Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in NM, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling.

Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in New Mexico, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling.

Spatial estimates of reservoir evaporation from satellite observations are often inaccurate due to biases in raw satellite-derived water surface temperature (Ts_sat) and the need for concurrent in situ measurements of water surface temperature (Ts_m) for correction. This study expands the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE) by developing transferable satellite-derived Ts_sat correction methods that eliminate the need for in situ Ts_m measurements at the application lakes. Analysis of 627 corresponding Ts_sat and Ts_m from 30 lakes across four climatic zones revealed climate-dependent biases, with a pooled mean bias of +1.53 °C and RMSE of 2.43 °C. Global (Method 1) and a climate-zone-based (Method 2) corrections were developed using 22 lakes and independently validated at eight lakes. Method 1 and Method 2 reduced satellite-derived Ts_sat RMSE from 2.92 °C to 2.15 °C and 1.87 °C, respectively. Incorporating these corrections into TEAREE reduced pooled daily evaporation RMSE from 1.37 mm/day to 0.81 mm/day (41%) and 0.62 mm/day (55%), respectively. Method 2 achieved comparable performance to the site-specific correction benchmark, with statistically equivalent mean evaporation estimates and absolute prediction errors at the pooled scale (TOST p < 0.001; ±5% equivalence margin). These findings demonstrate that climate-zone-based correction improves TEAREE evaporation estimates and supports broader application to lakes and reservoirs where in situ Ts_m measurements are unavailable.