Assessing water and environmental carrying capacity (WECC) requires a holistic approach that integrates ecological and hydrological aspects. This study introduces a novel integrated framework that couples projections of ecological footprint (EF), biological capacity (BC), and the water stress index (WSI) to evaluate WECC under climate change scenarios. The framework is applied to the Zarrinehrud Basin, the primary inflow source for Lake Urmia—one of the world's largest saltwater lakes—to support ecosystem preservation and sustainable development. Detailed time series analyses of BC and EF across multiple land uses reveal that carbon emissions constitute over 70% of the region's total EF. Using downscaled climate projections from five general circulation models (GCMs) and three shared socioeconomic pathways (SSPs), future carbon trends are forecasted, showing a potential increase of 6.43–6.52 million tons by 2040. This amplifies the existing per capita ecological deficit of 1.49 global hectares. To mitigate this impact, the study designs 15 targeted carbon-reduction packages spanning energy, transport, industry, and household sectors. Concurrently, WSI analysis identifies critical water stress threatening Lake Urmia's ecosystem, prompting the development of seven additional water-use optimization packages to improve WECC. The coupling of these mitigation strategies within the integrated framework identifies the most effective combination—packages P4, P5, P8, P11–P14, P17, P20, and P21—improves the ecological stress index from 2.52 to 0.9 and reduces the WSI from 0.7 to 0.43 by 2040 under the most probable climate change scenario. This intervention successfully transitions the basin from a “critically overloaded” state to “moderate capacity,” demonstrating that integrated water and environmental management can restore regional sustainability even under climate change pressures. This combined approach offers a powerful tool for restoring ecological balance and provides a decision-support system that can be adapted for other basins facing comparable sustainability challenges, thereby enhancing WECC evaluation and policy development.

This study presents an integrated modeling framework to quantify the relative impacts of climate change and anthropogenic activities (CCAA) on coupled surface and groundwater systems. The approach combines an integrated SWAT-MODFLOW (ISM) model with the fixing-changing (F-C) method to separate climate-driven and human-induced contributions to observed hydrological changes under historical conditions. The framework is applied to the Zarrinehroud basin and the Miandoab aquifer in northwestern Iran using observed hydro-meteorological, streamflow, and groundwater-level datasets for the period 1993–2017. Change-point detection based on Buishand, Pettitt, and SNHT tests identifies 2002 as a statistically significant shift, dividing the study period into a reference period (1993–2001) and a change period (2002–2017). The ISM model is calibrated and validated for the reference period and subsequently applied to the change period to quantify climate and anthropogenic impacts on surface runoff, groundwater levels, and their integrated response. Results indicate that anthropogenic activities, primarily groundwater abstraction, account for 83.4% of the combined surface and groundwater variability, while climate change contributes 16.6%. Surface runoff and groundwater levels both exhibit significant post-2002 reductions, accompanied by weakened surface-groundwater interactions, despite a slight increase in mean annual precipitation. Rising temperature and evapotranspiration intensify climate-driven stress but play a secondary role relative to human water abstraction. The findings highlight the dominant role of human water abstraction in altering both surface and subsurface components of the hydrological system and demonstrate the value of integrated surface-groundwater modeling for attribution analysis. The proposed framework provides a transferable tool for diagnosing historical drivers of water resources change and informing sustainable water management in heavily managed basins.

This study investigates how the seven core resilience principles are integrated into assessments of forest system resilience to natural or human-induced disturbances across engineering, ecological, and social-ecological resilience concepts. Following PRISMA guidelines, a literature search in the Web of Science database using the keywords “resilience”, “forest” and “ecosystem services” yielded 1828 studies, of which 330 met the selection criteria. The most commonly used criterion was diversity, a sub-criterion of “diversity and redundancy”, appearing in 50% of studies. The results indicate that social and governance-related principles, learning and experimentation (7%), participation (11%), and polycentric governance (9%) have not been frequently addressed. Although numerous studies have employed various principles for assessing forest resilience, none have considered all seven principles jointly. This highlights a significant research gap, emphasising the need to quantify these principles in forest systems. Understanding forest-community dynamics is essential for enhancing the long-term resilience and sustainability of both systems.

This paper presents a novel approach for quantifying and assessing water resources and environmental carrying capacity (WRECC) leveraging the Drivers-Pressures-State-Impacts-Responses (DPSIR) framework. Unlike previous approaches that were based on aggregating all DPSIR indicators to quantify WRECC, our framework explicitly integrates influence pathways into WRECC estimation through the “Impacts” component, capturing long-term systemic effects of upstream changes (e.g., drivers and pressures) on sustainability outcomes. Targeted strategies are designed to enhance WRECC under climate change scenarios (representative concentration pathway (RCP) 4.5, 6.0, and 8.5) by directly and/or indirectly modifying DPSIR criteria, with impacts evaluated across environmental, socio-economic, and cultural dimensions. The superior strategy is selected using the fallback bargaining method incorporating stakeholder utilities to ensure feasibility and acceptance. Applied to Iran's Zarrinehrud Basin, located within the Lake Urmia basin, the world's second largest saltwater lake, the framework reveals baseline conditions' WRECC as severely overloaded due to agricultural expansion and climate pressures. Simulations of 40 strategies for the periods 2000–2023 and 2024–2039, using climate change scenarios (i.e., RCP 4.5, RCP 6.0, and RCP 8.5) demonstrate that conventional measures (e.g., irrigation efficiency) alone fail to restore balance. In contrast, Strategy S39, reducing agricultural employment by 30 % while promoting industry and tourism, improves WRECC by 53 %, achieving a balanced state by 2039. Key findings highlight that strategies targeting “drivers” (e.g., employment shifts) outperform those focused solely on “pressures” or “state” components, underscoring the need for systemic interventions. The proposed framework's adaptability offers a transferable tool for basins facing similar sustainability challenges.

Tropical forest systems (TFSs), play a crucial role in maintaining the planet's ecological balance, supporting life on Earth, and providing different ecosystem services, which are vulnerable to environmental (e.g., severe droughts) and human-induced disturbances (e.g., deforestation).The resilience concept is usually considered in evaluating a forest system under these severe disturbances. However, while resilience evaluations have mainly focused on engineering and ecological perspectives, the integration of social core resilience principles (3SRPs)- learning and experimentation (P5), participation (P6), and polycentric governance (P7)- remains limited. This study performs a systematic review of papers published between 2000 and 2024, focusing on social resilience in tropical forest systems to assess the application of the 3SRPs, following the (PRISMA) framework for systematic reviews, and identify the research gaps in social-based resilience studies. The keywords “resilience”, “forest”, and “ecosystem services” were searched in the “Web of Science” and “Scopus” databases from 2000 to 2024. The 24-year timeframe captures the evolution of resilience theory from early ecological foundations to contemporary social-ecological applications. The results show that despite the recognition of social aspects in selected studies (49), 55% of studies have considered one social principle, 12% studies taken two principles into account (i.e., P6 and P7), and only 8% of reviewed studies have incorporated all three social principles together in their assessments. Social aspects such as stakeholders' participation and governance are often overlooked, with the majority of evaluations focusing on ecological criteria. There is a crucial need for an integrated approach that considers social and ecological criteria to assess forest resilience, with an emphasis on the effective application of 3SRPs.

Deepwater bodies such as dam reservoirs are among the most prominent water resources that supply societies' water demand. Hence, water quality in these resources requires continuous and meticulous monitoring. This study evaluated the water quality of Changuleh Dam, a dam under construction in western Iran, using the CE-QUAL-W2 model to predict fluctuations in sulphate (SO42−) and hydrogen sulphide (H2S) concentrations. A sensitivity analysis was conducted to assess the dependence of model outputs on input variables. Additionally, an uncertainty analysis was performed using MATLAB to address the inherent uncertainties of the input variables through an ARIMA (2, 2, 1) time series model and Monte Carlo simulations. The results indicated a high sulphate concentration averaging 955.6 mg/l, considerably exceeding the drinking water standard of 400 mg/l, identifying the reservoir as a sulphate-rich water resource. Seasonal fluctuations of H2S concentrations were also observed in the reservoir's anaerobic bed sediments, peaking at 87 mg/l. The sensitivity analysis showed that nitrate and total dissolved solids (TDS) varied more noticeably than dissolved oxygen (DO) at different depths, with the greatest variation at 50 m. This study highlights the critical need for continuous and meticulous monitoring of water quality in dam reservoirs to meet water supply standards.

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.