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In wastewater treatment plants (WWTPs), the stochastic nature of influent wastewater and operational and weather conditions cause fluctuations in effluent quality. Data-driven models can forecast effluent quality a few hours ahead as a response to the influent characteristics, providing enough time to adjust system operations and avoid undesired consequences. However, existing data for training models are often incomplete and contain missing values. On the other hand, collecting additional data by installing new sensors is costly. The trade-off between using existing incomplete data and collecting costly new data results in three data challenges faced when developing data-driven WWTP effluent forecasters. These challenges are to determine important variables to be measured, the minimum number of required data instances, and the maximum percentage of tolerable missing values that do not impede the development of an accurate model. As these issues are not discussed in previous studies, in this research, for the first time, a comprehensive analysis is done to provide answers to these challenges. Another issue that arises in all data-driven modeling is how to select an appropriate forecasting model. This paper addresses these issues by first testing nine machine learning models on data collected from three wastewater treatment plants located in Iran, Australia, and Spain. The most accurate forecaster, Bayesian network, was then used to address the articulated challenges. Key variables in forecasting effluent characteristics were flow rate, total suspended solids, electrical conductivity, phosphorus compounds, wastewater temperature, and air temperature. A minimum of 250 samples was needed during the model training to achieve a great reduction in the forecasting error. Moreover, a steep increase in the error was observed should the portion of missing values exceed 10%. The results assist plant managers in estimating the necessary data collection effort to obtain an accurate forecaster, contributing to the quality of the effluent. [Display omitted] • Bayesian network is benchmarked against eight AI models to predict wastewater quality. • Bayesian networks can handle missing value in input data due to probabilistic nature. • Key variables in WWTP quality and quantity were specified. • Input data quality has a huge role in successful development of data-driven models. • The prediction error sharply increases when the portion of missing data exceeds 10%. [ABSTRACT FROM AUTHOR]

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. • Adaptive reservoir operation model considering water quantity and quality objectives. • Using ECMWF-based rainfall forecasts with monthly time step and 6-month lead time. • Linking the optimization model with the CE-QUAL-W2 water quality simulation model. • Applying the real-time operation model to the Karkheh river-reservoir system in Iran. • Illustrating the efficiency of the proposed model in a real-world large-scale problem. [ABSTRACT FROM AUTHOR]

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. • Developing a new framework for coordinated operation of urban detention reservoirs. • Using deep-learning-based rainfall nowcasting data for real-time flood management. • Linking a stormwater simulation model with the cellular automata optimization model. • Applying the framework to the east drainage catchment of the Tehran metropolitan area. • Illustrating that the adaptive framework reduces the overflow volume by up to 60%. [ABSTRACT FROM AUTHOR]

Coastal areas are of paramount importance due to their pivotal role in facilitating a wide range of socio-economic activities and providing vital environmental services. These areas, as the meeting points of land and sea, face significant risks of flooding due to the ongoing rise in sea levels caused by climate change. Additionally, they are susceptible to extreme events like king tides and large waves in the future. This paper introduces a framework for estimating the extreme total water level (TWL) by considering the effects of regional sea level rise (RSLR) resulting from a warming climate under RCP 8.5. It also incorporates the contributions of high tides, 100-year storm surge, and 100-year wave setup and run-up. The proposed framework is utilized to evaluate the occurrence of extreme coastal flooding along the Persian Gulf coast of Iran, an area that is home to significant industries in the country. The results offer an estimated increase of RSLR by 0.23 m from 2020 to 2050 considering an ensemble of climate model projections. Extreme wave setup values are estimated to range between 0.19 and 0.66 m, while storm surge is projected to vary from 0.4 to 1.44 m across the studied coastline. These together yield in a projected extreme TWL along the coastline within the range of 3.18 and 3.90 m above the current sea level. This significant increase in sea level could lead to the inundation of approximately 513 km2 of low-lying coastal land, which accounts for about 16% of the studied domain and could pose serious flooding threat to the people and their assets in this region. Finally, relative ranking of flooded zones (i.e., 6 zones) helps determine the areas with higher chance of flood exposure, at which investment in flood mitigation measures should be prioritized. [Display omitted] • Regional sea level rise is quantified using GCMs under RCP 8.5. • Flooding maps are shown under regional SLR, tides, storm surge, wave setup and run-up. • Extreme total water level is expected to inundate important ports in Persian Gulf coast of Iran by 2050. • Research projects that 513 km of low-lying coastal areas will be inundated due to extreme total water level. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Proposing a real-time market-based framework for surface water allocation and pricing. • Calculating users' seasonal bid packages and using them in a gross pool option market. • The real-time feature of the market uses seasonal ANFIS-based rive flow predictions. • The framework can help users better cope with the available surface water uncertainty. • The water market applied to a large river basin and increased users' benefits by 45%. [ABSTRACT FROM AUTHOR]

Groundwater over-abstraction due to the absence of an effective management plan is often one of the main reasons for land subsidence in aquifer areas. This paper investigates this environmental problem at Salmas plain, Iran, by using the ALPRIFT framework, an acronym of a set of seven general-purpose data layers, introduced recently by the authors. It is capable of mapping Subsidence Vulnerability Indices (SVI) and the paper investigates an innovation to transform it into Time-variant SVI (TSVI) mapping capabilities through a three module strategy: Module 1 : maps SVI; Module 2 : develops a predictive model for Groundwater Levels (GWL); Module 3 : combines both modules to produces TSVI maps. Modules 1 and 2 employ Inclusive Multiple Modelling (IMM) practices, which promote learning from multiple models, as opposed to their ranking and selecting a 'superior' one. IMM is implemented through the same single modelling strategy for both Modules 1 and 2 at two levels: at Level 1 , multiple models are constructed by three Fuzzy Logic (FL) models: Sugeno FL (SFL), Mamdani FL (MFL) and Larsen FL (LFL). (ii) At Level 2 , FL models at Level 1 are reused by Support Vector Machine (SVM) as the combiner model. The results show that (i) the models at Level 1 are fit-for-purpose; (ii) the models at Level 2 are defensible owing to IMM strategies focussed on enhancing their accuracy and investigating their residuals; and (iii) according to TSVI maps, the north of the plain is vulnerable to hotspot areas and is exposed to subsidence risks due to unplanned over-abstraction of groundwater from the aquifer at Salmas plain. [Display omitted] • Study Time-variable Subsidence Vulnerability Indexing (TSVI) by Water T able (T). • Predict SVI using ALPRIF T in Module 1 (M1); predict T by site data in Module 2 (M2). • Use Inclusive Multiple Modelling strategies for M1 and M2, both at Levels 1 and 2. • Results of M1 and M2 at Level 1 are fit-for-purpose but are defensible at Level 2. • Predict TSVI maps for the near future by combining M1 with M2 – a proof-of-concept. [ABSTRACT FROM AUTHOR]

Ecosystems have a huge impact on social systems through providing diverse services. Sustainable management of social and ecological systems requires the quantification of spatial relationships between the supply, demand, and use of these ecosystem services. The main purpose of this paper was to provide a framework addressing all the complicated relationships of ecosystem services between ecological and social systems. Sustainable management cannot be achieved without considering these complex relationships. To simplify these complexities, management priorities must be identified. In this study, spatial changes of supply, use, and demand of ecosystem services and their main drivers were investigated. A Public Participation Geographic Information System was used to map the supply, demand and use of 19 ecosystem services in semiarid landscapes of Iran. Multi Criteria Decision Making methods were also used to link ecosystem services to social well-being. The main drivers of tradeoffs between supply, use, and demand of ecosystem services were determined using General Linear Method. The results showed that there was a synergy between supply, demand and use of ecosystem services in highlands. The deficit zones of ecosystem services were concentrated in lowlands. Provisioning services were the most important services for social well-being. The framework presented in this paper revealed all the complex relationships that the environmental management is faced with in ecosystem and social systems. Simplifying integrated relationships in both ecosystems (supply) and social systems (demand and use) helps sustainable management of ecosystems under environmental and social changes. • Ecosystems have a huge impact on social systems through providing diverse services. • A framework was provide to address all the relationships of ecosystem services. • There was a strong synergy between supply, demand and use of ecosystem services. • Biophysical factors were the most important factors driving synergies. • Cultural services were highly related to health index of social well-being. [ABSTRACT FROM AUTHOR]

Allocating water to organizational stakeholders poses a vital challenge to water managers. Organizations which benefit from water as the primary factor input attempt to achieve their objectives using cost-effective and quick-return strategies, such as increasing the water rights. In such circumstances, lack of water probably results in the conflict. Recognizing the management approaches, organizational priorities, and the stakeholders' influence power can play a dominant role in analyzing the future of such conflicts. In this paper, we analyzed the conflict of water allocation in Ilam dam among organizational stakeholders. We defined the strategies based on the background of the game and organizational objectives. The influence power of stakeholders and the numerical weights of strategies were quantified based on the expert judgment method. The relative priorities of strategies were then calculated for each state of the conflict. We used the GMCR + model to study the actions of stakeholders. Results suggest that the Jihad Agriculture Organization and the Water and Wastewater Company withdraw more water; hence, there exists no water to meet the environmental water right. In this case, the participation of the third party, such as the Governorship and the Justice can change the future of the conflict, and result in moving to the optimal state. However, results from Inverse GMCR analysis demonstrate that Justice is the most influential third party that can move the conflict towards a desired equilibrium (optimal case). [ABSTRACT FROM AUTHOR]

Incidences of disease, dieback, decline or mortality, some of which induced or enhanced by climate change, threaten the sustainability of forest stands in many ecosystems. Spatially explicit prediction of disease onset remains challenging, however, due to the involvement of several causative agents. In this paper, we developed a generic framework based on machine-learning algorithms and spatial analyses for landscape-level prediction of oak disease outbreaks caused by the charcoal fungus Biscogniauxia mediterranea in a mixed-oak forest of Mediterranean climate. For prediction, we used a set of fifteen causative factors as a cross-function of soil, site and stand-related predictors. A total of 80 sample plots, including 1134 affected trees, were surveyed and used for the modeling process at the 5600-ha landscape level of the southern Zagros, Iran, where the disease occurs in roughly 25% of forest lands. Ten machine learning algorithms were explored and the performance of each algorithm to predict oak disease outbreak was evaluated. The modeling framework used maximum entropy to remove the least influential variables and build the status-quo management scenario to which the results of the prediction models were compared. Results showed that the random forests algorithm (AUC = 0.96: Precision = 0.71: Accuracy = 0.90: F-Measure = 0.70) achieved significantly better results than the status-quo management (Precision = 0.13: Accuracy = 0.67: F-Measure = 0.12) and any other algorithm. Soil chemical properties (NPK, organic carbon and EC) and landform predictors (slope, distance to roads, and TWI) were major forecasters of oak disease outbreak identified by the random forest algorithm. Geostatistical analysis enabled the creation of a map that identified sites at higher risk of infestation, allowing epidemiologists and forest managers to find sites likely to be infested. Consequently, financial resources can be allocated and management practices such as sanitation felling treatments applied across large forest landscapes to minimize the risk of spread and severity to uninfested high-value trees on nearby or adjacent land zones that are in the early stage of epidemics. • Soil and landform attributes explain short-term presence of oak charcoal disease. • Random forest (RF) algorithms outperform nine other machine learning algorithms. • RF predicts 41% of Brant's oak at moderate disease intensity in southern Zagros. • RF algorithm predicts oak dieback, decline or mortality with 96% accuracy. • Machine-learning algorithms predict spatial charcoal disease intensity in Brant's oak. [ABSTRACT FROM AUTHOR]

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 groundwater market is proposed to promote economic and environmental situation. • The uncertainty of available water is incorporated in the proposed methodolgoy • Designed market mechanisms recover nearly 85% of groundwater loss in the study area. • The market framework raises the economic benefit by 128.7 billion rials annually. • METRIC algorithm approximates daily crop evapotranspiration with RMSE of 0.37 mm. [ABSTRACT FROM AUTHOR]

An understanding of traditional ecological knowledge systems is increasingly acknowledged as a means of helping to develop global, regional and national, but locally relevant policies. Pastoralists often use lands that are unsuitable for crops due to biophysical and climatic extremities and variabilities. Forage plants of pastures are utilized by herding communities by applying locally relevant multigenerational knowledge. We analyzed the forage-related knowledge of pastoralists and herders by reviewing scientific papers and video documentaries on forage plants and indicators, their use in land management, and plant-livestock interactions. Semi-structured interviews were also conducted with key knowledge holders in Iran, Mongolia, Kenya, Poland and Hungary. We found 35 indicators used by herders to describe forage species. The indicators described botanical features, livestock behavior during grazing, and the impact of plants on livestock condition and health. The indicators were used in context-specific management decisions, with a variety of objectives to optimize grazing. We identified ten global principles, including, among others, a livestock-centered perspective, close monitoring and targeted pasturing of various (preferred or avoided) forages, and the use of different livestock types and well-planned spatial movements at multiple scales to optimize the utilization of available plant resources. Although pastoralists vary greatly across the globe, the character and use of their traditional forage-related knowledge do seem to follow strikingly similar principles. Understanding these may help the local-to-global-level understanding of these locally specific systems, support bottom-up pastoral initiatives and discussions on sustainable land management, and help to develop locally relevant global and national policies. [Display omitted] • Pastoral traditional ecological knowledge is adapted to local conditions. • Globally, pastoral systems operate with strikingly similar forage-related knowledge. • We found livestock-eye perspectives, close monitoring and targeted use of forages. • Indicators focus on botanical features, livestock behavior and impact on livestock. • General principles can help develop relevant global/national land-use policies. [ABSTRACT FROM AUTHOR]

Drought is a major global environmental challenge. It affects the livelihoods of many people, mainly in developing countries. Iran is one of the most affected and vulnerable countries in the Middle East to drought. In this paper, we present a microlevel analysis by employing the Tobit multiplicative heteroscedasticity regression to examine the effects of drought on small farm household education expenditures in rural Iran. We collected primary data from a sample of 300 smallholders in Marvdasht County in Fars Province of Iran. The results revealed a negative relationship between the farm income and education expenditures. This means that the farm households in rural Iran that were affected by the drought increased their expenditures on education for their children. The analysis of income elasticity indicated that a one percent decrease in farm income led to a 0.86% increase in education expenditures, which indicated that the education expenditures were necessary. Furthermore, we observed that in drought-affected families, girls were more likely to be pulled out of university education than were boys; however, for school education, there were no significant differences between the boys and girls. Our findings revealed the need to provide improved facilities and further finances for education expenditures, especially for female university students, and to formulate environmental management policies that include the provision of education facilities by the government of Iran in drought-affected villages. Our findings also shed light on the presence of positive externalities and the important role of education in helping rural households better cope with the negative repercussions of drought on their livelihoods. • This paper presents a micro-level analysis of the effects of drought on small farm household education expenditures in rural Iran. • Importantly, households suffering from severe drought increase their per capita education expenditures and appear to be spending more on their children education. • The farm income elasticity of education expenditures is inelastic. • Underscores the policy importance of subsidized rural education and employment generation to sustain and boost rural livelihoods in the wake of drought risk in Iran. [ABSTRACT FROM AUTHOR]

The water footprint concept has been widely applied to the agriculture sector; however, little attention has been given to industrial products. In this paper, the concept of water footprint is applied to a large steel plant located in a semi-arid basin in the central part of Iran. The limitations of existing research, including the lack of any advanced approach for assessing the virtual water of personnel's food, working in the plant, and the lack of providing independent data sources, are addressed. An improved water footprint model of steel production is proposed based on the water footprint network methodology and the life cycle assessment framework. Ideas from food ecological footprint are applied to measure the water footprint of personnel's foods. The case of Iran demonstrates that the water footprint of steel production is considerably large compared with other industrial products. The results highlight the relevance of production line technology, energy efficiency measures, and human management on reducing the water footprint of steel products. The paper adds to a growing body of literature on environmentally friendly steel production. ⁃ We propose an improved water footprint model of steel production. ⁃ The virtual water of personnel, working in a steel plant, is addressed. ⁃ Independent data sources for steel's water footprint assessment are provided. ⁃ Results show that the environmental problem of steel production is significant. ⁃ Pathways to save water in steel production are presented. [ABSTRACT FROM AUTHOR]

Due to land-use and hydrology changes, people are constantly exposed to floods. The adverse impact of floods is greater on vulnerable populations that disproportionately inhabit flood-prone areas. This paper reports a comprehensive study on flood vulnerability of flood prone areas in residential areas of the Tajan watershed, Iran in two periods before 2006 and after 2006. Flood prone area were determined by the random forest (RF) and K-nearest neighbor (KNN) machine learning methods. To reduce time and cost, the vulnerability was assessed only in areas with very high flood hazard using 4 main criteria (social, policy, economic, infrastructure), 40 items, and 210 questionnaires across 40 villages. Independent t -test, Kruskal-Wallis, and paired t -test were used for statistical analysis of questionnaire data. The results of machine learning models (MLMs) showed that the RF model with AUC = 0.92% is more accurate in determining flood prone areas. The results of paired t -test showed that the three criteria of social (mean P1 = 2.97 and P2 = 3.35), infrastructure (mean P1 = 2.88 and P2 = 3.25), and policy (mean P1 = 3.02 and P2 = 3.50) had significant changes in both periods. The Kruskal-Wallis test also revealed the mean of all four criteria in both periods and all sub-watersheds, except three sub-watersheds 10 (Khalkhil village), 19 (Tellarem and Kerasp villages), and 23 (Dinehsar and Jafarabad), had a significant difference. The results of the t -test also showed a decrease in vulnerability in the second period (before 2006) compared to the first period (after 2006), so the number of sub-watersheds in the very high vulnerability class was more in the first period than in the second period. A vulnerability map was developed using three factors of risk zone area, area of each sub-watershed, and population of each sub-watershed. • Using machine learning methods and field survey to assess vulnerability in flood hazard areas. • Investigating temporal and spatial variability in flood vulnerability of Tajan watershed. • Prioritizing flood-vulnerable areas using related criteria and indicators. [ABSTRACT FROM AUTHOR]

In this paper, we present a new methodology for improving the results of seasonal drought forecasting by developing a Bayesian Maximum Entropy-based fusion (BMEF) model. The BMEF model combines the forecasts done by four individual (single-source) data-driven models to achieve better outcomes. Regional drought indices of Effective Drought Index (EDI) and Multiple Standard Precipitation Index (MSPI) are forecasted using the individual forecasting models of Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and M5tree. The outputs of the individual models with the best performances are selected to be fused using the BMEF model and the results are analyzed and compared. The effect of different large-scale climate signals on rainfall and drought forecasting is analyzed and the most effective climate variables are selected as predictors in the forecasting models. Next, the uncertainty analysis on the results of the individual models as well as those of the BMEF model is carried out by deriving the probability mass functions of the drought indices using a resampling technique and Monte Carlo analysis. Finally, the results of the uncertainty analysis are evaluated to compare the performance of individual models and the BME-based fusion model in decreasing the uncertainty of seasonal drought forecasting. The performance of the proposed methodology is evaluated by using it to forecast seasonal drought conditions in the southwest of Iran. Based on the results of the uncertainty analysis, the BMEF model provides more reliable forecasts particularly for severe drought events than the individual models. It is also inferred that adding the SST to the predictors, decreases the uncertainty of drought forecasts. • A new methodology is proposed to improve seasonal drought forecasting. • A BME-based fusion (BMEF) model is developed for combining the individual forecasts. • Drought indices of EDI and MSPI are forecasted under uncertainty. • Favorable effect of sea surface temperature (SST) on drought forecasting is shown. • The BMEF model reduces the uncertainty of the drought forecasts. [ABSTRACT FROM AUTHOR]

Desertification in Iran was recognized between the 1930s and 1960s. This paper traces Iran’s attempts to reclaim desertified areas, evaluates the anti-desertification approaches adopted, and identifies continuing challenges. Iran has areas vulnerable to desertification due to extensive areas of drylands and increasing population pressure on land and water resources. Over-grazing of rangelands is a particular problem. Initially desertification was combated mainly at the local level and involved dune stabilization measures, especially the use of oil mulch, re-vegetation and windbreaks. Insufficient technical planning in the early years has led to changed approaches to plant densities and species diversity in plantations, and increased on-going management of existing plantations. Since the late 1980s forage and crop production has increased in areas where runoff control techniques are practiced. The social and economic aspects of anti-desertification programs have assisted in poverty reduction by providing off-season employment in rural areas. In 2004 a national plan to combat desertification was ratified and this placed an emphasis on community participation. Continuing challenges include managing existing desertified areas as well as taking into account potential future problems associated with rapidly depleting groundwater supplies and a predicted reduction in the plant growth period accompanying climate change. [Copyright &y& Elsevier]

In this paper, principal component analysis (PCA) and hierarchical cluster analysis (CA) methods have been used to investigate the water quality of Jajrood River (Iran) and to assess and discriminate the relative magnitude of anthropogenic and “natural” influences on the quality of river water. T, EC, pH, TDS, NH4, NO3, NO2, Turb., T.Hard., Ca, Mg, Na, K, Cl, SO4, SiO2 as physicochemical and TC, FC as biochemical variables have been analyzed in the water samples collected every month over a three-year period from 18 sampling stations along a 50 km section of Jajrood River that is under the influence of anthropogenic and natural changes. Exploratory analysis of experimental data has been carried out by means of PCA and CA in an attempt to discriminate sources of variation in water quality. PCA has allowed identification of a reduced number of mean 5 varifactors, pointing out 85% of both temporal and spatial changes. CA classified similar water quality stations and indicated Out-Meygoon as the most polluted one. Ahar, Baghgol, Rooteh, Befor Zaygan, Fasham, Roodak and Lashgarak were identified as affected by organic pollution. A Scree plot of stations in the first and second extracted components on PCA also gave us a classification of stations due to the similarity of pollution sources. CA and PCA led to similar results, though Out-Meygoon was identified as the most polluted station in both methods. Box-plots showed that PCA could approximately demonstrate temporal and spatial variations. CA gave us an overview of the problem and helped us to classify and better explain the PCA results. [Copyright &y& Elsevier]

Environmental managers are commonly faced with sophisticated decisions, such as choosing the location of a new facility subject to multiple conflicting criteria. This paper considers the specific problem of creating a well-distributed network of hospitals that delivers its services to the target population with minimal time, pollution and cost. We develop a Multi-Criteria Decision Analysis process that combines Geographical Information System (GIS) analysis with the Fuzzy Analytical Hierarchy Process (FAHP), and use this process to determine the optimum site for a new hospital in the Tehran urban area. The GIS was used to calculate and classify governing criteria, while FAHP was used to evaluate the decision factors and their impacts on alternative sites. Three methods were used to estimate the total weights and priorities of the candidate sites: fuzzy extent analysis, center-of-area defuzzification, and the α-cut method. The three methods yield identical priorities for the five alternatives considered. Fuzzy extent analysis provides less discriminating power, but is simpler to implement and compute than the other two methods. The α-cut method is more complicated, but integrates the uncertainty and overall attitude of the decision-maker. The usefulness of the new hospital site is evaluated by computing an accessibility index for each pixel in the GIS, defined as the ratio of population density to travel time. With the addition of a new hospital at the optimum site, this index improved over about 6.5 percent of the geographical area. [Copyright &y& Elsevier]

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 (Res f), 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 new methodology is proposed for evaluating resilience of lakes under drought. • Some new criteria are proposed for assessing the lake's ecological condition. • The evidential reasoning approach is utilized for combining resilience sub-criteria. • Three new resilience indices have been proposed for evaluating management scenarios. • The methodology has been applied to Lake Urmia and Zarrinehrud river basin in Iran. [ABSTRACT FROM AUTHOR]

While being affected by economic and hydrological conditions, the behaviors of water market participants can also be caused by their psychological characteristics and social stimuli of the environment. This paper employs agent-based modeling (ABM) approach to simulate a local groundwater market in central Iran. The proposed ABM framework couples social, economic, and hydrological sub-models. The social sub-model benefits from the theory of planned behavior under field studies to design psychology-based behaviors of trading agents. Moreover, in continuous interaction with the FlowLogo hydrological sub-model, the economic sub-model simulates the inter-sectoral water trading under a double-auction mechanism. The inter-sectoral trading includes selling the farms' irrigation water to the industry sector. The calibration and validation results for an eight-year simulation period (2010–2018) confirm the acceptable performance of the proposed ABM framework. Water trading patterns experience relatively extreme variations in the first years. However, with the adaption of the agents' bids to the market conditions, they gradually emerge in a more stable form in the last years. Furthermore, updating the psychological factors increases the agents' intention of participating in the market, and thus, the competition level over time. Finally, the hydro-economic analysis reveals that implementing the dynamic cap-and-trade policy increases the total net benefits of market participants by an average of 27% per year while reducing the region's groundwater drawdown by 56 cm. Such inter-sectoral water markets can help with the sustainable exploitation of groundwater resources. [Display omitted] • An agent-based model dynamically integrates the hydro-economic concepts with the psychology-based behaviors of trading agents in inter-sectoral groundwater markets. • Participant's psychological factors gradually increase the market's competition level. • The cap-and-trade policy can help with the sustainable exploitation of the limited groundwater resources. [ABSTRACT FROM AUTHOR]

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 total resilience criterion is proposed for assessing water resources systems. • This criterion can evaluate the resilience against events with known and unknown probability. • The grade-based values of performance criteria are estimated and combined using ER. • The previous resilience evaluations /have underestimated the critical conditions. • The total cost and resilience of the selected scenario are 1272.5 × 106 $ and 0.85. [ABSTRACT FROM AUTHOR]

Understanding the complexity and feedbacks among food, energy, and water (FEW) systems is key to making informed decisions about sustainable development. This paper presents qualitative representation and quantitative system dynamics simulation of the water resources system in the Qazvin Plain, Iran, taking into account the energy intensity of water supply and interconnected water use sectors (e.g., urban, industrial, and agricultural). Qazvin Plain faces water resources challenges that are common to arid/semi-arid areas, including frequent droughts, declining surface water and groundwater, and increased urban and agricultural water demand. A system dynamics model is developed using historical data (2006–2016) to investigate the effects of anticipated dynamics of integrated water and energy sectors in the next two decades. The results of policy scenarios (2020–2039) demonstrate that the continuation of the existing management policies will cause severe damage to the water and energy sectors, pushing the system towards water resources limits to growth. An annual groundwater table decline of nearly 1 m is anticipated, indicating significant overshoot of the plain's natural recharge capacity, which may lead to the depletion of recoverable groundwater in the plain within the next three decades. The groundwater table decline will cause energy consumption of water supply to increase by about 32% (i.e., 380 GWh) to maintain irrigated agriculture. It is critical to implement a combination of water demand and supply management policies (e.g., net agricultural water savings and recycling treated wastewater) to delay the problem of water limits to growth in the region. • We present a system dynamics of model integrated water and energy sectors in an agricultural region. • The model facilitates evaluation of water management policies using a food-energy water nexux approach. • Policies that facilitate net agricultural water savings and water reuse can delay groundwater depletion. [ABSTRACT FROM AUTHOR]

Sustainable development is being reconsidered as a process with unknown endpoint. Outputs of sustainable urban water systems defined as 'policies, projects, laws, technologies, and consumption and reuse amounts associated with urban water sustainability goals' are therefore being viewed as inadequate monitoring instruments. I propose a new methodology for sustainability monitoring whereby normality of a system is diagnosed through normality of its supporting inputs in lieu of normality of its complex outputs. Supporting inputs are 'intents and behaviors that support system goals'. Supporting inputs follow a principle of self-organization to remain in the norm and behavior zone commonly associated with system goals. This implies that normality of supporting inputs can be inferred from their longitudinally normal or Gaussian distribution that can be explored by significance tests; in particular, the Shapiro-Wilk test which is most powerful for n < 50. We identify fourteen supporting inputs of sustainable urban water systems - such as internet searches, community campaigns, staff training, agent-principal reporting and legislation propositions about water sustainability – and define quantitative indicators for them. The Shapiro-Wilk test and Kolmogorov-Smirnov test (K–S) of these indicators and a subsequent Boxplot outlying examination of non-normal indicators are undertaken in Yazd – a desert city in central Iran with a historic record in water conservation – in the light of its complex wastewater speculation. Qualitative examination of non-normal supporting inputs confirms the ability of our statistical methodology to detect problems in the system. • This paper proposes a new methodology for monitoring sustainability of urban water systems. • Normality of the system is inferred from normality of its supporting inputs. • Supporting inputs are intents and behaviors that support system goals. • Normality of supporting inputs is inferred from their longitudinally normal distribution. • Fourteen supporting inputs and their quantitative indicators are identified for significance tests. [ABSTRACT FROM AUTHOR]

Today, an increase in the drought and water shortage all around the world is a challengeable threat for different governments and international committees. Water supply chain aims to manage the water consumption and to control the water shortage. Contrary to most of previous studies focused on the forward directions of the water supply chain, this paper also considers the reverse logistics known as a Closed-Loop Supply Chain (CLSC). Hence, an integrated Water Supply and Wastewater Collection System (WSWCS) under uncertainty is proposed. Furthermore, regarding the trend of sustainable development, the environmental dimensions and social benefits of the integrated WSWCS are contributed. To meet the standards of the sustainable development in developing countries, a case study in Iran is applied to a novel multi-objective stochastic optimization model based on triple bottom lines of sustainability. Having already been employed to similar optimization problems, the Social Engineering Optimizer (SEO) has been never applied in this research area. Another innovation of this study is to introduce an improved multi-objective SEO to solve this complicated model. Eventually, with regards to an extensive comparison contributing to the Pareto-based metrics and different sensitivities, some managerial implications are concluded as the main findings. • Developing a novel multi-objective stochastic optimization model to design a sustainable water supply network. • Considering triple bottom lines of sustainability for an integrated water supply and wastewater collection system. • Applying the life cycle assessment method to estimate the sustainability aspects. • Solving the case study of Urmia Lake by an improved social engineering optimizer. [ABSTRACT FROM AUTHOR]