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Source water areas are facing many potential water pollution risks. Risk assessment is an effective method to evaluate such risks. In this paper an integrated model based on k-means clustering analysis and set pair analysis was established aiming at evaluating the risks associated with water pollution in source water areas, in which the weights of indicators were determined through the entropy weight method. Then the proposed model was applied to assess water pollution risks in the region of Shiyan in which China's key source water area Danjiangkou Reservoir for the water source of the middle route of South-to-North Water Diversion Project is located. The results showed that eleven sources with relative high risk value were identified. At the regional scale, Shiyan City and Danjiangkou City would have a high risk value in term of the industrial discharge. Comparatively, Danjiangkou City and Yunxian County would have a high risk value in terms of agricultural pollution. Overall, the risk values of north regions close to the main stream and reservoir of the region of Shiyan were higher than that in the south. The results of risk level indicated that five sources were in lower risk level (i.e., level II), two in moderate risk level (i.e., level III), one in higher risk level (i.e., level IV) and three in highest risk level (i.e., level V). Also risks of industrial discharge are higher than that of the agricultural sector. It is thus essential to manage the pillar industry of the region of Shiyan and certain agricultural companies in the vicinity of the reservoir to reduce water pollution risks of source water areas., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Water quality data represent a critical resource for evaluation of the well-being of aquatic ecosystems and assurance of clean water sources for human populations. While the availability of water quality datasets is growing, the absence of a publicly accessible national water quality dataset for both inland and the ocean in China has been notable. To address this issue, we utilized R and Python programming languages to collect, tidy, reorganize, curate, and compile three publicly available datasets, thereby creating an extensive spatiotemporal repository of surface water quality data for China. Distinguished as the most expansive, clean, and easily accessible water quality dataset in China to date, this repository comprised over 330 000 observations encompassing daily (3588), weekly (217 751), and monthly (114 954) records of surface water quality covering the period from 1980 to 2022. It spanned 18 distinct indicators, meticulously gathered at 2384 monitoring sites, which were further categorized as daily (244 sites), weekly (149 sites), and monthly (1991 sites), ranging from inland locations to coastal and oceanic areas. This dataset will support studies relevant to the assessment, modeling, and projection of water quality, ocean biomass, and biodiversity in China, and therefore make substantial contributions to both national and global water resources management. This water quality dataset and supplementary metadata are available for download from the figshare repository at 10.6084/m9.figshare.22584742 (Lin et al., 2023b). [ABSTRACT FROM AUTHOR]

• Developing novel multiple parameters and pollution sources estimation method (MPSIE); • A new multi-scenario water quality prediction method (MEWQP) based on MPSIE; • Quantifying joint impacts of water intakes, pollution sources, and climate change; • Without intake can improve water quality and offset the impact of climate change; • An optimized scheme of water intake could improve dissolved oxygen by 6%. Water quality prediction is an important means for scientific water environment management and early warning measures. However, its reliability is often restricted by the uncertainties of parameters and pollution sources. Previous methods for resolving these uncertainties still have shortcomings due to low computational efficiency and incomplete coverage of mechanisms. With insufficient data, they also overlooked revealing the comprehensive impact of water intakes, pollution sources, and climate change on water quality in river networks. Insight understanding migration law and the main driving mechanisms under these factors is crucial for water resources and sustainable socioeconomic development. Here, we developed an efficient multi-scenario ensemble water quality prediction (MEWQP) approach to address the deficiency. The Downstream and Delta of Dongjiang River Basin was the demonstrative case. Firstly, we developed multiple parameters and pollution sources inverse estimation method by incorporating the Water Quality Analysis Simulation Program (WASP), general likelihood uncertainty estimation, and inverse methods to improve accuracy. Secondly, the Markov-Chain was used to predict flows affected by climate change. Thirdly, 60 scenarios were analyzed to quantify the impacts of multiple factors on water quality including 5-day biochemical oxygen demand (BOD 5), ammonia nitrogen (NH 3 -N), and dissolved oxygen (DO), and identify an optimized scheme of water intakes. Results show that: (1) WASP has good performance for BOD 5 , NH 3 -N, and DO with Mean Absolute Relative Error of 5.87%, 9.19%, and 15.25%, respectively; (2) pollution sources have the greatest impact, followed by extreme climate and intakes. Their combined impacts are greater than individuals; (3) under non-water intakes DO can be improved by 31%, also offset the impact of climate change by 16%; (4) the optimized scheme of intakes could improve DO by 6%. Therefore, decision-makers should simultaneously adjust water intake and reduce pollution sources to obtain high-quality water resources. [ABSTRACT FROM AUTHOR]

Meeting ecological water requirements (EWRs) is important for maintaining watershed system stability in many arid and semi-arid areas. Rainfall–runoff relationships under changing climate conditions could have many adverse effects on EWRs. The inherent uncertainties in water resource management and potential variations in EWRs should be considered to obtain suitable water allocation strategies under climate change. In this study, an integrated approach was proposed through incorporation of copula functions and a Markov Chain Monte Carlo (MCMC) simulation into a chance-constrained programming (CCP) model. The proposed approach had several advantages for water resource allocation under variable climatic conditions with respect to the following: (a) tackling correlated features of rainfall and watershed inflow under climate change based on copula–MCMC simulations, (b) obtaining runoff distributions using the copula sampling method under multiple climate change scenarios, (c) analyzing fluctuations in EWRs based on variable monthly flows and diverse runoff distributions, and (d) obtaining desired water allocation strategies through the developed CCP model with consideration of EWRs and water shortage risk. Application of the developed method to water resource management in the city of Dalian (China) indicated that the EWRs in the watersheds of Dalian would exhibit large variations under changing climatic conditions. Moreover, in comparison with the supply in 2025, an increase in water supply transferred from the Dahuofang Reservoir (Hun River) would be 6942–33,772, 6942–25,472, and 2849–14,259 Mt with risk tolerance levels of 20%, 50%, and 80%, respectively. [ABSTRACT FROM AUTHOR]

Highlights • A multi-stage fuzzy stochastic model was developed in this research. • The developed model was applied for supporting water resources management. • Ecological water demand was considered and reflected in the developed model. • Multiple ways were adopted for dealing with multiple uncertainties. • It can contribute to in-depth analysis and sustainable development of water-resource- and eco- systems. Abstract In this paper, a multi-stage fuzzy stochastic programming (MFSP) method is introduced to deal with uncertainties presented as fuzzy sets and probability distributions. Moreover, it is able to reflect dynamics of uncertainties and the related decision processes through constructing a series of representative scenarios within a multi-stage context under a set of fuzzy α-cut levels. A management problem about long-term planning of water resources system has been studied to illustrate applicability of the proposed approach. With ecological water demand being considered, the framework solves the complex problems that can hardly be solved in previous individual model research and promotes sustainable development. The results indicate that the dynamic and complexity of water resources allocation can be reflected through the multilayer discrete context tree. Moreover, real-time correction for reducing the risk of water shortage and low economic penalty can be presented. They can also help identify satisfaction degree of the goal and feasibility degree of constraints in an interactive way, enabling decision makers to generate a series of alternatives under various system conditions. Overall, it can not only contribute to decision makers for in-depth analysis, but also for sustainable development of ecosystem. [ABSTRACT FROM AUTHOR]

Traditional blue water resources assessment and management may not meet the needs of sustainable water resource utilization; ignoring the number of green water resources will underestimate the availability of water resources. To rationally allocate and scientifically manage the limited water resources, it is necessary to divide the rich and poor flow situation of blue water and green water. The MIKE SHE-MIKE HYDRO integrated coupled model was selected and used in the Yalong River basin to ascertain the blue and green water in the hydrological cycle. The model was calibrated by matching simulated discharge against observed streamflow discharge at the Tongziling Station. At the same time, the research analyzed the component of green water and the total amount of blue water or green water on a temporal scale. The set pair analysis (SPA) was introduced to classify blue water and green water, which can not only understand the amount and distribution characteristics of water resources in the Yalong River Basin but also rationally allocate the total of water resources in the basin from the perspective of the regional water cycle. Furthermore, according to the situation of blue water and green water in the basin, the related policies are formulated to realize the efficient utilization of water resources in the Yalong River basin. • A high-resolution hydrological model of the Yalong river basin is bulit. • The MIKE SHE system as the modeling platform is used to obtain the variation of Bluewater and Greenwater. • The set pair analysis (SPA) was introduced to study the variation of rich and poor Bluewater and Greenwater. • Table Captions. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid method integrated unbiased grey model (UGM) and artificial neural network (ANN) into an interval two-stage fuzzy credibility-constrained programming (ITFCP) framework is proposed for water resources allocation of the Yalong River research area. Through the grey correlation analysis and the eXtreme Gradient Boosting (XGboost) algorithm, the economic and social indicators are related to the water demands of different water sectors in different regions can be obtained for building water demand prediction model. According to the unbiased grey prediction of the socio-economic development data of each region in the Yalong River Basin (YRB), water demand prediction models are constructed by using neural network. The establishment of a hybrid two-stage interval fuzzy credibility-constrained programming model can analyze the uncertainties existing in the process of water resources allocation. Taking 2020, 2025, and 2030 as the planning years, the developed model studies and reveals the system benefits at different credibility levels, the water shortage of each user in sub-regions and the water resources allocation situation to provide suggestion for managers to optimize the allocation of water resources. Compared to the previous methods, this integrated model can help decision-makers set management policies more sustainably and profitably. • A simulation-optimization method was developed. • The method represents a combination of unbiased grey model (UGM) and artificial neural network (ANN). • Interval two-stage fuzzy credibility-constrained programming was used as a general framework. • The developed method was applied to support water management in Yalong River Basin, China. [ABSTRACT FROM AUTHOR]

To facilitate regional water resources allocation, an integrated bi-level multi-objective programming (IBMP) model with dual random fuzzy variables was developed in this research The proposed model was derived through incorporating dual random fuzzy variables, multi-objective programming, and interval parameter programming within a bi-level optimization framework. This approach improved upon the previous bi-level programming methods and had two advantages. Firstly, it was capable of reflecting tradeoffs among multiple conflict preferences for water related bi-level hierarchical decision-making processes. Secondly, random fuzzy variables were used to tackle the dual uncertainties in both sides of the constraints, which were characterize as probability density functions and discrete intervals. Then, a real-world water resources planning problem was employed for illustrating feasibility of the application of IBMP model in Dongjiang river watershed of south China. Results reflected the alternative decisions for water allocation schemes under a set of probability levels and fuzzy α - cut levels, which can support in-depth analysis of tradeoffs among multiple levels and objective values. Moreover, modeling comparison analysis was undertaken to illustrate the performances of the proposed model. [Display omitted] • Dual-randomness bi-level interval multi-objective programming was developed; • Multi-objective, chance-constrained, and interval parameter programming approaches were used; • Interactions between multiple decision-makers can be addressed through hierarchical strategies; • Uncertain information can be expressed as interval and probability density functions; • Water allocation schemes under multiple constraint-violated levels can be handled. [ABSTRACT FROM AUTHOR]

Water stress is a critical issue threatening the ecosystem health of many shallow lakes. Most lake management methods, however, employ indicators that are individually related to either water quantity or water quality to assess resource-induced and pollution-induced water stress. Based on the system dynamics principle, an integrated method was proposed for early warning of water stress in shallow lakes that considered interactions among 3 systems: water quantity, water quality, and aquatic ecosystems. By adopting the principal component analysis approach to analyze the indicators of these systems, a comprehensive assessment indicator covering 5 early warning levels was extracted to reflect the status of water stress. This method was applied to Lake Baiyangdian in North China, a lake with serious water-related problems. The results indicated that: (a) by comprehensively evaluating the 3 systems, water stress was serious throughout the year, which is more reliable than individual assessment with respective indicators, and the period from April to December had a high water-stress level and should be identified as a crucial early warning period requiring more attention; (b) due to the inherent long-term water stress in the lake, natural water replenishment could not alleviate water stress, even during a year with relatively abundant rainfall; and (c) the proposed method could provide an integrated indicator system to identify water stress status in shallow lakes subject to intensive anthropogenic activities. These findings could provide decision-making support for water resources management. [ABSTRACT FROM AUTHOR]

Abstract: Demands on fresh water by human beings have been continuously increasing due to population growth, living standard improvement, and economic development. At the same time, many regions are suffering greatly from floods and droughts. Those are the results of ineffective management of water resources due to the associated complexities. In this study, a decision support system (DSS) was developed for supporting integrated water resources management in Daegu city, Republic of Korea. The developed DSS contained four subsystems including database, modelbase, and knowledgebase, as well as general user interface (GUI). It was then connected with the National Water Management Information System (WAMIS). A flow prediction could be conducted through the incorporated HEC-HMS Version 3.0.1. Also, an urban water demand forecasting model was developed using an artificial neural network (ANN) based model. At the same time, a water resources management model based on genetic algorithm (GA) was developed in the DSS, facilitating efficient allocation of water resources among different regions within a city. The result indicated that the developed DSS is very useful to deal with complex water resources management problems and could be further applied to similar cities in South Korea. [Copyright &y& Elsevier]

• Interactions of water resources in adjacent regions was examined. • Variations in water supply and demand were analyzed through autoregressive models. • Interaction relationship between multi-regional nexus water shortage was solved. • Water resources allocation strategies were obtained through the optimization model. • The models were applied in Beijing-Tianjin-Hebei urban agglomeration of China. Growing agricultural and economic activities have brought tremendous pressure to regional water resources management. Considering the interactions of water resources allocation among adjacent regions with connected water systems, it is necessary to pay attention to the unification of socio-economic development and environmental protection between the regions. This is of significance to many agglomeration areas that are composed of multiple large cities such as the Beijing-Tianjin-Hebei urban agglomeration (BTH) in China. In this research, an ensemble approach was proposed to tackle water resources allocation problems under the constraints of water resources to reflect interdependence of the composed three areas, i.e., Beijing, Tianjin and Hebei. The approach included ARIMA (Autoregressive Integrated Moving Average) method, ANN (Artificial Neural Network) modelling, copula functions, and multi-objective chance-constrained programming model. It can 1) reflect the variations characteristic of regional water supply and demands, 2) solve the interactions between multi-regional water resources, and 3) obtain desired water resources allocation strategies under multi- scenario and scale conditions. Then, the approach was applied in typical regions of China, i.e., Beijing-Tianjin-Hebei urban agglomeration. Considering 27 scenarios of the nexus water shortage risk, multi-objective joint opportunity constraint programming model aimed at maximizing economic benefits and ecological benefits, in combination with economic, agricultural, water resources and environmental constraints. As the optimal strategies indicated, the maximum GDP in BTH would remain at 8514 billion yuan. The minimum amount of water allocation and wastewater discharge would be 24–26 billion m3 and 2100–2101 million m3 respectively. [ABSTRACT FROM AUTHOR]

Water is a fundamental resource in our daily lives and for social development. Currently, water resources are under stressed conditions in north China due to the ongoing growth of demand for water and the frequent occurrences of natural disasters in this area. This problem is further intensified by the deterioration of water quality, which is a side effect of economic development, industrial expansion, and agricultural growth. These problems interconnect with each other, leading to a variety of conflicts over water resources. To resolve such conflicts, a two-person game-theory based model is developed for water resource management in the trans-boundary regions of a river basin. The model is tested on a river basin within this area of China in which water resources are contested. Through the developed model, potential policy alternatives can be generated for water resource management and pollution control within the context of a river basin. Cooperative and/or competitive mechanisms can thus be formulated through competition and cooperation over issues of water quantity and quality among different levels of jurisdictions in the same river basin. Then, Nash Solution is used to solve scenarios of competition and cooperation between the upstream and downstream based on the integration of optimization and simulation models. The developed model and the solution process is then applied to resolve water conflicts between Beijing and Hebei province, which are in the upstream and downstream of the Guanting Reservoir Basin (GRB), respectively, representing two major stakeholders in the contest over water quantity and quality. Non-cooperative and cooperative scenarios for the two-player game scenarios are developed to investigate and compare potential economic benefits of these scenarios. Results indicate that the developed model can be effectively and flexibly employed to resolve water conflicts between upstream and downstream stakeholders in the same river basin. The results can also be used to help formulate economic compensation mechanisms within these jurisdictions. [ABSTRACT FROM AUTHOR]

Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of the water distribution process. Especially for the watershed including irrigation area with multiple water sources and water users, it is complicated that the joint risk among available water from each water source and multi-uncertainties under water resource allocation among multiple water users. In this research, an approach is developed that a copula-based interval multi-stage fuzzy stochastic programming (CIMFSP) model. This research aims at figuring out the planning of a case study for water-resources management. In the multi-sources water supply subsystem, the copula function is introduced to tackle the interaction of water availability between two water sources (i.e., A &B). Joint risk was set 0.05, 0.10, 0.15 to reflect the shortage risk of total available water at diverse levels. An interval parameter multi-stage fuzzy stochastic programming (IMFSP) model is developed for water resource distribution in a multi-users water-demand subsystem. Through this model, uncertainties presented as interval numbers and probability distributions, as well as fuzzy sets, were handled. The dynamics of the entire water resource system can be reflected by multi-stage discrete trees. A series of solutions can be generated under multiple scenarios (i.e., joint risk and α-cut levels). The modeling results will produce a series of alternatives under a battery of scenarios and help the decision-makers get an insight into the tradeoff between the system economic benefit and financial penalties under the corresponding risk level. This approach is valuable for improving the feasibility of optimal results in the watershed with irrigation region water resource management. • Joint risk analysis for multi-sources water resources supply based on copula function. • A multiple stage stochastic modeling approach for multi-users water allocation. • The dynamic decision-making process of water resources allocation under multiple joint risk & multiple α-cut scenarios. [ABSTRACT FROM AUTHOR]

In this research, a dual-randomness bi-level interval multi-objective programming (DR-BIMP) model was developed for supporting water resources management among multiple water sectors under complexities and uncertainties. Techniques of bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP) were incorporated into an integrated modeling framework to achieve comprehensive consideration of the complexities and uncertainties of water resources management systems. The DR-BIMP model can not only effectively deal with the interactive effects between multiple decision-makers in complex water management systems through the bi-level hierarchical strategies, but also can characterize the multiple uncertainties information expressed as interval format and probability density functions. It could thus improve upon the existing bi-level multi-objective programming through addressing discrete interval parameters and dual-randomness problems in optimization processes simultaneously. Then, the developed model was applied to a real-world case to optimally allocate water resources among three different water sectors in five sub-regions in the Dongjiang River basin, south China. The results of the model include determining values, interval values, and stochastic distribution information, which can assist bi-level decision-makers to plan future resources effectively to some extent. After comparing the variations of results, it is found that an increasing probability level can lead to higher system benefits, which is increased from [20,786.00, 26,425.92] × 108 CNY to [22,290.84, 27,492.57] × 108 CNY, while the Gini value is reduced from [0.365, 0.446] to [0.345, 0.405]. A set of increased probability levels gives rise to the lower-level objectives. Furthermore, the advantages of the DR-BIMP model were highlighted by comparing with the other models originated from the developed model. The comparison results indicated that the DR-BIMP model was a valuable tool for generating a range of decision alternatives and thus assists the bi-level decision-makers to identify the desired water resources allocation schemes under multiple scenarios. [Display omitted] • A dual-randomness bi-level interval multi-objective programming model was developed; • Interactions between multiple decision-makers can be addressed through bi-level hierarchical strategies; • Uncertain information associated with water can be expressed as interval and probability density functions; • Water resource allocation schemes under multiple constraint violated probability levels can be handled. [ABSTRACT FROM AUTHOR]

An integrated simulation-optimization modeling system (ISOMS) approach was developed for assessing adaptive strategies in response to coupled impacts of climate and landuse variations. The ISOMS can not only reflect future hydrological trends under changing environment, but also provide water-allocation plans under various uncertainties expressed as random or fuzzy feature systematically. A case study of Anning River Basin with dry and warm characteristics in the upper Yangtze River was applied to inspect the model's applicability. A range of alternatives to adaptive strategies were generated under combinations of climate and landuse scenarios with different satisfaction levels. Results reveal that: (i) there exist increasing trend of temperature and precipitation in future period (2021–2050); (ii) the streamflow variation is more sensitive to climate change than landuse change; (iii) the hydrologic system uncertainties would lead to changes in water resources allocation; (iv) a low level of uncertainty satisfaction or a high level of violation risk would reduce the system reliability for the water resources system. The ISOMS approach has tremendous significance for evaluating hydrologic variations with complicated uncertainties, and providing optimal water allocation schemes responding to the coupled impacts of climate and landuse variations among society, economy and environment. • An integrated simulation-based optimization modeling on MIKE SHE and MFSCP method. • A set of 31 GCMs and three CA-Markov modules for climate and land use projections. • Hydrological process and water availability vary under changing conditions. • Adaptive strategies for water resources generated under multiple scenarios. [ABSTRACT FROM AUTHOR]

• An interval two-stage fuzzy credibility constraint programming (ITSFCCP) method was proposed. • Multiple uncertainties can be tackled for the social and natural complexities of the water resources system. • The model was demonstrated in the city of China and Myanmar. • The research scope and research factors (water environmental pollution factors) need to further expanded. In this paper, an interval two-stage fuzzy credibility constraint programming (ITSFCCP) method is proposed to deal with multiple uncertainties that can be expressed as fuzzy sets, discrete intervals and probability distributions, which can be used for effectively reflect natural and social complexities of relevant decision-making processes. Lincang city of Yunnan province, which is located in the southwest border of China, is employed as the demonstrative case. It is a frontier window and an important channel for China to face the "radiation center" of Southeast and South Asia. Also, it is the intersection of the Tropic of Cancer and the Geographic Water Distribution Line between the Pacific and Indian Oceans. The city's water resources system is particularly unique due to its connection with Myanmar. Considering multiple uncertainties and complexities of the existing water resources system in the city, ITSFCCP is developed to optimize the allocation of water resources. The objective function of the model is the maximization of economic benefits of the water resources system. Fuzzy sets, discrete intervals and probability distribution were introduced to represent the multiple uncertainties associated with the natural and social complexities. Credibility levels were adopted to solve the complexity of multi-ethnic human society in Lincang. The results showed that the model could effectively deal with the uncertainties and complexities of Lincang's water resources system, and reflect the trade-offs between the system benefits and risks. [ABSTRACT FROM AUTHOR]

• We investigate the impacts of interregional trade on improved GWF in China. • Compared with traditional GWF, improved GWF of Chinese regions decreases by 19-35%. • Up to 35% of China's improved GWF is embodied in interregional trade. • Beijing-Tianjin and coastal regions outsource GWF to central and northern regions. • This study provides more accurate results for water resource conservation in China. Grey water footprint (GWF) is an essential indicator describing the effects of aquatic pollutants on water quantity, which is significantly influenced by interregional trade. Existing studies on the impacts of interregional trade on GWF in China have deviations. That is, traditional GWF is determined by the pollutant with the highest pollutant-specific GWF, but overlooks the influences of coexisting compounds. Coexisting compounds have large influences on GWF quantification. However, the impacts of interregional trade on the improved GWF in China have not been well characterized. This study integrates the multi-regional input-output analysis and an improved GWF quantification method considering multiple pollutants, to investigate the impacts of interregional trade on the GWF in China. Compared with traditional GWF, the improved GWF of Chinese regions decreases by 19-35%. Up to 35% of China's improved GWF is related to commodities that are produced outside of regions where they are finally consumed. Beijing-Tianjin and coastal regions outsource GWF to the central and northern regions. Sectors contributing the most to GWF outsourcing include corn, cattle, swine, poultry, and other animal husbandry. This study provides more accurate region-specific and sector-specific results for policy decisions on water resource conservation in China. [ABSTRACT FROM AUTHOR]

An entropy-based multi-objective interval stochastic programming (EMISP) approach based on interval hierarchical projection, interval information entropy, interval analysis and chance-constrained programming was developed for supporting agricultural water management. EMISP improved upon existing methods through measuring and balancing ecological and economic benefits of irrigated croplands under uncertainty. To demonstrate its applicability, the developed method has been applied to an arid Chinese watershed. The results suggested that grain crops would produce higher ecological benefits than economic crops. A series of benefit- and risk-explicit plans for agricultural water use were generated, which indicated that the relaxation of water availability constraints would generate higher comprehensive benefits, but increase the risks of system infeasibility. The results from the EMISP model were also compared to those from four potential alternative models. These comparisons revealed that EMISP could achieve higher water productivity and comprehensive benefits with controllable water-shortage risks. • An interval hierarchical projection method was proposed for measuring ecological benefits of croplands. • An entropy-based multi-objective interval stochastic model was developed to balance multiple objectives under uncertainty. • The results show that grain crops would produce higher ecological benefits than economic crops. • The developed model could achieve higher water productivity and comprehensive benefits. [ABSTRACT FROM AUTHOR]

Reservoir operation policies affect reservoir nitrogen (N) cycling and budgets through complex influences on physical and biogeochemical processes in water and sediment. However, the underlying influencing mechanisms, especially the impact of operations on sediment processes, remain unclear. This study combines a coupled hydrodynamic-eutrophication-sediment model and a reservoir operation model to clarify reservoir operation impact on N cycling processes in water and sediment as well as impact on stored and discharged water quality. Three typical reservoir operation policies (i.e., standard, hedging, and eco-friendly) were selected to create operation scenarios. The hydrodynamic-eutrophication-sediment model was used to simulate N cycling processes and N concentrations in stored and discharged water under each scenario. Using China's Danjiangkou Reservoir as a case study, results revealed that reservoir operation policies significantly affected spatiotemporal dynamics of N cycling processes and water quality. Specifically, reservoir sediment acted as a N source, contributing to annual net release of 9.5–16.1 Gg N. Higher water levels promoted sediment ammonium release through effects on reservoir submerged area and sediment temperature. Moreover, denitrification processes in water and sediment collectively contributed to annual N removal of 14.0–16.9 Gg N. Annual N removal was greater in the higher water level scenario despite its lower denitrification rate per unit area. Additionally, these processes and hydrodynamic conditions combine to affect N forms and concentrations. The N concentration in the lower water level scenario was generally higher, risking algal bloom outbreaks in the shallow-water zone. This study is intended to help guide reservoir operation policymaking to account for N management while also coordinating water quantity and quality targets. • Spatiotemporal dynamics in N cycling processes and water quality are assessed. • Higher storage levels can promote sediment ammonia release in deep reservoirs. • Denitrification in water and sediment significantly contributes to N removal. • Tradeoffs exist between the quantity and quality of water released to downstream. [ABSTRACT FROM AUTHOR]

• The distribution of green water resource was more homogeneous. • The annual average blue water was higher accounting for 65.1% of the total water flow. • The blue water has no significant variability in most river basins in 1992–2010. • The change of green water resource is caused mainly by green water flow. Poyang Lake basin has drought-prone pattern and floods in recent decades, posing a serious threat to water security and human life. In this research, the Soil and Water Assessment Tool (SWAT) was applied to investigate hydrological parameters characteristics, simulating uncertainties as well as spatial and temporal patterns of both blue and green water resources for the whole basin. The results revealed a strong correlation between the observed and simulated streamflow at six hydrological stations. The overall hydrological water balance analysis showed that 58.1 % of annual water yield is contributed by surface runoff, while groundwater flow contributes 38.8 % of annual water yield. The annual average blue water resource was higher accounting for 65.1 % of the total water resources. Green water coefficient was 34.9 % for the entire basin, varying from 26 % to 50 %. The spatial distribution of blue water resource was uneven in the basin with higher value in parts of the Ganjiang, Xinjiang and Raohe subbasins. While distribution of green water resource was more homogeneous. Blue water resource decreased significantly in Raohe subbasin with −37.1 mm/a whereas having no significant variability in other river basins from 1992 to 2010. The change of green water resource is caused mainly by the increase of green water flow in most subbasins. The results provide insights into prediction uncertainty reduction of simulation as well as green and blue water endowments. This research is of great significance to improve water resources management in Poyang Lake basin. [ABSTRACT FROM AUTHOR]

An improved multi-objective optimization model based on goal programming (GP) for supporting reservoir operation was developed under inflow senarios of multiple runoff guarantee rates (i.e., 25%, 75%, perennial mean, and 95%) and ecological goals with the combination of steady- and pulse-state ecological water demands. Under these four scenarios, discharge flows of Danjingkou Reservoir would be 358.40, 369.67, 268.91 and 98.14 × 10 8 m 3 /a, and those at Taocha Canal headwork would be 104.61, 86.62, 95.08 and 64.00 × 10 8 m 3 /a, respectively. The generated results for stream flows could successfully meet the predetermined operational goals for the project. Comparatively, under the scenario of 95% runoff guarantee rate, the obtained strategies could not satisfy the ecological water demands. The modeling results indicated that the capacity of water diversion and storage for Danjiangkou Reservoir would be enhanced due to the operation of the South-to-North Water Diversion Project. The results showed the risks associated with possible flooding would be comparatively low under those four runoff guarantee rates. This represents the current priority for flood control in Danjiangkou Reservoir needs to be changed into multiple ones including ecological water supply, water transfer, as well as downstream water security maintenance. [ABSTRACT FROM AUTHOR]

The time step used in the operation of eco-friendly reservoirs has decreased from monthly to daily, and even sub-daily. The shorter time step is considered a better choice for satisfying downstream environmental requirements because it more closely resembles the natural flow regime. However, little consideration has been given to the influence of different time steps on the ability to simultaneously meet human and environmental flow requirements. To analyze this influence, we used an optimization model to explore the relationships among the time step, environmental flow (e-flow) requirements, and human water needs for a wide range of time steps and e-flow scenarios. We used the degree of hydrologic alteration to evaluate the regime’s ability to satisfy the e-flow requirements of riverine ecosystems, and used water supply reliability to evaluate the ability to satisfy human needs. We then applied the model to a case study of China’s Tanghe Reservoir. We found four efficient time steps (2, 3, 4, and 5 days), with a remarkably high water supply reliability (around 80%) and a low alteration of the flow regime (<35%). Our analysis of the hydrologic alteration revealed the smallest alteration at time steps ranging from 1 to 7 days. However, longer time steps led to higher water supply reliability to meet human needs under several e-flow scenarios. Our results show that adjusting the time step is a simple way to improve reservoir operation performance to balance human and e-flow needs. [ABSTRACT FROM AUTHOR]

As the total water resources consumption control and carbon mitigation continuous improvement, the weak water-carbon incorporate management is increasingly exposed. In this study, a water-carbon nexus assessment framework is proposed to analyze the nexus relationship between water consumption and carbon emission, and distinguishes the coupled water-carbon transmission intensity and the transfer paths under regional and industrial scales. According to the practical input-output table, water consumption, and carbon emission information, the framework is applied to Beijing-Tianjin-Hebei urban agglomeration (BTHUA), a population, resource, and trade intensive area of China. Inter-regional/intra-regional water consumption and carbon emission transfer fluxes between sectors, the pairwise ecological relationship, and the water-carbon nexus were analyzed. Results indicated that the water-carbon transfer indexes from Hebei to Beijing and Tianjin were 161.85 kg/m3 and 113.88 kg/m3 in the study period, along with the most water consumption and carbon emission, and the worst water-carbon nexus. From the industrial perspective, electricity and gas supplying industry provided 7.8% and 29.1% of the total carbon transfer in Tianjin and Hebei, as the most key node sectors on the water-carbon nexus in the BTHUA. The research provides valuably supporting the adjustment of the existing urban agglomeration water-carbon nexus management schemes. • A new assessment framework was developed to analyze water-carbon nexus among regions/industries. • Water-carbon nexus among provinces and sectors was accounted in urban agglomeration. • It can identify key region/sectors for dual goals of water saving and carbon emission reduction. • Electricity and Gas Supplying was the most key node sector on the water-carbon nexus. [ABSTRACT FROM AUTHOR]

In this study, an integrated framework is proposed for agricultural water and land resources management under uncertain circumstances and energy-water nexus. The framework has two components: the optimized interval solutions generation of unknown market quotation and environment situation through the novel interval fuzzy-expectation programming, and the post-optimization decision-making of the tradeoff between system risk and return through risk-explicit interval programming. An optimization model based on the framework was developed from a real description for agricultural water resources system of Guangdong province under considering various crop, multiple water sources, and the related energy consumption in irrigation and drainage. The impact of energy consumption and risk level control on water resources allocation among different crops and system benefits were analyzed. The results indicated that the preference for surface water resources as the main water sources can effectively reduce regional irrigation energy consumption, and the planting area of each crop would have different variations duo to resources endowment constraints. Risk tradeoff-based results can provide valuable information and additional concern for developing low carbon-oriented agricultural water resources management schemes. [Display omitted] • An integrated framework for agricultural water resources management under uncertainty. • Influence of energy consumption on agricultural water resources allocation are explored. • Risk tradeoff-based results are obtained from risk explicit-interval fuzzy-expectation programming. [ABSTRACT FROM AUTHOR]

This paper develops an interval multi-stage robust programming model for regional low-carbon electric power system planning with energy-water nexus. The model integrates interval programming and multi-stage stochastic programming to handle uncertainties with different probability distribution information. In addition, under the framework of robust programming, the optimal solutions could provide tradeoff information between system cost and decision maker's risk preference. The hybrid programming method is tailored for a case study of Shandong Province, China to validate its applicability and provide useful managerial insights, where energy-water interactions are gain great concerns. The model helps to make robust strategies associated with risk control for capacity expansion, carbon capture and storage (CCS) technology retrofitting, power generation, imported electricity, and water allocation for electricity generation, with the goal of minimizing total cost. Besides, considering the variation of water resource conditions and environmental policies under climate change, the impacts of water resource availability and carbon emission caps on regional electric power system planning are explored and discussed. It is found that stricter carbon emission cap policy would facilitate the investment on CCS devices but have little impact on the installed capacity structure, meanwhile it would increase water consumption per power generation. However, scarcer water resource situation would promote the development of renewable energy generation, and limit the investment on CCS technology. • Low-carbon power system management optimization with energy-water nexus under uncertainties. • Impact analysis of various water resource availability and carbon emission cap on optimal strategies. • Useful managerial insights on energy system planning from energy-water nexus perspective. • Robust solutions with risk-cost tradeoff information to reflect different risk preferences. [ABSTRACT FROM AUTHOR]