International Journal of Environment and Climate Change

Reference evapotranspiration (ET_o) is a critical parameter in agricultural water management, irrigation scheduling and hydrological modelling. Spatially continuous estimation of ET_o remains challenging in data-sparse regions where ground-based meteorological networks are inadequate. This study presents the development and validation of a cloud-based decision support system (DSS) for spatiotemporal estimation of ET_o using the FAO-56 Penman-Monteith method driven by ERA5-Land reanalysis meteorological inputs. The system provides an interactive web interface through which users delineate a study area by drawing a polygon on a digital map, specify start and end dates, and instantly receive spatially distributed ET_o maps and time-series statistics without any software installation. Atmospheric pressure at each grid cell was derived from a digital elevation model to account for elevation effects on the psychrometric constant. The DSS was validated against station-observed ET_o computed from an automatic weather station (AWS) at Coimbatore, Tamil Nadu, for the year 2025. monthly simulated ET_o values showed satisfactory agreement with observed data, yielding a coefficient of determination (R²) of 0.83, Nash-Sutcliffe efficiency (NSE) of 0.83, root mean square error (RMSE) of 0.28 mm day^-1, mean absolute error (MAE) of 0.28 mm day^-1 and percent bias (PBIAS) of -0.17%. The annual mean ET_o for 2025 was 4.30 mm day^-1, corresponding to an annual total of 1567 mm. The developed DSS offers a freely accessible and scalable platform suitable for irrigation scheduling, drought monitoring and water balance studies across India and comparable semi-arid tropical regions. The principal novelty of this work is the first validated, freely accessible cloud-based ETo DSS integrating ERA5-Land reanalysis data with an interactive web interface, enabling on-demand spatiotemporal ETo estimation for any region globally without requiring local software installation or programming expertise.