Physically Informed Thresholds from Root-Zone Soil Moisture Loss Functions: Toward Improved Flash Drought Monitoring

Percentile-based thresholds widely used in flash drought monitoring lack a clear physical basis, as the same percentile may correspond to substantially different soil moisture states under different soil and climatic conditions. This study constructed a framework based on the soil moisture loss rate function L(SM) to identify root-zone loss stages, estimate physically informed thresholds, and compare them with statistical percentile thresholds. Using ERA5-Land reanalysis data from 1950 to 2025, the framework combines non-parametric identification of L(SM) with piecewise linear fitting to characterize root-zone soil moisture loss stages and estimate physically informed thresholds in the Dongjiang River Basin, southeastern China. Two physically informed critical thresholds were identified: SMWT, representing the wet–transitional boundary, and SMTD, representing the transitional–dry boundary. Their relationships with statistical percentile thresholds and reference soil hydraulic parameters were then evaluated through spatial comparison and regression analysis. Results showed that root-zone L(SM) curves were overwhelmingly dominated by a wet–transitional–dry three-stage structure, accounting for 92.5% of all grid cells, while the gravitational drainage stage was absent at the root-zone scale. The basin-mean SMWT and SMTD were 0.36 and 0.27 m3/m3, respectively, with mean bootstrap 95% confidence interval widths of 0.03 and 0.02 m3/m3. The 40th and 20th percentile thresholds were fully nested between SMWT and SMTD, and their correlations with the physically informed thresholds reached 0.98–0.99. However, the statistical threshold window covered only approximately 55.6% of the physically informed transition zone, indicating weaker ability to characterize terminal agricultural drought severity than flash drought onset. The proposed framework provides physically informed reference thresholds for flash drought monitoring and supports regional adaptation of percentile-based methods.