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Optimum irrigation programming at network level is important not only for maximum yield and benefit from the system but also for sustainable use of constrained resources. The subject of this investigation is to devise a method which enables optimum water allocation in irrigation networks, to apply it to a real system, and to analyze the results. In the first step, the irrigation district was divided into the most suitable water allocation zones considering the hydraulic properties of the canals. Next, alternative system rotation periods were defined in accordance with the properties of the research area, the hydraulic infrastructure of the irrigation network and the crop pattern. In this process, the model was run for five different water allocation strategies. The model was applied to the command area of Sarıkız Irrigation Association in the Ahmetli Regulator Right Bank Irrigation System in the Gediz Basin. Therefore, irrigation programs were prepared for the crop pattern, which receives water from 45 tertiary canals of the Y9 secondary. The irrigation time allocated to each tertiary canal and the amount of irrigation water were evaluated, together with the water shortage levels which occurred in these tertiary canals. The results indicated that the model defined the optimum system rotation period, the borders and the sizes of the most suitable water allocation zones, and the most suitable irrigation programs under the prevailing conditions. The proposed model can provide an insight for decision makers as a decision support tool.

Integrated and sustainable river basin management requires accurate assessment and optimized allocation of available water resources, considering the impacts of climate and anthropogenic changes. It can benefit from coupled modeling frameworks combining simulation models and optimization algorithms (OAs). This article reviews simulation-based optimization (SbO) techniques developed for integrated water resources management (IWRM) classified into four distinct categories: i) coupled Simulation-only Models (SM-o) and Simulation Models with water allocation skill (SM-wa); ii) coupled Simulation Models with water allocation skill (SM-wa) and OAs; iii) coupled SM-o and OAs, and iv) simultaneous coupling of SM-o, SM-wa, and OAs. These simulation-based optimization frameworks are constructed using various coupling strategies—isolated, loose, tight, and integrated—based on the required level of data exchange and interactions between model components. The first category (SM-o—SM-wa) is predominantly based on applying offline/isolated coupling methods, whereas subsequent categories witness the adoption of loose and tight coupling approaches. The findings of this review underscore the value of SbO frameworks in promoting comprehensive and sustainable management of water resources capable of addressing single-period and multi-period allocations, as well as optimizing reservoir operation policies within a single framework. Additionally, this review can be a valuable resource for researchers, modelers, and policymakers in selecting suitable simulation models, OAs, and coupling methods for effective decision-making in IWRM.

Hydrological restoration of wetlands has become a critical pressing issue in environmental preservation due to climate change. This study seeks to develop a novel methodology to identify which type of water resources available are the most appropriate for restoring a particular wetland, considering a holistic perspective based on the triple bottom line (TBL) assessment, which is a logical framework for identifying and integrating social, environmental, and economic factors into decision-making processes. The elicitation was addressed through a comprehensive holistic index using analytic hierarchy process for ranking TBL dimensions and drivers. This new hybrid technique was applied for elaborating sustainable rules of water allocation to restore the wetlands of the Tablas de Daimiel National Park, located in central Spain. The environmental dimension was analyzed using six drivers: the synergistic use of infrastructures, the water resources location, the wastewater reuse, the energy consumption, the landscape degradation, and the impact on water resources. The social dimension was evaluated measuring three drivers: community acceptance, political acceptance, and market acceptance. And finally, the economic dimension was assessed through the expropriation of land costs, the infrastructure costs, the maintenance costs, and opportunity costs associated. These drivers guarantee traceability and transparency in the elicitation process, becoming a novel allocation framework to support policy makers in wetland conservation. Applying the proposed methodology, Tagus-Segura interbasin water transfer is the best ranked option (83.13%), closely followed by pumping well areas (79.12 and 78.24%) and wastewater recycling plants (74.34 and 68.26%). The unique holistic index proposed is a transparent and traceable decision support tool to address water allocation in wetland restoration.

There is increasing concern about groundwater scarcity, environmental problems, and its socio-economic consequences. Therefore, questions have been raised about new approaches to allocating groundwater resources. This study aims to shed new light on these debates through a modeling effort to be applied to coastal groundwater management (CGM). The purpose of this study is to develop an approach for modeling coastal groundwater allocation based on equity, social welfare, and economic benefit efficiency principles. In this approach, the Gini coefficient and a modified Bentham-Rawls Criterion have been applied to increase water allocation equity in water-using sectors and subareas. Moreover, both economic development and water management have been taken into account to improve economic benefit efficiency. Four benchmark CGM problems with different degrees of difficulty have been selected to examine a variety of objective functions and constraints in this approach. The results demonstrate that this approach can be applied to assess various situations in real-case and large-scale problems. Economic efficiency loss levels have a significant influence on agricultural and industrial water allocation equity and the maximum economic efficiency loss rate. In the agricultural and industrial sectors, a greater loss tolerance threshold means less economic benefit and more equal groundwater allocation. To improve groundwater allocation efficiency, some socio-economic requirements should be considered in CGM. The results indicate that water authorities need to strike a balance between equity and economic benefit in groundwater allocation to adapt to groundwater allocation problems.

Water is becoming a scarce resource in many parts of the world, leading to increased competition amongst water users. Optimized water allocation is increasingly important to balance the growing demand for water and the limited supply of accessible clean water. The literature on water allocation schemes and decision support systems, developed for application in specific water management areas or watersheds, was critically reviewed. Although the literature is rich in studies on the application of a broad range of water allocation schemes, there is a lack of information available on the methodology and process of selecting the most applicable scheme that balances the local realities and requirements of stakeholders while considering the local context with regard to the economic, social and environmental impact of water usage. In this article, a framework is presented that water management practitioners can use to select applicable water allocation planning schemes and associated decision support systems based on the characteristics and requirements of the specific water management situation. The framework was used to analyse the water supply situation in South Africa (SA), taking broader factors into account. Based on this, a generic conceptualized water allocation planning and decision support framework for a typical SA water management area is proposed.

To mitigate the unfavorable effects of excessive water resources consumption, mainly induced by poor performance of irrigation practices, efficient water resource management strategies are required. In response to this need, we have, in an innovative way, enhanced the water resources management (WRM) strategies by both considering the regional conditions with the graph model for a conflict resolution (GMCR) decision support system, and linking the irrigation concept and water resources allocation theory to develop a coupled WRM simulation–optimization model. Typically, implementation of the modified WRM strategies may cause local conflicts because of losing the original water rights. To improve the current irrigation water allocation system with the minimum objections, the hypergame theory was utilized to enhance the capabilities of traditional GMCR models by including the parties’ misunderstandings in the negotiation process and assessing the partial perceptions rather than crisp options. Moreover, by dynamic monitoring of available water resources and water consumption patterns, a WRM simulation model was developed, which is applicable in real agricultural conditions of multi-agricultural zones with multi-crop and intercropping systems and variable water supply sources. The genetic algorithm was utilized to allocate the water resources and determine optimal WRM strategies with the lowest irrigation water shortage. The efficiency of the proposed framework was assessed in conventional agricultural zones in Oman. The recommended strategies not only address local conflicts during the implementation of optimal WRM strategies, but also demonstrate significant potential to reduce the water shortage as a serious environmental concern.

The escalating world population has led to a drastic increase in water demand in the municipal and drinking water, agriculture and industry sectors. This situation necessitates application of effective measures for the optimal and efficient management of water resources. With this respect, a two-objective socioeconomic model (aimed at job creation) has been presented in this study for the optimum allocation of water resources to industry, agriculture and municipal water sectors. In the agriculture sector, the production function of each product has been determined and then, based on the production functions, areas under cultivation, product yield and the income obtained from each product, the combined objective function has been specified. In the industry sector, since water demand is a function of the amount of produced products, price of supplied water and the price of other supplies, the demand function of this sector was determined regionally. Also, considering the existing necessity in meeting the municipal water requirement, the total amount of water needed by this sector was fully allocated. Then by using two meta-heuristic algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO), the objective functions were maximized and the water resources were optimally allocated between agriculture and industry sectors and the results were compared. Ultimately, comparing the results gained by PSO and GA algorithms, PSO with an economic and profit growth of 54 % and a 13 % rise in employment relative to the base condition, turned out to be more efficient in this application.

Optimal water resource allocation can go some way to overcoming water deficiencies; however, its achievement is complex due to conflicting hierarchies and uncertainties, such as water availability (WA) and water demand (WD). This study develops a robust water withdrawal scheme for drought regions that can balance the trade-offs between the sub-areas and water use participants, ensure sustainable regional system development, and guarantee robust solutions for future uncertainties. A bi-level affinely adjustable robust counterpart (AARC) programming framework was developed in which the regional authority as the leader allocates water to the sub-areas to maximize the intra- and intergenerational equity, and the sub-areas as the followers allocate water to their respective water departments to maximize their economic benefits and minimize water shortages. A case study from Neijiang, China, is given to illustrate the applicability and feasibility of this framework. The novelty of this study is to propose a sustainable bi-level AARC regional water allocation framework which integrates intra- and inter-generational equity of regional water use and priority rules reflected by goal preference programming between water departments under uncertainties of WA and WD simultaneously in water deficient regions.

This paper presents the evaluation and management of water resources at the basin scale, focusing on small reservoirs. Due to a lack of knowledge on these untapped resources, a semiautomatic procedure for their surface area estimation is presented. Multispectral images from the Pleiades satellite were used to extract water bodies from a combination of different bands. In particular, the normalized difference water index (NDWI) was used to evaluate the presence or absence of water. This methodology was applied in a test area located in the upper Tiber River Basin. The performance for identifying and analysing small reservoirs was determined by comparing the satellite information with a reference database. The potential volume available from the investigated small reservoirs was compared with the water deficit derived from a decision support simulation model at the basin/subbasin scale, which studies water allocation for multipurpose uses. Five irrigation districts were analysed. For three of these districts, more than 60% of the annual deficit can be balanced with the volume stored in small reservoirs.To date, water scarcity conditions have been increasingly frequent, so resilience in water resource management is a key requirement. Therefore, it is essential to evaluate the recovery and reuse of small reservoirs to strongly support water use, particularly irrigation and environmental uses.

Efficient water allocation is one of the most prominent issues in water resources management. In this research, a two-stage interval-parameter stochastic fuzzy programming with type 2 membership functions was used to allocate water resources optimally to different users under uncertainty. This method can handle uncertainties expressed as probability distributions, discrete intervals, and fuzzy sets. The model considers treated wastewater as an allocable water resource in a scenario, in addition to water that is extracted from wellheads, springs, and qanats. Moreover, the loss rate of water during distribution, surplus water in the reservoir in the previous and the next period, and treated wastewater parameters have been incorporated into the model. This model was applied to a case study of water resources allocation within a multi-user and multi-reservoir context in the Zarand region of Kerman, Iran. The results indicate that reasonable solutions have been generated, in the form of interval and fuzzy information under different scenarios that could help managers provide optimal water resources allocation plans. The results also demonstrate that establishing a wastewater treatment station increases the system net benefits, surplus water in wellheads for the next period, and system reliability (level of satisfying the fuzzy goal and constraints), and decreases encountering water shortages.