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.

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.

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 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.

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.

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.

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 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 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.

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.

Managing basin-wide ecosystem services using the bankruptcy theory.

Bankrupt ecosystems are those that cannot appropriately provide all their ecosystem services. In this paper, a novel bankruptcy-based methodology is developed to manage ecosystem services. To test the applicability of the developed methodology, it is used in the Zarrinehrud river basin in Iran. First, an integrated framework is used to assess regulating, supporting, provisioning, and cultural ecosystem services of the study area under three climate change scenarios of Representative Concentration Pathway (RCP) 4.5, 6.0, and 8.5. Then, for each ecosystem service, an aggregated utility is calculated that takes into account the stakeholders' different opinions toward ecosystem services. The utilities of the ecosystem services show that the Zarrinehrud river basin is bankrupt. To manage this ecosystem, six bankruptcy methods of Adjusted Proportional, Constrained Equal Loss, Constrained Equal Award, Piniles, Talmud, and Hybrid are developed and used in the study area. In this study, the summation of ecosystem services' aggregated utilities under each management scenario is considered as an asset, and all mentioned bankruptcy methods are used to redistribute these assets to different ecosystem services. Considering aggregated utilities, redistributed utilities, and each ecosystem service's claim, two different Root Mean Square Error-based approaches are developed to find the most applicable management scenario in a bankruptcy condition. Using the mentioned approaches, management scenario 128, which is comprised of all management packages, is chosen as the best option under all climate change scenarios. This scenario includes projects such as improving cropping patterns, allocating water to the lake from new water resources, and rehabilitating irrigation and draining systems. Moreover, analyzing the results derived from different bankruptcy methods shows that the Talmud, Hybrid, and Constrained Equal Loss methods have the best performance.

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 and improving the sustainability of ecosystem services in river basins under climate change.

This paper presents a new framework for evaluating the sustainability of basin-wide ecosystem services (ESs) including provisioning, regulating, supporting, and cultural services. In this framework, the Soil and Water Assessment Tool (SWAT) and MODSIM1 models and experts' opinions are used to evaluate the ESs. To show the applicability of the proposed framework, it is applied to the Zarrinehrud river basin under three different climate change (CC) scenarios (i.e., RCP 4.5, 6.0, and 8.5) for two different time horizons (i.e., 2020-2049 and 2020-2098). This basin is the main water supplier of the largest hypersaline lake in the Middle East, Lake Urmia. In the next step, 128 water resources management (WRM) scenarios are taken into account considering the projects defined by Urmia Lake Restoration National Committee (ULRNC). All ecosystem services are evaluated considering all WRM and CC scenarios. Finally, a group COPRAS-based decision-making approach is used to determine the best WRM scenario under climate change. The results show that WRM scenario 128 is the best scenario for improving ecosystem services in the study area. This scenario includes some projects such as allocating water to the lake from new resources, rehabilitating irrigation and draining networks, and improving cropping patterns.

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.

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.

A regret-based behavioral model for shared water resources management: Application of the correlated equilibrium concept.

The competition over water use in shared water resources systems may lead to conflict. Conflict can lead to strategic behaviors with the consequence of "Tragedy of Common" in water resources. In this paper, a novel approach is proposed for the quantity and quality management of shared water resources using the Correlated Equilibrium (CE) concept. For the first time in water resources management studies, a Reinforcement Learning (RL)-based method, namely Regret Matching (RM), is proposed to simulate agents' behaviors. In the proposed methodology, an agent, which is responsible for water allocations, tries to reduce illegal water withdrawal from resources, using some non-mandatory and mandatory suggestions. This agent's objectives are leading the system towards social optimality (SO) and reaching the environmental sustainability goal. A modified RM algorithm is also developed for behavioral simulation in urban areas. The proposed methodology's applicability and efficiency are evaluated considering some criteria such as the concentration of the nitrate pollutant in groundwater, the groundwater table fluctuations, the rate of illegal water extraction from the groundwater, and the stakeholders' general satisfaction. The results of applying the methodology to the western part of the Tehran metropolitan area show its ability to deal with the water and treated wastewater allocation problems in urban areas and increase in the learning and cooperation among agents. According to the results, a meaningful decrease in nitrate concentration in the aquifer and an increase in groundwater table levels are observed. The results also indicate that the model could teach the stakeholders to act more responsibly towards protecting the environment and conserving shared water resources.

Evaluating water resources management scenarios considering the hierarchical structure of decision-makers and ecosystem services-based criteria.

This paper introduces a new methodology for evaluating water resources management scenarios considering different aspects of their hydrological ecosystem services. The temporal variations of supplied water to different demands are assessed as provisioning hydrological ecosystem services. Then, three agricultural drought management policies have been defined for each water supply-demand alternative to reduce the irrigation water and cultivated area of the agricultural demand nodes during droughts. In addition to the net primary productivity criterion (NPP) and economic profit, the ecological condition of the system has been evaluated as an ecosystem services-based criterion. To prioritize and select the best water resources management (WRM) scenario(s), a game theory-based hierarchical evidential reasoning (ER) technique with multiple decision-makers has been used. The proposed methodology has been applied to the Urmia Lake basin, which is the largest saline lake in the Middle East. The grade-based values of the criteria have been used to compare the WRM scenarios. The results show that a scenario that includes supplying 100% of the lake's water demand has the highest priority. This scenario also suggests reducing the cultivated area of dominant crops and using deficit irrigation practices.

Planning for agricultural return flow allocation: application of info-gap decision theory and a nonlinear CVaR-based optimization model.

A new methodology is proposed for sizing the required infrastructures for water and waste load allocation in river systems receiving return flow from agricultural networks. A nonlinear optimization model with a constraint based on conditional value at risk (CVaR) is developed to provide water and waste load allocation policies. The CVaR-based constraint limits the probabilistic losses due to existing uncertainties in available surface water. The deep uncertainties of return flow simulation model parameters, which have significant impacts on the simulated quantity and quality of agricultural return flows, are handled by using the info-gap theory. Total dissolved solid (TDS) is selected as water quality indicator and diverting a fraction of return flows to evaporation ponds is considered to control the TDS load of agricultural waste load dischargers. Quantity and TDS load of agricultural return flows over a 1-year cultivation period are simulated by using a calibrated SWAP agro-hydrological model. The results of many runs of SWAP model for different combinations of important uncertain parameters in their ranges of variations provide some response (impact) matrixes which are used in optimization model. The applicability of the proposed methodology is illustrated by applying it to the PayePol region in the Karkheh River catchment, southwest Iran. The selected strategy for water and waste load allocation in the study area is expected to provide total annual benefit of 48.64 million US dollars, while 7.84 million m3 of total return flow should be diverted to evaporation ponds. The results support the effectiveness of the methodology in incorporating existing deep uncertainties associated with agricultural water and waste load allocation problems.

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.