Integrated water management and agroforestry planning in the Kulsi river basin: a data-driven decision-making approach

This study proposed an analytical hierarchy processes (AHP)- geographic information systems (GIS)-based multi-criteria decision model (AMCIDM) for agroforestry planning and applied in the Kulsi river basin, India for Pilot Testing. Over 25 years of hydrological data were analysed to assess rainfall patterns, flood risks and soil degradation. The Mann-Kendall test confirmed a significant increase in post-monsoon temperatures (Tau = 0.47, p < 0.04), which influenced runoff and discharge. The revised universal soil loss equation identified critical erosion zones, with maximum soil loss reaching 390 tons per hectare per year. The discharge regression analysis (R-2 approximate to 0.97-0.99) validated the accuracy of hydrological predictions. The livelihood vulnerability index (LVI) analysis indicates moderate adaptive capacity (0.43, LVI-IPCC: 0.023), high sensitivity (0.46) and high exposure (0.50) to climate risks, necessitating improved health infrastructure, water management, livelihood diversification, and disaster preparedness for resilience. The AMCIDM framework integrated GIS-based suitability analysis, economic feasibility (Net Present Value > INR 1,50,000 (Equivalent to USD 1723) per hectare) and environmental impact assessment (carbon sequestration up to 8.3 tons of CO2 per hectare per year). Zone 3 of the study area emerged as the most suitable for agroforestry, balancing soil retention, slope stability, and financial viability. The AMCIDM model provides a scalable solution for balancing ecological stability, economic viability, and climate resilience.