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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]

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]

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]

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]

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]

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]