Two-Stage Evapotranspiration Partitioning Under the Generalized Proportionality Hypothesis Based on the Interannual Relationship Between Precipitation and Runoff

• DOI: 10.3390/rs17071203
• Publication Date: 2025-03-28T10:06:35Z
• Authors: Changwu Cheng; Wenzhao Liu; Rui Chen; Zhaotao Mu; Xiaoyang Han

The generalized proportionality hypothesis (GPH) highlights the competitive relationships among hydrological components as precipitation (P) transforms into runoff (Q) and evapotranspiration (E), providing a novel perspective on E partitioning that differs from the traditional physical source-based approach. To achieve sequential partitioning of E into initial (Ei) and continuing (Ec) evapotranspiration under the GPH, a P-Q relationship-based Ei estimation method was proposed for the Model Parameter Estimation Experiment (MOPEX) catchments. On this basis, we analyzed the relationship between the GPH-based E components and the physical source-based ones separated by the Penman-Monteith-Mu algorithm. Additionally, we explored the differences between the calculated and inverse Budyko-WT model parameter (Ei/E) and discussed the implications for the Budyko framework. The results showed the following: (1) A significant linear P-Q relationship (p < 0.05) prevailed in the MOPEX catchments, providing a robust data foundation for Ei estimation. Across the MOPEX catchments, Ei and Ec contributed 73% and 27% of total E, respectively. (2) The combined proportion of evaporation from canopy interception and wet soil averaged about 25%, and it was much lower than that of Ei, indicating that it was difficult to establish a connection between Ei and the physical source-based E components. (3) The potential evapotranspiration (EP) satisfying the Budyko-WT model was strictly constrained by the GPH, while the inappropriate EP estimation method largely explained the discrepancy between the calculated and inverse Ei/E. This study deepens the knowledge of the sequential partitioning of E components, uncovers the discrepancies between different E partitioning frameworks, and provides new insights into the characterization of key variables in Budyko models.