A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting.

Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models' parameters on their performance.

Adaptive reservoir operation considering water quantity and quality objectives: Application of parallel cellular automata and sub-seasonal streamflow forecasts.

This paper presents a new framework for the adaptive reservoir operation considering water quantity and quality objectives. In this framework, using the European Centre for Medium-Range Weather Forecasts (ECMWF) database, the monthly precipitation forecasts, with up to 6-month lead time, are downscaled and bias corrected. The rainfall forecasts are used as inputs to a rainfall-runoff simulation model to predict sub-seasonal inflows to reservoir. The water storage at the end of a short-term planning horizon (e.g. 6 months) is obtained from some probabilistic optimal reservoir storage volume curves, which are developed using a long-term reservoir operation optimization model. The adaptive optimization model is linked with the CE-QUAL-W2 water quality simulation model to assess the quality of outflow from each gate as well as the in-reservoir water quality. At the first of each month, the inflow forecasts for the coming months are updated and operating policies for each gate are revised. To tackle the computational burden of the adaptive simulation-optimization model, it is run using Parallel Cellular Automata with Local Search (PCA-LS) optimization algorithm. To evaluate the applicability and efficiency of the framework, it is applied to the Karkheh dam, which is the largest reservoir in Iran. By comparing the run times of the PCA-LS and the Non-dominated Sorting Genetic Algorithms II (NSGA-II), it is shown that the computational time of PCA-LS is 95 % less than NSGA-II. According to the results, the difference between the objective function of the proposed adaptive optimization model and a perfect model, which uses the observed inflow data, is only 1.68 %. It shows the appropriate accuracy of the adaptive model and justifies using the proposed framework for the adaptive operation of reservoirs considering water quantity and quality objectives.

Extending the theory of planned behavior to predict the behavior of farmers in choosing low-water-intensive medicinal plants.

Water scarcity has become a serious challenge in many parts of the world due to increasing demands and the impacts of climate change. The agriculture sector globally accounts for a major portion of water consumption, yet it also holds substantial potential for water conservation. Among the most effective ways to conserve water is to cultivate low-water-demanding crops, such as medicinal plants (MPs), instead of water-demanding crops (WDC). However, the voluntary participation of farmers, largely influenced by socio-psychological drivers, is crucial for successfully implementing most water conservation programs and needs to be addressed. Therefore, the main objectives of this paper were: (1) to identify the determinants that explain farmers' intention and behavior in cultivating MPs instead of WDC; and (2) to examine the effectiveness and performance of an extended version of the theory of planned behavior (TPB) in predicting farmers' intention and behavior toward cultivating MPs by innovatively incorporating four new variables into the original TPB model: perceived barriers, moral norms, compatibility, and relative advantage. The applicability of the theoretical framework was evaluated in the Sojasroud Plain, Zanjan province, Iran. The results of the structural equation modeling revealed that: (1) farmers' intention to cultivate MPs instead of WDC is significantly influenced by perceived barriers, moral norms, subjective norms, and perceived behavior control (the strongest predictor); and (2) farmers' behavior in cultivating MPs instead of WDC is predicted by relative advantage, compatibility, and intention (the most prominent determinant). The R2 values for predicting intention and behavior were 55% and 53%, respectively. Based on the results, some practical policies were proposed to increase the cultivation of MPs in the study area.

A market-based mechanism for long-term groundwater management using remotely sensed data.

Groundwater markets improve the agricultural economy by transferring water entitlements from low-efficient users to high-efficient ones to maximize productivity. Aiming at developing an efficient groundwater market, the environmental effects of the market mechanism should be assessed, and a reliable method for monitoring water consumption needs to be employed. Toward this end, this paper proposes three annual smart groundwater market mechanisms to maximize water net benefits, minimize groundwater withdrawal, and precisely measure water consumption in agricultural fields. To guarantee the aquifer's safe yield in each mechanism, a groundwater simulation model (i.e., Groundwater Modeling System (GMS)) is used to control groundwater table drawdown at the end of the planning horizon. In addition, the fields' evapotranspiration (ET) is estimated using Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapo Transpiration at high Resolution with Internalized Calibration (METRIC) algorithm to measure the net groundwater consumption during the market. In this regard, we evaluated the algorithms' performances using observed data from a local lysimeter. They are applied to the Nough plain in Iran to assess the effectiveness of the proposed market framework. The findings illustrate their efficiency in recovering approximately 80% (23.33 million cubic meters (MCM)) of groundwater loss due to overexploitation in the study area and increasing the users' annual benefits by 10.6% compared to the non-market condition. In addition, results imply that the METRIC model approximates daily crop ET with a higher accuracy level than the SEBAL model with RMSE, MAE, and Percentage Error of 0.37 mm/day, 0.32 mm/day, and 14.92%, respectively. This research revealed that the proposed market framework is a powerful tool for reallocating water entitlements and increasing water productivity in arid and semi-arid regions.

A real-time market-based framework for basin-wide surface water pricing and allocation considering the available water uncertainty.

Market-based approaches are increasingly considered reallocating instruments that put water consumption at its highest economic value among competing water users. Setting up a water market can have a lot of environmental, social, economic, and legal complexities. One of the main issues is the uncertain nature of the available water, which can cause the failure of markets, especially during drought conditions. Therefore, there is a need for market mechanisms to consider and reduce the adverse impacts of available water uncertainty on market outcomes. Accordingly, this paper proposes a new real-time seasonal smart water market framework for basin-wide surface water pricing and allocation. The framework uses the results of the reservoir water allocation optimization models and ANFIS-based monthly river discharge forecasts to better assist the water users with their bidding. The market manager uses updated available information at the beginning of each season to provide users with a more accurate understanding of available water to adjust their tradings for the rest of the year. The applicability and efficiency of the proposed framework are evaluated by applying it to the Gorganrood River basin in Iran. According to the results, the framework increased users' benefits from 721 to 1050 billion rials, which is more efficient than an annual market. Water markets can use this framework to improve their ability to cope with the uncertainty of available water, increase their users' benefits, and encourage them to improve their efficiency. Furthermore, the proposed framework allows the decision-makers in water sectors (e.g., industrial, agricultural, etc.) to discover time and location specific water allocation and price for different water users.

Combining urban metabolism and reinforcement learning concepts for sustainable water resources management: A nexus approach.

Modeling Water-Energy-Food (WEF) nexus is necessary for integrated water resources management (IWRM), especially in urban areas. This paper presents a new urban water metabolism-based methodology for WEF nexus modeling and management. A behavioral simulation model is used to incorporate the characteristics of stakeholders in an urban area. Modified versions of the Borda count, Copeland rule, and fallback bargaining procedures are implemented to choose the socially acceptable management scenarios. Finally, the selected scenarios' effectiveness is evaluated using the fairness and total utility indices. The applicability of the proposed methodology is evaluated by applying it to the Kan River basin, Tehran, Iran, which is suffering from some water and environmental issues. The considered management scenarios include adding new water sources, leakage control plans, using rubber dams for enhancing groundwater recharge, revising water allocation priorities, and developing semi-centralized or decentralized reuse strategies for reclaimed wastewater. Results illustrate that considering different fluxes (i.e., water quantity, pollutants, energy, greenhouse gases (GHG), and materials) is as important as incorporating the social characteristics of stakeholders. Simulating the socially acceptable scenario shows that the aquifer's average water level improves by 3 (m), and its average nitrate concentration reduces by 16 (mg/L) in comparison with the business as usual (BAU) scenario. In addition, by implementing different water reuse strategies, which are energy-intensive, total energy consumption is reduced by 5% due to less groundwater pumping. Also, the selected scenario decreases GHG emissions by 18% and increases the sequestrated carbon dioxide by 20%. In conclusion, the proposed decision support tool can provide policies for sustainable water resources management considering water quality and quantity issues, energy usage, and GHG emission.

An uncertainty-based framework for evaluating and improving the long-term resilience of lakes under anthropogenic droughts.

This paper introduces a new framework to evaluate the resilience of lakes under climatic and anthropogenic droughts. The proposed hierarchical structure of criteria for assessing lake's resilience has four levels. The first level includes several indices such as long-term resilience, reliability, and implementation cost. In the second to fourth levels, four main resilience-based criteria (i.e. robustness, resourcefulness, redundancy, and rapidity) and some qualitative and quantitative sub-criteria are defined considering the factors affecting the ecological condition of lakes. To quantify the time series of the sub-criteria, a coupled SWAT-MODSIM-based simulation model has been applied. Also, the values of criteria and sub-criteria have been aggregated using the Evidential Reasoning (ER) approach. After estimating the annual resilience time series, three resilience indices, namely the recovery time (Tr), loss of resilience (LOR), and final resilience (Resf), have been calculated. The normalized values of these indices and reliability criteria have been aggregated to evaluate the overall performance of lake restoration scenarios. To show the applicability of the proposed methodology, the Zarrinehrud river basin and Lake Urmia have been selected as the case study. As one of the largest hypersaline lakes globally, Lake Urmia suffers from drastic changes in its water body and a high level of salinization. Also, the Zarrinehrud river basin, located in the southeastern of Urmia Lake, is the most significant sub-basin of the lake and is responsible for supplying 41% of the total annual inflow of the lake. The restoration scenarios of Lake Urmia have been assessed from 2019 to 2049. Eventually, the most effective scenario, which has an average overall performance of 0.72, the implementation cost of 17.1 million dollars, and the uncertainty band of 0.05, has been selected.

A distributed constraint multi-agent model for water and reclaimed wastewater allocation in urban areas: Application of a modified ADOPT algorithm.

Distributed Constraint Optimization (DCOP)-based approaches, as the distributed version of constraint optimization, provide a framework for coordinated decision making by a team of agents. In this paper, an agent-based DCOP model is developed to allocate water and reclaimed wastewater to demands considering the conflicting interests of involved stakeholders. One of the well-known DCOP algorithms, ADOPT1, is modified to incorporate an agent responsible for monitoring and conserving water resources. This new algorithm considers the social characteristics of agents and a new form of interaction between agents. For the first time in the literature, a real-world water and reclaimed wastewater allocation problem is formulated as a DCOP and solved using the Modified ADOPT (MADOPT) algorithm. To evaluate the MADOPT algorithm, it is applied to a water and reclaimed wastewater allocation problem in Tehran, Iran. The results illustrate the applicability and efficiency of the proposed methodology in dealing with large-scale multi-agent water resources systems. It is also shown that agents' selfishness and social relationships could affect their water use policies.

Evaluating the resilience of water resources management scenarios using the evidential reasoning approach: The Zarrinehrud river basin experience.

This paper introduces a new methodology for quantifying the total resilience of water resources management scenarios. The climate change impacts on water supply and demand have been investigated using a calibrated soil and water assessment tool (SWAT) and a MODSIM water allocation model. Several criteria have been defined to measure five aspects of water resources systems resilience. The first aspect defines resilience as system strength against crossing a performance threshold (reliability). In the second aspect, if the system crosses the performance threshold, the recovery rate of the system after a disturbance is evaluated. The violation from the performance threshold has been measured as the third aspect (vulnerability), which considers the failure's severity. The fourth aspect is the resilience under extreme events with unknown occurrence probability, which includes four sub-criteria, namely rapidity, robustness, resourcefulness, and redundancy (4 R). Finally, the fifth criterion considers the ecological condition of the system (ecological index). To compare water resources management scenarios (alternatives), an analytical evidential reasoning-based (ER) approach has been used. To show the applicability of the proposed methodology, it has been applied to the Zarrinehrud river basin, which is the leading water supplier of Lake Urmia in Iran. As one of the largest saline lakes globally, this lake has been suffering from drastic desertification and salinization in the past two decades. The grade-based results of the performance criteria are synthesized into a grade-based total resilience criterion to facilitate the comparison of water resources management scenarios. It is shown that a scenario which results in 40% reduction in agricultural water demand until 2023 has the highest resilience and an acceptable construction and operational cost.

Developing a methodology for real-time trading of water withdrawal and waste load discharge permits in rivers.

In this paper, a new methodology is proposed for the real-time trading of water withdrawal and waste load discharge permits in agricultural areas along the rivers. Total Dissolved Solids (TDS) is chosen as an indicator of river water quality and the TDS load that agricultural water users discharge to the river are controlled by storing a part of return flows in some evaporation ponds. Available surface water withdrawal and waste load discharge permits are determined using a non-linear multi-objective optimization model. Total available permits are then fairly reallocated among agricultural water users, proportional to their arable lands. Water users can trade their water withdrawal and waste load discharge permits simultaneously, in a bilateral, step by step framework, which takes advantage of differences in their water use efficiencies and agricultural return flow rates. A trade that would take place at each time step results in either more benefit or less diverted return flow. The Nucleolus cooperative game is used to redistribute the benefits generated through trades in different time steps. The proposed methodology is applied to PayePol region in the Karkheh River catchment, southwest Iran. Predicting that 1922.7 Million Cubic Meters (MCM) of annual flow is available to agricultural lands at the beginning of the cultivation year, the real-time optimization model estimates the total annual benefit to reach 46.07 million US Dollars (USD), which requires 6.31 MCM of return flow to be diverted to the evaporation ponds. Fair reallocation of the permits, changes these values to 35.38 million USD and 13.69 MCM, respectively. Results illustrate the effectiveness of the proposed methodology in the real-time water and waste load allocation and simultaneous trading of permits.

A Bayesian maximum entropy-based methodology for optimal spatiotemporal design of groundwater monitoring networks.

This paper presents a new methodology for analyzing the spatiotemporal variability of water table levels and redesigning a groundwater level monitoring network (GLMN) using the Bayesian Maximum Entropy (BME) technique and a multi-criteria decision-making approach based on ordered weighted averaging (OWA). The spatial sampling is determined using a hexagonal gridding pattern and a new method, which is proposed to assign a removal priority number to each pre-existing station. To design temporal sampling, a new approach is also applied to consider uncertainty caused by lack of information. In this approach, different time lag values are tested by regarding another source of information, which is simulation result of a numerical groundwater flow model. Furthermore, to incorporate the existing uncertainties in available monitoring data, the flexibility of the BME interpolation technique is taken into account in applying soft data and improving the accuracy of the calculations. To examine the methodology, it is applied to the Dehgolan plain in northwestern Iran. Based on the results, a configuration of 33 monitoring stations for a regular hexagonal grid of side length 3600 m is proposed, in which the time lag between samples is equal to 5 weeks. Since the variance estimation errors of the BME method are almost identical for redesigned and existing networks, the redesigned monitoring network is more cost-effective and efficient than the existing monitoring network with 52 stations and monthly sampling frequency.

River water quality management considering agricultural return flows: application of a nonlinear two-stage stochastic fuzzy programming.

In this paper, a new fuzzy methodology is developed to optimize water and waste load allocation (WWLA) in rivers under uncertainty. An interactive two-stage stochastic fuzzy programming (ITSFP) method is utilized to handle parameter uncertainties, which are expressed as fuzzy boundary intervals. An iterative linear programming (ILP) is also used for solving the nonlinear optimization model. To accurately consider the impacts of the water and waste load allocation strategies on the river water quality, a calibrated QUAL2Kw model is linked with the WWLA optimization model. The soil, water, atmosphere, and plant (SWAP) simulation model is utilized to determine the quantity and quality of each agricultural return flow. To control pollution loads of agricultural networks, it is assumed that a part of each agricultural return flow can be diverted to an evaporation pond and also another part of it can be stored in a detention pond. In detention ponds, contaminated water is exposed to solar radiation for disinfecting pathogens. Results of applying the proposed methodology to the Dez River system in the southwestern region of Iran illustrate its effectiveness and applicability for water and waste load allocation in rivers. In the planning phase, this methodology can be used for estimating the capacities of return flow diversion system and evaporation and detention ponds.

Water and waste load allocation in rivers with emphasis on agricultural return flows: application of fractional factorial analysis.

In this paper, a new methodology is developed to handle parameter and input uncertainties in water and waste load allocation (WWLA) in rivers by using factorial interval optimization and the Soil, Water, Atmosphere, and Plant (SWAP) simulation model. A fractional factorial analysis is utilized to provide detailed effects of uncertain parameters and their interaction on the optimization model outputs. The number of required optimizations in a fractional factorial analysis can be much less than a complete sensitivity analysis. The most important uncertain inputs and parameters can be also selected using a fractional factorial analysis. The uncertainty of the selected inputs and parameters should be incorporated real time water and waste load allocation. The proposed methodology utilizes the SWAP simulation model to estimate the quantity and quality of each agricultural return flow based on the allocated water quantity and quality. In order to control the pollution loads of agricultural dischargers, it is assumed that a part of their return flows can be diverted to evaporation ponds. Results of applying the methodology to the Dez River system in the southwestern part of Iran show its effectiveness and applicability for simultaneous water and waste load allocation in rivers. It is shown that in our case study, the number of required optimizations in the fractional factorial analysis can be reduced from 64 to 16. Analysis of the interactive effects of uncertainties indicates that in a low flow condition, the upstream water quality would have a significant effect on the total benefit of the system.

Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study.

In this paper, a new methodology is developed for integrated allocation of water and waste-loads in river basins utilizing a fuzzy transformation method (FTM). The fuzzy transformation method is used to incorporate the existing uncertainties in model inputs. In the proposed methodology, the FTM, as a simulation model, is utilized in an optimization framework for constructing a fuzzy water and waste-loads allocation model. In addition, economic as well as environmental impacts of water allocation to different water users are considered. For equitable water and waste load allocation, all possible coalition of water users are considered and total benefit of each coalition, which is a fuzzy number, is reallocated to water users who are participating in the coalition. The fuzzy cost savings are reallocated using a fuzzy nucleolus cooperative game and the FTM. As a case study, the Dez River system in south-west of Iran is modeled and analyzed using the methodology developed here. The results show the effectiveness of the methodology in optimal water and waste-loads allocations under uncertainty.

Optimal redesign of coastal groundwater quality monitoring networks under uncertainty: application of the theory of belief functions.

This paper presents a new methodology for the optimal redesign of water quality monitoring networks in coastal aquifers. The GALDIT index is used to evaluate the extent and magnitude of seawater intrusion (SWI) in coastal aquifers. The weights of the GALDIT parameters are optimized using the genetic algorithm (GA). A SEAWAT-based simulation model, a spatiotemporal Kriging interpolation technique, and an artificial neural network surrogate model are then implemented to simulate total dissolved solids (TDS) concentration in coastal aquifers. To obtain more precise estimations, an ensemble meta-model is developed using the Dempster-Shafer's belief function theory (D-ST) to combine the results obtained from the three individual simulation models. The combined meta-model is then used for calculating more precise TDS concentration. Some plausible scenarios are defined for variation of water elevation and water salinity at the coastline to incorporate uncertainty through the concept of value of information (VOI). Finally, the potential wells with the highest values of information are taken into consideration to redesign coastal groundwater quality monitoring network under uncertainty. The performance of the proposed methodology is evaluated by applying it to the Qom-Kahak aquifer, north-central Iran, which is threatened by SWI. At first, the individual and ensemble simulation models are developed and validated. Then, several scenarios are defined regarding the plausible changes in TDS concentration and water level at the coastline. In the next step, the scenarios, the GALDIT-GA vulnerability map, and the VOI concept are used for redesigning the existing monitoring network. The results illustrate that the revised groundwater quality monitoring network containing 10 new sampling locations outperforms the existing one based on the VOI criterion.

A resilience-based robustness evaluation framework for sustainable urban flood management under uncertainty.

Urban drainage systems (UDSs) may experience failure encountering uncertain future conditions. These uncertainties arise from internal and external threats such as sedimentation, blockage, and climate change. In this paper, a new resilience-based framework is proposed to assess the robustness of urban flood management strategies under some distinct future scenarios. The robustness values of flood management strategies are evaluated by considering reliability, resiliency, and socio-ecological resilience criteria. The socio-ecologic resilience criteria are proposed considering the seven principles of building resilience proposed by Biggs et al. (2012). The evidential reasoning (ER) approach and the regret theory are utilized to calculate the total robustness of the flood management strategies. In this framework, the non-dominated sorting genetic algorithms III (NSGA-III) optimization model and the storm water management model (SWMM) simulation model are linked and run to quantify the criteria. The novelty of this paper lies in presenting a new framework to increase the sustainability and resilience of cities against floods considering the deep uncertainties in the main economic, social, and hydrological factors. This methodology provides policies for redesigning and sustainable operation of urban infrastructures to deal with floods. To evaluate the applicability and efficiency of the framework, it is applied to the East drainage catchment of the Tehran metropolitan area in Iran. The results show that real-time operation of existing flood detention reservoirs, along with implementing five new relief tunnels with a construction cost of 37.1 million dollars, is the most robust non-dominated strategy for flood management in the study area. Comparing the results of the proposed framework with those of a traditional framework shows that it can increase the robustness value by about 40% with the same implementation cost.

A resilience-based framework for evaluating the carrying capacity of water and environmental resources under the climate change.

This paper proposes a new framework for evaluating water and environmental resources carrying capacity (WERCC) based on the concept of resilience under uncertainty. First, several quantitative and qualitative criteria based on the seven principles of resilience and the Pressure-Support-State (PSS) framework are defined to incorporate the positive and negative impacts of human interventions and natural factors on water resources and the environment. The resilience principles include redundancy and diversity, managing connectivity, managing slow variables and their feedbacks, fostering complex adaptive system (CAS) thinking, encouraging learning, broadening participation, and promoting polycentric governance. After evaluating the values of the criteria and sub-criteria using a two-point evidential reasoning (TPER) approach and considering the existing uncertainties, the monthly time series of WERCC with uncertainty bands are calculated. The proposed methodology is then used to evaluate the WERCC in the Zarrinehrud river basin in Iran for a given historical period (1991-2012), and the period of 2020 to 2049 under different climate change scenarios. The results of this analysis demonstrate the inadequacy of the WERCC during the historical period and indicate that the continuation of the existing trend (base scenario, MSC0) will cause many environmental issues. Hence, several water and environmental resources management (WERM) scenarios are proposed to enhance the WERCC. These scenarios are evaluated using a multi-agent-multi-criteria decision-making method to identify the preferable WERM scenario (MSC12356). This scenario, which encompasses various projects (e.g., development and enhancement of water transfer networks and upgrading cultivation methods), improves the average value of the WERCC by 26 %. The results of the proposed methodology are compared with those of a traditional decision-making method, which considers three criteria of average WERCC, the pressure-support index, and the implementation cost. The results demonstrate that the multi-agent-multi-criteria decision-making approach provides a more cost-effective management scenario, with 30 % less cost, leading to only 3 % less carrying capacity.

Application of multi-agent decision-making methods in hydrological ecosystem services management.

In this paper, a methodology is presented for managing hydrological ecosystem services by taking into account the hierarchy of stakeholders involved in the decision-making process. With this in mind, a water allocation model is first used for allocating water resources to demands. Then, several ecosystem services (ESs)-based criteria are defined to evaluate hydrological ESs of water resources management policies. A set of water and environmental resources management strategies (alternatives) are defined for decision-makers, and several drought management strategies are determined to decrease the area of key crops and water demands of agricultural nodes. To model a multi-agent multi-criteria decision-making problem for managing hydrological ESs, three main steps are considered as follows:•Different ES-based criteria (i.e., economic profit, NPP, and ecological index) are defined, and their grade-based values are estimated.•Several strategies are defined for stakeholders at different levels.•A recursive evidential reasoning (ER) approach, which considers a hierarchical structure for decision-makers and a leader-follower game, is used to select the best strategy for each decision-maker.The applicability and efficiency of the methodology are illustrated by applying it to a real-world case study. The methodology is general and can be easily applied to other study areas.

A multi-agent decision-making framework for evaluating water and environmental resources management scenarios under climate change.

This paper introduces a hierarchical multi-agent decision-making framework for Water and Environmental Resources Management Scenarios (WERMSs) under uncertain conditions of climate change and complex agent characteristics. The proposed framework utilizes three Game Theory concepts: the Stackelberg, Bayesian (Incomplete), and Imperfect games, in order to incorporate the hierarchical structure of the agents and the temporal distribution and accuracy of information between them. The methodology is applied to the Zarrinehroud River Basin (ZRB), the largest hypersaline lake in the Middle East. The area of the lake has decreased dramatically (about 50 %) during past decades causing various environmental, social, and economic problems. WERMSs were evaluated using qualitative and quantitative hydrological, social, economic, and ecological criteria under different climate change scenarios. The proposed methodology provides equilibriums in the decision-making process while considering different climate change scenarios. Applying the selected WERM results in an accumulated value of 2995 million m3 of water flow to the lake until 2049. Moreover, the lake's elevation reaches a new level of 1272.6 m above sea level at the end of the following 30 years, compared to the elevation of 1271.3 at the beginning of the evaluation period.

River water quality management under incomplete information: application of an N-person iterated signaling game.

One of the important issues in river quality management is to provide pollution control strategies which are acceptable for all stakeholders. When there is only one water quality checkpoint in a reach of a river which receives pollution loads of several dischargers and dischargers are penalized for any water quality violation, the game theory can be used for modeling the natural process of bargaining among load dischargers considering the assimilative capacity of a river. There are also some types of uncertainties in river water quality management which should be incorporated throughout the bargaining process. Signaling games can be utilized for modeling the bargaining among dischargers and developing perfect Bayesian equilibrium (PBE) strategies for pollution control. In this paper, a new methodology called N-person iterated signaling game is developed for river quality management considering the existing uncertainties in pollution loads of dischargers. The methodology can provide the stable PBE waste load allocation strategies. The practical utility of the proposed methodology is illustrated by applying it to a reach of the Zarjub River in Iran. This reach includes seven pollution load dischargers.