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

Changlong Li,Zhihai Gao,Zengyuan Li,Bin Sun

doi:10.3390/rs13071344

Abstract

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 R2 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 (R2 = 0.92 and 0.93), while the improved model had the best improvement effect in the arid region, with R2 increasing by 0.12. (3) Precipitation was the decisive factor affecting vegetation transpiration and ET, with R2 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.

Highlights

Evapotranspiration (ET) plays a very important role in the ecosystem, connecting the global water, carbon, and energy cycles [1,2]
After the three-part optimization algorithm was carried out at the same time, the R2 increased by 0.14 and the root mean square error (RMSE) decreased by 1.9 mm
From the change in the scattered point distribution, it can be seen that the shape of the optimized model in Figure 5 was slenderer, which indicates that the optimized results were more stable and the results were more accurate

Summary

Introduction

Evapotranspiration (ET) plays a very important role in the ecosystem, connecting the global water, carbon, and energy cycles [1,2]. Water-limited ecosystems (including arid, semi-arid and sub-humid regions) account for more than 40% of the Earth’s land surface [3]. In these areas, 90% of the effective precipitation returns to the atmosphere through ET. 90% of the effective precipitation returns to the atmosphere through ET This causes water resources to be the main limiting factor for economic development and ecological protection in arid areas [4,5]. The drylands experience the frequent occurrence of climatic natural disasters, such as soil degradation and droughts. Warnings of drought can be achieved through the dynamic monitoring of long-term

Methods

Results

Conclusion