Assessing the ability of SWAT as a water quality model in the Lake Victoria basin and its wetlands

3/20/2017 Report: assessing the
ability of SWAT as a
water quality model in
the Lake Victoria basin
and its wetlands
A pilot study on SWAT water quality
modelling in the Mara river basin
Brussée, Timo
MSc Hydrology thesis
Faculty of Earth Sciences
Vrije Universiteit Amsterdam
Boelelaan 1105,
1081HV Amsterdam
The Netherlands

ii
REPORT: ASSESSING THE ABILITY OF SWAT AS A WATER QUALITY MODEL IN THE LAKE VICTORIA
BASIN AND ITS WETLANDS
Abstract
There is a need for a water quality model for use in the Lake Victoria basin countries in East-Africa. The
region is characterised by data scarcity, a tropical climate and riverine, lacustrine tidal wetlands which form
an important buffer to riverine pollution of the lake. These characteristics of the basin form a challenge for
water quality models. The objective is to state the strengths and weaknesses of a potential water quality
model under these challenging conditions. This objective is executed with the soil water assessment tool
(SWAT) in a catchment of the Lake Victoria Basin as pilot area. The pilot area of the Mara river basin is
hydrologically complex containing tropical and plantation forest, savanna, grasslands, bi-annual agriculture,
shrublands and wetlands. It has varied soil types and bi-annual rain seasons
The study consist of literature research and flow simulation of the transboundary Mara river basin. The
model study aims to characterise the hydrology in the pilot area. The study includes a thorough analysis of
rainfall, stage and flow data. Model preparation steps include the use of weighted-area rainfall estimation
methods, climate model data and empirical derivation of soil input parameters. Discharge calibration
methods include multi-site calibration, by making use of an alternative objective function statistic for the
commonly used Nash-Sutcliffe Efficiency (NSE) called the Kling-Gupta Efficiency (KGE). The literature study
targets previous flow and water quality studies done in tropical or wetland areas, thereby looking to see how
these studies adapted to hydrological modelling with SWAT in tropical or wetland areas, and why theses
adaptions were made. The literature research also includes a comparison of wetland processes in SWAT
with the physical, biological and chemical processes as described in previous studies.
The Mara river basin flow simulation gave a satisfactory model performance for two out of three calibration
sites, thereby being able to give preliminary outputs on water-balance and other flow characteristics. During
research, a number of model, knowledge and data gaps were found to be critical for better understanding
the hydrological and water quality system workings in the Lake Victoria and Mara river basin. From the
model and literature study it is concluded that several issues on data scarcity and hydrological model
processes in the tropics can be overcome. These do not necessarily decrease model performance or
uncertainty in the SWAT model. However, wetland processes are oversimplified in SWAT. Modification and
coupled SWAT models yet have not been able to provide an alternative to the default model that adequately
represents the main flow, sediment and nutrients processes and fluxes that are present in Mara’s wetlands.
Keywords: SWAT, SWAT-CUP, wetlands, tropics, potential evapotranspiration, LAI, flow, sediments,
nutrients, modelling, WATCH, CFSR, Thiessen Polygon method, Kling-Gupta Efficiency, Nash-Sutcliffe
Efficiency, multi-site calibration, validation, water balance, pedotransfer functions, sensitivity analysis,
Mara river basin, Lake Victoria basin, Kirumi, water quality, vegetative filter strips, lake water level
fluctuations.

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Preface
This thesis is part of a fulfilment of the master’s degree of ‘Physical Hydrology’ at the Free University of
Amsterdam. The thesis is also written on behalf of the engineering company SWECO, which operated as a
consultant on an integrated water resource management programme in the Lake Victoria basin, for the Lake
Victoria Basin Commission. My role as MSc hydrology student herein was to help in the needs assessment
on a database and IWRM model, gathering water quality data and providing background literature on water
quality modelling in the Lake Victoria basin and testing the most promising water quality model as a pilot on
one of Lake Victoria’s subbasins. The findings of the general assessment of Lake Victoria Basin water
quality problems and processes and the results of the pilot with the ArcGIS Soil Water Assessment Tool
2012 Model and recommendation for the IWRM-programme are gathered in this report.
Target group
This report will target hydrology students, SWAT model users, LVB-IWRM Programme workers, the Lake
Victoria Basin Commission staff and personal on water quality modelling in the LVB partner states ministries
as an audience.
Personal Objective
This is study is meant to increase the capabilities of the master's student writing it and advance his
knowledge. Improvement in handling Access databases, Rstudio, ArcGIS, ArcSWAT and modelling and
calibration processes, in general, is to be expected. Further on an improvement in understanding of the
processes concerning water quality within the Lake Victoria Basin and its wetlands. Experience in working
on investment projects is a godsend in the LVB-IWRM Programme.
LVB-IWRM Programme description
The Lake Victoria Basin Integrated Water Resources Management Programme (LVB-IWRM) is a project
funded by the German KfW Bankengruppe. The LVB-IWRM Programme is a long-term cooperation project
between the LVBC and the KfW. The LVBC is a specialised institution of the East African Community. This
programme aims to: (a) improve the cooperation and data exchange between the national water institutes
of the Lake Victoria countries for water management on catchment scale, (b) allocate funds to the most
promising high priority investment projects on water quality improvement in the Lake Victoria Basin and
setting up a framework for future allocation of investments, (c) strengthen the role of the Lake Victoria Basin
Commission (LVBC) as an overarching organ of the national water institutes, (d) to harmonize current
national water databases and setting up an IWRM model for the Lake Victoria Basin Commission and the
national water institutes. The partners SWECO, Alterra, and Ecorys fulfil a role as a consultant for the LVBC
in this programme.

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Acknowledgements
During my research, I got help from many people. First of all, I would like to thank Martijn Westhoff (my
supervisor from the Free University of Amsterdam) and Marc Vissers (my supervisor during my internship
at SWECO) for their counsel and guidance during this educative and fun period of tough work and travel. I
would also like to express my gratitude towards my family and friends, and especially my father for his
advice. Furthermore, I would like to thank:
Dr Emmanuel Olet (consultant for
SWECO)
For helping me getting into contact with Douglas Nyolei
Douglas Nyolei (NBI/NELSAP,
Musoma, Tanzania)
For sharing me his data and counsel on the hydrology of the Mara basin
Johannes Hunnink (Future Water) For providing counsel on the Africover land use map processing
Maarten Zeylsmans- Van Ebbinckhoven
(Utrecht University)
For his counsel on ArcGIS use
Marjolein Vogels (Utrecht University) For her counsel on ArcSWAT use
Ann van Griensven (UNESCO-IHE
Delft)
For sending me requested literature
Petra Hulsman (Delft University) For sharing me her data and counsel on hydrologic modelling in the
Mara basin
Tadesse Alemayehu (Free University of
Brussel)
For his counsel on ArcSWAT use and discharge datasets in the Mara
River Basin
SWECO For giving me the chance to participate in this project
LVBC For hosting me in their office in Kisumu, Kenya & providing me with data
Evance Mbao & Fidelis Kilonzo
(Kenyatta University)
For sharing me their water quality data
Marwa Muraza (University of Dar Es
Salaam)
For sending me requested literature
Edwin Hes (UNESCO-IHE) For help getting met contacts in the Mara River Basin
Kelly Fouchy (UNESCO-IHE) For her counsel on current project activities in the Mara River Basin

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Contents
Abstract ....................................................................................................................................................ii
Preface.................................................................................................................................................... iii
Target group ........................................................................................................................................ iii
Personal Objective ............................................................................................................................... iii
LVB-IWRM Programme description...................................................................................................... iii
Acknowledgements..............................................................................................................................iv
List of figures .......................................................................................................................................... vii
List of tables.............................................................................................................................................xi
List of abbreviations ............................................................................................................................... xiii
1 Introduction ...................................................................................................................................... 1
1.1 The need for a LVB-IWRM Programme..................................................................................... 1
1.2 Problem definition..................................................................................................................... 2
1.3 Objectives & Research questions.............................................................................................. 4
1.4 Report structure........................................................................................................................ 6
2 Study area........................................................................................................................................ 7
2.1 Mara River Basin ...................................................................................................................... 7
2.2 Mara wetlands .......................................................................................................................... 9
3 Methods ......................................................................................................................................... 12
3.1 Literature study....................................................................................................................... 12
3.2 Model study............................................................................................................................ 13
3.2.1 SWAT model description................................................................................................... 13
3.2.2 Data description ................................................................................................................ 14
3.2.3 Pre-processing & data analysis ......................................................................................... 15
3.2.4 Modelling steps................................................................................................................. 18
3.2.5 Model Parameterization..................................................................................................... 19
3.2.6 Sensitivity analysis, calibration & validation ...................................................................... 19
4 Objective: SWAT modelling in wetlands.......................................................................................... 22
4.1 SWAT wetland processes....................................................................................................... 22
4.1.1 Wetlands modelled as filter strips in SWAT........................................................................ 22
4.1.2 Wetlands modelled as water bodies in SWAT.................................................................... 24
4.2 SWAT wetlands in the context of actual wetland processes..................................................... 25
4.3 Previous SWAT studies in wetland areas................................................................................ 26
4.3.1 SWAT – HEC-RAS coupled model .................................................................................... 27
4.3.2 Hydrologic Equivalent Wetland concept............................................................................. 27
4.3.3 SWAT wetland extension module for flow and sediments .................................................. 28
4.3.4 SWAT-HEW wetland extension module for flow, sediments and nutrients in a coupled model
28

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4.3.5 SWIM wetland processes.................................................................................................. 28
4.3.6 SWAT – SUSTAIN coupled model..................................................................................... 29
4.3.7 SWATrw: enhanced wetland module for use in riparian depressional wetlands.................. 29
4.3.8 A nitrogen improved SWAT wetland module...................................................................... 29
5 Objective: SWAT modelling in the tropics ....................................................................................... 30
5.1 Evapotranspiration in SWAT ................................................................................................... 30
5.2 Plant growth in SWAT versus the tropics................................................................................. 31
6 Objective: Hydrological model results ............................................................................................. 34
6.1 Data analysis.......................................................................................................................... 34
6.1.1 Weather data .................................................................................................................... 34
6.1.2 Flow data .......................................................................................................................... 34
6.2 Previous modelling efforts, model parameterization and water balance expectations............... 36
6.2.1 Water balances of previous studies ................................................................................... 37
6.3 First run with manual calibration.............................................................................................. 39
6.4 Sensitivity analysis.................................................................................................................. 40
6.5 Model results .......................................................................................................................... 42
6.5.1 Water Balance .................................................................................................................. 42
6.5.2 Model parameters ............................................................................................................. 43
6.5.3 Model performance ........................................................................................................... 44
6.5.4 Additional results............................................................................................................... 46
7 Discussion and recommendations .................................................................................................. 47
7.1 Hydrologic characteristics ....................................................................................................... 47
7.2 Model uncertainties................................................................................................................. 47
7.3 Weaknesses and strengths..................................................................................................... 50
7.4 Model adaptations in the context of the Mara .......................................................................... 51
7.5 Feasibility of a water quality model at LVB scale ..................................................................... 51
7.6 Recommendations.................................................................................................................. 52
8 Conclusion ..................................................................................................................................... 54
9 Recommendations for the LVBC on water quality modelling ........................................................... 56
10 References................................................................................................................................. 58
11 Appendix .................................................................................................................................... 76

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List of figures
Figure 1-1: The Lake Victoria Basin, its countries, main rivers, lakes and wetlands................................... 1
Figure 1-2: Water Hyacinth invasion at Lake Victoria, in August 2012 (Munyaga, 2012)............................ 2
Figure 1-3: Report structure. .................................................................................................................... 6
Figure 2-1: Relief of the Mara River Basin. Derived from the SRTM DEM 30m (USGS, 2016)................... 7
Figure 2-2: Mara River Basins protected areas and wetlands. .................................................................. 8
Figure 2-3: Change in size of the Kirumi wetland from 1973-2006 (Mturi, 2007)....................................... 9
Figure 2-4: a) Left side: Phosphorus forms and mechanisms within a wetland (Reddy et al., 1999). b) Right
side: Nitrogen cycle divided up into the aerobic and the anaerobic parts (Breuer et al., 2014). ................ 10
Figure 3-1: Scheme of the general process of achieving results in this study .......................................... 12
Figure 3-2: Locations of the flow stations and meteorological stations with rainfall observations that are
used in the data analysis........................................................................................................................ 18
Figure 3-3: Calibration scheme for SWAT flow modelling........................................................................ 21
Figure 4-1: A vegetated filter strip reducing runoff of water, sediment and nutrients towards the main
channel.................................................................................................................................................. 23
Figure 4-2: Two filter strip sections of an HRU, filtering a user-defined fraction of the total runoff flow,
before discharging in the main channel. ................................................................................................. 23
Figure 4-3: Scheme of the coupled SWAT wetland model containing HEW’s (Yang et al., 2016) ............ 28
Figure 4-4: SWIM nutrient processes implemented in the advanced approach:....................................... 29
Figure 5-1: Leaf area index as a function of the fraction of the growing season....................................... 32
Figure 5-2: MODIS LAI leaf area index for tropical forest plots in the upper Mara................................... 33
Figure 6-1: Data coverage of discharge data in the Mara River Basin for several flow stations................ 36
Figure 6-2: Seasonal pattern of SWAT computed PET with the (a) Penman-Monteith and (b) Hargreaves
method, using weather data from the SWAT weather generator, CFSR and WATCH data sets .............. 37
Figure 6-3: Water balance for the model its first run for the period from 1979-1982, with the Penman-
Monteith method. ................................................................................................................................... 40
Figure 6-4: The average water fluxes as given in SWAT for the model period of 1979-1986.................... 42
Figure 6-5: Monthly water balance for all the (sub-)basins that are used for calibration and validation..... 43
Figure 6-6: Potential evapotranspiration and actual evapotranspiration fluxes and soil water content
outcome of the SWAT model using the Hargreaves method. .................................................................. 43
Figure 6-7: Calibration and validation results for flow in the Mara basin, at different stations. .................. 45
Figure 7-1: Change in forest cover fraction in the Nyangores and Amala tributaries................................ 48
Figure 7-2: Time series of annual rainfall anomalies for the Musoma (Tanzania) rainfall station .............. 49
Figure 7-3: Monthly mean discharge (left) and monthly weighted arithmetic mean discharge (right) of the
main rivers draining into Lake Victoria over the period 1950-2000. ......................................................... 52
Figure 11-1: On the left, sources of nitrogen and phosphorus and their annual loads to the Lake Victoria,
on the right the external loadings of BOD, N and P to the lake Victoria for different pollution sources...... 79
Figure 11-2: Population density growth in the Lake Victoria Basin. ......................................................... 81

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Figure 11-3: Fertilizer Consumption of the Lake Victoria Basin Countries and the World Average........... 81
Figure 11-4: SWAT pathways for water transport (Kilonzo, 2014) ........................................................... 83
Figure 11-5: Pollutant transport mechanisms in ArcSWAT2012 (Immerzeel, 2016)`................................ 87
Figure 11-6: Soil nitrogen cycle (Neitsch et al., 2011). ............................................................................ 89
Figure 11-7: SWAT soil nitrogen pools and processes (Neitsch et al., 2011).......................................... 89
Figure 11-8: Phosphorus cycle (Neitsch et al., 2011).............................................................................. 92
Figure 11-9: SWAT soil phosphorus pools and processes (Neitsch et al., 2011) ..................................... 92
Figure 11-10: Logarithmic daily rainfall probability distribution for the rainfall gauging station in or near the
Mara River basin and for the CFSR mean rainfall for the Mara River basin............................................102
Figure 11-8: Locations of the selected rain gauges and their corresponding Thiessen Polygons............103
Figure 11-12: Average number of precipitation stations with observations per year in the Mara Basin....104
Figure 11-10: Station 9035227 – District office Bomet rain gauge double mass curve............................105
Figure 11-11: Station 9135025 – Ilkerin project rain gauge double mass curve ......................................105
Figure 11-15: 9035031 – Danson K. Ngugi rain gauge double mass curve ............................................106
Figure 11-16: Slope of the Mara river basin. Derived from the SRTM DEM 30m (USGS, 2016) .............107
Figure 11-17: AFRICOVER land use map (FAO,2004). .........................................................................109
Figure 11-18: Land use map from the MaMaSe project (Zheng, 2014)...................................................109
Figure 11-19: Scheme on how the KENSOTER and SOTERSA soil ISOC-SUID shapes have been
generalised from 34 into 21 soil types for the Mara river basin...............................................................116
Figure 11-20: Date coverage for station 1LB02 Amala river. ..................................................................119
Figure 11-21: Data coverage for station 1LA03 Nyangores river. ...........................................................119
Figure 11-22: Data coverage for station 1LA04 at Mara river. ................................................................119
Figure 11-23: Data coverage for station 1LA05 at Mara Serena.............................................................119
Figure 11-24: Data coverage for station 5H2 at Mara mines ..................................................................120
Figure 11-25: Flow duration curves for station 1LB02 at Amala river, for different time periods ..............121
Figure 11-26: Rating curves for station 1LB02 at Amala river for different time periods ..........................121
Figure 11-27: Cumulative discharge curves for each year (5 graphs) and a table with the missing
discharge data of station 1LB02 at Amala river......................................................................................122
Figure 11-28: Discharge over time for each individual year at station 1LB02 at Amala river. ..................122
Figure 11-29: Stage height over time for each individual year at station 1LB02 at Amala river and a table
with the missing stage data. .................................................................................................................122
Figure 11-30: Discharge over time versus the precipitation averaged with Thiessen polygons for the
upstream area of station 1LB02 at Amala river. .....................................................................................123
Figure 11-31: Discharge and precipitation averaged with Thiessen polygons over time for the upstream
area of station 1LB02 at Amala river......................................................................................................123
Figure 11-32: Discharge versus precipitation averaged with Thiessen polygons for the upstream area of
station 1LB02 at Amala river. ................................................................................................................124
Figure 11-33: Flow duration curve for station 1LA03 at Nyangores river.................................................125

ix
Figure 11-34: Rating curve for station 1LA03 at Nyangores river for the years 1964 till 2010. ................126
Figure 11-35: Cumulative discharge curve for station 1LA03 at the Nyangores river for each individual year
(5 graphs) and a table of the missing data for station 1LA03..................................................................126
Figure 11-36: Stage heights at station 1LA03 at the Nyangores river for each individual year. ...............127
upstream area of station 1LA03 at Nyangores river. ..............................................................................127
upstream area of station 1LA03 at Nyangores river, displayed at a logarithmic scale. ............................127
station 1LA03 at Nyangores river. .........................................................................................................128
Figure 11-40: Flow duration curves for monitoring station 1LA04 at Mara river, for each individual year.129
Figure 11-41: Rating curve for station 1LA04 at Mara river, for the period from 1970 to 2010.................129
Figure 11-42: Cumulative discharge plotted for each individual year at station 1LA04 at Mara river (5
graphs) and a table displaying the missing discharge data for each year. ..............................................129
upstream area of station 1LA04 at Mara river. .......................................................................................130
Figure 11-44: Discharge over time versus the precipitation over time averaged with Thiessen polygons for
the upstream area of station 1LA04 at Mara river, displayed at a logarithmic scale. ...............................130
station 1LA04 at Mara river. ..................................................................................................................130
Figure 11-46: Discharge records for monitoring station 1LA04 at Mara river. .........................................131
Figure 11-47: Flow duration curve for monitoring station 1LA04 at Mara river for each individual year....131
Figure 11-48: Stage height records for monitoring station 1LA04 at Mara river.......................................131
Figure 11-49: Rating curve for station 1LA05 at Mara river. ...................................................................132
Figure 11-50: Missing discharga data for station 1LA05.........................................................................132
Figure 11-51: Cumulative discharge record for station 1LA05 at Mara river for each individual year.......132
Figure 11-49: Stage height records for monitoring station Nyansurura at Mara river, for each individual
year. .....................................................................................................................................................132
Figure 11-53: Flow duration curves for station 5H2 at Mara Mines for each individual year. ...................133
Figure 11-54: Rating curves for station 5H2 at Mara Mines for the period of 1969 to 2013. ....................134
Figure 11-55: Discharge records for station 5H2 at Mara Mines for each individual year (5 graphs) and a
table containing the percentage of missing discharge and stage data per recorded year........................134
Figure 11-56: Stage height record for station 5H2 at Mara Mines for each individual year......................135
Figure 11-57: Cumulative discharge record for station 5H2 at Mara mines for each individual year. .......135
Figure 11-58: Discharge over time and the precipitation over time averaged with Thiessen polygons for the
upstream area of station 5H2 at Mara mines per individual year. ...........................................................136
Figure 11-59: Discharge over time versus the precipitation over time averaged with Thiessen polygons for
the upstream area of station 5H2 at Mara mines....................................................................................136
station 5H2 at Mara river.......................................................................................................................136

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Figure 11-58: Stage height records at station 5H3 at Kirumi bridge, the outlet of the Mara river basin (2
figures) and a table containing the percentage of missing data for station 5H3.......................................137
Figure 11-62: Agricultural land LAI growth curves in SWAT under different settings in the Mara basin ...141
Figure 11-63: Leaf area index growth curve for the years 1979-1982 of the model run...........................141
Figure 11-64: The shallow aquifer storage [mm] for some of the HRU’s for the final model run with the
Hargreaves method used for the period of 1979-1986. ..........................................................................142
Figure 11-65: Flow model results with the best parameters for different weather inputs .........................143
Figure 11-66: Flow model results with the best parameters for different Potential-evapotranspiration
estimate methods in the period 1979-1982. ...........................................................................................144
Figure 11-67: Water balance for the model run with the best parameters from calibration, but with the
settings on Penman-Monteith PET-method, for the years 1979-1986.....................................................145
Figure 11-68: Water balance for the model run with the best parameters from calibration, but with the
settings on Priestley-Taylor PET-method, for the years 1979-1986........................................................145
Figure 11-69: Flow model results with and without deep aquifer recharge for the period of 1979-1986...146
Figure 11-70: Forms of phosphorous in the water and sediment river column including the processes
transforming or transporting the phosphorus (Daldorph et al., 2015).....................................................149
Figure 11-71: Nitrogen in spiralling in streams.......................................................................................150

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List of tables
Table 2-1: Nutrient retention in the Masura Mara wetland.. ..................................................................... 11
Table 3-1: General performance ratings for recommended statistics for a monthly time step................... 12
Table 3-2: Literature sources of SWAT model studies ............................................................................ 13
Table 3-3: Keywords used in the SWAT literature study ......................................................................... 13
Table 3-4: Data used as SWAT model input and for data analysis purposes........................................... 14
Table 3-5: options on calculation methods for several hydrological processes available in SWAT........... 19
Table 4-1: Overview of previous wetland studies of SWAT wherein the model is coupled or modified. .... 27
Table 6-1: Statistics on the first model run.............................................................................................. 39
Table 6-2: Water balance ratios for the first model run, for the period from 1979-1982............................ 40
Table 6-3: Outcome of the global sensitivity analysis.............................................................................. 41
Table 6-4: Water balance ratio’s for the whole Mara river basin, the expected ratio’s from literature
assumptions and the model outcome using the Hargreaves PET method for the period 1979-1986. ....... 42
Table 6-5: Statistics on the calibration results for the Amala, Nyangores and Mara Mines calibration points
in the period of 1979-1982...................................................................................................................... 44
Table 6-6: Statistics on the validation results for the Amala, Nyangores and Mara Mines validation points
in the period of 1985-1986...................................................................................................................... 44
Table 7-1: Land cover change over the years in the Mara river basin (MEMR, 2012).............................. 48
Table 11-1: Lake Victoria Basin shoreline length and lake surface and catchment area (LVBC, 2007) .... 78
Table 11-2: Removal rates / retention of wetlands in the Lake Victoria Basin .......................................... 80
Table 11-3: SWAT studies done focusing on wetland areas and their model performances.. .................. 94
Table 11-4: Sensitivity analysis ranking of previous modelling efforts done in the Mara River Basin........ 96
Table 11-5: The calibrated parameter values of the Mara catchment as calibrated in previous studies.... 96
Table 11-6: Reported SWAT parameter values that are controlling losses in East-Africa ....................... 98
Table 11-7: Previous SWAT model studies in the Mara river basin ......................................................... 99
Table 11-8: Suggestions for alterations of plant and management operation parameters from previous
research in the Mara river basin. ............................................................................................................ 99
Table 11-9: Manning’s roughness coefficient n for overland flow in the lower Mara river basin...............100
Table 11-10: Water balance for previous researches done in the Mara basin.........................................101
Table 11-11: Data coverage and locations for the rainfall.......................................................................103
Table 11-12: Description of agro-ecological zones in the Mara River Basin............................................108
Table 11-13: Land use cover names in the MaMaSe land use map (Zheng, 2014) and their corresponding
SWAT class with a description on land use. ..........................................................................................108
Table 11-14: Required ArcSWAT2012 soil parameters for model set-up................................................110
Table 11-15: Reclassification FAO soil codes corresponding to soil types in the Mara ...........................111
Table 11-16: Potential pedotransfer functions (PTFs) used to calculate bulk density..............................111
Table 11-17: Statistical analyses used in determination of the most suitable pedotransfer function.. ......112

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Table 11-18: Statistical analysis on correlation PTFs. The pedotransfer functions were applied to
KENSOTERv2 database. ......................................................................................................................112
Table 11-19: Statistical analysis on correlation PTFs. The PTFs applied to KENSOTERv2 database for soil
types in the Mara catchment. ................................................................................................................112
Table 11-20: Statistical analysis on correlation PTFs. The pedotransfer functions applied to
SOTWISE_KENv1 database for soil types apparent in the Mara catchment...........................................113
Table 11-21: Textural soil classes according to the USDA soil texture triangle (simplified) .....................113
Table 11-22: Criteria set for soil hydraulic groups. .................................................................................113
Table 11-23: Conversion table of WISE soil database rootable depth, to SWAT max. rooting depth.......113
Table 11-24: Statistical comparison of different PTFs for the calculation of the available water content..114
Table 11-25: Percentage of soil types in the Mara falling within the soil sample range used to derive the
Jabro equation. .....................................................................................................................................114
Table 11-26: Conversion table for FAO SOTER database surface stoniness to the rock fragment required
by SWAT. .............................................................................................................................................115
Table 11-27: Flow station coordinates ...................................................................................................119
Table 11-28: Ratio of cumulative discharge and cumulative precipitation for the period between 1979 and
2014 for different sources of flow and discharge data.. ..........................................................................120
Table 11-29: Rating equations for station 1LB02 as found in the GLOWS report (Subalusky, 2011b).....121
Table 11-30: Rating equations for station 1LA03 as found in the GLOWS report (Subalusky, 2011b).....124
Table 11-31: Rating equations for station 1LA05 as found in the GLOWS report (Subalusky, 2011b).....131
Table 11-32: Best parameter values, that came out of the calibration procedure for the period from 1979-
1982 using the Hargreaves method.......................................................................................................140
Table 11-33: Characteristics of the HRU’s presented in Figure 11-64. ...................................................142
Table11-34: Statistics on flow model results for different weather inputs for the period 1979-1982. ........143
Table11-35: Flow model statistics results with the best parameters for the model that was calibrated with
WATCH data for different Potential-evapotranspiration estimate methods in the period 1979-1982........144
Table 11-36: A description of several water quality models....................................................................156
Table 11-37: Previous modelling efforts done in (East) African countries. ..............................................159

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List of abbreviations
AGL Above Ground Level
BOD Biological Oxygen Demand
DEM Digital Elevation Model
DIP Dissolved Inorganic Phosphorus
DON Dissolved Organic Nitrogen
DOP Dissolved Organic Phosphorus
EAC East African Community
GCM Global Circulation Model
HEW Hydrologic Equivalent Wetland
HYDATA Hydrological database and analysis system
KfW Kreditanstalt für Wiederaufbau – Bankengruppe
LVB Lake Victoria Basin
LVBC Lake Victoria Basin Commission
LVB-IWRM Lake Victoria Basin Integrated Water Resource Management
LVEMP I Lake Victoria Environmental Management Programme Phase I:
a comprehensive programme conducted by the then three EAC Partner States
namely, Republic of Kenya, Uganda and the United Republic of Tanzania.
LVEMP I was aimed at rehabilitation of the lake’s ecosystem for the benefit of
the 30 million people who live in the catchment, their national economies and
the global community.
LVEMP II Lake Victoria Environmental Management Programme Phase II:
an EAC regional initiative coordinated by the LVBC Secretariat and
implemented by the Five EAC Partner States. The programme’s purpose is to
contribute to “a prosperous population living in a healthy and sustainably
managed environment providing equitable opportunities and benefits” in the
LVB.
MRB Mara River Basin
MWE Uganda Ministry of Water and Environment Uganda
MWI Kenya Ministry of Water and Irrigation Kenya
N Nitrogen
NH4
+
Ammonium
NH4-N Ammonium-nitrogen
Nile Basin DSS Nile Basin Decision Support System
NO2
-
Nitrite
NO3
-
Nitrate
P Phosphorus
P-M method Penman-Monteith potential evapotranspiration method
P-T method Priestley-Taylor potential evapotranspiration method
PTF Pedotransfer Functions
SWAT Soil Water Assessment Tool
SWIM Soil and Water Integrated Model
TN Total Nitrogen
TP Total Phosphorus
TRP Total reactive phosphorus
TSS Total Suspended Solids
VFS Vegetative Filter Strips
WMO World Meteorological Organization
WRIS Water Resources Information System
WRMA Water Resource Management Authority (Kenya)

1 Introduction
This chapter describes why a Lake Victoria Basin Integrated Water Resources Management (LVB-IWRM)
Programme is needed. Secondly, the chapter describes on which cornerstone (aims1
) of the programme
this report focusses and how this cornerstone is delimited into an hydrology master thesis subject.
Thereafter the research question and objectives are formulated. The last section describes how the report
is structured.
1.1 The need for a LVB-IWRM Programme
This thesis is commissioned by SWECO Netherlands, who were employed as a consultant for the Lake
Victoria Basin Commission (LVBC) to work on an Integrated Water Resources Management (IWRM)
Programme. This programme was developed out of a need to improve the water quality within the Lake
Victoria Basin (LVB) and strengthen the tools, resources and cooperation between the water authorities
responsible for a healthy and sustained environment in the LVB.
Figure 1-1: The Lake Victoria Basin, its countries, main rivers, lakes and wetlands.
The need for this IWRM-project can be explained by the high population density of the Lake Victoria Basin
exerting a great pressure on its rivers and on the fragile ecosystem of the Lake Victoria (Cheruiyot, 2015;
Ouma et al., 2016). The lake is the second greatest lake of the world in surface area but has a low depth to
surface area rate as its average depth is only 40 meters. Its flushing time of 138 years makes it more
vulnerable to pollution impacts than smaller lakes with lower flushing times (Awange & Ong’ang’a, 2006).
1
These aims have already been discussed in the preface.
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Mwanza Gulf
Wetlands
36
37
42
41
40
39
38
44
45
43
47
49
48
46
46
50
Mori Bay
wetlands
Wazimenya Bay
wetlands
Kyojja
wetlands
Lake Wamala
Lake Mburo
Complex
Kagera Lakes
complex
Mara Wetlands
Insinga wetlands
complex
Nabugado
wetlands
Kijanebalola
lake/swamp
Nabajjuzi
wetlands
Minziro sango
bay swamp
Akanyaru
wetlands
Mabamba-Lutembe
Complex
Nai swamp
Katonga wetlands
Nyando / Kano
wetlands
Rwizi-Rufuha
Complex
Muzizi
wetlands
Kingwal
swamp
Rugesi
wetland
Sio Siteko
Wetlands
Saiwa swamp
Nyabarongo
wetlands
Ngono wetlands
system
Ukerewe
Island
Lake Burigi
wetlands
Yala swamp
Kome island
Napoleon Gulf
Complex
Lake Ikimba
wetlands
Kisii
wetlands
Kome
Island
Mfangano
island
Bumbire
Island Victoria Bunda
Bay wetland
Grumeti Wetlands
Simiyu Wetlands
Kalanga
23
12
5
17
34
2
1
8
4
31
33
3
30
32
16
28
15
18
25
21
24
10
29
19
22
27
20
9
13
14
26
6
7
11
35
Mara
Kager
a
Nzoia
Yala
Mwisa
Grumeti
R
uv
ubu
Simiyu
Katonga
Mig
ori
M
w
ogo
M
ba
lageti
S io
Gucha
Nyando
Sond u
Aka
nyaru
Nyab
a
rongo
Ngozi
Jinja
Kisii
Migori
Masaka
Tarime
Gitega
Butare
Kitale
Mwanza
Bukoba
Musoma
Kisumu
Mityana
Shyanda
Mutumba
Muyinga
Gatonde
Ruhondo
Entebbe
Kericho
Bungoma
Bariadi
Mbarara
Eldoret
Kakamega
Sengerema
Buseresere
Nyabugombe
Kigali
Kampala
Mara
Nzoia
Katonga
Nyabarongo
Simiyu
Grumeti
Ruvubu
Lower Kagera
Bukora
Isanga
Middle Kagera
Yala
Sondu
Gucha-Migori
Nyando
Sio
Southern shore streams
Mbalageti
Magogo-Moame
South Awach
Nyashishi
Eastern shore streams
North Awach
Biharamulo
Northern shore streams
Northern shore streams
Western shore streams
Lake Victoria Islands
36°0'0"E
36°0'0"E
34°0'0"E
34°0'0"E
32°0'0"E
32°0'0"E
30°0'0"E
30°0'0"E2°0'0"N 2°0'0"N
0°0'0" 0°0'0"
2°0'0"S 2°0'0"S
4°0'0"S 4°0'0"S
Wetlands
in the Lake Victoria basin
UGANDA
KENYA
TANZANIA
RWANDA
BURUNDI
DEMOCRATIC
REPUBLIC
OF CONGO
SOUTH
SUDAN
0 40 8020 km
Sources:
Background layer (Ecosystems of the lake Victoria, September 2013) :
IUCN, UNEP, 2013
Consultants modifications suggested by stakeholder’s consultation
LVBC, 2011 (b)
REMA, 2011
Google earth accessed in July 2013
Additional information:
MEMR, UNEP, 2012
Bogers, 2007
UNDP, NEMA, UNEP, 2009
MoWE et al, 2009
Google earth accessed in October 2014
Date: October 2014
Lake Victoria Basin
Water Resources
Management Plan
Phase 1
ETHIOPIA
SOMALIA
INDIAN
OCEAN
UGANDA
DEMOCRATIC
REPUBLIC
OF CONGO
RWANDA
BURUNDI
KENYA
TANZANIA
VictoriaNile
LAKE VICTORIA
LAKE KYOGA
LAKE ALBERT
LAKE EDWARD
LAKE KIVU
LAKE TANGANYIKA
LAKE MANYARALAKE EYASI
LAKE VICTORIA BASIN COMMISSION
Legend:
! Main cities
Main water system
Lake Victoria Basin
Lake Victoria sub-basinMara
Country boundaries
Wetlands
Lakes
Name of lakes:
Name of Islands:
1
2
1 Bunyonyi lake 13 Kanzigiri lake 25 Lac Rwanye
2 Lake Burera 14 Lake Rwihinda 26 Lake Mihindi
3 Lake ruhundo 15 Lake Bisongu 27 Lake Rushwa
4 Muhazi lake 16 Lake Mujunju 28 Lake Nakivali
5 Lake Cyohoha sud 17 Lake Ihema 29 Lake Mburo
6 Rumira lake 18 Lake Cyambwe 30 Kachira lake
7 Lake Cyohoha nord 19 Lake Nasho 31 Lake Ikimba
8 Lake Mugesera 20 Lake Mpanga 32 Lake Nabugado
9 Birara lake 21 Lake Hago 33 Lake Kijanebalola
10 Lake Sake 22 Lake Kivamba 34 Lake Wamala
11 Gaharwa lake 23 Lake Burigi 35 Lake Simbi
12 Rweru lake 24 Lake Lwelo
36 Lulamba Island 41 Vumba Island 46 Ikusa Island
37 Bunyama Island 42 Sagitu Island 47 Rubondo Island
38 Buyoyu Island 43 Lolui Island 48 Maisome Island
39 Bukesa Island 44 Ukora Island 49 Sigulu Island
40 Bugala Island 45 Victoria West Island 50 Dagusi island
1
2

2
REPORT: ASSESSING THE ABILITY OF SWAT AS A WATER QUALITY MODEL IN THE LAKE
VICTORIA BASIN AND ITS WETLANDS
Current pressures on the lake and its rivers are high erosion rates and sediment inputs caused by land use
change, deforestation and wetland destruction. Wetlands are abundant in the Lake Victoria Basin and retain
or remove a large part of the pollution carried into the lake by its rivers. Another pressure is dumping of
untreated sewage water, industrial effluents, animal waste and solid waste, as the LVB has relatively little
operating sewage treatment systems or sewage connections. The atmospheric deposition of nitrogen and
phosphorus has a large impact on the eutrophication of Lake Victoria due to the large surface area as it
deposits largely as wet deposition. This is not surprising considering that direct precipitation accounts for
82% of the lakes water inflows (Awange & Ong’ang’a, 2006; Lehman, 2009). The deterioration of the river
and lake water quality poses a threat to the health of the people in the lake riparian countries, its economy
and environment. About 70% of the LVB population utilises raw water in some form, therefore threatening
their health. Most problems are related to contaminated water and poor sanitation increasing typhoid,
cholera, dysentery, and malaria risks (Lubovich, 2009). The fisheries of Lake Victoria are a large share of
the economy of the riparian countries (Matsuishi et al., 2006). Effects of periods of anoxia in the lake are
increased due to increased eutrophication, thus causing higher fish mortality rates. Eutrophication
furthermore causes water hyacinths to invade the bays and shores and toxic cyanobacteria population to
bloom in the lake. Pressures are likely to increase due to (1) economic developments as commercial fish
cage farms increase lake eutrophication (SWECO, 2016), (2) Global climate change, increasing
meteorological extremes (Geoffrey, 2008), (3) a drastic increase in fertilizer use (International Fertilizer
Industry Association, 2016; The World Bank, 2016) and (4) the high population growth rate of 3 to 4% per
year in the LVB (UNEP, 2006).
Figure 1-2: Water Hyacinth invasion at Lake Victoria, in August 2012 (Munyaga, 2012).
1.2 Problem definition
My role was to help on the specification of an IWRM water quality model as this was one of the cornerstones
of the LVB-IWRM programme that is to be implemented in the coming years. For fulfilling its tasks, the IWRM
model needs to be able to correctly model the driving processes behind pollution, its causes, and its effects.
Topic delineation was done by delimiting on (1) the water quality parameters to be modelled, (2) the type
of model that can be used and tested, and (3) the model challenges that arise when using such a model in
the LVB. Delineation of the research topic was done by literature research and based on a needs
assessment wherein the LVB partner states its water authorities and ministries were interviewed.2
1) Water quality parameters to be modelled
Shortcomings in data availability complicate model calibration and validation and increases model
inaccuracies (Perrin et al., 2007). These shortcomings exclude certain water quality parameters from being
modelled. The data (March 2016) made available for the LVB-IWRM Programme were analysed on data
availability in time and space. The largest share of available water quality parameters in monitoring data
were the ones that were established as key parameters for water quality monitoring during the Lake Victoria
2
The purpose of this needs assessment and the summary of the outcomes are given in Appendix L).

3
Environmental Management Programme phase I (LVEMP I), because these are still being used as key
parameters by most LVB partner state countries. Evidence from the needs assessment and from literature
research indicated that most of the water quality problems and monitoring and research has focused on the
eutrophication, sedimentation and erosion problems of the LVB. These problems are not local but of regional
importance in the LVB in contrast to other problems as high levels of heavy metals in rivers (Cheruiyot,
2015; Kiragu, 2009; Lake Victoria Basin Commission, 2007; Muyodi et al., 2010). Therefore the parameters
Total Suspended Solids (TSS), Total Nitrogen (TN) and Total Phosphorus (TP) are chosen as most relevant
parameters in an IWRM model.
2) Type of model
The water quality model to be used to model for the Lake Victoria basin is to be determined. The model
should be ‘of the shelf’ and should already be able to model the total nitrogen, total phosphorus and total
suspended solids in the tributary rivers. An examples of such a model is the GIS-based Soil Water
Assessment Tool Model (SWAT).
In this study we chose SWAT as the model to test whether or not the model challenges can be
overcome.
The reason for using the SWAT model for the pilot study is that the needs assessment3
indicated that there
was a demand for a model that 1) can be used in data scarce areas, 2) is already being used by the LVB
and EAC countries/institutes/researchers/universities, so that there is less capacity building required, 3) is
already existing (an off-the-shelf model), 4) is relatively easy to use for novices, 5) includes erosion
modelling and 6) can be plugged-in as an adapter to the NILE Basin Decision Support System (NILE DSS)
framework that is being used by almost all the LVBC partner states and its Ministries of Water Resources
and Environment.4
3) Model challenges in the Lake Victoria Basin
In order for a water quality model to be successfully implemented as a tool for IWRM, three problems will
have to be overcome, depending on the process that is modelled.
The first problem is data scarcity, looking at the availability of meteorological, flow and water quality data.
Missing meteorological data is not necessarily problematic. Meteorological data can nowadays be largely
obtained from satellite products as climate models, although its accuracy on precipitation is often less
compared to rain-gauge data, especially at short time-scales (Li et al., 2015). Continuous meteorological,
water quantity and quality data records are something that usually needs to be bought at local water
authorities, and is often erroneous and hard to gather3
. Secondly, the model codes themselves may not be
able to simulate processes occurring on the detail level needed. Thirdly, process knowledge itself may be
lacking. In data scarce areas like the LVB a combination of these three challenges will be the cause for not
being able to model processes at the needed detail.
SWAT is the most commonly used hydrologic model in the LVB together with the MIKE model. Most studies
in the upper Nile basin countries have been restricted to the hydrologic part, in which SWAT performed
reasonable (Griensven et al., 2012b). Evidence that SWAT is able to perform reasonable in modelling water
quantity in the LVB is present (Alemayehu, Griensven, & Bauwens, 2015; Kilonzo, 2014; Kimwaga et al.,
2012; Mahay, 2008; Mulungu & Munishi, 2007; Nyolei, 2012). In contrast to water quantity models, nutrient-
and sediment model studies on river basin scale in the LVB are rare if available at all3
. SWAT is a model
that has originally been developed for the temperate climate of the United States. If one compares the area
where for SWAT was developed to the Lake Vitoria basin a number of differences stand out which can be
hypothesised as being challenging for SWAT to model water quality in the LVB:
3
See Appendix L) for the summary of the needs assessment.
4
An overview on frequently used water quality models is given in Appendix M) and N).

4
Firstly, the LVB contains numerous wetlands (Figure 1-1) that alter the flow, sediment- and nutrient inputs
into the lake. The hydrology and nutrient cycles in wetlands have complex interactions and feedbacks.
These processes become even more complex in wetlands whose presence and retention rates is partly
determined by the Lake Victoria’s water levels (Mturi, 2007; Mugisha et al., 2007). Annual water level
fluctuations have been up to 1.5 meter per year, whereas the water level fluctuates 0.25 meter seasonally
(Hassan & Jin, 2014). It is challenging to model wetlands on regional and catchment scale (Hattermann et
al., 2008). The modelling of wetlands in SWAT has rarely been done, especially considering water quality
modelling5
. This can be explained by the simple way the model deals with wetland considering them as
either filter strip or settling pond (Records et al., 2014). If SWAT is used as a hydrological and water quality
model in the Lake Victoria basin, it is important to know whether it can perform reasonable in a LVB pilot
study area (Mara river basin). There has been one study that has incorporated wetlands in its hydrological
model in the MRB. In this study SWAT was coupled to an HEC-RAS model which simulated the water level
in the wetland by coupling it to lake levels (Mahay, 2008). The strong and weak sides of wetland modelling
at basin scale with SWAT need to be highlighted. The best performing SWAT studies modelling water quality
and quantity in wetlands so far have been mostly coupled models or modified versions (Hattermann et al.,
2008; Liu et al., 2008; Breuer et al., 2014; Rahman et al., 2016; Yang et al., 2016). These and other previous
attempts done in order to improve SWAT wetland modelling need to be listed and discussed in order to
improve wetland modelling.
The second challenge for SWAT that is studied is related to its functioning in climate conditions the model
was not designed for. The Lake Victoria basin area is characterised by a tropical climate. There are
numerous differences in hydrological and nutrient processes between temperate and tropical zones (Lal,
1983; Vitousek, 1984; Singh et al., 1991; Bustamante et al., 2006; Wohl et al., 2012) . It is important to
explore how and if these differences possibly affect model outcomes in SWAT water (quality) modelling.
Thus far most studies have been restricted to hydrologic modelling wherein they have changed default
settings in SWAT or modified the model to cope with the different processes in a tropical environment
(Alemayehu et al., 2015; Mwangi, Julich, Patil, Mcdonald, & Feger, 2016).
1.3 Objectives & Research questions
This study aims to help fill the knowledge gaps stated in the previous section. To that end, this report will
provide an overview of the weaknesses and strengths of SWAT in water quality modelling of the Lake
Victoria Basin, focusing on wetlands as a key aspect needed for successful implementation of a model in
IWRM. This will hopefully be a step forward into specifying a well-functioning, easy to use water quality
model for the Lake Victoria Basin Commission. The knowledge gaps and project targets were combined
and translated into the following research question(s):
 What are the weaknesses & strengths of SWAT in modelling the Lake Victoria Basins total
suspended sediments, nitrogen and phosphorus?
This main research question can be divided into sub questions:
1. What are the hydrologic characteristics of the Mara river?
The first sub-question on hydrologic characteristics can be interpreted as a question to what the water
balance of the LVB pilot area (Mara river basin) looks like in general, seasonally and spatially according to
the SWAT model study and literature.
5
A summary of all relevant SWAT wetland modelling studies thus far is given in Appendix C).

5
2. What modifications/adaptions* have been made to SWAT in hydrological or water quality studies**
in basin areas of similar characteristics***?
* Coupled models, changes in default settings or SWAT runs in different environment with different
program code (added or adjusted formulas and parameters)
** Focusing on TSS, TN and TP parameters
*** Other SWAT models in tropics and/or wetland areas
The second sub-question can be interpreted as a question to how the SWAT model has been altered in the
past in order to copy with modelling flow, sediments and/or nutrients in basins that contain wetlands and/or
are located in the tropics.
3. Why are these modifications/adaptions made?
The third question looks at the nature of these changes. Why are these changes made or why weren’t any
changes made? In other words, what did previous SWAT users find lacking, weak or strong on the SWAT
model when modelling flow or water quality in an area similar to the Mara river basin?
From the research question the following objectives follow:
1. To assess the weaknesses and strengths of SWAT in water quality modelling in the Mara Basin.
2. To analyse the precipitation and flow gauge data available in the Mara River Basin.
3. To simulate hydrology7
and their spatial-temporal distribution in the Mara River Basin using
ArcSWAT 2012 and describe hydrological characteristics from this.
Besides for filling the objectives and answering the research question, this report will in its preparation also
serve the purpose of exploiting the pre-requisites for using SWAT as a water quality modelling tool in the
LVB by the LVBC and its partner states. Furthermore, it touches upon complexities in the modelling process
with SWAT and provides insight into the possibilities with SWAT. The objectives will, in the end, be placed
into the broader context of the Lake Victoria Basin scale and the current water quality model framework in
the East-African Countries. The report includes a series of recommendations for the Lake Victoria Basin
Commission considering the choice of future water quality modelling use and in specific the capability of
SWAT to fulfil this role. These recommendations hopefully will help the LVB-IWRM Programme to find a
suitable model and to give a preliminary answer to the question whether SWAT is or will be suitable as a
modelling tool in the LVB.
Pilot area chosen
The pilot area chosen is the Mara river basin (MRB). This was mainly done based on the higher data
availability of water quality data in the Mara river basin. Apart from the higher data availability, the choice
for the Mara as river basin as a pilot for the LVB can also be justified by some of its geographical and
hydrological characteristics, making it a complex and therefore useful pilot river basin. The Mara river
basin has a diverse land use, has a large relief in the west and north, and has soil types varying from
sand to clay. Hydrological features as rainfall and potential evapotranspiration are therefore spatially
variable (Defersha & Melesse, 2012). Furthermore the Mara river basin outlet contains riparian wetlands
with a tidal character (Kansiime et al., 2007).
7
Originally this study was supposed to include a water quality model run in the Mara being a pilot for water
quality SWAT model for the LVB, calibration data was however considered to be insufficient (because water
quality observations have generally been measured randomly in time and space in the Mara) and because
using a water quality model requires more knowledge of the Mara River basin system than could be gathered
within the time-span available. Recommended adjustments from literature can thus only add to the
understanding of the hydrologic part of the Mara model simulations.

6
1.4 Report structure
This report starts with a description of the study area and its wetlands. In the methodology, the outline of
literature study and model study is explained. This includes an overview of the used data inputs for the
model as well as an elucidation on the choice of these data, the assumptions and methods used in pre-
processing the data and a step-wise plan about how to proceed in calibration and validation of the model.
Hereafter the results on the literature study on SWAT functioning in the tropics and wetland areas follows in
chapters 4 and 5. In the results of the hydrological model, in chapter 6, the outcome of the data analysis
and hydrologic model is given. Thereafter the content of the discussion and conclusion will be about the
made observations, relate these finding to the issues in the introduction, criticise on made assumptions,
state the practical implications of the study, place the observation in the broader context of other literature
and answer the research question stated. It will furthermore elaborate on the functioning of SWAT in tropical
and wetlands areas with respect to hydrology, sediments and nutrients (nitrogen and phosphorus). Finally,
in the recommendations, the conclusion will be translated into a recommendation on the use and
improvement of a potential SWAT model for the LVBC and LVB partner states water authorities.
A large part of the work is explained in the appendix, wherein among other things the main hydrological,
sediment and nutrient processes of the SWAT model and data pre-processing steps are explained in detail
as well as the nutrient cycling processes. In Figure 1-3 below the relations between the chapters and the
appendices is shown.
Figure 1-3: Report structure. The arrows represent relations between chapter and appendices, the numbers/letters
belong to each chapter/appendix.

7
2 Study area
This chapter provides a description of the pilot study area the Mara river basin.8
In the first section the
geographic, hydrologic and geologic features as well as trends and land use will be described. In the second
part the characteristics, processes, utility and history of the wetlands present in the MRB is described.
2.1 Mara River Basin
The Mara river basin has a surface area of about 13.500 km2
. The basin is located in the tropics between
33º 56’ E and 35º 52’ E and 0º 22’ S and 1º 56’ S. It has a transboundary perennial river starting in the
Kenyan Mau forest at 2.900m above mean sea level travelling down 395km ending in the Lake Victoria in
Tanzania at 1134m above mean sea level (Figure 2-1). The Kenyan surface area of the Mara basin is 65%
of its total. The Mara river basin provides about 4.8% of the total inflows to the lake, equalling 37.5 m3
s-1
.
The Inter-Tropical Convergence Zone is the main influence on the basin's climate. The annual precipitation
in the upper MRB ranges from 1400 to 1800 mm yr-1
while the outlet receives a low amount of around 500
to 800 mm yr-1
(Mayo et al., 2013). This precipitation is divided into two rainy seasons in the lower basin:
long rains (Masika) from March to May/June and short rains (Vuli) between September/October and
December. In the long rains more rainfall falls than during the short rains. At high altitude unimodal regime
prevails from April to August. The annual mean temperature is about 25.5ºC. Potential evaporation varies
from 1400 mm yr-1
in the highlands to 1800 mm yr-1
in the Lake Victoria (WREM, 2008).
Figure 2-1: Relief of the Mara River Basin. Derived from the SRTM DEM 30m (USGS, 2016)
8
A description of the Lake Victoria Basin is given in Appendix A).

8
The basin has a fast growing population and livestock growth. The Mara catchment encompasses more
than 1.1 million people. The growth rate on the Tanzanian site is 2.6% and in Kenya about 3%. The
prognosis is that the population in the basin will double in 20 years at current growth rate. The Kenyan Mara
highlands are the most densely populated part of the area (LVEMP, 2005; WREM, 2008).
The geology of the Mara river basin consists mainly of volcanic rock in the eastern part, whereas the Tarime
region (south of Tarime city) composes granites of Archean age (2.5 to 4 billion years B.C.). A general
description of the geology would encompass granite gneiss, coarse feldspar-rich sandstone (arkosic) and
hard siliceous sandstone and quartzites. The Kenyan highlands have red, brownish well drained deep soils,
whereas the Kenyan middle basin is imperfectly drained9
with slightly less deep soils. The upper and middle
part is characterised by soils that have structural stability, high porosity, good water retention and medium
to high fertility, an example of this is cambisols. The lower part enclosing the national parks in the centre of
river basin has dark grey to black soils. The lower Tanzanian lands are rich in organic carbon and have a
high water holding potential. These soils require specialised techniques in order to be suitable for agricultural
use, such as e.g. vertisols10
(GLOWS-FIU & WWF-ESARPO, 2007; McCartney, 2010).
Figure 2-2: Mara River Basins protected areas and wetlands.
The Mara river basin is an area that mainly consists of agricultural area, nature parks and reserves
(savannah, grasslands, shrubs and forest), forest in the upstream areas and wetlands in the downstream
areas11
. There are few settlements in the area. The most densely populated areas lie upstream in the Amala
and Nyangores river tributaries. The major issues and activities in the area are the settlements and
agricultural areas providing nutrients and sediments, erosion, tea plantations, waste management and waste
from hospitals and impacts from tourism in the parks. The Amala tributary is surrounded by settlements, tea
9
in an intermediate condition between well-drained and poorly drained soils.
10
The main soil types are given in Appendix H) on soil data.
11
A more extensive description on land use and agro-ecological zones is given in Appendix G).

9
plantation, cypress and eucalyptus plantations and the in size declining 13-hectare Enapuiyapui swamp,
acting as a micro-catchment (WREM, 2008). The Nyangores area consists of agricultural area, tea farms,
settlements and forest. The Nyangores river contains the Mara river’s only dam, the Tenwek hydroelectric
dam (320kW), see Figure 2-2 (Kabere, 1999). This in 1986 constructed dam is losing capacity due to
increasing silting (GLOWS-FIU & WWF-ESARPO, 2007). The town of Bomet contains a wastewater
treatment plant. Down the Amala and Nyangores confluence, the land use is mainly large-scale agriculture
up to the nature reserves. Agriculture is the source of income for 70-80% of the Mara River Basin (MRB)
population. The World’s Heritage sites of the Kenyan Maasai Mara National Reserve and Tanzanian
Serengeti, lie largely within the Mara river basin boundaries, and are of major importance for wildlife in the
East African countries (Hoffman & Mcclain, 2007; Dessu et al., 2014). Around Mugumu mostly farming and
domestic activities are contributing to the water quality. The lower Mara area on Tanzanian site is mostly
rainfed crops, forest and grasslands with gold mining as one of the main activities. The river plane areas
are consisting of papyrus dominated wetlands (the Masura Mara Wetlands), with fishing and farming as
main economic activities (GLOWS-FIU & WWF-ESARPO, 2007; WREM, 2008).
2.2 Mara wetlands
The Mara river basin contains two wetlands, the largest at the outlet lying Masura Mara wetlands and the
tiny 13 ha Enapuiyapui swamp located at the source of the Mara river in the Mau forest. About 5.2 ha of the
Enapuiyapui swamp feeds into the Amala river and eventually the Mara river. Whereas the other part
contributes to the Njoro river (Okeyo-Owuor, 2007). These two wetlands form an import part of the Mara
river basin by providing erosion control, groundwater recharge, flood control, water filtration, nutrient cycling
and a refuge for wildlife (GLOWS-FIU & WWF-ESARPO, 2007; Tshering, 2011b; Raburu et al., 2012). The
Musura Mara floodplain wetlands are also called Mosori or Kirumi wetlands and formed in the 1960s after
heavy rains raised the lake’s water levels, causing the river banks to spill. Currently, the Musura Mara
wetlands can extend and shrink significantly depending on the season. The Masika long rains in the period
from March to May/June can cause flooding as has been the case in the 1970’s where wetlands expanded
by 387%. The wetlands maximum size is currently about 205km2
with a length of 36.8km and a maximum
width of 12.9km (WREM, 2008; Mayo et al., 2013; Muraza et al., 2013). Mturi (2007) estimates the size even
at 600km2
from remote sensing imagery. The morphology and size of the wetland have changed over the
last 50 years (Figure 2-3). Some experts claim its size is not influenced by local rainfall but by the backwater
effect of the lake on the wetland, whereas others claim it to be due to land use changes in the upper Mara
causing sedimentation (Mahay, 2008).
Wetlands, in general, are well known for their effect in retention of sediments, nutrients, heavy metals and
other pollutants. Retention can be seen as the capacity to remove a substance in the water column through
physical, chemical and biological mechanisms in order to keep it in such a form that it is not released under
normal conditions. Phosphorus retention paths encompass uptake and release by vegetation, algae and
micro-organisms, sorption and exchange with sediments and soil, precipitation, burial, leaching,
sedimentation and by carrying particles along in the current (entrainment). These paths are the same for
Figure 2-3: Change in size of the Kirumi wetland from 1973-2006 (Mturi, 2007).

10
nitrogen except that besides uptake by vegetation and sedimentation nitrogen can also escape in the volatile
phase through ammonification and denitrification. The microbial process of denitrification is reduced at low
temperature and pH (Verhoeven et al., 2006). Phosphorus can be present as dissolved inorganic
phosphorus, as dissolved organic phosphorus, particulate inorganic phosphorus and particulate organic
phosphorus (Figure 2-4). Nitrogen can be present as particulate or dissolved organic nitrogen in soluble
form as ammonium (NH4
+
), nitrate (NO3
-
) or nitrite (NO2
-
), or in gaseous forms (Figure 2-4). In general,
volatilization is the main form of nitrogen removal in wetlands. The anaerobic conditions in the root zones of
the macrophytes make that a significant reduction of nitrate is made possible.
Figure 2-4: a) Left side: Phosphorus forms and mechanisms within a wetland (Reddy et al., 1999). b) Right side: Nitrogen
cycle divided up into the aerobic and the anaerobic parts (Breuer et al., 2014).
The retention in wetlands is thus decreasing the load of a substance to a downstream water body. Wetlands
can also delay the transport of a substance in the order of days to years, depending on the substance’s
stability. Wetlands have a finite capacity, which is smaller without controlled harvesting, thus loads
exceeding the wetland capacity can be harmful to the downstream water quality and the wetland system
itself (Reddy et al., 1999; Kalin et al., 2013). Wetlands, therefore can be used as a treatment of domestic or
industrial waste as is the case in Kampala’s Nakivubo wetland, adjacent to the Lake Victoria. The nutrients
or pollutants in this wetland can be immobilised and incorporated in the plant, be lost through degassing,
can be adsorbed to organics, can enter into the metabolism at different trophic levels or can directly flow
through the wetland as solid particles or in solution. Commonly, the biological nutrient uptake is increased
at higher temperatures (Reddy et al., 1999; Zachariah, 2009).
The main nutrient retaining macrophytes in the Mara wetland are in diminishing order the floating Cyperus
papyrus L. (papyrus), the rooted Typha domingensis (southern cattail or cumbungi) and the rooted
Phragmatis australis (Mturi, 2007; Munishi, 2007; Muraza et al., 2013). Little research has been done on
the role of the Masura wetlands in the retention of water, sediments and nutrients, especially quantitatively.
The research focus so far has been more on nitrogen and the heavy metals associated with the Mara gold
mine in the region. The only studies involving nutrient load reduction and processes in the Mara wetland are
done by Zachariah (2009), Shahrizal & Razak (2011), Tshering, (2011a), Tshering (2011b), Mayo et al.
(2013) and Muraza et al, (2013). Some important finding of their work and of research on retention in other
(Lake Victoria Basin) wetlands and their use in treatment is described below:
A study of Zachariah (2009) about nutrient cycling in the Mara wetlands found that the Masura wetland
retention of total nitrogen (TN) ranges from 0.04 to 0.77 µgl-1
m-1
in the longitudinal direction of the river,
depending on the vegetation type, whereas this was 0.01 to 1.12 µgl-1
m-1
for total phosphorus (TP) in the
months November and December 2009 (Table 2-1). By harvesting the papyrus plant in the exponential

11
growth stage a large portion of nutrients could be removed that would else end up in the Lake Victoria,
considering that the Masura wetland papyrus incorporated 490g N m-2
and 98g P m-2
. A research done by
Mayo et al. (2013) found that the Mara wetland and sediment uptake accounted for 28.8% nitrogen removal.
This equals a removal of 75 tonnes N per year and 3.67 kg ha-1
year-1
. In comparison: the removal of nitrogen
by denitrification around the Lake Victoria by papyrus wetlands is about 1.3 * 106
tonnes N year-1
or 3.50 kg
ha-1
year-1
. (Kiwango & Wolanski, 2008). The main removal mechanism of nitrogen in the Masura Mara was
found to be deposition of organic nitrogen in wetland sediments (Mayo et al., 2013).
Table 2-1: Nutrient retention in the Masura Mara wetland. Water samples within wetland are taken by digging a hole,
wherein water was allowed to settle for one day. Sampling was done end November and beginning of December. So
measurements are not done in the main channel, except for the open water measurements (Zachariah, 2009).
Dominant vegetation Net TN retention (µgl-1
m-1
) Net TP retention (µgl-1
m-1
)
Papyrus 0.77 0.17
Typha 0.13 0.05
Mixed papyrus and typha 0.51 1.12
Open water 0.04 0.01
The functioning of the Mara wetlands as a nutrient filter over the years has been reduced by upstream land
use changes causing soil erosion, sediment build-up and floods as basin flow characteristics are altered.
Other influences are a loss of vegetation due to unsustainable grazing, overharvesting of papyrus and
timber, wetland burning in the dry season, pollutants from waste water discharge, animal husbandry,
agricultural and mining activities (Odada et al., 2004; Mati et al., 2008; Zachariah, 2009; Mayo et al., 2013).
These activities have started a decreasing trend in water infiltration capacity and soil fertility and have
increased soil erosion and sedimentation as well as river pollution (WWF, 2006; Nile Basin Initiative, 2007;
Bitala et al., 2009).

12
3 Methods
This report describes the strength and weaknesses of SWAT in modelling nutrient and sediment processes
in the Mara. Hereby the focus is laid on the aspects of modelling in tropical regions and in wetlands. This is
done by a literature study and a model study.
In the literature study SWAT studies are analysed that involve hydrological or water quality modelling in
wetlands and/or in the tropics as these are the most distinct features of the Mara River Basin expecting to
lower model performance. This literature study thereby shall elaborate on how SWAT currently tries to
incorporate wetlands and tropical processes, searching for weaknesses and strengths of this current
approaches and how researchers try to strengthen the current default SWAT model by changing settings,
adapting the model or coupling it to another model, and why they opted for that. Changes to default settings
can be incorporated in the model study. The model study will consist of 1) a model run with the default
settings of the ArcSWAT2012 version 2.16, 2) a model run that has adjusted the default setting to the
settings recommended in the previous SWAT studies found in the literature research (Figure 3-1).
Figure 3-1: Scheme of the general process of achieving results in this study
3.1 Literature study
The literature study consists of two parts. The first part describes how SWAT currently incorporates the
wetland or tropical processes. For the wetlands, this also consists of a theoretical part on sediment and
nutrients processes in rivers and wetlands12
. The second part describes how SWAT studies are trying to
solve weaknesses on modelling in the tropics and wetlands with SWAT by improving the model, thereby
also listing the reason why the default model was adjusted. The model accuracy can be used to evaluate
how well a model performed in a tropical or wetland area. Their performances are evaluated and reasoning
behind their performance rate are studied. The different ways to express model accuracy can be normalised
for the ratio of RMSE to the standard deviation of the observations (RSR), the Nash-Sutcliffe Efficiency
(NSE) and percentage of bias (PBIAS) according to Table 3-1 (Moriasi et al., 2007).
Table 3-1: General performance ratings for recommended statistics for a monthly time step (Moriasi et al., 2007)
Performance
Rating
PBIAS (%)
RSR NSE Streamflow Sediment N, P
Very Good 0.00 ≤RSR≤0.50 0.75<NSE≤1.00 PBIAS <±10 PBIAS <±15 PBIAS <±25
Good 0.50<RSR≤0.60 0.65<NSE≤0.75 ±10≤PBIAS<±15 ±15≤PBIAS<±30 ±25≤PBIAS<±40
Satisfactory 0.60<RSR≤0.70 0.50<NSE≤0.65 ±15≤PBIAS<±25 ±30≤PBIAS<±55 ±40≤PBIAS<±70
Unsatisfactory RSR≥0.70 NSE≤0.50 PBIAS≥±25 PBIAS≥±55 PBIAS≥±70
12
This theoretical part is available in Appendix K)

13
The SWAT studies are to be selected from the “SWAT Literature Database for Peer-Reviewed Journal
Articles”, and from SWAT studies in the area that were encountered during literature research in Google
Scholar (Table 3-2). Selection criteria were that the SWAT study had either taken place within the LVB
countries, had a wetland within the catchment area of significant size to influence model outcomes, or had
taken place within the tropics of Africa. Keywords that were used to find the SWAT studies are given in
Table 3-3.
Table 3-2: Literature sources of SWAT model studies
Name of literature source for SWAT models Website
SWAT Literature Database for Peer-Reviewed Journal
Articles
https://www.card.iastate.edu/swat_articles
Google Scholar https://scholar.google.nl/
Table 3-3: Keywords used in the SWAT literature study
Keywords
Lake Victoria (Basin) Burundi Bog Model Nutrients
Kenya Africa Fen Discharge Nitrogen
Tanzania Equator Swamp Water quality Phosphorus
Uganda Tropics Mire Erosion TSS
Rwanda Tropical Wetlands Retention Sediment
Penman-Monteith Hargreaves Priestley-
Taylor
Leaf Area Index Evaporation
3.2 Model study
In order to get the SWAT model running a Digital Elevation Model (DEM), a land use map, a soil map and
weather data are needed. Land use data is converted to general land use types which can be recognised
as SWAT standard land use types. Data on soil parameters is added by using the best available
pedotransfer functions (PTF’s) for tropical soils. Weather data statistics had to be calculated in order to run
the model. SWAT is able to model on a daily basis. The water quality observations in the Mara River Basin
have not been measured with a consistent monthly time interval, but rather random in time and space on a
daily basis. Therefore calibrating on sediments/nutrients would require a hydrological model that has been
calibrated well on a daily basis as poor hydrological model performance with satisfactory water quality model
performance would likely mean that the process is not well understood. This satisfactory performance on
water quality could be due to parameter non-uniqueness and Swiss cheese effect finding the same solution
with different optimisation programs (Abbaspour, 2015). Therefore a non-satisfactory performance on flow
modelling would require adjustment of input data and/or methods.
3.2.1 SWAT model description
A description of the main hydrological processes and nutrient/sediment processes in SWAT is given in short
in Appendix B). A more elaborate description can be found in the SWAT2009 theoretical documentation
(Neitsch et al., 2011)

14
Table 3-4: Data used as SWAT model input and for data analysis purposes.
Type of data Description Source
Weather data13
NCEP CFSR data (1979-2015)
0.5º x 0.5º
EU-WATCH-WFDEI (1979-
2014) 0.5º x 0.5º
http://globalweather.tamu.edu/
http://www.eu-watch.org/data_availability
Precipitation10
Station data (1955-2015) Hulsman (2015), Nyolei (2016)
Relief map SRTM DEM 30m x 30m (1Arc-
sec)
http://earthexplorer.usgs.gov/
Soil maps KENSOTERv2 & SOTERSA http://www.isric.org/content/data
Land Use AFRICOVER Kenya &
AFRICOVER Tanzania (FAO,
2004)
MaMaSe land use map (Zheng,
2014).
http://www.fao.org/geonetwork/srv/en/main.search
?title=africover%20landcover
http://maps.mamase.org/documents/
Water level /
Discharge
Water level gauge station
1LB02, 1LA03, 1LA04, 1LA05,
Nyansurura, 5H2, 5H3
Mbuya (2004), Hulsman (2015), LVBC (2016),
Nyolei (2016), Perron (2011), Ndomba (2009),
GLOWS-FIU (2011)
River cross-
sections
1LA03, 1LB02, 5H2 and Kirumi
wetland
Ndomba (2007), Mahay (2008), Ndomba (2009),
GLOWS-FIU (2011), (McClain et al., 2014), LVBC
& WWF-ESARPO (2010), Hulsman (2015)
Sediments Manual measurements on a
short time base by researcher
Hulsman (2015), McCartney (2010), GLOWS-FIU
(2011), WREM International Inc (2008), Kiragu
(2009)
Nutrient data Manual measurements on a
short time base by researcher
NTEAP (2005), McCartney (2010), Kilonzo et al.
(2014), GLOWS-FIU (2011),
3.2.2 Data description
The data used as model input is given in Table 3-4. All weather and calibration data is daily data. The
weather data used as a model input for SWAT is climate model data in combination with rain gauge data.
However, most global circulation model rainfall data, has been found to deviate strongly from rainfall gauge
data (Dessu & Melesse, 2013a). The first results found that CFSR climate model14
data did not give the
desired correlation between observed and simulated flow. Therefore as an alternative to the previous
approach rain gauge data was interpolated with an altered Thiessen Polygon method, gaps were filled based
on monthly weather statistics, other weather input parameters were filled with the EU-WATCH-climate data,
as recommended in Alemayehu et al. (2015) in combination with wind speeds from the CFSR climate
model15
.
The soil and land use maps initially chosen are respectively the KENSOTERv2 & SOTERSA, and the
AFRICOVER map because these maps were recommended in a study researching the best soil and land
use maps as input for SWAT in the Kenyan Tana catchment (Hunink & Droogers, 2010). Furthermore, these
13
The data-availability and coverage of the weather and discharge data is given in Appendix E) and I2).
14
CFSR: NOAA National Center for Environmental Prediction Climate Forecast System Reanalysis. CFSR
data is easy-to-use, complete, and gives, in general, satisfactory to very good model accuracies (Tobin &
Bennett, 2009; Dile & Srinivasan, 2014)
15
See Appendix E) for the method on how this weather input data is filled.

15
maps are easily available for most of the Lake Victoria Basin. Implying that in case model outcomes are
satisfactory they would also be possibly applicable in a future water quality model for the LVB countries.
Expert judgement has however led to the use of another land use map as the historic land use has never
been filled the way as was depicted in the AFRICOVER map. This alternative land use input data has been
obtained with Landsat 8 satellite imagery based on ground truth data (Zheng, 2014).16
This land use map
will further in this report be referred to as the MaMaSe land use map.
3.2.3 Pre-processing & data analysis
Weather data
The weather data used consist of climate model and rain gauge data. The CFSR weather data (Saha et al.,
2010) includes daily rainfall, maximum and minimum temperature, wind speed (at 1.7m), relative humidity
and radiation data for the period 1979-2015 as required by SWAT. The WATCH climate data includes
specific humidity, wind speed (at 10m), incoming short and longwave radiation, precipitation and 3 hourly
temperature values.
The precipitation and climate model data needed to be converted to monthly statistics data as required by
SWAT (Arnold et al., 2012). The monthly weather statistics are meant for gap filling. This gap filling in SWAT
is done with the WXGEN weather generator model from Sharpley & Williams (1990). Based on the statistics
and orography SWAT calculates the temperature and rainfall. The conversion to monthly weather statistics
is done in R-studio according to the formula’s given in Arnold et al. (2012). The rain gauge data was analysed
and corrected or omitted in several ways:
1. Based on maxima: daily rainfall values of >200mm day-1
were omitted.
2. Station records that deviated from the daily precipitation probability distribution were omitted, which
was only the case for the rain gauge installed at Ntimaru Chief’s office.
3. The rain gauge data was plotted as double mass curves17
. in order to look for irregularities in the
station observations (Searcy & Clayton, 1960).
4. The rain gauge data was derived from two different sources: Nyolei personal correspondence
(2016) & Hulsman personal correspondence, (2016). There were some shifts in data in time
between the records received. Therefore the records of Nyolei is used as correct one, as this one
was delivered in the same format as in which the WRMA historically stores its data.
SWAT uses the nearest by station to the centroid of the subbasin to calculate the average precipitation that
falls over a subbasin. Therefore the precipitation was calculated per subbasin in ArcGIS and R-studio before
inserting it to SWAT with the use of the Thiessen Polygons arithmetic mean weight method (Sen, 1998).
The Thiessen Polygon method was found to be cumbersome to use for data records that have many gaps,
varying in time and space. Therefore two Thiessen polygon areas were derived, 1.) one based on the
location the rain gauge stations, and 2.) the second one based on a selection of rain gauge stations in order
to cover a greater time period and area.
In situation 1: When a station contributing an area weighted percentage of precipitation to a subbasin is
missing, the other stations are weighted more heavily. A threshold to the area weight percentage that the
stations contributing precipitation to a subbasin have to deliver to the Thiessen polygon method on a certain
day is set to an arbitrary threshold of 33%. This threshold of 33% corresponds to an average data coverage
in the period of 1969 to 2014 of 51% for all subbasins.
In situation 2: The method is similar, but stations that have little coverage are either omitted from the record
or merged by coordinates with other stations in a range of 15km2
if this would result in a larger coverage.18
In this process, a balance was tried to be maintained between coverage of area and time. This resulted in
an increased data coverage of 76%, leaving the other 24% to be filled by weather statistics with the SWAT
16
Both land use maps are given in Appendix G).
17
The outcomes of this double mass curve analysis method are given in Appendix E).
18
The resulting Thiessen Polygon areas and station in both situations can be seen in Appendix E).

16
weather generator (WGEN). This mainly holds for the period of 1997-2015 as little precipitation data is
available for this period, decreasing with the years from 1997 on19
. SWAT WGEN data has proven to be
more accurate than WATCH and CFSR data in predicting precipitation in the Mara river basin, although this
also depends on the observed data provided to calculate these weather statistics (Alemayehu et al., 2015)
DEM
Digital Elevation data was obtained from the Shuttle Radar Data Topography Mission. All input layers
including the SRTM DEM 1-arc-second, providing the required elevation information, were converted to the
World Geodetic System (WGS) 1984 UTM 36S. The DEM is further processed during setup providing,
watershed delineation, streams and slopes.
Soil data
Soil data consisted of two soil maps, the KENSOTERv2 (Kenya) and SOTERSA (Countries within the south
of Africa) which needed geographic referencing, merging and conversion to WGS 1984 UTM 36S. Soil
names were harmonised based on FAO and WRB soil classification names. Both soil maps contained some
basic data (percentage sand, silt, clay, and soil organic carbon or carbon) that was coupled to the country
soil names called ISOC-SUID. The KENSOTERv2 soil map was more detailed and contained more soil
parameters than the SOTERSA map. The two maps have not been harmonised on FAO soil classification
names because the textures of the identical FAO soil classes were still different according to their
accompanied soil database. Only soils that had a transboundary connection were harmonised and given
the values of the, more complete, KENSOTERv2 soil database.
The addition of the key hydrologic parameters to the soil types was done according to their ISOC-SUID
country soil name. Alternatively, when this didn’t supply enough information to couple information on
hydrologic parameters to the soil type it was done by coupling these parameters to FAO soil classification
names. This coupling of parameters was done with queries in Access database. Querying was done based
on information given in ISRIC and FAO reports on the SOTER databases (Waveren van, 1995; Tempel,
2002; Batjes & Gicheru, 2004b; Batjes, 2005; Jahn et al., 2006; Engelen van & Dijkshoorn, 2013).
Missing soil parameter gaps in the soil database were, the Soil Hydraulic Group, the maximum rooting depth
of soil profile, the moist bulk density, the soil available water capacity, the organic carbon content, the moist
soil albedo and the USLE soil erodibility factor K. All these missing soil parameters were calculated with
pedotransfer functions (PTF)20
. The bulk density is a crucial input parameter in most PTF’s and was
therefore selected by comparing the correlation between calculated bulk density and bulk density of the
KENSOTERv2 and SOTWISE_KENv1 database.
Land Use maps
The AFRICOVER land use map was converted to SWAT land use classes with the classification table that
has been made for the Tana river basin in Kenya, as this area is comparable to the Mara river basin
(Droogers et al., 2006). The AFRICOVER land use map is composed of one to three land cover classes per
polygon. In this study only the most dominant land cover classes are converted to SWAT land use class.
Classes that were not available in SWAT were added, these were savanna, tea plantation and bare land.
Values for tea parameters were averaged from other agricultural crops in SWAT if the information was not
easily available in literature or could not be gathered in situ.
From the available land use maps the MaMaSe map was used, because the AFRICOVER map was
unreliable for the Mara river basin according to comparisons with other land use maps available and expert
judgement (Douglas Nyolei, personal communication, July 25, 2016).
19
See the precipitation observation point coverage per year in the Mara in Appendix E).
20
A description of the soil input parameters, their derivation and the choice of pedotransfer functions for
bulk density is given in Appendix H).

17
Discharge data
Over the years the discharge and stage data of the flow stations in the Mara basin have been transferred
and processed several times, original data has been lost, therefore datasets possessed by researchers and
water basin officers often differ (Kelly Fouchy, personal communication, Novembre 2, 2016; Douglas Nyolei,
personal communication, July 25, 2016; Emmanuel Olet, personal communication, July 2, 2016).
The water level and discharge data were obtained from multiple sources. Some datasets overlapped in time
and/or space. Several methods were used to check the data reliability in order to select a data set for
calibration and validation :
 Review of existing literature on stage gauge recording history in the Mara River Basin
 Plotting flow duration curves21
, cumulative plots, Q-t and h-t plots of datasets (from different
sources) overlapping in time and space, in order to look for differences
 Plotting (log) Q-h relations to see whether the equations to extract discharge data from gauge level
data were reliable.
 Plotting water levels over time, to see whether gauges were malfunctioning or have been replaced,
if so, then this was compared to the discharge and compared to rainfall data in those years.
Unrealistic values were excluded from use for calibration if they had:
1) water levels of zero meter where discharge values are greater than zero cumecs, 2) unrealistically high
water levels in comparison to other records in time, 3) discharge records that did not correlate with the
records at other nearby stations (e.g. for the Amala and Nyangores sub-catchment), 4) Q-h relations
resulting in flows that were out of bound with flow records over a similar time period, 5) flow duration curves
for a certain year that significantly differed from records in a 1-5 year range, 6) significantly less correlation
between stage or discharge and precipitation records for certain years in comparison to the whole record,
or if 7) existing literature dictated that stage or flow recordings over a certain period are unreliable.
Furthermore, only years that had less than 30% missing data in the discharge records are used for
calibration and validation.
21
Flow duration curves are plotted and analysed in years wherein data coverage for flow data is > 70%.

Assessing the ability of SWAT as a water quality model in the Lake Victoria basin and its wetlands

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