Implementation of Dynamic Effective Rooting Depth in Evapotranspiration Model Deepens Understanding of Evapotranspiration Partitioning Under Soil Moisture Gradients in China

Evapotranspiration (ET) is a key component of water cycle and is strongly modulated by below-ground (e.g., dynamic effective rooting depth, Zr) and above-ground (e.g., LAI and canopy height, CH) vegetation dynamics. Existing studies mainly focus on the effect of above-ground vegetation dynamics on ET, while it is still unclear how Zr affects ET. Moreover, it is challenging to parameterize Zr dynamically in large-scale hydrological and biogeochemical models due to data scarcity. Here, we estimate Zr in China from 1982 to 2015 based on Guswa's carbon cost-benefit model, and update ET partitioning algorithm by replacing CH with Zr in PT-JPLsm model to form the PT-JPLzr model. We find that root mean square error (RMSE) between modeled (based on PT-JPLzr model) and observed ET is 8.25% lower than that of PT-JPLsm model at 16 in-situ ET observation sites. Comparing with T/ET from the satellite-based products, our results highlight the improved performance of PT-JPLzr (R-2 = 0.66) due to incorporated Zr, which is superior to that of PT-JPLsm model (R-2 = 0.62). Transpiration (T) limited by changes in soil moisture (SM) is more sensitive to Zr than CH. Thus, Zr improves the PT-JPL model performance by affecting the sensitivity of T to soil moisture deficit. Moreover, SM and LAI are main drivers of T/ET spatial variability in drier and wetter regions, respectively. These findings highlight the critical role of Zr in regulating the effects of soil moisture deficit on ET.

Automated summary

Evapotranspiration (ET) is an important process in the water cycle, influenced by below-ground and above-ground vegetation dynamics. This study focuses on the effects of Zr, a measure of rooting depth, on ET and improves modeling performance by incorporating Zr in the PT-JPL model. The results show that the PT-JPLzr model performs better than the PT-JPLsm model, with lower error between modeled and observed ET. Zr influences the sensitivity of transpiration to soil moisture deficit, and soil moisture and LAI are key drivers of T/ET variability in different regions.