This study compared three uncertainty-based decision-making frameworks (considering/not considering the hierarchical structure of stakeholders) using resilience-based indices for evaluating different water resources management (WRM) scenarios under the impacts of climate change. The first step involved identifying significant stakeholders in the study area and establishing their relative weights. In the next step, stakeholders were asked to evaluate the management scenarios in the three different decision-making frameworks based on their decision criteria (nine resilience-based indices, implementation cost, and employment). Different types of weights (explicit and interval) were assigned to each stakeholder and their decision criteria, to account for the uncertainty associated with estimating their respective weights. This methodology was applied to the case of the Zarrinehrud River basin in northwest Iran. The best management scenario identified (MSC1346) was able increase lake elevation by 2.6 m (from 1271.3 m to 1273.9 m), improve the resilience of the system by 25 %, and enhance provisioning ecosystem services such as water and food supply and regulating services such as air quality. Comparing the results of the three decision-making frameworks revealed that the two which considered the hierarchical structure of stakeholders were more effective in determining the best scenario. The best scenario selected in the framework that ignored the hierarchical structure of stakeholders (MSC13567) had USD 202 million higher overall implementation and construction costs and gave a negligible difference in resilience value (0.04 difference) compared with scenario MSC1346.

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.

Given the substantial effects of agricultural practices on the environment, this paper introduces a novel stakeholder-based framework for assessing the ecosystem services (ESs) provided by agricultural areas. Ecosystem services include essential functions such as water supply, food production, carbon storage, soil erosion control, and habitat support. In addition to ESs, water footprint is also taken into account to evaluate the impacts of agricultural activities on water resources. Some of the mentioned ESs are assessed using the Soil and Water Assessment Tool (SWAT). Then, by extending and combining the Conflict Resolution Model with the Composition of Probabilistic Preferences (CRMCPP) method and the leader-follower game (LFG), while considering the hierarchical structure of decision-makers, the best scenario for enhancing the ESs is selected. The Zarrinehroud River Basin (ZRB) in Iran has been chosen as a case study to evaluate the performance of the proposed framework, as this basin is vital for supplying water to Lake Urmia, the largest hypersaline lake in the Middle East. In this paper, 16 Water and Environmental Resources Management (WERM) scenarios have been defined according to the Urmia Lake Restoration National Committee (ULRNC) projects. Then, the mentioned ESs have been evaluated under different WERM scenarios. Ultimately, by utilizing the CRMCPP-LFG method and taking into account the hierarchical structure of decision-makers, we can identify the optimal WERM scenario. The criteria for making this decision include various factors, such as ecosystem services and the costs involved in implementing the WERM scenarios. In the selected scenario, the average water inflow into Lake Urmia is projected to rise to 1329 million cubic meters per year, which is 6.3% more than the average inflow in the current condition. Key initiatives in this scenario include reducing cultivated areas, altering irrigation methods, changing crop patterns, and incorporating water-efficient plant species.

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

In this paper, a methodology is presented for managing hydrological ecosystem services by taking into account the hierarchy of stakeholders involved in the decision-making process. With this in mind, a water allocation model is first used for allocating water resources to demands. Then, several ecosystem services (ESs)-based criteria are defined to evaluate hydrological ESs of water resources management policies. A set of water and environmental resources management strategies (alternatives) are defined for decision-makers, and several drought management strategies are determined to decrease the area of key crops and water demands of agricultural nodes. To model a multi-agent multi-criteria decision-making problem for managing hydrological ESs, three main steps are considered as follows: • Different ES-based criteria (i.e., economic profit, NPP,1 and ecological index) are defined, and their grade-based values are estimated. • Several strategies are defined for stakeholders at different levels. • A recursive evidential reasoning (ER) approach, which considers a hierarchical structure for decision-makers and a leader-follower game, is used to select the best strategy for each decision-maker.The applicability and efficiency of the methodology are illustrated by applying it to a real-world case study. The methodology is general and can be easily applied to other study areas.

Due to the adverse impacts of severe droughts on various aspects of human life and ecosystem services (ESs), the spatio-temporal assessment of the resilience of ESs under droughts is essential. In this paper, we propose a new methodology for assessing ESs-based resilience, taking into account the seven resilience principles: a) redun-dancy and diversity, b) managing connectivity, c) managing slow variables and their feedback, d) complex adaptive system (CAS) thinking, e) experimentation and learning, f) broadening participation, and g) polycentric governance. These principles enable us to consider the main social, political, hydrological, economic, and environmental aspects concerning resilience which have been overlooked in previous studies. The methodology is evaluated by applying it to Zarrinehroud River Basin (ZRB) in north-western Iran. A set of qualitative and quantitative criteria and their sub-criteria are proposed for quantifying the ES-based resilience and generating time series of resilience against severe droughts in several sub-basins in the study area. To evaluate the criteria and sub-criteria, the required data are derived from calibrated SWAT and MODSIM models as well as experts' judjments. The time series of ES-based resilience under 128 Water and Environmental Resources Management (WERM) scenarios (for enhancing agricultural practices, altering and modernizing irrigation methods, improving irrigation network and drainage facilities) and Climate Change (CC) scenarios (RCP 4.5, RCP6.0, and RCP 8.5) are derived for each sub-basin based on short-term (2020-2049) and long-term (2020-2098) periods. The low resilience values (0.53-0.6) of all sub-basins under the base management scenario (the status quo scenario (SC0)) illustrate the need for implementing some projects to enhance the ESs in the study area. The results show that the WERM scenario SC12346 can improve the values of the resilience criterion in sub-basins up to 0.85 and reduce the vulnerability of the study area to droughts. By evaluating all management scenarios, scenario SC12346 is reported as the best scenario, since it can significantly increase the resilience of all sub-basins against extreme droughts with an acceptable cost of 636 million US dollars compared to other scenarios. Under this WERM scenario, the resilience values of sub-basins increase up to 40%. This scenario suggests implementing a set of projects such as improving irrigation networks and drainage facilities.