation (Zz»w_e/ev_i, High_elev_î), and a single defined land use as listed above. The

' Except in equations, italics is used to denote parameter names in the STELLA model.

93 Area (ha) 1996 Baseline Scenario Assumed or Land Use LANDFORM. Land Use (ha) Reported Estimated Element Area (ha) LAKES/PONDS Controlled Lake 80 80 LLE3 80 Pond, Circle-Style 133' 133 Intensive Pond. Water + Grass 381' 381 Total Ponds. Water + Grass 514 LLE5 514 Lotus 2T Water chestnut <33" SUBTOTAL AREA 594 CANALS' 124 124 LOW-POSITION PADDIES Middle-season rice 400 400 LUE 1 420 Tatami (annual area) (30 - 153)“ (67) Tatami : Rice : Rice (2-yr) 133 LUE 2 153 Root Lotus 27-40^" 40 Wild rice stem <0.T SUBTOTAL AREA 573 HIGH-POSmON PADDIES Rape; Middle-Rlce 106-422“ 400 LUE 4 600 Water-melon: Late Rice 33' 33 Other dry crop: Rice 167 SUBTOTAL AREA 600 DRY AREAS Vegetables 35 - 119' 100 Cotton : Wheat 41 -82“ 67 Other irrigated crops 100 Total irrigated area LUE 6 267 Agrcforestry* 31 Other non-irrigatecf 189 Total non-irrigated area LUE 7 220 SUBTOTAL AREA 486 Total Grain Production Area 1127-1379“ 1.200 Total Cultivated Land 1528" 1,440 Total Land, Xiaogang Farm 2378" 2378 2378 ‘See Table 4.4. "From farm manager discussions ‘Assumed to be about 25% of canal area ‘Does not include field ditches 'Includes residential, commercial, industrial, roads; determined b\ difference.

Table 5.1. Summary of reported information on Xiaogang Farm land areas devoted to various uses, and calculations or assumptions used to set estimated areas for land use elements in the farm-scale model.

94 total number of land use elements was limited to seven in order to control model size and complexity. Canals are a special category and are handled differently, as discussed below.

5.2.2. Surface and Soil Water Storage ( Water_ï)

For each fth land use element, a storage of surface and soil water. Water_i. was defined for time t as follows (Figure 5.6):

d(Water_/)/dt = Rain_/ - Seep_/ + lrrigate_/ - Drain_/ (5-1)

The surface soil layer was defined as roughly the upper 30 cm of soil, or that most

readily available for crop use. Negative values of Water_i denote unsaturated conditions at the soil surface, and positive values denote free water above the soil surface. The

maximum potential soil deficit was assumed to be the difference between saturation and wilting point, or no more than -12 cm for a 30-cm profile (derived from Hillel, 1980 and Ward and Elliot, 1995); this was used to define a minimum storage. The flows RainJ, Seep_i, IrrigateJ, and Drain_i are all time-variable and are defined below.

5.2.3. Precipitation and Evapotranspiration {Rain_î)

Rain_i was defined as daily precipitation minus évapotranspiration multiplied by land area. Daily precipitation and pan evaporation records for Honghu were used for all land areas. Evapotranspiration was determined as pan evaporation multiplied by an area- wide coefficient. Evapotranspiration coefficients were available for rice (Figure 4.10) but not for other crops; in addition, evaporation rate affects flooding duration, and when crops are submerged they are not able to transpire. Therefore, in the model, the value was set by calibration. The flow Rcân_i is bi-directional since negative flows (net evaporation) may occur.

95 Rainfall inner canal Rain 1 Rainfall 1 extra area inflow i V a ter 1

Seep 1 Drain 1 Irrigate 1 or"- Irrigation 1

Inner canal inflow s Seepage 1 to LUE 7 to LUE 2

Irrigation total

.

^ Irrigate Farm flow out 0 < f

farm outflow

to LUEs 3 - 6

Rgure 5.6. Storage and flows for land use element (LUE) I. LUE I is one of seven LUEs draining to the storage Inner_canals; the latter provides irrigation to all LUEs and excess water in Inner_canals flows out of the model. Symbols are as defined in Figure 3.3b.

9 6 5.2.4. Vertical Seepage (Seep_î)

Typical methods for preparation and flooding of paddy soils produce a less- pervious layer (' sole’) at a depth of 15 - 25 cm, which has a near-zero hydraulic conductivity (van de Goor, 1980). Water ponded by this layer is a temporary perched water table, not the ground water perse, and empirical knowledge rather than standard

drainage formulas must be applied to determine subsurface drainage (Ochs and Bishay. 1992). In the Four Lakes Region, typical loss by vertical seepage was reported to be <1 mm/day for low-position paddies and 2 -3 mm/day for high-position paddies. However, data from the 1996 flood event (see Figure 4.15) showed that Xiaogang Farm internal water levels were highly stable in the absence of pumping. If typical seepage (1-3 mm/day) had applied during this period, flood peak would have been lower and water level drop much more rapid. Since flooding periods are most germane, and to avoid a much more complex model of the regional water table, seepage rates for all LUEs were set at zero, and a calibrated évapotranspiration coefficient of 0.7 was determined. There is also lateral seepage flow between canals and fields. This lateral flow is important in the longer-term process of field drying, since soils in perennially waterlogged low fields are subject to gleization and reduced fertility. However, lateral flow was Judged not important in defining conditions for damage by excess flooding and was not included in the model.

5.2.5. Irrigation and Drainage (/mgare_/, DrcànJ)

Initial values forWaterJ were set at the midpoint of the normal minimum and maximum water levels from the water management schedules specific to each land use (see Figures 4.2 - 4.4). These schedules also governed irrigation and drainage. Whenever water depth (Water_i divided by land area) was less than the minimum, irrigation flowed in an amount to make up the deficit, bounded by a maximum rate. Whenever water depth

97 exceeded the maximum, drainage flowed out, bounded by a maximum rate. However, a rise in the inner canal elevation could reduce drainage outflow or cause net inflow; the latter procedures are discussed below.

5.2.6. Inner Canal Elevation-Volume Relationship (Cana/_a. CanalJ), and Ccmal_c)

When a given volume of water enters the farm as rainfall, and it is desired to compute flooding depth, it is first necessary to define the volume contained within the inner canal/field ditch drainage system. The remainder may then be defined as ‘within the fields' for the purposes of computing field depths. Configurations of inner canals and field ditches were discussed in Chapter 4 and are presented in more detail in Table 5.2- For the large canals, bottom elevations were specified. For small canals and field ditches, however, only depths below grade were given, and it was therefore necessary to make assumptions about the land elevations where these small canals and ditches were distributed in order to know the elevation. The total length of small canals was assumed to be distributed among land elevation types (low-position, high-position and dry lands) according to their relative proportions in Xiaogang Farm (see Table 5.1). A similar approach was used with ditches, except these were assumed to occur only in fields. Within the range of ditch spacings given in Chapter 4 (i.e., 40 to 340 linear meters of ditches per ha of field area), a relatively high value in this range (3(X) linear meters) was assumed for low areas, where water table lowering would be most necessary, an intermediate value (140 m) was used for high paddies, and a low value (40 m) was assumed for dry areas.

Water over the canal banks but above the canal was defined for these purposes as ‘within the drainage system,’ because canal area is not part of field area. Water in field ditches below field level was considered ‘in the drainage system,’ but water above the ditches was ‘within the field’ (see Figure 4.5). Relationship between water elevation and

98 Luruc Canals Smull Canals Field Pilches

Category/Elevation NA2 NAl EAl-4 NBl Total 1j )W High Dry Low High Dry Total

Bottom width (m)' 8.0 8.0 6.0 4.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0

Side slope ratio' 3 3 3 3 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5

Canal length (km)'’ 4.5 5.4 18.2 3.9 73.9 36.3 18.6 15.1 172.0 84.0 10.7 266.7

Canal depth/height (m)' - - - - - 0.35 0.35 0.35 0.25 0.25 0.25 -

Bottom elevation (m a.s.l.)‘ 18.10 19.10 19.60 19.60 - 21.15 22.15 23.15 21.25 22.25 23.25 -

Top elevation (m a.s,l.)' 23.10 23.10 23.10 23.10 - 21.85 22.85 23.85 21.50 22.50 23.50 -

Land-type elevation (m a.s.l)'* --- -- 21.5 22.5 23.5 21.5 22.5 23.5 -

« Land or field area (ha)' - ---- 1168 6(X) 486 573 6(X) 267 -

Field ditch density (m/ha)' ------3(X) 140 40 -

Surface area (ha)' 17.1 17.3 49.1 9.8 30.3 -_- . - - 66.7 ■ Reported by Xiuogung Furm managers " Large and small canal lengths measured from I: lO.tXK) map; small canals apportioned among land types by land area, field ditch lengths assumed based on densities and field areas Reported for large canals; assumed for small canals and field ditches based on mean land-type elevations and canal depth/height Approximated from elevations in 1996 baseline scenario • For small canals, all areas are included; for ditches, only field areas arc included ' Assumed based on reported range of ditch spacing (i.e., 5(), 1(X) or 2(X) m) • Computed as for trapezoidal channels; a total surface area (not including field ditches) of 123.6 ha was used in Table 5.1.

Table 5.2. Data used for calculation of inner canal and field ditch system volume at various water elevations. water volume for each type of canal or ditch was then determined based on trapezoidal channel configuration. Volumes for elevation increments of 0.1 m were then summed

across canal/ditch types to arrive at an overall relationship for the farm. As required for apportioning excess water between the drainage system and the fields, a second-order quadratic was fit for the critical elevation range of 20 - 23 m (Figure 5.7). The terms of the quadratic were input to the model as Canal_a, Canal_b, and Canal_c.

Because of the numerous assumptions required for this calculation, a sensitivity analysis of the canal configuration parameters was carried out. It was found that most individual assumptions had a less-than-proportional impact on the computed volume,

whereas canal length had proportional impact and canal depth had greater-than proportional impact (Table 53, Figure 5.7).

5.2.7. Inner Canal Storage (Inner_ccmals)

A storage representing water in the drainage system (which includes the inner canals and field ditches, see Figure 4.5) is defined as follows (Figure 5.6):

d(Inner_canals)/dt = Drain_/ + ... + Drain_n - Irrigate - Farm_flow_out (5-2) where, under nonflooding circumstances, DrainJ is the inflowing drainage from the Ah land use element. Irrigate is a summation of all valuesIrrigateJ, and Farm Jlow_out is flow from the inner canals to the outer canal. Under flooding circumstances, values of

DrainJ are positive (flowing to the canals) from high areas and negative (flowing from the canals) to low-lying areas.

5.2.8. Drainage and Hooding (Z>iain_/and F/oocf_e/ev Jinal)

The critical aspect of the model at the farm scale is the dynamic redistribution of drainage waters and computation of flooding level within the farm, since flood damage is a function of flood level and time. Hooding phenomena are computed in stepwise fashion. Step 1 is

1(X) 3.000.000 1-

10% D eeper y = 1 .57742+5x2 - 5.9747E+6X + 5.6699E+7 2,500.000

N o m i n a l Ç 2,000,000 - y = 1.54982+5x2 - 5.9031 E+6x + 5.63002+7 (D E 3 I O 1,500,000 b • o § (O § ü 1,000,000

+ 15%

500,000 -

10% Shallow er :y = 1.52182+5x2 - 5.82982+€x + 5.58892+7

21 22 Elevation (m a.s.l.)

Figure 5.7. Sensitivity analysis of relationship of canal and field ditch volume to water elevation: effect of a +10% change in assumed canal depth is +15% volume at 21.2 m elevation.

101 Parameter Varied S

Bottom width 0.55 Side slope 0.45 Canal length/ditch density 1.00

Canal/ditch depth 1.50

Land-type elevation -0.18 Field ditch density (m/ha) 0.09

Table 53. Sensitivity of canal/ditch system volume at 21.2 m water elevation to changes in canal configuration parameters. Sensitivity statistic S = (Ax/x)/( Ap/p) where x = volume, p = parameter and Ap/p = ± 10%.

to instantaneously compare the volume of water (Warer_0 in each land use element with the normal maximum volume{Water_max_i * LandjureaJ); if higher, the difference is defined as being in excess {Excess_î ). (Functionally, this assumes that farmers act to the benefit oftheir own crop regardless of system needs; this was verbally confirmed on several occasions.) Excess amounts from all land use elements together with the volume already ‘in the drainage system’ define the total excess volume for redistribution (Redisrrib_vo[).

102 Depending on its magnitude, this volume may have any of several geometries, as shown in Figure 5.8, a-c.

In Figure 5.8, the schematic originally presented in Figure 5.4 is made more complex by the division of low-position land into two land-uses (e.g.. one-crop rice and tatami : rice) which overlap in elevation, as may occur in reality. In Figure 5.8a, flood level does not exceed Low_elev_l + Water_tnax_l. Thus there is no flooding in LUE I or

the other LUEs, so Redistrib_vol is fully contained in shape M and the following is true:

Redistrib_vol = Canal_a*(flood elevation) + Canal_b*(flood (5-3)

elevation)+Canal_c

Subtracting Redistrib_vol from both sides and applying the quadratic formula, we solve for and define Flood_elev_0:

Flood_elev_0 = (-Canal_b + (Canal_b - 4*Canal_a * (Canal_c - (5-4)

Redistrib_vol)) )/(2*Canal_a)

In Figure 5.8b, flood level does exceed Low-elev_l + Water_max_l; thus flooding exists in LUE 1 but not in the other LUEs. Redistrib_vol is now fully contained in shapes

M + N, each of which is a quadratic function of flood elevation, and the following is true:

Redistrib_vol = [Canal_a*(flood elevation)* + Canal_b*(flood (5-5) elevation) + Canal_c] + [(Land_area_I/

(2*Elev_change_l)) * (flood elevation) * + (-Land_area_l/Elev_change_l*(Water_max_l + Low_elev_l )*(flood elevation) + (Water_max_l + Low_elev_l) " *Land_area_l/(2*Elev_change_l)

103 I I Ground level { I Maximum water level Flooding water level

a. Canal flooded 23-1 Lake Canals Low Paddies

cn CtS 2 2 -

c o OS

b. Canal and LUE 1 flooded 23- Lake Canals Low Paddies

CO Ct5 22 E c o ^ 21

2 0 -' Relative Area

Conti aued on next page

Figure 5.8. Profile view of Xiaogang Farm conceptual model, showing only the low- position fields (LUEs 1 & 2), lake (LUE 3) and canals, and illustrating computational approach for flooding elevation, (a) Canal is above criterion level; flood level is computed from shape M. (b) A portion of LUE 1 is also above typical maximum; flood level is computed from shapes M + N. (c) Flood level is computed from shapes M, N, P + Q.

104 Figure 5.8 (continued).

I I Ground level I I Maximum water level Flooding w ater level

c. Canal, LUE 1, LUE 2 and lake flooded 23 Lake Canals Low Paddies

to C6 2 2 - i . c o

g 21 -

Relative Area

105 where Bev_change_l = High_elev_l - Low_elev_l.

Rearranging to gather the quadratic terms a, b and c, we define new terms a_plus_l, b_plus_I and c _plus_l (denoting the inclusion of 1 flooded LUE):

a_plus_l = Canal_a + Land_area_I/(2*Elev_change_l) (5-6)

b_plus_I = Canal_b + -Land_area_I/Elev_change_l * (5-7) (Water_max_l + Low_elev_l )

c_plus_l = Canal_c + (Water_max_I+ Low_elev_I) (5-8)

*Land_area_l /(2*Elev_change_I)

Subtracting Redistrib_vol from both sides and applying the quadratic formula, we solve for and define Flood_elev_I:

Rood_elev_I = (-a_plus_I + ((b_p!us_l) -4*c_plus_I * (c_plus_I

- Redistrib_voi)) ) /(2*c_plus_I )

A similar procedure is used to define Flood_elev_2; the terms a_plus_2, b_plus_2 and c _plus_2 are defined based on the three shapes M, N and P. Next, if flooding levels exceed the maximum management level for the lake area, drainage will cease and part of Redistrib_vol will occupy shape Q (Figure 5.8c), and the terms of the quadratic defining flood elevation for the shapes M, N, P and Q are as follows:

a_plus_3 = a_plus_2 + 0 (5-10)

b_plus_3 = b_plus_2 + Land_area_3 (5-11)

106 c_plus_3 = c_plus_2 + (Water_max_3 + Mean_elev_3) * (5-12) Land_area_3

where Mean_elev_3 = (High_elev_3 + Low_eIev_3)/2.

The iterative computation of Flood_elevaiion_i based on successive definitions of the quadratic terms a, b and c is pictured in Figure 5.9. Of all the computed values

Flood_elevJi, it is not possible to know apriori which is the correct one (termed Flood_elevJjnal) for a given time step, so after computation they are successively tested {Flood? J; see Figure 5.9). If Flood_elev_n is greater than Low_eiev_n + Water_max_n, then Flood? ji > 1 and Flood_elev Jinal = Flood_elev_n; if not, Flood_elev_n-l is tested, and so on. If no flooding is found at any level, then Flood_elevJinal = Flood_elevJ) is accepted (Figure 5.10). This procedure requires that the LUEs be numbered in order of increasing design maximum water elevation; i.e., for all /:

Low_elev_/ + Water_max_i < Low_elev_i+/ + Water_max_/+7 (5-13)

If this requirement is violated, incorrect flooding elevations may be computed. The circle pond design was not used in the model, for example, because the practice of draining and refilling in May would cause a temporary violation of this restriction. The restriction had to be observed in the design of all test scenarios. Once Flood_elevJinal is known, its corresponding volume in each LUE

(^Flood_yol_î) can be determined using the same geometry (Figure 5.11). Drainage from each LUE is then determined as follows:

Drainage_r = Excess_/ - Flood_vol_/ (5-14)

107 redistrib vol ....

V--'- -©«fiaTa \ canal b' canal c \

flood elev 0

redistrib vol Low elev 1 Flood a 1 1 / Flood b 1 Flood c 1

c plus 1 \ C k a plus b plus 1 _V flood? 1 ^ ...,/ flood elev 1 Water max 1 redistrib vol "\ Low elev 2 Flood b 2 Flood 0 2

b plus 2 \ flood? 2 I ' flood elev 2 V .. ■ Water max 2 etc. etc. etc,

Figure 5.9. The variables flood_elev_l, flood_elev„2 Aood_elev_5are calculated from iteratively defined quadratic variables (e.g., canal_a, a_plus_l, etc.). Each is used to derive a test variable (flood?_l, flood?_ 2, etc) to determine whether a given land use element is flooded, at each dt. Symbols are as defined in Figure 3.3b. outer canal elev ( farm outflow Vol at outer elev pump on level

/criteria canal el pump flow exchange flow/ canal a

S ' / A X ' flood elev 0 flood elev 1 canal b / ..'■T"" '; flood elev 2 criteria c^al vol flood elev 3 A flood elev 4 s canal c J flood elev 5

——-Q ) flood? 1 flood elev final J flood? 2 canal vol final flood? 3

X flood? 4

X • flood? 5 'N .. •

Figure 5.10. The test variables flood?_l, flood?_2, etc. are used to determine which value (flood_elev_l, flood_elev_2 flood_elev_5) should be accepted as the actual flood elevation (flood_elev_rinal). The latter determines volume in the canal system (canal_vol_final) and is compared with a design level (criteria_canal_elev ) to determine amount of exchange with the outside (exchange JlowV Symbols are as defined in Figure ,T3b. Vater min 1 î

Irrig Capacity

runoff factV 1 Land area 1 ^ ^ a t e r 1 \ Irrigation 1 Vater level 1

Land area 1 Elev'fthange ^

Vater max 1 ; Vol m ax 1 V ater max

Flood b 1 V flood? 1 Low e le v 1

Flood 0 1 / ' flood vol 1 drainage 1

Elev change 1 Land area 1 flood elev final

Figure 5.11. Rood_elev_final defines the volume of water (e.g., flood_vol_I) assigned to each land use element (LUE I is shown) in the flood redistribution process. If the assigned amount exceeds the actual amount (Water_l), drainage (drainage_l) is negative; i.e., excess water flows in. Symbols are as defined in Figure 33b.

10 If the computed excess that is present exceeds the computed flood volume, the positive difference will drain out of the LUE to the inner canals, but if the reverse is true the negative difference will backflow in. Thus in the model, as in fact, runoff from the high

fields floods the low fields via the canal system. However, the procedure's accuracy relies on the correct determination of high and low land elevations, and the simplifying assumption of linear land slope for each LUE.

This redistribution is assumed to occur instantaneously at each dt. A time step of dt = 0.1 day was used in model runs. This interval may not be long enough to allow equilibration of water levels within an area the size of Xiaogang Farm; in figure 4.15, a one-day lag appears to occur between the rainfall events of July 16 and 18 and August 3

and their respective peaks of influence on the canal level. This lag could represent the necessary time for overland flow or interflow from fields to reach canals, management

actions to adjust pond or field drainage rates, or channel flow from ditches and canals in higher areas to lower areas. Therefore, a runoff factor {Runoff Jactor_i) was used to determine the fraction of water from each LUE defined as in excess. The result is that an incoming volume of precipitation drains as an exponential decay over several time steps rather than as a single lump. Values of Runoff^actorJ are determined below by calibration.

5.2.9. Outflow To Outer Canals{Farm Jlow_out)

Inner canals discharge to the outer canal through several openings housed in two flood gates (see figure 3.2 and refer to Chapter4). Under low water conditions, flow is by gravity and is controlled to maintain an inner canal water elevation of approximately

21.2 m; when the outer canal exceeds this level (but does not exceed a near bankfull level at which power is shut off) electric pumps are used to maintain the inner water level. To simulate water level control, the model compares three water elevations: actual inner canal level {Flood_eievJinat), inner canal criterion level {Criterion_canal_elev) and actual outer

111 canal level {Ouîer_ccmal_elev). It also determines inner canal volumes corresponding to each of these water levels (Cana/_vo/ Jinal, Canal_vnl_criterion, and Canal_vol_nuter) and determines by difference the amount that should flow in or out, consistent with gravity and

the criterion elevation. The resulting volume ^ExchangeJlow) flows out instantaneously at each time step, but not at a rate to exceed a ceiling rate (see Figure 5.10). In the initial stages of model development, the ceiling took any of three forms: weir flow, when water levels are so low that the outflow conduits are not submerged; pipe flow, when they are submerged; or pumping rate, when outer canal level exceeds a trigger value {Pump_onJeve[). However, testing of the model showed that only the pumping rate actually becomes limiting, indicating that the outflow conduits are large enough to

accommodate all necessary exchanges at low water. Therefore, the other two limits were removed from the model. Pumping rate is determined by pumping capacity per pump {Fixed_pump_cap = 2.2 m^/s), number of pumps {Pumpjtumber = 7) and electric power {Pump_power). Based on data from July, 1996 (see Figure 4.15), the model was set initially to reduce power to half at an outer canal level of 24.8 m, and to zero at 24.9 m, but these values were changed upon calibration.

5.2.10. Damage Due To Flooding {DamagejjreaJ,Max_daniage_areaJ and CropJossJ)

Crop damage or fishery loss depends on depth and duration of flooding of each LUE. Tolerance limits of rice for I-day or 3-day inundation periods were available, and 1- day limits for other crops were estimated from discussions with local experts (see Figures 4.2 - 4.3); limits for lake and ponds were determined by dike heights, above which fish could escape. The model determines flood depth {Flood_depth_i) for each LUE as height of flood level above the damage limit at the lowest elevation within that LUE; thus at the beginning of flooding only the lowest area of a LUE is flooded. Once a condition of flooding above the tolerance limit has already existed for 1 day or 3 days (depending on the

112 limit type), damage is occurring and damage area begins to be recorded {Damage_area _\). Model runs showed no appreciable difference in results using the two types of limits; therefore the 3-day limits were removed from the model. The maximum flood depth reached (Ma,r _flood_i) is used to calculate the greatest area of damage within that element {Max_cJamage_area_i)

Besides area inundated, the extent of crop loss from a production standpoint depends on growth stage: depth and duration of inundation; turbidity; water flow; and plant nutritional status (Grist, 1986; Reddy et al., 1991). Since the source of flooding at Xiaogang Farm is internal rainfall rather than surface inflow, turbidity and flow probably

are not important variables. (Quantitative information relating the remaining factors to production loss was not available in the region nor from the literature. Therefore, a simple index of crop loss {Crop_loss_i) was derived; the index accounts for time and area of inundation (and indirectly for depth, which determines area). To facilitate comparison across crops, the index is normalized by area planted; H Crop_loss_i = 2 (damage_area_ii * dt) * 10“* ha-m ' / A (5-15) t=T

where: Crop_loss_i = index of loss for crop i (day)

T = transplant date for crop I

H = harvest date for crop i

A = area planted to crop i = Land_area_/ / # years rotation (ha)

In addition to Max_flood_ccmal (which is the peak value of Flood_elev Jinal), the indices Max_damage_areaJ and CropJossJ are the critical outputs of the farm scale model, reflecting peak internal flood elevation and economic loss for each crop, respectively. It was not possible, however, to quantify the economic loss associated with

113 each of these indices, and therefore the objective of evaluating net economic benefit could not be fully achieved (see Chapter 4). Rather, these findings provide only qualitative indication of benefits.

5.3. Honghu Flood Diversion Area-Scale Model

5.3.1, Outer Canals (Sihu General Canal) And Honghu Lake

The farm-scale model is nested within the HFDA-scale model as shown conceptually in Figure 5.5. The latter contains two additional state variables, Honghu_Lake and Outer_canals (Figure 5.12):

d Honghu_lake/ dt = Inflow + Rain_Honghu Fartns_above - Xiaogang_gate (5-16)

d Outer_canal/ dt = Xiaogang gate + Farms_below + HFDAJrrigation - (5-17)

HFDA_outflows

Two points of connection exist between the farm-scale model and the remainder of the HFDA model. The bi-directional flow {Farm_Jlow_ouf, see Figure 5.6) representing flow through the water control gates at the farm boundary controls several bi-directional flows connected to the two HFDA storages. These latter flows represent exchanges between all HFDA production areas and either Honghu Lake or the outer canals. The second point of connection is the elevation of the outer canal. At the farm scale, outer canal elevation is a forcing function, determined by daily canal data. At the HFDA scale, canal elevation (DMfer_e/ev) is redefined as a function of canal volume {Outer_canals) based on assumptions about canal geometry. Thus, changes in farm outflow may change outer canal volume and water elevation, resulting in feedback effects at the farm level (since outer canal elevation determines ability to drain production areas). The connection between Honghu_Lake and Outer_canals enables flood storage effects to be examined.

114 flow splijK P minus ET I ,! I-armFarm tiflow out

Honghu area — land area ratio 1

HFDA irr Honghu elev Rain Honghu 'Ÿ^,-';,/'âesyno Irrigation

Xiaogang gate l\ Honghu outer ^ lake canal + 6 3 U\ D HFDA Inflows rm s above farms below • HFDA Outflows xiao flow Inflow rate

land area ratio 2 ■ . . . y xintankou flow i Farm flow out ■ . . . y

Figure 5.12. The Honghu Flood Diversion Area (HFDA) model adds two state variables, Honghu_lake and outer_canal. Flows include surface inflow to the HFDA (HFDAJnflows), net precipitation to the lake (Rain_Honghu) and flows to or from fami areas (farms_above, farms_below). The latter is divided into five delayed flows (split_0, split_l, etc.) to desynchronize storm flow peaks entering the outer canal. Symbols are as defined in Figure 3.3b. The lower portion of the Sihu General Canal is -60 km in length, and water elevation may differ by several meters over its length, although that gradient virtually disappears at times of high water (see Figure 4.1 1). Other canals that crisscross the area may also be classified as ''outer canals” (i.e., not within the control of a particular farming area). Information on their lengths and other dimensions was not available, but they were assumed to be smaller but of collectively greater length than the Sihu General Canal. In this model, these canals are collectively represented as a reservoir, within which flow is ignored. The reservoir is assumed to be trapezoidal in cross-section, with a resulting quadratic relationship between volume and elevation (Figure 5.13a). Specific dimensions were determined by calibration. On the other hand, the relationship between volume and water elevation for Honghu Lake has been established (Cai and Zhang, 1991) and is used in the model.

Many other parameters for the HFDA-scale model were similarly developed by calibration rather than measurement. The Sihu General Canal, including the gates at Futianshi, Xiaogang and Xintankou, is the primary conduit for water flow through the HFDA, but many other sites of inflow, conduction and outflow exist. Therefore, the model components that are analogous to these structures (i.e.. Inflows, Xlaogang_gate, and Outflows) cannot be parametrized directly based on these structures, but rather represent a lumping of many similar gates or canals. These components bad to be parameterized by calibration.

5.3.2. Flows To/From Production Areas {Farms_above, Farmsjbelow)

The total land area of HFDA was assumed to consist of many areas similar in basic form and function to Xiaogang Farm. The Xiaogang Farm model was therefore used to represent water management and production in the HFDA model, with the following modifications. LUEs were modified in respective area to reflect land use proportions for Honghu City (see Figure 4.6). Inflow to and outflow from the irmer canals {Flow_out)

116 a bank r

large lengt^'^

top elev med large ratio med l e n ^ bottom elev

outer canal

outer b outer a

outer c HFDA outer elev

outer depth

b. top width max elev min elev Fu below Xiao above length

bot width depth

bank \ wet perim

Manning

Inflow rate

Figure 5.13 Honghu Rood Diversion Area model computations: (a) computation of water elevation in the outer canal, based on a trapezoidal channel; (b) computation of surface inflow rate based on Manning equation for channel flow. Symbols are as defined in Rgure 33b.

117 was multiplied by area ratios based on estimated size of HFDA areas draining into the Sihu Canal above Honghu Lake or into Honghu Lake directly (Lcmd_area_rano_2). and into the

Sihu General Canal below Honghu Lake (,Land_area_ratio_l), respectively, to estimate outflow from those areas (see Figure 5.12). These areas were then adjusted by calibration. Describing rainfall for the HFDA was problematic, since data were available only for stations at Honghu and Jianli. The lower end of the HFDA is over 60 km from the nearest of these stations and would be poorly represented. Furthermore, even if average rainfall were very similar across the area, storms recorded at these stations would not strike all parts of the area simultaneously. To compensate as much as possible for these problems, daily rainfall for these two stations was averaged (pan evaporation for Honghu was subtracted to give net precipitation, PjninusJET) to estimate precipitation incident to Honghu lake and land areas. Outflow from farm areas to the outer canals was then divided into five flows {split_0, split_1, ..., split_4) which were desynchronized by increments of 2 days (i.e., 0, 2-, 4-, 6-, and 8 -day delays, respectively) to smooth outer canal responses.

This procedure was unnecessary for inflows to Honghu Lake because of its large storage volume.

5.3.3. Surface Inflow To Honghu Lake {HFDA_Inflows)

The primary surface inflow to the HFDA is through the Futianshi gate. Water level data above and below this gate are available for certain years, but these data cannot be used to estimate flow because the greatest head across the gate exists when the gate is closed, and thus flow is not directly proportional to head. However, the elevation gradient over the distance between Futianshi and Honghu Lake (~18 km) is also known and should be related to flow since no gate exists in that stretch. Flow was therefore estimated based on a trapezoidal channel and the Manning equation (Figure 5.13b). As mentioned above, however, other routes of inflow also exist, for which data are lacking. Therefore, the dimensions of the channel were set by calibration to represent all inflows.

118 5.3.4. Row From Honghu Lake To Lower HFDA {Xiaogang_gaie)

Xiaogang Gate is the primary outflow from Honghu Lake, leading to the lower part of the Sihu General Canal. Control of the gate is assumed to be according to a set of criteria governing lake and canal water levels, subject to limitations of flow across the gate. These criteria were assumed to vary over time, and were determined based on study of water elevation records. Criteria were thus established for optimal {OptjHonghu) and

minimal(Mm_f/bng/za) lake elevations, and maximal {Max_canal) and minimal (Min_cana[) canal elevations (Rgure 5.14a). A simplified, fully-open/fully-closed assumption caused the gate flow to ‘flutter’ from one time-step to the next but was assumed to adequately represent time-averaged flow. Row across the open gate was assumed to be

a function of head (i.e., lake level minus canal level) and two weir coefficients (linear and exponential, respectively) which were determined by calibration.

5.3.5. Outflow From HFDA To Rivers {HFDAjOutflows)

Water from the outer canals was assumed to flow out of the HFDA subject to the limitations of weir flow and time-variable maximum and minimum criteria for Yangtze River water level (Rgure 5.14b). (The latter criteria were determined by intercomparing situations where the gate was evidently open [inside and outside elevations similar] and

evidently closed [elevations different]). When the criteria were not violated, outflow was

by weir flow similar to Xiaogang Gate. Since actual outflow is through many gates, coefficients were again determined by calibration. When the criteria were violated, the gates were assumed to close and further outflow was subject to the limitations of pumping capacity. Nominal pumping capacity was known for 11 identified points of outflow (see Table 4.5), but it was assumed that this capacity was not realized in all situations, and so this value was also subject to calibration.

119 a. HFDA irr cap

m ax canal

HFDA irr min canal HFDA outer elev H opt Honghu Honghu elev • ^ min Honghu n

w eir a

w eir b xiao flow max ^

J y - - — xiao' flow

b. too high too low pump capacity

HFDA outer elev ("V. xintankou flow

xin head

Xintankou outside weir a2 weir b2

Figure 5.14. Honghu Rood Diversion Area model computations: (a) computation of HFDA irrigation demand and flow through Xiaogang Gate, based on elevation criteria for Honghu Lake and the Sihu General Canal; (b) computation of HFDA outflow through Xintankou, Gate, according to elevation criteria for the outer canal and pump capacity or weir flow. Symbols are as defined in Figure 33b.

120 5.4. Sensitivity Analysis and Calibration

5.4.1. Xiaogang Farm Scale

Simulation of July 14 - August 7. 1996

Two strategies were used for sensitivity analysis and model calibration at the farm scale. The first was based on the 25-day period from July 14 - August 7, 1996, the only time period for which inner canal elevation data were obtained. During this event, actual peak flood level was 22.72, or 1.52 m above the criterion level, and rice area damaged was approximately 680 ha. The initial simulation (Figure 5.15a) underpredicted internal flooding level by 0.43 m, and predicted only about 279 ha of rice damage, where total area is computed by adding areas damaged in LUEs 1 (mid-season rice) and 4 (rice : rape rotation) and half of the area damaged in LUE 2, since only half of the tatami : rice area was in tatami in a given year. A preliminary sensitivity analysis was used to rank parameters for calibration.

Adjustments to correct peak flood level were addressed first. The pumping schedule as reported did not match periods of observed water drop and was therefore assumed to be inaccurate; the schedule was changed in the model to obtain better agreement. It was noted that, once pumping was allowed to resume (i.e., after day 207), modeled water level drop was curvilinear, reflecting model assumptions of a constant pumping rate and reduced flooding area with depth, whereas actual water elevation drop was nearly linear. This trend suggested pump efficiency actually diminished as head increased. Linear

{PumpjieadJiactor) and exponential {Pump_head_exp) terms on head were therefore added to allow this relationship to be better calibrated. Additionally, canal depth was assumed to be 10% less than originally estimated, and runoff factor was set closer to unity.

Other corrections were made to assumed land elevations in order to increase area damaged. However, for a given volume of water, adjustments that increase area flooded tend to

121 1 : Simulated 2; Actual a. Pre-calibration 23.001

«S E ^ 22.00- O « >

LU

21.004------b. Post-calibration 23.001

(0 £ ^ 22 .00 -

I

LU

21.00 196.00 201.75 207.50 219.00

Julian Date

Figure 5.15. Simulation of Xiaogang Farm inner canal water elevation for flood event of July 14 - Aug 7, 1996, before (a) and after (b) model calibration. Recorded water elevations are shown for comparison.

122 decrease flooding depth. If the volume of water entering the farm was actually higher than the rainfall amount measured at Honghu, both flooding depth and damaged area would

show better agreement. Potential sources of inflow include flooding inflow from a canal breach, subsurface inflow, or higher rainfall. The observed water level pattern does not suggest a breach, since increases correlate well with timing of precipitation. Subsurface inflow also is not suggested, since internal levels were quite static in the absence of rainfall and pumping. Assuming a higher rainfall input also seems problematic, because although heavy precipitation tends to be spotty, the Honghu rainfall values used in the model are already extremely high. For the purposes of calibration daily rainfall values were increased by 10%. The calibrated model (Figure 5.15b) gives much better agreement in both canal elevation and damage area, although both still fall below reported levels. Final parameter values are reported in Table 5.4.

Simulation of May - August, 1980 - 1994

Following this calibration a second approach was used to test model operation. It was reported that destructive flooding occurred at Xiaogang Farm in 1980, 1983 and 1991 (see Chapter 4); it was therefore assumed that flooding in other years either was not pronounced or was absent. The model was tested with meteorological data for various years to determine whether the model would accurately reflect these assumptions. For the years 1987 - 1994, the land use configuration developed for 1996 was assumed to apply. However, land use was different during the early 1980s: a large area was planted to two- crop rice, lake area was larger, and pond area was less than at present. Therefore, a different land use configuration was developed for the years 1980 - 1986: much of the present pond area was assumed to be either lake or under cultivation, and low fields were divided between one-crop and two-crop rice. When moving land area between LUEs, a calculation was made to ensure that the total area of land apportioned to each of 4 elevation

123 classes (20.5 - 21.5; 21.5 - 22.5; 22.5 - 23.5; 23.5 - 25 m a.s.l.) was held constant to within 2%.

The nearest data for precipitation and pan evaporation, i.e., those from Honghu City ( 1980 - 1994), were used for these simulations in spite of recognized differences from Xiaogang Farm rainfall (see Figure 4.8). For all years other than 1980, Xiaogang Gate water levels were available (see Table 4.1) so outer canal elevation was known. For 1980.

Xintankou-inside elevation was known, and an analysis of years where both Xiaogang- below and Xintankou-inside were known (1983, and 1987-1994) showed that the former could be reasonably estimated from the latter, especially at high water levels (Figure 5.16). These estimated values were used in the 1980 simulation.

When farm managers described flood events, they stressed that governmental shut- off orders contributed to the extent of flooding (even while acknowledging that these orders were not always obeyed, especially at night). The outer canal elevation at which pump shut-off is assumed to occur{Pump_ojfjleve[) is therefore important. For the two

■flooding’ years in which Xiaogang gate data were available, 1983 and 1991, peak elevation outside Xiaogang Farm was 24.57 and 24.76 m a.s.l., respectively. During

■ nonflooding’ years from 1981 to 1994, peak canal elevation ranged from 22.75 - 24.24 m a.s.l., suggesting that shut-off was normally triggered within the range of 24.24 - 24.57 m a.s.l. Thus, although the execution of a shut-off order may be complex (for example, see Figure 4.15), a simple criterion of 24.5 m a.s.l. was set in the model as a condition of pump operation for the years 1980 - 1994 (see Table 5.4). Results of model runs for 1980 - 1986 (Figures 5.17 - 5.19) were basically as expected but with some important differences. Damaging flood events were found as expected during the ‘flooding years,’ 1980 and 1983, but the model showed a damaging event to have occurred in 1981 as well, which was not a reported flood year.

124 25 a. 24.5 Model: y = 9.016 \ + 9922.\ - 64003\ - - 494.6 24 r = 0.787 23.5

23 c 22.5 3 X 22 • > o u 21.5 SD + O ata{1983; 1987- 1994) 21 Regression Model +++ -H+f 20.5 • + 20 - 17 18 19 20 21 22 24 Inside Xintankou (m a.s.l.)

24.5

23.5 a S

g 22.5 - >s _u0 a 1 21.5 - 4— Inside Xintankou - 1980 data

—©— Below Xiaogang - 1980 estimated tromi 20.5 - regression model

121 152 182 213 243 Julian date

Figure 5.16. The relationship between water elevations inside Xintankou and below Xiaogang gates, respectively, was used to estimate 1980 missing data for water elevations below Xiaogang; (a) regression model; (b) estimation of 1980 elevations below Xiaogang. The relationship is strongest at high water elevations.

125 Inputs Units Runs Yearis) 1996 1980-1986 1987-1994 1987-1994 Run pcnod (calendar) l4Jul-7Aug I May-31 Aug IM av-3lAug lM av-3lA ug Run pcnod (Julian) 195-219 121-243 121-243 121-243 Scale Farm Farm Farm HFDA a. FarmScale Model Parameters Initial Conditions Inner_canals m^ 1.04E+6 6.93E+5 6.93E+5 6.93E+5 \Vater_l m* 2.I0E+5 -4.20E+5 -4.20E+5 -4.20E+5 Water_2 m' 3.44E+4 7.06E+4 6.58E+4 1.31E+4 Water_3 m' 9.00E+5 2.03E+6 — 1.38E+6 Water_4 m' 3.00E+5 -2.40E+5 -2.40E+5 -2.49E+5 Water_5 m^ 8.28E-H6 1.80E+6 4.32E+6 2.22E+6 Water_6 m^ -1.07E+5 — — -3.10E+5 Water_7 m' -2.64E-I-5 — — — Forcing Functions PFT multiplier - l.l II 1 ET multiplier - 0.7 —— — -Land Uses, Water Management LLFE I - Low-position mid-season rice LUE 2 - Low-position tatami : rice two-crop nee tw o-crop rice LUE 3 - Controlled lake carp polyculture LUE 4 - High-position rape : rice LUE 5 - Intensive pond carp polyculture LUE 6 - D r\ crop land irrigated rotation LUE 7 - Multiple uses non-irrigated -Inner Canal Water Management Crit_canal_elev m a.s.1. 21.2 Pump_number 7 Pump_cap day' 190,080 pump_on_level m a.s.1. 21.2 Pump head factor 0.04 Pump head exponent Pump_off_level m a.s.1. 96 calibrated* 24.5 24.5 24.5 Parameters Land_area_l m* 4.20E+6 Land_area_2 m- 1.53E+6 3.53E-+6 6.56E+5 Land_area_3 m- 8.00E+5 L80E+6 1.23E+6 Land_area_4 m’ 6.00E-I-6 6.23E+6 Land_area_5 m ' 5.14E+6 2.14E+6 2.64E+6 Land_area_6 m* 2.67E+6 7.74E+6 Land_area_7 m- 2.20E46 Low_elev_l m a.s.1. 21.2 Low_elev_2 m a.s.1. 21.2 2 1 3 21.3 Low_elev_3 m a.s.l. 20.5 Low_elev_4 m a.s.1.

continued on following page

Table 5.4. Final input values for calibration runs: (a) Farm Scale Model parameters (listed only where different from 1996 values); (b) Honghu Flood Diversion Area Scale Model parameters (HFDA-Scale runs only).

1 2 6 Table 5.4 (continued)

Inputs Units Runs Ycar(s) 1996 1980-1986 1987-1994 1987-1994 Run period (calendar) 14Jul-7Aug lMav-3 lAug l.Mav-31Aug lMav-31 Aug Run period (Julian) 195-219 121-243 121-243 121-243 Scale Farm Farm Farm HFDA

Low_elev_5 m a.s.1. 21.0 — —— —— Lo\v_cle\_6 m a.s.1. 22.5 —— — Low_elev_7 m a.s.1. 23.5 — — — High_ele\_l m a.s.1. 22.4 21.75 — 22.3 High_elev_2 m a.s.1. 22.4 — —— High_clev_3 m a.s.1. 21.0 —— — H i^_clev_4 m a.s.1. 23.5 ——— High_e[c\_5 m a.s.l. 21.6 ——— High_clev_6 m a.s.1. 24.0 - — — High_elev_7 m a.s.1. 25.0 — —— Dike_elev_3 m a.s.1. 22.1 — — — Dike_elev_5 m a.s.1. 22.6 -- - — RunoffJactorJ day' 0.9 — - — Seep_Rate_i m day ‘ 0 — — — lrrig_Capacit\_i m' day' 0.05 — - — Wi!ting_pointJ m -0.12 — — — Extra_area_factor - 0.1 — — — Canal _area m^ 1.24E+6 ——— Canal_a - 1.5218E+5 -— — Canal_b - -5.8298E+6 " — -- Canal_c 5.5889E+7 b. HFDAScale Model Parameters "" Initial Conditions Honghu lake nr* NA NA NA 2.64E+8 outer canal m^ NA NA NA 4.94E+7

Forcing Functions •Xiaogang Gate min canal m a.s.1. NA NANA 21.5 max canal m a.s.1. NA NANA 24.5 min Honghu m a.s.1. NA NANA 22.0 opt Honghu m a.s.1. NA NANA 22.4 HFDA irr cap m ' day' NA NANA 8.64E-1-6 -Xintankou Gate too low m a.s.1. NA NA NA 18.46 too high m a.s.1. NA NANA 23.21 pump capacity m^ d ay' NA NANA 2.72E+7 Parameters -Farm Flows land area ratio I - NA NANA 62.5 land area ratio 2 - NA NANA 37.5 desync day NA NANA

continued on following page

127 Table 5.4 (continued)

Inputs Units Runs Yearts) 1996 1980-1986 1987-1994 1987-1994 Run period (calendar) 14Jul-7Aug lMav-31 Aug lMav-31.Aug lMav-31Aug Run penod (Julian) 195-219 121-243 121-243 121-243 Scale Farm Farm Farm HFDA -Futianshi Gate NA NA NA top width m NANA NA 175 bank - NANA NA 4 max elev m a.s.1. NA NA NA 25.5 min elev m a.s.1. NA NA NA 21.2 length m NA NA NA 18.000 Manning - NA NA NA 0.035 -Xiaogang Gate weir a - NA NA NA 6.50E+7 weir b - NA NA NA -Outer Canals large length m NANA NA 150.000 large twidth m NANA NA 120 med large ratio - NA NA NA 3 med twidth m NA NA NA 50 top elev m a.s.1. NA NA NA 25 bottom elev m a.s.1. NA NA NA 18 -Xintankou Gate weir a2 - NANA NA 100,000 weir b2 - NANA NA 2.5

128 25.0 1980 m a.s.h- 198124.5

23.5 2. Ôüter’cànaï, à :tùàl

22.0

20.5 oi 1. Inner canal, simulated ê § 19.0 25.0 Ï982] 1983 24.5 m a.s.l. o> ^ 23.5 vO 22.0

20.5

19.0 121 151.5 182 212.5 121151.5243 182 212.5 243

Julian Date May June July August May June July August Continued on following page

Figure 5.17. Simulation of Xiaogang farm inner canal elevation (I) during May - August foryears 1980- 1986. Outer canal elevation (2), a forcing function, is also shown. At outer canal elevations above 24.5 m a.s.l., pumps were assumed to be shut down and the period of internal flooding was extended. Figure 5.17 (continued).

25.0 |Î984j Ï9851 2. Outer canaf, actual 23.5

22.0

1. Inner canal, simulated w Il986l 4> 23.5

22.0

20.5

19.0 121 151.5 182 212.5 243 121 151.5 182 212.5 243 Julian Date May June July August May June July August a. Land Use Element 1: 420 ha of mid-season rice 400 -1 Mid-season rice

SQ..

200 - 0 1

b. Land Use Element 2; 353 ha of two-crop rice 200 1 T ^ T Late rice T ^ T — T— T- Early rice

100 - o g 86

21.00 1 5 1 . 5 0 1 8 2 . 0 0 212.50 243.00 Julian Date May June July August

Figure 5.18. Simulated timing and area of Xiaogang Farm flooding damage (i.e., in excess of I-day tolerance depdi), with respect to periods of crop growth, during May - August for years 1980 - 1986, in Land Use Elements 1 (a, mid-season rice) and 2 (b, two- crop rice). Periods of transplant (T) and harvest (H) for each crop are shown. Year of each flood event is indicated; gray arrows indicate years that were not described as flooding years.

131 6 0 ^

CSSearly ncc 50^ - mid ricc ^^zalatc nce

40% - —g— flood elevation ë n 30% ^

20 % - g ------g( &

10% -

0% 84

3.5 3.0 I 2.5

I 2.0 - 1.5 - U 1.0 -

0.5 -r

0.0 - J 80 81 82 83 84 85 86 Year

Figure 5.19. Simulated flood elevation and crop damage, farm-scale model, 1980 - 86: (a) internal peak flood elevation and fraction of crop area damaged; (b) crop loss index, computed as area under the damage area-time curve, normalized to total crop area.

132 The 1980 simulation showed two flooding events in which internal water levels reached above 21.5 m a.s.l. (Figure 5.17). A brief event in mid-July had a minor effect on maturing early rice. This event evidently corresponds to the farm managers' report of a 1980 event in which 260 mm of rain fell in 2 hours, at a time when early rice was in the booting or heading phase (i.e., normally late June or early July). However, daily records from Xiaogang Farm were not available; at Honghu, 10 km away, only a 69-mm rainfall event was recorded. Therefore, the simulation showed no damage to early rice, and no damage to mid-season rice. In mid-August, a simulated event damaged >250 ha of maturing mid-season rice and 140 ha of late rice in the turn-green or stooling phase; this event was prolonged by outer canal elevations above 24.5 m a.s.l. (Figure 5.17). This result corresponds reasonably well to 1980 yield reduction data for Honghu City and Jianli County (Figure 4.7), in that late rice was more strongly impacted than early rice, although it does not explain the strong response of mid-season rice. In 1981, a 3-day event totaling 229 mm (i.e., having a 10-year return frequency; see Table 4.6) was recorded at Honghu during mid-July. The simulated result was a damaging inundation depth covering nearly 200 ha of one-crop rice in the booting phase and 150 ha of early rice in the heading or maturing phase. No such event was mentioned by farm managers or observable at the city/county level (see Figure 4.7); this finding illustrates the difficulty posed by the lack of daily rainfall records for Xiaogang Farm.

Outer canal levels did not exceed 24 m a.s.l. during 1981. The 1983 simulation showed repeated damaging events during May, June and July.

Events in May occurred so early in the growing season that crops may or may not have been transplanted or, if damaged, could have been replanted, and events in early June appeared to affect minimal area. In late June and mid July, repeated and more extensive impacts were shown, affecting up to 65 ha of early rice and 210 ha of mid-season rice.

Simulated inundations in July were also prolonged by outer canal elevations above 24.5 m

133 a.s.l. (Figure 5.17). Farm managers had specifically mentioned only early rice impacts, but city/county records showed impacts on both early and mid-season crops (Figure 4.7). As expected, no damaging events were simulated in 1982, 1984 or 1985. A brief event in 1986 would have caused little crop loss (Figures 5.17 and 5.18b). Results of model runs for 1987 - 1994 showed potentially damaging events occurring in most years (Figure 5.20 - 5.22). In these runs, LUE 2 is assumed to be in a

2-year rotation of mid-season rice, tatami and late rice, where in a given year half the total area is in the first year and half in the second. (Two-crop rice cultivation was not simulated, as it is now little practiced at Xiaogang Farm; therefore no early rice is shown.)

Actual outer canal elevations exceeded 24 m a.s.l. only during 1991 (Figure 5.20); simulated inner canal elevation approached 22 m a.s.l. in both 1987 and 1991 (Figures

5.20, 5.21a). The 1987 event occurred in early July, affecting mid-season rice in the booting phase and affecting tatami during harvest/drying. The 1991 simulation showed two major inundations. The first occurred in late May, during the period of transplanting and rooting (‘tura-green’) of mid-season rice, but before the susceptible, harvest period of tatami. The second event, in mid-July, was prolonged by external water levels > 24.5 m a.s.l.. and further affected mid-season rice. This event also came at the end of the tatami harvest, and a minor impact on tatami is shown (Figures 5.21 - 5.22). The actual effect on tatami reported by the farm managers for that year was a major loss of the crop because the long period of heavy rain in early July (Figure 5.20) prevented the crop from drying. This was not an inundation effect perse, and could not be predicted by the model. When the years

1987 - 1994 were expressed in terms of a crop loss index (Figure 22), the events of 1991 were most significant, as expected, and mid-season rice was the primary crop affected. A final sensitivity analysis of the farm scale model, conducted using a flooding period in 1991, showed that peak internal flood level {Max_inner_elev) was relatively insensitive to

134 25.0 1 9 8 7 19881

23.5. ictual

22.0 .

« 20.5 1. Inner canal, simulated

0 19.0 1 25.0 IÏ989 1 9 9 0

23.5

22.0

20.5

19.0 121 1 5 1 .5 182 2 1 2 ,5 2 4 3 121 151.5 2 1 2 .5 24 3182 Julian Date May June July August May June July August Continued on I'ollovving page

Figure 5.20. Simulation of Xiaogang farm innercanal elevation ( 1) during May - August for years 1987- 1994. Outer canal elevation (2), a forcing function, is also shown. At outer canal elevations above 24.5 m a.s.l., pumps were assumed to be shut down and the period of internal flooding was extended. Figure 5.20 (continued).

25.0 Ï9 9 ÏI — 24.5 m a.s.l 1992 Outer can^l, actual 23.5 i......

22.0 •

20.5 d 1. Innercanal, simulated I E § 19.0

% 1 " ° 119931 1994 «5 ï 23.5

22.0

20.5

19.0 121 151.5 182 212.5 243 121 151.5 182 212 5 243 Julian Date

May June July August May June July August a. Land Use Element 1 ; 420 ha of mid-season rice

400 T T ^ T - 4 - Mid-season rice

88 90 È 2 I 200-

Û

b. Land Use Element 2: 153 ha of rice - tatami - rice, 2-year rotation. Year 1 (77 ha): Mid-season rice

4 0 1 T ^ T - Mid-season rice 90 87

S ■ | 2 0 - «0» O

121.00 182.00151.50 212.50 243.00 Julian Date June July August Continued on following page

Figure 5.21. Simulated timing and area of Xiaogang Farm flooding damage (i.e., in excess of 1-day tolerance depth), with respect to periods of crop growth, during May - August for years 1987 - 1994, in Land Use Elements 1 (a, mid-season rice) and 2 (b,c, mid-season rice : tatami : late rice, 2-year rotation). Periods of transplant (T) and harvest (H) for each crop are shown. Year of each flood event is indicated; gray arrows indicate years that were not described as flooding years.

137 Figure 5.21 (continued).

c. Land Use Element 2: 153 ha of rice - tatami - rice, 2-year rotation. Year 1 (77 ha); Tatami followed by late rice

40 1 Tatami H—H Late rice 1

87. I 93 88 iI 20 - o

121.00 151.50182.00 212.50 243.00 Julian Date May Ju n e July August

138 0.7 - a. SSSam id rice - LUE 1 ■■■taïami - LUE 2 0.6 E^ZZImid ncc - LUE 2 faansJlate licc - LUE 2 1S —3 — flotxl elev aüon B "3

C3 Q.

3 C i

H.H 92 93 94

3.5 -

3.0 -

2.5 - 3 -3 i 2.0 - 1

A 1.5 - S- uW 1.0 -

a 89

Figure 5.22. Simulated flood elevation and crop damage, farm-scale model, 1987 - 19^; (a) inner canal peak elevation and fraction of crop area damaged; (b) crop loss index, computed as area under the damage area-time curve, normalized by area planted.

139 parameter variability. Only the elevation at which a pumping shutdown is ordered [Pump_offJeve[) had a sensitivity statistic near unity (Table 5.5). By contrast, area

damaged and crop loss indices were highly sensitive, with disproportionate responses to variations in precipitation and most parameters affecting pumping rate. Crop damage indices were found to be highly (S > 20) sensitive to pump_off_level, in agreement with statements by the farm managers.

5.4.2. Honghu Rood Diversion Area Scale

Simulation of May - August. 1987 - 1994

Calibration of the HFDA-scale model was limited to the years 1987 - 1994 because

inflow and outflow simulation requires data from Futianshi and Xintankou gates,

respectively (see Table 4 .1). Calibration was carried out by adjusting outer canal dimensions and weir flow parameters for the model structures analogous to Xiaogang and Xintankou gates. The évapotranspiration coefficient was also calibrated (Table 5.4).

The calibrated model could not closely simulate the daily fluctuations of the outer canal and Honghu Lake, as a result of its simplified structure and lack of spatially detailed data; however, it did simulate the rising and falling trend of these water bodies (Figures

5.23a,b - 530a,b). Drainage and water demand from production areas (Rgure 5.23d -

530d) was the primary influence on lake and canal hydrology; net precipitation incident to the lake (Rgure 5.23c - 5.30c) and surface inflow (Rgure 5.23f - 530f) were also important. The model invoked irrigation demand only occasionally (since the period modeled was the rainy period); this usually occurred in early May or in late August, if water requirements of (rooting or tillering) early or late rice, respectively, exceeded immediate rainfall (Rgure 5.23e - 5.3Oe). Xiaogang Gate operated intermittently as a result of falling canal levels and/or high lake levels (Rgure 5.23g - 530g), and Xintankou Gate {Outflows) operated in response to rising canal levels (Rgure 5.23h - 5.30h). Simulated

140 Sensitiviiv staustics Damage .4reas Loss Indices M ax_ LUE I LUE2 LUE I LUE 2 Inputs inner_eleV mid ricc earlv rice mid rice earlv ricc Units m a s.l. m- m- dav dav Baseline 21.72 6.6E+5 3.7E+5 0 3 2 0.57 fiiitial Conditions Inncr_canals m^ 8.03E+5 0.00 0.00 0.00 0.16 0.09 W alcr_l m ‘ 2.90E+5 0.00 0.00 0.00 0.16 0.00 Watcr_2 m^ 7.72E+3 0.00 0.00 0.00 0.00 0.00 Walcr_3 m ‘ 8.90E+5 0.00 0.00 0.00 0.16 0.09 Watcr_4 3.07E+5 0.00 0.00 0.00 0.16 0.00 Watcr_5 m' 8.44E+6 0.00 2.07 0.01 2.19 0.79 Watcr_6 m' -74383 0.00 0.00 0.00 0.00 0.00 Water_7 m^ -61301 0.00 0.00 0.00 0.00 0.00 Forcing Functions PPT multiplier -I 0 3 5 3.69 2.44 531 3.86 ET multiplier - 0.7 0.00 -0.16 -0.11 -0.31 -0.18 Min_factor - I 0.05 0 3 8 0.25 0.47 0 3 5 M a\_factor - I -0.05 -0.51 -0.34 -0.78 -0.53 Crit_cancil_elev m as.l." 21.2 0.23 2 3 0 1.52 4.22 4.82 Pump_number - 7 -0.15 -1.62 -1.06 -2.66 -2.02 Pump_cap nP day* 190,080 -0.15 -1.62 -1.06 -2.66 -2.02 pump_on_level m a. s. I. 21.2 0.00 0.00 0.00 0.00 0.00 Pump head factor - 0.04 O.IO 1.12 0.74 1.72 1.40 Pump head exponc - 2.0 0.25 2.56 1.69 4.22 3.42 Pump_off_level m as.l." 24.5 -1.05 -9.68 -7.20 -33.28 -26.05

Parameters Land_area_l m- 4.20E+6 0.00 0.81 -0.09 0.00 0.00 Land_area_2 m- 1.53E+6 0.00 -0.02 0.97 0.00 0.00 Land_area_3 m- 8.00E+5 0.00 0.10 0.07 0.16 0.09 Land_area_4 m- 6.00E+6 0.08 0.70 0.45 1.09 0.70 Land_area_5 m- 5.14E-h6 0.08 0.71 0.46 1.09 0.70 Land_area_6 m- 2.67E-H6 0.02 0 3 5 0.23 0.63 0.35 Land_area_7 m- 2.20E+6 0.02 0.28 0.18 0.47 0.26 Low_elev_l m a s . I." 21.2 0.35 -5.24 2.44 -8.91 3.16 Low_elev_2 m as.l.*’ 21.2 0.15 139 -4.48 1.88 -9.21 Lo\v_elev_3 m as.l." 20.5 0.00 0.00 0.00 0.00 0.00 Low_elev_4 m as.l." 22.2 0.00 0.00 0.00 0.00 0.00 Lx)\v_ele\_5 m as.l." 21.0 0.00 0.00 0.00 0.00 0.00 Low_elev_6 m as.l." 22.5 0.00 0.00 0.00 0.00 0.00 Low_elev_7 m as.l.*’ 23.5 0.00 0.00 0.00 0.00 0.00 High_elev_l m as.l." 22.4 0.10 -0.69 0.65 -0.47 0.70 High_elev_2 m as.l.*" 22.4 0.02 0 3 9 -1.44 0.47 -1.49 High_elev_3 m as.l.*’ 21.0 0.00 0.00 0.00 0.00 0.00 High_elev_4 m as.l.*’ 23.5 0.00 0.00 0.00 0.00 0.00 High_elev_5 m as.l.*’ 21.6 0.00 0.00 0.00 0.00 0.00 Continued on following page

Table 5.5. Sensitivity analysis of farm scale model, where baseline is simulation of June 19 - July 18, 1991, with LUE 2 set to two-crop rice (late rice was not affected). Sensitivity statistic is (Ax/x)/(Ap/p), and (Ap/p) = ± 10%, except as noted.

141 Table 5.5 (continued).

Sensitivity statistics Damage Areas Loss Indices M ax_ LUE I LUE2 LUE 1 LUE 2 Inputs inner_elev~* mid ricc earlv rice mid rice earlv nce Units m a.s.1. m* m- dav day Baseline 21.72 6.6E+5 3.7E+5 032 0.57 High_ele\_6 m a.s.1." 24.0 0.00 0.00 0.00 0.00 0.00 High_elev_7 m a.s.1." 25.0 0.00 0.00 0.00 0.00 0.00 Dike_elev_3 m a.s.1." 22.1 0.00 0.00 0.00 0.00 0.(X) Dike_elev_5 m a.s.1." 22.6 0.00 0.00 0.00 0.00 0.00 RunoffJactorJ d av ' 0.9 0.02 0.19 0.12 0.31 0.18 Seep_Rate_i m day' 0.0020 -0.02 -0.26 -0.17 -031 -0.26 lrrig_Capacity_i m' dav' 0.05 0.00 0.00 0.00 0.00 0.00 Wilting_point_i m -0.12 0.00 0.00 0.00 0.00 0.00 E\tra_area_factor - O.l 0.02 0.22 0.15 0.31 0.26 Canal_arca m^ 1.24E+6 0.02 0.20 0.13 031 0.26 Canal.a"' - 1.5498E4-5 Canal_bF - -5.9031 E- h6 -0.02 -0 3 8 -0.25 -0.63 -0.44 C an al.f - 5.6300E+7 'For this variable, numerator of sensitivity statistic is A.x/(2 m). "For this variable, denominator of sensitivity statistic is Ap/(2 m) and Ap = ± 0.2 m. These variables were varied as a group, according to values shown in Figure 5.7; denominator of sensitivitv statistic is 0.2.

142 1987 a. Actual water e|e\ ations

1. Honghu Lakd £ 2. Sihu General jCanal (outer cana a b. Simulated wafer elevations !

§

40 c. Computed raiaftûi input to Hongl^ Lake

40-

40 e. Simulated puiftpcd irrigation to HtTDA

0 g 40 f. Computed surface inflow to HFDA

_0 40 g. Simulated flo^ through Xiaogang! Gate

40 h. Simulated ouCflow from HFDA

H i I 121 May 151.5 June 182 July 212.5 August 243 Julian Date

Figure 5.23. 1987 water elevations and water flows in the Honghu Flood Diversion Area-Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

143 1988 a. Actual water cjevauons

d 1. Honghu Lakcl £ '•2- 20 2. Sihu General panal (outer cana .3 25 b. Simulated wafier elevations ? •2

I

2 0

40 c. Computed raiitfall input to Hongho Lake

0-

40-

d. Simulated outflow from HFDA faims o -40

40 e. Simulated punjped irrigation to HfDA

§ 40 f. Computed surface inflow to HFDA

_0 40 g. Simulated floti' through Xiaogang •Gate

_0_ 40 h. Simulated outflow from HFDA

121 May 151.5 June 182 July 212.5 August 243 Julian Date

Figure 5.24. 1988 water elevations and water flows in the Honghu Flood Diversion Area-Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

144 1989 a. Actual water elevauons

w 1. Honghu Lake a 2 0 2. Sihu General Canal (outer canal) a I.3 b. Simulated wafer elevations I

40 c. Computed raiiüall input to Honghii Lake

0 -

40-

00 a d. Simulated outflow from HFDA Tanns s g 40 e. Simulated putiped irrigation to HÎDA

§ 40 f. Computed sur&tce inflow to HFDA

_0 40 g. Simulated floip through Xiaogang!Gate

■< A 40 h. Simulated outflow from HFDA

l U i L 121 May 151.5 June 182 July 212.5 August 243 Julian Date

Figure 5.25. 1989 water elevations and water flows in the Honghu Flood Diversion Area-Scale model following calibration. Computed inflows (c, D are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

145 1990 a. Actual water elevauons

« 1. Honghu Lakcl £ 2. Sihu General banal (outer canal ¥ I 25 b. Simulated waœr elevations •3 I larvl

40 c. Computed raiiifall input to Honghii Lake

ys:

40"

CO 0 d Simulated out^ow from HFDA fahns 2 $ ^ e. Simulated punhped irrigation to HtTDA â 0 g 40 f. Computed surface inllow to HFDA

_0 40 g. Simulated floik through XiaogangjCate

_0_ 40 h. Simulated outflow from HFDA i

121 May 151.5 June 182 July 212.5 August 243 Julian Date

Figure 5.26. 1990 water elevations and water flows in the Honghu Flood Diversion Area-Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

146 1991 a. Actual water c|e\ aüons

1. Honghu Lake! 2 0 2. Sihu Générai Canal (outer canal; 25 b. Simulated water elevations AI 7 = ^

I

20 40 : Compu d ratmall input to Honghu Lake

0 -

40

0 -

% d Simulated out@ow from HFDA faÂns S -40 ^ 40 e. Simulated pun&ped irrigation to H ^ A

I 40 f. Computed surt^e inflow to HFDA

_0_ A 40 g. Simulated flovV through XiaogangiGate

■I _âL 40 h. Simulated ou^ow from HFDA

121 May 151.5 June 182 July 212.5 August 243 Julian Date

Figure 5.27. 1991 water elevations and water flows in the Honghu Flood Diversion Area-Scale model following calibration. Computed inflows (c, 0 are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

147 1992 a. Actual water ejevaüons

« 1. Honghu LakW 0 2 0 2. Sihu General Canal (outer canal) I b. Simulated wafer elevations I I

40 c. Computed raitifall input to Honghii Lake

0-

40-

00 0 d Simulated outflow from HFDA fatms

0 Î 40 f. Computed surface inllow to HFDA

_0 40 g. Simulated flotk through XiaogangjGate

h. Simulated outllow from HFDA |

T jzUiiii T" 121 May 151.5 June 182 July 212.5 August 243 Julian Date

Figure 5.28. 1992 water elevations and water flows in the Honghu Flood Diversion Area-Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

148 1993 a. Actual water e(e\ ations

w 1. Honghu Lakej a 2. Sihu General Canal (outer canal) 25 •5I b. Simulated water elevations §

20

40 c. Computed raidfall input to Honghii Lake

O'

40 e. Simulated puniped irrigation to H I0A

I 40 f. Computed surfjuze inflow to HFDA

g. Simulated flow through Xiaogang pate iiL L iw . b. Simulated outSow from I

0 liw It* 121 May 151.5 June 182 July 212.5 August 243 Julian Date

Figure 5.29. 1993 water elevations and water flows in the Honghu Flood Diversion Area-Scale model following calibration. Computed inflows (c, 0 are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

149 1 9 9 4 a. Actual water e^vations

1. Honghu Lake a 20 2. Sihu General lanal (cuter canal) a a 25 b. Simulated water elevations

-i~ I

20

40 c. Computed raitfall input to Honghii Lake

4 0 -

0 - ph/vLL^‘■ ■ ■ CO a d. Simulated outflow from HFDA faiins S -40 40 e. Simulated puniped irrigation to HEDA s

I 40 f. Computed surface inflow to HFDA

g. Simulated flow tfnough Xiaogang pate

_0^ 40 h. Simulated outâow from HFDA II Mil ITT## 121 May 151.5 June 182 July 212.5 August 243 Julian Date

Rgure 5.30. 1994 water elevations and water flows in the Honghu Rood Diversion Area-Scale model following calibration. Computed inflows (c, f) are forcing functions; simulated elevations (b) and flows (d, e, g) are model outputs; actual elevations (a) are shown for comparison.

1 5 0 crop damage in the HFDA (Figure 5.31 ) followed patterns similar to those of the farm model for these years.

Sensitivity analysis of parameters specific to the HFDA scale (Table 5.6) also showed that damage indices were sensitive to pumping capacity (in this case, pumping to the Yangtze River). Apportionment of farm outflow between Honghu Lake {Land_area_ratio_2) and the outer canal iLand_area_ratio_I) was also important.

(Information on the actual drainage patterns within the HFDA was not obtained). The synchrony of those flows into the outer canals was also critical, and outer canal network dimensions, also not obtained, were important as well.

Although information on several critical variables was poor, these results confirmed that the HFDA-scale model was responding logically and was reasonably calibrated to the system. Based on these results it was concluded that the model satisfactorily represents hydrologie responses at the farm and HFDA scales and can be used to investigate the impacts of flooding on various engineering strategies.

151 0.7 early nce - LUE 2 a. mid rice - LUE 1 21.9 }/MUa late rice - LUE 2 ! ' inner canal max 3

i

earlv nce - LUE 2 mid nce - LUE 1 laZZalate rice - LUE 2 4.0 - outer canal max 24.5 : f X 3.0 - “3u S u9-

0.5 -

Figure 531. Simulated flood elevation and crop damage, HFDA-scale model, 1987 - 94 : (a) inner canal peak elevation and fraction of crop area damaged; (b) outer canal peak elevation andcrop loss index, computed as area under the damage-area curve, normalized by area planted.

152 Sensitivitv statistics Peak Elevation Damaee Areas Loss Indices Max_ Max_ LUE I LUE2 LUE 1 LUE 2 Inputs inner_elev‘ outer_elcv’ mid rice earlv rice mid rice earlv rice Units m a.s.1. m a.s.1. m- m- dav dav Baseline 21.7 25.13 6.6E+5 l.OE+5 o .œ 0.91 Initial Conditions Honghu lake m^ I.I5E+9 0.28 -0.50 3.12 3.14 5.62 5.71 outer canal m^ 1.29E+8 0.03 -0.10 0.25 0.26 0.29 0.29 Forcing Functions -Xiaogang Gate min canal m as.l." 21.5 0.00 0.00 0.00 0.00 0.00 0.00 max canal m a.s.1." 24.5 0.00 0.00 0.00 0.00 0.00 0.00 min Honghu m a.s.1." 210 0.00 0.00 0.00 0.00 0.00 0.00 opt Honghu m a.s.1." 2 1 4 0.00 0.00 0.00 0.00 0.00 0.00 HFDA irr cap m ’ day' 8.6E+6 0.00 0.00 0.00 0.00 0.00 0.00 -Xintankou Gate too low mas.l." 21.75 0.00 0.00 0.00 0.00 0.00 0.00 too high mas.l." 23.21 0.08 0.02 0.84 0.85 0.96 0.93 pump capacity m ' day' 17E+7 -0 3 0 -1.08 -336 -4.61 -7.28 -7.71 Parameters -Farm Flows land area ratio I - 61 5 0.40 1.28 4.56 5.73 9.61 10.22 land area ratio 2 - 37.5 0.10 -0.05 1.28 130 1.40 132 desvnc dav 1 0 -0.42 0.23 -4.90 -6.07 -8.09 -835 -Futianshi Gate top width m 175 0.10 -0.05 1.28 130 1.40 132 bank - 4.0 0.00 0.02 0.00 0.00 0.00 0.00 max elev m a.s.1." 25.5 0.00 -0.02 0.00 0.00 0.00 0.00 min elev m a.s.1." 21.2 -0.08 0.00 -0.84 -0.85 -0.96 -0.93 length m 18000 -o.œ -0.05 -0.42 -0.43 -0.51 -0.55 Manning - 0.035 -0.08 -0.02 -0.84 -0.85 -0.96 -0.93 -Xiaogang Gate weir a - 6.5E+7 0.00 0.02 0.00 0.00 0.06 0.05 weir b - 1 0 -0.03 -0.05 -0.42 -0.43 -0.56 -0.60 -Outer Canals large length m 150,000 -0.23 -035 -2 3 4 -2 3 6 -2.87 -2.80 large twidth m 120 -0.15 -0.10 -1.54 -1.74 -1.97 -1.87 med large ratio - 3.0 -0.15 -0.12 -1.54 -1.74 -1.97 -1.87 med twidth m 50.0 -0.15 -0.18 -1.54 -1.74 -1.97 -1.98 top elev m a.s.1." 25.0 0.03 0.15 0.42 0.43 0.56 0.60 bottom elev m a.s.1." 18.0 0.00 0.00 0.00 0.00 0.00 0.00 -Xintankou Gate weir a2 - 100000 0.00 0.00 0.00 0.00 0.00 0.00 weir b2 - 2.5 0.00 0.00 0.00 0.00 0.00 0.00 ’For this variable, numerator of sensitivity statistic is Ax/(2 m). "For this variable, denominator of sensitivit\’ statistic is Ap/(2 m) and Ap = ± 0.2 m.

Table 5.6. Sensitivity analysis of Honghu Rood Diversion Area scale model, where baseline is simulation of June 19 - July 18, 1991, with LUE 2 set to two-crop rice (late rice was not affected). Sensitivity statistic is (Ax/x)/(Ap/p), and ( A p / p ) = ± 10%, except as noted.

153 CHAPTER 6

SIMULATION OF ECOLOGICAL ENGINEERING ALTERNATIVES FOR IMPROVING FLOODING RESISTANCE

6.1. Introduction

In this chapter, the model developed in Chapter 5 is used to test the various engineering strategies introduced at the close of Chapter 4. Those strategies were as follows:

1. increasing pumping capacity; 2. deepening inner drainage canals;

3. converting existing rice production areas to other wetland crops;

4. engineering wetland cropping areas to increase passive water storage capacity, and

5. focusing cropping changes at the lowest elevations. Model simulations based on these strategies will partially address the study hypotheses (as introduced in Chapter I):

(a) Shifting land use in empoldered areas of the JH-DT Plain toward productive uses with greater water storage capacity and inundation tolerance will produce net economic benefit, and

(b) This benefit will become more pronounced as the spatial scale of evaluation increases.

154 Although the indices used cannot be quantitatively translated into increased production or avoided costs, the results will qualitatively reflect changes in the degree of flood damage to crops.

Sufficient data on météorologie and hydrologie forcing functions are available to allow each strategy to be tested for two sets of scales and conditions:

(a) at the Xiaogang Farm scale during the May - August rainy season. 1980 - 1994 and during the July 14 - August 7, 1996 flood event;

(b) at the farm and Honghu Flood Diversion Area (HFDA) scale during the May - August rainy season, 1987 - 1994.

Simulations at the Xiaogang Farm scale are carried out to determine how hydrologie and cropping conditions at that farm would respond to the proposed changes. Therefore,

these simulations use land-use element (LUE) area-elevation configurations representing recent conditions at Xiaogang Farm. However, the two low-position LUEs (LUEs 1 & 2)

are modified to facilitate testing of land use changes. They are set equal in area and elevation, and LUE 1 is assumed to be divided in area between one-crop and two-crop rice

(Table 6.1). This usage is held constant through all simulations, so that indices of damage to these respective crops provide a basis of comparison when either drainage system parameters or LUE 2 cropping practices are varied.

Simulations at the HFDA scale are carried out to examine scale effects; that is, to compare the results of implementation on a single farm with those of HFDA-wide implementation. These runs use an LUE configuration derived from Honghu City data (see Figure 4.6; Table 6.2). Each engineering strategy is simulated with and without the HFDA submodel operating. Without the HFDA submodel, implementation for a single farm (but one representing an average farm in the HFDA rather than Xiaogang Farm per se) is simulated; with the HFDA submodel, implementation over the whole HFDA is simulated. When these two conditions are compared, differences may be attributed to scale.

155 Inputs Units Test Strategies Strategy Number 0 1 2 3a 3b 3c 3d 4a Strategy Baseline; Increase Deepen Change test Change test Change Change test Change test Description test cix>p = pump number canals and crop to tw o- crop to test crop crop to wild crop to lotus; mid-season from 7 to 10 ditches by crop rice tatami : rice to lotus ricc stem use as passive rice 20% rotation storage a. Test Settings Switch_mtxlel . 3 — — ------Pump_numbcr - 7 10 ------Switcti_canai - I — 3 -* -- -- Switch_crop - 0 -- I 2 3 4 3 Raise_dike m 0 0.75 b. Farnt'Scaie Model Parameters lnner_canals m‘ 6.93E+5 - - 9.30E+5 -- — -- -- W ater, 1 m' -1.14E+5 ------W ater,] m' -2.86E+5 — 5.72E+4 1.23E+5 7.32E+5 3.(X)E+5 7.32E+5 W ater,3 m' 9.00Ef5 ------W atcr,4 m* -2.40E+5 — ------W ater,5 m' 4.32E+6 — -* -* -- LUE 1 - Lxiw-position divided* — ------LUE 2 - l-t)W-position mid rice -- two-crop ricc tatami : rice lotus wild ncc stem lolus lumd_area_l m' 2.86E+6 ------I^nd_area_2 2.86E+6 ------— -- -- Lx)w_elev_l m a.s.l. 21.2 ------High_clev_2 m a.s.l. 22.4 ------Canal_a - t.52l8E+5 -- 1.5774E+5 - -- -- — -- C anai,b - -5.8298E+6 -- -5.9747E+6 ------Canal_c - 5.5889E+7 -- 5.6699E+7 ------

‘LUE i was divided bclwccn iwo uses: miü-seuson ricc and iwo crop rice.

Continued on next page

Table 6.1. Experimental runs for Xiaogang Farm, Farm-Scale model: (a) Switch settings used for test strategies; and (b) input values. Only input values that varied are shown; others are as shown for 1996 run in Table 5.4. Values shown are those for 1980 - 1994 runs. 1996 runs differed in state variable initial conditions because run period differed: those values are not shown. Table 6.1 (continued).

Inputs Units Test Slialegics Case Number 4b 5a 5b 4+.5a 4r5b Case Description Change test crop to Change icst crop lo Change test crop to Change test crop to Change test crop to w ild wild rice stem; use us lotus; shil't to lowest w ild ricc stem; shil't to lotus; shill to lowest ncc stem; sluH lo lowest passive storage position lowest |X)sllion |X)Silion; use as [xtssivc |X)siiion; use as [xissne stonigc stonige

<1. TeU Settings

Switch_mixlel - - m 8 8 8 K Pump_number - ---- Switch_cunal ------Switch_crop 4 3 4 3 4

Raisc_dikc m 0.75 -- ■■ 0.75 0.75 b. Farm-Scale Model Parameters ! 5 Inncr_cana!s ni' -- -• Waicr_t m' — ------Watcr_2 m ' 3.(X)E+5 7.32K+5 3.(K)E+5 7.32I-+5 3.IK)lvi5 Watcr_3 m' - "■ -- Walcr_4 m' -- Watcr_5 m' ----

LUE I - Low-position -- --

LUE 2 - Low-position wild ricc stem lotus w ild rice stem lolus w ild rice stem Land_area_l -•

1 l-and_urea_2 -- Lo\v_clcv_l m a.s.l. 21.0 21.0 21.0 21.0 High_elcv_2 m a.s.l. 22.0 22.0 22.0 22.0 CuiiuLu CunuLb Canal c 'LUE 2 vvus divided bclwccn iwti uses; mid-scason ncc and iwn crop ricc. inputs Units Test Strategies Strategy Number 0 1 2 3 a 3b 3c 3d 4a Strategy Baseline; Increase Deepen Change test Change test Change test Change test Change test Description test crop = pump canals and crop to two- crop to crop to lotus crop to w ild crop to lotus; mid-season number irom ditches by crop ricc tatami : rice rice stem use as passi\ c rice 7 to 10 20% rotation storage a. Test Settings

Switcti_model 4/5' ------* - - Pump_numbcr 7 to -- -■* -- ” *- Switcli_canai 1 -- 3 ------Switch_crop 0 -- -- 1 2 3 4 3 Raisc_dikc m 0 ------0.75 b. Farm~Sca!e Model Parameters lnner_canais m3 6.93E+5 - - 9.30E+5 • - - - - - » - Water, 1 m3 -4.92E-M -- -* — -- — -- Watcr_2 m3 -1.23E+5 -- -- 2.46E+4 5.29E+4 3.15E+5 I.29E+5 3.L5E+5 LUE 1 - Low-position divided** ------LUE 2 - Low-position mid rice -- two-crop rice tatami ; rice lotus w ild ricc sten lotus Land_arca_l m2 1.23E+6 ------•- l-and_area_2 m2 I.23E+6 — ------Low_ciev_l m a.s.i. 21.2 -- — ------la)W_e!cv_2 m a.s.l. 21.2 ------High_elcv_l m a.s.1. 22.4 ------*- High_clev_2 m a.s.l. 22.4 — ------Canal_a 152.180 -- 1.5774E-I-5 — ------Canal_b -5.829.800 -- -5.9747E+6 — -- -- Canul_c 55,889.000 -- 5.6699E+7 ------Tlic listing of two settings indicates that each case was run at both the farm and the HFDA scale. ''LUE 2 was divided bclwccn Iwo uses; mid-season ricc and iwo crop ricc. Continued on next page

Table 6.2. Experimental runs for HFDA, Farm- and HFDA-Scaie models; (a) Switch settings used for lest strategies; and (b) input values. Only input values that varied are shown; others are as shown for 19% run in Table 5.4. Table 6.2 (continued).

Inputs Units Test Strategics Case Number 4b 5a 5b 4+5a 4+5h Case Description Change test crop Change test Change test crop Change test crop to Change test crop lo to wild rice stem; crop lo lotus; to wild ncc lotus; shift to lowest w ild ricc stem; shift to use as passive shift to lowest stem; shift to position; use as lowest position; use as storage position lowest position passive storage passive storage a. Test Settings Switch_moUel 6/7 6/7 6/7 6/7

Pump_numbcr --- - - ■'

Swiich_canal ------Switch_crop 4 3 4 3 4

Raise_dike m 0.75 ---- 0.75 0.75 b. Farm-Scale Model Parameters

!nnei_canals m3 - - - - - • .. Water_l m3 ---- Water_2 m3 !.2yE+5 3.I5E+5 I2VE+5 3.I5E+5 I.26K+5

LUE ! - Low-position -- -• LUE 2 - Low-position w ild rice stem lotus Wild ncc slcm lolu.s w ild ricc sicin Land_arca_l m2 ---- Land_area_2 m2 ---- Lou_cle\ _ 1 m a.s.i. 21.6 21.6 2 16 21.6 U)w_ele\_2 m a.s.l. ---- High_elev_l m a.s.l. ------Uigh_ele\_2 m a.s.l. 22.0 22.0 22.0 22.0 Canal_a ------Canal_b - --

Canal_c ------In each test, the effects are evaluated by comparing flooding depth and crop loss indices with and without implementation of the engineering strategy. (Areas damaged were not reported because area is largely determined by flood peak; therefore these comparisons added little to the analysis.) Since these comparisons were only considered relevant for flooding years, these runs were carried out only for those years in which simulated peak inner canal elevations had exceeded 21.7 m a.s.l. (i.e.. an internal flooding depth of 0.5

m), corresponding to years with a crop loss index exceeding 0.5 day (see Figures 5.16 and 5.19). For the Xiaogang Farm scale, these included the years 1980. 1981. 1983, 1987.

1988, 1991 and 1996. For the HFDA scale, these included only 1987, 1988 and 1991.

6.2. Simulation Results

6.2.1. Baseline Simulations

Flood peaks and crop loss indices for the baseline runs were similar to those obtained in the calibration runs (see Chapter 5), but they were not identical because of the establishment of slightly different LUE configurations (Table 6.3). In the table, results for the various years are ordered according to severity (determined by a simple sum of loss indices for LUE 1) to enable any trends to be observed. At the farm scale, where four identified flooding years ( 1980, 1983, 1991, 1996) are evaluated, three of these years were ranked above the other years, but 1981 was ranked higher than 1983. The severity ordering by crop loss agrees well with an ordering by inner canal peak elevation, but not as well with an ordering by outer canal elevation, which is important but is less proximal to the damage. At the FIFDA scale, the simulated outer canal peaks exceeded the actual values, reflecting the imperfect calibration of the model (see Figures 5.23a,b - 530a,b). The three years modeled at this scale— 1988,1987 and 1991 — offer a limited range of damage compared with the seven years considered for Xiaogang Farm, and therefore the ability to draw conclusions is also limited. Only in 1991 did the outer canal rise high

160 Year Peak canal LUE 1 Rice Cron Loss LUE 2 Cron Loss Index (day) elevation (m a.s.l.) Index(day) inner outer early mid­ late mid­ early late season season a. Farm Baseline; test crop (LUE 2)= mid-season - rice rice 87 22.00 23.94 1.10 0.87 0 0.87 NA NA 88 21.82 23.68 0.92 0.98 0.13 0.98 NA NA 83 21.92 24.56 1.65 0.97 0 0.97 NA NA 81 21.98 23.78 1.83 1.45 0 1.45 NA NA 91 21.94 24.76 2.24 235 0.57 235 NA NA 80 22.00 24.58 0 2.51 4.26 2.51 NA NA 96 22.54 24.96 11.57 8.73 0.05 8.73 NA NA b. HFDA Baseline; test crop (LUE 2)= mid-season rice Farm Scale rice 88 21.62 23.67 0.22 035 0.17 035 NA NA 87 21.9 23.95 0.65 0.50 0.10 0.50 NA NA 91 21.97 24.76 2.89 2.65 0.00 2.65 NA NA HFDA Scale 88 21.62 24.05 0.40 031 0.17 031 NA NA 87 21.9 24.05 0.62 0.50 0.10 0.50 NA NA 91 22.03 24.98 5.06 438 0.00 438 NA NA

Table 63. Baseline runs for determining effects of implementing engineering strategies on peak canal water elevations and crop loss indices for (a) Xiaogang Farm (at the farm scale) and (b) the Honghu Flood Diversion Area (at the farm and area scales).

1 6 1 enough to invoke a pumping shutdown. At both scales, the data are considered too few and the events too dissimilar to allow the use of summary statistics.

Mid-season rice is established as the baseline in LUE 2, against which to substitute other uses: note that in both the Xiaogang Farm and HFDA simulations indices for this crop are identical between LUEs 1 and 2, since the LUE sizes and elevations are identical. It must be bom in mind, however, that the Xiaogang Farm and the HFDA farm scale simulation may not be directly comparable, even for a given year. Not only are the LUE configurations different, but rainfall is different as well. The HFDA simulations use an average of Jianli and Honghu daily values, whereas the Xiaogang runs use only Honghu data. In the HFDA runs, crop loss indices for were lesser in 1988 and 1987 compared to the Xiaogang runs, but in 1991 the two were quite similar.

6.2.2. Test Strategy I: Increase Pump Number from 7 to 10

Xiaogang Farm managers plan to increase the number of flood gate pumps from 7 to 10, giving a nominal 43% increase in pumping capacity. As long as pumping is permitted, increasing capacity is an effective means for more rapidly reducing inundation at the local scale. In the Xiaogang Farm simulations (Table 6.4), a substantial reduction in simulated impact was observed. Inner canal peak was reduced by 8 - 16% compared to baseline. It may be noted that the peak reduction appeared to be lesser in the more severe events, possibly reflecting pump shutdown at the time of the peak. No such trend was apparent for crop loss, however, illustrating the importance of inundation time as well as peak in determining these indices. Loss indices were reduced by 27 - 80%.

In the HFDA simulations (Table 6.5), inner canal peak reduction ranged from 8 - 32% at the farm scale and reduction appeared to be inversely related to event severity. Internal peak reduction for the 1988 and 1987 events was unaffected by scale, but for 1991, when the pump capacity increase was implemented at the HFDA scale, the internal peak was only marginally reduced. Loss indices were still improved (i.e., by 27%), but

162 Year Peak canal cievauon LUE 1 Rice Crcip Loss Index LUE 2 Croo Loss Index 0? chanee from baseline) ( % chanee from baseline) (da\ 1 inner* outei* early mid- laie mid­ early late season season

/. Increase pump number from 7 to 10 nee

87 -14% — -43% -45% — 0.48 NA NA 88 -16% — -39% -45% -69% 0.54 NA NA 83 -14% — -44% -46% — 0.52 NA NA 81 -13% — -43% -47% — 0.77 NA NA 91 -S% — -27% -29% -37% 1.67 NA NA 80 —— — -38% -29% 1.56 NA NA

96 -8% — -50% -54% -80% 4.04 NA NA

2. Deepen canals and ditches by 20% nee 87 -\%c — -5% -3% — 0.84 NANA 88 -2% -9% -7% -8% 0.91 NA NA 83 -1% — -4% -6% — 0.91 NA NA 81 -1% —— -4% -5% — 1.38 NA NA 91 -1% —— -3% -3% -4% 2.29 NA NA 80 -2% — -4% -3% 2.42 NA NA

96 -1% — -1% -2% -20% 8.59 NA NA

3a. Change LUE 2 crop to two-crop rice rice nee 87 2% — 6% 7% - NA 1.17 0.00 88 5% - — 8% 5% -8% NA 0.99 0.12 83 3% — 7% 3% — NA 1.76 0.00 81 1% —— 3% 3% — NA 1.89 0.00 91 -1% 11% 1% 0% NA 2.49 0.57 80 -2% —— —— -5% -3% NA 0.00 4.12

96 -1% — -1% -1% 20% NA 11.46 0.06 3b. Change LUE 2 crop to tatami-rice rotation rice tatami rice 87 0% -1% -1% — 0.86 0.64 0.00 88 0% —— 3% 1% 0% 0.99 0.22 0.13 83 0% —— 3% 0% — 0.97 0.50 0.00 81 0% —— 0% 0% — 1.45 1.07 0.00 91 0% —— 0% 0% 2% 2.35 0.28 0.58 80 -1% —— -2% -2% 2.45 0.00 4.19 96 -1% —— -1% -2% -20% 8.57 11.39 0.04 Continued on following page

Table 6.4. Effects of implementing engineering strategies, including ecological engineering strategies, at the scale of Xiaogang Farm. For canal elevations and LUE I crop loss indices, percent change with respect to baseline is reported; negative values indicate peak canal water elevation or crop loss index was lowered as a result of the strategy; positive values indicate increases. For LUE 2 index values are not compared with baseline since LUE 2 crop varied among strategies.

163

i Table 6.4 (continued).

Y ear Peak canal elevation LUE I Rice Croo Loss Index LUE 2 CroD Loss Index change from baseline) ( Yf change from baseline) (day) inner* outer" early mid­ late mid­ early late season season

3c. Change LUE 2 crop to lotus lotus 87 10% 12% 16% 0 NA NA 88 21% — 23% 30% 69% 0 NA NA 83 17% 13% 27% 0 NA NA 81 12% — 3% 6% — 0 NANA 91 1% 10% 11% 16% 0 NA NA 80 15% — — 33% 21% 0 NA NA 96 4% — 7% 9% -40% 0 NA NA 3d. Change LUE 2 crop to wild rice stem rice stem 87 2% -9% -8% 0 NA NA 88 21% 10% 6% -31% 0 NA NA 83 17% 8% 21% — 0 NA NA 81 4% — -10% -8% — 0 NA NA 91 -1% 6% 6% 14% 0 NA NA 80 10% — — 19% 10% 0 NA NA 96 3% — 1% 3% -40% 0 NA NA

4a. Change LUE 2 crop to lotus and use as passive storage reservoir lotus 87 7% - -1% 1% 0 NA NA 88 11% -8% -6% -31% 0 NA NA 83 10% — 7% 4% — 0 NA NA 81 9% —— -2% 1% — 0 NA NA 91 1% —— 5% -2% 12% 0 NA NA 80 5% — — 7% 2% 0 NA NA 96 4% — -2% 0% 100% 0 NA NA 4b. Change LUE 2 crop to wild rice stem and use as passive storage reservoir rice stem 87 7% - -5% 0% - 0 NA NA 88 11% -9% -10% -31% 0 NA NA 83 10% — 7% 3% — 0 NA NA 81 9% — -2% 1% — 0 NA NA 91 1% —— 3% -6% 5% 0 NA NA 80 5% —— — 7% 1% 0 NA NA 96 2% —— -8% -7% 120% 0 NA NA

continued on following page

164 Table 6.4 (continued).

Year Peak canal elevation LUE I Rice Croo Loss Index LUE 2 CroD Loss Index (Of change from baseline) (% chanee from baseline) (day) in n er outer" early mid- late m id­ early late season season

So. Change LUE 2 crop to lotus and shift to lowest position lotus 87 190f -45% -51% 0 NA NA 88 35% — -64% -66% -100% 0 NA NA 83 28% — -55% -44% — 0 NA NA 81 23% — -50% -63% —— 0 NA NA 91 7% — -57% -63% -33% 0 NA NA 80 21% —— -35% -11% 0 NA NA 96 5% — 1% -11% -lOOOl 0 NA NA

5b. Change LUE 2

4+5a. Change LUE 2 crop to lotus, shift to lowest position and use as passive storage lotus 87 20% -49% -53% 0 NA NA 88 31% — -79% -82% -100% 0 NANA 83 25% *- -57% - 5 6 % — 0 NA NA 81 22% — -50% -61% — 0 NA NA 91 7% — -58% -65% -56% 0 NA NA 80 14% —— -53% -27% 0 NA NA

96 5% — - 5 % -17% 0% 0 NA NA

4+Sb. Change LUE 2 to wild rice rtem, shift to . low position and use as passive storage rice stem 87 20% -49% -53% 0 NA NA 88 32% — -78% -81% -100% 0 NA NA 83 26% — -57% -56% — 0 NA NA 81 22% — -50% -61% — 0 NA NA 91 7% -- -60% -66% -56% 0 NA NA 80 14% —— -54% -27% 0 NA NA

96 4% — -11% -24% 20% 0 NA NA 'Percentage reductions for inner canal are with respect to elevation above 21.2 tn a.s.1. "At the farm scale, outer canal elevation is a forcing function

165 Year Peak canal élévation LUE I Rice Cron Loss Index LUE 2 CroD Luss Index eye chanee from baseline) C? chanee irom baseline) (Jay) inner" outer* early mid-season late mid-season early late /. Increase pump number from 7 to 10 Farm Scale nee 88 -36% -50% -71% -82% 0.10 NA NA

87 -24% ” -55% -64% -80% 0.18 NA NA 91 -8% — -36% -36% —— 1.70 NA NA HFDA Scale 88 -36% 0% -65% -71% -82% 0.09 NA NA 87 -24% 0% -58% -64% -80% 0.18 NA NA 91 -1% -2% -27% -27% — 3.20 NA NA 2. Deepen canals and ditches by 20% Farm Scale rice 88 -5% -27% -23% -12% 0.00 NA NA 87 -4% — -9% -10% -20% 0.27 NA NA

91 0% — -4% -4% — 0.45 NA NA HFDA Scale 88 -5% 0% -13% -23% -12% 0.00 NA NA 87 -4% 0% -8% -10% -20% 0.24 NA NA 91 -1% 0% -4% -4% — 0.45 NA NA 3a. Change LUE 2 crop to two-crop rice Farm Scale nee rice 88 0% 0% -3% -6% MA 0.00 0.00 87 1% —— 5% 4% -10% NA 0.22 0.16 91 0% — 2% 2% — NA 0.68 0.09 HFDA Scale 88 0% 0% 0% 0% -6% NA 0.00 0.00 87 1% 0% 5% 4% -10% NA 0.40 0.16 91 4% 0% 5% 5% — NA 0.65 0.09 3b. Change LUE 2 crop to tatami-rice rotation Farm Scale rice tatami rice 88 0% 0% 0% 0% 0.00 0.00 0.17 87 -1% —— -2% -2% -10% 0.35 0.33 0.09 91 0% — 0% 0% — 0.49 0.58 0.00 HFDA Scale 88 0% 0% 0% 3% 0% 0.00 0.00 0.17 87 -1% 0% 0% -2% -10% 0.32 0.33 0.09 91 2% 0% 3% 3% —— 0.49 2.75 0.00 Continued on following page

Table 6.5. Inter-scale comparison of effects of implementing engineering strategies, including ecological engineering strategies, for the Honghu Flood Diversion Area. For canal elevations and LUE I crop loss indices, percent change with respect to baseline is reported; negative values indicate peak canal water elevation or crop loss index was lowered as a result of the strategy; positive values indicate increases. For LUE 2 index values are not compared with baseline since LUE 2 crop was varied.

166 Table 6.5 (continued).

Year Peak canal elevaiion LUE 1 Rice Crop Loss Index LUE 2 Croo Loss Index ( % chanee from baseline) (% chanee from baseline) (day) inner* outer* early mid-season late mid-season early late 3c. Change LUE 2 crop lo lotus Farm Scale lotus 88 14% 0% 17% 29% 0.00 NA NA 87 6% — 8% 10% 30% 0.00 NA NA 91 1% — 11% 10% — 0.00 NA NA HFDA Scale 88 14% 0% -5% 23% 29% 0.00 NA NA 87 6% 0% 10% 10% 30% 0.00 NA NA 91 12% 1% 15% 16% - 0.00 NA NA 3d. Change LUE 2 crop to wild rice stem Farm Scale rice stem 88 2% -5% -11% -6% 0.00 NA NA 87 3% -2% 0% 0% 0.00 NA NA 91 0% 8% 8% — 0.00 NA NA HFDA Scale 88 2% 0% -10% -10% -6% 0.00 NA NA 87 3% 0% 0% 0% 0% 0.00 NA NA 91 11% 0% 10% 11% — 0.00 NA NA 4a. Change LUE 2 crop to lotus and use as passive storage reservoir Farm Scale lotus 88 -7% - -36% -37% -29% 0.00 NA NA 87 1% —— -8% -6% -60% 0.00 NA NA

91 1% —— -3% -3% — 0.00 NA NA HFDA Scale 88 -7% 0% -28% -39% -29% 0.00 NA NA 87 1% 0% -6% -6% -60% 0.00 NA NA 91 0% -1% -10% -10% — 0.00 NA NA 4b. Chaise LUE 2 crop to wild rice stem and use as passive storage reservoir Farm Scale rice stem 88 -5% ~ -36% -34% -24% 0.(X) NA NA 87 1% —— -8% -6% -50% 0.00 NA NA 91 1% —— -6% -6% — 0.00 NA NA HFDA Scale 88 -5% 0% -28% -39% -24% 0.00 NA NA 87 1% 0% -6% -6% -50% 0.00 NA NA 91 -4% -1% -13% -13% — 0.00 NA NA

continued on following page

167 Table 6.5 (continued).

Year Peak canal elevation LUE I Rice Croo Loss Index LUE 2 Cron Loss Index {He chanee from baseline) (% chanee from baseline) (dav) inner* outer* early mid-season late mid-season early late So. Change LUE 2 crop to lotus and shift to lowest position Farm Scale lotus 88 26% — -100% -100% -100% 0.00 NA NA 87 11% — -75% -86% -100% 0.00 NA NA 91 3% — -62% -63% — 0.00 NANA HFDA Scale 88 26% 0% -100% -100% -100% 0.00 NA NA 87 11% 0% -74% -86% -100% 0.00 NA NA 91 18% 0% -41% -48% — 0.00 NA NA Sb. Change LUE 2 crop to iwild rice stem and shift to lowest position Farm Scale nee stem 88 19% — -100% -100% -100% 0.00 NANA 87 9% — -83% -94% -100% 0.00 NANA 91 3% — -64% -66% — 0.00 NA NA HFDA Scale 88 19% 0% -100% -100% -100% 0.00 NA NA 87 9% 0% -82% -94% -100% 0.00 NA NA

91 18% 0% -41% -49% — 0.00 NANA 4+Sa. Change LUE 2 crop to lotus, use as passive reservoir and shift to lowest position Farm Scale lotus 88 7% — -100% -100% -100% 0.00 NANA 87 10% — -82% -92% -100% 0.00 NANA

91 3% — -65% -65% — 0.00 NANA HFDA Scale 88 7% 0% -100% -100% -100% 0.00 NANA 87 10% 0% -81% -92% -100% 0.00 NA NA

91 7% -1% -57% -63% — 0.00 NANA 4+5b. Change LUE 2 crop to wild rice stem, use as passive reservoir and shift to lowest position Farm Scale rice stem 88 10% — -100% -100% -100% 0.00 NA NA 87 10% — -82% -92% -100% 0.00 NA NA 91 3% — -67% -66% — 0.00 NA NA HFDA Scale 88 10% 0% -100% -100% -100% 0.00 NA NA 87 10% 0% -81% -92% -100% 0.00 NA NA

91 5% -1% -61% -66% - 0.00 NA NA •Percentage reductions for inner canal are with respect to elevation above 21.2 m a.s.1. "At the farm scale, outer canal elevation is a forcing function

168 not as much as shown at the farm scale (36%). This apparent scale effect was not as dramatic as might be expected if capacity of all farms were to be increased to the degree planned for Xiaogang Farm. The reason is linked to the desynchronization of farm outflows in the model. Under this test strategy, the total amount of water to be pumped from the farms is constant; only the speed of pumping is increased. The desynchronization used—of five subflows spaced 2 days apart, determined by calibration to best match system behavior— substantially ameliorated the 1991 peak flows, even with increased pumping. A very large storm could increase the synchrony of peak flows, but information on storm size and intensity was not obtained.

6.2.3. Test Strategy 2: Deepen Canals and Ditches by 20%

This scenario assumes that both large and small canals and field ditches are deepened by 20% while retaining the same bottom width. The resulting change in storage volume at 21.2 m a.s.l. is 30% (see Figure 5.7). In the Xiaogang Farm simulations (Table

6.4), this change resulted in modest reductions in peak internal elevation (I - 2%) and crop loss indices (usually 1 - 9%). In the HFDA simulations (Table 6.5), crop loss reduction was greater (9 - 27%) in 1988 and 1987; this improvement may indicate that the comparatively small magnitude of increased storage was more able to ameliorate the smaller events. Crop loss index reduction appears to be inversely severity related in all cases.

Thus, this strategy appears to have marginal benefit during minor events and virtually none in more significant events.

6.2.4. Test Strategy 3a; Change LUE 2 to Two-crop Rice This change has not been suggested as a flood reduction strategy; indeed, it was suggested that two-crop rice is more susceptible to flooding than mid-season rice. This susceptibility arises because periods requiring low water levels (maturing/harvest of early rice, and transplanting and control of stooling in late rice) occur during the middle of the flooding season rather than at the margins (see Figure 4.2). Furthermore, because of its 169 longer growing time, mid-season rice achieves a greater height and has higher tolerance limits. These vulnerabilities were reflected in increases in most crop loss indices at both scales (Tables 6.4,63). Effect on internal peak was variable with event timing, however, since the two rotations are staggered with respect to each other in water schedule. Events occurring relatively early (late June or early July) had ( 1-2%) higher peaks under two-crop

rice because the accelerated schedule of early rice resulted in higher existing field water levels at that time, whereas the reverse was true with events arriving in late June or August.

6.2.5. Test Strategy 3b: Change LUE 2 to Tatami : Rice Rotation Tatami is maintained at water levels similar to rice during its growth, although the timing of the water requirement is different as it is a winter crop (see Figure 43a). It must

be drained for harvest in late June just before early rice. In addition, it is grown in a 2-year rotation with late and mid-season rice. Thus a change from mid-season rice to the tatami : rice rotation causes little change in overall system vulnerability. The fact that it is resistant to flooding during much of its growth is of little benefit because of the lack of flood risk during that time. Thus changes in peak floods or rice loss indices were generally ± <3%. for both Xiaogang Farm and HFDA simulations (Tables 6.4,6.5).

6.2.6. Test Strategy 3c: Change LUE 2 to Lotus

Lotus, on the other hand, has a high tolerance to flooding throughout the rainy period. However, it is maintained at water depths (i.e.. 25 - 50 cm) that are near to or greater than the tolerance limits for rice. Therefore, rice grown at the same elevation is put at risk because water from the higher elevations that floods the canals will cause more rapid increases in the rice paddies since the lotus fields are already flooded. This negative impact on iso-elevation rice is clearly seen in the Xiaogang farm simulations (Table 6.4), where peak internal water level increased I - 21% and damage tended to increase by 3 - 30%.

A negative scale effect was also suggested. The HFDA simulation at the farm scale (Table 6.5).showed that the negative effect of this in-farm water burden was of lesser

170 significance as the magnitude of the flood event increased (i.e., it fell from 14% for 1988 to 1% for 1991 ). At the area scale, however, this trend disappeared. The reduced internal storage capacity increased the volume pumped out, slightly raising the peak outer canal elevation ( 1%). extending the period of pump shut-off and increasing the inner canal elevation ( 12%).

6.2.7. Test Strategy 3d: Change LUE 2 to Wild Rice Stem

The effect of a change to wild rice stem was more variable. Like lotus, it is highly flood tolerant during the rainy period. According to the information given, however, wild rice stem is managed between wider limits (0-50 cm). Whether these limits obtain among growers or within a given field over time was not determined. In the model, the crop was neither irrigated nor drained if it was between these limits. Low water levels preceding heavy rains represented available storage capacity, whereas high levels served to concentrate flooding within the rice paddies. Therefore, at both the farm and area scales, rice loss indices were sometimes increased and sometimes decreased (Tables 6.4,6.5). A negative scale effect was suggested by the 11% increase in internal peak that was noted in 1991 at the HFDA scale but not at the farm scale.

6.2.8. Test Strategies 4a and 4b: Change Test Crop To Lotus Or Wild Rice Stem and Use as a Passive Storage Reservoir

Tests were made of the effects of switching to lotus or wild rice stem while also raising dikes around these fields to 0.75 m to provide passive storage capacity. It was assumed that imder normal conditions these crops would be grown normally, with supermaximal water being drained, but that a certain elevation of internal canal water would trigger the withholding of water. Under the LUE configurations being used, 0.75 m was the highest dike elevation that could be used without the possibility of violating the ordering of water elevation by LUE required by the model (see Chapter 5). The effectiveness of this arrangement was found to be very sensitive to the “trigger” of water level increase

171 employed, since an early start during a rain event made all the difference. It was assumed that 0.05 m was the minimum practical increase; above that level the maximum undrained water level that would be permitted in the field without triggering drainage was changed to 0.75 m.

Under this arrangement, the locally negative effects of switching to lotus or wild rice stem were minimized or reversed. Peak inner concentrations were still elevated with respect to baseline but not as greatly; and rice loss indices showed minor improvements in most years.

6.2.9. Test Strategies 5a and 5b: Change Test Crop To Lotus Or Wild Rice Stem and Selectively Cultivate at the Lowest Positions

When most of the lotus or wild rice stem was shifted according to microlandform so as to occupy the lowest elevations within the low position fields, internal flood peak was further elevated when compared to cultivating these crops without the microlandform shift (Tables 6.4,6.5). However, rice crop loss indices were strongly reduced; in most years and crops, reduction exceeded 50%. (The reduction was weaker in the years of most severe flooding, 1980 and 1996.) A negative effect of scale was suggested in 1991, however, as internal flood peaks were higher, and crop damage benefits reduced, at the area compared to the farm scale.

6.2. lO.Test Strategies 4+5a and 4+5b; Change Test Crop To Lotus Or Wild Rice Stem, Selectively Cultivate at the Lowest Positions and Use as a Passive Storage Reservoir When passive storage capacity was added to the latter configuration, two effects were noted: ( 1) a slight further reduction of crop loss was observed; and ( 2) the negative effect of scale noted previously disappeared (Tables 6.4,6.5).

172 6.3. Discussion

These results can be used to qualitatively rank the effectiveness of these strategies for improving the flood resistance of the Four Lakes Area depressional agriculture system. They can also be used to suggest how the net benefits of each should be evaluated. For this ranking, the strategies were separated into two dissimilar groups, according to whether or not a reduction of rice cultivation area was required for implementation (Tables 6.6, 6.7). Those not requiring such a reduction constitute the traditional flood protection approach of improving pumps and canals. Those requiring rice reduction rely on cultivation of other plants which can impart different hydrologie properties to the system based on their manner of growth.

6.3.1. Traditional Engineering Approaches That Maintain Current Land Area Devoted To Rice Production Increasing farm-level pumping capacity is a highly effective means of increasing lowland rice production at the local scale; its effectiveness is blunted, yet still positive, when pumping capacity increases are widespread. This finding, however, depends on the degree of synchrony of pumped outputs from individual polders as storms traverse the area. This property could be more effectively studied by a spatially distributed hydrologie model, but data gathered during the present study did not allow such an approach. A significant deepening of farms’ drainage network might have benefits at the level of the field, where speed of drainage to the canals would be increased (this micro-scale effect was not studied) but it would have little impact on the level or duration of internal flooding, except during more minor events. The change from mid-season to two-crop rice is not a protection measure, as stated above, but is shown for comparison. (In fact, the reverse change does confer some flood protection.)

The costs of either of these changes may be determined conventionally as the cost of public works, although that information was not gathered as part of the present study.

173 Implemented at Farm Scale Effect of Widening the Ranking for Strategy* Impact on Internal Impact on Low Scale of Overall Flood Peak Position Rice Implementation Effectiveness HliGH 1. Increase Peak substantially Damage strongly Probably pumping capacity lower reduced Adverse by 43%

2. Deepen Inner Peak slightly Damage slightly None Canals/Ditches by lower reduced 20%

1! 3a. Change 50% of Peak effect slight, Damage usually Possibly low-position area to variable increased Adverse two-crop rice LOW •Numbering of strategics mutches that of text and Tables 6.1 - 6.5.

Table 6.6. A ranking of strategies for flood protection that maintain land areas presently in lowland rice production, based on simulation results. Implemented at Farm Scale Effect of Widening the Is Modified Ranking for Strategy* Impact on Internal Impact on Low Scale of Area Flood- Overall Flood Peak Position Rice Implementation Resistant? Effectiveness 4+5a.b. Chanee lowest Hli3H area to lotus or wild Damage more rice stem and raise Higher Probably none Yes dikes for passive strongly reduced storage

5a.b. Chanee lowest Probably Damage strongly Yes area to lotus or wild Higher reduced rice stem adverse 4a,b. Change 50% of low position area to Probably lotus or wild rice stem Usually higher Variable Yes beneficial and raise dikes for passive storage 3b, Change 50% of low position area to tatami : Little or none Slight, variable Slight No rice rotation 3c,d. Change 50% of Damage often Yes low position area to Often higher increased Possibly adverse lotus or wild rice stem LC)W •Numbering of slralcgics matches that of text and Tables 6.1 - 6.5.

Table 6.7. A ranking of strategies for flood resistance that rely on changing some biological components of the lowland production system; based on simulation results. Benefits might be determined for the local scale based on the results of this model, with the caveat that it was not possible to calibrate these crop loss indices to specific tonnage or

monetary reductions. However, this study confirms that there is an additional cost of degrading the ability of the "commons' canal system to handle drainage needs at times of peak flow. Whenever a given empoldered area increases pumping capacity, all the other areas experience some decrement of pumping effectiveness, which is presumably uncompensated.

6.3.2. Ecological Engineering Approaches That Restructure The Biological Components ŒThe Lowland Agricultural System

Changing low-position paddy areas to alternative wetland crops such as lotus or tatami : rice rotation can often result in higher returns to land and labor (see Table 4.4; economic data on wild rice stem were not available). However, a simplistic approach to increasing the use of alternative wetland crops can harm the more hydrologically sensitive

components of the system (Table 6.7). Although areas converted to lotus or wild rice stem are themselves flood resistant, their increased cultivation at the same elevations as rice tends to increase the impact on rice of local flooding and may have an area-wide effect of reducing storage capacity, thereby increasing pressure on canals. In spite of tatami s ability

to withstand high levels of inundation during most of its growth, increased cultivation of tatami confers little benefit to the rest of the system because it must be drained, dried and harvested in the middle of the flood season.

Two other strategies that may be employed in conjunction with a shift to alternative wetland crops appear to work best when used together. The use of higher dikes alone, to enable passive storage of excess rainwater in wetland crop fields, probably confers a benefit at the scale of area-wide canals, but it is variable in its effect on rice grown at the same elevations. (Carefully selecting the lowest of the low-position fields for wetland crop cultivation is more beneficial because it removes the most vulnerable areas from rice production. However, since the lowest areas, which usually are hit by flooding, are then 176 already filled to a higher level, there is a loss of storage capacity with possible negative ramifications at the area scale. Combining these two strategies appears to have the greatest overall benefit at both scales. Vulnerable areas are converted, but an important degree of storage capacity is retained.

This combined strategy fulfills many of the criteria of ecological engineering. It is a case of designing with nature, as the selection of species is better fitted to microlandform.

The raised-dike fields that would be constructed at the lowest land elevations would more closely mimic the depressional lakes and wetlands they replaced, without sacrificing agricultural production capacity. The resulting water storage is passive; the benefit is gained without increased pumping; peak demand on the drainage system is thereby reduced and installed capacity need not be increased. Rather, the change is accomplished through earthworks (field dikes raised to 0.75 m) using human labor.

The passive storage approach would not necessarily achieve a net reduction in pumping. The same volume, minus some évapotranspiration, would eventually have to be eliminated. If water could be retained until early October, falling canal levels would allow drainage by gravity. Harvest of lotus root, which requires drainage for digging, begins in September, but not all areas need to be harvested immediately. Wild rice stem can be harvested without drainage. Therefore, an overall reduction of energy use for pumping might be possible as well.

As for reducing nutrient additions by increasing recycling, it is not clear that this criterion would be met. Root lotus, for example, requires more superphosphate and urea fertilizers than middle-season rice. Increasing water depth might increase the fertilizer requirement due to dilution.

It is also not clear that a species symbiosis could be designed to further enhance productivity of these raised-dike low fields. The utility of the system for passive storage depends on maintenance of relatively low water levels (~ 03 m) during the flooding

177 season, which is the hottest part of the year. Except at very low density, fish would survive with difficulty under these conditions. It is likely that many aquatic and wildlife species would use these fields, but whether the use would be greater or lesser than that of rice paddies cannot be determined.

This strategy would appear to provide benefits to human society, because the system becomes more hydrologically resilient overall and rice losses are reduced.

However, several problems would need to be overcome to evaluate those benefits. The first is a question of grain policy. Maintaining a high degree of self-sufficiency in food supply is a high priority in China:

Only when the Chinese people are free from food supply worries can they concentrate on and support the current reform, thus ensuring a sustained, rapid and healthy development of the economy. With its huge population ... China has no other choice but to rely on its own efforts to meet its food demand. (Xinhua News Service, 1996).

In spite of a low degree of mechanization and therefore poor domestic competitiveness with foreign grain, China has maintained grain import limits to support higher grain prices, with a goal of producing 95% of its grain needs (Brown, 1994;

Xinhua, 1996). Maintaining domestic rice acreage is therefore an important policy goal which complicates economic evaluation of strategies involving land use changes away from rice production. Reducing area of rice is also difficult to evaluate because of demand inelasticity of the potential substitutes. Hydric crops such as lotus and wild rice stem have a high degree of acceptance as traditional foods in China, but there are severe limits to the present market. Whereas the role of rice in the local and national economy is well established, farmers must carefully regulate their production of lotus and wild rice stem to avoid local surplus, and a system for regional distribution and marketing is lacking. The tatami produced at Xiaogang Farm is provided directly to a special local industry that is limited in size, and excess

178 production evidently is not easily disposed of. Therefore, ecological engineering strategies cannot be fully evaluated without further study of regional markets for these crops.

The matter of compensation must also be considered. Although crops such as lotus and wild rice stem can withstand relatively high inundation, their optimal growth is achieved at lower depths where sunlight and temperature are higher and added nutrients are more concentrated. Therefore, the receives no benefit, or even sacrifices some production, for withholding water during flooding events. A compensation system would be required to induce individuals to manage water in such a way as to benefit the larger system.

In spite of these problems, cultivation of alternative wetland crops may be more sustainable than rice over the long term. Rice cultivation benefits from seasonal aeration of the soil; if soils are wet for too long, gleization occurs, which must be compensated with additional fertilization. Operation of the Three Gorges Dam to reduce Yangtze River peak flows is expected to result in Yangtze River elevations that are 1.0 - 1.5 m higher during the remainder of the year in the area of the JH-DT Plain (S. Cai, personal communication). These higher flows are expected to raise the regional water table and complicate wintertime aeration of soils; therefore, existing problems from gleization of low-position soils are likely to be exacerbated in the near future, and rice production in these areas will become less economical.

6.4. Recommendations for Further Research

To improve the reliability of these findings, and to complete a more fully integrated ecological and economic analysis, several types of additional study are needed.

6.4.1. Modeling Hydrology and Crop Damage

Some key parameters regarding system structure at the HFDA level, including canal dimensions and land elevations, were either assumed, inferred, or set by calibration;

1 7 9 additional information on these aspects should be collected. Information on forcing functions such as rainfall and canal elevations was limited to certain years and locations; as a result the models (especially at the HFDA scale) could be run for a limited set of flooding circumstances. Daily rainfall records are needed for Xiaogang Farm, and for locations in the HFDA in addition to Honghu and Jianli. Canal elevations for Futianshi for 1980, 1983 and 1996, and Xintankou data for 1996, would enable additional runs of the HFDA-scale model. Data on flows through Futianshi would allow surface inflow to be more accurately represented.

In addition to data on system structure and forcing functions, data sets for calibration and validation are needed. One primary need is for records of flooding damage, in terms of areas damaged and yield reductions for specific crops, since 1980. This information is needed for Xiaogang Farm and for the HFDA; for the latter, information for Honghu City and Jianli County would need to be disaggregated to eliminate areas outside the HFDA. Another primary need is for daily internal water level data for Xiaogang Farm, besides the 25-day period in 1996. A secondary need is for flow volume data for Xiaogang and Xintankou Gates, to calibrate flows determined in the model. Data sets obtained should be divided for use in further calibration and model validation, respectively.

6.4.2. Economic Policy

This study has identified changes based on ecological engineering that could reduce the flooding vulnerability of the JH-DT Plain agricultural/hydrologic system. Any such engineering changes must be implemented in the context of the region’s institutional system of economic actors and incentives. Research is therefore needed to identify the existing incentive structures and to evaluate institutional changes that could minimize the transaction costs associated with ecological engineering. The actors and incentives affecting the following types of decisions need to be addressed: planting decisions, water management decisions and decisions affecting the development of re^onal marketing systems.

180 Planting decisions by farmers presumably depend on each crop's familiarity and acceptability, its anticipated profitability, and its perceived flooding risk. However, the structure of rents or taxes applied to croplands and the structure of relief payments following flooding damage may also be important determinants. Policies and incentives governing the design of these payments need to be understood, and their use to make ecological engineering socially viable should be investigated, both in state-run areas such as Xiaogang Farm and in more typical areas.

The decision to temporarily retain excess water in a field in order to reduce flood peaks elsewhere in the region will likely not be made without compensation to the farmer. Monitoring and financial incentives instituted by the regional hydrologie management

system to induce individual polders (e.g., Xiaogang Farm) to reduce pumping during times of peak flow might spur innovation at the level of the polder. Such innovation may take the form of rewarding farmers who practice in-farm water storage. These rewards, coupled with long-term tenancy assurances, might be sufficient to induce individual farmers to raise

field dikes for managing stormwater. Such an incentive-based approach could prove more effective than pumping shut-down orders and should be investigated.

Finally, policies and incentives affecting the potential for establishing interregional marketing systems—such as at the county or province level—should be examined.

Research should determine to what extent agricultural areas are induced to maximize grain production, and whether consideration is also given to identifying each region’s potential comparative advantage in production of other crops. The objective functions of the individuals who determine regional economic policy should be clarified, and incentive schemes that would be compatible with the implementation of ecological engineering need to be investigated.

181 6.4.3. Other Ecosystem Services Besides the services of hydrologie regulation and production that are considered in this study, wetlands may also improve water quality and provide habitat for biodiversity. Sediment and nutrient budgets for this system may be determined by measuring the quality of water crossing system boundaries at the field, farm and area scales. Invertebrate and vertebrate fauna in rice paddies and alternative cropping systems, respectively, may be surveyed to identify any comparative biodiversity benefits of instituting changes based on ecological engineering.

6.4.4. Sustainability

Sustainability over the next 50 years for this wetland agroecological region will depend in part on improving net system benefits, such as through the ecological engineering measures modeled in this study, but other long term trends need to be considered as well. Rate of population growth relative to economic growth is critically important Long term trends in soil fertility are important as well, since conversion from natural to cultivated wetland is relatively recent for many low-position areas, finally, the Yangtze River hydrologie regime will be altered by the Three Gorges Project, and the regional impacts of these alterations on inundation frequency, groundwater hydrology and soils will have to be determined.

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189 APPENDIX A

STELLA MODEL EQUATIONS

crop_loss_early_crop_2(t) = crop_loss_eariy_crop_2(l - dt) + (accum_2_early) * dt INTT crop_loss_early_crop_2 = 0 accum_2_early = [F{time>l21 AND time<212) THEN Ma.\(dama2e_area_2_\r2.0)* Subdivision_2 (Land_area_2) ELSEO crop_loss_carly_rice_l(t) = crop_Ioss_early_rice_l(t - dt) + (accum_l_early_2) * dt INTT crop_!oss_early_rice_l = 0 accum_l_early_2 = IF(time>I21 AND time<2l2) THEN .\Ia.x(dainage_area_l_type_2,0)* Subdivision_l ( Land_arca_ 1 ) ELSE 0 cropJossJate_rice_l(t) = cropJoss_late_rice_l(t - dt) + (accum_l_late_2) * dt INTT cropJoss_late_rice_ I =0 accum_ljate_2 = IF(time>212) THEN Max(damage_area_l_type_2,0)*Subdivision_l (Land_area_l) ELSE 0 crop_lossjate_rice_2(t) = crop_lossJate_rice_2(t - dt) + (accum_2Jate) * dt INTT crop_Ioss_Iate_rice_2 = 0 accutn_2_Iate = IF(time>2l2) THEN Max(damage_area_2_}r2,0)*Subdivision_2 (Land_area_2) ELSE 0 cropJoss_mid_rice_l(t) = crop_loss_mid_rice_l(t - dt) + (accum_I) * dt INTT crop_loss_mid_rice_l = 0 accum_I = max(damage_area_1.0)*Subdivision_l Land_area_I crop_loss_inid_rice_2(t) = crop_Ioss_mid_rice_2(t - dt) + (accum_2_mid) * dt INTT crop_Ioss_inid_rice_2 = 0 accum_2_mid = max(damage_area_2_yrI,0)'(Land_area_2Subdivision_2) fef_proxy(t) = fef_proxy(t - dt) + (Noname_3) • dt INTT fef_proxy = 21.2

Noname_3 = nood_eIev_finaI-fer_proxy HFDA_irr_total(t) = HFD.A_irr_total(t - dt) + (Ncname_2) * dt INTT HFDA_irr_total = 0

Noname_2 = HFDAJrr Honghu_lake(t) = Honghu_lake(t - dt) + (HFDAJnflows + Rain_Honghu + famis_above - Xiaogang_gate) * dt INTT Honghujake = (0.19 l3*Xiao_above*2 - 5.4942*Xiao_above + 31.268)* 10*8

HFD.-\_InfIows = Inflow_rate Rain_Honghu = Honghu_area*P_minus_ET fanns_above = Iand_area_ratio_2*Fann_no\v_out Xiaogang_gate = xiao_flow

190 lnncr_canals(t) = Inner_canals(t - dt) - (Drain_! + Drain_2 Dtain_3 - Dram_4 - l>ain_5 - l>ain_^ - Drain- Inner_canal_inflo\vs - Imgate - Farm_now_out) * dt INIT Inner_canals = IF Year = 96 THEN canal_a*21,7''2 canal_h*2I,7 j- canal_c EI.SF canal_a“critena_canal_elev''2 - canal_b*cntena_canal_elev -r canal_c

Drain_l =drainage_l Drain _2 = drainaae_2 Drain_.^ = drainage_3 Drain_4 = drainage_4 Drain_5 = drainage_5 Drain_6 = drainage_6 Drain_7 = drainage_7 lnner_canal_inflo\vs = Rainrall_inner_canal+extra_area_intlow Irrigate = Irrigationjotal Farm_now_out = farm.outflow inaxJlood_l(t) = max_nood_l(t - dt) + (max_count_l) * dt INIT max_flood_l = 0 max_count_l - if fIood_depth_I>max_flood_I then ((flood_depth_l - max_nood_l) dt) else 0 max_flotxl_2(t) = max_tlcxxl_2(t - dt) + (max_count_2) * dt INIT max_fIood_2 = 0 max_count_2 = IF(tirae>l2l AND time<212 AND flood_depth_2_jr2>tnax_nood_2) then ((tlood_depth_2_jr2 - max_flood_2) dt) else 0 tnax_fIood_3(t) = max_nood_3(t - dt) + (max_count_3) * dt INIT max_fIood_3 = 0 max_count_3 = if fIood_depth_3>max_fIood_3 then ((f1ood_depth_3 - max_fIood_3) dt) else 0 max_nood_4(t) = max_fIood_4(t - dt) + (max_count_4) * dt INIT max_nood_4 = 0 max_count_4 = if flood_depth_4>ntax_ncxxl_4 then ((fIood_depth_4 - ntax_nood_4) dt) else Ü max_fIood_5(t) = max_fIocxl_5(t - dt) + (max_count_5) * dt INIT raax_fIood_5 = 0 max_count_5 = if (time>212 .AND fIood_depth_2_jT2>max_nood_5) then ((fIood_depth_2_jr2 - max_fIood_5) dt) else 0 max_fIcKxi_6(t) = max_flood_6(t - dt) + (max_count_7) • dt INIT max_flcxxl_6 = 0 ma.x_count_7 = if (fIood_depth_2_yr_I>max_fIood_6) then ((fIood_depth_2_\ r_I - max_fIood_6) dt) else 0 max_fIood_7(t) = max_flood_7(t - dt) + (max_count_9) * dt INIT tnax_fIood_7 = 0 max_count_9 = if (time>212 AND fIood_depth_I_type_2>ma.x_fIood_7) then ({flcxxl_depth_l_type_2 - ma.x_flood_7)/dt) else 0 max_fIoodJ8(t) = max_nood_8(t - dt) + (max_count_8) • dt INIT max_fIood_8 = 0 ma.x_count_8 = IF(time>l2l AND time<2l2 AND flood_depth_I_type_2>max_flood_8) then ((fIood_depth_l_type_2 - ma.x_flood_8) dt) else 0 ma.xjloodjnnerft) = max_flood_inner(t - dt) + (max_count_canai) * dt INIT max_flood_inner = 0 max_count_canaI = if flood_eIev_nnaI>ma.x_flood_inner then ((flood_elev_final - max_flood_inner) dt) else 0 max_flood_outer(t) = max_riood_outer(t - dt) + (tnax_count_6) * dt INIT max_nood_outer = 0 191 ma.\_count_f) = if outcr_canal_clev>max_nixxl_outer then ((outer_canal_elcv - ma\_nood_outer) dt) else i) outer_canal(t) = outer_canal(t - dt) - (\iaogang_gate - larms_helo\v - HFDA_Irrigation - HFDA_(’)uttlo\vs) * dt IN'IT outer_canal = outer_a*(Xiaogang_belovv-bottom_elev)''2 * outer_b*(Xiaogang_belo\v-bottom_elev)

Xiaogang_gate = xiao_riow farms_heIow = split_0+split_l+split_2-i-split_3-t-split_4 HFDA_lrrigation = HFDA_irr HFDA_OutfIow.s = ’tintankou_now Tntal_upper_no\v(t) =Total_upper_no\vft - dt) - (fpperjlow ) * dt INTT Total_upper_flow = 0

Lpper_rio\v = Inllo\v_rate Water_l(t) = Water_l(l - dt) + (Rain_l + lrrigatc_l - Drain_l - Seep_l) * dt INIT \Vater_I = Land_area_lAX((\\ater_ma\_l4-Water_min_I) 2, \Vilting_point_I)

Rain_l = RainfaII_l Irrigate_I = Irrigation, I Drain_I = drainage, I Seep, I = Seepage, 1 NVater,2(t) = NVater,2(t - dt) + (Rain_2 + Irrigate,! - Seep,2 - Drain_2) * dt INIT \Vater_2 = Land,area,2*MAX((\\’ater,max_2+\Vater,min,2) 2, \\'iIting,poinl_2)

Rain_2 = Rainfall,2 Irrigate,2 = Irrigation,2 Seep_2 = Seepage,! Drain,2 = drainage,2 Water,3(t) = Water,3(t - dt) + (Rain,3 + Irrigate_3 - Seep,3 - Drain,3) * dt INTT Water,3 = Land,area,3*(\Vater,max,3+\Vater,min_3) 2

Rain,3 = RainfaII_3 Irrigate,! = Irrigation,! Seep,! = Seepage,! Drain,! = drainage,! Water,4

Rain,4 = RainfaII_4 Irrigate,4 = Irrigation_4 Seep,4 = Seepage_4 Drain_4 = drainage,4 Water,5(t) = Water,5(t - dt) + (Rain_5 + Irrigate,! - Seep,! - Drain,!) • dt INTT Water,! = Land,arca,!*0.7*(Water,max,!+W ater,min,!) 2

Rain,! = Rainfall,! Irrigate,! = Irrigation,! Seep,! = Seepage,! Drain,! = drainage,! Water,6(t) = Water_6(t - dt) + (Rain,6 + Irrigate,6 - Seep,6 - Drain,6) * dt INIT W'ater,6 = Land,area,6*MAX((Water,max,6+W’ater,tnin,6);2,W’iIting,point,6)

Rain,6 = RainfaII,6 Irrigate,6 = lrrigation,6 Seep,6 = Seepage,6 Drain,6 = drainage,! W'ater,7(t) = Water,7(i - dt) + (Rain,7 - Seep,7 - Drain,7) * dt INIT W'ater,7 = Land,area,7*MAX((Water,max,7+W'ater,tnin,7) 2,W'iIting,point_7) 192 Rain_7 = Raintall_7 Scep_7 = Seepage_7 Drain_7 = drainage_7 \iaogale_totaI(t) = xiaogate_total(t - dt) - (Noname_l) * dt INTT \iaogate_total = 0

Noname_ I = xiao_flow area_frac_early_rice_l = MAX(max_nood_8 Bev_change_l. 0) arca_frac_late_rice_l = \I.AX(max_flood_7 Elev_change_l, 0) a_plus_l = canal_a+Flood_a_l !t_plus_2 = a_pIus_I^nood_a_2 a_plas_3 = a_pIas_2+Rood_a_3 a_plus_4 = a_plus_3+Flood_a_4 a_plus_5 = a_plus_ 44-Flood_a _ 5 bank = 4 {designates 4; 1 bank slope} bottom_elev = 18 {m as.l.} Bottoni_elev_5 = ((High_elev_5+Low_elev_5) 2) - 1.3 bot_width = top_width-2*(max_elev-min_elev)*bank DOCllvlENT: assumes trapezoidal channel; calculates width or channel bottom fc*_plus_l = canal_b+Hood_b_l b_pius_2 = b_plus_l+Rood_b_2 b_plus_3 = b_plus_2+Rood_b_3 b_plus_4 = b_plus_3+Rood_b_4 b_plus_5 = b_plus_4+Rood_b_5 canal_a = IF switch_canal=l THEN 1.5218E5 ELSE IF switch_canal=2 THEN 1.5498E5 ELSE 1.5774E5 Canal_area = 124 {ha} * le4 {m''2 ha} canal_b = IF switch_canal=l THEN -5.8298E6 ELSE IF switch_canal=2 THEN -5.903IE6 ELSE -5.9747E6 canal_c = IF swttch_canal=l THEN 5.5889E7 ELSE IF switch_canal=2 THEN 5.6300E7 ELSE 5.6699E7 canal_vol_final = canal_a*flood_elev_final''2 + canal_b*nood_elev_final + canal_c criteria_canal_elev = 21.2 {m} criteria_canal_vol = canal_a*criteria_canal_elev''2 + canal_b*criteria_canal_elev+ canal_c c_plus_l = canal_c+Rood_c_l c_plus_2 = c_plus_l+Rood_c_2 c_plus_3 = c_plus_2+Rood_c_3 c_plus_4 = c_plus_3+Rood_c_4 c_plus_5 = c_plus_4+Rood_c_5 damage_area_l = MAX((nood_depth_l 'Qev_change_l) * (Land_area_l Subdivision_l). 0) damage_area_l_t\pe_2 = MAX((flood_depth_l_t\'pe_2 Elev_change_l) * (Land_area_l Subdivision_l). 0) damage_area_2_yrl = MAX((flood_depth_2_yr_l Elev_change_2) * (Land_area_2 Subdivision_2),0) damage_area_2_xr2 = M.\X((flood_depth_2_j’r2 Elev_change_2) * (Land_area_2 Subdivision_2), 0) DOCL'MENT: if growing tatami then half area grows year 1 (mid rice —> tatami) and half grows year 2 (tatami -> late rice) if not then all is in two-crop rice damage_area_3 = IF flood_depth_3>0 THEN Land_area_3 ELSE 0 damage_area_4 = S1AX( (lood_depth_4 Elev_change_4 * Land_area_4,G) depth = Fu_below-min_elev DOCUMENT: assumes trapezoidal channel, uss quadratic formula to solve for depth based on known dimensions (bottom-width, bank-slope and length) and total volume □a=bank-slpe ab= bottom width □c=-(vol length) □depth = [-b + sqrt(ly'2 - 4ac)| 2a desync = 2

193 Dike_clcv_3 = 22.1 dike_elev_5 = 22.6 dikejrigger = .05 drainage_l = E'ccess_l-ncxxl_vol_I drainage_2 = Excess_2-flood_vo[_2 drainage_3 = E\cess_3-n(X)d_vol_3 drainage_4 = &tces.s_4-riood_vol_4 drainage_5 = E’ccess_5-llood_vol_5 drainageJS = Excess_6 drainage_7 = Exccss_7 dn_crop_ma.\ = 0 dr\_crop_min = - 08 EIev_change_l = High_elcv_l-Low_elev_l EIev_change_2 = High_elev_2-Low_elev_2 Elev_change_4 = High_elev_4-l.ow_elev_4 Eiev_change_6 = High_elev_6-Low_elev_6 EIev_change_7 = High_elev_7-Low_elev_7 ET = (if Year=80then ET_80a ELSE lFYear=8l TEIEN ET_81a ELSE IF Year=82THEV ET_82a ELSE IF \ ear=X3 THEX ET_83a ELSE IF Vear=^THEN ETj84a ELSE IF Year=85 THEN ET_8:» ELSE IF Year=86Tl EN ET_86a ELSE IF ’i ear=87 THEN ET_87a ELSE IF \ car=88 THEN ET_88a ELSE IF Year=89 THEN ET_89a EI2^E IF l'ean=90 THEN ET_90a ELSE IF \'ean=91 THEN ET_9la ELSE E \'ear=92THEN ET_92a ELSE IFYear=93 THEN ET_93a ELSE IF Year=94THEN ET_94a ELSE IF Year=96THEN ET_% ELSEO)* 001 *ET_multiplier ET_96 = 3 .7 ET_muItipIier = 0.7 Excess_l = M.\X(({Water_I-VoI_ma\_I)*runorf_factor_alI).0) E.xcess_2 = MAX(((\Vater_2-Vol_ma\_2)*runotTJactor_alI).0) E.xcess_3 = \LAX(((Water_3-Vol_max_3)*ninofr_factor_alI).0) Excess_4 = MAX(((\Vater_4-VoI_max_4)*ninotT_factor_alI),0) Excess_5 = \L\X{Water_5-Vol_max_5,0)*ninoff_factor_ail Excess_6 = MAX(\Vater_6-\oI_max_6,0)*runolT_factor_alI Exccss_7 = MAX(Water_7-VoI_max_7,0)*ninotT_factor_aII exchange_riow = IF ((outer_canaI_eIev-criteria_canaI_eIe% ) (tlcxxl_elev_nnal-criteria_canal_eiev) > 1) THEN 0 EI^E canal_voI_final - (IF (ncxxi_elev_finaI>outer_canal_eIev) THEN MAX (VoI_al_outer_eIev.criteria_canal_vol) ELSE MIN (Vol_al_outer_elev.criteria_canaI_vol)) extra_area_faclor = . I exira_area_inflow = (Excess_4+Excess_5+Excess_6+Excess_7)*e.xtra_area_tactor farm.outflow = IF(outer_canaI_eIev0)THEN(nood_elev_5)ELSE(lF(nood?_4>0)THEN(nood_elev_4)ELSE(lF(notxl?_3>0)r HEN(nood_elev_3)ELSE(lF(nood?_2>0)THEN(nood_elev_2)ELSE(IF(nood?_l>0)THEN(nocxl_elev_l)El.SE (nood_elev_0)))))). 19.5) llocxl_vol_l = lF(riocxl?_l<=G)THEN(Q)ELSE(Flcxxl_a_l*nood_elev_final''2 + Rood_b_l*nood_ele%’_final 4- Flood_c_l) llood_vol_2 = IF(nood?_2<=G)THEN’(0)ELSE(Rocxi_a_2*flood_elev_finaH2 + Rood_b_2*nocxi_clev_final -r Rood_c_2) nood_vol_3 = lF(nood?_3<=0)THEN(0)ELSE .M.\X((Rcxxl_a_3*nood_elevjlnal''2 + Rcxxl_b_3*flood_e!ev_final + Rood_c_3),0) flood_vol_4 = lF(flcxxl?_4<=0)THEN’(G)ELSE(Rood_a_4*nood_elev_final''2 + Rcxxl_b_4*flocxl_elev_rinal - Rood_c_4) llood_vol_5 = IF(fl5 THEN 22.0 ELSE 22.4) High_elev_3 =(IFswitch_modeI=0THEN21 ELSE IFswitch_model=l THEN21 ELSE21) High_elev_4 = 23.5 High_elev_5 = 21.6 High_elev_6 = 24 High_elev 7 = 25 Honghu ea =350 {km^2} * lc6 {m''2. km''2} increase = 0 Inflow_rate = IF slope>0 THEN (1.5/Manning) * (X_sec_arca/wel_perim*3.281{ft'm»''.67 * (slope^O.5) * (X_sec_area*10.76{fl''2 m''2}) * 86400{sec d>*0.0283{m'^3ffl'^3} ELSEO DOCL^IENT: L'ses Maning equation: q = (1.5) n X 67 x sqrt(S) x a where q is in cfs. R (area/wetted perimeter)is in ft and area is in ff'Z 195 (rngation_l = [F(\\atcrJcvel_l-\Vater_min_l<0) THEN(.\IlN((\Vater_min_l-WalerJevcl_l i IÎ1'. lrn2_(rapacity_all)*Land_area_ 1 ) ELSH(O) [mgation_2 = (F(\Vater_level_2-\V'aier_min_2<0) TMEN(MIN((\Vater_min_2- \VaterJcvcl_2) Dr.Irne.Capacitj.aHV"’ and_area_2) ELSE(O) Imgation_3 = IF(\Vater_level_3-\Vater_min_ i)) THEN(MIN((Water_min_3- NVater_level_3) DT.Img_Capacily_all)*Land_area_3) ELSE(O) lrrigation_4 = [F(Watcr_level_4-\Vater_min_4<0) THEN(M[N((Water_rnin_4- \VaterJevel_4) Drr.Irrig_Capacity_all)*Land_area_4) ELSE(O) frrigation_5 = lF(\VaterJevel_5-Waler_min_5<0) THEN(M[N((Water_inm_5-\Vater_level_5) DT. Irrig_Capacity_all)*Land_area_5*0.7) ELSE(O) Irrigation_6 = IF(\Vater_level_6-\Vater_inin_6<0) THEN(.\IlN((Waler_tnin_6-Water_leve[_6) DT. Irrig_Capacity_all)*Land_area_6) ELSE(O) lrrigalion_7 = lF(NValer_level_7-Water_min_7<0) THEN(MlN((NVater_min_7-Water_level_7) DT. Irrig_Capacity_alI)*Land_area_7) ELSE(O) Irrigation_total = Iirigation_l+[rrigation_2+[rrigalioti_3+Irrigation_4+lrTigation_5+(rrigation_6 lrrig_Capacity_all = 05 JL_PPT = (IF Vear=87THEN JL_PPT_87a ELSE IF Vear=88 THEN JI._PPT_88a ELSE IF Vcar=89 THEN JL_PPT_89a ELSE IF \'cai=90 THEN JL_PPT_90a ELSE IF \'ear=91 THEN JL_PPr_9la ELSE IF \ ear=92 THEN JL_PPT_92a ELSE 0)*0.00l»PPT_rauItipIier lalce_max = (if (time>350) then 1.15 else il (time > 320) then 0.35 else 1.15) lake_min = (if (time>320) then 0.3 else if (time > 75) then l.l else 0.3) Land_area_l = (IF switch_model=0 THEN 420 ELSE IF switch_model=l THEN 420 ELSE IF s\vitch_mcxlel=2 Tl I EN 420 ELSE IF switch_modeI=3 OR s«.itch_model=8THE.N 286 ELSE 123) * 10^4 {m^2 ha) Land_area_2 = (IF switch_model=0 THEN 353 ELSE IF s»itch_modeI=l THEN 153 ELSE IF switch_model=2 THEN 65.6 ELSE IF switch_model=?3 OR switch_modeI=8 THEN 286 ELSE 123) • 10^4 {m''2 ha} Land_area_3 = (IF swiich_model=0 THEN 180 ELSE IF switch_mcxiel=l OR switch_model=3 OR s\vitch_model=8 THEN 80 ELSE 123) * 10^4 {m'^2 ha} Land_area_4 = (IF switch_model=0 OR switch_modeI=I OR switch_model=3 OR switch_model=8 THEN 600 ELSE 623) * 10^4 {m-'2ha> Land_area_5 = (IF switch_model=0 THEN 214 ELSE IF switch_modeI= I OR switch_model^ OR switch_mcxlel=8 THEN 514 ELSE 26+) » 10^4 {m''2 ha} Land_area_6 = (IF switch_model=0 OR switch_modeI=l OR switch_modeI^ OR switch_model=8 THEN 267 ELSE 774) » ICT4 {m'^2 ha} Land_area_7 = 220 * lOM {m''2'ha} land_area_ralio_l = 1500 24 {HFDA km''2Xiaogang Fann km''2} Iand_area_ratio_2 = 900/24 {HFDA km''2'Xiaogang Farm km''2} largejength = 150 {km} * 1000 {m km} large_twidth = 120 length = 18{km}*1000{m'km} Iimit_lday_3 = (if (time>320) then 10 else if (time > 75) then (Dike_elev_3-Mean_elev_3) else 10) limit_Iday_4 = one_rice_Iday Iimit_Iday_5 = (if (time>320) then 10 else if (time > 75) then (dike_elev_5-Bottom_elev_5) else 10) limit_LLï2_jTl = IF (switch_crop=0 OR switch_crop=2) THEN one_rice_Iday ELSE IF switch_crop=3 THEN lotus_lday ELSE IF switch_ciop=4THEN WRS_lday ELSE 2 limit_LL'E2_jT2 = IF switch_crop = I THEN two_rice_Iday ELSE IF swiich_crcp=2 THEN tatami_yr2_lim ELSE 2 Low_eIev_I = (IF switch_model=0 THEN 21.2 ELSE IF switch_modeI=l THEN 21.2 ELSE IF switch_model>5 THEN 21.6 ELSE 21.2) Low_eIev_2 = IF (switch_model=0 OR switch_modeI=2) THEN 213 ELSE 21.2 Low_elev_3 = (IF switch_model=0 THEN 20.5 ELSE IF switch_model= I THEN 20.5 ELSE 20.5) Low_elev_4 = 22.2 Low_elev_5 = 21 Low_elev_6 = 22.5 Low_elev_7 = 23.5 Manning = 0.035 DOCL'MENT: 0.035 for earth, winding & slugish channel, dense weeds in deep channel [clean earth channel is 0.020; unmaintained w dense weeds is 0.08] 196 tna’t_canal = if time >258 and lime < 535 then 23 else 24.5 (m a.s.l} Max_damaae_area_I - M.\X( ma.x_flixxl_I EIev_chanae_i*I.and_area_l. 0) max_damage_arca_3 = IF(max_n<)) THEN l^d_area_3 E1L5E0 max_damaae_area_4 = max_flcKxl_4 EIev_change_4 * Land_area_4 \lax_damage_area_early_2 = \L\X(ma.x_nood_2 Elcv_change_2*Land_area_2 Subdivision_2. 0) Max_damage_area_late_2 = MAX(max_Hood_5 Elev_changc_2*Land_area_2 Subdivision_2. 0) Ma.x_damage_area_mid_2 = \(AX( maxJlood_6 Bev_change_2*Land_area_2 Subdivision_2, 0) max_clev = 25.5 maxj'actor = I .Mean_elev_3 = (High_elev_3+Low_elev_3) 2 medJarge_ralio = 3 med_length = med_large_ratio*large_length med_iwidth = 50 {m} min_canal = if time >105 and time <260 then 21.5 else 20 {m a.s.l} min_elev =21.2 m injactor = 1 min_Honghu = 22{m a.s.l.} non_irr_max = 0 non_irr_min = -1 OC_multiplier = 1 opt_Honghu = if time >213 and time < 335 then 22.9 else 22.4 {m a.s.l} outer_a = (large_length+med_length)*bank outer_b = large_twidth*large_length + med_twidth*med_length - 2*bank*(top_elev- bottom_elev )*( large_length-*-med_length) outer_c = -outer_canai outer_canal_elev = IF (switch_modcl = 2 OR switch_model = 5 OR switch_model=7) THEN HFD.\_outer_elev EI.SE (Xiaogang_below * OC_multiplier) outer_depth = ( -outer_b+(outer_iy'2-l-*outer_a*outer_c)'K).5)’ (2*outer_a) PPT_multiplier = IF Year = 96 THEN 1.1 ELSE 1 pump_cap = 2.2{m''3 s'pump} * 86400 {s.d} pump_capacity = 315 {m''3 s} * 86400 {s d} pump_flow = lF(outer_canal_elev 92) THEN HH_PPT-ET ELSE (JL_PFT+HH_PPT),2 - EF Rainfall_l = IF Water_IeveI_l0)Tl{EN(seepage_rate_all*Land_area_l)ELSE(0) Seepage_2 = IF(Water_level_2>0)THEN(seepage_rate_all*Land_area_2)ELSE(0) Seepage_3 = (F(Water_level_3>0)THEN(.seepage_rate_all*Land_area_3)ELSE(0) Seepage_4 = lF(Water_level_4>0)TFŒN(seepage_rate_aH*Land_area_4)ELSE(0) Se€page_5 = lF(Water_level_5>0)TtIEN(seepage_rate_alI*Land_area_5)ELSE(ü) Seepage_6 = IF(\Vater_level_6>0)THEN(seepage_rate_all*Land_area_6)ELSE(0) Scepage_7 = [F(Water_level_7>0)THEN(seepage_rate_all*Land_area_7)EiLSE(0) Seepage_Rate_l = 0{mm day}*0.001 {m nun} Seepage_Rate_2 = 0{mm.'day}*0.00l{m.nim} Seepage_Rate_3 = 0{mm day}*0.001 {m mm} Seepage_Rate_4 = 0{mm.day}*0.001{m,'mm} Seepage_Rate_5 = 0{mm day}*0.001 {m mm} Seepage_Rate_6 = 0{mm'day}*0.001{m'mm} Seepage_Rate_7 = 0{mm day}*0.001 {m mm} seepage_rate_all = 0 slope = (Fu_below-Xiao_above) length split_0 = now_split split_l = DEL.4Y(now_spIit,desync*l) split_2 = DELAY(now_split.dcsync*2) split_3 = DELAY(now_split,desync*3) splil_4 = DELAY(now_split,desync*4) Suixlivision_l = IF switch_modeI>2 THEN 2{half in 1-crop half in 2-crop rice} ELSE 1 DOCL^MENT: corrects land area for situation where land area is divided between 2 crops or rotation is 2 years

Subdivision_2 = IF switch_crop=2 THEN 2{half in mid-rice half in tatami-late rice} ELSE 1 switch_canal = I DOCL’MENT: I = 10% Shallow (baseline run condition) 2 = nominal (used for 96 precalibration only) 3 = 10% deep (used in test scenario)

switch_crop = 1 DOCLMENT: 0 = mid-season rice (LL^ 2 sub 1 ) 1 = two-crop rice (LUE 2 sub 2) 2 = mid-season rice (LL^E 2 sub 1); tatami-late rice (LLE 2 sub 2) 3 = lotus 4 = wild rice stem

switch_model = 2 DOCL^MENT: 0 = 80 FARM (80 - 86) 1 = 90 FARM (87 - 94; 96 [change times)) 2 = HFDA (87 - 94) 3 = Xiaogang farm experimental (80, 81. 83. 87. 88. 91. 96["[) 4 = HFDA farm scale (87. 88. 91) 5 = HFDA. HFDA scale (87.88.91) 6 = HFDA farm scale, land tailored 7 = HFDA. HFDA scale, land tailored 8 = X farm, land tailored too_high = (IF(time < 225) THEN 2510 ELSE 2590) 100-1.89 198 to o jo w = (('OS((time+l50)*2*3 1416 3f>5)*325+2050) 100-1 NO top_eIev = 25 {m a.s.l.} lop_width - 175 Vol_at_outer_elev = canal_a*outer_canal_clev''2 + canal_b*outer_canal_elcv+ canal_c \'ol_ma.\_l = Land_area_l*Water_ma\_I Vol_ma.\_2 = Land_area_2*Water_cnax_2 \'ol_max_3 = Land_area_3*\Vater_max_3 \'ol_ma.\_4 = Land_area_4*Water_ma\_4 \'ol_ma\_i5 = Land_area_5*0.7*\Vater_ma.\_5 V'ol_tna\_6 = Land_area_6*\Vater_ma\_6 \'ol_ma.x_7 = Land_area_7*\Vater_max_7 Water_level_l = \Vater_rLand_area_l Water_level_2 = Water_2 Land_area_2 \Vater_Ievel_3 = Water_3 ' Land_area_3 Waler_leve!_4 = \Vater_4 Land_area_4 \VaterJcvel_5 = Water_5'{Land_area_5*0.7) \Vater_Ievel_6 = \Vater_6 Land_area_6 \Vater_Ievel_7 = Water_7 Land_area_7 \Vater_tim_l = (IF switch_model<3 THEN one_rice_ma.x ELSE (one_rice_max+iwo_rice_max).2)*niaxJactor \3'ater_max_2 = IF fef_prox\'>21.2+dike_irigger AND raise_dike>0 THEN raise_dike ELSE(IF switch_crop=0 THEN one_rice_inax ELSE IF switch_crop=l THEN two_rice_niax ELSE IF switch_crop=2 THEN tataini_rice_max ELSE IF switch_crop=3 THEN IoHis_max ELSE IF switch_crop=4 THEN WRS_max ELSE 0) *max_faclor \Vater_max_3 = Iake_max*max_factor Watcr_max_4 = one_rice_max*inax_factor VVater_max_5 = max_factor*intens_pond_max_a Water_max_6 = dry_crop_ma.x*inax_factor Water_iuax_7 = non_irr_max*ma.x_factor Water_min_l = (IF switch_inodel<3 THEN one_rice_min ELSE (one_rice_min+two_rice_min)/2)*tnin_raclor Waler_min_2 = (IF switch_crop=0THEN one_rice_niin ELSE IF switch_crop=I THEN two_rice_min ELSE IF switch_crop=2 THEN tatami_rice_min ELSE IF swi(cb_crop=3 THEN Iotus_niin ELSE IF switch_crop=4 THEN WRS_min ELSE 0) *min_factor Water_min_3 = Iake_min*min_factor \Vater_min_4 = Rice_rape_min*min_factor Water_min_5 = (intens_pond_min_a+increase)*min_factor Water_min_6 = dry_crop_min*min_factor Water_min_7 = non_irr_min*min_factor weir_a = 65000(X)G \veir_a2 = 100(X)0 weir_b = 2 weir_b2 = 23 weir_fIow_2 = weir_a2*xin_head''weir_b2 wet_perim = bot_width + 2*depth*sqrt(bank''2 + I) DOCL'XIENT: For trapezoidal channel, □wetted perimeter = bot-widtb + 2*depth*sqrt(bank''2 + I)

WiIting_point_I = -.12 WiIting_point_2 = -.12 \ViIting_point_4 = -0.12 WiIting_point_6 = -. 12 \V'iIting_point_7 = -.12 WiIting_point_all = -.12 Niaogang_beIow = (IF Year=80 THEN Xiao_B_80_synth ELSE IF Year=81 THEN Xiao_B_81a ELSE IF Year=82 THEN ,Xiao_B_82a ELSE IF Year=83 THEN Xiao_B_83a ELSE IF Year=84 THEN Xiao_B_84a ELSE IF Year=85THEN Xiao_BJ85a ELSE IF Yeai=86THEN Xiao_BJ86a ELSE IF Year=87 THEN Xiao_B_87a ELSE IF Year=88 THEN Xiao_B_88a ELSE IF Y ear^9 THEN Xiao_B_89a ELSE IF Year=90 THEN Xiao_B_90a

199 ELSH IF \ear=9l TFIFN \iao_U_9la EELSF IF Year=92THEN Xiao_B_‘X!a ELSE IF Year=93 Tl I EX Xiao_B_93a ELSE IF Year=94TllEN Xiao_B_94a ELSE IF Year=96 THEN Xiao_B_<^)A ELSE 0) I0()-1 Xiao_abovc = (IF Year=S7THES Xiao_A_87a ELSE IF Year=B8THEX Xiao_A_88a ELSE IF \ ear=89 THEN Xiao_A_89a ELSE IF Year=90 THEN Xiao_A_90a ELSE IF Yean=91 THEN Xiao_A_9la ELSEIFYear=92 THEN Xiao_A_92a ELSE IF Year=93 THEN Xiao_A_93a ELSE IF Ycar=94THEN Xiao.AJMa ELSE 0) l(X) - 1.89 xiaojlow = IF Honghu_elev>opt_Honghu THEN (IF HFD.A_outer_elev>max_canal THEN 0 ELSE \iao_now_tnax) ELSE IF HFD.A_outer_elevtoo_high THEN pump_capacity ELSE (IF HFD.A_outer_elev ear=92 THEN Xin_I_92a ELSE IF Year=93 THEN Xin_I_93a ELSE IF Year=94THEN Xin_l_94a ELSE 0) 100-1.89 Ximankou_outside = (IF Year=87 THEN Xin_0_87a ELSE IF Year^SS THEN Xin_0_88 ELSE IF "t ear=89 Tl lEN Xin_0_89a ELSE IF Year=90THEN Xin_O_90a ELSE IF Yean=91 THEN Xin_0_9la ELSE IF Year=92THEN Xin_0_92a ELSE IF Year=93 THEN Xin_0_93 ELSE IF ’t ear=94 THEN Xin_0_94a ELSE 0) 100-1 89 xin_head = MAX(HFD.\_outer_elev-Xintankou_oulside. 0) -X_sec_area = depth''2*bank+depth*bot_width Year = 94 .\clual_inner_96 = GR.\PH(time) ET_80a = GR.APH(üme) ET_81 a = GRAPH(time) ETj82a = GRAPH(time) ET_83a = GR.4PH(time) ETj84a = GRAPH(time) Er_85a = GR.\PH(time) ET_86a = GRAPH(time) ET_87a = GR.4PH(üme) ET_88a = GRAPH(time) ET_89a = GR.APH(time) ET_90a = GRAPHCüme) ET_91a = GRAPH(time) ET_92a = GRAPH(time) ET_93a = GRAPH(time) ET_94a = GRAPH(time) Fu_B_87 = GRAPH(time) Fu_B_88 = GRAPH(tiine) Fu_B_89 = GRAPH(thne) Fu_B_90 = GRAPH(time) Fu_B_9I =GR.APH(tirae) Fu_B_92 = GRAPH(üme) Fu_B_93 = GRAPH(time) Fu_B_94 = GRAPH(time) HH_PPT_80a=GRAPH( TIME) HH_PPT_8Ia=GRAPH( TIME) HH_PFT_82a= GRAPH( TIME) HH_PPT_83a =GRAPH( TIME) HH_PPT_84a =GRAPH( TIME) HH_PPT_85a = GR-\PH( TIME) HH_PPT_86a =GR.XPH( TIME) HH_PPT_87a =GRAPH( TIME) HH_PPT_88a =GR-\PH( TIME) HH_PPTJS9a =GR.APH( TIME) 200 PrH.PPT.SOa =ÜR.\PH( TIMB HH_PPT_9la =GR.APH( TIME) HH_PPT_92a =GR.\PH( TIME) HH_PPr_93a =GR.\PH( TIME) HH_PPT_94a =GR.\PH( TIME) IIH_PPr_96 =GRAPH( TIME) Honghu_elev = GR.\PH(HonghuJake)

(0.00, 20.7). (4e+07. 21.1). (8e+07, 21.2). (I.2e+08. 21.4). ( 1 . 6 C + 0 8 . 21.5). (2e+08. 21.7). (2.4c+08. 21.8). (2.8C+08. 22.0). (3.2e+08. 22.1). (3.6e+08. 22.2). (4e+08. 22.4). (4.4e+08. 22.5). (48e+08. 22.6). (5 2e+08. 22.8). (5.6e+08. 22.9). (6e+08. 23.0). (6.4e+08. 23.1). (6.8e+08. 23.2). (7.2e+08. 23.3). (7.6e+08. 23.4). (8c+08. 23.6). (8.4e+08. 23.7). (8.8e+08. 23.8). (9.2e+08. 23.9). (9.6e+08. 24.0). (le+09. 24.1). (le+09. 24.2). (l.le+09. 24.3). (l.le+09. 24.4). (I.2e+09. 24.5). (1.2e+09. 24.6). (1.2e+09. 24.7). (1.3e+09. 24.8). (I.3e+09. 24.9), (I.-le+09. 25.1). (I.4e+09. 25.1). (1.4e+09. 25.2). (1.5e+09. 25.3). (1.5e+09. 25.4). (l.6e+09. 25.6). (1.6e+09. 25.6) intens_pond_ma.x_a = GRAPH(lime) intens_poiKi_min_a = GRAPH(time) JL_PPT_87a = GR.4PH( TIME) JL_PPT_88a = GR.\PH( TIME) JL_PPT_89a = GR.4PH( TIME) JL_PPT_90a = GR.APHC TIME) JL_PPT_9!a = GR.APH1 TIME) JL_PPT_92a = GR.\PH( TT.ME) lotus_lday = GRAPH(time) lotus_ma\ = GR.4PH(time) lotus_min = GR.4PH(time) one_rice_lday = GR.4PH(time) one_rice_ma\ = GR.\PH(time) one_rice_inin = GR.\PH(time) pump_power_96_cal = GR.4PH(time) (195. 1.00). (196. 1.00). (197. 1.00). (198, 1 00). (199. 1.00). (200. 1.00). (201, 1.00). (202. 1.00). (203. 0 5). (204. 0.5). (205. 0.00). (206. 0.00). (207. 0.00). (208. 0.00). (209. 1.00). (210. 1.00). (211. 1.00). (212. 1.00). (213. 1.00). (214. 1.00). (215. 1.00). (216. 1.00). (217. 1.00). (218. 1.00). (219. 1.00) Rice_rape_min = GRAPH(time) talanü_rice_ina.\ = GRAPH(üme) tatami_rice_min = GR.APH(time) tatami_yr2_lim = GRAPH(lime) lwo_rice_lday = GR.APH(time) two_rice_inax = GRAPH(time) two_rice_min = GRAPH(time) WRS_lday = GRAPH(time) WRS_max = GRAPH(ümc) WRS.min = GRAPH(time) Xiao_.A_87a = GRAPH(time) .Kiao_A_88a = GR.APH(üme) Xiao_.A_89a = GRAPH(time) Xiao_.A_90a = GRAPH(üme) .Xiao_A_91a = GRAPH(time) .Xiao_A_92a = GRAPH(time) Xiao_A_93a = GRAPH(time) Xiao_A_94a = GRAPH(time) .Xiao_B_80_synth = GR.APH(time) Xiao_B_81a = GRAPH(time) .Xiao_B_82a = GRAPH(time) Xiao_B_83a = GRAPH(time) Xiao_B_84a = GR-APH(time) Xiao_B_85a = GRAPH(time) Xiao_B_86a = GR.APH(tiine) 201 Xiao_B_87a = GRAPH{time) Xiao_B_88a = GR.A.PH(time) Xiao_B_89a = GRAPH(time) Xiao_B_90a = GRAPH(time) Xiao_B_91a = GRAPH(üme) Xiao_B_92a = GRAPH(time) Xiao_B_93a = GRAPH(lime) Xiao_B_94a = GR.APH(time) Xiao_B_96 = GRAPH(time) Xin_l_87a = GRAPH(time) Xin_I_88 = GRAPH(lime) Xin_l_89a = GRAPHdime) Xin_l_90a = GRAPH(Umc) Xin_I_91a = GR.APH(time) Xin_[_92a = GRAPH(Ume) Xin_[_93a =GRAPH(Ume) Xin_[_94a = GRAPH(lime) Xin_0_87a = GRAPHdime) Xin_0_88 = GR.APH(üme) Xin_0_89a = GR.APH(time) Xin_O_90a = GRAPH(time) Xin_0_91a = GR.APH(time) Xin_0_92a = GRAPH(time) Xin_0_93 = GR.APH(time) Xin_0_94a = GRAPHdime)

202