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MODELLING LAND - WATER INTERACTIONS IN SIMIYU RIVER CATCHMENT
USING THE SOIL WATER ASSESSMENT TOOL (SWAT)
Deogratias M.M. Mulungu1 and Simon H. Mkhandi2
1University
of Dar es Salaam, Water Resources Engineering Department
P.O. Box 35131 Dar es Salaam, Tanzania.
Tel. / Fax.: +255 22 2410029
E-mails: deo@wrep.udsm.ac.tz; deorgm@yahoo.co.uk
2University
of Dar es Salaam, Water Resources Engineering Department
P.O. Box 35131 Dar es Salaam, Tanzania.
Tel. / Fax.: +255 22 2410029
E-mails: s_mkhandi@yahoo.com; mkhandi@wrep.udsm.ac.tz
Abstract
The physically based Soil Water Assessment Tool (SWAT) model is applied on the Simiyu River
catchment at Ndagalu outfall, Tanzania. Input data are spatially distributed data set such as
topographical, land cover and soil data, climatic and river flow time series data. The weather
generator tool of the SWAT was used to fill in the missing climatic data and enable flow
simulation in the periods with missing data. The model was calibrated for period 1976-1980
based on the availability of coinciding climatic data and validated for the period 1981-1983. The
calibration involved fine-tuning the land cover-soil parameters to match the observed discharge
at Ndagalu flow gauging station. Also, comparison of simulated results using the single lumped
land area combination (a dominant land use-soil combination) and distributed combinations was
done. Several model fit techniques and water balance matching techniques was used to assess
model performance. Results and analysis was presented to show model performances in
calibration and validation periods. Results showed that the distributed combination achieved
better model performance than the lumped combination. Also, the study has identified optimum
parameters for better discharge simulations. However, since some model parameters were
obtained manually by fine-tuning and sparsely distributed rainfall stations, in the future, it is
recommended to consider the more physical basis. Conclusions were drawn based on the results
of SWAT in this study. Since the model is comprehensive in land surface processes
representation, the model is very promising for land and water management studies and expected
to give valuable information to land and water resources managers.
Keywords: Land-Water Interactions, Simiyu catchment, SWAT, weather generator, spatially
distributed
1
Corresponding Author
2
INTRODUCTION
The wide diversity in some characteristics may require different parameter values. Also, lack of
observed data provides the major problem of runoff modeling in arid regions (Pilgrim et al.,
1988). Moreover, evapotranspiration and interception are particularly important components of
arid zone models. The interaction between vegetation and hydrology is probably more
pronounced in the arid zone than in humid regions. In arid zone, general storms of relatively low
intensities provide much of the rainfall but interception may dispose of an appreciable portion of
the rain. With the high potential rates in most arid and semiarid regions, evapotranspiration
accounts for most of the water entering the soil especially since drainage to groundwater occurs
only in extreme events in most cases. Infiltration capacity is also high around vegetation and
conversely vegetation may only grow where the infiltration capacity is relatively high.
In arid and semiarid regions, current knowledge indicates that the predominant runoff mechanism
is Hortonian overland flow where the rate of rainfall exceeds the potential rate of infiltration.
Partial-area runoff (due to saturation in valley bottoms where virtually all rainfall becomes runoff
and potential rates of infiltration prevent any runoff on the remainder of the basin) has also been
found to occur in several arid regions. Giusti and Neal (1993) is an example of one of recent field
studies of this runoff mechanism. Evidence from such studies indicates the cause in all cases was
spatial differences in infiltration capacity and areas producing runoff were in the valley bottoms
and along stream channels (Pilgrim et al., 1988). The spatial differences in infiltration capacity
stem from different soil or land cover types. The Soil Water Assessment Tool (SWAT) model
uses the Curve Number (CN) method to simulate surface runoff and therefore indirectly model
the infiltration process. Previous study in Simiyu catchment using a lumped SWAT model by
Mulungu et al. (2005) recommended among other things a distributed model structure. Also,
since SWAT has not been widely and critically applied in Tanzania for hydrological analysis,
therefore a step-by-step application is important to warrant its suitability. Consequently, the
SWAT model structure considered in this study is a distributed land use-soil combinations in subbasins (multi-basins) for simulations in 1976-1983. In this study, more time series data were
collected and for multi basins combination a distributed network of rainfall stations is expected to
improve and contribute much on runoff generation. The lumped/single (single-basin) land usesoil combination has also been considered for comparison. In the simulation period the model
was calibrated and validated. This allowed investigating sensitive model parameters for better
river discharge estimation and for monitoring so as at the end to come up with an effective
management tool.
The impact of land use on runoff is a function of many variables but the most important ones
being the water regime of the plant cover in terms of evaporation/evapotranspiration (ET), the
ability of the soil to hold water (infiltration capacity) and the ability of the plant cover to intercept
moisture. As a result this study considered contribution of different land use/cover and their
infiltration capacities for generation of the runoff. Interception is usually modeled as a function of
land cover (interception capacity) and it has been taken care of implicitly in the SWAT model
daily simulations. Conversely, the impact of runoff on land use is mainly a function of soil
moisture and infiltration capacity, which can culminate in form of extreme events (droughts or
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floods). Once processes or interactions are well represented by a model, will ensure effective
management tool for water quantity and quality (transport of pollutants).
OBJECTIVES
The general aim of the study is to obtain baseline-modeling results, which in the long run would
serve the understanding of land-water interactions in Simiyu catchment as well as possible. This
will foster determination / detail investigation of land-water processes for better ways of water
resources management in the catchment.
METHODOLOGY
Description of the study area
The Simiyu River catchment (Fig. 1) covers an area of about 11,000 km2 to the outfall into the
Lake Victoria and is located between 33o 50’ – 35o 00’ E and 2o 40’ – 3o 30’ S. Simiyu River is
ephemeral, which contain water during and immediately after a storm event and dries up during
the rest of the year with exception of some dead channel storage. The flow in the river is highest
during wet seasons and very low or zero during dry seasons. The catchment is in the semi arid
area of Tanzania. The area of the Simiyu River catchment at Ndagalu gauging station (112012 at
33.568oE 2.639oS) is 5320 km2. The catchment map also shows the location of other hydroclimatic stations that were used in this study. The Simiyu River drains from the Serengeti
National Park plains to the Lake Victoria. On the downstream, Duma River joins Simiyu River
and form a main tributary. The Duma River is a result of the confluence of Ngasamo and Bariadi
rivers on the upstream. In this study, model calibration was performed at the Ndagalu flow
gauging station following previous data analysis and models application in the study by Mulungu
et al., 2003.
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Fig. 1: Map of the Simiyu catchment
The land cover in Simiyu catchment is dominated by grassland, woodland and cultivated land.
The main soil types in the catchment are sandy loam, loam and sand clay loam but according to
the United States Geological Survey (USGS) data, the soil can be categorized in only one
hydrologic soil group (2 or B), which is either shallow loess or sandy loam or loamy. This soil
group has moderate infiltration rates if thoroughly wetted and therefore has a moderate rate of
water transmission. Based on the geological map of Tanzania (GURT, 1967), the Simiyu is
dominated by granite (Syn-orogenic and Migmatite) and sedimentary and metamorphic rocks
(Nyanzian). Volcanic rocks (Neogene) cover a small part of Simiyu catchment around the
Serengeti plains.
Determination of catchment characteristics
The topography of the Simiyu catchment, as analysed from a Digital Elevation Model (DEM),
can be categorized as mild with minimum and maximum elevation ranging from 1143 m.a.s.l. on
the catchment’s most downstream points to 1927 m.a.s.l. on the catchment’s headwaters
respectively. Table 1 gives the Simiyu catchment characteristics derived data from DEM.
Table 1: Simiyu River tributary catchment physical (geometry) and topographic data
Parameter
Area (sq. km)
Average slope length (m)
Average slope (m/m)
Average elevation (m.a.s.l)
DEM estimate
5091
121.0
0.011
1387.79
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Data
The spatial datasets included the Africa 1km x 1km data such as Digital Elevation Model (DEM),
land cover and soil data from the United States Geological Survey (USGS). Climatological time
series data such as daily rainfall, temperature, radiation, wind speed and relative humidity were
obtained from the Tanzania Meteorological Agency (TMA) and river flow data from the Ministry
of Water and Livestock Development (MWLD). The time series data coverage is summarized in
the Table 2. The rainfall stations in the catchment are not uniformly distributed. There are two
stations (09333005 at 33.767oE 3.167oS and 09333015 at 33.483oE 3.000oS) located around the
downstream end and one at the middle (09334006 at 34.150oE 3.050oS) of the catchment and the
headwaters do not have a station at all (Fig. 1). One station is in the neighborhood of the
catchment (09234011 at 34.250oE 2.750oS) just upstream of the plane of the middle of the
catchment. Absence of rainfall data in the headwaters and uneven distribution is likely to affect
the accuracy of runoff simulations. From the climatological time series data (0933329 at
33.767oE 3.017oS), the weather simulation data (monthly averages) was calculated and used in
SWAT to generate the missing data for the weather stations.
Table 2: Available data sets for rainfall, climatic (at 09333029) variables and river discharge data
Data set
Rainfall at 09333005 (mm/day)
Rainfall at 09334006 (mm/day)
Rainfall at 09234011 (mm/day)
Rainfall at 09233046 (mm/day)
Min.Temp. (oC)
Max. Temp. (oC)
Radiation (Langleys/day)
Wind speed (Km/day)
Relative humidity (-)
Discharge at 112012 (cumecs)
Discharge at 112022 (cumecs)
Discharge at 112032 (cumecs)
Data coverage
Jan-1928 - Dec-1993
Jan-1976 - Dec-2003
Jan-1976 - Dec-2003
Jan-1976 - Dec-1998
Jan-1970 - Dec-1979
Jan-1970 - Dec-1979
Jan-1970 - Jul-1984
Jan-1970 - Apr-1984
Jan-1970 - Jun-1984
Jan-1970 - Dec-1996
Jan-1969 - Dec-2000
Jan-1970 - Sep-1978
Mean
2.26
3.12
2.60
2.25
16.47
28.65
17.72
1.58
0.55
11.82
27.00
13.35
Std. deviation
7.26
9.70
8.11
7.07
1.59
2.12
3.95
0.63
0.12
36.92
45.42
35.53
% Missing
8.97
63.41
58.37
11.47
20.50
20.50
0.02
0.02
0.02
0.00
21.89
0.00
Hydrologic Modeling
Modelling considerations were on physical processes occurring in the catchment as far as
possible. These include: (1) little or absence of baseflow where appropriate parameters were used
(2) the tendency of rainfall being more variable in both space and time in arid/semi arid regions
than in humid regions, although the magnitude of these variations differs for different regions.
Therefore, distributed influence of rainfall was considered in setting up the model (3) following
lack of observed data, especially the much lower rain gauge density, an investigation of whether
the use of lumped inputs is adequate or not, and quick assessment of likelihood of errors resulting
from sparsely gauged data being unrepresentative of average rainfalls over the drainage basin was
carried out. This was achieved by comparing the lumped and distributed sub-basins with single
and four rainfall inputs respectively (4) Since dominant land covers are grassland and shrubland,
the sparse vegetative cover may favour generation of surface runoff and probably has significant
effect on many other processes including interception, infiltration, evapotranspiration and
subsurface runoff. The following explains the SWAT model set-up procedure for the Simiyu
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catchment. Details of the model set-up and operation in Simiyu catchment can be found in the
customized user manual / guideline (Bauwens and Mulungu, 2004).
Basin delineation
The Simiyu catchment at Ndagalu was delineated from a DEM and formed the base of all spatial
data and SWAT model set-up. This location was strategically selected in order to use the
observed river flows for comparison with the SWAT simulated flow during calibration. The land
use and soil data were then clipped in the basin using the facility provided within the SWAT
software (AVSWAT2000) (Di Luzio et al., 2002) operated under the ArcView 3.2 GIS platform.
Soil spatial data selection and analysis
The soil hydraulic properties are important for determination of the amount of water available for
runoff and to the plants in the soil profile. The soil spatial data was related to the soil hydraulic
properties that were taken from the United States Department of Agriculture (USDA) database.
This was considered sufficient, as in the Simiyu catchment there is no detailed data for the
subsurface layers. However, this is likely to bring some inaccuracy in the simulations and finetuning the parameters may not reflect the actual ground conditions. The key soil water parameters
such as field capacity (fc), permanent wilting point (wp) and available soil water (Saw), as
presented in Table 3, were used to obtain soil type and properties data for the Simiyu catchment
from the USDA database.
Table 3: Some typical available soil water for various textural classes of soils
Soil
Sandy
Sandy loam
Loam
Clay loam
Silty clay
Clay
fc (vol.%)[pF=2]
10-20
15-27
25-36
31-41
35-46
39-49
wp (vol.%)[pF=4.2]
4-10
6-12
11-17
15-20
17-23
19-24
Saw (vol.%)
6-10
9-15
14-20
16-22
18-23
20-25
Saw (mm/m)
60-100
90-150
140-200
160-220
180-230
200-250
(Source: Agricultural Compendium 1981: 75 Table 2.3/7)
Land use spatial data classification and analysis
With the base spatial data for the catchment at Ndagalu, the land use and soil distributed dataset
were imported and overlaid. The different land use classes used are shown in Table 4 and Fig. 2.
Table 4: Simiyu Land use classes matched with the SWAT land use classes
USGS Land use Class
Grassland
Shrubland
Deciduous Broadleaf Forest
Dryland Cropland & Pasture
Cropland / Grassland Mosaic
SWAT Land use
Class
RNGE
RNGB
FRSD
PAST
AGRR
% Total catchment
area
82.89
15.34
0.94
0.45
0.37
Dominancy
Rank
1
2
3
4
5
7
Fig. 2: Re-classed Land use data for the SWAT simulations
For the single land use-soil combination (single basin), the Simiyu catchment was divided into a
single catchment/subbasin (computational element) that discharge at Ndagalu. This combination
represent (comprise) a Grassland (RNGE) land use - Loamy soil combination for the Simiyu
catchment at Ndagalu from the different land use classes shown in Table 4 and Fig. 2 and formed
a dominant Hydrologic Response Units (HRU) distribution with a single HRU. This single and
lumped land area combination does not make the model to explicitly reflect differences in
evapotranspiration and other hydrologic conditions/fluxes for different land covers and soils. In
order to account for such differences of fluxes, distributed combinations was used to provide a
much better physical description of the water balance.
For the distributed land use-soil combinations (multi basins), the Simiyu catchment was divided
into 13 contributing subbasins (computational elements) each with different land covers
according to the land cover data (Table 4 and Fig. 2) and soil data. These land use-soil data
combinations formed distributed Hydrologic Response Units (HRUs) in each subbasin, which are
used to simulate flow hydrographs using nearest rainfall station for each subbasin and route the
flow for each HRU to the subbasin outfall where are combined and then route through
downstream subbasins till the catchment outfall. The catchment simulated discharges for the
single and distributed land use-soil combination was then compared with observed discharge.
Model calibration
The SWAT model was run on daily time step using the Curve Number method to simulate
surface runoff and hence indirectly model the infiltration process. The water available for
infiltration and subsequent percolation is obtained as the difference between the rainfall and
surface runoff. The Muskingum scheme was used for river flow routing to the catchment outlet at
Ndagalu. It is imperative to note that in the model set-up, attention should be on the classification
of land use and soil type to match the SWAT’s classification and type respectively. During model
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set-up, it was noted that the land use classification (especially the vegetation types) was sensitive
to the assigned SWAT land use classes for the river flow estimation.
The model was calibrated for the period 1976-1980 based on the availability of coinciding
climatic data and validated for the period 1981-1983. Four rainfall stations data (Table 2) were
used in the model simulations for the distributed combinations while only one from the station
09333005 was used in the single combination. The weather generator tool of the SWAT was used
to fill in the missing climatic data and enabled flow simulation in the periods with missing data.
The calibration involved fine-tuning the land cover-soil parameters to match the observed
discharge at Ndagalu flow gauging station. One parameter was chosen at a time and tuned, then
simulate the discharge. The optimum parameters for the single basin (RNGE-Loamy soil) were
maintained and used in the distributed combination that comprised five combinations (Table 5).
Several model fit techniques and water balance matching techniques were used to assess model
performance. This was achieved by comparing of observed and estimated annual average water
variables for total water yield, surface runoff and base flow. The Nash and Sutclif model
efficiency criteria, R2 were also used to assess the model performance. Moreover, plots of
observed and estimated daily flow hydrographs were compared. When the best fit on annual and
daily basis is obtained, the model parameters set were considered optimum and used to estimate
the river discharge. Further check was carried out on the simulated potential evaporation if it is
within the expected range in a semi arid region.
In modeling the ground water flow, the following considerations were made. Among the areas of
the Lake Victoria basin, it seems that a large part of the Simiyu catchment has low ground water
potential (World Bank and Danida, 1995). Also, the analysis of the flow duration curve at
Ndagalu flow gauging station suggested that groundwater storage is not much released on the
river discharge at Ndagalu station (Mulungu et al., 2003), implying deep aquifer water storage.
Consequently to account for the water movement (by evaporation and deep-rooted plants) from
the saturated zone to the unsaturated zone through the capillary fringe, a low value of the
GW_REVAP was used in the simulations. A value of 0 for the parameter restricts the movement
from the shallow aquifer to the root zone while a value of 1 implies the movement nearly equals
the potential evapotranspiration rate. Another parameter is REVAPMN, a value of zero (0)
showed more or less good fit for the low flows. That is the movement of water from the shallow
aquifer to the unsaturated zone or to the deep aquifer is allowed at any time. Another important
parameter is RCHRG_DP for deep aquifer percolation in an ephemeral river catchment.
RESULTS AND DISCUSSIONS
The result of land use-soil combination for the multi basin model set-up gave 31 distributed
HRUs as shown in Table 5.
Table 5: Distributed HRUs and their areas for Simiyu River sub-catchments.
Subbasin
1
2
Number of
HRUs
2
2
Land cover-Loamy soil area (km2)
AGRR FRSD PAST RNGB RNGE
10.00
290.00
150.00 521.00
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Subbasin
3
4
5
6
7
8
9
10
11
12
13
Total
Number of
HRUs
4
2
3
2
3
2
2
2
3
2
2
31
Land cover-Loamy soil area (km2)
AGRR FRSD PAST RNGB RNGE
6.00
20.00
315.00 60.00
18.20
344.80
12.45
137.99 236.56
35.00
178.00
120.00 272.00
28.00
12.00
240.00
26.00
424.00
2.00
3.00
97.00
18.00
1030.00
101.00 148.00
17.00
234.00
18.00
46.45 315.00 786.19 3941.36
From Table 5, the order of decreasing area coverage for the HRUs is: RNGE, RNGB, PAST,
FRSD and AGRR, which appeared in basins 11, 2, 3, 7 and 11 respectively. This order was
considered during the model calibration to obtain optimum parameters. The following parameters
were found to be very sensitive to the discharge simulations and Table 6 presents optimum
parameters values for the SWAT model set-up in Simiyu catchment. Other and some of these
parameters were left at their default values.
Table 6: Tuned and optimum model parameters for the single and multi basins 1976-1983.
Parameter
SOL_K (1)
SOL_K(2)
SOL_AWC(1)
SOL_AWC(2)
CN2
REVAPMN
RCHRG_DP
GW_REVAP
SURLAG
EPCO
ESCO
GWQMN
Single basin
RNGE
900 (32)
500 (18)
0.14 (0.14)
0.12 (0.12)
65 (69)
0 (1)
0.9 (0.05)
0.02 (0.02)
2 (4)
0 (0)
0 (0)
0 (0)
FRSD
900 (32)
500 (18)
0.14 (0.14)
0.12 (0.12)
51 (66)
0 (1)
0.9 (0.05)
0.02 (0.02)
2 (4)
0 (0)
0 (0)
0 (0)
PAST
900 (32)
500 (18)
0.14 (0.14)
0.12 (0.12)
61 (69)
0 (1)
0.9 (0.05)
0.02 (0.02)
2 (4)
0 (0)
0 (0)
0 (0)
Multi basins
RNGB
900 (32)
500 (18)
0.14 (0.14)
0.12 (0.12)
50 (61)
0 (1)
0.9 (0.05)
0.02 (0.02)
2 (4)
0 (0)
0 (0)
0 (0)
RNGE
900 (32)
500 (18)
0.14 (0.14)
0.12 (0.12)
65 (69)
0 (1)
0.9 (0.05)
0.02 (0.02)
2 (4)
0 (0)
0 (0)
0 (0)
AGRR
900 (32)
500 (18)
0.14 (0.14)
0.12 (0.12)
74 (78)
0 (1)
0.9 (0.05)
0.02 (0.02)
2 (4)
0 (0)
0 (0)
0 (0)
The description of parameters in Table 6 is as follows: SOL_K(1) and SOL_K(1) is saturated
hydraulic conductivity (mm/h) in layer 1 and 2 respectively. SOL_AWC(1) and SOL_AWC(2) is
available water capacity (-) of the soil layer 1 and 2 respectively. REVAPMN is threshold depth
of water in the shallow aquifer for “revap” or percolation to the deep aquifer to occur (mm). CN2
is SCS runoff Curve Number for moisture condition II. GW_REVAP is groundwater “revap”
coefficient (-) and ALPHA_BF is baseflow alpha factor. EPCO is plant uptake compensation
factor and ESCO is soil evaporation compensation factor. The default parameter values are in the
brackets. From Table 6, the main difference in the runoff generation for the single and multi
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basins lies on the differences of Curve Number (CN2) for the different land covers, which is
responsible for surface runoff generation. Since the dominant the runoff generation mechanism in
the arid area is Hortonian type, the different CN2 obtained are expected to represent the runoff
processes adequately so as to agree with mechanism for runoff generation in arid regions.
Table 7: Observed and simulated long term average (annual) water balances.
Water variable
Total Water Yield
(WYLD)
Surface
Runoff
(SURQ)
Base flow (GWQ)
Observed (mm)
1976-1983
93.10
Single HRU Estimated
(mm) 1976-1983
93.32
Distributed HRU Estimated
(mm) 1976-1983
93.87
55.16
56.49
56.79
37.94
36.83
37.08
From Table 7, the estimated average annual water balance in the simulation period matched well
with observations and showed that the water balances matched well. The multi basin set-up
attained relatively high efficiency and from Table 9 it indicates better physical description of the
water balances.
Table 8a: Daily model performance efficiencies for single basin for the period 1976-1983.
Year(s)
1976
1977
1978
1979
1980
1981
1982
1983
R2 (-)
-0.1061
0.1300
0.4429
0.0057
-0.8235
0.2783
0.3445
-2.0207
Cumulative R2 (-)
0.1563
0.2442
0.1985
0.1345
0.1599
0.1828
0.1439
Consecutive R2 (-)
0.2266 (1977-1978)
0.3078 (1981-1982)
0.1647 (1981-1983)
Table 8b: Daily model performance efficiencies for multi basins for the period 1976-1983.
Year(s)
1976
1977
1978
1979
1980
1981
1982
1983
R2 (-)
-0.1566
0.2464
0.4881
-0.4679
-0.4484
0.4787
0.3156
-0.4284
Cumulative R2 (-)
0.2651
0.3338
0.1802
0.1422
0.2016
0.2158
0.2068
Consecutive R2 (-)
0.3210 (1977-1978)
0.4061 (1981-1982)
0.3595 (1981-1983)
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Comparing Table 8a and 8b, consistent more positive values of cumulative R2 from the beginning
of simulation to a certain year shows that the multi basins set-up has better performance than the
single basin. Model performances in consecutive years were calculated to reduce uncertainty and
to get the average performance in the intervals, which had better model performance on yearly
evaluation. For both single and multi basins results showed on average model performance
improvement over the individual years and beginnings of the simulations to a certain year’s
performances. In all cases, the multi basins set-up achieved better performance than the single
basin.
Table 9: Daily model performance efficiencies during calibration and validation periods.
Model performance index
Mean of observed flow [Cumecs]
Index of Volumetric Fit (IVF) [-]
Model efficiency (R2) [100%]
Calibration (1976-1980)
Validation (1981-1983)
Single basin
Multi basins Single
Multi basins
15.65
15.65
13.28
13.28
1.27
1.15
0.83
0.84
13.45
16.76
14.22
36.17
From Table 9, the model underestimated water volumes (through IVF values) during calibration
(1976-1980) and tended to conserve the water volumes during validation period (1981-1983).
This may be attributed by relatively high peak flows, which were observed during the calibration
period than in the verification period. The difference in the peak flows can be reflected in mean
flows during calibration and validation periods. The multi basins model set-up attained relatively
high model efficiency for both periods.
Two discharge series plots for the years 1978 and 1981 were selected to represent the model
performance in the calibration and validation periods. During these years the model had best
performance than in other years as shown in Table 8. Fig. 3 and 4 shows plots of observed and
estimated river discharge for single and multi basins.
Fig. 3: Observed and estimated discharge for 1978 for single and multi basins.
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Fig. 4: Observed and estimated discharge for 1981 for single and multi basins.
The model flow simulation hydrographs for the single and multi basins followed the pattern of
the observed discharge with the latter basin’s set-up achieving better fits. Both model set-ups
underestimated the peak discharges and this may be attributed by several reasons: (1) missing
settlement land cover that may lead to impermeable/semi-impermeable surfaces, which contribute
significantly to peak discharges. The settlements could not be captured/registered on the Satellite
derived land cover data used (2) assumption of uniform soil texture and land use in the entire
catchment, which does not reflect the reality. (3) Uneven rainfall stations distribution in the
catchment. Also, some peak flows were not captured at all. This may be attributed to local
rainfall storms that were not well represented by the rainfall data used in the hydrologic
simulations. Moreover, rainfall distribution in the year 1978 is as presented in Fig. 5. The
headwaters in the National parks have lower rainfall than the downstream. This unexpected
representation of rainfall is likely to affect the flow estimations.
13
Fig 5: Annual rainfall distribution in Simiyu catchment 1978
Fig. 6: Rainfall and Potential evaporation for single basin.
14
Fig. 7: Rainfall and Potential evaporation for subbasin 11.
Plots of the annual rainfall and potential evaporation during the period 1976-1983 for both single
basin and subbasin number 11 (Figs. 6 & 7) shows a more or less constant evaporation. The year
1976 showed value around 1800 mm/y. The expected value and pattern for a semi arid area of
Tanzania is evaporation rate (atmospheric driven) of about 1800+ mm/y (FAO, 1986), which do
not vary much throughout the year. However, the river flow estimation for the year was poor
probably because of the model warming up and other problems including the rainfall input.
Beyond 1976 the values are nearer to the threshold value but underestimated and it might be due
to the fact that the weather-generator was used to produce some of the weather data (see Table 2).
Therefore, with some exceptions, the SWAT model tried to simulate the potential evaporation
close to the expected values.
CONCLUSIONS AND RECOMMENDATIONS
The study is a step further from previous hydrological work on SWAT in Simiyu catchment and
has set up the model in two styles: the single and multi basins. Results showed that calibrating
independently each land cover-soil combinations improved the model performance. This
emphasizes the need for distributed (semi-distributed) model set-up for better river discharge
estimation. Distributed input data such as rainfall, soil texture and more detailed land cover in the
catchment are required for better fit of peak flows.
The study also recommends to carryout a set of observations in order to get basic observed data
such as ground water levels or/and soil moisture at representative land cover points, which are
important for assessing the situation of subsurface water and their hydraulic properties. One of
the reasons for poor model performance is likely to be unrepresentative rainfall in the catchment.
Efforts should also be made to get rainfall data from the headwaters areas, although these data
might be difficult to get because the area falls within the restricted wildlife parks. Satellite
derived rainfall data may therefore be useful. This is imperative to continuously supplementing
the basic hydro-geological data on the catchment and by developing the tools and field
observations (more physically based) it is possible to manage water resources with minimum
15
costs and therefore offer more information on availability and sufficiency for water development
projects and increased population in the Lake Victoria region. The SWAT model deficiencies for
good results can be solved in a step-by-step manner, addressing one problem after another.
ACKNOWLEDGEMENT
We are very thankful to the SIDA/SAREC support, which financed this research. Thanks are also
to the Department of Water Resources Engineering, University of Dar es Salaam for facilitation
to carryout the study in terms of office space and access of some hydro-meteorological data from
its database.
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