Evaluation and Application of the PT-JPL Physical Model Optimized with XGBoost Algorithm in Latent Heat Flux Estimation

The static parameterization scheme in the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model limits the dynamic capture of latent heat flux (LE) in different plant functional types (PFTs). Therefore, this study employs the Extreme Gradient Boosting (XGBoost) algorithm to optimize the constrai...

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Veröffentlicht in:	Water resources management Jg. 39; H. 10; S. 4971 - 4988
Hauptverfasser:	Wang, Lizheng, Kong, Jinling, Zhang, Qiutong, Dong, Lixin, Zhong, Yanling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.08.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Atmospheric Sciences Civil Engineering Climate change Comparative analysis Constraints Deserts Dynamic response Earth and Environmental Science Earth Sciences Ensemble learning Environment Geotechnical Engineering & Applied Earth Sciences Heat Heat flux Heat transfer Hydrogeology Hydrology Hydrology/Water Resources Latent heat Optimization Parameter sensitivity Parameterization Parameters Physiology Precipitation Propulsion systems Radiation Remote sensing Resource allocation Resource conservation River basins Simulation Transpiration Vegetation Water conservation Water resources XGBoost algorithm Combining ensemble learning algorithm with physical model PT-JPL model Physical constraints Evapotranspiration
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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