Modelling at the basin scale offers crucial insights for policymakers as they make decisions regarding the optimal utilization of water resources. This study employed the MIKE HYDRO Basin model to analyse water demand and supply dynamics in the Parvati Basin of Rajasthan, India, for the period 2005–2020. The MIKE11 NAM model showcased strong alignment between simulated and observed runoff during both the calibration (NSE = 0.79, PBIAS = −2%, R² = 0.79, RMSE = 4.95, RSR = 0.5, and KGE = 0.84) and validation (NSE = 0.67, PBIAS = −12.4%, R² = 0.68, RMSE = 8.3, RSR = 0.62, and KGE = 0.67) phases. The MIKE HYDRO Basin model also exhibited excellent agreement between observed and simulated reservoir water levels, with R², NSE, RMSE, PBIAS, RSR, and KGE values of 0.86, 0.81, 3.87, −2.30%, 0.43, and 0.88, respectively. The MIKE HYDRO Basin model was employed to create six distinct scenarios, considering conveyance efficiency, irrigation method, and conjunctive water use, to assess irrigation demands and deficits within the basin. In the initial simulation, featuring a conveyance efficiency of 45%, flood irrigation, and no groundwater utilization, the average water demand and deficit throughout the study period were estimated as 43.15 MCM and 3.45 MCM, respectively, resulting in a sustainability index of 0.506. Enhancing conveyance efficiency to 75% under flood irrigation and 5% conjunctive use could elevate the sustainability index to 0.92. Transitioning to sprinkler irrigation and a lift irrigation system could raise the system’s sustainability index to 1. These developed models hold promise for real-time reservoir operation and irrigation planning across diverse climatic conditions and varying cropping patterns.

Soil erosion causes sedimentation in reservoirs, limiting storage capacity by occupying space available for water. This reduction in capacity affects flood control and restricts the amount of water available for different purposes. Combating soil degradation requires estimating soil loss and locating hotspot locations. The purpose of this study is to use high-resolution geospatial data and the Universal Soil Loss Equation model to estimate the rate of soil loss and locate hotspot locations in the Bina basin of Madhya Pradesh, India. Rainfall data was used to calculate the rainfall erosivity factor (R), soil data to determine the soil erodibility factor (K), DEM data to get the topographic factor, and Land Use Land Cover maps to determine the crop cover and conservation practice factors. ArcGIS 10.4 tool was used to integrate different geospatial layers in order to determine the rate of soil erosion from the basin soil. An estimated 144,888.45 tons of soil were lost from the basin annually, with an average annual soil loss rate of 2.26 t/ha/year. In the basin, the average annual rate of soil loss ranged from 0 to 340 t/ha/year. Spatial risk categorisation was 82.31% slight, 13.36% moderate, 3.64% high, 0.53% very high, 0.13% severe, and 0.02% very severe. The findings showed low vegetative cover, a lack of conservation measures, and significant erosion on steep slopes. This assessment provides vital information for policymakers and planners to devise strategies for the sustainable use of soil and water resources.