Evaluating Soil Resistance Formulations in Thermal-Based Two-Source Energy Balance (TSEB) Model: Implications for Heterogeneous Semiarid and Arid Regions

Relatively small fluctuations in the surface energy balance and evapotranspiration in semiarid and arid regions can be indicative of significant changes to ecosystem health. Therefore, it is imperative to have approaches for monitoring surface fluxes in these regions. The remote sensing-based two-source energy balance (TSEB) model is a suitable method for flux estimation over sparsely vegetated semiarid and arid landscapes since it explicitly considers surface energy flux contributions from soil and vegetation. However, previous studies indicate that TSEB generally underestimates sensible heat flux (H) and hence overestimates latent heat flux (LE) or evapotranspiration for these regions unless soil resistance coefficients are modified based on additional ground information. In this study, TSEB is applied over semiarid and arid regions on three continents using the original soil resistance formulation with modified coefficients and a recently developed physically based soil resistance formulation. Model sensitivity analysis demonstrates the high sensitivity of TSEB with original soil resistance formulation to soil resistance coefficients, while TSEB with the new soil resistance formulation has relatively low sensitivity to uncertainties in all coefficients. The performance of TSEB using different soil resistance formulations is evaluated by comparing modeled H against eddy covariance measurements in six semiarid and arid study sites and ranking the error statistics. Our results indicate that incorporating the new soil resistance formulation into TSEB would enhance its utility in flux estimation over heterogeneous landscapes by obviating its reliance on semiempirical coefficients and thus provide more robust fluxes over sparsely vegetated regions without model calibration and/or parameter tuning.Spanish Ministry of Science and Innovation. Grant Number: CGL2016‐78075‐P DINCOS. Grant Number: CGL2016‐78075‐P Swiss National Science Foundation. Grant Number: P2EZP2‐165244 Strategic Priority Research Program of Chinese Academy of Sciences. Grant Number: XDA1904050