Geospatial Assessment of Soil Erosion Intensity and Sediment Yield Using the Revised Universal Soil Loss Equation (RUSLE) Model

Abstract

Land degradation caused by soil erosion is considered among the most severe problems of the 21stcentury. It poses serious threats to soil fertility, food availability, human health, and the world ecosystem. The purpose of the study is to make a quantitative mapping of soil loss in the Chitral district, Pakistan. For the estimation of soil loss in the study area, the Revised Universal Soil Loss Equation (RUSLE) model was used in combination with Remote Sensing (RS) and Geographic Information System (GIS). Topographical features of the study area show that the area is more vulnerable to soil loss, having the highest average annual soil loss of 78 ton/ha/year. Maps generated in the study show that the area has the highest sediment yield of 258 tons/ha/year and higher average annual soil loss of 450 tons/ha/year. The very high severity class represents 8%, 16% under high, 21% under moderate, 12% under low, and 13% under very low soil loss in the Chitral district. The above study is helpful to researchers and planners for better planning to control the loss of soil in the high severity zones. Plantation of trees and structures should be built like check dams, which effectively control the soil erosion process.

GIS and remote sensing-based assessment of soil erosion risk using RUSLE model in South-Kivu province, eastern, Democratic Republic of Congo

“…Since the cover management is closely related to the interception of rainfall energy by crops during the growing season (Morgan 1995), it is recommended to integrate into the equation the seasonal variation of crops production along with the variation of rainfall fraction in each season (Maqsoom et al 2020). The mean time series NDVI for the two growing seasons of 2019 has taken into account six representative months (March, April, May, September, October, and November) when the erosion is active and the vegetation at a peak.…”

Assessment of denudation rate and erosion susceptibility in the upper Tamakoshi basin in the higher Himalayas, Nepal

“…In the Tamakoshi watershed, LS ranges from 0.06 to 65.74 (Figure-9), with an average of 0.3±0.56. More than 90% of the basin has LS value ranging from 0.06-1.1 indicate a moderate level of erosion susceptibility (Maqsoom et al, 2020). Moreover, mean LS is found as 0.63±0.82, 0.43±0.62, and 0.38±0.5 for the altitudinal distribution of less than 3000m, 3000-5000m, and more than 5000 m, respectively.…”

“…Since soil erodibility factor K represents soil's susceptibility to erosion and the amount and rate due to runoff effect. Soil data of study area for both Nepal and China were obtained from the Soil and Terrain Database (SOTER), compiled and prepared by World Food Organization (FAO) (Figure-4).Similarly, K factors were initially extracted from the monograph published by the US Department of Agriculture (USDA, 2000) with necessary modification referring to the past studies on similar mountain regions (Gardner and Gerrard, 2003;Maqsoom et al, 2020;Veeranarayana, Raju and Wuletaw, 2019) (Table 1). Where, LS = Topographical factor; Qa = Flow Accumulation grid; Sg = Grid slope in percentage; M =Grid size (x X y), y = dimensionless exponent which depends on slope steepness, being 0.5 for slopes exceeding 4.5%, 0.4 for 3-4.5% slopes, 0.3 for 1-3%, and 0.2 for slopes less than 1% (Wischmeier and Smith, 1978).…”

Torrential Flood Water Management: Rainwater Harvesting through Relation Based Dam Suitability Analysis and Quantification of Erosion Potential

“…The factors of RUSLE are generated using modern techniques of GIS and RS [17]. The implementation of RUSLE for the estimation of SE based on the integration of GIS and RS was also used in different studies [33].…”