Articles in press are presented at https://www.sciencedirect.com/journal/water-science-and-engineering/articles-in-press
2026, 19(1): 1-10.
doi: 10.1016/j.wse.2026.01.002
Abstract:
Accurate and efficient hydrological simulation is critically important to sustainable water resources management amidst escalating climate change. As an indispensable scientific tool, hydrological modeling employs mathematical frameworks and computational techniques to quantitatively characterize hydrological processes, thereby playing a vital role in water resources assessment, the prediction and management of extreme hydrological events, and climate change impact evaluation. This review article systematically synthesizes recent advances in traditional hydrological models while critically examining their inherent methodological limitations. It further delineates the evolutionary trajectory of machine learning (ML) techniques in hydrological simulation and highlights the comparative advantages of data-driven ML approaches over conventional paradigms. Through a rigorous analysis of contemporary research, this review article establishes that coupling physically-based hydrological models with data-driven ML architectures represents the most promising pathway for overcoming fundamental bottlenecks in hydrological simulation. Furthermore, this review article concludes by identifying persistent challenges within existing coupling frameworks and projecting key future research directions in this rapidly evolving field.
Accurate and efficient hydrological simulation is critically important to sustainable water resources management amidst escalating climate change. As an indispensable scientific tool, hydrological modeling employs mathematical frameworks and computational techniques to quantitatively characterize hydrological processes, thereby playing a vital role in water resources assessment, the prediction and management of extreme hydrological events, and climate change impact evaluation. This review article systematically synthesizes recent advances in traditional hydrological models while critically examining their inherent methodological limitations. It further delineates the evolutionary trajectory of machine learning (ML) techniques in hydrological simulation and highlights the comparative advantages of data-driven ML approaches over conventional paradigms. Through a rigorous analysis of contemporary research, this review article establishes that coupling physically-based hydrological models with data-driven ML architectures represents the most promising pathway for overcoming fundamental bottlenecks in hydrological simulation. Furthermore, this review article concludes by identifying persistent challenges within existing coupling frameworks and projecting key future research directions in this rapidly evolving field.
2026, 19(1): 11-22.
doi: 10.1016/j.wse.2025.11.001
Abstract:
Effective management of multi-purpose reservoirs requires precise planning and accurate data to balance competing objectives and constraints. Reservoir inflow forecasting is critical in this process, with deep learning models increasingly applied across various time scales, from hourly to annual predictions. This study integrated a two-layer stacked long short-term memory network with decomposed data and a rolling window technique to enhance multi-day reservoir inflow forecasting accuracy. The proposed framework was applied to the Lam Takhong Dam in northeastern Thailand, a tropical monsoon region characterized by distinct wet and dry seasons. The dataset included daily reservoir inflow, river discharge, and average rainfall records spanning multiple years. Four forecasting strategies were compared for up to 7-d predictions: multi-step prediction, rolling prediction, multi-step prediction with decomposition, and rolling prediction with decomposition. The results indicated that while all models performed similarly for short-term predictions, accuracy declined over longer forecasting horizons. The rolling window approach with decomposition consistently outperformed others, achieving an average correlation coefficient of 0.92 and an average Nash—Sutcliffe model efficiency coefficient of 0.78 at the 7-d forecasting horizon. These findings demonstrate the practical advantages of integrating decomposition into a dynamic forecasting framework, particularly in reducing error accumulation in extended hydrological predictions.
Effective management of multi-purpose reservoirs requires precise planning and accurate data to balance competing objectives and constraints. Reservoir inflow forecasting is critical in this process, with deep learning models increasingly applied across various time scales, from hourly to annual predictions. This study integrated a two-layer stacked long short-term memory network with decomposed data and a rolling window technique to enhance multi-day reservoir inflow forecasting accuracy. The proposed framework was applied to the Lam Takhong Dam in northeastern Thailand, a tropical monsoon region characterized by distinct wet and dry seasons. The dataset included daily reservoir inflow, river discharge, and average rainfall records spanning multiple years. Four forecasting strategies were compared for up to 7-d predictions: multi-step prediction, rolling prediction, multi-step prediction with decomposition, and rolling prediction with decomposition. The results indicated that while all models performed similarly for short-term predictions, accuracy declined over longer forecasting horizons. The rolling window approach with decomposition consistently outperformed others, achieving an average correlation coefficient of 0.92 and an average Nash—Sutcliffe model efficiency coefficient of 0.78 at the 7-d forecasting horizon. These findings demonstrate the practical advantages of integrating decomposition into a dynamic forecasting framework, particularly in reducing error accumulation in extended hydrological predictions.
2026, 19(1): 23-34.
doi: 10.1016/j.wse.2025.11.005
Abstract:
Flood process simulation in karst basins is challenging due to complex runoff generation and concentration mechanisms, often resulting in low accuracy. This study investigated two typical karst basins (the Maiweng and Liudong river basins) in Guizhou Province, China, and developed two hydrological models for flood simulation: the karst-Xin'anjiang (Karst-XAJ) model, a modified Xin'anjiang (XAJ) hydrological model adapted for karst runoff characteristics, and the long short-term memory (LSTM) deep learning model. Their performances were compared, and their results were integrated using Bayesian model averaging (BMA). The Karst-XAJ model accurately simulated flood peak time and runoff depth but showed limited peak flow accuracy. The LSTM model performed well within a 2-h computational window, with accuracy declining for longer computational windows (3-4 h) yet maintaining a Nash—Sutcliffe model efficiency coefficient above 0.7. The BMA approach further enhanced simulation accuracy beyond individual models. Overall, both models effectively captured flood dynamics in karst basins, with the LSTM model achieving superior precision. This study offers a novel framework for simulating flood processes in karst regions with complex runoff processes.
Flood process simulation in karst basins is challenging due to complex runoff generation and concentration mechanisms, often resulting in low accuracy. This study investigated two typical karst basins (the Maiweng and Liudong river basins) in Guizhou Province, China, and developed two hydrological models for flood simulation: the karst-Xin'anjiang (Karst-XAJ) model, a modified Xin'anjiang (XAJ) hydrological model adapted for karst runoff characteristics, and the long short-term memory (LSTM) deep learning model. Their performances were compared, and their results were integrated using Bayesian model averaging (BMA). The Karst-XAJ model accurately simulated flood peak time and runoff depth but showed limited peak flow accuracy. The LSTM model performed well within a 2-h computational window, with accuracy declining for longer computational windows (3-4 h) yet maintaining a Nash—Sutcliffe model efficiency coefficient above 0.7. The BMA approach further enhanced simulation accuracy beyond individual models. Overall, both models effectively captured flood dynamics in karst basins, with the LSTM model achieving superior precision. This study offers a novel framework for simulating flood processes in karst regions with complex runoff processes.
2026, 19(1): 35-46.
doi: 10.1016/j.wse.2025.12.004
Abstract:
Turbulent flow around bluff bodies like square cylinders involves complex vortex shedding and flow separation, challenging traditional computational methods. This study developed a novel approach using a generative artificial intelligence (GenAI) model to predict turbulent flow over a single square cylinder. The GenAI model was trained using high-fidelity simulation data generated from an advanced differentiable physics framework (PhiFlow), which can efficiently capture the nonlinear dynamics of turbulent flow. Flow predictions from the GenAI model were validated against numerical results, demonstrating high accuracy in capturing key flow characteristics, including vortex shedding frequency. Stability and spatial—temporal frequency analyses revealed strong agreement between the diffusion model and numerical simulations. This study highlights the potential of GenAI models to significantly enhance the prediction and analysis of turbulent flow, offering a powerful tool for fluid dynamics research and engineering applications.
Turbulent flow around bluff bodies like square cylinders involves complex vortex shedding and flow separation, challenging traditional computational methods. This study developed a novel approach using a generative artificial intelligence (GenAI) model to predict turbulent flow over a single square cylinder. The GenAI model was trained using high-fidelity simulation data generated from an advanced differentiable physics framework (PhiFlow), which can efficiently capture the nonlinear dynamics of turbulent flow. Flow predictions from the GenAI model were validated against numerical results, demonstrating high accuracy in capturing key flow characteristics, including vortex shedding frequency. Stability and spatial—temporal frequency analyses revealed strong agreement between the diffusion model and numerical simulations. This study highlights the potential of GenAI models to significantly enhance the prediction and analysis of turbulent flow, offering a powerful tool for fluid dynamics research and engineering applications.
2026, 19(1): 47-55.
doi: 10.1016/j.wse.2025.12.003
Abstract:
Water sources in volcanic regions have long been a focal point in hydrogeology. Tianchi Lake of the Changbai Mountain in Northeast China, the world's highest volcanic lake, has historically faced water imbalance issues. This study offered a comprehensive analysis of the water sources of Tianchi Lake, examining water volume, hydrodynamics, hydrochemistry, and isotopic evidence. Flow simulations of the Changbai Mountain waterfall during the glacial period indicated that besides local precipitation stored within the mountain during the non-freezing period, other groundwater sources were involved. Additionally, the volume of spring water and the geological structures in the Tianchi Lake area suggested that even expanding the watershed boundary cannot fully account for water balance within the region. Comparative analysis of hydrogen and oxygen isotopes in groundwater and local precipitation within the Changbai Mountain region revealed that external water recharged Tianchi Lake via deep circulation, sustaining the stable flow of Tianchi Lake and its surrounding springs. This study provides valuable insights into the mechanisms and recharge processes of groundwater circulation in volcanic regions.
Water sources in volcanic regions have long been a focal point in hydrogeology. Tianchi Lake of the Changbai Mountain in Northeast China, the world's highest volcanic lake, has historically faced water imbalance issues. This study offered a comprehensive analysis of the water sources of Tianchi Lake, examining water volume, hydrodynamics, hydrochemistry, and isotopic evidence. Flow simulations of the Changbai Mountain waterfall during the glacial period indicated that besides local precipitation stored within the mountain during the non-freezing period, other groundwater sources were involved. Additionally, the volume of spring water and the geological structures in the Tianchi Lake area suggested that even expanding the watershed boundary cannot fully account for water balance within the region. Comparative analysis of hydrogen and oxygen isotopes in groundwater and local precipitation within the Changbai Mountain region revealed that external water recharged Tianchi Lake via deep circulation, sustaining the stable flow of Tianchi Lake and its surrounding springs. This study provides valuable insights into the mechanisms and recharge processes of groundwater circulation in volcanic regions.
2008, 1(1): 37-43 .
doi: 10.3882/j.issn.1674-2370.2008.01.005
Abstract:
2011, 4(1): 101-109.
doi: 10.3882/j.issn.1674-2370.2011.01.010
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2011, 4(3): 258-269.
doi: 10.3882/j.issn.1674-2370.2011.03.003
Abstract:
2012, 5(3): 243-258.
doi: 10.3882/j.issn.1674-2370.2012.03.001
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2010, 3(3): 321-330.
doi: 10.3882/j.issn.1674-2370.2010.03.008
Abstract:
2012, 5(1): 26-33.
doi: 10.3882/j.issn.1674-2370.2012.01.003
Abstract:
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2008, 1(1): 37-43 .
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2011, 4(1): 101-109.
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2011, 4(3): 258-269.
doi: 10.3882/j.issn.1674-2370.2011.03.003
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2012, 5(3): 243-258.
doi: 10.3882/j.issn.1674-2370.2012.03.001
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2010, 3(3): 321-330.
doi: 10.3882/j.issn.1674-2370.2010.03.008
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2012, 5(1): 26-33.
doi: 10.3882/j.issn.1674-2370.2012.01.003
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2010, 3(2): 132-143.
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2010, 3(3): 241-256.
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2011, 4(1): 101-109.
doi: 10.3882/j.issn.1674-2370.2011.01.010
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2010, 3(1): 1-13.
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2012, 5(3): 243-258.
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Volume 19，Issue 1，
Mar. 2026
Editor-in-ChiefZhong-bo Yu
Edited byEditorial Board of Water Science and Engineering
Distributed byEditorial Office of Water Science and Engineering
News
- WSE Special Issue on Security and Sustainability for Hydraulic Structures November 01，2021
- WSE Special Issue on Water Security and Sustainability April 14，2021
- WSE Special Issue for CORE2021 March 09，2021
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