Abstract
Evapotranspiration (ET) is a critical climate and ecosystem variable that interconnects water, energy, and carbon cycles. Breathing Earth System Simulator (BESS) is one of the state-of-the-art biophysical models capable of producing spatio-temporal continuous ET results. However, we found that since the BESS model does not use an explicit constraint on soil moisture (SM), it has a relatively lower performance under drier conditions. Given that changes in land surface temperature (LST) are closely associated with surface water status and sensible heat energy, we hypothesize that integrating LST changes could explicitly add the soil moisture constraints and thus enhance BESS's ability to estimate ET. Here we used the morning rise rate of LST (Trate) as a proxy of LST change because of the low noise level in Trate as well as Trate's close relationship with daily mean sensible heat. To test the hypothesis, this study first assessed whether the performance of BESS ET can be explained by the LST change, targeting grassland sites of the AmeriFlux network in the US Midwest and Great Plains. Specifically, the ET deviation (i.e., the difference between BESS-modeled ET and field-measured ET) and Trate deviation, as well as their relationships, were investigated under different conditions of precipitation, SM, and vapor pressure deficit at the AmeriFlux sites. Results indicated that BESS ET exhibited consistently higher performance under well-watered conditions than water-deficit conditions. Also, the deviations of ET and Trate became more negatively correlated under water-deficit conditions. Leveraging the empirical relationship between ET and Trate deviations, this study developed a new way to calibrate BESS ET based on Trate calculated from LST diurnal observations, particularly under soil or atmospheric water-deficit conditions. After calibrating BESS ET, the statistical indicators between the calibrated ET and the ground measurements showed meaningful improvements relative to those before calibration. Specifically, in the Midwest (Great Plains), R2 increased from 0.42 to 0.51 (from 0.45 to 0.46), and RMSE and absolute bias decreased by 12% and 42% (11% and 45%), respectively. This study highlights that the morning rise rate of LST can effectively constrain the ET models that have no SM constraints under water-deficit conditions and also sheds lights on improved ET estimation for crop, biofuel, and pastureland production in dryland and semi-dryland ecosystems.
| Original language | English |
|---|---|
| Article number | 132201 |
| Journal | Journal of Hydrology |
| Volume | 645 |
| DOIs | |
| State | Published - Dec 2024 |
| Externally published | Yes |
Funding
This work was partially funded by the NASA ECOSTRESS Science and Applications Program and partially by the USDA NIFA's AIFARMS project. We also acknowledge the support from the DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award no. DE-SC0018420. Any opinions, findings, conclusions, and recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of NASA, U.S. Department of Energy, or the U.S. Government. We acknowledge the following AmeriFlux sites for their eddy covariance measurements: US-AR1, US-AR2, US-KFS, US-KM4, US-Kon, US-Ro4, US-xCP, US-xDC, US-xKA, US-xKZ, US-xNG, and US-xWD. We thank Dennis Baldocchi and Dan Li for their helpful suggestions and comments on the manuscript development. This work was partially funded by the NASA ECOSTRESS Science and Applications Program and partially by the USDA NIFA \u2019 s AIFARMS project. Any opinions, findings, conclusions, and recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of NASA or the U.S. Government. We acknowledge the following AmeriFlux sites for their eddy covariance measurements: US-AR1 , US-AR2 , US- KFS , US-KM4 , US-Kon, US-Ro4, US-xCP, US-xDC, US-xKA, US-xKZ, US-xNG, and US-xWD. We thank Dennis Baldocchi and Dan Li for their helpful suggestions and comments on the manuscript development.
Keywords
- BESS model
- Energy balance
- Evapotranspiration
- Land surface temperature
- Soil moisture
- Vapor pressure deficit
