Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions
Existing evapotranspiration (ET) estimation models face inherent limitations when relying solely on physics-based or data-driven paradigms. To address this issue, we propose three data-physics hybrid modeling methods for improving instantaneous ET estimation in this study. A Physics-Data Learning (P...
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Elsevier
2025-08-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425003488 |
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| author | Binrui Liu Xinguang He Wenkai Lyu Lizhi Tao |
| author_facet | Binrui Liu Xinguang He Wenkai Lyu Lizhi Tao |
| author_sort | Binrui Liu |
| collection | DOAJ |
| description | Existing evapotranspiration (ET) estimation models face inherent limitations when relying solely on physics-based or data-driven paradigms. To address this issue, we propose three data-physics hybrid modeling methods for improving instantaneous ET estimation in this study. A Physics-Data Learning (PDL) model is first formed by adding a complementary physical variable generated by Penman–Monteith (PM) equation to a deep learning (DL) model along with driving variables to regress latent heat flux. Building on the PDL, a Physics-Augmented Learning (PAL) model is then formulated by introducing a physics-augmented term into the loss function. Finally, a Physics-Augmented Residual Learning (PARL) model is developed by using the residual learning technique to deeply integrate the PM and pure DL baseline models. Using the FLUXNET dataset, three proposed models are compared with the baselines on ten vegetation types (VTs) across the globe. The results show that all proposed models improve the accuracy of two baselines and reduce the uncertainty of pure DL to different extents. Among them, the PARL achieves the highest accuracy and robustness, with NSE (RMSE) ranging from 0.71–0.82 (22.40–43.14 W/m2) across ten VTs. The PAL ranks second and effectively mitigates the PDL’s sensitivity to imperfect physical knowledge. Although three proposed models show better extrapolation ability than the pure DL under conditions of limited data, the PARL stands out for its superior generalization under four created extreme climate scenarios. These results confirm the potential of data-physics hybrid modeling in ET estimation, which is conducive to supporting efficient irrigation water management. |
| format | Article |
| id | doaj-art-7bfacda45f2c4a24a2a73489f85515b2 |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
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| series | Agricultural Water Management |
| spelling | doaj-art-7bfacda45f2c4a24a2a73489f85515b22025-08-20T03:56:08ZengElsevierAgricultural Water Management1873-22832025-08-0131710963410.1016/j.agwat.2025.109634Physics-augmented deep learning models for improving evapotranspiration estimation in global land regionsBinrui Liu0Xinguang He1Wenkai Lyu2Lizhi Tao3Key Laboratory of Geospatial Big Data Mining and Application, College of Geographic Sciences, Hunan Normal University, Changsha 410081, China; National Centre for Groundwater Research and Training, and College of Science and Engineering, Flinders University, Adelaide, SA 5001, AustraliaKey Laboratory of Geospatial Big Data Mining and Application, College of Geographic Sciences, Hunan Normal University, Changsha 410081, China; Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education, College of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China; Corresponding author at: Key Laboratory of Geospatial Big Data Mining and Application, College of Geographic Sciences, Hunan Normal University, Changsha 410081, China.Key Laboratory of Geospatial Big Data Mining and Application, College of Geographic Sciences, Hunan Normal University, Changsha 410081, ChinaKey Laboratory of Computing and Stochastic Mathematics of Ministry of Education, College of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China; Key Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education, and School of Geography and Environmental Science, Jiangxi Normal University, Nanchang 330022, ChinaExisting evapotranspiration (ET) estimation models face inherent limitations when relying solely on physics-based or data-driven paradigms. To address this issue, we propose three data-physics hybrid modeling methods for improving instantaneous ET estimation in this study. A Physics-Data Learning (PDL) model is first formed by adding a complementary physical variable generated by Penman–Monteith (PM) equation to a deep learning (DL) model along with driving variables to regress latent heat flux. Building on the PDL, a Physics-Augmented Learning (PAL) model is then formulated by introducing a physics-augmented term into the loss function. Finally, a Physics-Augmented Residual Learning (PARL) model is developed by using the residual learning technique to deeply integrate the PM and pure DL baseline models. Using the FLUXNET dataset, three proposed models are compared with the baselines on ten vegetation types (VTs) across the globe. The results show that all proposed models improve the accuracy of two baselines and reduce the uncertainty of pure DL to different extents. Among them, the PARL achieves the highest accuracy and robustness, with NSE (RMSE) ranging from 0.71–0.82 (22.40–43.14 W/m2) across ten VTs. The PAL ranks second and effectively mitigates the PDL’s sensitivity to imperfect physical knowledge. Although three proposed models show better extrapolation ability than the pure DL under conditions of limited data, the PARL stands out for its superior generalization under four created extreme climate scenarios. These results confirm the potential of data-physics hybrid modeling in ET estimation, which is conducive to supporting efficient irrigation water management.http://www.sciencedirect.com/science/article/pii/S0378377425003488Residual learningPhysical knowledgePenman–Monteith equationGeneralization capabilityExtreme eventsLimited data |
| spellingShingle | Binrui Liu Xinguang He Wenkai Lyu Lizhi Tao Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions Agricultural Water Management Residual learning Physical knowledge Penman–Monteith equation Generalization capability Extreme events Limited data |
| title | Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions |
| title_full | Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions |
| title_fullStr | Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions |
| title_full_unstemmed | Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions |
| title_short | Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions |
| title_sort | physics augmented deep learning models for improving evapotranspiration estimation in global land regions |
| topic | Residual learning Physical knowledge Penman–Monteith equation Generalization capability Extreme events Limited data |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425003488 |
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