An optimization model for the allocation of water resources

The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic, social and environmental benefits of regional water consumption. In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources, multiple users and multiple objectives like “curse of dimensionality” or sinking into local optimum, this paper proposes a particle swarm optimization (PSO) algorithm based on immune evolutionary algorithm (IEA). This algorithm introduces immunology principle into particle swarm algorithm. Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population. Also, the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum. After applying this model to the allocation of water resources in Zhoukou, we obtain the scheme for optimization allocation of water resources in the planning level years, i.e. 2015 and 2025 under the guarantee rate of 50%. The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable. Thus it offers a new idea for solving the issue of optimal allocation of water resources.

The water deficit is one of the primary challenges faced by developing countries, stemming from several factors such as limited water resources, population growth, and climate change. Optimal allocation of water resources represents a comprehensive strategy for water resource management, acknowledging the intricate connections between water systems and their repercussions on the environment, society, and economy. It serves as a means of integrating diverse elements of development plans into a cohesive approach for land and water planning and management. In the current study, we undertook the optimal allocation of land and water resources across different sectors for the water years 2016-17, 2017-18, and 2018-19. The study area chosen was the Munneru basin, situated in the lower section of the Krishna River Basin in India. This basin is predominantly agricultural, covering 63.17% of the area, and was selected to validate the proposed framework concept. Within the study area, we identified six distinct water-demanding sectors and calculated their sectoral water demands at a basin level. To assess water availability in the basin, we conducted hydrological modeling employing the Soil and Water Assessment Tool (SWAT). Furthermore, we determined the crop water requirements for various crops using CROPWAT. For the optimal allocation of water resources, we applied the Non-dominated Sorting Genetic Algorithm-II (NSGA – II) optimization model, considering two different objectives that account for social and economic aspects. To identify superior solutions from the Pareto front, we employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Compromising Programming (CP) methods. Through this methodology, we achieved maximum utilization of water and land resources and maximized returns from the agricultural sector. Following the optimal allocation of land and water, we observed an average annual increase of 3.61% in agricultural sector returns. These outcomes demonstrated a substantial enhancement in the water use efficiency across all pertinent water use sectors. As a result, decision-makers may contemplate the implementation of this framework in large-scale regions, with potential expansion to encompass a national sustainable development strategy at the country level.

A model for regional optimal allocation of irrigation water resources under deficit irrigation and its applications

The water resources optimization disposition of multi-water-source, the multi-consumer is the large-scale system question of multi-objective and multi-decision-making, the reasonable allocation of water resources is one of the effective regulative measures for implementing sustainable utilization of water resources. Using the inherent parallelism mechanism of genetic algorithms and global optimization characteristics, the optimal allocation of water resource problem was modeled as the problem of biological evolution, survival of the fittest was carried out by judging the degree of optimization of each generation of individuals, thus produces the new generation, iterating so repeatedly to complete the optimal allocation of water resources. The case analysis shows that this algorithm which was applied to optimize the allocation of water resources was a success.

The optimal allocation of agricultural water and land resources is of great significance in ensuring sustainable food production and economic benefits of farmers. However, agriculture, as an important carbon cycle ecosystem, has paid limited attention to carbon sequestration in the optimal allocation of water and land resources. Therefore, this study developed a new water-carbon-economy coupling model (WCECM) for optimal allocation of agricultural water and land resources. In this model, the minimum water scarcity, maximum carbon sequestration and maximum economic benefits are taken as the optimization objectives. In addition, surface water volume and groundwater volume and planting area etc. were defined as constraints, respectively. Then, the model was solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and the Entropy-weighted-TOPSIS evaluation method. The developed model was demonstrated in the largest Farm, Youyi Farm, which is one of commercial grain production base in China to analyze the optimization of water and land resources from 2010 to 2019. We found that the new WCECM, based on the simulation of a complex coupled water-carbon-economy system, can realize the optimal allocation of agricultural water and land resources to protect regional water resources, increase carbon sequestration and adjust the agricultural planting structure. In detail, through the multi-objective optimization model, the planting structure and the allocation ratio of surface water and groundwater irrigation water consumption are more suitable for this study area. After the optimization, the area planted with Rice was significantly reduced, the area planted with Maize was increased, and the area planted with Soybean did not change significantly compared with the first two crops. The planting structure has changed from focusing on paddy cultivation to dryland cultivation, with the ratio of Rice area, Maize area and Soybean area being 3:6:1. The water consumption is constrained within manageable limits, with an average annual irrigation water consumption of 2.01 × 108 m3. The amount of carbon sequestered has increased significantly, with an average annual increase of 7.8 × 108 kg. Meanwhile, the optimized economic benefits increased slightly, with a value of ¥2.35 billion. In short, optimization of water and land resources is beneficial for improving farmers' incomes, increasing carbon sequestration in agriculture, and conserving water resources.

Based on the mine water produced by mining, to improve the ecological environment, the optimal allocation of mine water resources is studied. To reduce the uncertainty of the calculation results of ecological water demand, the wolf colony algorithm neural network model is used for long-term rainfall forecast. Combined with the forecast annual rainfall, the ecological water demand is classified and calculated. The results show that the ecological water demand based on rainfall forecast can reduce the allocation of water resources in wet years to ecological, so that the surplus water resources can be used in industries, irrigation, and other aspects that can create economic benefits, and improve the utilization efficiency of water resources. The ecological allocation model of mine water based on long-term rainfall forecast can reduce the uncertainty of regional water resources allocation based on rainfall forecast, which has good guiding significance and practical value for the optimal allocation of water resources in arid and water shortage areas.

Global warming and anthropogenic changes can result in the heterogeneity of water availability in the spatiotemporal scale, which will further affect the allocation of water resources. A lot of researches have been devoted to examining the responses of water availability to global warming while neglected future anthropogenic changes. What’s more, only a few studies have investigated the response of optimal allocation of water resources to the projected climate and anthropogenic changes. In this study, a cascade model chain is developed to evaluate the impacts of projected climate change and human activities on optimal allocation of water resources. Firstly, a large set of global climate models (GCMs) associated with the Daily Bias Correction (DBC) method are employed to project future climate scenarios, while the Cellular Automaton–Markov (CA–Markov) model is used to project future Land Use/Cover Change (LUCC) scenarios. Then the runoff simulation is based on the Soil and Water Assessment Tool (SWAT) hydrological model with necessary inputs under the future conditions. Finally, the optimal water resources allocation model is established based on the evaluation of water supply and water demand. The Han River basin in China was selected as a case study. The results show that: (1) the annual runoff indicates an increasing trend in the future in contrast with the base period, while the ascending rate of the basin under RCP 4.5 is 4.47%; (2) a nonlinear relationship has been identified between the optimal allocation of water resources and water availability, while a linear association exists between the former and water demand; (3) increased water supply are needed in the water donor area, the middle and lower reaches should be supplemented with 4.495 billion m3 water in 2030. This study provides an example of a management template for guiding the allocation of water resources, and improves understandings of the assessments of water availability and demand at a regional or national scale.

Coordinated allocation of conventional and unconventional water resources considering uncertainty and different stakeholders

Prediction of regional water resources carrying capacity based on stochastic simulation: A case study of Beijing-Tianjin-Hebei Urban Agglomeration

Risk assessment and configuration of water and land resources system network in the Huang-Huai-Hai watershed

Large-scale water resource system with multi-water resources and multi-users consists of several independent supply and drainage network systems. It is hard to solve the problem well of large-scale water resource system with traditional planning method. We model the problem of optimal allocation of water resource using the principles of biological evolution, and eliminate the inferior by judging the optimization degree of each generation individuals, then produce a new generation, thus the optimal allocation of water resource is accomplished through iteration repeated. The disadvantage is that MOGA solution out of the quasi-global optimal solution, rather than the true optimal solution.

Construction and application of a refined model for the optimal allocation of water resources — Taking Guantao County, China as an example

In this paper, we first establish the model of optimal allocation of water resources within the region and convert it into a dynamic planning problem. Then, under drought conditions, the models of optimal allocation of intra-regional water resources among industries and different crops are established, respectively, and the water production function model is established using sensitivity coefficients. Under the condition of satisfying the water quantity opportunity constraints and with the objective of ensuring obtaining a higher system return, the water resources optimal allocation model based on interval multi-stage stochastic opportunity constraint planning is established by combining multi-stage planning, interval planning and stochastic opportunity constraint planning. A hybrid intelligent algorithm combining stochastic simulation, multilayer neural networks, and genetic algorithms is used to solve the model. A case study of optimal water resource allocation is used to verify the feasibility and validity of the proposed model and algorithm. Under the traditional water resource allocation scheme, the total water supply and water shortage of Nandan County are 0.97 and 0.58 billion m3, and the water shortage rate is 37.55%. Under the optimized water resources allocation scheme of this paper, the water shortage of Nandan County is 0.05 billion m³, and the water shortage rate is 6.09%. The model of this paper’s allocation scheme can effectively meet the water demand.

This paper presents a simple bi-level multi-objective linear program (BLMOLP) with a hierarchical structure consisting of reservoir managers and several water use sectors under a multi-objective framework for the optimal allocation of limited water resources. Being the upper level decision makers (i.e., leader) in the hierarchy, the reservoir managers control the water allocation system and tend to create a balance among the competing water users thereby maximizing the total benefits to the society. On the other hand, the competing water use sectors, being the lower level decision makers (i.e., followers) in the hierarchy, aim only to maximize individual sectoral benefits. This multi-objective bi-level optimization problem can be solved using the simultaneous compromise constraint (SICCON) technique which creates a compromise between upper and lower level decision makers (DMs), and transforms the multi-objective function into a single decision-making problem. The bi-level model developed in this study has been applied to the Swat River basin in Pakistan for the optimal allocation of water resources among competing water demand sectors and different scenarios have been developed. The application of the model in this study shows that the SICCON is a simple, applicable and feasible approach to solve the BLMOLP problem. Finally, the comparisons of the model results show that the optimization model is practical and efficient when it is applied to different conditions with priorities assigned to various water users.

The rational allocation of water resources for irrigation is important to improve the efficiency in utilization of water resources and ensuring food security, but also effective control measures need to be in place for the sustainable utilization of water resources in an irrigation area. The progress of research on the rational allocation of water resources in irrigation districts both at home and abroad may be summarized in four key aspects of the policy regarding water resources management: ① The mechanism of water resource cycle and ② Transformation in irrigation district, ③ The water resources optimal allocation model and ④ The hydrological ecosystem analysis in irrigation district. Our analysis showed that there are four major problems in domestic irrigation water resources allocation: Policies for rational water resources allocation and protection are not in place, unified management mechanism of water resources is not perfect, the model for optimal water resources allocation is not practical, and the basic conditions for optimal allocation of water resources is relatively weak. In order to solve those problems in water resources allocation practice, six important aspects must be the focus in China in future research: More attention need to paid to studying the unified management policy and mechanism of water resources, studying the water resources cycle and transformation under environmental change, studying new methods for water resources carrying capacity and evaluation in irrigation districts, studying the water resources control technology in irrigation districts by hydrology ecological system, studying the technologies of real-time risk dispatching and intelligent management in irrigation districts, and finally studying the technology of coupling optimal allocation technology in irrigation districts.