GLOBAL REFERENCE EVAPOTRANSPIRATION ESTIMATION USING ERA5-LAND HOURLY REANALYSIS DATA IN GOOGLE EARTH ENGINE

Abstract

Reference evapotranspiration (ETo) is a critical variable for water resources management, irrigation scheduling, drought assessment, and global climate studies. This study presents a cloud-based, scalable methodology for global ETo estimation using ERA5-Land hourly reanalysis data processed within the Google Earth Engine (GEE) platform. The FAO-56 Penman-Monteith equation was implemented using six meteorological variables derived from the ERA5-Land dataset at approximately 11 km spatial resolution. The implemented pipeline processes hourly data and aggregates results to daily, monthly, and annual scales. The resulting global ETo dataset reveals distinct spatial and seasonal patterns consistent with known global climate zones, with maximum values exceeding 10 mm/day in arid subtropical regions such as the Sahara, Arabian Peninsula, and Australian interior, and minimum values near 0 mm/day in polar and high-altitude zones. The GEE-based pipeline enables efficient large-scale computation without local data storage requirements, making it accessible for operational global hydrological applications. The proposed methodology and dataset provide a valuable tool for agricultural water demand assessment, drought monitoring, and climate impact studies worldwide.