Sediment Yield Assessment of a Large Basin using PSIAC Approach in GIS Environment

Abstract

Reservoirs are the key infrastructure for the socio-economic development of a country. The reservoirs are proven to be a remedial solution of highly erratic spatial and temporal availability of water. The growth in population and consequent developmental activities within a catchment area has shown to aggravate the problem of sedimentation which comprised of erosion, sediment transport and its deposition in these reservoirs. Among all above mentioned, reservoir sediment deposition is most important as it reduces its useful life and impairs the purposes of these vast water resource. The sediment yield has been considered as comprehensive index for assessing sustainability of such resources. The present study investigates the suitability of Pacific Southwest Inter-Agency Committee (PSIAC) model in determining the sediment yield rate for a drainage basin considering nine basin factors in geographical information system (GIS) environment. For the analysis, a large river basin at the foothill of Himalayas in India has been considered as case study. It was realized that the GIS approach made large basin characteristic sampling very easy and efficient for this hilly basin. A regression equation between specific sediment yield and effective model factors was established based on geomorphic features for this basin. It was observed that most of the basin area is falling under moderate to high sediment yielding potential zone, leading to high sediment yield.

Soil Erosion Prediction Using Morgan-Morgan-Finney Model in a GIS Environment in Northern Ethiopia Catchment

“…This could be related to the reason that the study catchment could be characterized by very low soil infiltration, low soil water holding capacity and poor vegetation cover (inappropriate land use system), and low conservation measures. Consistent with this finding and explanation many previous reports indicated that a higher soil loss rate is strongly associated with high runoff resulting from absence of runoff flow obstacles such as vegetation cover, conservation measures, impoundments, and soil with low infiltration rate and low soil water holding capacity [54][55][56][57][58][59][60]. If an average annual soil generation rate of 6 t ha −1 y −1 [61] is considered, the soil loss rates estimated by the model in most parts of the catchment could be beyond this acceptable level.…”

Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia

“…In this study, we used the Revised Universal Soil Loss Equation (RUSLE) model (Renard et al, 1997) to estimate soil loss and map its spatial variability. This model has been selected because it requires minimal data and has a low computational cost, rendering it more suitable for data scarce regions (Garg and Jothiprakash, 2012;Chowdary et al, 2013). In addition, some of the key model parameters have been calibrated for Ethiopian conditions (e.g., Hurni, 1985).…”

Estimating soil erosion in sub-Saharan Africa based on landscape similarity mapping and using the revised universal soil loss equation (RUSLE)

“…For areas where quality data for model building and calibration are scarce, empirical models such as the RUSLE may give better approximation of soil loss (Renard et al 1997) compared to complex physical based models that require detailed data (Garg and Jothiprakash 2012;Chowdary et al 2013). Accordingly, the RUSLE adjusted for SDR is used in this study to assess soil erosion risk with a spatial resolution of 250 m at regional scale.…”