Linking climate drought indices with spatial monitoring data to project agricultural water storage requirements

Climate change is intensifying drought risk and hydroclimatic variability in many rainfed agricultural regions, posing increasing challenges for smallholder farming systems. Access to reliable on-farm water storage is essential, particularly during short-term droughts affecting critical stages of crop growth. Small agricultural ponds are a low-cost and widely adopted solution for buffering rainfall variability, yet their adequacy is rarely evaluated through integrated monitoring frameworks that link climate drought indicators with spatial observations of land use and water storage at the local scale. Despite increasing availability of climate data and remotely sensed land and water information, a clear gap remains in systematically connecting drought monitoring with on-farm water storage requirements under future climate scenarios in rice-based systems. This study assesses how climate change may alter agricultural pond requirements in the rice-growing region of Palakkad, Kerala (India) by integrating multi-temporal climatic drought indicators with spatially explicit land-use and water storage datasets. Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were derived for historical (1984–2014) and future periods (2020–2050 and 2070–2100; CMIP6) under SSP4.5 and SSP8.5 scenarios. These indicators were combined with spatial rice cultivation maps of Palakkad and georeferenced pond distribution data to construct a composite agricultural water stress index. By comparing present and future conditions, the study identifies areas where water stress is projected to intensify and where existing pond availability may become insufficient. The results highlight priority zones where additional pond infrastructure will be critical to sustain rainfed rice farming systems in Palakkad. By bridging climate drought monitoring and spatial water storage assessment, this research advances a transferable, monitoring-based decision-support framework for climate-resilient agricultural water management.