资源环境科技发展态势分析平台

Despite offering spatially and temporally continuous measurements, the use of remotely sensed P and E in data-scarce catchments is hindered by the lack of ground-based measurements that enable comprehensive validation. This study proposes a novel validation framework that characterizes the combined error in the long-term average estimates of remotely sensed P and E by making use of the Budyko hypothesis, specifically Fu's equation. A Root Mean Square Error (RMSE)-based error metric that is capable of translating individual biases in P and E estimates onto the Budyko space is developed. A controlled sensitivity experiment using data from Model Parameter Estimation Experiment (MOPEX) catchments in the United States showed that the developed error metric is more sensitive to biases in P compared to biases in estimates of E. Validating the framework using combinations of different satellite-based estimates of P and E revealed that the framework succeeds in arriving at the same conclusions as a traditional validation method with regards to the quality of P and Edam sets. The framework offers a physically consistent, parametrically efficient basis for the selection of remotely sensed P and E data sets for hydrologic studies. Due to lack of consideration for catchment storage in the formulation of Fu's equation, the developed error metric is limited to long temporal time scales. As a result, the error metric is capable of characterizing the bias in P and Edam sets and not the variance.