Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau

Abstract

More accurate and process-based satellite evapotranspiration (ET) estimation for the Tibetan Plateau (TP)—the Third Pole of the world—have long been of major interest in hydrometeorology. Combining recent advances in satellite-based ET mechanistic algorithms and data-oriented methods allows ET hybrid modeling by coupling physical constraints with machine learning (ML). Specifically, we developed two hybrid models, a surface conductance-based ML model (ML-Gs) and a soil evaporation-based ML model (ML-Es), to estimate regional ET on the TP. These hybrid models have biophysical framework, under which one of the parameters or components is modeled using ML. Hybrid models make ML complementary to the process-based ET framework, which to find an optimal junction between well physical mechanism and high model performance. The daily ET estimates were evaluated at 28 eddy covariance flux tower sites, as well as by comparison with two process-based ET algorithms (a Penman–Monteith-based ET-PM algorithm and a Priestley-Taylor-based ET-PT algorithm) and a data-oriented pure ML method. The hybrid models decreased the root-mean-square-error (RMSE) of two physical algorithms (1.11 mm/day for ET-PM, 1.09 mm/day for ET-PT) to 0.50 mm/day, and increased the Kling-Gupta efficiency (KGE) (0.35 for ET-PM, 0.36 for ET-PT) to 0.92. Our hybrid models also showed improved performance (KGE of 0.65) than pure ML (KGE of 0.62) at data-sparse regions as well as for the responses to extreme weather events. It indicates that our approach does not only boost the ET simulation accuracy, but also improve the physical understanding of ML-based ET estimation. More importantly, ML-Es focuses on the ET components on the TP and is more well-defined than ML-Gs. An innovation of our approach is that for data-sparse regions and extreme cases, the more robust physical mechanism was coupled, the better generalization performance of hybrid model could achieve. The spatiotemporal ET patterns based on our hybrid models were consistent with the variations in local climatic regions and could provide critical information on the understanding of hydrological processes under the global and regional climate changes.