Geoscience Reference
In-Depth Information
Mtalo and Ndomba (2002) have reported alarming high rate of soil erosion of up to 2,400
t/km
2
/yr or 24 t/ha-yr in the upstream of Pangani river basin covering parts of Arusha,
Kilimanjaro and Tanga regions (Fig. 2.1). Mtalo and Ndomba used USLE equation to map
and estimate on site potential soil loss. In the basin high erosion rates can be measured in
different perspectives such as increased agricultural and other human activities. For
instance, in Arumeru district, which is one of the districts in the Arusha region, soil erosion
is one of the major obstacles to increasing or sustaining the agriculture production. The
whole district is affected by soil erosion, but the reasons differ from place to place. The
amount of livestock in the district is considered to be far above the carrying capacity of the
present land area devoted to grazing. Agricultural activities are a contributing factor to
increased soil erosion rates in the Pangani basin upstream of Nyumba Ya Mungu reservoir
(Mtalo and Ndomba, 2002).
Of recent, there have been attempts to apply complex distributed, physics-based sediment
yield models such as Soil and Water Assessment Tool (SWAT) for poor data large
catchments, Kagera, Simiyu and upstream part of Pangani River catchment, in Kagera,
Mwanza and Arusha/Kilimanjaro regions, respectively, in Tanzania. SWAT model uses the
Modified Universal Soil Loss Equation (MUSLE) to estimate sediment yield (Arnold
et al
.,
1995). The model operates at daily time step with output frequency of up to month/annual.
However, in order to adopt the model for general applications in watershed management
studies, researchers recommended for SWAT model improvements (Ndomba
et al
., 2005;
Ndomba
et al.
, 2008).
One would note that most of the previous sediment yield estimates studies in Tanzania and
the region at large were catchment specific. The results could not be transferred easily to
other hydrologic similar catchments (Rapp
et al
., 1972; Mulengera and Payton, 1999;
Mulengera, 2008; Ndomba, 2007, 2010). In order to estimate catchment yield researchers
were forced to use uncertain factor such as sediment delivery ratio (Ndomba
et al
., 2009). In
some studies attempts were made to develop only a simple procedure which would
distinguish between dams that will silt up rapidly from dams that will have a sedimentation
lifetime well in excess of twenty years (Lawrence
et al
, 2004). The estimation tools used were
either complex for operational and wider application or data intensive (Ndomba
et al
. 2008).
In some cases due to limitation in data the developed Sediment yield predictive tools could
not be validated (Rapp
et al
., 1972; Lawrence
et al
, 2004). As acknowledged by Faraji (1995)
and others, at present, there is very scanty knowledge about reservoir sedimentation in
Tanzania. Previous studies were done on few reservoirs/dams. The studies gave some
guidelines on the rate of sedimentation of the respective areas (Rapp
et al
., 1972). However,
this knowledge should be backed with further extensive surveys and resurveys to get
improved relationships. Critical tools in this context include sediment yield and/or
reservoir life estimation. The country has limited resources in terms of funding and human
capital for developing the planning tools (Mulengera, 2008). The latter problem might be
common to most of the developing countries.
Based on the discussions above and literature, generally, sediment yield models may vary
greatly in complexity from simple regression relationships linking annual sediment yields to
climatic physiographic variable such as regional regression relationships to complex
distributed simulation model (Garde and Ranga Raju, 2000). Modelling as one of the
approaches for estimating catchment sediment yields, if properly applied, can provide
information on both the type of erosion and its spatial distribution across the catchment.
Sediment mobilized by sheet and rill erosion may be deposited by a variety of mechanisms
Search WWH ::
Custom Search