Estimation of Evapotranspiration in Sparse Vegetation Areas by Applying an Optimized Two-Source Model

Evapotranspiration (ET) is an important part of the water, carbon, and energy cycles in ecosystems, especially in the drylands. However, due to the particularity of sparse vegetation, the estimation accuracy of ET has been relatively low in the drylands. Therefore, based on the dry climate and sparse vegetation distribution characteristics of the drylands, this study optimized the core algorithms (canopy boundary resistance, aerodynamic resistance, and sparse vegetation coverage) and explored an ET estimation method in the Shuttleworth-Wallace two-layer model (SW model). Then, the Beijing-Tianjin sandstorm source region (BTSSR) was used as the study area to evaluate the applicability of the improved model in the drylands. Results show that: (1) The R-2 value of the improved model results was increased by 1.4 and the RMSE was reduced by 1.9 mm, especially in extreme value regions of ET (maximum or minimum). (2) Regardless of the spatial distribution and seasonal changes of the ET (63-790 mm), the improved ET estimation model could accurately capture the differences. Furtherly, the different vegetation regions could stand for the different climate regions to a certain extent. The accuracy of the optimized model was higher in the semi-arid region (R-2 = 0.92 and 0.93), while the improved model had the best improvement effect in the arid region, with R-2 increasing by 0.12. (3) Precipitation was the decisive factor affecting vegetation transpiration and ET, with R-2 value for both exceeding 0.9. The effect of vegetation coverage (VC) was less. This method is expected to provide a more accurate and adaptable model for the estimation of ET in the drylands.

Automated summary

Based on the specific characteristics of drylands, this study optimized the ET estimation model and found improved results in extreme value regions. Different vegetation regions can represent different climate areas, with precipitation being the decisive factor affecting ET.