Articles in press are presented at https://www.sciencedirect.com/journal/water-science-and-engineering/articles-in-press
2025, 18(4): 401-411.
doi: 10.1016/j.wse.2025.09.001
Abstract:
Water conservation, a critical ecosystem service, is primarily quantified through water retention (WR), which plays a pivotal role in sustainable socio-economic development and water resources management. However, the absence of multi-temporal modeling of land use and climate change impacts on eco-hydrological processes limits the accurate estimation of WR, particularly in humid regions. This study employed the Soil and Water Assessment Tool (SWAT) model coupled with the water balance principle to estimate WR in the source area of the Xin'an River (SXAR) in China from 2009 to 2017. The multi-temporal variations of WR and its response to climate and land use changes were analyzed through scenario-based hydrological simulations. Results indicated that annual WR ranged from 256.4 mm to 412.7 mm, monthly WR varied between 0 mm and 67.6 mm, and peak daily WR coincided with extreme rainfall events. Precipitation and evapotranspiration were identified as the primary factors influencing WR variability at daily and monthly scales. Spatially, higher WR values were observed in the northeastern SXAR, reflecting the influences of land use patterns and topography. Notably, agricultural land exhibited negative WR during summer months due to crop water storage demands. Overall, climate change exerted more immediate effects on WR at shorter timescales, whereas land use change produced longer-term impacts. This study offers valuable theoretical insights into WR mechanisms of response to environmental changes and provides practical guidance for water resources planning and management in humid and sub-humid regions.
Water conservation, a critical ecosystem service, is primarily quantified through water retention (WR), which plays a pivotal role in sustainable socio-economic development and water resources management. However, the absence of multi-temporal modeling of land use and climate change impacts on eco-hydrological processes limits the accurate estimation of WR, particularly in humid regions. This study employed the Soil and Water Assessment Tool (SWAT) model coupled with the water balance principle to estimate WR in the source area of the Xin'an River (SXAR) in China from 2009 to 2017. The multi-temporal variations of WR and its response to climate and land use changes were analyzed through scenario-based hydrological simulations. Results indicated that annual WR ranged from 256.4 mm to 412.7 mm, monthly WR varied between 0 mm and 67.6 mm, and peak daily WR coincided with extreme rainfall events. Precipitation and evapotranspiration were identified as the primary factors influencing WR variability at daily and monthly scales. Spatially, higher WR values were observed in the northeastern SXAR, reflecting the influences of land use patterns and topography. Notably, agricultural land exhibited negative WR during summer months due to crop water storage demands. Overall, climate change exerted more immediate effects on WR at shorter timescales, whereas land use change produced longer-term impacts. This study offers valuable theoretical insights into WR mechanisms of response to environmental changes and provides practical guidance for water resources planning and management in humid and sub-humid regions.
2025, 18(4): 412-421.
doi: 10.1016/j.wse.2025.08.004
Abstract:
Accurate streamflow prediction under climate change is essential for mitigating natural disasters and optimizing water resources management. However, streamflow prediction is subject to considerable uncertainties due to the complexity of hydrological model structures, parameterization, and input forcing data. This study predicted monthly streamflow in the upper Han River Basin in China under three Shared Socioeconomic Pathways (SSP) scenarios, using climate projections from five Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. Bias correction of climate model outputs was performed prior to streamflow simulation using four deep learning approaches: long short-term memory, gated recurrent unit, temporal convolutional network, and transformer. To reduce uncertainties inherent in individual deep learning models, Bayesian model averaging (BMA) was employed to integrate their predictions. The results showed that the three deep learning models achieved satisfactory performance with Nash-Sutcliffe model efficiency coefficient (NSE) values exceeding 0.8, while BMA exhibited superior robustness and accuracy, with the highest NSE and lowest root mean square error. Projected precipitation, mean air temperature, and potential evapotranspiration generally decreased during 2026-2100 relative to the historical period (1970-2017), suggesting a colder and drier regional climate. Streamflow was projected to decline significantly across all three scenarios, particularly from June to September, highlighting the potential for exacerbated water scarcity in the future.
Accurate streamflow prediction under climate change is essential for mitigating natural disasters and optimizing water resources management. However, streamflow prediction is subject to considerable uncertainties due to the complexity of hydrological model structures, parameterization, and input forcing data. This study predicted monthly streamflow in the upper Han River Basin in China under three Shared Socioeconomic Pathways (SSP) scenarios, using climate projections from five Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. Bias correction of climate model outputs was performed prior to streamflow simulation using four deep learning approaches: long short-term memory, gated recurrent unit, temporal convolutional network, and transformer. To reduce uncertainties inherent in individual deep learning models, Bayesian model averaging (BMA) was employed to integrate their predictions. The results showed that the three deep learning models achieved satisfactory performance with Nash-Sutcliffe model efficiency coefficient (NSE) values exceeding 0.8, while BMA exhibited superior robustness and accuracy, with the highest NSE and lowest root mean square error. Projected precipitation, mean air temperature, and potential evapotranspiration generally decreased during 2026-2100 relative to the historical period (1970-2017), suggesting a colder and drier regional climate. Streamflow was projected to decline significantly across all three scenarios, particularly from June to September, highlighting the potential for exacerbated water scarcity in the future.
2025, 18(4): 422-430.
doi: 10.1016/j.wse.2025.09.003
Abstract:
Pesticides are widely used in agriculture and can enter river-lake systems through surface runoff, adversely affecting non-target organisms and threatening ecological security. This study investigated the occurrence and distribution of 52 pesticides in a typical river-lake system in China and evaluated their ecological risks to aquatic organisms. The average total pesticide concentration in surface water was 203.05 ng/L, with carbendazim being the dominant pollutant, contributing 23.66% to the contamination. In sediments, the average pesticide concentration was 6.34 ng/g, with tebuconazole being the primary contributor at 28.57%. Fungicides were the main pesticide type in both river water and sediments, accounting for 76.86% and 85.10%, respectively. Pesticides predominantly accumulated in lake sediments, with the small lake showing high pesticide concentrations near river outflow areas and the large lake accumulating pesticides near lake inlets. Pesticide concentrations in both water and sediments increased downstream along rivers. Ecological risk assessment revealed high mixed risks to algae, daphnia, and fish, with risk levels rising along with trophic levels of aquatic organisms. The correlation between pesticide concentration and mixed ecological risk was weaker for algae than for daphnia and fish, and certain pesticides posed high risks to algae even at low concentrations, indicating more targeted toxicity for lower trophic organisms. These findings provide reference data for ecological risk assessment and pesticide pollution management in river-lake systems in agricultural regions.
Pesticides are widely used in agriculture and can enter river-lake systems through surface runoff, adversely affecting non-target organisms and threatening ecological security. This study investigated the occurrence and distribution of 52 pesticides in a typical river-lake system in China and evaluated their ecological risks to aquatic organisms. The average total pesticide concentration in surface water was 203.05 ng/L, with carbendazim being the dominant pollutant, contributing 23.66% to the contamination. In sediments, the average pesticide concentration was 6.34 ng/g, with tebuconazole being the primary contributor at 28.57%. Fungicides were the main pesticide type in both river water and sediments, accounting for 76.86% and 85.10%, respectively. Pesticides predominantly accumulated in lake sediments, with the small lake showing high pesticide concentrations near river outflow areas and the large lake accumulating pesticides near lake inlets. Pesticide concentrations in both water and sediments increased downstream along rivers. Ecological risk assessment revealed high mixed risks to algae, daphnia, and fish, with risk levels rising along with trophic levels of aquatic organisms. The correlation between pesticide concentration and mixed ecological risk was weaker for algae than for daphnia and fish, and certain pesticides posed high risks to algae even at low concentrations, indicating more targeted toxicity for lower trophic organisms. These findings provide reference data for ecological risk assessment and pesticide pollution management in river-lake systems in agricultural regions.
2025, 18(4): 431-443.
doi: 10.1016/j.wse.2025.09.005
Abstract:
Frequent saltwater intrusion induced by extreme climate events poses significant challenges to water supply security in coastal cities. This study developed a supply-demand balance model for urban water supply systems based on the system dynamics (SD) method, employing the supply-demand gap and water stress index (WSI) as risk indicators. Dynamic simulations were conducted in Zhongshan City in China across five development and 17 saltwater boundary scenarios, and corresponding emergency water reserve requirements were proposed for emergency durations of 10-60 d. The results showed no supply-demand gaps from 2016 to 2023, although water supply was notably affected by saltwater intrusion. The highest risk occurred in 2021 when saltwater fronts reached the Renyi and Dafeng intakes, resulting in a peak WSI of 0.45. Water demand would peak in 2035 across all development scenarios. The economic development scenario exhibited the highest demand, the conservation development scenario the lowest, and the comprehensive development scenario the second lowest, with the latter balancing economic and social development with resource conservation, enhancing its policy relevance. Across the 17 saltwater boundary scenarios, the conservation development scenario demonstrated the lowest WSI values (0-6.74) and water supply risk level, followed by the comprehensive development scenario (with WSI values of 0-7.11), while the economic development scenario demonstrated the highest WSI values (0-7.84) and water supply risk level. Under worst-case saltwater conditions with 60-d emergency reserves, supply-demand gaps in 2030 would reach 7.938 × 107 m3 and 8.928 × 107 m3 in the conservation and economic development scenarios, respectively, and increase to 10.164 × 107 m3 and 12.354 × 107 m3 by 2035. This methodology offers actionable insights for coastal cities to optimize development strategies and emergency water reserve planning.
Frequent saltwater intrusion induced by extreme climate events poses significant challenges to water supply security in coastal cities. This study developed a supply-demand balance model for urban water supply systems based on the system dynamics (SD) method, employing the supply-demand gap and water stress index (WSI) as risk indicators. Dynamic simulations were conducted in Zhongshan City in China across five development and 17 saltwater boundary scenarios, and corresponding emergency water reserve requirements were proposed for emergency durations of 10-60 d. The results showed no supply-demand gaps from 2016 to 2023, although water supply was notably affected by saltwater intrusion. The highest risk occurred in 2021 when saltwater fronts reached the Renyi and Dafeng intakes, resulting in a peak WSI of 0.45. Water demand would peak in 2035 across all development scenarios. The economic development scenario exhibited the highest demand, the conservation development scenario the lowest, and the comprehensive development scenario the second lowest, with the latter balancing economic and social development with resource conservation, enhancing its policy relevance. Across the 17 saltwater boundary scenarios, the conservation development scenario demonstrated the lowest WSI values (0-6.74) and water supply risk level, followed by the comprehensive development scenario (with WSI values of 0-7.11), while the economic development scenario demonstrated the highest WSI values (0-7.84) and water supply risk level. Under worst-case saltwater conditions with 60-d emergency reserves, supply-demand gaps in 2030 would reach 7.938 × 107 m3 and 8.928 × 107 m3 in the conservation and economic development scenarios, respectively, and increase to 10.164 × 107 m3 and 12.354 × 107 m3 by 2035. This methodology offers actionable insights for coastal cities to optimize development strategies and emergency water reserve planning.
2025, 18(4): 444-453.
doi: 10.1016/j.wse.2025.06.002
Abstract:
Understanding the processes and dynamics of tidal wave propagation in estuaries is critical for assessing the impacts of natural processes and human interventions on estuarine systems. However, current knowledge of tidal dynamics in micro-tidal estuaries and their variability across various timescales remains limited. This study used an analytical framework and field observations to investigate the fundamental physical processes and mechanisms governing tidal wave propagation in the Yongjiang Estuary, a micro-tidal estuary on the eastern coast of China. The analytically computed tidal amplitude and wave propagation timing aligned with observed data. Significant wet/dry and spring/neap variations in tidal wave properties were identified, primarily influenced by the interplay between channel convergence and bottom friction. Given the high siltation rates in the Yongjiang Estuary, analytical simulations suggested that human-induced dredging enhances tidal dynamics, while channel bed siltation weakens hydrodynamic processes, potentially exacerbating local sedimentation. The findings of this study provide valuable insights for estuarine management and establish a foundation for future research on sediment dynamics in the Yongjiang Estuary.
Understanding the processes and dynamics of tidal wave propagation in estuaries is critical for assessing the impacts of natural processes and human interventions on estuarine systems. However, current knowledge of tidal dynamics in micro-tidal estuaries and their variability across various timescales remains limited. This study used an analytical framework and field observations to investigate the fundamental physical processes and mechanisms governing tidal wave propagation in the Yongjiang Estuary, a micro-tidal estuary on the eastern coast of China. The analytically computed tidal amplitude and wave propagation timing aligned with observed data. Significant wet/dry and spring/neap variations in tidal wave properties were identified, primarily influenced by the interplay between channel convergence and bottom friction. Given the high siltation rates in the Yongjiang Estuary, analytical simulations suggested that human-induced dredging enhances tidal dynamics, while channel bed siltation weakens hydrodynamic processes, potentially exacerbating local sedimentation. The findings of this study provide valuable insights for estuarine management and establish a foundation for future research on sediment dynamics in the Yongjiang Estuary.
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.
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(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(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.
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.
2019, 12(1): 45-54.
doi: 10.1016/j.wse.2018.11.001
摘要：
Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling
water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the
adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more
suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being
trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO)
and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood
River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and
gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved
solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using
SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with
ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.
Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling
water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the
adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more
suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being
trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO)
and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood
River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and
gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved
solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using
SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with
ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.
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.
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(1): 105-119.
doi: 10.3882/j.issn.1674-2370.2012.01.010
6
2010, 3(4): 418-430.
doi: 10.3882/j.issn.1674-2370.2010.04.005
Volume 18，Issue 4，
Dec. 2025
Editor-in-ChiefChao Wang
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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