Resource Use Efficiency (RUE) Dynamics of Indian Forests Through an Eco-Hydrogeological Approach Using Machine Learning

 Resource Use Efficiency (RUE) serves as a critical indicator of forest ecosystem functionality, reflecting the efficiency of forests in utilizing light, water, and carbon for biomass production. This study investigates the spatiotemporal dynamics of RUE across 14 major Indian forest types from 2014 to 2023 by integrating Light Use Efficiency (LUE), Water Use Efficiency (WUE), and Carbon Use Efficiency (CUE) derived from MODIS satellite products. Using an eco-hydrogeological framework coupled with Random Forest modeling, the study evaluates the influence of climatic, topographic, and hydrological variables on forest productivity. Results reveal considerable spatial heterogeneity and temporal variation in RUE, with the highest efficiencies observed in wet evergreen and semi-evergreen forests and the lowest in dry deciduous and thorn forests. WUE demonstrated substantial variability across forest types and years, particularly impacted by the 2016 drought. CUE was strongly influenced by elevation (R2 = 0.82), and slope emerged as a limiting factor in drier ecosystems. The study highlights that subtropical pine and montane forests exhibit resilience and adaptive efficiency, while arid-zone forests remain vulnerable to climatic stressors. These findings provide actionable insights for sitespecific sustainable forest management and climate resilience planning in India’s diverse forest landscapes.