Articles
Natural Hazards (15730840)
Drought disasters can affect socio-economic systems and ecosystems. Furthermore, based on the observations, the Eastern Middle East (EME) region has experienced significantly faster warming compared to other populated areas. Wherein, Iran has also suffered from several long droughts. Predictions regarding the spatial and temporal distribution of drought events over Iran and its neighboring countries improve our understanding of this process and help reduce exposure to water scarcity and its destructive effects by preparing timely measures. In this study, complex networks are applied to analyze the paths and propagation distances of droughts, identifying the sources and vulnerable regions in EME. Additionally, event synchronization techniques to detect drought events that simultaneously impact multiple regions are utilized. For this purpose, important events are determined according to the SPEI index during 1970–2020. Based on the developed network over the country, the inward and outward strength criteria of drought events across different time scales and eight geographical directions are calculated. The findings reveal a significant vulnerability in the western and northwestern regions of Iran, highlighting their susceptibility to drought events originating in its southwestern and northwestern neighboring countries. This pattern suggests a strong influence of regional meteorological conditions and hydrologic al connectivity on drought propagation within these regions. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Scientific Reports (20452322)15(1)
Agriculture, being a major consumer of water resources, is crucial for ensuring global food security. Current patterns of water use and agricultural practices, if continued, are projected to cause severe water shortages, particularly in agriculture, by 2054. This water scarcity has already reduced crop cultivation, threatening future food and water security. This study introduces a dynamic system-based model to simulate water resources, focusing on agricultural water consumption and groundwater reclamation from 2025 to 2054. The model evaluates cultivated areas using three indicators: physical productivity, economic productivity, and water consumption. Under projected conditions, significant water shortages and declining underground water levels are anticipated. The most effective scenario involves halting cultivation of water-intensive crops, reducing groundwater withdrawal by 25%, and transferring 250 million cubic meters of water annually. This approach increases surface and underground water levels by 29.5% and 36.5%, respectively, and offsets 65.1% of the water shortage. These results emphasize the urgent need for sustainable water management to address future water scarcity and ensure agricultural and food security. The proposed model serves as a valuable tool for policymakers to design and implement strategies in water-scarce regions. © The Author(s) 2025.
Environmental Quality Management (15206483)34(2)
In addressing the complex challenges of sustainable development, the Water-Energy-Food (WEF) nexus underscores the critical role of wastewater management. This study evaluates centralized and decentralized wastewater treatment systems using the innovative Water-Energy-Food-Economy-Environment Nexus Index (WEFEENI). Centralized systems are traditionally favored in urban settings due to economies of scale, yet they encounter high costs and environmental impacts. In contrast, decentralized systems offer flexibility and lower operational costs, making them suitable for less populated regions. Our findings reveal that decentralized systems significantly reduce energy consumption by 72.88%, investment costs by 52.01%, and operating costs by 87.98%, while also lowering greenhouse gas emissions. Although centralized systems excel in food production and annual income, decentralized systems perform better in water and energy productivity, green space development, and aquifer recharge. The study highlights the importance of adopting a holistic approach, tailored to specific contexts and priorities, to achieve sustainable wastewater management. © 2024 Wiley Periodicals LLC.
Water Resources Management (09204741)38(3)pp. 881-892
In this study, variations of macroinvertebrates are considered as a criterion for assessing biological diversity and ecosystem health in the downstream reach of a river-reservoir system. Unlike most of the previous studies, this biological diversity index is then used in a multi-objective reservoir operation optimization model as an objective function instead of a constraint. Two objectives of supplying water demand and ecological diversity were maximized for the case of the Aboulabbas Dam in Khuzestan Province in southwest of Iran. Based on the historical records of water quality and macroinvertebrate samples, a relationship between these two parameters was used in the optimization model formulation. Evaluation of the results in a 10-year period and comparison with single-objective optimization shows that using the proposed methodology, the biodiversity and ecosystem health has been improved while achieving an acceptable level of water supply reliability. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Journal of Hydrology (00221694)626
Satellite-based terrestrial water storage changes have been recorded using the Gravity Recovery and Climate Experiment (GRACE) satellite which causing it an important dataset in hydrology and other related fields. GRACE dataset is widely utilized in many studies, but its coarse spatial resolution is a limiting drawback. Machine-learning approaches (e.g., ANN and SVM) are commonly applied in spatially downscaling. However, their input formation, which is in vector form, is a limitation of considering neighbor relations between the gridded-based inputs, specifically in spatial downscaling. Thus, developing an appropriate, simple, fast, and novel model to spatially downscale GRACE resolution is initially necessary for its utilizations. In this study, a Spatially Promoted Support Vector Machine (SP-SVM) model is innovatively proposed for GRACE downscaling from 0.5° to 0.25°. This promotion is investigated utilizing the distances between the unknown target points (with 0.25°) and their surrounding GRACE-valued points (0.5°), called their Distance Effect Coefficient (DEC), as the SP-SVM model input. In addition, the efficiencies of different in-situ and satellite-based datasets (fifteen variables from May 2005 to August 2020) are evaluated as the inputs of the GRACE downscaling models. After finding the most influential datasets, showing the best correlation with the GRACE, their best combinations in GRACE downscaling are identified. Based on the results, the set of PERSIANN-CDR without delay, the in-situ evaporation with a 1-month delay, and the soil moisture in 0–10 cm depth with a 1-month delay show the best performance in GRACE downscaling. The results of GRACE downscaling by the SP-SVM approach are also compared with the ones based on a usual statistical SVM (S-SVM) model, consisting of an intermediate bias interpolation to improve the estimations through a bias correction step. The results show that the SP-SVM model outperforms the common statistical SVM-based. Thus, compared with the usual S-SVM approach, the proposed SP-SVM (linear) model could be used as a simpler and more accurate model for downscaling any variable in a hierarchical process. © 2023
