Reply on RC1

Abstract

Actual evapotranspiration (ET) is the key link between water and energy cycles. However, accurate evaporation estimation in alpine barren areas remains understudied. In this study, we aimed to improve the satellite-driven Process-based Land Surface ET/Heat fluxes algorithm (P-LSH) by introducing two frameworks for quantifying moisture constraints to ET, and to test the applicability of satellite soil moisture and precipitation data for improving ET retrieval. As a result, it formed two improved P-LSH algorithms. The first framework normalizes the surface soil moisture to represent moisture stress, while the second framework takes the ratio of cumulative precipitation to cumulative equilibrium evaporation to quantify soil water stress. We systematically assessed the performances of the two improved P-LSH algorithms and six existing remote sensing ET retrieval algorithms on two barrens-dominated basins of the Tibetan Plateau using reconstructed ET estimates derived from the terrestrial water balance method as a benchmark. The two frameworks largely improved the performance of the P-LSH algorithm and showed better performance in both basins (root mean square error (RMSE) = 7.36 and 7.76 mm month-1; R2 = 0.86 and 0.87), resulting in a higher simulation accuracy than all six existing algorithms. We used five soil moisture and five precipitation datasets to investigate the impact of moisture constraint uncertainty on the improved P-LSH algorithm. The ET estimates of the improved P-LSH algorithm, driven by the GLDAS_Noah soil moisture, performed best compared with those driven by other soil moisture and precipitation datasets, while ET estimates driven by various precipitation datasets generally showed a high and stable accuracy. These results suggest that high-quality soil moisture can optimally express moisture supply to ET, and that more accessible precipitation data can serve as a substitute for soil moisture as an indicator of moisture status for its robust performance in barren evaporation.