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.
2021, 14(2): 139-148.
doi: 10.1016/j.wse.2021.06.006
摘要：
To assess the magnitude of water quality decline in the Turag River of Bangladesh, this study examined the seasonal variation of physicochemical parameters of water, identified potential pollution sources, and clustered the monitoring months with similar characteristics. Water samples were collected in four distinct seasons to evaluate temperature, pH, dissolved oxygen (DO) concentration, five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), electrical conductivity (EC), chloride ion (Cl−) concentration, total alkalinity (TA), turbidity, total dissolved solids (TDS) concentration, total suspended solids (TSS) concentration, and total hardness (TH) using standard methods. The analytical results revealed that 40% of water quality indices were within the permissible limits suggested by different agencies, with the exception of EC, Cl− concentration, TA, turbidity, DO concentration, BOD5, and COD in all seasons. Statistical analyses indicated that 52% of the contrasts were significantly different at a 95% confidence interval. The factor analysis presented the best fit among the parameters, with four factors explaining 94.29% of the total variance. TDS, BOD5, COD, EC, turbidity, DO, and Cl− were mainly responsible for pollution loading and were caused by the significant amount of industrial discharge and toxicological compounds. The cluster analysis showed the seasonal change in surface water quality, which is usually an indicator of pollution from rainfall or other sources. However, the values of different physicochemical properties varied with seasons, and the highest values of pollutants were recorded in the winter. With the change in seasonal temperature and increase in rainfall, the seasonal Turag River water followed a self-refining trend as follows: rainy season > pre-winter > summer > winter.
To assess the magnitude of water quality decline in the Turag River of Bangladesh, this study examined the seasonal variation of physicochemical parameters of water, identified potential pollution sources, and clustered the monitoring months with similar characteristics. Water samples were collected in four distinct seasons to evaluate temperature, pH, dissolved oxygen (DO) concentration, five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), electrical conductivity (EC), chloride ion (Cl−) concentration, total alkalinity (TA), turbidity, total dissolved solids (TDS) concentration, total suspended solids (TSS) concentration, and total hardness (TH) using standard methods. The analytical results revealed that 40% of water quality indices were within the permissible limits suggested by different agencies, with the exception of EC, Cl− concentration, TA, turbidity, DO concentration, BOD5, and COD in all seasons. Statistical analyses indicated that 52% of the contrasts were significantly different at a 95% confidence interval. The factor analysis presented the best fit among the parameters, with four factors explaining 94.29% of the total variance. TDS, BOD5, COD, EC, turbidity, DO, and Cl− were mainly responsible for pollution loading and were caused by the significant amount of industrial discharge and toxicological compounds. The cluster analysis showed the seasonal change in surface water quality, which is usually an indicator of pollution from rainfall or other sources. However, the values of different physicochemical properties varied with seasons, and the highest values of pollutants were recorded in the winter. With the change in seasonal temperature and increase in rainfall, the seasonal Turag River water followed a self-refining trend as follows: rainy season > pre-winter > summer > winter.
2019, 12(4): 274-283.
doi: 10.1016/j.wse.2019.12.004
摘要：
Increased urbanisation, economic growth, and long-term climate variability have made both the UK and China more susceptible to urban and river flooding, putting people and property at increased risk. This paper presents a review of the current flooding challenges that are affecting the UK and China and the actions that each country is undertaking to tackle these problems. Particular emphases in this paper are laid on (1) learning from previous flooding events in the UK and China, and (2) which management methodologies are commonly used to reduce flood risk. The paper concludes with a strategic research plan suggested by the authors, together with proposed ways to overcome identified knowledge gaps in flood management. Recommendations briefly comprise the engagement of all stakeholders to ensure a proactive approach to land use planning, early warning systems, and water-sensitive urban design or redesign through more effective policy, multi-level flood models, and data driven models of water quantity and quality.
Increased urbanisation, economic growth, and long-term climate variability have made both the UK and China more susceptible to urban and river flooding, putting people and property at increased risk. This paper presents a review of the current flooding challenges that are affecting the UK and China and the actions that each country is undertaking to tackle these problems. Particular emphases in this paper are laid on (1) learning from previous flooding events in the UK and China, and (2) which management methodologies are commonly used to reduce flood risk. The paper concludes with a strategic research plan suggested by the authors, together with proposed ways to overcome identified knowledge gaps in flood management. Recommendations briefly comprise the engagement of all stakeholders to ensure a proactive approach to land use planning, early warning systems, and water-sensitive urban design or redesign through more effective policy, multi-level flood models, and data driven models of water quantity and quality.
2023, 16(4): 333-344.
doi: 10.1016/j.wse.2023.04.003
摘要：
Clean drinking water is one of the United Nations Sustainable Development Goals. Despite significant progress in the water purification technology, many regions still lack access to clean water. This paper provides a review of selected water contaminants and their impacts on human health. The World Health Organization (WHO) guidelines and regional standards for key contaminants were used to characterise water quality in the European Union and UK. The concept of safe drinking water was explained based on the non-observed adverse effect level, threshold concentrations for toxic chemicals, and their total daily intake. Various techniques for monitoring water contaminants and the drinking water standards from five different countries, including the UK, USA, Canada, Pakistan and India, were compared to WHO recommended guidelines. The literature on actual water quality in these regions and its potential health impacts was also discussed. Finally, the role of public water suppliers in identifying and monitoring drinking water contaminants in selected developed countries was presented as a potential guideline for developing countries. This review emphasised the need for a comprehensive understanding of water quality and its impacts on human health to ensure access to clean drinking water worldwide.
Clean drinking water is one of the United Nations Sustainable Development Goals. Despite significant progress in the water purification technology, many regions still lack access to clean water. This paper provides a review of selected water contaminants and their impacts on human health. The World Health Organization (WHO) guidelines and regional standards for key contaminants were used to characterise water quality in the European Union and UK. The concept of safe drinking water was explained based on the non-observed adverse effect level, threshold concentrations for toxic chemicals, and their total daily intake. Various techniques for monitoring water contaminants and the drinking water standards from five different countries, including the UK, USA, Canada, Pakistan and India, were compared to WHO recommended guidelines. The literature on actual water quality in these regions and its potential health impacts was also discussed. Finally, the role of public water suppliers in identifying and monitoring drinking water contaminants in selected developed countries was presented as a potential guideline for developing countries. This review emphasised the need for a comprehensive understanding of water quality and its impacts on human health to ensure access to clean drinking water worldwide.
2020, 13(3): 202-213.
doi: 10.1016/j.wse.2020.09.007
摘要：
In this experiment, cobalt ferrite-supported activated carbon (CF-AC) was developed and characterized via the wet impregnation method for the removal of Cr and Pb(II) ions from tannery wastewater. Batch adsorption was carried out to evaluate the effect of experimental operating conditions (pH of solution, contact time, adsorbent dose, and temperature), and the removal efficiencies of Cr and Pb(II) ions by the developed adsorbents were calculated and recorded for all experimental conditions. These variables were estimated and reported as removal efficiencies of 98.2% for Cr and 96.4% for Pb(II) ions at the optimal conditions of 5, 0.8 g, 80 min, and 333 K for pH, adsorbent dose, contact time, and temperature, respectively. The equilibrium for the sorption of Cr and Pb(II) ions was studied using four widely used isotherm models (the Langmuir, Freundlich, Dubinin-Radushkevich, and Temkin isotherm models). It was found that the Freundlich isotherm model fit better with the coefficient of determination (R2) of 0.948 4 and a small sum of square error of 0.000 6. The maximum adsorption capacities (Qm) of Pb(II) and Cr adsorbed onto CF-AC were determined to be 6.27 and 23.6 mg/g, respectively. The adsorption process conformed well to pseudo-second order kinetics as revealed by the high R2 values obtained for both metals. The thermodynamic parameters showed that adsorption of Cr and Pb(II) ions onto CF-AC was spontaneous, feasible, and endothermic under the studied conditions. The mean adsorption energy (E) values revealed that the adsorption mechanism of Cr and Pb(II) by CF-AC is physical in nature. The results of the study showed that adsorbent developed from CF-AC can be efficiently used as an environmentally friendly alternative adsorbent, for removal of Cr and Pb(II) ions in tannery wastewater.
In this experiment, cobalt ferrite-supported activated carbon (CF-AC) was developed and characterized via the wet impregnation method for the removal of Cr and Pb(II) ions from tannery wastewater. Batch adsorption was carried out to evaluate the effect of experimental operating conditions (pH of solution, contact time, adsorbent dose, and temperature), and the removal efficiencies of Cr and Pb(II) ions by the developed adsorbents were calculated and recorded for all experimental conditions. These variables were estimated and reported as removal efficiencies of 98.2% for Cr and 96.4% for Pb(II) ions at the optimal conditions of 5, 0.8 g, 80 min, and 333 K for pH, adsorbent dose, contact time, and temperature, respectively. The equilibrium for the sorption of Cr and Pb(II) ions was studied using four widely used isotherm models (the Langmuir, Freundlich, Dubinin-Radushkevich, and Temkin isotherm models). It was found that the Freundlich isotherm model fit better with the coefficient of determination (R2) of 0.948 4 and a small sum of square error of 0.000 6. The maximum adsorption capacities (Qm) of Pb(II) and Cr adsorbed onto CF-AC were determined to be 6.27 and 23.6 mg/g, respectively. The adsorption process conformed well to pseudo-second order kinetics as revealed by the high R2 values obtained for both metals. The thermodynamic parameters showed that adsorption of Cr and Pb(II) ions onto CF-AC was spontaneous, feasible, and endothermic under the studied conditions. The mean adsorption energy (E) values revealed that the adsorption mechanism of Cr and Pb(II) by CF-AC is physical in nature. The results of the study showed that adsorbent developed from CF-AC can be efficiently used as an environmentally friendly alternative adsorbent, for removal of Cr and Pb(II) ions in tannery wastewater.
2016, 9(1): 58-66.
doi: 10.1016/j.wse.2016.02.002
摘要：
This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcasting of cyclone wind fields is first examined, and then two modification methods are proposed to improve the hindcasted results. The first one is the superposition method, which superposes the wind field calculated from the parametric cyclone model on that obtained from the Cross-Calibrated Multi-Platform (CCMP) reanalysis data. The radius used for the superposition is based on an analysis of the minimum difference between the two wind fields. The other one is the direct modification method, which directly modifies the CCMP reanalysis data according to the ratio of the measured maximum wind speed to the reanalyzed value as well as the distance from the cyclone center. Using these two methods, the problem of underestimation of strong winds in reanalysis data can be overcome. Both methods show considerable improvements in the hindcasting of tropical cyclone wind fields, compared with the cyclone wind model and the reanalysis data.
This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcasting of cyclone wind fields is first examined, and then two modification methods are proposed to improve the hindcasted results. The first one is the superposition method, which superposes the wind field calculated from the parametric cyclone model on that obtained from the Cross-Calibrated Multi-Platform (CCMP) reanalysis data. The radius used for the superposition is based on an analysis of the minimum difference between the two wind fields. The other one is the direct modification method, which directly modifies the CCMP reanalysis data according to the ratio of the measured maximum wind speed to the reanalyzed value as well as the distance from the cyclone center. Using these two methods, the problem of underestimation of strong winds in reanalysis data can be overcome. Both methods show considerable improvements in the hindcasting of tropical cyclone wind fields, compared with the cyclone wind model and the reanalysis data.
2019, 12(1): 11-18.
doi: 10.1016/j.wse.2019.03.001
摘要：
Hydraulic models for the generation of ?ood inundation maps are not commonly applied in mountain river basins because of the dif?culty in modeling the hydraulic behavior and the complex topography. This paper presents a comparative analysis of the performance of four twodimensional hydraulic models (HEC-RAS 2D, Iber 2D, Flood Modeller 2D, and PCSWMM 2D) with respect to the generation of ?ood inundation maps. The study area covers a 5-km reach of the Santa Barbara River located in the Ecuadorian Andes, at 2330 masl, in Gualaceo. The model's performance was evaluated based on the water surface elevation and ?ood extent, in terms of the mean absolute difference and measure of ?t. The analysis revealed that, for a given case, Iber 2D has the best performance in simulating the water level and inundation for ?ood events with 20- and 50-year return periods, respectively, followed by Flood Modeller 2D, HEC-RAS 2D, and PCSWMM 2D in terms of their performance. Grid resolution, the way in which hydraulic structures are mimicked, the model code, and the default value of the parameters are considered the main sources of prediction uncertainty.
Hydraulic models for the generation of ?ood inundation maps are not commonly applied in mountain river basins because of the dif?culty in modeling the hydraulic behavior and the complex topography. This paper presents a comparative analysis of the performance of four twodimensional hydraulic models (HEC-RAS 2D, Iber 2D, Flood Modeller 2D, and PCSWMM 2D) with respect to the generation of ?ood inundation maps. The study area covers a 5-km reach of the Santa Barbara River located in the Ecuadorian Andes, at 2330 masl, in Gualaceo. The model's performance was evaluated based on the water surface elevation and ?ood extent, in terms of the mean absolute difference and measure of ?t. The analysis revealed that, for a given case, Iber 2D has the best performance in simulating the water level and inundation for ?ood events with 20- and 50-year return periods, respectively, followed by Flood Modeller 2D, HEC-RAS 2D, and PCSWMM 2D in terms of their performance. Grid resolution, the way in which hydraulic structures are mimicked, the model code, and the default value of the parameters are considered the main sources of prediction uncertainty.
2023, 16(1): 1-13.
doi: 10.1016/j.wse.2022.10.004
摘要：
Nature-based coastal protection is increasingly recognised as a potentially sustainable and cost-effective solution to reduce coastal flood risk. It uses coastal ecosystems such as mangrove forests to create resilient designs for coastal flood protection. However, to use mangroves effectively as a nature-based measure for flood risk reduction, we must understand the biophysical processes that govern risk reduction capacity through mangrove ecosystem size and structure. In this perspective, we evaluate the current state of knowledge on local physical drivers and ecological processes that determine mangrove functioning as part of a nature-based flood defence. We show that the forest properties that comprise coastal flood protection are well-known, but models cannot yet pinpoint how spatial heterogeneity of the forest structure affects the capacity for wave or surge attenuation. Overall, there is relatively good understanding of the ecological processes that drive forest structure and size, but there is a lack of knowledge on how daily bed-level dynamics link to long-term biogeomorphic forest dynamics, and on the role of combined stressors influencing forest retreat. Integrating simulation models of forest structure under changing physical (e.g. due to sea-level change) and ecological drivers with hydrodynamic attenuation models will allow for better projections of long-term natural coastal protection.
Nature-based coastal protection is increasingly recognised as a potentially sustainable and cost-effective solution to reduce coastal flood risk. It uses coastal ecosystems such as mangrove forests to create resilient designs for coastal flood protection. However, to use mangroves effectively as a nature-based measure for flood risk reduction, we must understand the biophysical processes that govern risk reduction capacity through mangrove ecosystem size and structure. In this perspective, we evaluate the current state of knowledge on local physical drivers and ecological processes that determine mangrove functioning as part of a nature-based flood defence. We show that the forest properties that comprise coastal flood protection are well-known, but models cannot yet pinpoint how spatial heterogeneity of the forest structure affects the capacity for wave or surge attenuation. Overall, there is relatively good understanding of the ecological processes that drive forest structure and size, but there is a lack of knowledge on how daily bed-level dynamics link to long-term biogeomorphic forest dynamics, and on the role of combined stressors influencing forest retreat. Integrating simulation models of forest structure under changing physical (e.g. due to sea-level change) and ecological drivers with hydrodynamic attenuation models will allow for better projections of long-term natural coastal protection.
2022, 15(1): 29-39.
doi: 10.1016/j.wse.2021.12.006
摘要：
In this article, current research findings of local scour at offshore windfarm monopile foundations are presented. The scour mechanisms and scour depth prediction formulas under different hydrodynamic conditions are summarized, including the current-only condition, wave-only condition, combined wave-current condition, and complex dynamic condition. Furthermore, this article analyzes the influencing factors on the basis of classical equations for predicting the equilibrium scour depth under specific conditions. The weakness of existing researches and future prospects are also discussed. It is suggested that future research shall focus on physical experiments under unsteady tidal currents or other complex loadings. The computational fluid dynamics-discrete element method and artificial intelligence technique are suggested being adopted to study the scour at offshore windfarm foundations.
In this article, current research findings of local scour at offshore windfarm monopile foundations are presented. The scour mechanisms and scour depth prediction formulas under different hydrodynamic conditions are summarized, including the current-only condition, wave-only condition, combined wave-current condition, and complex dynamic condition. Furthermore, this article analyzes the influencing factors on the basis of classical equations for predicting the equilibrium scour depth under specific conditions. The weakness of existing researches and future prospects are also discussed. It is suggested that future research shall focus on physical experiments under unsteady tidal currents or other complex loadings. The computational fluid dynamics-discrete element method and artificial intelligence technique are suggested being adopted to study the scour at offshore windfarm foundations.
2019, 12(1): 27-36.
doi: 10.1016/j.wse.2019.04.003
摘要：
This study aimed to investigate the biosorption potential of Na2CO3-modified Aloe barbadensis Miller (Aloe vera) leaf (MABL) powder for removal of Ni(II) ions from a synthetic aqueous solution. Effects of various process parameters (pH, equilibrium time, and temperature) were investigated in order to optimize the biosorptive removal. The maximum biosorption capacity of MABL was observed to be 28.986 mg/g at a temperature of 303 K, a biosorbent dose of 0.6 g, a contact time of 90 min, and a pH value of 7. Different kinetic models (the pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion models) were evaluated. The pseudo-second-order kinetic model was found to be the best fitted model in this study, with a coefficient of determination of R2 = 0.974. Five different isotherm models (the Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, and Brunauer-Emmett-Teller (BET) models) were investigated to identify the best-suited isotherm model for the present system. Based on the minimum chi-square value (χ2 = 0.027) and the maximum coefficient of determination (R2 = 0.996), the Langmuir isotherm model was found to represent the system well, indicating the possibility of monolayer biosorption. The sticking probability (S*) was found to be 0.41, suggesting a physisorption mechanism for biosorption of Ni(II) on MABL. The biosorbent was characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential, and BET surface area, in order to understand its morphological and functional characteristics.
This study aimed to investigate the biosorption potential of Na2CO3-modified Aloe barbadensis Miller (Aloe vera) leaf (MABL) powder for removal of Ni(II) ions from a synthetic aqueous solution. Effects of various process parameters (pH, equilibrium time, and temperature) were investigated in order to optimize the biosorptive removal. The maximum biosorption capacity of MABL was observed to be 28.986 mg/g at a temperature of 303 K, a biosorbent dose of 0.6 g, a contact time of 90 min, and a pH value of 7. Different kinetic models (the pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion models) were evaluated. The pseudo-second-order kinetic model was found to be the best fitted model in this study, with a coefficient of determination of R2 = 0.974. Five different isotherm models (the Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, and Brunauer-Emmett-Teller (BET) models) were investigated to identify the best-suited isotherm model for the present system. Based on the minimum chi-square value (χ2 = 0.027) and the maximum coefficient of determination (R2 = 0.996), the Langmuir isotherm model was found to represent the system well, indicating the possibility of monolayer biosorption. The sticking probability (S*) was found to be 0.41, suggesting a physisorption mechanism for biosorption of Ni(II) on MABL. The biosorbent was characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential, and BET surface area, in order to understand its morphological and functional characteristics.
2020, 13(2): 136-144.
doi: 10.1016/j.wse.2020.06.005
摘要：
Based on conventional particle swarm optimization (PSO), this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight (ARIW) strategy, referred to as the ARIW-PSO algorithm, to build a multi-objective optimization model for reservoir operation. Using the triangular probability density function, the inertia weight is randomly generated, and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution, which is suitable for global searches. In the evolution process, the inertia weight gradually decreases, which is beneficial to local searches. The performance of the ARIW-PSO algorithm was investigated with some classical test functions, and the results were compared with those of the genetic algorithm (GA), the conventional PSO, and other improved PSO methods. Then, the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China, including the Panjiakou Reservoir, Daheiting Reservoir, and Taolinkou Reservoir. The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.
Based on conventional particle swarm optimization (PSO), this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight (ARIW) strategy, referred to as the ARIW-PSO algorithm, to build a multi-objective optimization model for reservoir operation. Using the triangular probability density function, the inertia weight is randomly generated, and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution, which is suitable for global searches. In the evolution process, the inertia weight gradually decreases, which is beneficial to local searches. The performance of the ARIW-PSO algorithm was investigated with some classical test functions, and the results were compared with those of the genetic algorithm (GA), the conventional PSO, and other improved PSO methods. Then, the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China, including the Panjiakou Reservoir, Daheiting Reservoir, and Taolinkou Reservoir. The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.
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
Abstract:
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
Abstract:
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 .
doi: 10.3882/j.issn.1674-2370.2008.01.005
2
2011, 4(1): 101-109.
doi: 10.3882/j.issn.1674-2370.2011.01.010
3
2011, 4(3): 258-269.
doi: 10.3882/j.issn.1674-2370.2011.03.003
4
2012, 5(3): 243-258.
doi: 10.3882/j.issn.1674-2370.2012.03.001
5
2010, 3(3): 321-330.
doi: 10.3882/j.issn.1674-2370.2010.03.008
6
2012, 5(1): 26-33.
doi: 10.3882/j.issn.1674-2370.2012.01.003
1
2010, 3(2): 132-143.
doi: 10.3882/j.issn.1674-2370.2010.02.002
2
2010, 3(3): 241-256.
doi: 10.3882/j.issn.1674-2370.2010.03.001
3
2011, 4(1): 101-109.
doi: 10.3882/j.issn.1674-2370.2011.01.010
4
2010, 3(1): 1-13.
doi: 10.3882/j.issn.1674-2370.2010.01.001
5
2012, 5(3): 243-258.
doi: 10.3882/j.issn.1674-2370.2012.03.001
6
2012, 5(1): 105-119.
doi: 10.3882/j.issn.1674-2370.2012.01.010
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
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