Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing Data and Numerical Modeling

Imesh Chanaka Bihawala Vithanage March, 2009

Imesh Chanaka Bihawala Vithanage

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation. Specialisation: Integrated Watershed Modeling and Management.

Thesis Assessment Board

Professor Dr. Z. Bob Su, Chairman, WREM Department, ITC Dr. Ir. D.C.M. Augustijn, External Examiner, WEM Department, UT Dr. Ir C.M.M. Chris Mannaerts, First Supervisor, WREM Department, ITC Dr. B.H.P. Maathuis, Second Supervisor, WREM Department, ITC M. Yevenes, Advisor, WREM Department, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Disclaimer

This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Abstract

The water quality in catchments is influenced by the complex combinations of land use, point sources combined with weather and other natural and human influences. Agricultural non point source pollution is usually considered a major cause of water quality deterioration in larger agricultural catchments. Farmers usually tend to use more fertiliser with the intention of making more harvest without considering the optimum doses, and their effects on the environment. Excess nutrients leached through the watershed and are collected on the downstream water bodies, making them eutrophic, being excessive algae and/or plant growth due to an abundance of nutrients. The excess growth of algae and phytoplankton has various deleterious effects of the water storage and distribution systems like clogging of filters, reduction of dissolved oxygen (DO), unpleasant taste and odour. Some algae species (e.g., blue green algae) may be toxic to fish, animals (like birds) and even humans (Chapra, 1997). In addition ammonia (NH3), nitrates (NO3) and nitrites (NO2) can be harmful when present in excessive amounts in water.

In this study, our aim was to research the hydrological and environmental processes associated with Nitrogen (N) and Phosphorous (P) compounds and their dynamics in the hydrological system of the Roxo catchment. A model is developed that simulates the catchment flow hydraulics and the water quality in the catchment and Roxo reservoir. A special effort was put in place to model the effects of the Beja city waste water treatment plant (WWTP) as a test case for the model. This WWTP is an identified nutrient point source in the upper catchment. The hydrological, physical and biochemical water quality processes have been developed using Duflow Modeling Studio (DMS) including its Rainfall Runoff component model (RAM), based on linear reservoir theory. RAM elements (runoff areas) permit simulation of runoff hydrograph at the detailed sub-catchment level and permit to simulate distributed nutrient source (N, P) apportionment of agricultural areas to streams.

Because no direct stream flow gauging station was present existing in the upper Roxo, a reservoir water balance technique was used to estimate daily catchment stream flow to the reservoir. An extensive data set was available for this purpose. The hydrological model calibration and validation was based on this, was judged satisfactory. During summer periods, some small negative inflows were computed. These small estimation errors were considered due the precision in the level measurements, as well as uncertainties in some water abstraction values (extra unregistered withdrawals) or losses (evaporation, groundwater) from the reservoir especially during summer periods. The flow calibration of DMS was finally performed using cumulative decadal inflow values.

Besides running of the DMS model using interpolations from daily rain gauge data, we have run the model with remote sensing derived rainfall data (i.e. the Meteosat Multi-sensor Precipitation Estimate or MSG-MPE). We used a density of 18 data extraction points to represent catchment rainfall inputs. A simulation period starting from Jan 2007 to May 2008 with daily time steps was used for the purpose, and was a function of the availability of the MSG satellite rainfall data products. The study showed that the MSG daily rainfall data is not properly correlated with the daily gauge precipitation records. However when compared with the entire period, the cumulative result showed good agreement with the

i aggregated gauge station total rainfalls. The hydrologic responses produced using MPE showed that there are discrepancies with runoff hydrographs and this can be explained by the occasional high magnitude rainfalls recorded in the MPE that are significantly different from the gauge records.

After calibration and validation of the flow model, and comparison of the rainfall runoff response using ground gauged rainfall and satellite precipitation inputs, nutrient export from the agriculture areas, and the impact of a point source (i.e., the WWTP) was simulated. These first results were interpreted and compared to observed stream and reservoir water quality data. Although, initial water quality modeling results are encouraging, and confirmed we were able to simulate the magnitudes and local spatial and temporal variations of the nutrient processes, much more model and parameter uncertainties need to be eliminated before the water quality model can be judge as appropriate for the upper Roxo catchment and the reservoir system. This can be obtained through further study and research.

ii Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Acknowledgements

First and the foremost I am grateful to my employer National Water Supply and Drainage Board, Sri Lanka for allowing me to take this valued long-term fellowship to pursue with Master of Science Degree. At the same time I am thankful to ITC for offering me this esteemed MSc degree programme and also providing me with a scholarship for financing all expenses during 18 months period. I express my heartiest appreciation to my 1st supervisor Dr Ir. C.M.M Mannaerts for the support and guidance extended to me during this whole research period. The encouragement and the critical reviews had immensely helped in shaping up the thesis to the due expectations. Further, during the field campaign at Portugal, Chris’s assistance was enormous as he had not limit himself just a supervisor’s task but guided us at all possible occasions to get the maximum out of field visit.

I would also acknowledge the Dr B.H.P Maathuis, my 2nd supervisor. He often supported me at critical junctures when struggling with technical issues. At the same time I am grateful to Drs J.B de Smeth for providing space at the laboratory and supplying ancillary logistics for performing water sample analysis and the supervision over performing water quality testing in due manner.

I would also like to remember the Technical Director I. Oliveira and his staff of COTR, Engineer Alexandre Leal of EMAS, The Director ABROXO and Engineer R. Nobre from IPB (Beja) for the corporation extended to us in providing essential information for the success of this research study. I am also thankful to Mariella, Murat and colleagues Fransiska, Prescilla and Daphne, who were with me during the field campaign and for working as a team in attending the objectives. Moreover they made the field campaign more enjoyable. I would also like to acknowledge the lecturers of the WREM programme, whom made me equipped with proper tools and techniques to proceed with the research work. It is also essential to thank ITC library in providing essential information and resources, when I made a simple request to them. I also wish to thank all my friends in the WREM 2008 for giving suggestions, thoughtful reviews during this research phase.

Last but not least, I would like to express my sincere gratitude to my family, Niroshini, Iresh and Harshi for the moral support and love they have showed me during this long duration I was away from them. Especially the tolerance they have showed during this tough time made this task possible for me and to concentrate more on my studies. Finally I wish thank my parents for supporting me in all possible ways and looking after my family with utmost care during this 18 months period.

iii Table of contents

1. Introduction...... 1 1.1. Background...... 1 1.2. Research problem...... 2 1.3. Objectives ...... 3 1.4. Research questions ...... 3 1.5. Research hypothesis ...... 3 1.6. Description of the study area ...... 4 1.7. Climate ...... 5 1.7.1. Temperature...... 5 1.7.2. Precipitation and Evapotranspiration ...... 6 1.7.3. Topography...... 6 1.7.4. Hydrology...... 6 1.7.5. Land use ...... 7 2. Materials and Methods ...... 9 2.1. Work plan...... 9 2.1.1. Pre-field work ...... 9 2.1.2. Field work...... 9 2.1.3. Post-field work...... 9 2.2. Remote sensing data ...... 10 2.2.1. ASTER pre-processed digital elevation model (DEM)...... 10 2.2.2. Multi-sensor Precipitation Estimates (MPE ) ...... 10 2.3. Data collection from Beja water authorities...... 11 2.4. Field data collection...... 11 2.4.1. Geo-referencing data ...... 12 2.4.2. Data collection for CORINE land cover accuracy assessment...... 12 2.4.3. Stream section details...... 12 2.4.4. Water sample collection...... 12 3. Data Analysis and Integration...... 15 3.1. ASTER DEM ...... 15 3.2. Meteorological information...... 18 3.2.1. Rainfall estimation ...... 18 3.2.2. Evapotranspiration estimation...... 20 3.3. Data preparation for reservoir water balance...... 22 3.3.1. Inflow ...... 23 3.3.2. Outflow...... 23 3.3.3. Estimation of rainfall & evaporation volumes...... 23 3.3.3.1. Stage volume, Stage surface area relationships ...... 24 3.3.3.2. Water surface evaporation...... 25 3.3.3.3. Direct rainfall (input) ...... 26 3.3.4. Spill occurrences...... 27 3.3.5. Change in storage-Roxo reservoir ...... 28

iv Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

3.3.6. Water abstractions...... 28 3.4. Water balance and Estimation of inflows...... 28 3.5. Water quality data ...... 30 3.5.1. In-situ water tests ...... 30 3.5.2. Laboratory analysis ...... 31 3.5.3. Analysis of results ...... 32 3.6. Flow measurements ...... 33 3.7. Soil data...... 33 3.8. Land cover data...... 34 3.9. Historical water qauality data ...... 35 4. Numerical Modeling...... 37 4.1. Duflow Modeling Studio (DMS)...... 37 4.2. Duflow flow model ...... 37 4.3. Duflow quality model ...... 38 4.4. Duflow rainfall runoff (RAM) component...... 39 4.4.1. Open water surface...... 41 4.4.2. Paved surface ...... 41 4.4.3. Unpaved surfaces ...... 43 4.5. RAM quality model (DUFLOW, 2004b)...... 44 4.6. Quality model definition...... 45 4.7. Previous modeling studies with Duflow...... 46 4.8. Model simplifications and limitations (DUFLOW) ...... 47 5. Integration of satellite remote sensing data ...... 49 5.1. MSG_Multi-sensor Precipitation Estimate (MPE)...... 49 5.2. MPE precipitation data ...... 49 5.3. Data formats and pre-processing...... 50 5.4. Data extraction...... 50 5.5. Spatial variability of the rainfall with in the catchment ...... 53 5.6. Comparison of MPE rainfall with gauge data ...... 54 5.7. Conclusion ...... 54 6. Model Development and Implementation...... 55 6.1. Modeling objectives and the scope...... 55 6.2. Schematization of the study area ...... 55 6.3. Basic data inputs and data formats...... 56 6.4. Initial and Boundary conditions...... 57 6.5. Sensitivity analysis ...... 59 6.6. Model calibration...... 60 6.7. Model validation...... 60 6.8. Results-water quality (nutrient) modeling ...... 62 6.9. Scenario analysis ...... 63 7. Conclusion and recommendation...... 67 7.1. Conclusion ...... 67 7.1.1. Estimation of runoff...... 67 7.1.2. Numerical flow and water quality Modeling...... 67

v 7.1.3. Integration of rainfall remote sensing data in Duflow...... 68 7.2. Recommendation...... 69

References ...... 71

Appendixes

Appendix 1 Research phases and key activities...... 76 Appendix 2 Maps ...... 77 Appendix 3 Potential water surface evaporation calculation (sample calculation sheet)...... 79 Appendix 4 Data constancy checks using double mass technique...... 80 Appendix 5 Land cover ...... 82 Appendix 6 Data for crop evapotranspiration estimates ...... 84 Appendix 7 Roxo Stage Volume curve (Mannaerts , 2006)...... 85 Appendix 8 Soil Information ...... 86 Appendix 9 MSG MPE data gaps ...... 88 Appendix 10 Field data collection for water quality ...... 89 Appendix 11 Salt dilution gauging...... 91 Appendix 12 Water consumption/abstraction data (sample) ...... 92 Appendix 13 Collected ground control points for Geo-referencing...... 93 Appendix 14 Historical water quality data...... 94 Appendix 15 Water quality description (Nutrients) ...... 96 Appendix 16 Water balance calculation...... 98 Appendix 17 Results of the sensitivity analysis...... 100 Appendix 18 RAM surface parameters and calibrated parameter settings...... 102 Appendix 19 Temporal Phosphates concentration and load variation at reservoir section ...... 103

vi Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

List of figures

Figure 1-1 Geographical location of the Roxo catchment...... 4 Figure 1-2 Variation of mean daily temperatre over the two weather (Beja , Aljustrel) stations ...... 5 Figure 1-3 Variation of precipitation and reference crop evapotranspiration...... 6 Figure 2-1 Water sampling locations...... 13 Figure 2-2 Different land cover types observed in the Roxo catchment...... 14 Figure 3-1 SNIRH station points considered in accuracy assessment ...... 16 Figure 3-2 Accuracy assessment of the ASTER DEM...... 16 Figure 3-3 DEM hydro processing procedure for hydrologic parameter extraction ...... 17

Figure 3-4 Cumulative ETo comparison of the two COTR weather stations...... 20

Figure 3-5 Weighted crop factors (Kc) for catchment #01...... 21 Figure 3-6 Important terms of the water balance of a reservoir ...... 22 Figure 3-7 Stage area relationship for the Roxo reservoir ...... 24 Figure 3-8 Albufeira Do Roxo floating station ...... 25 Figure 3-9 Double mass graph between Beja and Albufeira stations...... 27 Figure 3-10 Basic configuration of the Ogee type crest spillway extracted from (Khatsuria, 2005) ...... 27 Figure 3-11 Monthly runoff estimation and the rainfall graph for the simulation period (2001-2007) ...... 29 Figure 3-12 Nitrification process along the river...... 32 Figure 4-1 Duflow RAM modeling framework (DUFLOW, 2004b)...... 40 Figure 4-2 Chemical and water quality processes modelled in RAM (DUFLOW, 2004b) ....44 Figure 4-3 Nitrogen cycle ...... 45 Figure 4-4 Phosphor cycle ...... 45 Figure 4-5 Structure of the water quality model description...... 46 Figure 5-1 Roxo catchment with 18 pts overlaid on top of MPE image: date 10/02/2007 ....51 Figure 5-2 Comparison of MPE data and ground based data ...... 51 Figure 5-3 Consistency check for random 5 points...... 53 Figure 6-1 Model calibration: simulated discharge vs. observed runoff ...... 61 Figure 6-2 Model validation: simulated discharge vs. observed runoff...... 61 Figure 6-3 Duflow model structure showing all key elements...... 62 Figure 6-4 Nitrification along the main river (Chamine) section...... 62 Figure 6-5 Temporal variation of the nitrate, ammonium concentrations ...... 64 Figure 6-6 Nutrient concentrations in the Roxo reservoir according to EMAS data ...... 65 Figure 6-7 Runoff simulation based on the two rainfall inputs ...... 66

vii List of tables

Table 1-1 COTR automatic weather stations in the vicinity of Roxo catchment ...... 5 Table 1-2 Monthly average temperature over last seven years ...... 5 Table 1-3 Monthly average reference crop evapotranspiration and precipitation ...... 6 Table 1-4 Land cover based on the European CORINE 2000 classification...... 7 Table 2-1 Water authorities visited during the field visit...... 11 Table 3-1 List of weather stations in the vicinity of the catchment ...... 19 Table 3-2 Interpolation weights for Thiessen polygon method ...... 19 Table 3-3 Interpolation weights for Inverse distance method...... 19 Table 3-4 Crop factors for different crop types ...... 21 Table 3-5 Growing stages (days)...... 21 Table 3-6 Landsat ETM + images used in extending the stage area relationship Roxo ...... 24 Table 3-7 Details of the weather stations located close to Roxo reservoir...... 25 Table 3-8 Field test results ...... 31 Table 3-9 Results of the laboratory analysis ...... 32 Table 3-10 Major soil types in the Roxo catchment...... 34 Table 3-11 Reclassification of CORINE land cover classes to DUFLOW RAM...... 35 Table 5-1 Cumulative rainfall figures ...... 52 Table 6-1 Shows the data required for the model...... 57 Table 6-2 Typical values of concentrations of nutrients...... 58 Table 6-3 RAM sensitivity analysis results ...... 59

viii Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

1. Introduction

1.1. Background The water quality is a complex combination of land use, point sources, natural processes in each catchment (Chen et al., 2005). The sewage treatment effluents and agricultural sources are respectively the main contributors to phosphorus and nitrates in water bodies (Neal et al., 2008).

The nitrogen (N) and phosphorus (P) are the two essential nutrients required for the growth of plant life. - - + Nitrogen exists in different oxidation forms Nitrate (NO3 ), Nitrite (NO2 ), Ammonium (NH4 ), Organic

Nitrogen, Molecular Nitrogen (N2), Nitrous Oxides (N2O) in the environment (Chapman, 1996). There are different sources contributing nutrients to the lake/reservoir water systems and it could be originating from the use of fertilizer, the mineralization of dead plant and animals, effluent from the urban and waste water treatment systems and water flows through igneous rock sources (Chapman, 1996). In the last few decades, human activities have enhanced the enrichment of water bodies with nutrients, (Leston et al., 2008) particularly nitrogen and phosphorus.

Phytoplankton and macro algae are capable of taking the advantage of the available resources in transient environments. Their high surface area to volume ratio and high affinity for nutrients, especially N and P, favour a rapid nutrient uptake and high growth and production rates leading to very large biomass values (Leston et al., 2008). The biological response to excess nutrient input to the water system is referred to as the Eutrophication (Chapman, 1996). Nutrient inputs generally are increased by human-induced land use changes specially the intense farming practices can lead to eutrophication and impairment of surface water quality (Dodds and Oakes, 2006)

The factors affecting the growth of phytoplankton are temperature, light and the nutrients, when each of these contributing factors comes to its optimum level there could be out break of heavy algal blooms (Alam et al., 2001). There are numerous adverse effects with the phytoplankton, water becomes turbid making the water more turbid, this can reduce the photosynthetic activities of the aquatic plant and ultimately causing the extinction of aquatic life forms.

The effects of land use and land use changes in a catchment are key aspects to understand the stream/catchment nutrient dynamics. Further the effect of rain events and the seasonality of nutrient dynamics are of interest to assess the maximum nutrient inflows in to an important water body and the associated delay after particular event are factors that affect the temporal water quality. The Nutrient dynamics is also dependent heavily on the application pattern of fertilizer in agricultural catchments (Poor and McDonnell, 2007).

1.2. Research problem

Roxo reservoir and the catchment is located in Alentejo province in southern Portugal and has high economic importance as the reservoir water uses for irrigation and public water supply purposes for the Beja city.

There is large agricultural area in the upper catchment and extensive use of fertilizer and subsequent problem of leaching nutrients to the down stream Roxo reservoir and eventually affecting the water quality is reported. Water quality impairment due to the high use of fertilizer for agricultural activities are being evident in the reservoir waters since several years (Chisha, 2005). Several research activities based on water quality had been carried out in the past due to the fact that there had been concerns over eutrophication problem and associated deterioration of surface water quality.

Rodriguez (2003) studied the influence of the waste water treatment plant for the quality of water in the Roxo-reservoir. Conteh (2003) in his study, did a surface and ground water quality assessment for a Chamine river of Pisoes part of Roxo catchment. A research study on inflow of pollutants into the reservoir from the Roxo catchment with more emphasis on Pisoes sub-catchment had been carried out by Shakak (2004) and concluded that major pollutant sources are intense agriculture practices in the catchment associated with miss-managed fertilizer application. He further stated that the effluent from the waste water treatment plant is also affecting the deterioration of water quality. Chisha (2005) did a study on assessing nutrient pollution contribution of the Outeiro catchment of the Roxo reservoir and able to identify main sources of nutrient initiator as agricultural runoff and waste water treatment plant located with in the Outeiro catchment.

Nutrient concentration fluctuations with in the river flow system prompts sudden algal growth and can lead to toxic algae types and the bio accumulation in animal and plant may cause to toxicity (Poor and McDonnell, 2007). Thronson and Quigg (2008) in his paper states that in a research study carried out in US confirmed that the leading cause for fish kills was found to be the low dissolved oxygen concentrations caused by both physical and biological factors and further identified that this happened during the warmest months, particularly in August. This DO reduction has direct correlation with the eutrophication as a result of subsequent excessive nutrient availability in the water systems.

Thus it has been identified, the study of the nutrient dynamics in the Roxo catchment is timely and important research that contributes greatly toward understanding the dynamics of the system and possible mitigatory measures to address this problem.

2 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

1.3. Objectives In order to address the research problem of studying the dynamics of the nutrients in the Roxo catchment, it was decided to build a numerical model using the Duflow Modeling Studio (DMS) with rainfall runoff components (RAM). The main objectives of the research study can be listed as follows;

• Build an operational numerical water quality model for the Roxo catchment using Duflow Modeling Studio (DMS) and rainfall runoff (RAM) components

• Perform model Calibration and Validation through reservoir water balancing technique as no in- stream discharge measurements available

• Utilization of satellite remote sensing precipitation data as model input and compare the performance with the gauge station precipitation records

1.4. Research questions In addressing the above objectives, following research questions had been identified and efforts were made to answer the subsequent research questions during the research phase.

• Can the watershed water quality model be build using DMS with RAM component and can it efficiently represents the water quality and nutrient dynamics of the Roxo catchment?

• What kind of temporal and special variation of nutrient concentrations could be observed with in the catchment?

• Whether the reservoir water balance technique provides stream inflows that can be used for hydrologic model calibration and validation

• What type of satellite sensors data can effectively be used for watershed water quality model?

• Whether the use of two precipitation input methods will provide significant differences in results?

1.5. Research hypothesis The following are hypothesized in order to proceed with the research work

• The hydrological model developed using DMS for the Roxo catchment can be calibrated and validated effectively by the inflows calculated using the reservoir water balance technique.

• The land use conditions in riparian zones are highly correlated with in-stream and reservoir nutrient concentrations.

1.6. Description of the study area The Beja district is having approximately population of 165,000 with in an area of 10,224 km2. Beja municipality is the capital of Beja district and there is about 35,000 population lives in 18 communes of an area of 1140 km2 in the municipality with low population density of 31 per km2 (FOTW, 2009).

Figure 1-1 Geographical location of the Roxo catchment

The Roxo watershed delimited by 37°46'44"N to 38°02'39"N latitudes and 7°5'47"W to 8°12'24"W longitudes and is having an area of 353 km2 located in the Beja district, Alentejo province of southern Portugal (Gurung, 2007). The province is reputed for its agricultural productivity as it produces approximately 75% of the country’s total production of wheat.

Roxo reservoir was constructed artificially and it covers a surface area of 13.8 km2 when it is at its full capacity. The main objectives of constructing the Roxo reservoir were in providing water to downstream agricultural activities (irrigation), cater the drinking water requirement at the Beja city and some industries (mining).

4 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

1.7. Climate

1.7.1. Temperature The temperature in Portugal varies considerably from north to south within its territory as it stretches through wide latitude range. Usually it is cool and rainy in north while warmer and drier in southern Mediterranean parts of the country. The Table 1-1 lists the locations of the nearby COTR weather stations and the daily temperature data of past seven years have been collected and summarised in the subsequent table and graph.

Table 1-1 COTR automatic weather stations in the vicinity of Roxo catchment Station Coordinate Elevation (masl) Beja 38°02'15.00"N, 7°53'06.00"W 206 Aljustrel 37°58'17.00"N, 8°11'25.00"W 104 Datum73

Table 1-2 Monthly average temperature over last seven years Monthly temperature at Beja station ( oC) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Average 7.9 8.6 10.9 12.7 15.6 19.3 20.9 21.8 18.6 15.2 10.7 8.0 Maximum 13.4 14.2 16.8 19.0 22.7 26.9 29.3 30.3 25.9 20.7 15.8 12.8 Minimum 3.9 4.4 6.2 7.1 9.2 12.2 13.1 14.3 12.8 11.1 7.0 4.6

Mean Daily Temperature Beja Station Mean Daily Temperature Aljustrel Station

30 30

25 25

20 20

15 15

10 (oC) Temperature Temperature (oC) Average Temperature Average Temperature Poly. (Average Temperature) 5 Poly. (Average Temperature) 5

0 0 Jul Jul Jun Jan Jan Jul Jul Oct Feb Apr Sep Aug Dec Dec Mar Jan Jan Jun Nov May Oct Sep Feb Apr Dec Aug Dec Mar Nov May Month Month Fig (b) Aljustrel station Fig (a) Beja station Figure 1-2 Variation of mean daily temperatre over the two weather (Beja , Aljustrel) stations

As shown in the Table 1-2 maximum temperature occurs during the months of June to September and max temperature may reach up to about 40 0C. The annual mean temperature stays about 150C in Beja according to the Table 1-2. In addition Figure 1-2 shows that monthly mean temperature varies in between 8 to 25 centigrade during a year. The temperature at Beja is comparatively higher with respect to the average annual temperature in the Portugal.

1.7.2. Precipitation and Evapotranspiration Daily rainfall and evapotranspiration data of the last seven years had been considered in making averages. Based on past 7 years records (Table 1-3) of the automatic weather stations at Beja and Aljustrel the study area receives an average annual rainfall about 525 mm. The annual reference crop evapotranspiration is about 1300 mm. According to Figure 1-3a, Figure 1-3b the dry months occur from May to September where there is very low precipitation and associated with high evapotranspiration. These characteristics generally correspond to long term averages listed in literature (Bhandari, 2004), (The Weather Network, 2009).

Monthly Average Precipitation and Reference Crop Evapotranspiration (Beja Station) Monthly Average Precipitation and Reference Crop Evapotranspiration (Aljustrel Station)

120 250 120 250

100 100 200 200

80 80 150 150

60 60

100 100 Precipitation (mm) Precipitation Precipitation (mm) 40 40

50 50 20 20 Reference Crop Evapotranspiration /(mm Evapotranspiration Crop Reference Reference Crop Evapotranspiration/(mm) Crop Reference

0 0 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Month Precipitation ETo Precipitation ETo Fig(a) Beja station data Fig(b) Aljustrel station data

Figure 1-3 Variation of precipitation and reference crop evapotranspiration

Table 1-3 Monthly average reference crop evapotranspiration and precipitation Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec ETo 37.83 49.33 82.11 108.54 146.86 188.04 211.86 188.53 130.60 79.47 44.34 33.26 Aljustrel Precipitation /(mm) 38.61 48.25 56.16 57.57 29.09 6.14 1.17 4.94 30.49 104.28 97.30 54.55 ETo 39.18 50.73 85.14 114.50 153.14 196.29 222.46 200.25 136.53 82.49 46.61 33.43 Beja Precipitation /(mm) 41.01 49.39 49.31 48.33 29.26 11.06 0.80 4.91 48.51 101.46 83.60 48.91

1.7.3. Topography The catchment has a gentle slope towards western direction and the highest elevations are about 245 m above the mean sea level (masl) while lowest elevation is around 125 m. The higher elevated areas are located northern, north eastern and south western areas of the catchment.

1.7.4. Hydrology The study area belongs to dry zone of Portugal and the dry period occurs during the months of May to September. The wet season starts from October and last till April, confirming Sen and Gieske (2005) Table 1-3 shows that 85% of the annual rainfall occurs during the wet period in the catchment. Most of the streams in the catchment are ephemeral or seasonal in nature where water flow occurs only during the wet season and steams tend to get dry out during the summer time. According to our observations the Pisoes river can be considered as perennial having very minimal flow during the dry season. The

6 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Roxo is a closed boundary catchment with no trans-boundary water transfers among adjacent catchments.

1.7.5. Land use

The main land use type in the catchment is agriculture and the types of crops grown are maize, wheat, sunflower and olives. It was observed that very limited land spaces are occupied in growing grapes (Vine yards). There were some coniferous forests in the western part of the catchment. In addition scattered Eucalyptus forest plantations were also observed in the area, of which some are matured while others are at the immature initial growing stage. In the south-western part of the catchment there was a large matured Eucalyptus forest.

It is also observed that there were livestock farming practices in the catchment and cattle, sheep and pig farms were amongst them. There were few small scale water bodies (i.e., ponds) in the catchment and those were used for agriculture (irrigation) and drinking source for animals.

Almost all the people are concentrated in or around Beja city that is located north eastern part of the catchment. In addition to this major population centre at Beja, there are few suburban townships (villages) with very low population.

The land cover-land use map of the study area is shown in Appendix 2 fig.(b) while Table 1-4 lists the fractions of lands cover land use types in the Roxo catchment using the European CORINE land cover- land use classification.

Table 1-4 Land cover based on the European CORINE 2000 classification Land use-Land cover classification Extent km2 Percentage fraction Agro-forestry areas 39.64 11.42% Annual crops associated with permanent crops 21.99 6.33% Broad-leaved forest 16.97 4.89% Complex cultivation patterns 0.68 0.20% Discontinuous urban fabric 2.90 0.83% Land principally occupied by agriculture, with 0.42 0.12% significant areas of natural vegetation Mixed forest 0.26 0.07% Natural grasslands 5.49 1.58% Non-irrigated arable land 236.94 68.26% Olive groves 6.67 1.92% Permanently irrigated land 7.32 2.11% Transitional woodland-shrub 0.52 0.15% Vineyards 0.00 0.00% Water bodies 7.34 2.11% Total 347.13 100.00%

8 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

2. Materials and Methods

2.1. Work plan The thesis work plan is divided in to three major phases and at each phase predetermined task was performed and detailed description is provided below. The entire research phase and key activities are schematically shown in the Appendix 1 with a flow diagram.

2.1.1. Pre-field work

During this phase, basically review of the past research that had been carried out with relevant to the research area (Roxo catchment) was investigated. The effort had been made to assess the current know how and to identify the gaps existing for further research. In addition we tried to locate the data sets essential for the subsequent modeling and analysis exercises. Further this time period has been utilised in down loading an important remote sensing images and pre-processing the same.

The initial research methodology had been drafted and the field data collection planning was carried out so that all essential information can be collected efficiently during the field work.

2.1.2. Field work

Field work was carried out during 2 weeks time 29th Sep 2008 to 12th Oct 2008 in Beja Portugal. The detail description of field data collection is given in section [2.4].

2.1.3. Post-field work

Post-field work involved data integration, estimation of stream flow using water balance technique, building up flow model, sensitivity analysis, model calibration and validation, integration of remote sensing data defining quality model, analysis results and making conclusions.

The data integration involved the water sample analysis, filling data gaps, preparation of data inputs, image processing, handling errors and integration of past data essential for subsequent modeling work.

2.2. Remote sensing data

2.2.1. ASTER pre-processed digital elevation model (DEM)

In order to perform rainfall runoff modeling various information applicable to the topography of the study area is obligatory, usually catchment extraction, drainage paths, slopes, longest flow paths, are among them. The most of these information are extracted by processing and analysis of digital elevation model (DEM) (Maathuis and Wang, 2006). The most commonly used remote sensing data source for DEM is the Shuttle Radar Topographic Mission (SRTM) which has 80% of the global coverage and it is a free source for general use. The current updated version 4 data set is of 90 m horizontal spatial resolution and available for the downloading from the National Map Seamless Data Distribution System, or the USGS ftp web server. It is stated that the vertical maximum error in SRTM DEM can go up to about 17 meters (CGIAR-CSI, 2009).

Due to the comparatively low spatial resolution of SRTM and the vertical accuracy (height), it was decided to use significantly superior spatial resolution (30 m) ASTER DEM for the hydro processing. ASTER DEM is produced by Land Process Distributed Active Archive Center (LPDAAC) of USGS using bands 3N and 3B from an Aster level L1A data sets through NASA’s Earth Observing System (EOS) data gateway (NASA-JPL, 2009).

2.2.2. Multi-sensor Precipitation Estimates (MPE )

Satellite remote sensing data is becoming and increasingly important data source for modeling studies and its special and temporal aspects give additional flexibility to models. One of the key objectives of this research is to investigate the possibility of integrating the remote sensing data (meteorological forcing) in driving the model. The precipitation is primary meteorological forcing in hydrologic modeling and the objective was to drive the model with Meteosat MSG_MPE extracted precipitation due to the data availability at near real time and with high temporal (15 minutes) and spatial resolution (3x3 km2).

• Temporal resolution : Images are available at 15 minutes intervals (96 per day) • Spatial resolution : Precipitation pixel size of 3x3 km2

The MPE is a product of Meteosat-7 and Meteosat-9 combine with SSM/I onboard of the US-DMSP satellite and produced at near real-time. The data can be readily down loaded from the EUMETSAT server. Initially raw data comes in the form of GRIB files need to be converted in to ILWIS readable formats, and followed by pre-processing and aggregation to a day sum with a special scripting made in ILWIS GIS software for batch processing. Then the precipitation data extraction from the images was carried out in tabular form, subsequently data analysis and integrating of data in to the model was performed. The detail description of the complete procedure is explained in Chapter 5 in the thesis.

10 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

2.3. Data collection from Beja water authorities

During the field data collection, following Beja water management institutes listed in the Table 2-1 were visited and able to gather some vital information relevant to the research study. The collected data was in the forms of cartographic information (i.e. soils and land use) in the study area, water quality information, water consumption/release information, weather station data and fertiliser application data.

Table 2-1 Water authorities visited during the field visit No Institution Information

01 EMAS – Beja Past water quality test results-Roxo reservoir, Municipal Water Supply & Services provided and their involvement with the Roxo Sanitation Authority reservoir http://emas-beja.pt/ 02 COTR –Beja Information on the soil and other ancillary maps Center for Irrigation Technology Automatic weather stations details [14 nos] Data is readily available in the net. http://www.cotr.pt/ 03 ABROXO -Aljustrel Water demand data Association of beneficiaries of the Background information on Roxo reservoir Roxo Reservoir and Irrigation area http://www.abroxo.pt/ 04 Poly-technical institute of Beja Information on fertiliser application, cropping patterns etc 05 INETI-Beja-National Institute for No information was available that relates to water Engineering, Technology and quality at Beja office. Innovation

The soil data is one of the essential information needed for the parameterization of the model. The COTR provided the soil family map of the study area and some extracts of the Portugal soil reference manual (Conference on Mediterranean Soils, 1966). In addition COTR provided us with the European CORINE 2000 land cover land use classification map that corresponds to the study area.

2.4. Field data collection

The field data collection was done during two weeks period and the target was to familiarize the study area and to collect the maximum possible information in building up and driving the model. Initially a reconnaissance survey was carried out to investigate the entire study area as to finalise the sampling points for soils, waters and stream section data collection.

We further observed that most of the cereal crops had been harvested at the time of field data collection, and we made an effort to identify the type of crops grown in the respective observation site based on the remaining residues. We scrutinized the catchment was at relatively dry state and almost all the streams

were dry and few were having very low water flows. It is evident that streams were running with base flow as there were no substantial rainfalls during that time period.

The data that was collected from the field was basically used for defining model input variables, model parameters, verification of the results and accuracy assessment processes. In addition it is used for reasoning out the some physical process in the catchment system.

2.4.1. Geo-referencing data The ground control points are essential piece of information for geo-referencing images data. Thus 15 ground control points (GCP) were collected in the Roxo catchment and surrounding area and attention was made to located those tie points in spatially distributed manner in the Beja and the surrounding areas. The locations were selected at main road junctions so that points could easily be located in the maps/images relatively easily. The Appendix 13 lists the details of the 15 GCP points taken during the field data collection.

2.4.2. Data collection for CORINE land cover accuracy assessment A random land cover sampling was used to perform an accuracy assessment for the CORINE land Cover 2000 (CLC 2000) classification. The ground truth information of approximately 40 random locations (Appendix 5.3) had been collected representing the entire catchment. The location was selected such that the point is as far as possible inside the particular land block of uniform land cover class that is to assure the point reside within the respective class polygon of the classification map. The Figure 2-2 shows some of the typical land plots observed during the field data collation.

2.4.3. Stream section details

The stream section details are essential information required in building up the model defining corresponding stream elements in Duflow. The locations were decided at the site and it was found that almost all stream sections are of the similar shape and size. Generally the streams are of trapezoidal shape having top width of 3-4 m and bottom width approximately 1-1.5m and the height vary from 1.5- 2.5 m and the similar condition was prevailed in almost every part in the catchment.

Another important observation made during the field survey was that most streams in the catchment were at dry state in September- October months and therefore streams can be considered ephemeral or seasonal in nature. The streamline flows were visible in the Chamine River in the Pisoes catchment, however the flow rate were very low and it is conveniently identify as the base flow was occurring. Further there were no hydraulic structures obstructing the stream flow paths in the upper Roxo.

2.4.4. Water sample collection The sample points were selected so that it represents the processes occurring along the flow path and also considered the ease of access to a particular location. The Figure 2-1 shows the locations of the water sampling points.

12 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Eight water samples were collected in the Roxo catchment and 7 of it were collected from Chamine river of the Pisoes sub-catchment and one sample from the Roxo reservoir. Pisoes was the only sub- catchment we found stream flow during the time of field campaign, thus sample collection was resolute to that stretch. Furthermore most past water quality sampling and testing were also limited to the Pisoes sub-catchment. The sample points located down stream of the waste water treatment plant, thus it could also helps in analyzing the effect of waste water treatment plant.

Figure 2-1 Water sampling locations

fig.(a) Harvested maize field with crop residue fig.(b) Sparsely spaced olive grove with pine trees (stalk) on field, and pivot irrigation system in back in the back

fig.(c) Infront young pines with Eucalyptus fig.(d) Mature Pines plantation plantation in the back

fig.(e) Vine yard fig.(f) Young Olive plantation (under drip irrigation, with sparse cork oak trees in back)

Figure 2-2 Different land cover types observed in the Roxo catchment

14 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

3. Data Analysis and Integration

This chapter discuss in detail the methodologies adopted in data analysis and integration in order to create model input parameters, input variables, meteorological forcing, etc. The Roxo catchment was divided in to 13 sub catchments and this discretization was based on the main contributory streamlines to the Roxo reservoir. Since the Pisoes catchment is the most important with respect to flow and this catchment is having more past information, we split the Pisoes catchment in to four sub catchments based on the stream intersection points. The Roxo catchment is ungauged and the historical daily reservoir water levels and water abstraction data were the information available. Thus reservoir water balance method was used and invented to estimate the total catchment stream flow rate towards the reservoir. The simulation period is from 2001 to 2008 and the daily time step had been selected as the discrete time step. In the subsequent sections following aspects are discussed in detail; • DEM hydro processing • Integration of meteorological data • Reservoir water balance and runoff calculation • Laboratory analysis of water samples • Soil and land cover land use data integration • Historical water quality data

3.1. ASTER DEM The Advanced Space borne Thermal Emission Reflection Radiometer (ASTER) sensor onboard the NASA terra satellite provide visible and near infrared (VNIR) bands in 15 m spatial resolution and that also have short wave and thermal infrared and they are comes at 30 m and 90 m spatial resolution respectively (Abrams, 2000). An ASTER DEM is an on demand product of ASTER images and the DEM is derived using the ASTER band 3N (nadir viewing) and 3B (backward viewing) level 1A stereoscopic image data sets and based on an automated stereo-correlation method (Hirano et al., 2003) utilising a software available at the Land Process Distributed Active Archive Center (LPDAAC) of USGS. The spectral ranges of the images (ASTER band 3) used for the DEM are 0.78 to 0.86 μm.

Some basic information on the DEM is listed below; • Area coverage of ASTER DEM: 60x60 km2 • Pixel resolution: 30 m • Data type: 16 bit signed integer • Data format: GeoTIFF • According to NASA DEM product description documentation, the current final validated version of ASTER DEM is generated without using ground control points. The product description specifies that the absolute accuracy of the DEM is greater than or equal to 7 m while the relative accuracy of the DEM is great than or equals to 10 m (LPDAAC, 2001).

An accuracy assessment for the ASTER DEM had been carried out using the elevation data of the SNIRH weather stations and it was found that almost all the point are with in the specified accuracy limits stated by the USGS. The Figure 3-1 shows the overlaid SNIRH weather stations on top of the ASTER DEM considered. There are substantial number of points in the selected area and the statistical analysis of the station elevations and the respective ASTER DEM pixel elevations (Figure 3-2) show that there is good correlation among the actual elevation and the ASTER DEM. The Figure 3-2 further confirms that regression line has a gradient of 0.982 and which is very much close to 1 and it verifies the agreement between the data points. The correlation coefficient is equals to 0.959 and only very few out-liars noted out of 77 points. Thus the ASTER DEM is better choice for the hydrologic parameter extraction with higher spatial resolution with the more realistic elevation data.

The primary procedure adopted in obtaining key parameters is schematically illustrated in Figure 3-3.

X – SNIRH station points

Figure 3-1 SNIRH station points considered in accuracy assessment

Accuracy Assesment of the ASTER DEM

350 y= 0.982x + 7.627 Ground Elevation x ASTER DEM

300

250

200

150 ASTER DEM Elevation (masl) 100

0 0 50 100 150 200 250 300 Ground Elevation ( masl)

Figure 3-2 Accuracy assessment of the ASTER DEM

16 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Figure 3-3 DEM hydro processing procedure for hydrologic parameter extraction

The DEM hydro-processing was carried-out using the capabilities of the ILWIS- GIS package. The process followed are schematically shown on the Figure 3-3 and the main catchement, sub-catchments based on the stream intersection points and drainange paths were extracted. The resulting sub- catchement map [Appendix 2-fig.(d)] is an essential output of the DEM hydro-process as to obtrain the area average values (aggregated statistics) for each sub-catchment based on the GIS information like land use, soils, etc, .

3.2. Meteorological information

The meteorological information is available for the catchment through two automatic weather stations operated under the Operating Center and Irrigation Technology (COTR) and Portuguese Water Resources Information System (SNIRH) data base and the information is readily available through the net. The two COTR stations are located just close to the boundary of the Roxo catchment and The two weather stations selected in this study (Beja, Aljustrel) from the COTR data base has the daily weather information available since 2001 September till to-date.

There are several meteorological stations in the vicinity of the catchment under SNIRH data base (6 nos) where one station situated inside the catchment and others are very close to the catchment boundary but the consistency of the data set is not satisfactory as the there are number of data gap during last 2 years and most of the other relevant meteorological information like humidity, wind speed, temperature were not recorded in most of these station data sets. Since the simulation period start from 2001, it was decided to proceed with modeling work with information available with two COTR stations. The map showing all weather stations in the vicinity of the Roxo catchment are shown in Appendix 2 fig.(c).

3.2.1. Rainfall estimation The precipitation is an essential input (meteorological forcing) for hydrologic modeling and the accuracy of the precipitation data depends on the point measurement and the spatial conversion in to area averages. In the calculating are average precipitation we considered 8 nearby weather stations. The stations under COTR are automated and it is having information continuously since 1st November 2001. The missing data (interruptions) were negligible (less than 20 days for the entire 7 years period) and according the COTR office these interruptions were usually due to malfunctioning of the equipment. The consistency of the data set had been investigated with regression analysis and it was found that there weren’t any significant changes during the study period. Whilst other stations had apparent data gaps mostly after 2007 and those were filled using the normal ratio method described by the Dingman (2002) with data from nearby stations. The method can be described by the equation [3.1].

G = 1 P o po ∑ pg [ 3.1] G g =1 P g

Where

Po Missing data value (daily)

Pg Observed value at the designated date (gauge # g : 1,2,….G) G Stations with data on specific day

⎯Po Annual average precipitation of data missing station

⎯Pg Annual average precipitation of other stations

The double mass curve technique was adopted for the testing the homogeneity of the long continuous data set. The double mass curves for stations are listed in the Appendix 4.

18 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Table 3-1 List of weather stations in the vicinity of the catchment Elevation Station Name Latitude Longitude (masl) Beja-COTR 38.038 -7.884 206 Aljustrel -COTR 37.972 -8.189 104 Albernoa -SNIRH 37.857 -7.962 133 Albufeira Do Roxo -SNIRH 37.929 -8.080 135 Aljustrel -SNIRH 37.868 -8.167 193 Santa Clara Do Louredo -SNIRH 37.966 -7.874 195 Santa Victoria -SNIRH 37.964 -8.023 150 Trindade -SNIRH 37.886 -7.893 172

The thirteen sub catchments were assigned spatially averaged rainfall based on the nearby station precipitation records. In the rainfall data transformation to watershed both the Thiessen polygons (nearest neighbour) method and Inverse distance interpolation method considered and created two rainfall schemes. The Table 3-2 and Table 3-3 respectively shows the sub catchments and the corresponding Thiessen weights and Inverse distance weights. Thiessen method is simple in application and gives reasonably good estimate when the orographic effect of the area is negligible as the maximum elevation deference is less than 100 meters (Dingman, 2002). Thus considering the above limitations and the distance effect of ground station, it was decided to used the Inverse distance scheme for the model. It is also found that Thiessen polygon method gives slightly high area average rainfall estimates than inverse distance method.

Table 3-2 Interpolation weights for Thiessen polygon method

Sub Catchment Station 12345678910111213 Beja - Cotr 11.7 100.0 100.0 29.4 78.9 100.0 93.7 99.6 0.3 Aljustrel - Cotr 16.5 70.6 100.0 12.8 6.3 0.4 77.5 Albernoa Albufeira Do Roxo 50.0 67.0 49.9 25.9 22.3 Aljustrel 50.1 33.0 74.1 Santa Clara Do Louredo Santa Vitoria 21.9 8.4 Trindade Thiessen weights

Table 3-3 Interpolation weights for Inverse distance method

Sub Catchment Station 12345678910111213 Beja - Cotr 22.4 21.3 20.1 2.6 8.3 13.8 17.0 16.1 7.5 10.2 14.6 21.6 20.7 Aljustrel - Cotr 12.3 15.6 18.8 20.3 19.4 11.6 9.0 20.1 21.6 16.3 17.5 13.8 13.7 Albernoa 9.9 6.3 5.2 14.5 13.7 15.8 15.8 9.0 12.6 14.9 13.0 8.0 11.9 Albufeira Do Roxo 5.4 8.2 8.1 14.4 11.8 7.4 5.1 9.1 13.5 10.0 8.1 7.8 2.4 Aljustrel 6.0 8.9 14.4 22.5 21.5 16.2 13.5 18.8 22.7 19.6 18.9 6.6 12.5 Santa Clara Do Louredo 18.8 16.8 14.7 4.8 7.4 14.3 16.8 11.3 4.4 10.5 12.3 17.6 17.5 Santa Vitoria 10.1 11.2 8.8 9.4 5.9 4.0 4.8 5.5 8.0 4.1 1.8 11.4 5.4 Trindade 15.1 11.8 10.0 11.4 12.0 16.8 17.9 10.2 9.6 14.3 13.7 13.2 15.8 Inverse distance weights

3.2.2. Evapotranspiration estimation Evapotranspiration is one of the key inputs for the hydrologic model and it represents aggregated result of transporting water from the surface to the atmosphere. The reference crop evapotranspiration of the two near by COTR station data is based on the modified Penman-Monteith equation (Allen et al., 1998).

900 0.408Δ(R − G) + γ u (e − e ) n + 2 s a ET = T 273 [ 3.2] o Δ + γ + (1 0.34u2 )

Where -1 ETo Reference crop evapotranspiration [mm day ], -2 -1 Rn Net radiation at the crop surface [MJ m day ], G Soil heat flux density [MJ m-2 day-1], T Mean daily air temperature at 2 m height [°C], -1 u2 Wind speed at 2 m height [m s ], es Saturation vapour pressure [kPa], ea Actual vapour pressure [kPa], es - ea Saturation vapour pressure deficit [kPa], Δ Slope vapour pressure curve [kPa °C-1], γ Psychometric constant [kPa °C-1].

The reference crop evapotranspiration (ETo) daily time series data is available at the two COTR weather stations since September 2001 till to date. Further it was found that the ETo data of the two stations are homogeneous during the study period and had a good correlation among the reference crop evapotranspiration values. The Figure 3-4 depicts that two weather stations have almost the same reference crop Cummulative ETo Recorded at Beja and Aljustrel weather stations 10000 Evapotranspiration values. The

dd 9000 y = 0.9582x - 53.129 coefficient of determination 2 R = 0.9998 2 8000 (R ) is almost equal to one

7000 showing excellent agreement,

6000 hence adoption of Thiessen

5000 polygon interpolation is

4000 considered reasonable. Thus,

3000 the spatial interpolation was Double mass relation Linear (Double mass relation) carried out by the Thiessen

Cummulative ETo Aljustrel /(mm) 2000

1000 polygon method in order to

0 calculate ETo at sub catchment 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 level. Cummulative ETo Beja (mm)

Figure 3-4 Cumulative ETo comparison of the two COTR weather stations

20 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Table 3-4 Crop factors for different crop types

Crop Type Winter Maize Growing- Forests- Grazing Stage Sunflower Wheat (sweet) Olives Grapes Olives Bare Soils Coniferous Pasture Kc ini 0.45 0.7 0.45 0.65 0.3 0.65 0.45 1 0.3 Kc mid 1.15 1.15 1.15 0.7 0.7 0.45 0.45 1 0.75 Kc end 0.35 0.25 1.05 0.7 0.45 0.65 0.45 1 0.75 Max Crop height/(m) 2 1 1.5 3 to 5 1.5-2 10 0.1 Source FAO 56

Table 3-5 Growing stages (days)

Crop Type Init.(Lini)Dev.(Ldev)Mid.(Lmid)Late.(Llate) Total Planting Date

Sunflower 25 35 45 25 130 April Winter Wheat 30 140 40 30 240 November Maize (sweet) 20 25 25 10 80 May/June Growing-Olives 30 90 60 90 270 March Grapes 30 60 40 80 210 April Olives 30 90 60 90 270 March Source FAO 56

Daily area average crop coefficient plot for the catchment #01

0.950 Catchment 1_ Resultant Kc Value 0.900

0.850

0.800

0.750

0.700

Resultant Kc Factor Factor Kc Resultant 0.650

0.600

0.550

0.500 JJFMAMJJASOND Period

Figure 3-5 Weighted crop factors (Kc) for catchment #01

= ETc Kc ETo [ 3.3] Where: ETc Crop Evapotranspiration

Kc Crop factor

The basic information needed for estimation of crop evapotranspiration (ETc) had been collected from the FAO 56, Irrigation and Drainage paper and the data is summarised in Appendix 6. A GIS analysis was carried out to assess the different fractions of land use types within each sub-catchment.

During non growing periods surface is usually similar to bare soil and Allen et al (1998) recommends to use the Kc value equivalent to Kc,initial for non growing time periods. Accordingly weighted Kc factors were calculated considering the crop types, growth stage and individual crop factors, growing periods, etc. in a sub catchment. The area average (weighted) crop factors at daily time step were estimated for individual sub catchments. The weighted Kc variation for a period of one year in sub-catchment #01 is shown in the

Figure 3-5, similarly for all other sub-catchments weighted Kc values were calculated for the estimation of crop evapotranspiration based on the equation [3.3].

3.3. Data preparation for reservoir water balance

The basic information needed for flow model calibration and validation is the inflow of the stream network from the upper catchment. Since there were no gauging stations for inflow measurements in the entire stream network, it is crucial to calculate the inflows through the only available technique of reservoir water balance. The basic information needed is water inflows, water out flow and the change in storage of the reservoir for specified time interval and Figure 3-6 shows the general considerations in a schematic form, the terms involved in reservoir water balance. Since historical water level records were available on daily basis, the water balance calculation was also carried out at daily time steps.

The water balance could be expressed in the following continuity equation

dV = Q − Q [ 3.4] dt in out

Where dV/dt is the variation in the storage volume of reservoir with time, Qin is the total inflow and Qout is the total out flow from the reservoir.

Main river Water Surface flows Ground water Rainfall with transfers in the basin

Inflows

Change in Water Balance Storage

Outflows

Discharges Different Uses Losses • Spillways • Water for dinking • Evaporation • Bottom outlets • Irrigation • Leakages • Animals • Seepage • Bottom outlets

Figure 3-6 Important terms of the water balance of a reservoir

22 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

3.3.1. Inflow

As displayed in the Figure 3-6 basic inflow to a reservoir could be listed as follows Qriver ; water flow in through the main river and tributaries, Qtran ; water flow accumulated through tributaries or trans- boundary flows, Qsurface ; surface flows due to rain events (overland flows directly comes in to reservoir.

Qgf ; ground water flow comes in to the reservoir and Qrain ; water comes in due to rain events directly over the reservoir.

Qin = Qriver + Qtran + Qsurface + Qgf + Qrain [ 3.5]

It is assumed that ground water flow in to the reservoir is very much equal to the ground water flow out hence Qgf term could be neglected in the final equation. So far there aren’t any trans-boundary flow in or out from the Roxo catchment and we can delete the term Qtran from the equation as well. Rain water come in (Qrain) can be assessed by the rainfall records from Albufeira Roxo floating rain gauge station.

The only unknown parameter of the above equation [3.5] is Qriver and the main purpose of performing the water balance is to assess this parameter.

3.3.2. Outflow

The main outflows from the reservoir are discussed below Q uses ; discharge to various uses , drinking water supplies, Irrigation , and commercial purposes, Q operations ; discharges made under reservoir controls, spillway and bottom out let releases during flooding conditions, Q evp ; losses due to water surface evaporation from the reservoir water body, Q seepage ; seepage losses from the reservoir bed, The general equation for Qout could be written as follows

Qout = Q uses + Q operations + Q evp + Q seepage [ 3.6]

It is assumed that water loss as seepage is negligible due to relatively impervious nature of the reservoir bed, its acceptable as the reservoir sites are generally located in locations where there is relatively impervious soils. In addition with time bed become even more impervious due to the deposition of fine particles making it further water tight. This assumption was also affirmed by the ABROXO technical department where they stated no evidence of water loss observed as seepage. Further during the field visit we inspected the down stream of the reservoir and did not observe any dampness confirming our assumption.

3.3.3. Estimation of rainfall & evaporation volumes The regular monitoring information available about the reservoir is the daily water levels at 1 cm precision. In estimation of daily water balances, the key information needed are water comes as direct precipitation on to the surface and water loss as evaporation from the surface. The water level in a reservoir relates to surface area with the stage area relationship and utilising this relationship it is possible to estimate water exchange quantities from the reservoir surface. Following section discuss about the stage area relationship of the Roxo reservoir.

3.3.3.1. Stage volume, Stage surface area relationships

The stage-volume and stage-area relations are the most essential information to assess the quantity of water available in the reservoir and the corresponding areal coverage of water surface respectively. In estimating the daily rainfall and evaporation volumes, stage area (area coverage of the reservoir) relationship was used. Similarly stage volume was used for to estimate the change in reservoir storage at daily water balance calculation. According to the bathymetric survey carried out in 2003 by an ITC research group, a partial stage surface area curve had already been prepared up to the reservoir water lavel of 129.00 masl (Mannaerts, 2003). There was a shortage of the data to make it a full stage-area relation, as reservoir top water level can reach up to 136 masl (spill level). Therefore, using four predetermined Landsat images based on the water levels, the reservoir stage-surface area relationship was extended and areas were calculated using digital techniques. Image dates were selected so that the recorded water level exceeds 130 masl on the respective image acquisition date. The detail of the images and the sources are listed in the Table 3-6 and final stage area curve and the best fitting polynomial equation is shown in the Figure 3-7.

Table 3-6 Landsat ETM + images used in extending the stage area relationship Roxo

No Date of Acquisition Image / Source Water Level (masl)

1 April 1, 2001 L7 ETM+, Global Land cover facility 134.55

2 April 20, 2002 L7 ETM+, ITC RSG Lab 132.79

4 May 25, 2003 L7 ETM+, ITC RSG Lab 132.59

3 June 23, 2003 L7 ETM+, ITC RSG Lab 131.96

Stage-area relation curve for the Roxo reservoir 18

16 y = 1414.2x3 - 483742x2 + 6E+07x - 2E+09 2 14 R = 0.998

bbb 12 Millions ) )

2 10

6 Surface Area / (m Area Surface 4 Bathymetric data pts Digitise Pts 2 Poly. (Bathymetric data pts)

0 110 112 114 116 118 120 122 124 126 128 130 132 134 136 Elevation (masl)

Figure 3-7 Stage area relationship for the Roxo reservoir

24 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Its it seen by Figure 3-7 that the new digitize points continue with the trend of the initial data set based on the bathymetric survey and the coefficient of determination [R2] value is very close to 1 indicating model predicts the stage surface area relation quite well with the data points. The stage-volume relation for the Roxo reservoir is already formulated by Mannaerts (2006) and it had been used in calculating the daily changes in storage based on daily water level changes and is shown in the Appendix 7.

3.3.3.2. Water surface evaporation

The evaporation from the free water surface is an important term in water balance calculation. The accuracy of the water balance calculation would consequently lead to an increase in reliability of the other terms of the water balance calculation. There are different possible ways in estimation of the water evaporation on the basis of data availability (Ali et al., 2008). According to SNIRH data base there were two stations close to Roxo reservoir (Barragem and Albufeira) and The Barragem station is already extinct and the Albufeira floating station shown Figure 3-8 is still under operation.

Figure 3-8 Albufeira Do Roxo floating station

Table 3-7 Details of the weather stations located close to Roxo reservoir Barragem Do Roxo Albufeira Do Roxo Duration 1958-07-31 to 2001-03-31 2001–04-01 to current Location (lat, lon) 37.934296 N, 8.083004 W 37.928975 N, 8079641 W Status Extinct Active ET Method Class A Pan Evaporation Penman

Water surface evaporation estimation from the reservoir surface prior to April, 2001 was based on the Pan evaporation and used the following simple equation.

= Eo Kc E p [ 3.7] Where;

Kc Pan coefficient

Ep Pan Evaporation

Eo Potential water surface evaporation

The Albufeira station has essential information that could be used for calculating the potential water surface evaporation. However since station does not have all the essential parameters measured, it was not possible to adopt the original Penman water surface evaporation equation, but the simplified method proposed by Valiantzas (2006) has been used for water surface evaporation. The accurate approximate version for Penman of Valiantzas (2006) is shown in the equation [3.8] below. The sample calculation sheet for the potential water surface evaporation is attached in the Appendix 3.

⎛ R ⎞ ⎛ ⎞ = −α + − ()− ⎜ s − ⎟ ⎜ − ()+ + 2 RH ⎟ E PEN 0.051(1 )Rs T 9.5 0.188 T 13 ⎜ 0.194⎟x⎜1 0.00014 0.7Tmax 0.3Tmin 46 ⎟ ⎝ R A ⎠ ⎝ 100 ⎠ [ 3.8] ⎛ RH ⎞ + 0.049()T +16.3 ⎜1− ⎟()a + 0.536u max ⎝ 100 ⎠ u

Where

EPEN Potential water surface evaporation

aU Wind function coefficient (1: for original penman equation) u Wind velocity in (m/s)

RA Extraterrestrial radiation in (MJ/m2/d)

RS Measure solar radiation at surface (MJ/m2/d)

RH Relative humidity in (%). T Temperature in °C α Water surface albedo (0.08)

Valiantzas (2006) further stated that the accuracy of the above equation was tested for different countries and different climatic conditions and conclude that the above equation performs relatively good as the results were perfectly close and in agreement with the original penman water surface evaporation equation.

3.3.3.3. Direct rainfall (input)

Daily rainfall data observed at the Albufeira do Roxo floating station have been used to calculate the rain water falling directly on the reservoir surface since 2001 to 2008, while the Barragem do Roxo station data have been used for same calculations prior to that period. There were few data gaps and those were filled considering the regression analysis performed with the nearby Beja (COTR) weather station as it was found a reasonably good correlation with the two stations as shown in the Figure 3-9. Finally the volume of water comes on to the reservoir surface as direct precipitation was estimated using the stage area relationship at daily time steps.

26 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Double mass relation between Beja and Albufeira Do Roxo

700 y = 0.8856x - 1.4291 2

jjj R = 0.9946 600

500

400

300

200

Doube mass curve Cum precipitation of Albufeira Do Roxo (mm) (mm) Roxo Do Albufeira of precipitation Cum 100 Linear (Doube mass curve) 0 0 100 200 300 400 500 600 700 800 Cum precipitation Beja (mm) jjj

Figure 3-9 Double mass graph between Beja and Albufeira stations

3.3.4. Spill occurrences The spill overflow is another important term in a reservoir water balance equation. In observing water levels, it was found that there were four spill occurrences during the period of year 1990 to 2008. The spill structure of the Roxo dam has an uncontrolled Ogee type crest and the discharge equation of the spillway could be written based on Khatsuria (2005) in the form of equation [3-9].

Figure 3-10 Basic configuration of the Ogee type crest spillway extracted from (Khatsuria, 2005)

2 Q = C b 2gH 3 [ 3.9] 3

Where Q Discharge (m3/s) C Coefficient of discharge b Crest width (m) H Water head/spill height above the crest (m) g Gravitational acceleration (m/s2)

According to Khatsuria (2005), the coefficient of discharge C is equal to 0.75 for uncontrolled spillways with elliptical inclination of the upstream face. Therefore with a known head H (difference between top water level and the spill crest level) at a particular instant, we can estimate the spill discharge rate through the spill structure.

Basic Information about the Roxo Reservoir: Constructed in year 1967 Spill Location At the centre Control type Uncontrolled Spillway type Over the dam Sill Elevation 136 m Sill length 27 m Max discharge capacity 64 m3/s Bottom outlet Max discharge capacity 47 m3/s Source : http://cnpgb.inag.pt/gr_barragens/gbingles/RoxoIng.htm (CNPGB, 2009)

3.3.5. Change in storage- Roxo reservoir The daily reservoir volume changes were estimated by first converting daily reservoir water levels to reservoir volume and then calculating the difference of reservoir storage between the two consecutive days. The stage volume relationship of Mannaerts (2006) was used for this calculation and is shown in the Appendix 7.

3.3.6. Water abstractions Water in the Roxo reservoir is basically used for irrigation, water supply for drinking, industrial and small quantity for animal consumption. This information was available with ABROXO and we manage to collect data since 1990 till 2007. The data was available as monthly records and it had to be distributed in to daily basis. This we performed by dividing monthly totals by the number of days in the particular month in order to calculate the daily water balances. A sample data set of the water abstractions from the Roxo reservoir is shown there in the Appendix 12.

3.4. Water balance and Estimation of inflows Based on the water balance formula (equation [3-4]) the inflows were estimated for daily, decadal and monthly time steps. Due to the low precision of the level measurements (10 mm) there is a possible inaccuracy in the calculation creating small negative values especially during dry months. The effect could be lowered by considering larger time intervals. In addition there is inaccuracy introduced in distributing the water abstractions equally on daily time steps. Thus, some deviation was anticipated in the final water balance calculations.

28 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Monthly Precipitation (mm) jjj (mm) Precipitation Monthly

160 140 120 100 80 60 40 20 0

Nov-07

Sep-07

Jul-07

May-07

Mar-07

Jan-07

Nov-06

Sep-06

Jul-06 May-06

Monthly Precipitation volume /(mm) day (m3/second) Inflow

Mar-06

Jan-06

Nov-05

Sep-05

Jul-05

May-05

Mar-05

Jan-05

Nov-04

Sep-04 (months) Time

Jul-04

May-04

Mar-04

Jan-04 Nov-03

Monthly runoff rainfall estimation graph for Roxo catchment 2001 Sep to 2007 Dec

Sep-03

Jul-03

May-03

Mar-03

Jan-03

Nov-02

Sep-02

Jul-02 May-02

Monthly runoff estimation and the rainfall graph for the simulation period(2001-2007) period(2001-2007) simulation the for graph rainfall the and estimation runoff Monthly Mar-02 Jan-02

3-11 3-11 Nov-01

9 8 7 6 5 4 3 2 1 0

Sep-01 jjjj /s) 10 (m Runoff Estimated 3 Figure

The runoff values were estimated for the time period starting from January 1990 to December 2007. In order to perform model calibration and validation, decadal runoff data have been used since September 2001. The monthly runoff hydrograph for the simulation period is shown in Figure 3-11 and it shows the inflows are having few peaks after strong rainfall incidents and during the dry months the inflow are very minimal or almost zero. The Appendix 16 illustrates the sample water balance calculation excel sheet and the estimated decadal runoff hydrograph for the Roxo reservoir.

3.5. Water quality data Water quality data is important information in the subsequent modeling work, therefore water samples were collected at suitable locations of the streams and Figure 2-1 shows the exact locations. The sample collection and analysis were carried out according to the guidelines set by the standard reference text books at ITC (Chapman, 1996), (Dost, 2006). Before going to field work, the sample collection bottles (HDPE) were cleaned with 1%-3% Nitric acid for 24 hours and consequently rinsed with distilled water.

At each sampling point, key information on the particular location were noted down, the date, time, GPS coordinates, water colour, possible contaminants, flow condition, surrounding land cover, were among the recorded information. The water samples were collected on the 10th Oct 2008 so that samples remained a minimum time at Beja before returning back to ITC. Seven water samples were collected along the Chamine River and one from the Roxo reservoir. There were low water flows occurring in the stream and mostly the width of the water flow was less than 2 m and depth of the flow was about 2-10 cm depending on the location. The effort had been made to take water sample without disturbing the bed sediments. Generally the samples were taken close to the top and at the centre of the flow section.

The samples were filtered with disposable syringe mounted 0.45 micron Whatman filters and two samples bottles were collected from each site, one having 200 ml of water sample acidified with HNO3 while the second sample has 100 ml of sample water acidified with HCl. The sample bottles were filled up to the top and caps were properly tightened so that no interaction with air. Each sample bottle was named giving the date, time, location ID and the acid type used as preservative. As soon as the samples were brought to the shelter care had been taken to keep them inside the cooler (below 4oC).

3.5.1. In-situ water tests The temperature, electrical conductivity (EC), pH and alkalinity tests were carried out at the sampling location and the HACH HQ 40D multi probe device was used to measure temperature and EC. Prior to taking the measurements the sensor probe was calibrated with the standard solution provided with the equipment. An alkalinity test kit was used to test the alkalinity. The in situ test results are show in the Table 3-8.

30 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Table 3-8 Field test results

- HCO 3 (mmol/l) Sample ID Date Time Longitude Latitude Elevation (m) pH Temp oCEC (μS/cm) Ks 8.2 Ks 4.3

D01 10/10/2008 11:30 -8.067 37.943 176 8.47 20.5 1301 0.7 3.6

D02 10/10/2008 12:15 -7.976 37.960 165 8.20 18.7 1463 1.8 8.5

D03 10/10/2008 12:45 -7.967 37.954 149 7.84 17.1 7540 1 8.9

D04 10/10/2008 13:00 -7.971 37.974 163 8.35 19.9 1480 1 9

D05 10/10/2008 14:25 -7.948 37.998 187 8.80 24 950 0.9 6.8

D06 10/10/2008 14:45 -7.915 38.019 213 8.10 23.3 2109 2.8 10

D07 10/10/2008 15:00 -7.907 38.020 222 7.88 22.7 2146 3.7 10 D08 10/10/2008 15:15 -7.897 38.016 229 7.73 20.7 2210 1 10

3.5.2. Laboratory analysis

The cations and the anions analysis of the collected water samples were carried out at the ITC laboratory and following cations (Ca+2, Al+3,Fe+2, K+1, Mg+2, Mn+2, Na+1 were tested with Inductively Coupled Plasma (ICP-AES) instrument. Two independent cation tests were carried out with the two sets of samples acidified with HNO3 and HCl respectively. The following anions analysis was performed with HACH DR/2010 spectrometer and a brief description of the anion analysis method use is listed below.

-3 3- PO4 Test ‘N tube (0-5 mg/l PO4 ), Program 535, Phos Ver 3, 20/10/08 -2 SO4 Sulphate (0-70 mg/l range), program 680, SultaVer 4, 23/10/08 -1 - NO3 -N Nitrate, HR (0- 30 mg/l NO3 -N) program 355, NitraVer-5, 21/10/08 -1 - NO2 Nitrite, LR, (0- 0.30 mg/l NO2 -N), program 371, NitraVer-3 , 24/10/08 Cl-1 Chloride (0 to 20.0 mg/L Cl-) , Program 70, Mercuric Thiocyanate Method, 23- 23/10/08

NH3 -N Test ‘N tube (0-50 mg/l), program 343, Salicylate regent powder, Cynurate Regent, 21/10/08 Nitrogen, Total Test ‘N tube (10-150 mg/l) program 395, TNT Per sulphate Digestion Method, 22-23/10/08 COD Reactor digestion method

-1 Results of the in-situ (field) and the subsequent laboratory test results are shown on Table 3-9 and NO3 -3 –N, NH3 –N and PO4 are the most important parameters for the water quality modeling. According to -1 laboratory test results, it is observed that some nitrite (NO2 ) concentration is also present at points close to the waste water treatment plant.

+1 -1 The sample D-01 was taken at the reservoir and shows very low concentrations of NH4 and NO3 , while sample D-06, 07 and 08 were taken quite close to waste water treatment plant. Those samples are +1 -1 showing very high concentrations of NH4 and NO3 . This is due to the fact that the WWTP effluent water still contains substantial nitrogen.

Table 3-9 Results of the laboratory analysis Water Sample ID Test D 01 D 02 D 03 D 04 D 05 D 06 D 07 D 08 EC μS/cm 1301 1463 7540 1480 950 2109 2146 2210 Ca+2 mg/l 64.38 104.62 520.31 105.98 78.79 91.97 88.71 88.65 Al +3 mg/l 0.14 1.37 3.34 1.24 0.80 2.29 1.12 1.34 +2 Fe mg/l 0.45 0.35 0.37 0.45 0.62 1.06 0.38 0.56 K +1 mg/l 6.31 3.59 18.23 3.41 3.23 27.29 25.02 27.61 Mg+2 mg/l 43.48 55.79 345.69 61.41 39.27 47.68 46.43 47.42 Mn+3 mg/l < Det.Lim < Det.Lim 0.36 < Det.Lim < Det.Lim 0.07 < Det.Lim 0.02 Na+ mg/l 106.43 109.45 550.41 103.09 50.36 197.24 195.96 199.83 + NH4 mg/l 0.61 0.12 0.00 0.12 0.12 34.53 35.50 48.27 -2 SO4 mg/l 62 75 625 70 75 125 135 135 -1 NO3 mg/l 0.00 13.28 0.00 17.26 55.77 19.47 16.38 9.74 -3 PO4 mg/l 0.03 0.24 0.78 0.21 0.33 6.55 9.85 11.65 Cl-1 mg/l 144 165 2130 236 125 446 392 390 -1 NO2 mg/l 0.003 0.004 0.003 0.004 0.009 0.375 0.475 0.125 -1 HCO3 mg/l 219.66 518.65 543.06 549.16 414.92 610.18 610.18 610.18

3.5.3. Analysis of results Subsequent to the water sample analysis for its concentrations, each sample had been checked for its reliability and accuracy of the results based on Hounslow (1995) of chemical water quality and the results are shown in the Appendix 10. It is found that there is reasonable agreement with the anion- cation balance and the measured EC-iron sum calculation.

60.00 Nitrification process along the Chamine river 55.77 Ammonium 50.00 48.27 Nitrate

40.00

35.50 34.53 30.00

Concentration (mg/l 20.00 19.47 16.38 17.26 13.28 10.00 9.74

0.00 0.12 0.12 0.00 0.12 0.000.61 D08D07D06D05D04D03D02D01

Sample Location

Figure 3-12 Nitrification process along the river

We have observed low nitrate concentrations in the reservoir waters but in the upstream of the Pisoes catchment close to waste water treatment plant, the nitrogen pollution is significant. According to the Figure 3-12, nitrification process (conversion of ammonium to nitrate along the river) is evident along the Chamine river just down stream of waste water treatment plant.

32 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

3.6. Flow measurements

An estimation of stream flow of the Pisoes river was carried out at sampling point D_04 (37o,57’ 34.5” N, 7o 58’ 33.8” W). Due to time constraints, we could only manage to do a single flow measurement. The standard procedure listed in the Butterworth et al., (2000) was adopted for the salt dilution method. Some key information with respect to the salt dilution test is listed below.

Salt - NaCl Applied Quantity (NaCl) - 400 g Distance of the stretch - 20 m (application of salt and subsequent EC measurement point) Instrument - HACH HQ 40D multi- probe device for EC measurements

The primary objective of the dilution gauging is to make a realistic assessment over the stream flow rate of the selected stretch. The flow rate was estimated using the mass conservation during the advective and dispersive solute transport equation and it is listed below.

M = ()xo [ 3.10] Q ∞ ()− ∫ C()x1,t Cb t0

Where

M(xo) - Salt load applied at the application position [g] Q - Flow rate [m3/sec]

C(x1,t) - Salt concentration at time “t” at the selected position [mg/l]

Cb - Initial salt concentration [mg/l]

The estimated stream flow at sample point D_04 is 0.12 m3/s and it is relatively similar to the initial quick guess made through the float at the selected position. The electrical conductivity (EC) measurements and other relevant information on the salt dilution gauging technique is listed Appendix 11.

3.7. Soil data

Soil data is key information needed in defining RAM surface properties in Duflow. The rainfall runoff elements (RAM) needs several parameters that are relevant to soil properties of the catchment. According to the soil map of the study area shown in Appendix 2_fig.(b), it is found that there are about 40 soil units present, but in modeling work we need to give and area-averaged parameter value for sub-catchment.

The data of the soil properties of individual soil class were collected from the soil reference books (Cardoso, 1965), (Conference on Mediterranean Soils, 1966). According to the Portugal soil map, the major soil units present in the Roxo catchment and its percentages are listed in the Table 3-10.

Table 3-10 Major soil types in the Roxo catchment

Soil type I Soil type II Detailed classification type IV Symbol Percentage % Incipient Soils Alluvial Soils Modern alluvial soil non calcareous A;A(h); Asoc 2.58%

Brown Calcareous Soils Brown Calc.S. of Sub-humic and semi arid climates Pc ;Pcx 1.14% Calcareous Soils Red Calcareous Soils Red Calc.S. of Sub-Hum and Semi Arid Climates Vcx; Vc 10.71%

Non Calcareous Black Barros Bp 1.47%

Barros Black Barros Calcareous Black Barros Strongly decarbonated Bpc 8.16%

Calcareous Black Barros Slightly decarbonated Cp 1.40%

Brown Medit.S.from calcareous rocks Pac; 0.92% Brown Mediterranean Soils Soils Lessives Brown Medit.S.from non-calcareous rocks - Normals Px 13.68% (Not Strongly Brown Medit.S.from non-calcareous rocks - Para Hydromophic Pag 1.55% Unsaturated) Red-Yel. Medit.S.from non-calcarioos rocks - Normals Vx; Vx(d); Vx(d,p);Vx(p) 21.42% Red-Yellow Mediterranean Soils Red-Yel. Medit.S.from non-calcareous rocks - with plinthitic materials Sr* 16.63% Hydromorphic Soils With Eluvial Horizon Planosols with Eluvial Horison Ps 9.37% Hydromorphic Peaty Soils Organic Soils Peaty Soils Sp 1.61%

The Table 3-10 summarises that the high percentage of soils in the catchment belongs to Mediterranean soil types. The basic soil properties relevant in modeling work are porosity, moisture content at different pF values (saturation, field capacity and wilting point), initial and constant infiltration rates,. The relevant information extracted from the literature are summarised in the Appendix 8. This information is used as the initial estimate for setting up the model parameters for unpaved surfaces of the RAM elements. The Appendix 2_fig.(b) shows the composite soil map of the Roxo catchment.

3.8. Land cover data Land cover-land use (LCLU) data is essential information required in hydraulic and water quality modeling. In defining a RAM element in Duflow for a selected basin, we have to categorise sub basin or RAM area in to open water, green houses, sewer, other-paved and unpaved surfaces then need to input the fractions of the land in each category as percentages.

Thus, it was essential to have a detailed land cover information for the model building. It was decided to use CORINE land cover classification that have 44 detailed classes in the surface classification and it is comparatively easy to categorise areas according to the requirements of Duflow. The CORINE land- cover land-use is produced using Landsat 7 ETM + sensor and is available in vector data format with minimum mapping units of 25 hectares (Caetano and Araújo, 2006) and also is having an overall thematic accuracy beyond 82 % (Caetano et al., 2006). The land cover map of the area is given in the Appendix 2_fig.(a) and during the field visit we have collected information for an accuracy assessment. It was found that the overall accuracy is very close to 90%. Thus, CORINE LCLU was judged a reasonable data set for the surface classification according to the model requirements. The land cover classes had been re-categorised as presented in the Table 3-11.

34 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Table 3-11 Reclassification of CORINE land cover classes to DUFLOW RAM

Code CLC_Level 1 CLC_Level 2 CLC_Level 3 Re classification

112 Artificial surfaces Urban fabric Discontinuous urban fabric Other paved

211 Agricultural areas Arable land Non-irrigated arable land Unpaved

212 Agricultural areas Arable land Permanently irrigated land Unpaved

221 Agricultural areas Permanent crops Vineyards Unpaved

223 Agricultural areas Permanent crops Olive groves Unpaved

241 Agricultural areas Heterogeneous agricultural areas Annual crops associated with permanent crops Unpaved

242 Agricultural areas Heterogeneous agricultural areas Complex cultivation patterns Unpaved

Land principally occupied by agriculture, with 243 Agricultural areas Heterogeneous agricultural areas significant areas of natural vegetation Unpaved

244 Agricultural areas Heterogeneous agricultural areas Agro-forestry areas Unpaved

311 Forest and semi natural areas Forests Broad-leaved forest Unpaved

313 Forest and semi natural areas Forests Mixed forest Unpaved Scrub and/or herbaceous vegetation 321 Forest and semi natural areas associations Natural grasslands Unpaved Scrub and/or herbaceous vegetation 324 Forest and semi natural areas associations Transitional woodland-shrub Unpaved

512 Water bodies Inland waters Water bodies Open Water

In the Roxo catchment land cover fractions, falling under the definition of sewered areas and green- house were considered negligible based on the CORINE land-cover classification. During the field visits also we did not observe land use that comes under above categories. The land cover information was further used in the estimation of crop evapotranspiration. The procedure followed in estimation of crop evapotranspiration was discussed in chapter 3.2.2 in detail.

3.9. Historical water qauality data Historical water quality data is obligatory for the water quality model assessment in its prediction. Generally it’s difficult to find continuous time series of water quality data and the condition is same for Roxo catchment. Few past water quality test records have been collected from the EMAS. The EMAS data refers to the water quality tests performed at their intake of the reservoir. Test data was available since Jan 2005 to Jan 2008 at monthly time steps and is show in the Appendix 14.1.

In addition, some of the previous ITC’s research MSc were focused on water quality in the Roxo catchment that also provides information on water quality in the Roxo stream network (Awino, 2003). This information is summarized in the Appendix 14.2.

36 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

4. Numerical Modeling

4.1. Duflow Modeling Studio (DMS) DUFLOW is a software package for simulating one-dimensional steady, unsteady flow in open-channel systems One can acquire it for a relatively low cost (Clemmens et al., 1993). Duflow facilitates dynamic management of water systems, with for example, applications in diverse areas like agriculture, water supply, fisheries, energy and water quality control (DUFLOW, 2004a). The program is provided with a graphical user interface (GUI) thus, it is interactive and reasonably fast in learning. The programme is frequently used by the water authorities, designing hydraulic structures and for teaching/academic purposes.

The DMS program is designed for simple networks of channels with simple structures. The result of a modeling can be displayed in time and space for immediate analysis. The Duflow Modeling Studio consists of following key features;

1. Duflow water quantity model Used for unsteady flow calculation in streams and rivers 2. Duflow water quality model Simulation complex water quality processes in the streams and water bodies 3. RAM precipitation runoff module Used for modeling rainfall runoff processes from land areas 4. MODUFLOW This program is used to link the Duflow model with MODFLOW (ground water model)

In this research, first three tools in DMS listed above have been used and the results of the RAM calculations are used as input boundary calculation to the DUFLOW calculation.

4.2. Duflow flow model The water levels and flow rates are determined by solving the Saint Venant’s equations of continuity and momentum using the four point implicit Preissmann numerical scheme. The basic 1-D equations used in the model are listed as follows;

∂B ∂Q + = o [ 4.1] Continuity equation ∂t ∂x

∂Q ∂H ∂(αQν ) g Q Q + gA + + = aγw2 cos()Φ −φ [ 4.2] Momentum equation ∂t ∂x ∂x C 2 AR

Where; t Time [s] x Distance as measured along the channel axis [m] H(x, t) Water level with respect to reference level [m] v(x, t) Mean velocity (averaged over the cross-sectional area) [m/s] Q(x, t) Discharge at location x and at time t [m3/s] R(x, H) Hydraulic radius of cross-section [m] a(x, H) Cross-sectional flow width [m] A(x, H) Cross-sectional flow area [m2] b(x, H) Cross-sectional storage width [m] B(x, H) Due to gravity [m/s2] C(x, H) Coefficient of De Chézy [m1/2/s] w(t) Wind velocity [m/s] Φ(t) Wind direction in degrees [degrees] f(x) Direction of channel axis in degrees, measured clockwise from the north [degrees] γ(x) Wind conversion coefficient [-] α Correction factor for non-uniformity of the velocity distribution in the advection term

After solving the above equations [4-1, 4-2] using the 4 point Preissmann scheme, each channel of the network will have 2 equations with unknown Q and H for the new time level. For the unique solution of the set of equations, the additional conditions have to be specified at boundaries of the network and at the sections defined as structures. These boundary conditions are in the form of heads (H), discharges (Q) or combination of both (Q-H).

4.3. Duflow quality model

Duflow quality model is based on the one dimensional transport equation listed as equation [4.3] and this partial differential equation is used to describe concentration of constituents at space and time in 1- D flow system.

∂(BC ) ∂(QC) ∂ ⎛ ∂C ⎞ = − + ⎜ AD ⎟ + P [ 4.3] ∂t ∂x ∂x ⎝ ∂x ⎠ where: C Constituent concentration [g/m3] Q Flow [m3/s] A Cross-sectional flow area [m2 ] D Dispersion coefficient [m2 /s] B Cross-sectional storage area [m2 ] x x-coordinate [m] t Time [s] P Production of the constituent per unit length of the section [g/ms]

38 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

The general advection-diffusion/dispersion processes are represented in the first two terms of the right hand side of the equation 4-3. The production term listed in equation (P) represent all the physical, chemical and biological processes that undergo by a particular constituent. The user has the freedom of defining the processes (Windemuller et al., 1997). This model/ process description file can be created and edited in the user interface and later complied with the DUPROL. During this compilation stage the process descriptions are converted in to readable format of the programming part of Duflow (DUFLOW, 2004a). Finally, these process descriptions are coupled with the transport equation to produce a numerical solution. The model has to be provided with initial and boundary condition to run for all state variables (concentrations). The initial conditions are provided to all nodes and the initial conditions are based on historical records, best approximate guesses or by using the results of the former computations. The boundary conditions are provided at the physical boundaries of the system or at the internal boundaries. The boundary conditions can be provided either as concentrations or loads for quality model.

4.4. Duflow rainfall runoff (RAM) component The rainfall runoff modeling component describes the processes in the hydrologic cycle that occur at the land surface after incidental precipitation with the discharge as a runoff to the ground water or the surface water. In order to describe rainfall runoff processes with RAM, the evaporation, precipitation intensity, soil and terrain properties of the catchment or land area are essential input information. The soil information includes actual, maximum and minimum moisture contents, infiltration rates, depth to the water table, etc are also essential in describing the processes.

In RAM, the rainfall runoff processes is described at land area i.e. catchment level and uses a conceptual rainfall runoff (linear reservoir) model rather than a physical-mathematical model. The framework of the precipitation runoff model is shown in the Figure 4-1 and it was extracted from the Duflow manual (DUFLOW, 2004b).

Source RAM manual

Figure 4-1 Duflow RAM modeling framework (DUFLOW, 2004b)

The surfaces are classified in to three basic types for the simplified representation in the model considering the differences in precipitation runoff processes (DUFLOW, 2004b).

• Open water surface • Paved surface • Unpaved surfaces (soils)

The linear reservoir model had been used in calculating the discharges for each surface category. The basic equations used in the model set up are described below. In the linear reservoir model, runoff are calculated in two stages, initially the specific discharge [mm/day] is calculated and it is the specified as the discharge during the time step t-1 to t later this specific discharge converts to momentanious discharge [m3/s].

40 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

4.4.1. Open water surface The water bodies like reservoirs, rivers and lakes in a catchment considered under the surface category of open water bodies, and water loss only by means of evaporation and generally there aren’t any delays in runoff generation from the open water surfaces.

= − PN ,open _ water ,t Pb,t fo Er,t [ 4.4]

−Δt −Δt k ⎛ k ⎞ q = q e o + P ⎜1− e o ⎟ [ 4.5] open _ water ,t open _ water ,t−1 N ,open _ water ,t ⎝ ⎠ = Qopen _ water ,t aAopen _ water ,t qopen _ water ,t [ 4.6]

Where;

Input

Pb,t [mm/day] Precipitation intensity Er,t [mm/day] Reference crop evaporation Makkink qopen_water,t-1 [mm/day] Specific discharge open water surface Aopen_water [ha] Open water surface

Model parameters

f0 [-] Crop factor Makkink for open water k0 [day] Time constant reservoir open water surface Δt [day] Time step a [-] Conversion factor units

Output

PN,open_water,t [mm/day] Effective precipitation open water surface 3 Qopen_water,t [m /s] Discharge open water surface

4.4.2. Paved surface The paved areas are further divided in to 3 categories as paved rural, urban and green houses. Discharge from the paved surface can occur by means of (a) paved surface discharge directly through the drainage system, (b) surface discharge by mean of separated sewer systems or (c) surface covered with green house. However in the study area there were very limited paved surface area and all belongs categorized under the 1st category (paved surface discharge directly through the drainage system). The governing equations [4.5] are listed below.

B I = P − f E + o,t−1 [ 4.7] o,t b,t o r,t Δt

B − B P = P − f E + o,t 1 − I − v,max [ 4.8] N ,o,t b,t o r,t Δt o,t Δt −Δt ⎛ −Δt ⎞ koo koo q = q − e + P ⎜1− e ⎟ [ 4.9] o,o,t o,o,t 1 N ,o,t ⎝ ⎠ = Qo,o,t aAo,oqo,o,t [ 4.10]

−Δt −Δt k ⎛ k ⎞ q = q e oi + I ⎜1− e oi ⎟ [ 4.11] o,i,t o,i,t−1 o,t ⎝ ⎠ = ( − ) Qo,i,t aqo,i,t Ao,r Ao,o [ 4.12]

Where

Input

Pb,t [mm/day] precipitation intensity

Bo,t-1 [mm] storage at open paved surface

Er,t [mm/day] reference crop evaporation Makkink

qo,o,t-1 [mm/day] specific discharge other open paved surface (formula

Ao,o [ha] other open paved surface

qo,i,t-1 [mm/day] specific discharge infiltrated water

Ii,o [mm/day] infiltration intensity open paved surface

Ao,r [ha] open paved surface with sewer system

Model parameters

Io,max [mm] infiltration capacity

fo [-] crop factor Makkink open water Δt [day] Time step

Bv,max [mm] maximum storage at surface

ko,o [day] time constant reservoir other open paved surface

ki [day] time constant reservoir a [-] Factor output

Io,t [mm/day] infiltration intensity

PN,o,t [mm/day] surface runoff at open paved surface

qo,o,t-1 [mm/day] specific discharge other open paved surface 3 Qo,o,t [m /s] discharge from other open paved surface

qo,i,t-1 [mm/day] specific discharge infiltrated water 3 Qo,i,t [m /s] discharge infiltrated water from open paved surface

42 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

4.4.3. Unpaved surfaces The RAM categorizes three type of processes in the unpaved surface category namely (i) Infiltration in to unsaturated zone (ii) percolation to ground water and (iii) the ground water discharge in to drainage system. The most of the surface types that have in Roxo catchment fall under this category of unpaved surfaces. The corresponding linear reservoir equations are listed below.

=Φ −Φ + [ 4.13] It pF=0 t−1 Pper.max B B [ 4.14] P = P + t − I − max N,sur,t b,t Δt t Δt Φ =Φ +( − − )Δ [ 4.15] t t−1 It Ea,t Pper,t t −Δt ⎛ −Δt ⎞ [ 4.16] q = q e ksur + P ⎜1 − e ksur ⎟ sur,t sur,t−1 N ,sur,t ⎝ ⎠ = [ 4.17] Qsur,t aAunpaved qsur,t

Where; Input

Pb,t [mm/day] Precipitation intensity

Bt −1 [mm] Storage in surface depressions

It −1 [mm/day] Infiltration intensity

Φt 1 [mm] Actual moisture storage (at time t-1)

Ea,t [mm/day] Actual Evapotranspiration

Pperc,t [mm/day] Percolation into the saturated zone

Aunpaved [ha] Unpaved surface

Model Parameters

Imax [mm/day] Infiltration capacity Δt [day] Time step

Pperc,max [mm] Percolation to the saturated zone at pF=0

ΦpF =0 [mm] Moisture storage if pF=0

Bmax [mm] Maximum storage in surface depressions

Ksur [day] Time constant reservoir surface runoff unpaved surface a [-] Conversion factor units

Output

It [mm/day] Infiltration intensity

PN, sur,t [mm/day] Effective precipitation surface runoff

Φt [mm] Actual moisture storage

qsur,t [mm/day] Specific surface runoff unpaved surface 3 Qsur,t [m /s] Surface runoff unpaved surface

Further the ground water discharge is spit in to slow and rapid runoff components. The percentage fractions of the slow and rapid contribution are to be determined during the model calibration stage(DUFLOW, 2004b). The slow and fast components of the ground water discharge could be modelled using system of linear reservoirs, different configurations could be taken by having different number of reservoirs and different time constants and the Figure 4-1 is clearly displayed the concept of the application of multiple linear reservoirs to the soil moisture storage.

An individual RAM element can consist of several surface types as above, but the properties are considered homogenous within an individual surface of the RAM.

4.5. RAM quality model (DUFLOW, 2004b)

The RAM water quality part is confined to the nitrogen and phosphor balance of the RAM area system. Schematically the process can be illustrated as follows.

Figure 4-2 Chemical and water quality processes modelled in RAM (DUFLOW, 2004b)

For the RAM quality model it is essential to define the input schemes for nitrate, phosphorous and ammonium concentrations for different surface classifications depending on the land use.

= Sxx,yy,t C xx,yy,t Q yy,t [ 4.18] = + + SNH 4−N ,total,t S NH 4−N,openwater,t S NH 4−N, paved,t S NH 4−N ,unpaved,t [ 4.19] = + + S NO3−N ,total,t S NO3−N,openwater,t S NO3−N, paved ,t S NO3−N,unpaved ,t [ 4.20] = + + SP−total,total,t S P−total,openwater,t S P−total, paved,t S NP−total,unpaved,t [ 4.21]

44 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

“S” [g/s] represents the nutrient load of each constituent and it is calculated by concentration “C” [mg/l] multiplied by the corresponding surface discharge rates “Q” [m3/s]. The equations listed in [4.18, 4.19, 4.21] provide the total ammonium, nitrate and phosphor loads in a RAM surface area respectively.

Simplified typical concentration values for different surface types of land-use categories and degree of fertilization are available in literature and could be used with some uncertainty. Nevertheless, if available some previous studies carried out on soil N, P and NH4 can be used for designing the input concentrations schema. In addition supplementary measuring campaign can also be used to gather information on the nutrient inputs to the surfaces. The errors introduced in the above method can be minimized by comparing the concentration of N, P and NH4 in the receiving water body as these tests can be performed easily.

4.6. Quality model definition The aim of the modeling was to simulate the processes associated with N and P nutrient transport in the Roxo catchment. The N and P cycle are shown in the Figure 4-3, and Figure 4-4 respectively it is clear that these constituents are in different forms and different states in the physical system. However, in modeling we have tried to simplify the reality. For example the Eutrophication depends on the species of the phytoplankton but here, modeling every phytoplankton species was not possible with in this time period. In addition the sediment exchange fluxes of O2, ammonia and phosphor considered time independent.

Nitrogen cycle Phosphor cycle

Sources Source http://www.starsandseas.com/SAS%20Ecology/SAS%20chem http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/wat3350 cycles/cycle_nitrogen.htm Figure 4-3 Nitrogen cycle Figure 4-4 Phosphor cycle

In this analysis a predefined EUTROF 1 (DUFLOW, 2000) model structure had been used with minor modifications and the Table 6-1 in chapter 6 lists the state variables considered in the quality model description. The processes modelled in the quality model are described in the schematic form in the following Figure 4-5.

Figure 4-5 Structure of the water quality model description

The modified process description file is attached in the Appendix 15. Prior to the compilation and running the quality model it is essential that the flow model is stable and has been well calibrated.

4.7. Previous modeling studies with Duflow In the literature review it was found that some studies carried out using Duflow as a basic modeling tool. Duarte et al (1999) carried out a study to compare the numerical techniques in solving dispersion problems in the river Mondego and the numerical modeling was tested with tracer injection to the river. With his results he showed that Duflow aided with hydrodynamic and water quality modeling tools provide results that show best agreement with experimental data.

A flow modeling with Duflow was carried out by Windemuller et al(1997) in the Lake Okeechobee in the South Florida ecosystem because of the its importance of the lake due to the water resources for water supply and that was subjected to water quality deterioration with particulate prosperous discharged from the agriculture areas. His objective was to build and calibrate a flow model in the primary canal network as an initial stage of developing a water quality (nutrient and sediment) model.

Duflow DMS had effectively being used by Ochir (2008) to model surface water quality assessment and modeling Tuul river (Mongolia) and he modelled the waste water treatment plant impacts. Efforts were made by Ochir (2008) to assess the impact on river water quality using ammonium, nitrate, nitrite, DO, COD and BOD as quality indicators using Duflow flow and quality modeling tools. Makkinga (1998) had also used DUFLOW for modeling dissolved oxygen in the river Regge was able to attain a reasonable simulation and concluded that DUFLOW is valuable tool for scenario analysis and also suitable tool to study hydrology and nutrient dynamics (Makkinga, 1998).

46 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Mwannuzi et al (2003) used DUFLOW model to study the buffering effect of the wetland in lake Victoria and to assess the capacity of the wetland to absorb nutrients and other pollutants. The focus of his study was on nitrogen and phosphorus but DO, BOD, DOM, SS, EC were also included in the wetland model. He further stated that the quantity and quality are shown good agreement with the measured values and concludes that Duflow is good tool for study water quality and buffering capacity.

4.8. Model simplifications and limitations (DUFLOW) The precipitation runoff module in DMS is developed so that processes are described at sub-catchment level and the area average value of sub catchment has to be entered as input parameters, however in reality the surface properties and parameter values vary from location to location.

The capillary rise from the ground water to the unsaturated zone are not been considered. Another model simplification is that Infiltration immediately results in percolation to the deep ground water. The parameters specified in the precipitation runoff module does not have direct physical relevance and strongly empirical in nature.

All equations are for one dimensional setup and model can not take in to consideration the variation on the flow in other dimensions. It models flow velocity averaged over the flow section (depth and width) though there are obvious variations. Modeling can not be performed if there is a significant difference of inflow velocity over the vertical profile (stratified wasters).

48 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

5. Integration of satellite remote sensing data

One of the objectives of this research was to investigate the possibilities of integrating remote sensing derived precipitation data in to numerical model as a driving meteorological force. In order to perform this task of integration of the remote sensing data in to the model following activities had been carried out in sequential order, downloading, pre-processing, extraction and comparison for the reliability of the remote sensing derived data sets and creating input data schemes for the model.

5.1. MSG_Multi-sensor Precipitation Estimate (MPE) The Multi-sensor Precipitation Estimate (MPE) is a product distributed by EUMETSAT and is providing instantaneous rain rates by utilising METEOSAT data in combination with SSM/I on board US Defence Meteorological Satellite Program (DMSP). The data is provided at an intensity of mm/15min for each METEOSAT image in its original pixel resolution. Currently METEOSAT 7, 8 and 9 are being used for producing the real-time rainfall products.

Rainfall data is produced by a technique called blending utilising two remote sensing data products (EUMETSAT, 2009; Heinemann et al., 2002). The concept of blending is that to utilise the high temporal and spatial resolution of geostationary Meteosat-IR sensor with high accuracy in rain rate retrieved from the microwave sensor on the polar orbiting US DMSP satellites. There are three DMSP satellite carrying SSM/I sensors (F13, F14 & F15) with approximately 6 hour repeat cycle (NGDC, 2009) .

The derivation of rain rates from microwave sensors are already established (Heinemann et al., 2002) and brightness temperature calculated from the Meteosat IR channel is correlated with rainfall rates of the microwave sensor to produce MPE rain rates. This method is based on the concept that colder clouds are more likely to produce higher rain rates. Heinemann et al (2002) further explains that MPE algorithms are more appropriate in estimating convective type of rains.

5.2. MPE precipitation data The data comes as General Regularly-distributed Information in Binary (GRIB) files and each consists of two quality indicators stated according to the Heinemann et al (2002) the standard deviation and correlation coefficient. The standard deviation and correlation coefficient is calculated considering the MSG IR sensor data and SSM/I data comparison. Heinemann et al (2002) further states that when the correlation is less than 0.4 MPE rainfall products are not suitable for the use. According to EUMETSAT these information on correlation and standard deviation are appended to the GRIB files and the information could be extracted from the standard GRIB decoder.

The algorithm that is used for rainfall estimation is explained comprehensively in Heinemann et.al.(2002). In simple terms the algorithm is based on the theory that the coldest cloud temperature is

related to highest rainfall intensity and there is a threshold temperature value when above that no rain can occur. The used algorithm has to change geographical position and with time as it depends on the current weather situation. Due to the low temporal coverage of the microwave imager, the algorithm is not modified/revised each and every image but do it for the aggregated MSG IR images. So there are possible shortcomings in the calibration of cloud top temperature and even calibration is not performed for the entire area but just on a portion of the image.

5.3. Data formats and pre-processing

As stated in the previous paragraph MPE data comes as GRIB file format and there are 96 GRIB files on a particular day at 15 minutes time intervals. These GRIB files format had to be converted all into ILWIS through DOS based batch processing, Ultimately 96 images are aggregated to produce the daily precipitation image. The MPE data had been processed is starting from January 2007 to May 2008. The data is originally in MSG geo-reference and the projection parameters are as follows.

Projection parameters Geostationary satellite Ellipsoid User defined Ellipsoid Parameters a = 6378140, 1/f =298.252981 False Easting = 0.0 False Northing = 0.0 Central Meridian = 0 0 Scale Factor = 0 1 Height Persp Center = 0 35785831

It was observed some data gaps and missing GRIB files during the study period (Jan 2007 to May 2008), and relevant information is listed in the Appendix 9. For data compilation and subsequent analysis the missing information was checked against the occurrence or rainfall measured on the ground. For these days that there was missing information no rainfall was recorded.

5.4. Data extraction MPE data was compiled in to daily aggregated rainfall map since January 2007, there were 478 days of records (ILWIS map files) in a layer stack. The MPE rainfall image covers approximately half of the globe and to handle it conveniently, sub-maps were created so that it covers the study area. Then 18 random data extraction points had been selected from the catchment considering the size and the location of the catchment. There were one or more pointes selected from each sub catchment for subsequent rainfall data extraction. The Figure 5-1 shows the locations of the 18 points within the catchment. An ILWIS script function had been used to extract the data from rainfall image stack that consists of 478 layers. This data set was compared with the nearby weather station data for consistency checks before implementing it in the model.

50 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Figure 5-1 Roxo catchment with 18 pts overlaid on top of MPE image: date 10/02/2007 (MSG geo-reference)

Comparison of MPE data with gauge station data 700 y = 0.7675x + 52.691 2 Beja R = 0.9558

600 y = 0.831x + 54.909 2 Aljustrel R = 0.9386

500 ppp

400

300

200 Cum. Rain COTR Station Data (mm) Data Station COTR Rain Cum. Beja Aljustrel 100 Linear (Aljustrel) Linear (Beja)

0 0 100 200 300 400 500 600 700 Cumulative of Average (18 pts MSG_MPE) precipitation (mm)

Figure 5-2 Comparison of MPE data and ground based data The 18 data extraction points are spatially distributed as shown in the Figure 5-1 and there were approximately 40 MSG-MPE grids/pixels falling with in the catchment. Based on the area coverage of the sub-catchment one or more point have placed in the sub catchment (e.g catchment 12 having 3

points considering its area coverage). The 18 points approximately represents 50% of the pixels falling with in the catchment and hence it’s a quite a dense sample for rainfall representation

A comparison was made for the calculated average rainfall of 18 MPE data extraction points with the Beja and Ajlustrel ground weather station using mass balance technique. The cumulative rainfall graph is shown in Figure 5-2 and aggregated rainfalls by both methods provide very much the same result. That is also evident in Table 5-1 where the cumulative rainfall average of MPE amounts to 639 mm with a standard deviation of 12.7 mm furthermore the difference of cumulative MPE rainfall compared to both the Beja and Aljustrel are very small (< 20 mm) and fall with in Average ± STD.

Figure 5-2 shows a different trend in the graph with two distinct gradients (slope) and this explains that there are differences in precipitation estimation with time (seasonal effect). However the cumulative totals are in good agreement with station data. This is considered as an indication for the favourability to use of MPE as precipitation data source for annual or long time interval simulations.

Table 5-1 Cumulative rainfall figures Cumulative rainfall Catchments Point UTM Co-ordinate (mm) 1 pt 1 (579928.6,4191573.3) 617 2 pt 2 (582598.0,4190996.1) 620 3 pt 5 (587142.2,4194953.1) 638 4 pt 10 (595298.3,4207788.6) 645 5 pt 11 (592289.8,4206408.3) 649 6 pt 15 (584708.0,4208566.4) 638 6 pt 16 (583760.3,4204228.0) 644 7 pt 17 (580540.5,4204052.5) 636 8 pt 6 (591610.4,4198156.9) 649 8 pt 7 (587474.6,4198423.0) 638 9 pt 8 (594235.4,4203776.8) 644 9 pt 9 (591432.5,4201961.0) 654 10 pt 13 (588359.3,4206237.0) 653 10 pt 14 (586089.9,4203872.6) 656 11 pt 12 (587865.9,4201351.2) 648 12 pt 3 (576612.8,4185764.6) 614 12 pt 4 (584253.8,4187397.5) 627 13 pt 18 (582831.9,4198263.9) 634

Thin the above table consists of rainfall data of 478 days and during the period of 1/1/2007 to 5/23/2007. Some statistics are listed below Average rainfall over 18 points 639 mm Standard deviation 12.7 mm Beja Rainfall total during on the specific days 627 mm Aljustrel Rainfall total during on the specific days 648 mm

Thus the averaged total (MSG_MPEavg) ± Standard deviation fails in the range of min 626.3 mm , max 651.7 mm and are quite close to nearby two weather station rainfall total during the study period.

52 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

5.5. Spatial variability of the rainfall with in the catchment

The Figure 5-3 depicts scatter plot of daily average rainfall (18 points) verses rainfall of 5 random points (pt 1, 4, 13, 15 and 18). According to the best fit line in the graph, the slope is almost equal to 1 while coefficient of determination [R2] equals to 0.9903. This explains that individual point rainfalls are very similar to the average of 18 points. Thus we can distinguish the spatial variability of the MPE rainfall with in the catchment is minimum.

Scatter plot for MPE points rainfall distribution 20

18 y = 1.0018x + 0.0065 2 16 R = 0.9903

14 jjj

Point rainfall data (mm) rainfall data Point 6 pt1 4 pt4 pt13 pt15 2 pt18 Linear (pt13) 0 02468101214161820 Average rainfall (mm)

Figure 5-3 Consistency check for random 5 points

5.6. Comparison of MPE rainfall with gauge data

The MPE performs best in strong convective type of rain, it can also detect frontal precipitation but the exact position could be misled by about 100 kms according to Heinemann et al (2003), most rainfalls in the Portugal have the strong influence of ocean and that could be one reason for the deviation. The SSM/I data used to calibrate MSG-IR rain-rates are only available at satellite overpass times possibly 3-4 times in a day. If rain does not occur during that time calibration may have problem in representing the rain rates. In addition Heinemann et al (2003) sited (Berge,2003) states that precipitation in general is underestimated by the MPE products. Thus, there are possibilities in underestimating rain rates derived through the MSG IR channel images.

5.7. Conclusion

Referring Figure 5-3 it can be said that variability with in the catchment is minimal and 18 data points are sufficient to represent the rain estimation as the spatial variability within the catchment is negligible. According to the analysis there is an inconsistency in daily rainfalls values between the MPE data and nearby ground station data. MSG MPE is a better option in a situation where there is scarcity of weather data.

54 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

6. Model Development and Implementation

6.1. Modeling objectives and the scope Hydrologic and water quality models are extensively used in current context and their applications are on basically guiding water resources policy, water resources management and water resources regulations (Harmel and Smith, 2007). In this particular modeling study, it is intended to use the model for basic guidance for water quality management such are pollution control of ground and surface waters and predict the possible contaminant loads. Further this could be used to manage the drinking water quality issues. It is observed that EMAS water treatment plant at Beja uses expensive activated carbon filter media due to the risk of contamination of water with algal toxins (blue green algae). Thus the regulation and water quality management practice aided with numerical models could reduce expensive water treatment costs.

The modeling study is limited to Roxo catchment having approximately 350 km2. Modeling is carried out with Duflow Modeling Studio(DMS), and emphasis is laid on the nutrients nitrates, ammonium and phosphates. Considering the availability of data, a simulation period was selected spanning from September 2001 to December 2007 and a daily time interval (temporal resolution) was selected for the analysis.

6.2. Schematization of the study area The physical schematization is a key process in modeling. It depends on the data availability and the expected degree of accuracy of the Modeling. In this sense, Duflow allows the user to define the level of detail of the physical modeling system. To formulate the Duflow model the study area was schematized into following elements:

• Nodes, schematization points, calculation points • Stream Lines • Flow section definitions (cross section) • RAM runoff areas components • Structures (weir, discharge point WWTP )

Initially the Roxo catchment delineated using the Aster DEM was further sub-divided in to 13 sub- catchments. The number of sub catchments was decided based on the stream discharge points to the Roxo reservoir. Since Pisoes sub-catchment has more information and contains a small stream (Chamine) that is considered to be the only perennial stream of the catchment, it was divided in 4 sub catchments. The Appendix 2_fig.(d) shows the delineated sub catchments.

Nodes were introduces at the key points like junctions, discharge points of individual sub catchments, outlets of the streams and by both sides of structures. The streamlines were designed considering the longest flow path determined by the hydro-DEM process. The schematization points (bending) introduced so that the stream path follows the actual drainage profile on background streamline map. As a model simplification all streams sections were considered to be uniform in size and shape and it not far from reality as most channel section are found to be of a similar size in most field observations. The stream sections were considered to be trapezoidal. Each sub catchment was assigned with rainfall runoff area (RAM) component and individual RAM component was assigned following information.

• Surface classification • Rainfall and potential Evapotranspiration (ET) • Soil Parameters and surface properties • Nutrient Schemes

Natural basins show greater variability and complexity with respect to soils, land cover and topographic characteristics (Lenhart et al., 2002). In Duflow the sub basins are represented by RAM elements and each have to be provided with area average parameter values for each surface. Since each sub catchment level calibration was not possible due to the un-gauged nature of the catchment runoff, general values (area average for entire catchment) have been used so that degrees of freedom in model calibration would reduce to manageable proportion. The information used in the model for different surface types are listed in the Appendix 18. The only structure in the catchment is Roxo dam and it was modelled with a weir structure.

The rain and evaporation data was available at daily time intervals and hence daily calculation steps for flow and quality were selected in the model simulations. The simulated runoff output was taken at decadal (10 day aggregated) time intervals for the model calibration and validation as to minimize the errors in the water balance calculations due to low precision of the water level measurements.

6.3. Basic data inputs and data formats Data input can be done in different techniques in Duflow, the evaporation, precipitation, had a very long time series data and it was converted in to ASCII file format. Those ASCII files can easily be imported and define the input data schemes. In addition there is a possibility in defining data non equidistant, Fourier or constant time series settings. Water quality parameters change seasonally and hence non- equidistant time intervals were also used. The general boundary condition can be provided any of the above methods. In this model flow and head boundary conditions were given as constant values with time.

The input parameters were defined in the respective menus and those are constant with time and Duflow will now allow temporal variation in parameter settings. Initial conditions are also time invariant and values were set at the beginning of the simulation.

56 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

6.4. Initial and Boundary conditions Initial and boundary condition are the most important piece of information for numerical model to start simulation. The initial conditions reflect the state of the system at the onset of the simulation period and usually its hard to estimate true value prevailed at the initial state as such information is hard collect of not available. This is same even for this study and the data required are in the form of water levels, flows, and concentration of state variables of the stream sections. It is often recommended practice to run the model for trail runs and to use the subsequent model run output as initial conditions (Mwanuzi et al., 2003). This methodology had been adopted in modeling work and Duflow provides replacement of the simulation results of the state variable as initial conditions of the system for the next simulation run.

Table 6-1 Shows the data required for the model State Variable Element- Flow Discharge (Q) Sections Levels Sections Quality Algae Sections

Ortho-PO4 Sections

Particulate PO4 Sections Organic –N Sections

NH4 -N Sections

NO3-N Sections

The boundary conditions are applied at the physical boundaries of the system or at internal nodes of the system. At the upstream nodes flow boundary conditions were applied, introduced flow was very low value just to make sure to keep the streams wet. At the downstream end, a head (level) boundary condition was applied. There were no internal boundary conditions used but the RAM itself considered as boundary condition at the connected schematization point.

The proper operation of RAM components also needs a definition of its boundary conditions. The precipitation and evapotranspiration are main boundary condition while water quality boundary condition are more complicated as quality definitions are used for ammonium, nitrate and phosphorous and each component need to be broadly classified into different surface categories like open water, paved surfaces, open unpaved surfaces, unpaved (slow), unpaved (quick) and as seepage. These boundary conditions were defined as constant with time or function of time series. Based on the boundary conditions and surface parameter RAM calculates the additional flow and the concentration or the load for quality inputs to the network. This flow and chemical concentration or load act as Q add scheme to the particular area point in the Duflow schematization..

The different quality boundary schemes applied are listed in the following table. Most information was collected from the literature (DUFLOW, 2004a) combined with field data. The Duflow reference manual provides the general concentrations of nutrients at different surfaces and it also categorizes these concentrations based on the basic soils type (sand, clay, peat) and application of fertiliser of farmlands.

These recommended valued have been used as a quality boundary condition of the model and values are summarized in Table 6-2.

Table 6-2 Typical values of concentrations of nutrients

Plausible ranges Basic model Constituent Surface category Type (mg/l) (mg/l)

Open water 1.3,2.4,6.1 * 1.30 Const

Paved surface 0.15-2.5*1 1.50 Const

Unpaved (open) 0*2 0.00 Blank Ammonium Unpaved surface (slow) 0.08-0.36*2 0.20 Const

Unpaved surface (quick) 0*2 0.00 Blank

Seepage 0.08-0.36*2 0.20 Const

Open water 0.9,1.2,1.8* 1.20 Const

Paved surface 0.1-0.7*1 0.40 Const

Unpaved (open) 0*1 0.00 Const Nitrate Unpaved surface (slow) 21-39*3 40.00 Const

Unpaved surface (quick) 2*3 2.00 Const

Seepage 21-39*3 40.00 Const

N 1 1.00 Const

Open water 0.06,0.11,0.27* 0.11 Const

Paved surface 0.22-1.5*1 0.86 Const

Unpaved (open) 0-1*2 0.20 Const Phosphorous Unpaved surface (slow) <0.01*2 0.01 Const

Unpaved surface (quick) 0 0.00 Blank

Seepage <0.01*2 0.01 Const

* min, mean and max concentration values *1 typical concentration values for residential areas *2 unfertilized land (sand) *3 fertilized other farmland (sand)

These typical concentration schemes defined based on the RAM manual (DUFLOW, 2004b) and the past water test carried out by the previous ITC research teams.

58 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

6.5. Sensitivity analysis The model parameters are uncertain to some extent due to numerous constraining factors such as budgets, high spatial variability of parameters and difficulty in access. Therefore knowledge of sensitivities of the parameters is quite important for model development and application. Thus it is possible to reduce the uncertainty by means of better understanding and better estimation of values (Lenhart et al., 2002). The sensitivity analysis is carried out by using one parameter which is varied at a time keeping all other fixed. In general most sensitive parameters for modeling for hydrology and water quality are physical soil properties (Lenhart et al., 2002). The sensitivity is expressed as a dimensionless index and which expresses the relative change of model output and the relative change of a parameter. The objective functions used in the sensitivity analysis is the Root Mean Squared Error (RMSE), Relative Volume error (RVE) and Nash-Sutcliffe coefficient of efficiency (E) that are listed in equation 6.1, 6.2 and 6.3 respectively.

N 2 ()− ∑ Oi Pi RMSE = i=1 [ 6.1] N

= ( − ) RVE Oi Pi [ 6.2]

N ∑ ()O −P 2 [ 6.3] E = 1− i=1 i i N 2 ∑ ()O − O i=1 i

Where Oi = measured data, Pi = observed data,⎯O = mean of measured data and N is the number of data points.

Summary results of the sensitivity analysis are shown in Table 6-3 and the detailed description of the sensitivity analysis results of the selected parameters are listed in Appendix 17. The analysis shows that the model is highly sensitive for the following parameters.

Table 6-3 RAM sensitivity analysis results Parameter plausible ranges Surface Type Hydraulic head difference covering layer and water Not specified Seepage/unpaved transporting package DH (m) Vertical hydraulic resistance of the covering layer C (days) Not specified Unpaved Infiltration capacity I max(mm/day) 20-40 mm/day Unpaved

Initial depth of the unsaturated Zone LBv0 1000-1500 mm Unpaved

Maximum capillary rise Cmax (mm/day) 1- 20 mm/day Unpaved Nash Cash Cades 0-128 Unpaved

6.6. Model calibration The calibration is the process by which a output of the model is matched with the observed field data. Generally in hydraulic Modeling flow observations are matched by changing the input parameters. As illustrated before only measurement available in this catchment is the reservoir water levels and abstractions, and the reservoir inflow time series derived from these data.

The flow model calibration was performed by adjusting the different parameters of the model and plausible ranges of some the parameters were found from the literature. In addition there are guide lines in the Duflow user manual for some parameter values (reservoir constants reservoir [k], initial storage [Bmax], etc.). The calibration was done for the period starting from the Sep 2001 to Dec 2004 period and the discharge results were aggregated at ten days intervals (decadal).

The calibration at individual sub-catchment parameter setting was not feasible as the sub catchments were not gauged at their individual outlet points. The goodness of fit was assessed by the considering the Root Mean Squared Error as listed in the equation [6.1] and visual examination of the behaviour of the simulated hydrograph. The Table 6-3 below shows the calibrated flow model and the Appendix 18 list out the adjusted parameters. The flow model calibration produces relatively acceptable runoff simulation pattern with calculated inflows, however some peak are not perfectly coincide with the calculate inflows. The goodness of fit values shows that it is of an acceptable order. However model could have been better calibrated still if it had actual gauge measurements.

The inflow values calculated based on the reservoir water balance show that there were some negative values appearing and this can be explained by the accuracy in the level measurements, under estimated discharge from the reservoir or even it could be due to actual evaporation from the water surface, which can be higher than the calculated. In addition some of the simplifications made during schematization and parameterization of the model could also affect the accuracy of the simulated discharge. The general surface properties of sub-catchments were assumed to be uniform during the model building, but in reality due to the diverse nature of the soil prevailed in the catchment the resultant (area average) parameter values could be deferent from what had been used in the model at individual catchments. Thus each catchment could have its own optimum parameter setting, but due the limitation in calibration data and time constraint. This prompt us to simplify the model at this stage.

6.7. Model validation

The validation is the process by which a second set of independent stress conditions are applied to the model to test and assure the reliability, accuracy and predictive capability of the model. Initially the flow model parameters were calibrated for stress conditions of just 3 years and the next 3 years are used for the validation. Since there was a very long drought period there was a long time period where zero or no flow conditions and there were major runoff can be observed during Oct-Dec 2006 and it has been simulated perfectly well. The results show in Figure 6-2 that the flow model is simulating the inflow condition reasonably well.

60 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

3 Model Calibration- Decadal stream flow (m /sec)

Decadal inflow (m3/sec) 9 Model output (m3/sec)

ddddd 7 /sec) /sec) 3 6

5 Decadal inflow (m inflow Decadal 4

0 Jul-02 Jul-03 Jul-04 Jan-02 Jan-03 Jan-04 Jun-02 Jun-03 Jun-04 Sep-01 Feb-02 Sep-02 Feb-03 Sep-03 Feb-04 Sep-04 Oct-01 Oct-01 Oct-02 Oct-03 Oct-04 Dec-01 Apr-02 Aug-02 Dec-02 Apr-03 Aug-03 Dec-03 Apr-04 Aug-04 Dec-04 Mar-02 Mar-03 Mar-04 Nov-01 Nov-02 Nov-03 Nov-04 May-02 May-03 May-04 Time

Figure 6-1 Model calibration: simulated discharge vs. observed runoff

Model Validation - Decadal stream flow (m3/sec) 26

Decadal inflow (m3/sec) 24 Model output (m3/sec) 22

16 /sec) 3 14

Decadal inflow (m inflow Decadal 10

0 Jul-07 Jul-06 Jul-05 Jun-07 Jun-06 Jan-07 Jun-05 Jan-06 Jan-05 Feb-07 Sep-07 Feb-06 Sep-06 Feb-05 Sep-05 Sep-04 Oct-07 Oct-06 Oct-05 Oct-04 Dec-07 Dec-06 Apr-07 Aug-07 Dec-05 Apr-06 Aug-06 Dec-04 Apr-05 Aug-05 Mar-07 Mar-06 Mar-05 Nov-07 Nov-06 Nov-05 Nov-04 May-07 May-06 May-05 Time

Figure 6-2 Model validation: simulated discharge vs. observed runoff

In addition, the peak flows correspond to each other and there is a small time shift which can be due to (a) possible errors of having reservoir water levels records (b) the number of RAM element used in the modeling, which could have been improved by splitting the sub catchments further into few more RAM elements and introducing surface properties that corresponds to each sub catchment. With the validation results, the model is considered sufficiently adjusted for the subsequent water quality modeling process.

6.8. Results-water quality (nutrient) modeling

The results of the water quality simulation are as shown in the Figure 6-5 below. It is observed that the there are concentration fluctuations in the ammonium and nitrate at the reservoir. It is clear that nitrate concentration is usually high in November-March. This can be explained by the higher nitrate flow to the river after every rain event.

The nitrate concentration is fluctuating around 2.5 mg/l at the peak while it can be low as 0.3 mg/l during months with lowest concentrations. According to the Figure 6-6b the ammonium concentrations are very low in the reservoir and this agrees with the field concentration measurements made during the early Oct 2008.

Figure 6-3 Duflow model structure showing all key elements Figure 6-4 Nitrification along the main river (Chamine) section

The above Figure 6-4 shows the Nitrification process (Nitrification: the oxidation of ammonia to nitrate, via nitrite) occurring along the main river stretch. - + NH3 + CO2 + 1.5 O2 + Nitrosomonas → NO2 + H2O + H - - NO2 + CO2 + 0.5 O2 + Nitrobacter → NO3

With this result, it can be stated that the quality model reasonably simulate the process of nitrification happening along the river and the concentration are also with in the observations.

62 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

The simulation results for the phosphorous does not show reasonable concentration observed in the Roxo reservoir. Generally the values are less than 0.2 mg/l while model simulates relatively higher phosphate concentrations in the reservoir (see. Appendix 19) and this could be mainly due to the fact that model may not adequately describe the phosphate interaction with in the system, especially the interactions with the bed sediments,. Hence phosphor description needs further refinement and in the quality model description.

6.9. Scenario analysis

A Duflow scenario was built to make a comparison of the runoff generation in the Roxo catchment based on the two rainfall methods. In this analysis the daily precipitation from the ground stations and the MSG-MPE were considered for 1.5 years period. The runoff generated from MSG-MPE was compared with that of measured flow based on the reservoir water balance technique which was available from Jan 2007 till Dec 2007 (one year). All the data processing was carried out at equivalent daily time steps.

The results are plotted in Figure 6-7 and it is evident that gauge station data is inline with the runoff calculated by RWBT. However MSG shows large runoff during the month of Sep 2007 amounting approx 42 m3/s and other two runoffs methods (gauge and calculated) are not showing such a peak runoff on sep 2007 moreover the year 2007 is considered as a dry year without major inflow to the reservoir. Hence the runoff shown in MPE data integrated scenario can be considered as a result generated du to an out liar. In addition MPE shows rather strange results in the year 2008 with two major flow one is rather low in magnitude (Feb 2008) and isolated runoff in March 2008.

The Figure 6-8 shows the cumulative rainfall change of MPE and two COTR weather stations during the selected time period and this clearly indicates the changes occurring in the rainfall and during Sep 2007 and Apr 2008 MPE shows very high rainfall values in comparison to the two automatic weather stations. These high rainfall values are leads to generate high runoff events during those time periods in model.

Fig. (b) Temporal variation of nitrate concentration at the Roxo reservoir section section Roxo reservoir at the concentration of nitrate variation Temporal (b) Fig. reservoir Roxo at concentration ammonium in variation Temporal (d) Fig.( section

SEC2:CLC00029->CLC00030 SEC4:CLC00074->CLC00075 SEC28:CLC00211->CLC00212 SEC2:CLC00003->CLC00004 SEC4:CLC00063->CLC00064 SEC28:CLC00211->CLC00212 SEC2:CLC00015->CLC00016 SEC4:CLC00063->CLC00064 SEC16:CLC00123->CLC00124 SEC2:CLC00029->CLC00030 SEC3:CLC00057->CLC00058 SEC16:CLC00123->CLC00124 Time Date Variation of Nitrate concentration Ammomium Concencentration along Pisoes river along Concencentration Ammomium SEC2:CLC00003->CLC00004 SEC3:CLC00057->CLC00058 SEC15:CLC00115->CLC00116 SEC2:CLC00015->CLC00016 SEC3:CLC00031->CLC00032 SEC15:CLC00115->CLC00116 SEC2:SCH00022->CLC00008 SEC3:CLC00031->CLC00032 SEC14:CLC00104->CLC00105 SEC2:SCH00022->CLC00008 SEC2:CLC00004->CLC00005 SEC4:CLC00074->CLC00075

5 0 Sep-01 Feb-02 Aug-02 Feb-0 3 Aug-03 Feb-04 Aug-04 Feb-05 Aug-05 Feb-06 Aug-06 Feb-07 Aug-0 7 5 0

Sep-01 Feb-02 Aug-02 Feb-03 Aug-03 Feb-04 Aug-04 Feb-05 Aug-05 Feb-06 Aug-06 Feb-07 Aug-07 45 40 35 30 25 20 15 10

50 45 40 35 30 25 20 15 10

Nitrate concentration (mll) (mll) concentration Nitrate Ammonium Concentration (mg/l) (mg/l) Concentration Ammonium Fig.( a)Nitrate concentration variation along the stream sections sections stream the along variation concentration a)Nitrate Fig.( stream sections the along change concentration Fig. (c) Ammonium Figure 6-5 Temporal variation of the nitrate, ammonium concentrations

64 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

The following Figure 6-6 shows the measured temporal variation of nitrates, ammonium and phosphor concentrations in the Roxo Reservoir (EMAS intake).

Temporal Variation of Nitrate Concentration in the Roxo Reservoir 6 Nitrate Concentration 5 4 3 2

Nitrates mg/l 1 0 Jul-05 -1 Jul-06 Jan-05 Jan-06 Jun-07 Sep-05 Feb-07 Feb-08 Oct-06 Oct-07 Aug-06 Dec-06 Apr-07 Aug-07 Dec-07 Mar-05 Mar-06 Nov-04 Nov-05 May-05 May-06 Time

Fig. (a) Nitrate

Temporal Variation of Ammonium Concentration in the Roxo Reservoir 0.6 Ammonium Concentration 0.5

0.4

0.3

0.2 Ammonium mg/l 0.1

0 Jul-06 -0.1 Jul-05 Jun-07 Jan-06 Jan-05 Feb-07 Feb-08 Sep-05 Oct-06 Oct-07 Aug-06 Dec-06 Apr-07 Aug-07 Dec-07 Mar-06 Mar-05 Nov-05 Nov-04 May-06 May-05 Time

Fig. (b) Ammonium

Temporal Variation of Phosphor concentration in the Roxo Reservoir 0.14

0.12 Phosphor Concentration

0.1 0.08

0.06

0.04 Phosphor mg/l Phosphor 0.02 0

-0.02 Jul-06 Jul-05 Jun-07 Jan-06 Jan-05 Feb-07 Feb-08 Sep-05 Oct-06 Oct-07 Aug-06 Dec-06 Apr-07 Aug-07 Dec-07 Mar-06 Mar-05 Nov-05 Nov-04 May-06 May-05 Time

Fig (c) Phosphorous

Figure 6-6 Nutrient concentrations in the Roxo reservoir according to EMAS data

Figure 6-7 Runoff simulation based on the two rainfall inputs 100 Simulation with two rainfall methods simulation with MSG_MPE Simulation with gauge data

/sec) 3 1

(m Runoff

0.1

0.01

10/10/06 4/28/07 11/14/07 6/1/08 Date The runoff simulation comparison for MSG_MPE with gauge data rainfall was done from January 2007 to May 2008.The Figure 6-7 shows the comparison of the discharge simulated by the two rainfall methods and the discharges are plotted in log vertical scale and the simulations are not perfectly in agreement with each other.

Comparison of rainfall patterns 800

700 Two high intensity rainfalls recorded in MPE data 600

500

400

300 Cumulative precipitation (mm) j (mm) precipitation Cumulative

200

MSG_MPE Average Beja 100 Aljustrel

0 Jul-07 Jan-07 Jan-08 Jun-07 Feb-07 Sep-07 Oct-07 Feb-08 Dec-06 Dec-07 Apr-07 Aug-07 Apr-08 Mar-07 Nov-07 Mar-08 May-07 May-08 Time

Figure 6-8 Comparison of cumulative rainfalls in the catchment between MSG-MPE and station data

66 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

7. Conclusion and recommendation

7.1. Conclusion

The aim of this research was to build a numerical water quality Modeling and analysis scheme for the upper Roxo reservoir watershed. The Duflow Modeling Studio (DMS v.3.8.2) and an inverted reservoir water balance approach was used for calibration and validation of the flow model. The second aim was to investigate the possibilities of using remote sensing data (satellite precipitation) in Duflow, more specifically to force the rainfall runoff sub-model RAM.

Duflow is an integration of hydrodynamics, morphology and water quality (Van Waveren et al., 1999) and a model was build for Modeling the nutrient dynamics of the upper Roxo agricultural catchment. The emphasis was on Modeling of reservoir inflows including nutrients, ammonium, nitrate and phosphorus. RAM, the rainfall runoff module was used extensively to model the water quality of land- based runoff i.e. the diffuse source pollution. Each RAM area, defining a user-defined sub-catchment was defined with different land cover inputs as well as surface and soil parameter settings as a function of availability of information. The conceptual linear reservoir based RAM model needs a considerable amount of parameters to be defined and not all those do have always physical meanings or are directly related to the physical basin properties as e.g. the reservoir time constants (DUFLOW, 2004a). The specific conclusions made in this research can be resumed as follows:

7.1.1. Estimation of runoff

The reservoir water balance calculation basically relied on registered reservoir water level fluctuations, meteorological (e.g. evaporation) and the reservoir water demand / consumption data. The reservoir inflow calculation, by basically inverting the balance equations (Eqs. 3.4 to 3.6) was done at different time steps (daily, decadal and monthly) for the past 18 years period. The reservoir water balance calculation provided the all important inflow information from the catchment to the reservoir. The calculation showed some minor negative values in the flow values, at some time periods. This could be explained by the relatively low precision of the level measurements (1 cm) or an underestimation of reservoir water releases, and uncertainties in lake evaporation and groundwater in/outflows. However, this uncertainty effect has been substantially overcome by considering decadal inflow values. Basically, the flow calibration and validation was performed using the peak runoff flows occurring during the strong rainfall events and those are only minimally affected by the precision of the level measurements.

7.1.2. Numerical flow and water quality Modeling

The predictions made by the model for the flow simulation can be considered satisfactory even with several simplifying assumptions. The model calibration and validations were done with decadal inflow (m3/sec) figures. However it was found that model predictions are somewhat peculiar at early stages of

the Modeling period, while the validation period (2005-2007) is well simulated, showing a Nash & Sutcliff (NS) coefficient close to 0.8 and Relative Volume Error (RVE) value within 5% range from the measured. The Duflow water quality model definitions considered processes as denitrification, nitrification, growth of algae (eutrophication) and phosphor decay. The results of the water quality simulations show a reasonable agreement and are in range with the EMAS measured data. Especially the nitrates (NO3-) show typical concentration values (0.5-5.5 mg/l) as observed in the Roxo reservoir and the temporal variation of NO3- shows relative peaks during winter months (November- March) that is quite similar to the EMAS monthly NO3 concentration data set. This agreement also prevails for the ammonium (NH4+) concentration equally showing very low values at the reservoir. Further we could clearly identify the fast nitrification process occurring on the Pisões stream after the WWTP effluent discharge point. These results are quite similar to the results obtained by Wriedt (2007) and in his analysis NO3-N concentration has seasonal dynamic and the nitrate dynamics are partly due to the drying and wetting pattern of the basin. The study shows that the surface water nitrate concentration is positively related to discharge and this can be explained by seasonality of soil leaching, increased denitrification during summer months and plant uptake of nitrogen (Dhondt et al., 2002; Wriedt et al., 2007). The study shows the highest nitrate concentration in winter coinciding the high runoff and ground water discharge and lowest in summer months coinciding the low flows and low ground water discharge.

7.1.3. Integration of rainfall remote sensing data in Duflow

The investigation of the possibility of integrating rainfall remote sensing data i.e. the MSG-MPE or Meteosat multi sensor precipitation estimate into the numerical model (Duflow) as rainfall forcing or input was one of the primary objectives of the research. The whole integration process was considered satisfactory from pre-processing up to generate the rainfall time series inputs to the model. MSG-MPE time series can be coupled to user-defined RAM areas as precipitation boundary condition. In the analysis we found that MSG-MPE data in this particular study area in Southern Portugal were not always fully in agreement with observed rainfall from ground station data. As discussed in the previous Chapter 6, the there were two strong storm events recorded in the MSG-MPE dataset, but those rainfall incidents were not recorded with the same depth by the ground weather stations. Further the calculated catchment runoff or reservoir inflows with the reservoir water balance technique were more in agreement with measured station rainfall values. Thus the MSG-MPE data are still not entirely safe for predicting precipitation for individual or daily storm events. When the model is set on a daily or sub- daily time step, one should be careful for using only satellite precipitation data for driving the model. This can be explained partly that the precipitation pattern in Portugal is usually governed by stratiform rainfall from Atlantic depressions, which is also known as frontal rain or cyclonic rain. But the MSG- MPE is best suited for predicting more convective type of rainfall, that usually has high rainfall intensities and is concentrated to particular area (Heinemann et al., 2002).

Heinemann et al.(2002) indicates that the algorithms used in estimating rainfall rate initially correlates microwave SSM/I rain rates to brightness temperature of MSG TIR 10.8 um channel and that brightness temperature is near independent of the SSM/I precipitation in frontal precipitations. As such it is quite difficult to perform rainfall estimations with this algorithm in such geographic areas.

68 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

In contrast, as shown in Chapter 5 (Fig.5.2), the cumulative long-term or annual rainfall sums derived from MSG-MPE data, coincide very well with all the station data. This could be due to the fact that integral conserving characteristic of the matching algorithm (Heinemann et al., 2002). This permits us to use the MSG-MPE data for seasonal or annual rainfall estimations in this area. The duration of analysis is however not sufficient to draw a strong conclusion but further studies would enlighten the application of MSG-MPE in this sense.

Another technique which can be proposed is to use data assimilation techniques, by which satellite and ground observations are coupled to improve the rainfall estimates and inputs for the model. This is recommended for further study.

7.2. Recommendation

The model could have been further refined in case there were few flow gauge stations at least for the major stream discharge points to the reservoir. This could yield better and more extensive RAM surface definitions, via calibration. In such case, RAM elements can be more extensively used to model more detailed semi-distributed runoffs and nutrient dynamics. A major problem encountered in using the precipitation runoff module elements in the hydraulic Modeling is that the information on the parameters and its plausible ranges are not easily found in literature. Due to the conceptual nature of the precipitation runoff module (RAM), the parameter definition process is by calibration with not always direct physical meaning (DUFLOW, 2004a).

The Corinne land cover dataset Level 2 was used to parameterize the surface conditions (vegetation, crops, etc.) of the RAM areas. This dataset corresponded well with the Landsat ETM imagery and field observations. This land classification plays a major part in RAM area definitions and in addition it is used for derivation of the resultant (weighted) crop coefficients at the sub-catchments level. Thus a more comprehensive model could theoretically be developed with the use of high spatial resolution images.

Further it was more useful if the RAM component of Duflow would also have the capability of importing direct GIS data (land cover, soil properties, rainfall maps etc.). Only the Duflow main engine has this capability (flow network). Our GIS source data needed to be converted to ASCII file formats prior to import into RAM and this makes model setup time consuming. The coupling of the Duflow RAM with GIS further would enhance the user friendliness and is more research time available for other process subject areas.

In addition, we could not find many publications that were relevant to the use of the RAM rainfall runoff module of Duflow, where according this research, it has good potential of doing hydrologic as well as diffuse pollution Modeling. This issue make it also difficult to find the published parameters values typical for specific local conditions. Basically all starting values were based on the guidelines provided in the Duflow manual and own hydrological judgement. These initial guesses in combination with many parameters make it difficult to perform sensitivity analysis. To do an effective hydrologic or water quality Modeling, an essential requirement is information and calibration data and we could have done a much detailed analysis with still more experimental data.

The negative values of the reservoir balance inflow calculation could be due to different reasons such as

Low precision of the level measurements as stated above: the current water level records are available at nearest 1 cm precision and it is low precision compared to the order of the evaporation and precipitation data. That may lead to a possible uncertainty of ±5 mm in each water level reading and can lead have ± 0.45 m3/sec uncertainty in the individual daily readings (considering an average surface area of 8 km2);

Uncertainties in estimation of water surface evaporation: Ali (2008) in his paper explains that precise estimation of evaporation from a water body requires extensive databases and often becomes very difficult to select the most appropriate single evaporation method. Consequently the accuracy of the method will improve the reliability of the results obtained in the water balance calculation.

Demand and reservoir water release data: It is generally possible that water releases that were recorded in the ABROXO data could at some time points be underestimating the amounts of water released. In addition when calculating daily water balances monthly demand data have been distributed evenly over the days. That might also lead to some inaccuracies in the calculation.

Integration of remote sensing data has been carried out satisfactorily through the results are not as good as expected, especially for some individual daily time step rainfall runoff estimations. The MSG-MPE product is quite useful as it provides rainfall intensities at 15 minutes intervals (96 images per day). The processing and data extraction is somehow tedious task but can be automated. It would be better if more studies are carried out in developing an active data screening system in extracting data (at points, space windows, intensity graphs, etc) followed by subsequent data processing.

The observed short term rainfall patterns were at some periods quite different from the ground observations. It is recommended to perform more MSG-MPE data application research to assess the validity of the MSG-MPE algorithm and data. Heinemann et al (2002) in his paper recommends that MSG-MPE best suited for tropical African conditions, so more investigations are needed and more applications in higher latitude areas. He further explains that validity of the blending technique and its application to semi arid climates can vary. In addition it is stated in the literature that MSG-MPE is not the best to estimate rainfall occurring in frontal weather systems. Thus it could be one reason for the discrepancies found between the MSG-MPE rainfall and ground rainfall observations. More research on algorithm adjustment or data assimilation is recommended here to improve the estimates.

70 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

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72 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

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Neal, C. et al., 2008. Water quality along a river continuum subject to point and diffuse sources. Journal of Hydrology, 350(3-4): 154-165. NGDC, 2009. DMSP Data Availability, http://www.ngdc.noaa.gov/dmsp/availability.html, (accessed on February 9, 2009). Ochir, A., 2008. Surface water quality assessment and modelling a case study in the Tuul river, Ulaanbaatar city, Mongolia, ITC, Enschede, 87 pp. Poor, C.J. and McDonnell, J.J., 2007. The effects of land use on stream nitrate dynamics. Journal of Hydrology, 332(1-2): 54-68. Ricardo Rodriguez, J.E., 2003. Influence of wastewater treatment plant on water quality in Beja, Alentejo region Portugal, ITC, Enschede. Sen, P.K. and Gieske, A., 2005. Use of GIS and remote sensing in identifying recharge zones in an arid catchment: a case study of Roxo River basin, Portugal. Journal of Nepal Geological Society, Vol. 31: 25-32. Shakak, N.B.I., 2004. Assessment of pollution risk using the AGNPS watershed model : case study of upper Roxo catchment, Portugal, ITC, Enschede, 117 pp. The Weather Network, 2009. Statistics: Beja, Portugal, Long term mean weather parameters: 1961- 1990, http://www.theweathernetwork.com/index.php?product=statistics&pagecontent=C03482, (accessed on February 12, 2009). Thronson, A. and Quigg, A., 2008. Fifty-five years of fish kills in coastal Texas. Estuaries and Coasts, 31(4): 802-813. Valiantzas, J.D., 2006. Simplified versions for the Penman evaporation equation using routine weather data. Journal of Hydrology, 331(3-4): 690-702. Van Waveren et al., 1999. Good Modelling Practice Handbook. STOWA/RWS-RIZA, Utrecht/Lelystad, in Dutch, 149 pp. Windemuller, P., Anderson, D.L., Aalderink, R.H., Abtew, W. and Obeysekera, J., 1997. Modeling flow in the Everglades agricultural area irrigation/drainage canal network. Journal of the American Water Resources Association, 33(1): 21-34. Wriedt, G., Spindler, J., Neef, T., Meißner, R. and Rode, M., 2007. Ground water dynamics and channel activity as major controls of in-stream nitrate concentrations in a lowland catchment system. Journal of Hydrology, 343(3-4): 154-168.

74 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendixes

Appendix 1 Research phases and key activities

76 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendix 2 Maps Maps 1-2 - Soil classification and CORINE Land cover classification

Fig. (b) Land cover classification map

catchment Roxo the of map Soil (a) Fig.

Map 3-4 - Weather stations and Schematised sub-catchments

Fig (d) Schematized 13 sub-catchments sub-catchments 13 Schematized Fig (d)

ered in estimation of area estimation of in ered average rainfall average Fig.(c) stationsweather consid

78 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendix 3 Potential water surface evaporation calculation (sample calculation sheet)

Albureira Do Roxo Elevation z 100 m Atmospheric pressure P 100.12 kpa Psychrometric constant γ 0.067 kPaoC-1 Latitude ϕ 37.929 N Longitude 8.080 W Albedo water α 0.08

Wind function coefficient au 1

Daily mean Daily cum. Daily Equation 31 relative solar Daily mean Max Min water surface

Julian humidity radiation mean velocity temp. temp. RS RA evaporation 2 2 Date day (%) (W/m ) temp. (°C) (m/s) (°C) (°C) /MJ/m /d /MJ/m2/d 9/1/01 244 64 6237 21.5 2.4 31.5 13.8 22.45 33.80 6.52 9/2/01 245 58 6154 23 0.8 30.7 17.9 22.15 33.59 5.94 9/3/01 246 58 5832 24.8 2.3 33.3 19.7 21.00 33.38 6.82 9/4/01 247 62 5777 24.9 1 30.3 19.4 20.80 33.17 5.78 9/5/01 248 76 5376 23.1 2 27.6 18.0 19.35 32.95 5.21 9/6/01 249 74 5964 21.4 1.3 24.7 17.4 21.47 32.73 5.19 9/7/01 250 78 3031 19.6 0.8 30.9 15.5 10.91 32.51 3.18 9/8/01 251 61 5727 22.8 0.9 33.6 17.4 20.62 32.29 5.75 9/9/01 252 53 5808 24.5 0.8 33.2 18.3 20.91 32.07 5.96 9/10/01 253 56 5748 23.9 1.2 29.3 16.2 20.69 31.85 5.75 9/11/01 254 66 5928 21.7 2 26.5 16.1 21.34 31.62 5.65 9/12/01 255 70 5811 19.8 1.8 26.2 15.5 20.92 31.39 5.25 9/13/01 256 74 5561 18.2 1.3 28.9 12.5 20.02 31.16 4.84 9/14/01 257 67 5478 19.7 1.4 28.9 12.3 19.72 30.93 5.08 9/15/01 258 66 5467 19.9 2.5 31.7 12.7 19.68 30.70 5.74 9/16/01 259 52 5081 23 0.9 32.3 18.0 18.29 30.47 5.36 9/17/01 260 59 4700 23.6 1 31.7 17.2 16.92 30.24 4.99 9/18/01 261 65 5114 21.7 1.3 27.6 15.6 18.41 30.00 4.88 9/19/01 262 73 4430 19.8 0.9 27.4 15.3 15.95 29.77 4.03 9/20/01 263 68 4799 20.2 1.3 26.6 15.5 17.28 29.53 4.47 9/21/01 264 73 3370 19.8 2.3 20.1 15.6 12.13 29.29 3.48 9/22/01 265 92 1552 17.7 2.4 20.9 15.5 5.59 29.05 1.70 9/23/01 266 91 2737 18 0.6 22.7 15.9 9.85 28.81 2.30 9/24/01 267 83 4446 17.9 0.9 22.7 13.8 16.01 28.57 3.47 9/25/01 268 77 4654 17 1.5 23.4 12.2 16.75 28.33 3.81 9/26/01 269 72 5135 17.3 1.4 25.7 12.2 18.49 28.09 4.29 9/27/01 270 63 5039 18.6 0.8 23.5 13.5 18.14 27.85 4.16 9/28/01 271 76 2971 18.9 1.8 23.2 16.2 10.70 27.61 3.06 9/29/01 272 82 3250 19.5 2.8 24.6 17.6 11.70 27.37 3.28 9/30/01 273 88 3202 19.3 0.8 24.7 16.1 11.53 27.13 2.70 10/1/01 274 78 4364 18.9 0.8 27.2 15.0 15.71 26.89 3.66 10/2/01 275 79 4623 21.3 1.2 25.9 18.4 16.64 26.65 3.95 10/3/01 276 83 3174 20.9 0.7 24.6 17.8 11.43 26.40 2.82 10/4/01 277 72 3835 18.5 1.8 25.2 12.4 13.81 26.16 3.64 10/5/01 278 65 4731 19.2 0.8 24.4 15.6 17.03 25.92 3.93 10/6/01 279 87 2251 19.7 0.9 22.1 15.5 8.10 25.68 2.10 10/7/01 280 84 3140 17.7 0.9 21.9 14.6 11.30 25.44 2.57 10/8/01 281 81 3915 18.3 0.6 21.7 13.6 14.09 25.21 2.96 10/9/01 282 80 3067 15.6 0.9 19.0 10.8 11.04 24.97 2.43 10/10/01 283 83 3542 14.2 1.4 20.4 9.5 12.75 24.73 2.64 10/11/01 284 78 2525 16.9 2.9 25.7 14.6 9.09 24.49 2.93 10/12/01 285 80 2840 20.6 2.9 21.3 17.2 10.22 24.26 2.92 10/13/01 286 84 2821 18.2 2.8 22.4 16.3 10.16 24.02 2.69 10/14/01 287 84 2913 18 0.8 24.2 13.9 10.49 23.79 2.43 10/15/01 288 81 3804 19 0.8 22.4 16.2 13.69 23.56 2.91 10/16/01 289 83 3050 17.9 0.5 23.4 14.7 10.98 23.33 2.42 10/17/01 290 78 3049 18.8 1.8 23.5 15.6 10.98 23.10 2.84

Appendix 4 Data constancy checks using double mass technique 4.1 Double mass curve relations: Beja weather station vs other nearby weather stations

Double mass curve ralation Beja vs Aljustrel (SNIRH) Double mass curve ralation Beja vs Aljustrel (COTR) 2000 3000 y = 0.8004x - 117.74 y = 1.0661x - 162.38 2800 2 R2 = 0.9962 1800 R = 0.9968 jjj 2600 jjj 1600 2400

2200 1400 2000

1200 1800

1600 1000 1400

800 1200

1000 600

Cum. Aljustrel (COTR) precipitation /(mm) 800

400 600

Cum. precipitation Aljustrel(SNIRH) /(mm)) Aljustrel (SNIRH) Precipitation /(mm) 400 cum. Aljustrel (COTR) 200 Linear (Aljustrel (SNIRH) Precipitation /(mm) Precipitation /(mm)) 200 Linear (cum. Aljustrel (COTR) Precipitation /(mm) ) 0 0 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 3000 Cum. Beja precipitation /(mm) Cum. Beja precipitation /(mm)

Fig (a) Beja vs Aljustrel (SNIRH) Fig(b) Beja vs Aljustrel (COTR)

Double mass curve ralation Beja vs Santa clara do louredo precipitation Double mass curve ralation Beja vs cum. Santa vitória precipitation

2000 2200 y = 0.9009x - 59.453 y = 0.8409x - 20 2 R2 = 0.9985 1800 R = 0.9984 2000

jjj jjj jjj 1800 1600 jjjjjj

1600 1400

1400 1200

1200 1000 1000 800 800 600 600

400 /(mm) precipitation vitória Santa Cum. 400 Cum. Santa clara do louredo cum. Santa vitória Cum. Santa clara do louredo precipitation /(mm) /(mm) precipitation louredo do clara Santa Cum. Precipitation /(mm) Precipitation /(mm) 200 200 Linear (Cum. Santa clara do Linear (cum. Santa vitória louredo Precipitation /(mm) ) Precipitation /(mm)) 0 0 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 Cum. Beja precipitation /(mm) Cum. Beja p recip itation /(mm)

Fig(c) Beja vs Santa Clara do Louredo Fig(d) Beja vs Santa Vitoria

Double mass curve ralation Beja vs Albernoa Double mass curve ralation Beja vs Trindade precipitation 2000 2000 y = 0.779x - 67.251 2 y = 0.7934x + 8.5351 1800 R = 0.9981 1800 R2 = 0.9967

1600 1600 jjj jjj 1400 1400

1200 1200

1000 1000

800 800

600 600 Cum. Albernoa precipitation /(mm) /(mm) precipitation Albernoa Cum. Cum.Trindade precipitation /(mm) /(mm) precipitation Cum.Trindade 400 400 Cum. Albernoa precipitation /(mm) Cum. Trindade Precipitation 200 Linear (Cum. Albernoa 200 /(mm) Linear (Cum. Trindade precipitation /(mm)) Precipitation /(mm) ) 0 0 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 Cum. Beja precipitation /(mm) Cum. Beja precipitation /(mm) Fig(e) Beja vs Albernoa Fig(d) Beja vs Tridade

80 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

4.2 Table below shows the slop/gradient of the individual double mass curves shown about in appendix 4.1 and the corresponding coefficient of determination values of the graphs

Precipitation ratio and coefficient of determination

Coefficient of Beja Determination R2

Beja 1.00 1

Aljustrel (SNIRH) 0.80 0.9968

Aljustrel (COTR) 1.07 0.9962 Santa Clara do louredo 0.90 0.9985

Santa vitória 0.84 0.9984

Albernoa 0.78 0.9981

Trindade 0.79 0.9967

Albufeira do Roxo 0.89 0.9946

4.2 The Long term precipitation ratios between nearby rainfall stations used in normal ratio method for filling data gaps:

Long term precipitation ratios

Beja Aljustrel Aljustrel Santa Clara Santa Albufeira (COTR) (SNIRH) (COTR) do lured vitória Albernoa Trindade do Roxo

Beja (COTR) 1.00 1.25 0.94 1.11 1.19 1.28 1.26 1.13

Aljustrel (SNIRH) 0.80 1.00 0.75 0.89 0.95 1.03 1.01 0.90

Aljustrel (COTR) 1.07 1.33 1.00 1.18 1.27 1.37 1.34 1.20

Santa Clara do louredo 0.90 1.13 0.85 1.00 1.07 1.16 1.14 1.02

Santa vitória 0.84 1.05 0.79 0.93 1.00 1.08 1.06 0.95

Albernoa 0.78 0.97 0.73 0.86 0.93 1.00 0.98 0.88

Trindade 0.79 0.99 0.74 0.88 0.94 1.02 1.00 0.90

Albufeira do Roxo 0.89 1.11 0.83 0.98 1.05 1.14 1.12 1.00

Appendix 5 Land cover

5.1 Accuracy assessment CORINE land cover classification An accuracy assessment was performed using the ground data and result shows that the overall accuracy is about 90% with a reliability of 85%

5.2 Reclassification results of the individual sub catchment level It is evident that the most common surface type in the Roxo catchment is unpaved, as the catchment dominated by the agriculture. Further in there are few water bodies in the sub catchment and its minor fraction compared to dominant unpaved surfaces. The catchment # 3 is considered to represent the surface runoff directly from the near by area to the reservoir. Moreover the Roxo surface area was not considered in RAM water surface classification as that area was considered in the water balance calculations. Table shows the different percentages of different surface types in each sub-catchment

Sub-catchment Area (km2) Unpaved Other Paved Open Water

1 12.74 98% 0% 2% 2 21.96 100% 0% 0% 3 19.91 100% 0% 0% 4 17.39 90% 10% 0% 5 7.27 100% 0% 0% 6 18.78 100% 0% 0% 7 14.33 98% 2% 0% 8 37.13 100% 0% 0% 9 35.38 98% 2% 0% 10 39.87 99% 1% 0% 11 8.07 100% 0% 0% 12 82.58 100% 0% 0% 13 24.28 99% 1% 0% Roxo reservoir area not considered in catchment 13

82 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

5.3 Summary of the field the data used for accuracy assessment (Synchronized ground data with CORINE classification) line Soil Sampling also performed atclose position (SS) Streamflowwater is located no w=2m,d=1m Small mountain and streamflowing down w=4m,d=1m, SS Undulating terrain Close to Roxo reservoir What kind of trees? What trees? Sunflower Maize Fenced, appears protected, trees? what sort of 1-3m_high- 5m to Up Also tilled Sunflower Wheat Very extensiveplantation Dark brownish red tilled soil Geodetic point Mature eucalypts Young pines, no more than tall 2m Looks likemixed use- cropping andpasture, given thecow many droppings Crop appeareddiseasedand looking scaleon unsuccessful. Fungi underside and of leaves grapes were driedstill on and vine Irrigated young olives,brown orange - soil, mixedcrop, image show may shadow from trees Unirrigated matureolives, backgroundsoil is reddish brown, grassquite green Unirrigated matureplantation, soilreddish is brown, patchy dry grass Vegetation is shrubby, somecork oaks interspersed. Soils are veryreddish, high quartz and schist content. Pine plantationclose by. Grasses are dry butMature look like andirrigated drounght resistant grove. species Soil appears dark and loamy thought quite rocky.grass No soil on undulatingtilled. Landscape is slightly Mature vineyard,unirrigated. Rockysoil, high quartzcontent Beja city centre 1/10/2008 C01 37.826 -8.103 311 Broad leafed forest Eucalypt plantation 1/10/2008 C021/10/2008 C031/10/2008 C041/10/2008 C051/10/2008 37.827 C061/10/2008 37.858 C071/10/2008 37.858 -8.104 C08 37.9371/10/2008 -8.076 C091/10/2008 37.921 -8.078 C101/10/2008 211 37.958 -8.081 Non irrigated C11 arable land1/10/2008 313 37.915 -8.071 Broad C12 leafed forest2/10/2008 211 38.001 -8.058 Non irrigated C13 arable land 3112/10/2008 37.960leafed Broad forest -8.035 C142/10/2008 211 37.988 -7.901 Non irrigated C15 arable land2/10/2008 241 38.011 -7.990 Annual crops C16 associatedwith permanentcrops 2412/10/2008 38.014 -8.034 Annual crops associated C17with permanentcrops 2/10/2008 241 38.013 -8.047 Sparsely Annual crops C18 distributed associatedwith trees permanentcrops Arable land, harvested 3/10/2008 211 38.004 Arable -7.944 Non irrigated land, C19harvested arable land 2413/10/2008 38.002 Annual Tree -7.944 crops plantations associated C20with permanentcrops Arable land, harvested 3/10/2008 244 Mature 37.949 pine forest -7.936 Agro-forestry C21 areas3/10/2008 211 37.958 Olive -8.007 Non grove irrigated C22 arable land Dry Scatteredgrass trees3/10/2008 211 37.995 Culvert visible, but -7.915 Non not irrigated drainage C23 arable land3/10/2008 212 37.962 -7.896 Permanently C24 irrigated land3/10/2008 211 37.964 -7.906 Non irrigated C25 arable land3/10/2008 311 37.965 -7.977 Broad C26 leafed forest Arable land, harvested 3/10/2008 311 37.967 -8.015 Broad C27 leafed forest 2113/10/2008 38.007 -8.015 Non irrigated Adjacent C28natural tois this woodland Roxo-permanent or crop? arable a land Planted in rows, set quitefarapart 3/10/2008 211 37.976 -8.032 Non irrigated C29 arable land Arable land, harvested Randomly_planted_trees7/10/2008 211 37.973 -8.102 Non irrigated C34 arable land Arable land, harvested 2417/10/2008 37.941 Arable land -8.086 Annual crops associated C35with permanentcrops 7/10/2008 241 37.905 -7.820 Annual crops C36 associatedwith permanentcrops Arable land, harvested 7/10/2008 223 37.872 -7.785 Mixed Olive groves crops C37 2217/10/2008 37.959 Vineyards -7.851 Arable C38 land,harvested 7/10/2008 223 Forest 37.836 -7.868 Olive groves C39 Arable land, harvested 7/10/2008 241 Eucalypt 37.252 plantation -7.931 Annual crops C40 associatedwith permanentcrops Arable land, harvested 7/10/2008 312 37.830 -8.201 Coniferous C41 forest Arable land, harvested 211 37.878 -8.202 Arable Non irrigated land arable land7/10/2008 C42 241 -8.166 Annual crops associated 0.000with permanentcrops7/10/2008 Olives and sunflower C43 311 37.928 -8.019 Broad leafed forest7/10/2008 C44 324 37.866 Oak Trees Transitional woodland shrub1/10/2008 0.000 TW1 211 -7.602irrigated Non arable land 37.808 221 -7.611 Vineyards Olive grove 37.971 223 38.053 Olive groves -7.627 223 Vineyard Olive groves Olive grove 38.012 Arable land, harvested -7.563 244 Pineplantation Agro-forestry areas -7.467 321 -7.863 Pine plantation Natural grassland 223 Olive groves Eucalypt plantation Pasture / arable 221 Vineyards 112 Discontinuousurban fabric Vineyard Cork plantation Olive groves Olive grove Uncultivated undulatingterrain Paved surface Olive grove Vineyard Date Code Lat Lon CLC_3 _3 Class Description Corine Description#01 Description#02

Appendix 6 Data for crop evapotranspiration estimates

Growing Seasons

Crop Type Init.(Lini) Dev.(Ldev) Mid.(Lmid) Late.(Llate) Total Planting date

Crop factors at different growth stages

Crop type and crop factors at different growth stages Winter Maize Growing- Forests- Grazing Stage Sunflower Wheat (sweet) Olives Grapes Olives Bare Soils Coniferous Pasture

Kc, ini 0.45 0.7 0.45 0.65 0.3 0.65 0.45 1 0.3

Kc, mid 1.15 1.15 1.15 0.7 0.7 0.45 0.45 1 0.75

Kc, end 0.35 0.25 1.05 0.7 0.45 0.65 0.45 1 0.75 Max crop height/(m) 2 1 1.5 3 to 5 1.5-2 10 0.1

Percentages of individual land cover types at each sub catchment

Sub catchment land cover percentage (%) CORINE Code Type 12345678910111213

112 Discontinuous urban fabric 10.2 1.95 1.5 0.66 0.06

211 Non-irrigated arable land 63.78 37.72 79.45 86.96 88.87 60.81 68.87 77.57 82.32 64.04 59.82 70.41 47.96

212 Permanently irrigated land 1.64 4.37 5.13 1.34 9 3.89 3.28 0.59 17.24

221 Vineyards 0.03

223 Olive groves 10.41 6.32 0.57 0.58 2.7 4.74 0.49 0.71 Annual crops associated with 241 permanent crops 1.58 1.51 2.13 18.44 26.51 2.43 8.71 16.98 22.45 0.32 3.66 242 Complex cultivation patterns 0.7 1.39 Land principally occupied by 243 agriculture, with significant areas of natural vegetation 0.94 1.37 0.05

244 Agro-forestry areas 27.96 23.05 2.99 11.29 13.47 12.99 18.22 9.8

311 Broad-leaved forest 3.31 33.54 0.05 1.9 10.14 0.14

313 Mixed forest 1.17

321 Natural grasslands 0.15 0.43 1.72 2.07 0.15 0.23 14.51

324 Transitional woodland-shrub 0.62

512 Water bodies 2.36 0.78 21.78 100 100 100 100 100 100 100 100 100 100 100 100 100

84 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendix 7 Roxo Stage Volume curve (Mannaerts , 2006)

Appendix 8 Soil Information

Different soil percentages at sub-catchments according to soil map

athment 12 Cathment 13 Whole Roxo 3.700.00 3.40 0.00 0.20 0.00 2.60 0.00 1.50 0.00 6.70 0.00 4.60 0.00 2.40 0.00 3.10 0.00 3.00 0.00 3.50 0.00 0.00 3.70 5.50 0.00 2.58 0.90 83.10 82.50 8.50 0.00 0.00 2.00 0.00 4.20 6.40 9.80 5.30 35.20 20.90 21.42 Class01 Cathment 02 Cathment 03 Cathment 04 Cathment 05 Cathment 06 Cathment 07 Cathment 08 Cathment 09 Cathment 10 Cathment 11 Cathment C Total 100.00 100.00 100.00 100.10 99.90 99.70 99.90 100.00 99.70 100.10 100.10 83.50 100.10 96.05 At; At(p) At; SbVt(p)Pc ;Pcx VcVcx; Bp;Bpc;CpCb 0.00 0.00Bvc 0.00Pac; 0.00 0.00Px 3.20 0.00Pm 0.00 0.00 0.00Pag 0.00 0.00Vcm; 0.00 0.00 0.00 4.70Pv 0.00 0.00 0.00 0.00Vx(d,p);Vx(p) Vx(d); Vx; 12.50 0.00 0.00 0.00 0.00 0.00 0.00Sr* 0.00 1.00 0.00 11.50 0.00Sag 0.00 24.40 0.00 5.30 0.00 0.00 0.00Pcz 62.60 0.00 0.00 0.00 0.50 0.60 0.00 CaacCaa; 15.50 17.00 0.00 0.00 2.30 0.00 0.00 21.80Ps 0.00 0.00 0.00 0.00 67.00 0.00 0.00 0.00 2.00Sp 0.00 5.60 0.00 0.00 0.60 9.80 0.00 12.50 0.00 12.50 0.00 8.30 0.00 0.00 0.00 0.20 0.10 1.40 0.00 0.00 0.10 0.00 0.00 3.50 9.20 0.00 0.00 0.00 0.00 0.00 9.00 0.00 2.60 0.70 0.00 6.10 0.00 0.00 0.00 2.50 0.90 42.90 0.00 0.00 0.00 0.00 14.70 0.00 0.00 0.00 11.20 0.00 0.00 1.50 0.00 0.00 7.20 0.60 3.70 4.60 0.00 0.00 17.30 0.00 0.00 15.90 0.00 0.00 2.40 0.40 6.30 0.50 1.70 2.40 0.00 0.00 1.40 0.90 0.00 2.30 0.00 0.00 17.90 18.30 0.00 0.00 0.00 0.50 0.00 0.00 7.70 0.00 26.20 0.70 0.00 0.00 0.80 0.00 0.00 10.40 0.00 0.00 0.00 0.00 3.00 0.00 27.30 0.00 0.00 11.00 2.40 0.00 0.00 0.00 5.30 0.70 0.50 0.00 0.00 0.00 6.70 0.40 3.40 1.70 0.17 10.71 0.00 0.00 0.00 6.20 0.00 0.32 0.00 1.90 8.80 0.00 0.00 0.00 0.00 46.70 1.14 0.00 0.00 0.00 0.60 4.70 0.30 0.00 8.50 0.00 19.80 5.00 0.00 1.00 0.00 0.70 24.00 0.00 1.47 0.00 0.00 0.00 4.50 8.00 8.16 35.70 0.00 3.40 0.00 0.00 10.40 0.00 0.00 0.00 1.40 0.00 0.00 0.80 0.00 15.90 0.90 19.60 0.00 0.00 50.10 0.00 0.52 0.00 0.20 0.92 0.00 0.00 13.68 0.00 12.10 1.10 0.00 3.50 0.40 1.50 0.50 0.56 0.00 15.90 0.00 13.90 0.00 0.00 0.00 0.27 1.55 0.00 8.10 9.80 16.63 0.00 0.56 0.00 0.00 1.80 5.70 0.00 0.00 6.30 0.15 0.00 0.18 0.17 9.37 0.00 1.61 Ex Asoc A;A(h); 0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.90 0.00 0.71

86 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Summary of the soil properties

Permeability cm/h water % water Available Available F P 2 2.7 4.2 Initial Constant Holding Max. water Max. Capacity % 52.00 52.05 37.80 36.75 14.29 23.52 0.90 0.80 Porosity % Porosity Notation Sr*Ps 41.87 32.42 41.38 21.80 35.53 16.60 23.48 8.93 17.03 13.06 10.98 14.33 12.47 10.75 7.80 4.60 PxPm 38.67Vcm 30.17 43.73Pv 22.26Vx; Vx(d); 44.87 37.45Vx(d,p);Vx(p) 20.24 28.18 37.23 9.12 50.27 23.15 21.72 13.14 9.93 45.68 17.47 0.58 18.26 38.45 13.06 0.80 6.92 32.70 8.66 4.80 16.64 0.00 21.81 0.91 0.35 0.48 Vcx; Vc 47.87Bpc 48.60 24.77Cp 27.94 50.90Cb 15.43 51.00Bvc 47.70 9.34 40.80 1.55 47.28Pac 45.63 35.55 2.88 41.48 20.40 52.76 38.65 37.18 20.40 23.73 50.89 20.35 43.80 2.11 18.73 25.33 21.13 1.68 35.88 10.73 28.49 0.45 25.38 13.00 17.48 0.75 12.91 21.00 7.85 9.01 9.68 3.59 15.95 5.93 3.32 2.93 Detailed TypeIV Red-Yel. Medit.S.from non-calcareous non-calcareous Medit.S.from Red-Yel. materials plinthitic with - rocks Horison Eluvial Planosols with Brown Medit.S.from non-calcareous rocks - Medit.S.from non-calcareous Brown Normals rocks - Medit.S.from non-calcareous Brown Barros Para rocks - calcareous Medit.S.from Red-Yel. Barros Para non-calcareous Medit.S.from Red-Yel. - Normals rocks non-calcareous Medit.S.from Red-Yel. - Normals rocks Brown Calc.S. of Sub-humic and semi Calc.S. and arid of Sub-humic Brown climatesand Arid of Sub-Hum Semi Calc.S. Red Climates Black Barros Calcareous Non Barros Strongly Black Calcareous decarbonated Bp Pc ;Pcx Barros Slightly Black Calcareous decarbonated Brown Dark Reddish Calcareous Non Barros 46.65 51.92 Reddish Barros Brown Dark Calcareous Decarbonated Strongly 39.30 45.53 31.20 rocks Medit.S.from calcareous Brown 39.00 23.25 31.27 5.80 18.47 25.40 20.53 0.90 0.63 1.00 0.70 Soil Type II Brown Mediterranean Mediterranean Brown Soils Red-Yellow Soils Mediterranean With Eluvial HorizonWith Eluvial Black Barros Black Barros Brown Dark Reddish Borros Brown Calcareous Soils Calcareous Brown SoilsRed Calcareous Soil type I type Soil Calcareous Soils Calcareous Soils Lessives (Not Strongly Unsaturated) Barros Barros Hydromorphic Soils Some of the soil data collected from the literature

Appendix 9 MSG MPE data gaps

Missing data MSG-MPE Year Month Day Missing data or problem data (all times are inclusive) 01 17 13.45 12 - 20 2007-02-12 from 10.30 to 2007-2 -20 13.15 data missing 02 20 00.00 - 13.00 3 12.00 -13.45 4 12.15 5 08.30 - 23.45 03 6-11 No data for whole period 12 08.30 - 23.45 17 00.00 - 12.45 22 11.00 6 15.30 - 21.00 04 9 16.30 - 16.45 28 0400 - 1645 10 23.15 - 23.45 05 11 00.00 - 06.45 23 13.15 5 05.45 - 07.30 06 19 00.00 07 30 13.15 - 13.30 and 14.15 3 12.15 2007 4 12.30 - 12.45 7 13.00 10 00.00 repeatedly fails to process 08 27 13.00 - 15.00 24 00.00 repeatedly fails to process 25 00.00 repeatedly fails to process 28 12.00 - 12.45 31 07.15 - 11.15 8 00.15 - 00.45 09 10 16.00 - 16.30 and 17.45 -18.00 17 00.45 - 01.15 11 11.45 10 17 07.15 - 08.45 25 15.30 - 16.15 17 05.30 - 09.00 11 19 05.30 - 07.30 3 11.45 - 23.45 4-11 No data for whole period 12 12 00.00 - 08.15 16 04.15 - 23.45 17 00.00 - 09.00 01 29 00.00 repeatedly fails to process 4 16.30 5 10.00 02 12 13.00 - 14.45 14 09.15 3 13.45 - 23.45 4 00.00 - 06.45 2008 04 5 00.00 - 02.45 9 08.30 23 08.30 5 08.00 - 13.45 13 21.15 - 23.45 05 19 00.00 - 09.45 20 05.00 - 06.15

88 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendix 10 Field data collection for water quality

10.1 Water sampling locations

10.2 Results of the reliability checks for chemical water quality analysis Accuracy assessment of the water sample analysis is shown in table below, where checks were made to see the electro neutrality of cation and anions. The sum of captions must be equal to sum of anions and it is evident that this condition is reasonably satisfies by the 8 water samples. Further the charge balance is also satisfactory and percentage difference is below 15% for all samples. According to the observations the water sample taken close to waste water treatment plant shows higher charge balance difference. It could be due to the other constituents present in the sample water, specially the possible organic ions present in significant quantities (Hounslow, 1995).

Sample ∑Cations ∑Anions EC/100 % Difference 100* ∑Cation/ECmea 100* ∑Anion/ECmea D 01 11.94 8.95 13.01 14.30% 0.92 0.69 D 02 14.96 14.94 14.63 0.08% 1.02 1.02 D 03 80.03 82.02 75.4 -1.23% 1.06 1.09 D 04 15.27 17.40 14.8 -6.53% 1.03 1.18 D 05 9.63 12.80 9.5 -14.14% 1.01 1.35 D 06 20.04 25.71 21.09 -12.41% 0.95 1.22 D 07 19.86 24.45 21.46 -10.37% 0.93 1.14

D 08 20.68 24.34 22.1 -8.13% 0.94 1.10

90 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendix 11 Salt dilution gauging Field observations (37o,57’ 34.5” N, 7o 58’ 33.8” W) The salt (NaCl) quantity used in the test is 400 g and the distance between salt application and EC measuring point is 20 m. EC EC EC Time (s) (μs/cm) Time (s) (μs/cm) Time (s) (μs/cm) 0 1559 220 3370 380 1982 10 1559 230 3340 390 1952 20 1559 240 3230 400 1923 30 1556 250 3070 410 1887 40 1556 260 2960 420 1873 50 1555 270 2800 440 1837 60 1554 280 2660 450 1820 70 1553 290 2540 460 1796 80 1551 300 2510 470 1778 90 1549 310 2410 480 1758 120 1547 320 2350 490 1745 160 2151 330 2280 500 1735 170 2700 340 2210 510 1724 180 2970 350 2160 520 1731 190 3190 360 2090 640 1637 200 3370 370 2030 700 1611 210 3450

Graph displaying the variation of electrical conductivity after introducing the NaCl upstream. The area under the graph is equivalent to the equation [7.1] and the area was calculated using the trapezoidal rule. The flow was calculated subsequently and was found to equal to 0.12 m3/sec.

∞ ()C −C ∫ ()x1,t b [ 0.1] t0

Electrical conductivity measurement at stream section (μS/cm) with time 3500

Electrical conductivity (ms/cm) 3300

3100

jjjjj 2900

S/cm 2700 μ

2500

2300

2100 Electrical conductivity conductivity Electrical

1900

1700

1500 0 100 200 300 400 500 600 700 Time in Seconds /(s)

Appendix 12 Water consumption/abstraction data (sample)

Summary of the water consumptions extracted from ABROXO (since Nov 2001 –Oct 2005)

3 Consumption (m ) Monthly total water Mon/Yr Days Irrigation Animal Industrial Drinking consumption (m3) Nov-01 30 311850 311850 Dec-01 31 302770 302770 Jan-02 31 0 0 279750 279750 Feb-02 28 0 0 251060 251060 Mar-02 31 4320 0 258240 262560 Apr-02 30 247707 0 248850 496557 May-02 31 1911573 0 296210 2207783 Jun-02 30 3933423 0 304160 4237583 Jul-02 31 5681610 0 343130 6024740 Aug-02 31 4796010 41544 333230 5170784 Sep-02 30 1357488 108468 259090 1725046 Oct-02 31 64296 2520 340460 407276 Nov-02 30 0 0 354640 354640 Dec-02 31 0 0 331080 331080 Jan-03 31 0 0 0 295650 295650 Feb-03 28 0 0 0 259280 259280 Mar-03 31 0 0 0 309950 309950 Apr-03 30 360 0 0 280100 280460 May-03 31 1472508 864 0 339930 1813302 Jun-03 30 4015566 2340 0 329010 4346916 Jul-03 31 4787892 5508 0 372320 5165720 Aug-03 31 4389966 1476 139392 387860 4918694 Sep-03 30 1442790 3888 28152 342840 1817670 Oct-03 31 32508 0 0 323960 356468 Nov-03 30 0 0 0 340730 340730 Dec-03 31 0 0 0 368670 368670 Jan-04 31 0 0 0 366250 366250 Feb-04 29 0 0 0 355150 355150 Mar-04 31 0 0 0 372350 372350 Apr-04 30 437562 252 0 353040 790854 May-04 31 1908756 252 0 370690 2279698 Jun-04 30 4407174 3536 0 419180 4829890 Jul-04 31 5968188 2304 0 428700 6399192 Aug-04 31 4271148 1368 105408 378240 4756164 Sep-04 30 1705518 504 95904 365110 2167036 Oct-04 31 437328 0 0 351050 788378 Nov-04 30 0 0 0 296774 296774 Dec-04 31 0 0 0 349916 349916 Jan-05 31 0 0 0 335097 335097 Feb-05 28 0 0 0 294696 294696 Mar-05 31 0 0 0 336963 336963 Apr-05 30 0 0 0 336611 336611 May-05 31 48888 0 0 301274 350162 Jun-05 30 62787 0 0 321290 384077 Jul-05 31 60282 0 0 299919 360201 Aug-05 31 81585 0 153648 254076 489309 Sep-05 30 66348 0 28800 299271 394419 Oct-05 31 0 0 0 275843 275843

92 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendix 13 Collected ground control points for Geo-referencing

Elevation Date Point Latitude Longitude (m) Description

10/3/2008 A01 38.006 -7.869 258 Opposite to Beja Porque hotel , inside the round about

10/3/2008 A02 37.989 -7.916 222 Ervidel & Beja junction Mina da Juliana junction,surroundings have residential 10/3/2008 A03 37.965 -8.023 166 buildings, opposite road leads to beja

10/3/2008 A04 37.967 -8.031 147 Mombeja junction

10/3/2008 A05 37.967 -8.082 159 Fourway junction, (Beja, Ervidel, Adejas, Aljustrel)

10/3/2008 A06 38.057 -8.120 137 Lisbon junction, mercado area

10/3/2008 A07 37.859 -8.086 161 Barragem Roxo junction, with open fields around

10/3/2008 A08 37.948 -8.096 142 Cotre VTE anesl surrounded by cultivated land

10/3/2008 A09 38.003 -7.866 251 Round about opposite to McDonalds

10/3/2008 A10 37.978 -7.827 230 Beja, Salvada junction, opposite to Quintos junction

10/3/2008 A11 37.941 -7.784 209 Round about

10/3/2008 A12 37.907 -7.869 174 Accesso local junction

10/3/2008 A13 37.911 -7.872 171 T junction

10/3/2008 A14 37.893 -7.896 171 Beja Evora Ourique Castro verde junction

10/3/2008 A15 37.972 -7.874 207 Penedo Gordo Junction, 4 way junction

Appendix 14 Historical water quality data

14.1 Information from past ITC work : Surface Water: 2003 may

3- Sample PO4 - - No. sample type E(X) N(Y) Sample date EC μS/cm NO3 (mg/l) NO2 (mg/l) (mg/l) B4 River 598261 4209069 5/1/03 1457 72 3.4 14 B1 Stream 595792 4208082 5/1/03 679 81 1.5 10 S02 Surface water 597765 4208992 5/1/03 1280 43.2 0.492 0.6 S03 Surface water 595904 4208932 5/1/03 940 34.1 0.023 0.54 S05 Surface water 596058 4208777 5/1/03 1941 43 0.175 10.7 S06 Surface water 597072 4208377 5/1/03 2080 67 0.069 9.3 S07 Surface water 595365 4208755 5/1/03 1690 57 0.14 7 S09 Surface water 593886 4207854 5/1/03 1510 67 0.28 5.1 S10 Surface water 592485 4206357 5/1/03 1138 103 0.44 0.5 S11 Surface water 595334 4208084 5/1/03 783 46 0.04 0.07 S12 Surface water 593343 4208083 5/1/03 758 64.5 0.049 0.13 S13 Surface water 593233 4207408 5/1/03 1407 85 1.38 2.9 S15 Surface water 593511 4207140 5/1/03 1303 47 1.205 2.25 S16 Surface water 594802 4207252 5/1/03 803 42.9 0.072 0.76 S17 Surface water 592048 4204321 5/1/03 1472 37 0.135 0.22 S18B Surface water 590104 4202280 5/1/03 1224 38.4 0.118 0.5 S19 Surface water 590694 4201628 5/1/03 1845 13 0.136 0.59 S20 Surface water 588794 4201641 5/1/03 1776 11 0.202 0.39 S21 Surface water 597519 4208261 5/1/03 724 10.7 0.134 2.1 S26 Surface water 586830 4200828 5/1/03 1946 11.5 1.21 1.56 S27 Surface water 586799 4199543 5/1/03 2580 6.7 0.188 1.03 S28 Surface water 583821 4196019 5/1/03 1026 3.4 0 0.1 S29 Surface water 584729 4196590 5/1/03 2100 8 0.048 0.07 S30 Surface water 586192 4198637 5/1/03 2260 3.5 0.054 0.08 S31 Surface water 586143 4200668 5/1/03 1036 0 0.068 0.04 S33 Surface water 580863 4198543 5/1/03 1016 5.6 0.019 0.04 S34 Surface water 578229 4198710 5/1/03 1371 29 0.056 0.08 S35 Surface water 588919 4201063 5/1/03 1103 35 0.266 0.12 S36 Surface water 590536 4202645 5/1/03 1164 5.1 0.101 0.01 S37 Surface water 590923 4204438 5/1/03 898 36.9 0.538 0.09 S38 Surface water 590980 4204448 5/1/03 1220 44.2 0.136 0.73 59 B19 Tap water 598053 4210760 5/1/03 753 0.8 14

94 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

14.2 Water quality information collected from EMAS The sampling location is the Roxo reservoir and the concentration data for the Roxo waters are available since Jan 2005 till Jan 2008.

Data de Fósforo Azoto Azoto Amoníaco não ID-RQA colheita Nitratos total Fosfatos Kjedahl amoniacal ionizado Nitritos 5210 11-01-2005 5.09 0.054 <0,023 1.13 0.26 0.0119 0.027 5210 09-02-2005 0.93 0.064 0.034 0 0.26 0.0212 0.032 5210 08-03-2005 1.24 0.06 0.121 1.45 0.26 0.0826 0.037 5210 05-04-2005 1.28 0.369 0.042 0 0.16 0.0156 0.034 5210 03-05-2005 0.58 0.031 0.032 0.74 0.11 0.0012 0.017 5210 31-05-2005 2.39 0.059 0.036 0 0.04 0.0079 0.001 5210 27-06-2005 0.56 0.057 0 1.24 0.16 0.0343 0.003 5210 26-07-2005 0.59 0.046 0.026 0 0.11 0.0042 0.003 5210 06-09-2005 0.63 0.054 <0,023 1.1 0.04 0.0377 0.01 5210 03-10-2005 1.11 0.09 0.023 0 0.15 0.0307 0.001 5210 02-11-2005 0.8 0.123 0.037 1.49 0.15 0.0251 0.026 5210 29-11-2005 3.01 0.135 0.061 0 0.42 0.3519 0.065 5210 10-01-2006 3.19 0.042 0.037 1.03 0.49 0.0006 0.072 5210 07-02-2006 3.19 0.029 <0,023 0 <0,04 0.00338 <0,001 5210 07-03-2006 2.97 0.039 0.025 0.41 0.05 0.0201 0.003 5210 04-04-2006 0.97 0.045 0.029 0 0.07 0.0173 0.034 5210 02-05-2006 1.02 0.049 0.027 0.83 0.13 0.0264 0.011 5210 30-05-2006 0.04 0.047 <0,023 0 0.18 0.04286 0.002 5210 26-06-2006 1.2 0.069 0.045 0.76 0.17 0.0226 <0,001 5210 26-07-2006 <0,09 0.125 0.04 0 0.11 0.0116 0.001 5210 05-09-2006 0.13 0.159 0.059 1.16 0.1 0.0205 0.001 5210 02-10-2006 <0,09 0.077 0.115 0 0.14 0.0048 0.003 5210 30-10-2006 <2,00 0.069 <0,023 1.26 0.15 0.0186 0.014 5210 28-11-2006 <2,00 0.059 0.113 0 0.42 0.0096 0.155 5210 02-01-2007 3.8 0.09 0.05 0.78 0.12 0.0109 0.02 5210 30-01-2007 3.1 0.049 0.035 0 0.19 0.0066 0.062 5210 27-02-2007 3.2 0.035 <0,023 0.73 0.15 0.005 0.054 5210 27-03-2007 <2,00 0.037 0.033 0 0.23 0.0071 0.065 5210 02-05-2007 <2,00 0.038 <0,023 0.51 0.06 0.0304 0.08 5210 29-05-2007 <2,00 0.499 <0,023 0 <0,04 0.0013 0.012 5210 25-06-2007 <2,00 0.033 <0,023 1.3 <0,04 0.0019 0.008 5210 23-07-2007 <2,00 0.026 <0,023 0 <0,04 0.0001 0.003 5210 11-09-2007 <2,00 0.044 0.023 0.94 <0,08 0.002 0.004 5210 01-10-2007 3.9 0.039 <0,023 0 <0,08 0.0009 <0,002 5210 29-10-2007 <2,00 0.204 <0,023 0.59 0.12 0.0026 0.019 5210 27-11-2007 <2,00 0.066 <0,023 0 0.36 0.00138 0.123 5210 02-01-2008 <2,00 0.03 <0,023 0.45 0.22 0.0096 0.072

Appendix 15 Water quality description (Nutrients)

/* River water quality model for the Nutrients */ /* Imesh Vithanage */ /* In case of Roxo Catchment River, Beja District, Portugal */ /* MSc research */ /* WREM, ITC, 2009 */

WATER A [2.00] mg C/l;ALGAE CONCENTRATION WATER Phosphor [0.01] mg/l ;ORTO-P CONCENTRATION WATER PP [0.10] mg/l ;PARTICULATE-P CONCENTRATION WATER NORG [0.20] mg/l ;ORGANIC-N CONCENTRATION WATER Ammonium [0.10] mg/l ;AMMONIUM-N CONCENTRATION WATER Nitrate [0.20] mg/l ;NITRATE-N CONCENTRATION

PARM UMAX [1.00] 1/day ; MAXIMUM INCREASE SPEED PARM ALFA [0.02] m2/W ; LIGHT EFFICIENCY PARM IOPT [40.0] W/m2 ; OPTIMUM LIGHT INTENSITY PARM PIOPT [1 ] - ;KEUZE LICHTLIMITATIE (1=SMITH,2=STEELE) PARM ACA [30] UG CHL/mg C; CHLOROPHYLL CARBON PROPORTION PARM KLOSS [0.1] 1/day ;SPEED OF CONSTANT ALGAE LOSES PARM KMIN [0.1] 1/day ;SPEEDS CONSTANT MINERALISATIE PARM KNIT [0.2] 1/day ;SPEEDS CONSTANT NITRIFICATION PARM KDEN [0.05] 1/day ;SPEEDS CONSTANT DENITRIFICATION PARM KP [0.005] mg/l ;MONOD CONSTANTE P PARM KN [0.010] mg/l ;MONOD CONSTANTE N PARM EPS0 [2.0] 1/m ;ACHTERGRONDEXTINCTIE PARM EPSALG [0.016] m2/mg CHL ;SPECIFIEKE EXTINCTIE CHLOROFYLL PARM THGA [1.04] - ; TEMPERATURE COEFFICIENT ALGAE INCREASE PARM THMIN [1.04] - ; TEMPERATURE COEFFICIENT MINERALISATION PARM THNIT [1.06] - ; TEMPERATURE COEFFICIENT NITRIFICATION PARM THDEN [1.06] - ; TEMPERATURE COEFFICIENT DENITRIFICATION PARM VSO [1.0] m/day ; SEDIMENTATION SPEED ORGANIC SUBSTANCE PARM ANC [0.176] mg N/mg C ; NITROGENING CARBON PROPORTION PARM APC [0.024] mg P/mg C ; PHOSPHATE CARBON PROPORTION

XT IO [20000] kj/m2 ;Average Daily Solar Radiation XT L [12] UUR ;DAY LENGT XT T [20] oC ;TEMPERATURE

XT NFLUX [0.0] g/m2,day ;AFTER SUPPLY FLUX NITROGENING XT PFLUX [0.0] g/m2,day ;AFTER SUPPLY FLUX PHOSPHATE

FLOW Z [1.0] m ;Water depth

96 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

{ FT=THGA^(T-20); FN=MIN(Phosphor/(Phosphor+KP),Ammonium/(Ammonium+KN)); IOMAX=1000*2*IO/(L*3600); CHLA=ACA*A; ETOT=EPS0+EPSALG*CHLA; IF (PIOPT==1) { H1=(1+(ALFA*IOMAX)^2)^0.5; FL=L*(LN(ALFA*IOMAX+H1+(1-H1)/(ALFA*IOMAX)))/(24*ETOT*Z); }

IF (PIOPT==2) { f=(L/24); ALFA1=ALFA*exp(-1*ETOT*Z); FL=2.718*f*(exp(-1*ALFA1)-exp(-1*ALFA))/(ETOT*Z); } GA=UMAX*FT*FN*FL; K1(A)=GA-KLOSS;

KMINT=KMIN*THMIN^(T-20); K1(NORG)=-KMINT-VSO/Z; K0(NORG)=KLOSS*ANC*A;

KNITT=KNIT*THNIT^(T-20); K1(Ammonium)=-KNITT; K0(Ammonium)=KMINT*NORG-GA*ANC*A+NFLUX/Z;

KDENT=KDEN*THDEN^(T-20); K1(Nitrate)=-KDENT; K0(Nitrate)=KNITT*Ammonium;

K1(PP)=-KMINT-VSO/Z; K0(PP)=KLOSS*APC*A;

K1(Phosphor)=0; K0(Phosphor)=KMINT*PP-GA*APC*A+PFLUX/Z; KJN=NORG+Ammonium+ANC*A; NTOT=KJN+Nitrate; PTOT=PP+Phosphor+APC*A;

}

Appendix 16 Water balance calculation 16.1 The following table shows the sample calculation sheet (Excel)

Daily Calculation

Potential Total Water Water Chanage in Spill Average Pricipi- Volume of Surface Release the reservor Surface Area/ release release tation/ precipi- Evapora-tion Evaporated Consump- from Spill storage Inflow Date Stage (m) Volume m3 (m2) (m3/s) (m3/s) (mm) tation (m3) (mm) volume (m3) tions (m3) (m3) (m3) m3/sec 9/1/2001 132.14 51,204,219 9393761 0.0 0.0 0.0 0.0 8.30 77968.22 55915 0.00 -192,460 -0.68 9/2/2001 132.13 51,108,249 9379403 0.0 0.0 0.0 0.0 7.20 67531.70 55915 0.00 -95,970 0.32 9/3/2001 132.11 50,916,825 9350732 0.0 0.0 0.0 0.0 8.50 79481.22 55915 0.00 -191,423 -0.65 9/4/2001 132.09 50,726,090 9322122 0.0 0.0 0.0 0.0 7.10 66187.07 55915 0.00 -190,735 -0.79 9/5/2001 132.08 50,630,980 9307840 0.0 0.0 0.0 0.0 5.30 49331.55 55915 0.00 -95,110 0.12 9/6/2001 132.06 50,441,273 9279323 0.0 0.0 0.0 0.0 5.30 49180.41 55915 0.00 -189,707 -0.98 9/7/2001 132.04 50,252,248 9250867 0.0 0.0 0.0 0.0 3.30 30527.86 55915 0.00 -189,025 -1.19 9/8/2001 132.03 50,157,991 9236662 0.0 0.0 0.0 0.0 5.90 54496.30 55915 0.00 -94,257 0.19 9/9/2001 132.01 49,969,985 9208297 0.0 0.0 0.0 0.0 8.00 73666.38 55915 0.00 -188,006 -0.68 9/10/2001 131.99 49,782,655 9179993 0.0 0.0 0.0 0.0 9.40 86291.94 55915 0.00 -187,330 -0.52 9/11/2001 131.97 49,596,000 9151751 0.0 0.0 0.0 0.0 7.20 65892.61 55915 0.00 -186,656 -0.75 9/12/2001 131.96 49,502,924 9137653 0.0 0.0 0.0 0.0 5.90 53912.15 55915 0.00 -93,076 0.19 9/13/2001 131.95 49,410,016 9123569 0.0 0.0 0.0 0.0 4.40 40143.71 55915 0.00 -92,908 0.04 9/14/2001 131.94 49,317,275 9109501 0.0 0.0 0.0 0.0 6.90 62855.56 55915 0.00 -92,741 0.30 9/15/2001 131.93 49,224,701 9095449 0.0 0.0 0.0 0.0 7.40 67306.32 55915 0.00 -92,574 0.35 9/16/2001 131.92 49,132,295 9081411 0.0 0.0 0.0 0.0 8.20 74467.57 55915 0.00 -92,407 0.44 9/17/2001 131.91 49,040,054 9067389 0.0 0.0 0.0 0.0 7.30 66191.94 55915 0.00 -92,240 0.35 9/18/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 7.90 71521.71 55915 0.00 -92,074 0.41 9/19/2001 131.89 48,856,072 9039390 0.0 0.0 0.0 0.0 4.00 36157.56 55915 0.00 -91,908 0.00 9/20/2001 131.88 48,764,329 9025413 0.0 0.0 0.0 0.0 5.30 47834.69 55915 0.00 -91,743 0.14 9/21/2001 131.87 48,672,751 9011451 0.0 0.0 0.0 0.0 4.70 42353.82 55915 0.00 -91,577 0.08 9/22/2001 131.87 48,672,751 9011451 0.0 0.0 11.7 105434.0 8.00 72091.61 55915 0.00 0 0.26 9/23/2001 131.87 48,672,751 9011451 0.0 0.0 8.7 78399.6 1.30 11714.89 55915 0.00 0 -0.12 9/24/2001 131.86 48,581,339 8997504 0.0 0.0 0.0 0.0 3.00 26992.51 55915 0.00 -91,412 -0.10 9/25/2001 131.86 48,581,339 8997504 0.0 0.0 0.0 0.0 4.30 38689.27 55915 0.00 0 1.09 9/26/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 4.30 38629.36 55915 0.00 -91,248 0.04 9/27/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 5.40 48511.29 55915 0.00 0 1.21 9/28/2001 131.84 48,399,009 8969657 0.0 0.0 0.0 0.0 3.60 32290.76 55915 0.00 -91,083 -0.03 9/29/2001 131.84 48,399,009 8969657 0.0 0.0 10.7 95975.3 2.80 25115.04 55915 0.00 0 -0.17 9/30/2001 131.84 48,399,009 8969657 0.0 0.0 0.5 4484.8 1.90 17042.35 55915 0.00 0 0.79 10/1/2001 131.83 48,308,090 8955756 0.0 0.0 0.0 0.0 3.40 30449.57 10355 0.00 -90,919 -0.58 10/2/2001 131.83 48,308,090 8955756 0.0 0.0 0.0 0.0 4.10 36718.60 10355 0.00 0 0.54 10/3/2001 131.83 48,308,090 8955756 0.0 0.0 0.0 0.0 3.30 29553.99 10355 0.00 0 0.46 10/4/2001 131.82 48,217,335 8941869 0.0 0.0 0.0 0.0 5.00 44709.35 10355 0.00 -90,755 -0.41 10/5/2001 131.82 48,217,335 8941869 0.0 0.0 0.0 0.0 5.50 49180.28 10355 0.00 0 0.69 10/6/2001 131.84 48,399,009 8969657 0.0 0.0 28.1 252047.4 2.50 22424.14 10355 0.00 181,674 -0.44 10/7/2001 131.84 48,399,009 8969657 0.0 0.0 0.0 0.0 2.80 25115.04 10355 0.00 0 0.41 10/8/2001 131.84 48,399,009 8969657 0.0 0.0 0.5 4484.8 2.80 25115.04 10355 0.00 0 0.36 10/9/2001 131.84 48,399,009 8969657 0.0 0.0 0.3 2690.9 3.10 27805.94 10355 0.00 0 0.41 10/10/2001 131.84 48,399,009 8969657 0.0 0.0 0.4 3587.9 2.30 20630.21 10355 0.00 0 0.32 10/11/2001 131.85 48,490,092 8983573 0.0 0.0 12.5 112294.7 2.80 25154.00 10355 0.00 91,083 0.17 10/12/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 3.50 31442.51 10355 0.00 0 0.48 10/13/2001 131.85 48,490,092 8983573 0.0 0.0 0.7 6288.5 2.30 20662.22 10355 0.00 0 0.29 10/14/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 2.40 21560.58 10355 0.00 0 0.37 10/15/2001 131.85 48,490,092 8983573 0.0 0.0 2.5 22458.9 2.50 22458.93 10355 0.00 0 0.12 10/16/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 1.90 17068.79 10355 0.00 0 0.32 10/17/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 2.90 26052.36 10355 0.00 0 0.42 10/18/2001 131.85 48,490,092 8983573 0.0 0.0 1.3 11678.6 2.40 21560.58 10355 0.00 0 0.23 10/19/2001 131.88 48,764,329 9025413 0.0 0.0 28.3 255419.2 1.50 13538.12 10355 0.00 274,237 0.49 10/20/2001 131.89 48,856,072 9039390 0.0 0.0 6.6 59660.0 2.30 20790.60 10355 0.00 91,743 0.73 10/21/2001 131.89 48,856,072 9039390 0.0 0.0 1.0 9039.4 3.00 27118.17 10355 0.00 0 0.33 10/22/2001 131.89 48,856,072 9039390 0.0 0.0 0.0 0.0 3.10 28022.11 10355 0.00 0 0.44 10/23/2001 131.90 48,947,980 9053382 0.0 0.0 4.4 39834.9 1.60 14485.41 10355 0.00 91,908 0.89 10/24/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 1.90 17201.42 10355 0.00 0 0.32 10/25/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 2.80 25349.47 10355 0.00 0 0.41 10/26/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 2.10 19012.10 10355 0.00 0 0.34 10/27/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 2.10 19012.10 10355 0.00 0 0.34 10/28/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 3.20 28970.82 10355 0.00 0 0.46 10/29/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 5.00 45266.91 10355 0.00 0 0.64 10/30/2001 131.90 48,947,980 9053382 0.0 0.0 0.0 0.0 6.80 61562.99 10355 0.00 0 0.83 10/31/2001 131.89 48,856,072 9039390 0.0 0.0 0.0 0.0 4.80 43389.07 10355 0.00 -91,908 -0.44 11/1/2001 131.89 48,856,072 9039390 0.0 0.0 0.0 0.0 3.50 31637.86 10355 0.00 0 0.49 11/2/2001 131.89 48,856,072 9039390 0.0 0.0 0.0 0.0 3.00 27118.17 10395 0.00 0 0.43 11/3/2001 131.88 48,764,329 9025413 0.0 0.0 0.0 0.0 3.10 27978.78 10395 0.00 -91,743 -0.62 11/4/2001 131.88 48,764,329 9025413 0.0 0.0 0.0 0.0 3.40 30686.40 10395 0.00 0 0.48 11/5/2001 131.88 48,764,329 9025413 0.0 0.0 0.3 2707.6 1.70 15343.20 10395 0.00 0 0.27 11/6/2001 131.88 48,764,329 9025413 0.0 0.0 5.8 52347.4 0.70 6317.79 10395 0.00 0 -0.41 11/7/2001 131.88 48,764,329 9025413 0.0 0.0 0.0 0.0 1.90 17148.28 10395 0.00 0 0.32 11/8/2001 131.88 48,764,329 9025413 0.0 0.0 0.0 0.0 1.70 15343.20 10395 0.00 0 0.30 11/9/2001 131.88 48,764,329 9025413 0.0 0.0 0.0 0.0 4.60 41516.90 10395 0.00 0 0.60 11/10/2001 131.87 48,672,751 9011451 0.0 0.0 0.0 0.0 7.90 71190.46 10395 0.00 -91,577 -0.12 11/11/2001 131.87 48,672,751 9011451 0.0 0.0 0.0 0.0 6.70 60376.72 10395 0.00 0 0.82 11/12/2001 131.87 48,672,751 9011451 0.0 0.0 0.0 0.0 4.10 36946.95 10395 0.00 0 0.55 11/13/2001 131.86 48,581,339 8997504 0.0 0.0 0.0 0.0 2.70 24293.26 10395 0.00 -91,412 -0.66 11/14/2001 131.86 48,581,339 8997504 0.0 0.0 0.0 0.0 3.20 28792.01 10395 0.00 0 0.45 11/15/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 6.10 54799.80 10395 0.00 -91,248 -0.30 11/16/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 4.10 36832.65 10395 0.00 0 0.55 11/17/2001 131.85 48,490,092 8983573 0.0 0.0 0.0 0.0 2.30 20662.22 10395 0.00 0 0.36 11/18/2001 131.86 48,581,339 8997504 0.0 0.0 13.8 124165.6 0.10 899.75 10395 0.00 91,248 -0.25 11/19/2001 131.86 48,581,339 8997504 0.0 0.0 0.0 0.0 1.00 8997.50 10395 0.00 0 0.22 11/20/2001 131.86 48,581,339 8997504 0.0 0.0 0.0 0.0 1.40 12596.51 10395 0.00 0 0.27 11/21/2001 131.86 48,581,339 8997504 0.0 0.0 0.0 0.0 2.40 21594.01 10395 0.00 0 0.37

98 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

16.2 Graph showing decadal runoff calculated based on reservoir water balance

Nov-07

Sep-07

Aug-07

Jun-07 Apr-07

Inflow GraphInflow

Feb-07

Dec-06

Oct-06

Aug-06

Jun-06

Apr-06

Feb-06

Dec-05

Oct-05

Aug-05

Jun-05

Apr-05

Feb-05

Dec-04

Oct-04

Aug-04

Time (Decadal)

Jun-04

Apr-04

Feb-04

Dec-03

Oct-03

Aug-03

Jun-03 Apr-03

Decadal runoff calculated based on Roxo-reservoir water balance (Sep-2001 Dec-2007) to balance water Roxo-reservoir on based calculated runoff Decadal

Feb-03

Dec-02

Oct-02

Aug-02

Jun-02

Apr-02

Feb-02

Dec-01

Oct-01 Sep-01

8 4 0

24 20 16 12

/sec) (m Inflow 3

inflow Decadal hydrograph

Appendix 17 Results of the sensitivity analysis

Root mean Relative Nash squared error volume error Shutcliffe k=0.0014 1.288 44.0% -0.11 Open water k (day) k= 0.00 1.288 44.0% -0.11 k =0.0014 1.288 44.0% -0.11 k (day) k =0.01 1.288 44.0% -0.11 k =1 1.288 43.8% -0.11 Paved Surface Imax=25 1.288 43.8% -0.11 Imax (mm/day) Imax =10 1.288 43.8% -0.11 DH=0.2 3.143 150.2% -5.59 DH=0.3 3.815 229.9% -8.71 DH=0.15 2.306 108.4% -2.55 DH=0.12 1.777 71.6% -1.11 DH (m) C=225 days DH=0.08 1.130 17.7% 0.15 DH=0.05 1.124 -15.1% 0.16 DH=0.07 1.104 3.3% 0.19 DH=0.09 1.153 30.6% 0.11 DH=0.075 1.101 9.8% 0.19 C=250 1.099 5.7% 0.19 C=275 1.114 -0.1% 0.17 C=200 1.153 30.6% 0.11 C=175 1.347 50.3% -0.21 Seepage C (days) DH=0.08 m C=235 1.097 12.1% 0.20 C=215 1.150 23.7% 0.12 C=260 1.103 3.4% 0.19 C=300 1.133 -4.9% 0.14 C=175 3.553 194.5% -7.42 C=300 2.306 108.4% -2.55 C=350 1.900 78.5% -1.41 C=400 1.577 63.9% -0.66 C (days) C=500 1.161 30.9% 0.10 DH=0.2 m C=550 1.115 18.6% 0.17 C=530 1.145 26.3% 0.13 C=510 1.179 28.5% 0.07 C=520 1.162 25.8% 0.10 C=525 1.158 26.2% 0.11 Imax=25 2.666 332.8% -3.74 Imax=30 1.158 -40.0% 0.11 Imax=28 1.173 -39.1% 0.08 Imax (mm/day) Imax=32 1.147 -40.7% 0.12 Imax=40 1.146 -41.0% 0.12 Imax=20 1.360 -28.2% -0.23 Unpaved Surface LBvo = 1000 1.129 -37.3% 0.15 LBvo = 1500 1.244 -62.3% -0.03 LBvo = 1200 1.196 -50.6% 0.05 Lbvo (mm) LBvo = 1000 2.736 111.6% -3.99 LBvo = 1100 2.472 99.1% -3.08 LBvo = 1100 1.190 30.0% 0.06

100 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Root mean Relative Nash squared error volume error Shutcliffe Bslow = 0.4 1.190 30.0% 0.06 Bslow = 0.3 1.190 30.0% 0.06 Bslow = 0.7 1.190 30.0% 0.06 Bslow Bslow = 0.8 1.190 30.0% 0.06 Bslow = 0.9 1.190 30.0% 0.06 Bslow = 1 1.190 30.0% 0.06 K quick=2 1.190 30.1% 0.06 K quick=3 1.190 30.0% 0.06 Kquick with Bslow K quick=4 1.190 30.0% 0.06 0.6 K quick=5 1.190 30.0% 0.06 K quick=1 1.171 33.6% 0.09 Kslow=10 1.171 33.6% 0.09 Kslow=20 1.171 33.6% 0.09 Kslow=30 1.171 33.6% 0.09 K slow Kslow=40 1.171 33.6% 0.09 Kslow=50 1.171 33.6% 0.09 Kslow=100 1.171 33.6% 0.09 Kslow=200 1.171 33.6% 0.09 Cmax=10 1.130 -4.9% 0.15 Cmax=20 1.112 -14.2% 0.17 Cmax=66 1.131 -12.7% 0.15 Cmax Cmax=0 4.089 197.4% -10.15 Cmax=2 2.041 108.3% -1.78 Unpaved Surface Cmax=4 1.486 57.6% -0.47 I max=25 1.154 12.3% 0.11 I max=30 1.093 8.9% 0.20 I max=28 1.103 9.1% 0.19 I max I max=32 1.090 8.8% 0.21 I max=40 1.094 8.6% 0.20 I max=20 1.376 17.2% -0.26 slow=10 2.661 4.6% -3.72 slow=25 2.718 -86.1% -3.93 Nash Cascade Slow slow=50 2.661 -15.1% -3.72 Quick=125 slow=75 2.661 -15.1% -3.72 slow=100 2.661 -15.1% -3.72 slow=125 2.661 -15.1% -3.72 quick=1 2.804 -38.3% -4.25 quick=10 2.821 47.0% -4.31 quick=50 2.689 45.1% -3.82 quick=75, 2.662 8.7% -3.73 Nash cascade Fast quick=100 2.658 -8.1% -3.71 Slow=125 quick=115 2.660 -12.8% -3.72 quick=120 2.660 -14.1% -3.72 quick=110 2.660 -11.2% -3.72 quick=90 2.661 -3.1% -3.72 quick=95 2.659 -5.7% -3.72

101

Appendix 18 RAM surface parameters and calibrated parameter settings

Rainfall Runoff (RAM) parameter settings k Reservoir Time Constant (days) 0 Open water surfaces f Crop Factor Makkink 1

k Reservoir time constant (days) 0.0014 Bmax Maximum storage in depression (mm) 2 Breservoir Storage in resevoirs (mm) N/A Paved Surfaces [-] % Open surface 0 pumpc Pump over capacity (mm/day) N/A I max Infiltration capacity (mm/day) 10

Imax Infiltration capacity. [mm/day] 32 f Cropfactor Makkink. [-] 1 F0 Moisture storage at pF = 0. [mm] 450 F2 Moisture storage at pF = 2. [mm] 200 F4.2 Moisture storage at pF = 4.2. [mm] 100 Phi0 Initial moisture storage [mm] 120 LBv0 Initial depth of the unsaturated zone [mm] 1100 n Pore content [-] 0.5 Storage value in linear reservoir at which capillary rise is activated Blmin Unpaved surfaces [mm] 100 Percolation to saturated zone between pF = 0 en pF =2 (maximum). Ppercmax [mm/day] 32 Cmax Maximum capillary rise [mm/day] 5 Ksurface Time constant reservoir unpaved surface [day] 0.0001 Kquick Time constant fast groundwater discharge. [day] 1 Kslow Time constant slow groundwater discharge. [day] 200 B slow Distribution formula for fast and slow groundwater discharge. [-] 0.6 [-] 2 Nash cascades number of reservoirs slow/quick 125/125 Advance percolation evaporation relation exponent [-] percolation/evaporation 2_2

C Vertical hydraulic resistance of the covering layer (days) 250 Hydaulic head difference covering layer and water transporting Seepage DH package (m) 0.08 [-] Percentage to unpaved 95%

102 Analysis of Nutrient Dynamics in Roxo Catchment Using Remote Sensing data and Numerical Modeling

Appendix 19 Temporal Phosphates concentration and load variation at reservoir section (SEC00028:NOD00025->CLC00211)

Temporal Phophate Concentration variation at reservoir section SEC00028:NOD00025->CLC00211

1.8 Phophate Concentration

1.6

llll 1.4

1.2

0.8

0.6 Phosphate concentration (mg/l) Phosphate (mg/l) concentration 0.4

0.2

0 Oct-07 Feb-07 Feb-06 Feb-05 Feb-04 Feb-03 Sep-01 Feb-02 Nov-06 Nov-05 Nov-04 Nov-03 Nov-02 Nov-01 Aug-07 Aug-06 Aug-05 Aug-04 Aug-03 Aug-02 May-07 May-06 May-05 May-04 May-03 May-02 Time fig.(a) Phosphor - Concentration

Temporal Phophate load variation at reservoir section SEC00028:NOD00025->CLC00211 6

Phophate Load g/s

llll 4

Phosphate Load (g/s) 2

0 Oct-07 Sep-01 Feb-02 Feb-03 Feb-04 Feb-05 Feb-06 Feb-07 Nov-01 Nov-02 Nov-03 Nov-04 Nov-05 Nov-06 Aug-02 Aug-03 Aug-04 Aug-05 Aug-06 Aug-07 May-02 May-03 May-04 May-05 May-06 May-07 Time fig.(b) Phosphor -Loads

103