Articles | Volume 19, issue 21
https://doi.org/10.5194/gmd-19-10699-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-10699-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
11 Nov 2026
Model description paper |
| 11 Nov 2026
| 11 Nov 2026
PLSTM-Reg v1.0: a regional physics-encoded LSTM model for simulating reservoir operations without site-specific training
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Yanan Chen
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
School of Engineering, Westlake University; Hangzhou 310030, Zhejiang Province, China
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Songjun Wu, Doerthe Tetzlaff, Yi Zheng, and Chris Soulsby
Geosci. Model Dev., 19, 2257–2278, https://doi.org/10.5194/gmd-19-2257-2026, https://doi.org/10.5194/gmd-19-2257-2026, 2026
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We developed EcoTWIN v1.0, a new fully distributed tracer-aided ecohydrological model that tracks water, isotopes, and nutrients fluxes. The model was successfully tested in 17 large European catchments across diverse geological and climatic backgrounds. As a tracer-aided model, EcoTWIN not only captures flow paths but also estimates water ages/travel times, thus bridging hydrology with water quality. This opens new possibilities for understanding the synergy between water and nitrogen cycles.
Chao Wang, Stephen Leisz, Li Li, Xiaoying Shi, Jiafu Mao, Yi Zheng, and Anping Chen
Earth Syst. Dynam., 15, 75–90, https://doi.org/10.5194/esd-15-75-2024, https://doi.org/10.5194/esd-15-75-2024, 2024
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Climate change can significantly impact river runoff; however, predicting future runoff is challenging. Using historical runoff gauge data to evaluate model performances in runoff simulations for the Mekong River, we quantify future runoff changes in the Mekong River with the best simulation combination. Results suggest a significant increase in the annual runoff, along with varied seasonal distributions, thus heightening the need for adapted water resource management measures.
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Short summary
We introduce a deep learning approach that extracts shared operating patterns from diverse, data-rich reservoirs and transfers this knowledge to limited-data reservoirs, yielding skillful simulations of storage and release. Using satellite observations, the model also reconstructs historical behavior in no-data reservoirs. Together, this work provide a scalable foundation for addressing pressing data gaps in reservoir operations and better supporting large-scale and long-term water management.
We introduce a deep learning approach that extracts shared operating patterns from diverse,...
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