Estimating the volume of evaporation from the main dams of Iran

• A nationwide assessment of reservoir evaporation across 117 Iranian dams was conducted using six empirical models and four satellite-based datasets. • SEBAL and ERA5 outperformed other methods, showing the highest agreement with observed data (R² > 0.91), especially in arid and topographically diverse regions. • Major reservoirs are predominantly located in regions with extreme evaporation (>3000 mm/year), intensifying water loss challenges in already stressed hydrological zones. • MODIS consistently underestimated evaporation due to coarse spatial resolution, reinforcing the need for hybrid or corrected modeling frameworks. • The study provides a scalable, model-integrated framework for operational evaporation monitoring using Google Earth Engine, with direct implications for adaptive water management under climate change Freshwater scarcity poses a critical challenge in arid and semi-arid regions such as Iran, where evaporation from dam reservoirs contributes substantially to water loss. This study quantifies evaporation volumes from 117 major Iranian reservoirs using a combination of six empirical models and four satellite-based datasets (MODIS, GLDAS, SEBAL, and ERA5). Observational data from 339 synoptic stations were used to validate model performance using statistical indices (R² and RMSE). Among empirical models, Penman-Monteith achieved the highest accuracy (R² = 0.54, RMSE = 688 mm), while SEBAL and ERA5 demonstrated superior performance among satellite products (R² > 0.91, RMSE < 13 mm). Estimated annual evaporation volumes ranged from 2.2 to 2.8 billion cubic meters, with MODIS showing consistent underestimation. The highest evaporation losses were observed in southwestern and southeastern provinces, particularly Khuzestan and Sistan-Baluchestan, which face concurrent water scarcity. The findings emphasize the value of hybrid modeling approaches and satellite integration for national-scale hydrological assessments. This study provides a practical framework for improving water resource management and adapting to climatic variability in data-scarce environments.