Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa - Advances in Data, Methods, Models and Their Applications in Geoscience - page 262

  0.56

Sed  11.8 Q q Area K C P LS CFRG (1)

surf peak hru USLE USLE USLE USLE

Where Sed is defined as Sediment yield rate (tones/day), Q surf is the surface runoff volume
(mm/day), q peak is the peak runoff rate (m 3 /s), Area hru is the area of the HRU (ha), K USLE is the
USLE soil erodibility factor (0.013 metric ton m 2 hr/(m 3 -metric ton cm)), C USLE is the USLE
crop management factor or cover management factor, P USLE is the USLE support practice
factor, LS USLE is the USLE topographic factor, and CFRG is the coarse fragment factor.
The runoff component of the SWAT model supplies estimates of runoff volume and peak
runoff rate using the curve number method (SCS, 1972) and modified rational method,
respectively, which, along with the subbasin area, are used to calculate the runoff erosive
energy variable. The crop management factor or cover management factor is recalculated
every day that runoff occurs. It is a function of above-ground biomass, residue on the soil
surface and the minimum cover factor for the plant. The K USLE factor is estimated using an
equation proposed by Mulengera and Payton (1999) for tropics. Other factors of the erosion
equation are estimated as described by Neitsch et al . (2005). The current version of the
SWAT model uses the simplified stream power equation of Bagnold's (1977) to route
sediment in the channel. The maximum amount of sediment that can be transported from a
reach segment is a function of the peak channel velocity. Sediment transport in the channel
network is a function of two processes, degradation and aggradation ( i.e. deposition),
operating simultaneously in the reach (Neitsch et al ., 2005).
The SWAT model includes an automated calibration procedure. The calibration procedure is
based on the Shuffled Complex Evolution-University of Arizona algorithm (SCE-UA) as
proposed by Duan et al. (1992). The autocalibration option in SWAT provides a powerful,
labour saving tool that can be used to substantially reduce the frustration and uncertainty
that often characterizes manual calibration (Van Liew et al , 2005). In one of the study cases
other calibration tools such as the 'Sequential Uncertainty Fitting Algorithm' (SUFI-2)
program (Abbaspour et al ., 2004, 2007) were used.
Although general SWAT applications have shown that the model performs satisfactorily
(Ndomba and Birhanu, 2008), its suitability for specific applications such as sediment yield
modelling has yet to be ascertained. In this paper, various sediment yield modelling issues
involved with using SWAT such as data requirements and analysis, calibration, sensitivity
and uncertainty are critically evaluated in three well-studied cases, the Nyumba Ya Mungu
(NYM) Reservoir subcatchment located in the upstream part of the Pangani River catchment
(PRC), (a trans-boundary catchment shared between Kenya and Tanzania); the Simiyu River
catchment (SRC), (a Lake Victoria Basin subcatchment in Tanzania); and the Koka Reservoir
catchment (KRC) in Ethiopia. Growing population, growing demand of cultivated land,
mostly inaccurate traditional land usage and dangerously increasing deforestation have
increased soil erosion. Erosion has a major impact on nature and diminished the agriculture
potential of the selected study cases. Excessive exploitation increases the susceptibility of the
soil to fluvial and upland erosion, which is responsible for the increased sediment transport
and deposition into the reservoirs.

2. Materials and methods
2.1 Description of the study cases
Case study 1, the Koka Reservoir catchment (KRC) lies within the western part of the Awash
Basin and has an area of approximately 11,000 km 2 (Figure 1(a-d) & Table 1). The Awash