Application of analytic hierarchy process for the assessment of soil erosion risk in the Urmodi River watershed of Maharashtra state, India

• The article cover geological, climatic, drainage, and hydrological factor for soil erosion risk zone prediction. • AHP-derived soil erosion risk zone compared with RUSLE zone for cross verification. • This study is used for hilly areas to understand the severity of water-induced soil loss. The current research work established a geoinformation-based decision support system designed to facilitate informed decision-making, specifically aimed at mitigating soil erosion within the Urmodi River watershed in Maharashtra, India. Soil erosion poses a critical environmental challenge with far-reaching implications on ecosystem health, land productivity, and sustainable land use management. This study employs a multi-criteria analysis approach to assess erosion-prone areas within Urmodi River watershed by integrating diverse geospatial data layers and GIS techniques. Ten key factors from domains of climatic condition, geology and biophysical status influencing erosion vulnerability, including Rainfall erosivity, Geology, Geomorphology, Slope, Lineament Density, Drainage Density, Drainage Texture, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Soil Texture, were evaluated using the Analytic Hierarchy Process (AHP). The results revealed distinct soil erosion vulnerability categories across the landscape, ranging from "very low" to "very high" susceptibility zones. The area proportions for each vulnerability category were computed as follows: very low (47.00 %), low (23.88 %), moderate (13.90%), high (10.86 %), and very high (4.36 %) over the entire watershed. The AHP-based SER was compared with Revised Universal Soil Loss Equation based soil erosion risk zone. The performance was assessed by computing Root mean square error and Mean Absolute Percentage Error (MAPE) and found 31.67 and 28.03 % respectively. By computing AHP with geoinformation science and earth observation data plays a significant role in developing a resilient Spatial Decision Support System (SDSS), effectively exposing the present condition of areas prone to soil degradation and highlighting its irreversible impact on the environment. The results of AHP and RUSLE show similarities in the spatial extent over the entire watershed. This study focuses on the Urmodi River watershed due to increased development from the 'Kaas plateau' a world heritage site. This study compares soil erosion risk using RUSLE, superimposed over AHP-based output to highlight the differences. It integrates various climatic, geological, and biophysical factors with ‘specific constants’ to compute erosion in the neighboring watershed, which resembles nearly the same characteristics.