Potential evaporation at eddy-covariance sites across the globe

Abstract

Potential evaporation ( E p) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of E p exist, which reflect a diverse range of methods to calculate it. A comparison of the performance of these methods against field observations in different global ecosystems is urgently needed. In this study, potential evaporation was defined as the rate of terrestrial evaporation (or evapotranspiration) that the actual ecosystem would attain if it were to evaporate at maximal rate for the given atmospheric conditions. We use eddy-covariance measurements from the FLUXNET2015 database, covering 11 different biomes, to parameterise and inter-compare the most widely used E p methods and to uncover their relative performance. For each of the 107 sites, we isolate days for which ecosystems can be considered unstressed, based on both an energy balance and a soil water content approach. Evaporation measurements during these days are used as reference to calibrate and validate the different methods to estimate E p. Our results indicate that a simple radiation-driven method, calibrated per biome, consistently performs best against in situ measurements (mean correlation of 0.93; unbiased RMSE of 0.56 mm day ĝ'1; and bias of ĝ'0.02 mm day ĝ'1). A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman-based, Penman-Monteith-based or temperature-driven approaches. We show that the poor performance of Penman-Monteith-based approaches largely relates to the fact that the unstressed stomatal conductance cannot be assumed to be constant in time at the ecosystem scale. On the contrary, the biome-specific parameters required by simpler radiation-driven methods are relatively constant in time and per biome type. This makes these methods a robust way to estimate E p and a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.