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2005). The surface runoff (Q
surf
) as input to the MUSLE equation is simulated by the runoff
component of SWAT. The SWAT model uses water balance Equation 2 as a driving force
behind everything that happens in the watershed (Neitsch
et al
., 2005).
t
SW
SW
R
Q
E
w
Q
(2)
t
t
1
day
_
i
surf
_
i
a
_
i
seep
_
i
gwi
_
i
1
SW
t
is the final soil water content (mm), SW
t-1
is the initial soil water content on day i (mm),
t = 1, 2, 3,…,n where “n” is the total number of days during the simulation (days), R
day_i
is
the amount of precipitation on day i (mm), Q
surf_i
is the amount of surface runoff on day i
(mm), E
a_i
is the amount of evapotranspiration on day i (mm), W
seep_i
is the amount of water
entering the vadose zone from the soil profile on day i (mm), and Q
gw_i
is the amount of
return flow on day i (mm).
SWAT uses Manning's equation to define the rate and velocity of flow. Water is routed
through the channel network using the variable storage routing method or the Muskingum
River routing method. Both the variable storage and Muskingum routing methods are
variations of the kinematic wave model (Neitsch
et al
., 2005).
2.3 Data and data analysis
The sediment flow data are readily available (Table 2a-c). The quality and adequacy of data
varies from one catchment to the other. In the two study cases, the Simiyu River and Koka
Reservoir catchments, secondary data on streams flows, climate, sediment flow and spatial
data were used to setup, calibrate and validate the model. These are typical data types used
in most of the SWAT applications elsewhere in the region (Andualem and Yonas, 2008;
Shimelis
et al
., 2010). Most of the sediment flow data are intermittent instantaneous
sediment flow data. In one of the cases, the NYM Reservoir subcatchment, primary data on
sediment flow was collected to complement the analysis. These are continuous subdaily
sediment concentrations data plus multi-temporal reservoir survey information. As Table 2
stipulates, various sources of data were explored. The data preparation and analysis task
involved analyzing statistics such as season mean, percent missing data, identifying outliers,
length of the records, temporal and spatial variability of rainfall, and wet years' period. The
wet years' period is defined as the period when the annual total rainfall is above the long
term annual average. The analysis was meant to guide and provide data for the SWAT
modelling. For instance, spatial variability justified the need for distributed modelling. The
derived statistics were also used as inputs to weather generator module of SWAT. The
module generates climatic data or fills in gaps in measured records. As presented in Tables
2a-1, 2b-1 and 2c-1, the input spatial data included base maps such as readily available
topographic maps in the Ministries and global spatial thematic maps (
i.e.
Digital Elevation
Models, DEM; Soil, and Landuse-cover) of various resolutions. One of the case studies
reviewed in this paper (Mulungu and Munishi, 2007), used high-resolution data on land use
from the 30 m LandSat TM Satellite, the 90 m Digital Elevation Model and the Soil and
Terrain Database for Southern Africa (SOTERSAF). In some cases such as NYM, the soil
types were extracted from Pauw (1984) digital map and complemented by the Soil Atlas of
Tanzania (Hathout, 1983). Similarly, climatic data included rainfall data from the regular
ground monitoring network.
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