Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India

Abstract

Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan–Morgan–Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).

Soil erosion vulnerability assessment in the eco-sensitive Himalayan region using modeling approach

“…The forest, forest scrub, grassland and wetland were set values close to 1 due to the absence of conservation practices, but the presence of vegetative cover in these land uses. The outcomes of the present study authenticate with the findings of Biswas & Pani (2015) for the Jharkhand (Barakar basin) (0-1), Srinivasan et al (2019) for Odisha, India (0-1) and Das et al (2021) for the Nogpoh micro watershed of Ri-Bhoi district of Meghalaya (0.6 to 1). Thus, the presence of anti-erosion practices aids in the reduction of soil disintegration in regions of steeper slants where erosion rates are otherwise comparatively extreme (Imajjane & Belfoul, 2020).…”

GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects

“…This index (K value) designates the susceptibility of soil to get erode (Das et al, 2021). It is mainly dependent on the land/soil features, the texture of the soil, organic matter content, and unsaturated hydraulic conductivity.…”

geoinformatics and RUSLE model-based soil erosion assessment in a tropical mountainous area of Chite watershed, Mizoram, India

“…In a similar study, Das et al (2018) predicted a mean soil loss below 5 t ha -1 year -1 , while Chatterjee (2020) estimated an erosion rate of 3.4 t ha -1 year -1 . On the other hand, Dabral et al (2008), Pandey et al (2009), Das et al (2020), and Barman et al (2020) reported a comparatively higher mean erosion rate of 51, 57.06, 59.94, and 115.4 t ha -1 year -1 , respectively. Such an extensive disparity may be attributed to regional distinctions in the parameters as well as the varying methodology adopted while preparing these parameters.…”