Multi-objective optimization of quantitative-qualitative operation of water resources systems with approach of supplying environmental demands of Shadegan Wetland

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical "water-quantity water-scarce region" in China, with those of the Yangtze River Basin, a "water-quality water-scarce region." The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical-virtual water system challenges within river basins.

Multi-level decisions for addressing trade-off in the cross-regional water-environment-agriculture interactive system under constraint of water ecological carrying capacity

The complex-enhanced hierarchical relationship among multiple stakeholders in the water-environment-agriculture interactive system has been overlooked. This study develops a leader-follower-enhanced framework (named as FCMLP) that integrates variable-weight combination prediction model, multi-level programming, and fuzzy credibility constrained programming, which can effectively address the above problems under uncertainties. Five water ecological carrying capacity (WECC) statuses are treated as a critical constraint into the modeling framework to improve the accuracy of decision-making. An interactive fuzzy satisfaction algorithm is advanced for solving this multi-level problem, in which COD discharge minimization, economic benefits maximization, and grain yield maximization are taken as the upper-, middle-, and lower-level goals, respectively. The framework is applied to plan the cross-regional water-environment-agriculture interactive system in the Beijing-Tianjin-Hebei and Yangtze River Economic Belt. Solutions reveal that increased WECC status and credibility level would decrease 1.40%-1.74%, 0.71%-9.61%, and 1.63%-2.26% of water resources allocation, COD emissions, and economic benefits, respectively. Crop area and grain yield would dramatically decline by 4.13%-4.46% and 4.03%-4.67% when a credibility level increases from 0.8 to 1, respectively. The overall satisfactory degree would range from 0.58 to 0.70, which illustrates interactive decision-making process of multiple stakeholders. Significant differences can be observed in the optimized schemes of water resources allocation and environmental-economic-agricultural performances among various models. The amounts of allocated water resources, pollutant discharge, and economic output from the FCMLP model would be respectively 11.30%-13.45%, 14.90%-15.21%, and 73.12%-73.48% higher than those from the environment- and agriculture-oriented schemes, yet 13.81%, 32.05%, and 15.29% lower than those from the economy-oriented scheme. Some water adaptability countermeasures are given for ensuring the scientific operation of the South-to-North Water Transfer Project and alleviating conflicts between water source and receiving areas. Further exploration of the optimization scheme of water-environment-energy-agriculture system driven by climate change is still required for guaranteeing the dynamic balance of regional resources.

An integrated simulation-optimization approach for combined allocation of water quantity and quality under multiple uncertainties

Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.

Exploring a multi-objective optimization operation model of water projects for boosting synergies and water quality improvement in big river systems

Due to excessive nutrient enrichment and rapidly increasing water demand, the occurrence of riverine environment deterioration events such as algal blooms in rivers of China has become more frequent and severe since the 1990s, which has imposed harmful consequences on riverine ecosystems. However, tackling river algal blooms as an important issue of restoring riverine environment is very challenging because the complex interaction mechanisms between the causes are impacted by multiple factors. The contributions of our study consist of: (1) optimizing joint operation of water projects for boosting synergies of water quality and quantity, and hydroelectricity; and (2) preventing algal bloom from perspectives of hydrological and water-quality conditions by regulating water releases of water projects. This study proposed a multi-objective optimization methodology grounded on the Non-dominated Sorting Genetic Algorithm to simultaneously minimize the excess values of algal bloom indicators (water quality, O1), minimize the used reservoir capacity for water supply (water quantity, O2), and maximize the hydropower generation (hydroelectricity, O3). The proposed methodology was applied to several catastrophic algal bloom events that took place between 2017 and 2021 and thirteen water projects in the Hanjiang River of China. The results indicated that the proposed methodology largely stimulated the synergistic benefits of the three objectives by reaching a 36.7% reduction in total nitrogen and phosphorus concentrations, a 33.1% improvement in the remaining reservoir capacity, and a 41.0% improvement in hydropower output, as compared with those of the standard operation policy (SOP). In addition, the optimal water release schemes of water projects would increase the minimum streamflow velocity of downstream algal bloom control stations by 8.6%-9.4%. This study provides a new perspective on water project operation in the environmental improvement in big river systems while boosting multi-objectives synergies to support environmentalists and decision-makers with scientific guidance on sustainable water resources management.

Multi objective simulation-optimization operation of dam reservoir in low water regions based on hedging principles

The high level of reliability of water resources is always an advantage for consumers, but in arid and semi-arid regions where the inflow to the reservoir is faced with severe fluctuations, it makes sense to decrease the percentage of reliability of the system and allocate less water to consumption zones to prevent critical conditions such as emptying of the reservoir. In this research, the employed operation model is based on the simulation-optimization combination by considering the objectives of minimizing the violation of the allowed capacity of the reservoir and maximizing the percentage of supplying the demands. The optimal hedging variables are specified by linking the WEAP (Water Evaluation and Planning System) to the MOPSO multi-objective optimization algorithm. According to the available data, the duration of the simulation and optimization period in the model is 360 months. After 1000 iterations, the optimal reservoir volume values are obtained at the hedging level and hedging coefficient in different months. Finally, the model results are compared with the results obtained from the standard operation policy (SOP). The results show that the proposed model is able to manage the allocation to needs in the dry months and prevent the reservoir from emptying. Also, by storing a part of the flow in the reservoir in watery months and consuming it in low water months, it increases the supply of needs by 20 to 35% and reduces the failure rate in dry months.

Multi-objective Optimization of water resources in real time based on integration of NSGA-II and support vector machines

One of the management strategies of water resources systems is the combination of simulation and optimization models to achieve the optimal policies of reservoir operation in the form of specific optimization. This study utilizes an integration of the NSGA-II multi-objective algorithm and WEAP simulator model so that the first objective is to maximize the reliability of providing the needs in front of the second goal, i.e., to minimize the drawdown the water table at the end of the operation time. The dam rule curve or the amount of released volume from the reservoir is optimized to supply downstream uses in these conditions. However, in certain optimizations, the optimal solutions cannot be generalized to other possible inputs to the reservoir, and if the inflow to the reservoirs changes, the obtained optimal solutions are no longer efficient and the system must be re-optimized in the form of an optimizer algorithm. Therefore, to solve this problem, a new method is extended on the basis of the combination of the support vector machine and NSGA-II algorithm for optimal real-time operation of the system. The results demonstrate that the average error rate of optimal rules derived from support vector machines is less than 2.5% compared to the output of the NSGA-II algorithm in the verification step, which indicates the efficiency of this method in predicting the optimal pattern of the dam rule curve in real time. In this structure, based on the inflow to the reservoir, the volume of water storage in the reservoir and changes in the reservoir storage (at the beginning of the month) and the downstream demands of the current month, the optimal release amount can be achieved in real time. Therefore, the developed support vector machine has the ability to provide optimal operation policies based on new data of the inflow to the dam in a way that allows us optimally manage the system in real time.

Rainwater harvesting and water balance simulation-optimization scheme to plan sustainable second crop in small rain-fed systems

Environmental degradation in the form of water shortage and uncertainty has severely affected the food systems across the globe. Especially in India, which is dominated by rain-fed farmers, the need for sustainable water resource and its management at farm level is imperative for farming livelihoods and food security of the country. Rainwater harvesting in on-farm reservoirs (OFR) can enable crop diversification, year round cropping and seasonal vegetable cultivation in rain-fed farming systems in India. However appropriate sizing of OFR remains a serious concern especially for small and marginal farmers with limited land holdings. In this study, a novel and comprehensive simulation-optimization model was developed to determine the optimal size and utilization of OFR. The simulation consisted of water balance of soil and OFR using hydrological analysis for last 28 years, through which supplement irrigation needs and, rainwater harvesting potential was estimated. Optimal use of available water in OFR was designed using a multi-stage process wherein the model generated, compared and screened appropriate vegetable plans for Rabi cultivation. The model was simulated for different OFR sizes and the optimal size was chosen based on its economic feasibility. To demonstrate the model, a case study was simulated wherein high supplement irrigation was estimated, indicating a severe limitation in rain-fed farming. A minimum OFR size of 9.9% of the total land was required. With an increase in OFR sizes, the profits increased however, the growth rate declined as the cropping area was reduced. An OFR size of 15.5% of total land was found to be optimal which gave benefit-cost ratio and payback period of 2.4 and 6.8 years respectively. Trends in cultivation plans for different sizes of OFR was observed wherein for small OFR sizes, the model generated fewer options of cultivation plans and preferred crops with high water productivity over crops with high profitability. The proposed model is generic and applicable at multiple scales and scenarios. The model could be used by environmental decision makers, farm managers, policy makers and researchers to determine the feasibility of any water resource intervention using an ecosystem centric approach when multiple scenarios of cultivation are possible.

Research on Water Rights Allocation of Coordinated Development on Water–Ecology–Energy–Food

Water rights trading is an important way to solve the problem of water shortage by market mechanism. The allocation of water rights among ecological water, energy water, and grain planting water are the basis of the regional water rights trade. In this paper, the concept of coordinated development of water–ecology–energy–food is proposed. We build a water rights allocation model with fairness, efficiency, and coordinated development as the goal, to achieve water security for various industries. Taking Yinchuan city as an example, the results showed that compared with the current water rights the water rights of life increased by 1.07%, the water rights of ecology increased by 1.85%, the water rights of energy industry decreased by 1.09%, the water rights of food planting decreased by 3.27%, the water rights of other agriculture increased by 0.83%, and the water rights of the general industry increased by 0.65%. After the allocation of water rights, the cooperativity of water–ecology–energy–food increased by 7.56%, and the total value of water resources in various industries increased by 2.31 × 108 CNY. A new water rights allocation model is developed in this paper, which can provide a reference for the allocation of water rights among regional industries.