Global Lake Evaporation Estimates by Integrating Penman Method with Equilibrium Temperature Approach

Modeling evaporation E from inland water bodies is challenging largely due to the uncertainties of input data, particularly surface water temperature that plays a key role in the available energy, i.e., net radiation R_n minus rate of water heat storage change G. The equilibrium temperature approach (ETA) for estimating water surface temperature offers an alternative method to calculate R_n and G using standard meteorological data. This study evaluates the global lake E estimates from the widely used Penman model (PM) coupled with the ETA (PM-ETA) against field observations and model simulations from the Lake, Ice, Snow, and Sediment Simulator (LISSS). Our analysis reveals that the PM-ETA tends to overestimate E by approximately 36% and 24% compared to observations and the LISSS simulations, respectively, despite being driven by the same input data. The biases of the PM-ETA E are more pronounced in the cold and polar regions with distinct seasonality of R_n and G. Furthermore, the E trends from the PM-ETA deviate from the LISSS simulations over the period of 2001–16 due to the bias trends in the available energy. By incorporating the LISSS-simulated R_n and G into the PM, the bias in E is reduced to less than ±5% compared to the LISSS results. This study highlights the need to improve the available energy input of the PM to improve the estimates of global lake E for better water resource management and planning. SIGNIFICANCE STATEMENT: This study addresses a crucial challenge in modeling evaporation E from inland water bodies}uncertainties in surface water temperature and available energy inputs, particularly net radiation R_n and rate of heat storage change G. By evaluating the widely used Penman model (PM) coupled with the equilibrium temperature approach (ETA), we reveal a tendency for the PM-ETA to overestimate E globally, with the largest biases observed in cold and polar regions. Incorporating higher-quality R_n and G estimates from the Lake, Ice, Snow, and Sediment Simulator (LISSS) significantly reduces these biases. These findings highlight the importance of alternative higher-quality data products for available energy inputs for improving E estimates by the PM.