Sediment Yield Estimation and Prioritization of Watershed Using Remote Sensing and Gis

Abstract

ABSTRACT:Soil erosion is the greatest destroyer of land resources in Indravati catchment. It carries the highest amount of sediment compared to other catchment in India. This catchment spreading an area of 41,285 square km is drained by river Indravati, which is one of the northern tributaries of the river Godavari in its lower reach. In the present study, USLE is used to estimate sediment yield at the outlet of river Indravati catchment. Both magnitude and spatial distribution of potential soil erosion in the catchment is determined. From the model output predictions, it is found that average erosion rate predicted is 18.00 tons/ha/year and sediment yield at the out let of the catchment is 22.31 Million tons per year. The predicted sediment yield verified with the observed data. The Indravathi basin is divided into 424 sub-watersheds and prioritization of all 424 sub-watersheds is carried out according to soil loss intensity for soil conservation purpose. Generated soil loss map will be useful to soil conservationist and decision makers for watershed management. Overall 19.71 % of the area is undergoing high erosion rates which are a major contributor to the sediment yield (78.04 %) in the catchment. This area represents high-priority area for management in order to reduce soil losses, which are mostly found in upstream of the catchment. It is indicated that the areas of high soil erosion can be accounted for in terms of steep unstable terrain, and the occurrence of highly erodible soils and low vegetation cover.

Prioritization of Watershed Using Remote Sensing and Geographic Information System

“…Pixel-level soil erosion was averaged at the watershed boundaries so that watersheds could be prioritized. The critical level of the sub-watersheds was then determined using the criteria suggested by the authors of [38]. Each sub-watershed was ranked according to its soil erosion risk.…”

“…These practices together with persistent plant cover that helps control soil losses will be crucial for sustainable development [39]. Therefore, controlling large amounts of runoff and enhancing organic matter buildup in the soil will suffice for the purpose [32,38].…”

Assessment of ecosystem services of rice farms in eastern India

“…However, rice is cultivated in a bunded system in this region. The bund height ranges from 0.50 to 0.75 m. Since the average size of field is less than 0.1 ha, the sediment delivery ratio (SDR) is assumed to be 0.3 (Sreenivasalu 2012), which is ultimately lost from the rice field. This factor is multiplied by the reference soil erosion data of the region to get the actual soil loss.…”

A Union of Dynamic Hydrological Modeling and Satellite Remotely-Sensed Data for Spatiotemporal Assessment of Sediment Yields

“…These findings emphasized that Case 3 is a much better representation of the actual sediment prediction for different watersheds with varying spatial and temporal environments compared to Case 2. Hence, the connectivity of the dynamic estimates of C-factor and K-factor into SWAT realistically predicted SY when compared to the dynamic assessment using the singular C-factor model [96,97]. The correlations and predictions of Case 3 indicated that the combined application of C-factor and K-factor powerfully represented the SY estimates both for the whole watershed and for the HRUs in the watersheds.…”