Exploring the potential of GIS-based integrated agroforestry cum soil conservation measure on spring discharge in data scarce region, Kumaon Himalaya, Uttarakhand

Springs in Kumaon region represent one of the largest and most precious sources of fresh water, necessary to meet the drinking water of the population. Understanding the temporal variability of spring discharge is therefore crucial for sustainable water management. Knowledge of the discharge characteristics, organized in a coherent framework, is essential for protecting spring water and preventing shortages. Sentinel 2A and SRTM DEM data were used to study topography, delineate springshed boundary, analyze structural setting, and surface water flow pattern. This study uses a novel approach to assess the impact of agroforestry cum soil conservation measure at selected sites in springshed on spring discharge. The result indicated that average discharge of the period 2020–2023 is 11.49 m 3 /day, which is almost twice the average value of 2019–2020 (6.93 m 3 /day) before intervention. Statistical analyses indicated a clear improvement in spring discharge after intervention, with linear regression showing an increase in R 2 from 0.27 ( p = 0.16) before intervention to 0.43 ( p = 0.0000012) after intervention. One-way ANOVA ( F = 14.813, p = 0.0003) and Spearman’s correlation ( ρ increased from 0.250 to 0.521) further confirmed a statistically significant enhancement in spring discharge following the integrated agroforestry cum conservation measures. The spring discharge dynamics was accessed using recession curve analysis. It was found that the fitting of recession-curve (of the Attadhar spring under study) with one exponential component gives accurate results. The value of exponential coefficient (i.e., 0.0206) represents the major contribution to drainage from the spring-catchment’s portion with highest permeability. We conclude that the quantitative insights gained from this analysis offer a valuable addition to conventional spring classification methods, which typically rely on qualitative assessments. Our proposed approach refines these traditional schemes by increasing objectivity and reproducibility, thereby fostering greater consistency across hydrological disciplines.