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Rapid urbanization has increased impervious areas, leading to a higher flood hazard across cities worldwide. Low Impact Development (LID) practices have shown efficacy in reducing urban runoff; nevertheless, choosing the best combinations in terms of implementation cost and performance is of great importance. The present study introduces a framework based on green infrastructure, multi-objective optimization, and decision support tools to determine the most cost-effective LID solutions. The Storm Water Management Model (SWMM) was employed for rainfall-runoff and hydraulic modeling in Region 1, District 11 of Tehran, Iran. Six scenarios of different combinations of LID practices were developed. The system for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) was used to optimize and evaluate each scenario. The selected solutions were imported to the SWMM to evaluate the stormwater system performance. Then, two multi criteria decision making (MCDM) models, including TOPSIS and COPRAS, were employed to rank the scenarios based on four technical and economic criteria. Results showed that scenario 4, consisting of rain barrels, porous pavements, and vegetated swales, had the best performance under TOPSIS with a 7.68 million USD and reduced the runoff volume and peak flow by 20.77% and 19.2%, respectively. However, Under the COPRAS method, Scenario 2 with a combination of rain barrels, bio-retention cells, and vegetated swales showed higher performance than the other scenarios with 3.25 million USD and led to a 15% reduction in the runoff volume and 4.30% in the peak flow. The COPRAS method was more sensitive to cost weights and chose the most economical scenario as the ideal. However, Scenario 4 concluded to be more feasible due to spatial limitations in the study area. The proposed SWMM—SUSTAIN—MCDM framework could be helpful to decision-makers in the design, performance evaluation, cost estimation, and selection of optimal scenarios.

Assessing the status of water resources is essential for long-term planning related to water and many other needs of a country. According to climate reports, climate change is on the rise in all parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects.

Urban treated wastewater can be used as a sustainable water supply alternative; however, it contains chemical and biochemical contaminants and poses risks to the affected area. This study aims to assess the risk of treated wastewater application in four sectors: agriculture, landscape irrigation, groundwater artificial recharge, and industry, considering environmental, social, economic, and technical criteria using the novel Hybrid Bayesian Network. The risk of using 27 Iran’s wastewater treatment plants’ effluent in the four sectors were calculated and the Bayesian Networks results demonstrate that four risk models were well trained and were capable of predicting the risks with acceptable performance. Using the trained risk frameworks, the risk of applying the treated wastewater of Tabriz metropolitan wastewater treatment plant in Iran for various sectors was obtained. The results showed that the risk of using the treated wastewater in agriculture, landscape irrigation, artificial recharge, and industry was 26.9%, 17.7%, 18.7%, and 11.9%, respectively. The developed Hybrid Bayesian Network framework in this study can help water resources managers for risk-based prioritizing the effluent reuse scenarios with high performance.

An increase in stormwater frequency following the rapid development of urbanization has drawn attention to the mitigating strategies in recent decades. For the first time, the present study aims to conduct a local rehabilitation in stormwater collecting systems by (i) detecting the critical nodes along with the canal network and (ii) redesigning the critical canal reaches using ant colony optimization (ACO) to create maximum capacity for flood discharge with minimum reconstruction cost while considering the probability of exceedance of the flood as a constraint. Hence, using the SWMM model, the flow in the collection system was simulated, and the inundation points in the study area in the eastern Tehran metropolis were determined. After determining the critical points, the hydraulic stimulation model for the selected canal flows was developed using HEC-RAS software to accurately simulate each critical bridge's flow. Then, the optimal parameters for the canal bed width and canal depth were obtained using ACO and defining a probability objective function using the flood probability exceedance as the redesign constraint. The results from the optimizer were compared with those of the LINGO nonlinear model. Finally, the operational performance of the redesigned system was evaluated using the optimal selected parameters. The results showed that in redesigning the studied canals, the two widening and deepening options are needed to obtain a discharge with sufficient flow capacity in various return periods (RPs). The optimization results for the first to third critical sections for a design discharge with a 100-year RPs showed that the calculated cost was 19.765(*106), 13.327(*106), and 43.139(*106) IR rials (1 USD = 202000 IRR), respectively. For the selected sections, the optimal bed width is 6.97, 8.97, and 10.93 m, and the optimal depth is 3.68, 4.81, and 4.04 m, respectively. The results indicate that the local modification in the eastern flood control canal adequately improved inundation problem reduction in various RPs – i.e., for a 10-year RP, the number of node flooding dropped from 4 to zero, the inundated area from 17% to zero, and the overflow volume from (10–45) to zero. It also reduced overflow volume from (30–65), (43–74), and (70–92) in the status quo to (4–12), (11–27), and (24–36) percent for precipitations with 25, 50 and 100-year RPs, respectively. HIGHLIGHTS Redesigning critical segments in stormwater collection system was conducted.; SWMM model was performed to identify the probable critical points along with the system.; A preprocessing analysis included field visiting, brainstorming sessions and hydraulic modelling with Hec-Ras was conducted.; The optimal redesigning of the critical points was conducted based on the flood exceedance probability via ACO and LINGO.

To identify and assess the impact of various hazards threaten agriculture water supply systems, the development of a risk analysis framework is inevitable in promoting sustainable agricultural development. This study aims to develop a novel multi-hazard risk assessment model by applying a Hybrid Bayesian Network for agricultural water supply and distribution systems. This model consists of discrete and continuous nodes and their probable interactions. The structure of this model is designed to assess the risk associated with the agricultural water system's for supply and distribution sections separately considering various factors such as river discharge, the inflow of water distribution system and its fluctuation, and the demand of water. The developed model is applied to the Roodasht Irrigation district located in the center of Iran to demonestrate its capability. This Irrigation district is under the threat of drought, improper performance of the ditch-riders, and operational losses. The results showed that the model in both training and test datasets has proper accuracy and performance with the root mean square error of 0.069 and 0.076, coefficient of determinations equal to 0.717 and 0.690, and the overall index of model performance equal to 0.787 and 0.671, respectively. The results of this research and the proposed model will help stakeholders and decision-makers to be aware of the probable causes and the extent of system failure and its components due to the threatening hazards. Also, it will assist in planning the allocation of irrigation water based on predictable risk associated with various hazards.

Increasing water use efficiency in the agricultural sector requires the use of appropriate methods for intelligent performance evaluation of surface water distribution systems in agriculture. Therefore, in this study a systematic approach was developed for operational performance appraisal of the agricultural water distribution systems. For this purpose, Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to evaluate the technical performance of irrigation network, considering the uncertainties in the water exploitation process. The performance of the developed models was studied on the Roodasht irrigation canal, located in central Iran, which suffers from severe fluctuations in the inflow, by evaluating the adequacy, efficiency, and equity of surface water distribution. Hydraulic simulation of water distribution system, as well as providing the information required for training and validation of the intelligent models, were performed using the HEC-RAS model. The results showed that compared to the FIS model, ANN and ANFIS models similarly predicted the model outputs with lower errors at almost the same level. The adequacy, efficiency, and equity indicators were predicted by ANFIS model with MAPE of 0.16, 0.01 and 0.23, respectively. Also, FIS model was only able to predict the efficiency and could not predict the adequacy and equity with appropriate performance. The findings of this study reveal that since the ANFIS model uses both FIS and ANN models in its structure, it considers the model uncertainty reliably, and it can be used to evaluate the performance of agricultural water systems.

Urban water supply, transmission, and distribution systems are considered as the basic infrastructures of each community. Deficiency of water resources in system leads to lack of some subscribers’ access to safe water at any time. The water treatment units have a high potential for massive crisis and importance of sustainable performance in water treatment plants, due to their special role in high quantity and quality water supply even in critical situations, requires a comprehensive approach in assessing and increasing the reliability. A comprehensive approach in assessing the performance of water treatment plants and reducing their vulnerability can lead to cost reduction of inappropriate performance in critical situations and also a focus on the most important vulnerable parts of water treatment plants to decrease their vulnerability, as well as cost-effective budgeting in recovery programs. In this research, due to the uncertainties in the input information, two methods of fuzzy fault tree analysis (FFTA) and fault tree analysis (FTA) based on Monte Carlo simulation were evaluated in Jalaliyeh water treatment plant in Tehran, and results of both approaches were compared in terms of uncertainty analysis. The results indicate the low to moderate failure risk in the treatment plant in both approaches and a small difference between results in both cases. Failure probability of top event for FFTA and Monte Carlo-based FTA methods is 0.194 and 0.27, respectively. In the basic event rating, it was found that threats such as inappropriate reservoir design, power equipment failure, transfer pipe failure, and inappropriate maintenance of the pumps play the major role in treatment plant failure.

The present study’s main objective is to simultaneously minimize operational and seepage losses in agricultural water distribution systems, relying on the Ant Colony Optimization (ACO). For this purpose, the following arrangements were made: i) Hydraulic flow simulation of the distribution systems was conducted by developing an Integrator Delay (ID) model using MATLAB and appraisal performance of the water distribution system, ii) The seepage simulation alongside the system employing a calibrated and validated estimation equation, iii) developing the ACO model to minimize operational and seepage losses within the agricultural water distribution Systems. Two single-objective and one multi-objective functions were considered to minimize seepage loss, operational loss, and both loss components simultaneously. The Moghan irrigation water distribution system, Iran, was selected as the case study. Optimization results revealed that the first through third objective functions managed to reduce the total losses in the Moghan water distribution systems by, respectively, 0.39, 3.1, and 4% compared to the existing conditions. A comparison with the optimization results from LINGO, a nonlinear optimization model, was suggestive of the advantages of the ACO in terms of the optimal result and optimization time. The proposed method can be used as a practical measure to improve water productivity within the scale of agricultural water distribution systems by improving the manual operating system’s performance in the status quo.

Groundwater resources are the only source of water in many arid and semi-arid regions. It is important to manage these resources to have a sustainable development. However, there are many factors influencing the accuracy of the results in groundwater modeling. In this research, the uncertainty of two important groundwater model parameters (hydraulic conductivity and specific yield) were considered as the main sources of uncertainty in estimating water level in an unconfined aquifer, in Iran. For this purpose, a simple method called Rosenblueth Point Estimate Method (RPEM) was used to assess groundwater modeling parametric uncertainty, and its performance was compared with Monte Carlo method as a very complicated and time-consuming method. According to calibrated values of hydraulic conductivity and specific yield, several uncertainty intervals were considered to analyze uncertainty. The results showed that the optimum interval for hydraulic conductivity was 40% increase–30% decrease of the calibrated values in both Monte Carlo and RPEM methods. This interval for specific yield was 200% increase–90% decrease of the calibrated values. RPEM showed better performance using the evaluating indices in comparison with Monte Carlo method for both hydraulic conductivity and specific yield with 43% and 17% higher index values, respectively. These results can be used in groundwater management and future prediction of groundwater level.

This study aimed to quantitatively evaluate the performance of practical alternatives in modernization projects of surface water distribution systems in irrigation networks based on the water-food-energy nexus using the AHP-Entropy-WASPAS technique. Three methods of improved manual operation, decentralized automatic operation, and centralized automatic operation were developed under normal and water shortage operation scenarios and modeling the current status of water distribution in the main canal of the Isfahan Rudasht irrigation network as a case study. Water-based indicators (i.e., surface water delivery, surface water productivity, and surface water economic productivity), energy-based indicators (i.e., energy consumption, energy productivity, and economic energy productivity), and food-based indicators (i.e., food productivity and food economic productivity) were used to develop the nexus evaluation framework. The results showed that the average values of the water-food-energy nexus index in the manual operation method were estimated at 0.49 and 0.16 under normal and water shortage operation scenarios, respectively. These average values were estimated at 0.53 and 0.17 under normal and water shortage operation scenarios, respectively, by improving the method to the improved manual operation method. The decentralized automatic operation method improved these average values to 0.82 and 0.39 under normal and water shortage operation scenarios. Finally, using the centralized automatic operation method, these average values were 0.94 and 0.35 under normal and water shortage operation scenarios. Since the downstream secondary off-takes of the irrigation network receive no water even by upgrading the surface water distribution system to the decentralized automatic operation method under the water shortage operation scenario, it can be said that the performance of the centralized automatic operation method is more efficient than the decentralized automatic operation method due to the fair and uniform distribution of water in both normal and water shortage operation scenarios.

This study investigates the effectiveness of the reduction policies of the apparent and real losses of non-revenue water. For this purpose, using technique for order of preference by similarity to ideal solution (TOPSIS) model, a decision matrix is proposed, involving apparent and real losses risk reduction values induced by the implementation of each non-revenue water reduction policy. First, decision criteria, including technical, economic, and social criteria, were selected and introduced. Then, the technical criterion was calculated using a fuzzy Bayesian model. In the following, using group decision-making and analytic hierarchy process method, the criteria were weighted. In the next step, the bests of decision criteria were determined by the TOPSIS method. This method was applied in the water distribution network of district 4 of Tehran, Iran. The results indicated that the most basic non-revenue water reduction policy by risk-based group decision-making is to identify unauthorized connections in the apparent water loss sector and increase the speed and quality of maintenance and repairs in the real water loss sector.

Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and R2 indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation.

In many parts of the world, especially in metropolitan areas with dry climates, shortages of freshwater resources have become a significant challenge in water resources management. Rapid population growth and climate change in these areas impose additional burdens on water consumption, especially on surface water resources, which are increasingly becoming scarce. In this study, a change from the Standard Operating Policy (SOP) to the Modified Linear Decision Rule (MLDR) policy is considered as a solution to mitigate the impacts of climate change on dams and reservoir operation. The two policies were compared by using five reservoir system performance indices (reliability, resiliency, vulnerability, sustainability and meeting demands) and three hydropower energy generation indices. This study pertained to the Karaj hydropower dam reservoir in Iran and speculated on 2020–2039. Changing the operation policy from current SOP to the MLDR policy can mitigate the adverse effects brought by climate change. According to the results, the SOP policy works best for reservoir operation if there is enough water (optimistic scenario). In the case of pessimistic scenarios (shortage of water), operating the reservoir system with SOP policy would turn into a crisis in supplying drinking water. On the other hand, the MLDR policy prevents the complete draining of the reservoir. In all water-demand scenarios, the MLDR policy reduces the amount of time (by over 90%) in which the reservoir remains empty, compared to the SOP policy. The final results demonstrate how the MLDR policy can mitigate the undesirable effects of climate change.

Due to the impacts of climate change on agriculture and water allocation, an investigation of the farmers’ perceptions and stakeholders’ views on the adaptation strategies to climate change has a great of importance for sustainable development in the future. In this study, a fuzzy based decision support system has been developed to evaluate and rank the proposed adaptation scenarios to climate change in the Jarreh agricultural water resources system in southwest of Iran. Using output of ten coupled models inter comparison project phase 5 (CMIP5) under two representative concentration pathway scenarios (RCP 4.5, and RCP 8.5), the results indicated an increasing the annual mean temperature (1.64–1.84 °C under RCP 4.5, and 1.85–2.1 °C under RCP 8.5), reducing the amount of runoff into the reservoir (17.83–46.24% under RCP 4.5, and 21.54–50.91%under RCP 8.5), as well as increasing the amount of agricultural water requirement. Also, the results showed decreasing in reliability of system (12–53% under RCP 4.5, and 23–63% under RCP 8.5). Following, due to the main purpose of the system, six adaptation scenarios by using a questionnaire and stakeholders’ opinions are proposed to mitigate the effects of climate change. In the next step, by fuzzy mode of the technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE II), the proposed scenarios have been ranked according to the performance criteria. The final results of this study indicated the superiority of improving the irrigation efficiency and decreasing the area under cultivation among other proposed scenarios.

Water crisis management in drinking water supply systems is a crucial issue. Many different natural and unnatural disasters cause great damages to these systems. Prioritizing effective strategies by expert decision-makers before any incidence can greatly reduce this damage. Moreover, cost minimization in the provision of these strategies is very essential as the urban crisis management organizations are financially limited. Therefore, this study provided a model for the selection of the most appropriate drinking water supply strategies in crisis conditions, considering possible limitations. Fuzzy PROMTHEE V, a multi-criteria decision-making method, is a new approach developed in this research to help the decision-makers in selecting a set of possible alternatives for drinking water supply management, which are ranked based on five criteria determined by decision-makers (water supply reliability, implementation speed and simplicity, implementation cost, social satisfaction, water quality) and budget limitation as the constraint for the optimization problem. This model was applied in a case study of Tehran city in Iran. Due to the uncertainties in expert opinions and parameters needed for drinking water supply risk management, this model was solved with three fuzzy methods and one non-fuzzy method, and the results were compared. Findings showed that strengthening passive defense in water supply, transportation, and distribution systems; providing water consumption management; encouraging people to save water for emergency conditions and planning to exploit popular forces and performing maneuvers are top-ranked alternatives.

According to the impact of climate change on the thermal stratification which results in a quality change of dams outflow, this study aims to present a framework to assess the impacts of climate change on altering thermal regimes of dam reservoirs. Therefore, five Atmosphere–Ocean Global Circulation Models (AOGCM-AR5 models) under two emission scenarios (RCP2.6 and RCP8.5) are used to investigate the change of air temperature, precipitation, wind speed, relative humidity, dam inflow and water temperature in 2020–2039. To quantify these impacts, three models are used, namely rainfall–runoff IHACRES, hydrodynamic model CE-QUAL-W2 and a downscaling model of LARS-WG. The efficiency of the proposed framework was evaluated in Latian Dam in Iran. Results show an increment of long-term air temperature average by 0.7 °C and, consequently, water surface temperature by 0.59 °C in 2020–2039 relative to the baseline period (1990–2009). Precipitation, wind speed and relative humidity decrease due to climate change in the future by 57%, 17% and 11%, respectively. The simulated dam inflows show that the maximum of 89% of the dam inflow decreases in the future period relative to the baseline period. The results indicate that the water temperature will increase in the coming period as the air temperature increases and this will lead to the extension of thermal stratification period up to 52 days.

The purpose of this study is to develop a flood warning system based on Atmospheric Ensemble Forecasts. Although ensemble forecasts are increasingly employed for flood forecasting, developing a flood warning system based on ensemble forecasts has not been adequately addressed yet. In this study, first a Weather Research and Forecasting (WRF) model was used to forecast the heavy precipitation in Kan Basin, Iran. Ensemble storms were forecasted using five cumulus schemes including Kain-Fritsch, Betts-Miller-Janjic, Grell 3D ensemble, Multi-scale Kain-Fritsch and Grell-Devenyi ensemble cumulus scheme. Then, a Bayesian Networks (BN) was developed to estimate the flood peak using the atmospheric ensemble forecasts. Finally, a Fuzzy-TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) model was prepared for making decisions for flood warning scenarios considering all effective factors in flood warning and uncertainty associated with them. Assessment of the proposed flood warning system was examined for various scenarios. It showed that when a significantly high probability was assigned to a warning level, that level had the maximum closeness coefficient and consequently chosen as a warning level. Yet, if the probability was distributed equally between some warning levels, the flood warning system acts cautiously since the decision-making model allocated the highest rank to the stronger warning level. Regarding the reasonable results of this study, applying the Fuzzy-TOPSIS model to develop a flood warning system based on atmospheric ensemble forecasts is recommended to apply in similar catchments for addressing the uncertainties.

Sustainable utilization of water resources requires preventive measures that must be taken to promote optimal use of water resources together with consideration of stakeholder interests and the economic value of water. The main objective of this study is to present an integrated hydro-economic model for allocating agricultural water based on its economic value. The study region covered six irrigation networks downstream of the Zayandeh Rood Dam in Iran. In fact, this study addresses questions of how to allocate scarce water to different consumers, in order to achieve the highest efficiency and economic benefits. To gain this goal, the existing agricultural activities in each irrigation network were simulated by applying the Positive Mathematical Programming (PMP) economic model and then by coupling the economic model with a water allocation planning model of the basin (MODSIM), the hydro-economic framework was generated. These tools helped to allocate water based on its economic value, maximize net profit by determining the optimal cultivating area and analyze the effects of various allocation scenarios on employment, economic productivity, and reliability indicators. Moreover, Bayesian Networks and Random Forest algorithms were developed as an automated substitute to simplify the process and reduce computational complexity. The results showed that the Nekoabad Network enjoys top priority followed by the Barkhar, Mahyar, Sonati, Abshar, and Rodasht Networks. After implementing the Bayesian Network, the four criteria of MAE, MAPE, R2, and the Nash-Sutcliffe index for the irrigation networks were 9 MCM, 24%, 0.738, and 0.644 respectively, which indicated the model has good accuracy. Random Forest method was also employed as a novel technique in automated allocation, and the values obtained for the four mentioned criteria were 7 MCM, 15%, 0.861, and 0.80, which showed it is more accurate. The results indicated the capability of the presented hydro-economic model as well as the intelligent models substituting it in allocating agricultural water.

Design and performance of stormwater infrastructure systems in urban areas have direct implications in social, environmental and public health problems and are of utmost importance to urban authorities and managers. Risk analysis in urban stormwater systems has become a must because of the extensive consequences of flooding in urban areas and limited funding for the rehabilitation and renovation of stormwater systems. Complexity, multidimensionality, and inherent uncertainties of the urban stormwater systems require the risk analysis approach to be comprehensive and able to address different uncertainties and spatial aspects of the problem. The objective of this study is to provide a comprehensive framework for risk analysis in urban stormwater systems. Multi Criteria Decision Making (MCDM), geographic information systems (GIS), and fuzzy sets theory are used to consider the diverse risk-affecting criteria, facilitate the analysis of spatial data and information and formulate the ambiguity and uncertainty of problem, respectively. The presented framework uses a Fuzzy Analytic Hierarchy Process (FAHP) for determining the weights of risk-affecting criteria (i.e. Hydrological and Hydraulic, Traffic, Social, Economic, Environmental, Structural, and Green space) in the presence of multiple decision makers. The Autodesk Storm and Sanitary Analysis model is used for the Hydrological and Hydraulic simulations. FSAW and FTOPSIS methods are used to provide the final product of the framework, i.e., a risk map that presents a risk level for each channel in the network. The framework is applied to District 11 of the capital city of Iran, Tehran as a real case study. The resulted risk maps indicate a high or very high flooding risk for 17.07 to 41.95 km of the stormwater channels in the study area, covering about 10.5 to 26% of the total length of the channels. The presented framework was found to be a suitable risk analysis tool in urban stormwater systems.

This research proposes an enhanced Graph Model for Conflict Resolution (GMCR) with application to an international wetland. The integrated model enhances GMCR using the Analytical Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to extract the relative preferences of decision-makers in conflict resolution. The Hawizeh/Hoor-Al-Azim Wetland is facing increased dust storms in recent decades which are attributed to drying up of Mesopotamian wetlands, creating the potential for conflicts among riparian countries. The proposed integrated approach was applied in the Hawizeh/Hoor-Al-Azim Wetland fed by the Tigris and Karkheh Rivers. Turkey, Iran, Iraq, and Syria are located in the Tigris River basin, and the Karkheh River originates from the Zagros Mountains in western Iran. The results indicate that the achievement of an environmental balance depends on cooperation between Iran and Iraq to persuade Turkey to release the environmental flow to the wetland. Three strong equilibrium points were identified among 15 feasible states: (a) establishing a regional agreement among Iran, Iraq, and Turkey to reduce the effects of conflicts in the Hawizeh/Hoor-Al-Azim Wetland; (b) coalition of Iran and Iraq to motivate Turkey to reduce water withdrawal from the Tigris River; and finally, (c) exchanging water release for commodity market in Iran and Iraq for Turkey. In general, the economic cooperation among countries can be used as a negotiation tool to solve water conflicts in international wetlands.

Comprehensive assessment of debris flow hazards is a challenging issue due to the complexity and uncertainty of its factors. For this reason, the practical forecasting of debris flows requires developing a reliable and realistic forecasting model. In this paper, a Bayesian Networks (BNs) model is proposed for identification of debris flows events in the northern basins of Iran. BNs model illustrates the uncertainty of the results as probability percentage of debris flow occurrence in different categories (non-occurrence, occurrence with low-intensity and occurrence with high-intensity). In this research, average basin elevation, average basin slope, watershed area, the current rainfall, antecedent rainfalls of 3-day ago and discharge of 1-day ago were used as the predictor variables. Moreover, K-means clustering method was applied in modeling by the BNs model. To identify the effective predictors in debris flow occurrence, sensitivity analysis was performed. For this purpose, scenarios which employ various predictor variables were tested. The scenario which uses all predictor variables has a forecasting accuracy of 91%. This scenario was selected as the best scenario. However, a scenario which employs only effective predictors also proposed for practical uses. The results of the various forecasting scenarios showed that average basin elevation, watershed area, current rainfall and discharge of 1-day ago are the effective predictors in the forecasting debris flows. The BNs model may be proposed for future tests in the other debris flow prone regions.

Sustainable water resources management in most river basins needs appropriate techniques to implement the conjunctive use of surface and groundwater resources with consideration of uncertainties. In this study, first a linear fuzzy optimization model was used to find the optimal surface and groundwater withdrawal. Then, by using the results of this model, a Fuzzy Inference System (FIS) was developed to determine the groundwater withdrawal, automatically. The capability of this approach is studied in the Astaneh-Kouchesfahan Plain in north of Iran. To do this, the groundwater of this Plain was simulated using MODFLOW code. Then, two fuzzy optimization methods were developed to minimize the water shortage. Results showed that the average water shortage was equal to 215 and 138 MCM according to current water withdrawal and the best solution for the fuzzy optimization model which was 22.0% and 14.6% of the water demand, respectively. The results of the optimization model were used to predict the optimal water withdrawal of the aquifer using the FIS, considering the predictor variables. These variables were applied to the developed FIS model under different scenarios in order to determine the best scenario with the highest prediction performance for water withdrawal from the aquifer. The evaluation criteria were coefficient of determination, normalized root mean square error, and the mean absolute error which were equal to 0.94, 0.26 MCM and 3.8 MCM, respectively for the best scenarios. These scenarios were able to predict the optimal amount of groundwater withdrawal and can replace the numerical optimization methods.

Groundwater resources are steadily subjected to increasing water demands. The aquifers are considered as the most accessible source of fresh water. In recent years, they have been faced with severe water withdrawal in arid and semi-arid countries like Iran and thus some aquifers was considered as forbidden aquifers that it means the water withdrawal from these aquifers is unauthorized. Given a critical situation, groundwater resources management in the form of tools such as monitoring the level of the aquifers and developing the restoring scenarios is essential. Therefore, for this purpose, a framework has been developed based on prediction of groundwater level using Bayesian Networks (BNs) model. Furthermore, Multi Criteria Decision Making methods (MCDM) techniques proposed and employed for ranking of proposed groundwater management scenarios. This framework was evaluated for restoring the Birjand aquifer in Iran in different hydrological conditions. A probabilistic Dynamic BN was proposed for groundwater level prediction under uncertainties. After analyzing the obtained results, the applicable short term scenarios for groundwater management as well as appropriate economic, social and technical criteria were defined for decision making procedure. Then, using elicitation of decision makers’ opinions on the relative importance and performance of criteria, SAW, TOPSIS and PROMETHEE-II techniques were applied to rank the scenarios and the obtained results were aggregated by Borda method for final ranking of the scenarios. Lastly, the final results demonstrates the capability of the proposed framework for groundwater resources planning and management which can be employed for reducing the risk of aquifer level declining.

In this research, cropping pattern optimization models have been developed to maximize the benefits and minimize the potential negative effects of quantitative-qualitative conjunctive use of unconventional surface water (treated wastewater) and groundwater in agricultural irrigation. Three objective functions were considered: maximizing the benefits from crop patterns, reducing nitrogen leaching, and improving the rate of aquifer recharge. The developed models use particle swarm optimization (PSO) integrated with an additive weighting method and a multi-objective particle swarm optimization (MOPSO) algorithm for different single- and three-objective optimization scenarios in the Varamin irrigation network in Iran, and then the results of the models were compared. The solutions resulting from the three-objective model using the PSO algorithm with the additive weighting method indicated that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 49%, and 20%, respectively. Meanwhile, the conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 35% and 88%, respectively. The solutions resulting from the three-objective model using the MOPSO algorithm, forming Pareto front, and then using TOPSIS to select the optimal solution from among the non-dominated solutions showed that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 47% and 15%, respectively. The objectives of conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 36% and 89%, respectively. The difference between the objective function values of the two algorithms is about 0.4% to 4%, which shows the proximity of the models in finding the optimal solution. The results of this research can be used to optimize the use of water resources, increase farmers’ benefits, and decrease nitrogen leaching from irrigation and drainage networks.

Improving the efficiency of main Agricultural Water Conveyance and Delivery Systems (AWCDS) has a significant impact on improving water productivity in agriculture. Therefore, risk assessment of mentioned systems is necessary to increase reliability of operational performance. Accordingly, this study for the first time presents a unique framework to assess the adequacy, equity, and efficiency of agricultural water distribution and delivery risk assessment within AWCDS. In this way, the Fault Tree Analysis (FTA) technique is employed for risk assessment of “undesirability of supply and delivery”. The west Dez main irrigation canal in Khuzestan province of Iran was determined as the case study of the research. A set of questionnaires filled up by managers and experts of this irrigation district, the failure probabilities of the basic events are gathered in the form of linguistic terms. Due to the uncertainty in these terms, the system’s risk assessment to determine the failure probability of the top event was performed based on Fuzzy Fault Tree Analysis method (FFTA). The results of the study showed that the failure probability in the fuzzy approach is 0.55 which is roughly 0.15 more than crisp approach. Also, the rating of the basic events based on their contribution to the occurrence of the top event was carried out using importance measures. Five major events were identified with an emphasis on operational and socio-economic issues related to distribution and delivery of water. Comparing the results of risk assessment with the mathematical model reveals that the latter’s failure probability will be less than the system’s FTA due to non-consideration of some important factors.

The present study deals with the optimal extraction of groundwater resources. This approach has been developed for optimal integrated operation in an aquifer in Iran. The results of simulation / optimization models have been used to develop a predictive model based on machine learning. In the first stage, adaptive neuro-fuzzy inference system (ANFIS) was used to predict the Optimal Groundwater Exploitation (OGE) amount in each month having the amounts of several inputs, including the drop and amount of surface water at the end of the previous month and two months earlier, and the water demand of the current month. The results showed that the model’s performance in predicting the test data was undesirable. Therefore, to improve the prediction results, in the second stage several evolutionary optimization algorithms, i.e., particle swarm optimization (PSO), gray wolf optimization (GWO), and Harris hawk optimization (HHO), were used to train ANFIS model. The results indicated the appropriate performance of HHO in ANFIS training, which significantly improved the prediction accuracy of this model. The best scenario for the ANFIS-HHO model included all the input parameters, which resulted in RMSE = 1.45, MAE = 1.15, and R2 = 0.99 respectively, for the test data. In addition, the Taylor diagram (RMSD = 1.40, STD = 15.5 and CC = 0.99) showed ANFIS-HHO accuracy in estimating the OGE value. ANFIS-HHO was also able to improve the accuracy of anfis by RMSE = 4 and MAE = 2 MCM. In general, ANFIS-POS, ANFIS-GWO and ANFIS-HHO had good predictive accuracy compared to ANFIS. The results assure the authors to suggest the developed approach to experts for timely and cost-effective prediction of OGE in similar study areas.

To secure operation of wastewater collection systems and treatment plants, it is so necessary to detect any probable vulnerability and estimate undesirable events and severity of their effects. In this paper, a model has been developed to evaluate system operation management in order to deal with serious conditions. The risk assessment phase contains three parts including evaluation of probability of threats, severity of their effects–consequences and vulnerability of the system components. These parameters could be measured by fuzzy multi-attribute decision-making techniques by questionnaire and through defining some criteria for threat assessment. In the risk management phase, risk-controlling approaches are developed by professionals based on risk assessment results and prioritizing the threats. Risk assessment and management model has been conducted through two case studies in Tehran, respectively, as a sample of wastewater collecting and the biggest wastewater treatment system in Iran. The results show that “entry of chemical contaminant” and “change in wastewater quality” were taken the highest score for the west Tehran wastewater collection network and “earthquake” in processing units, sludge treatment and gas storages were taken the highest score in the south Tehran wastewater treatment, reduction strategies of which were presented for dealing of each risk.

The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN.