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The composition of food baskets can significantly influence the water consumption rate and society's environmental impact. This study evaluates the sustainability of Iran's food and nutrition security by evaluating five food baskets using water and carbon-footprint indices. These food baskets are the common Iranian food basket (CIFB), the vegetarian food basket (VFB), the Ministry of Health desired food basket (MHDFB), the Swiss Society for Nutrition food basket (SSNFB), and the Mediterranean food basket (MFB). This study also examines the role of Iran's agricultural sector under the Paris Climate Change Agreement (COP21). The TOPSIS multi-criteria decision-making method was used to identify the most desirable food basket. The results of this study show that the VFB could be the most desirable alternative to the CIFB. Replacing the CIFB with the VFB causes a 10% and 21% lower water and carbon footprint, respectively. From a 30-year perspective (around 2050), choosing the VFB would reduce the blue water footprint by 19.7 BCM and the gray water footprint by 3.6 BCM compared to the CIFB. According to the provisions of COP21 regarding Iran's voluntary program to reduce greenhouse gas emissions, the agricultural sector will be one of the sources of greenhouse gas emissions that will face an upward trend. Hence, to adhere to this voluntary commitment, these emissions should be reduced by changing the CIFB to the VFB or reducing emissions in other sectors, such as industry. Choosing the VFB can reduce 10.7 million tons of CO2 emissions, which can be a significant step for the agricultural sector in Iran in reducing greenhouse gas emissions. Overall, considering the characteristics of farm products (water and carbon footprints) in Iran, a country located in a semi-arid climate, the VFB is recommended. [ABSTRACT FROM AUTHOR]

Vulnerability maps of groundwater provide an efficient means of identifying environmental trends and prioritizing regions for prevention plans. The GIS-based DRASTIC method, however, does not consider the impact of contamination, so there is a need to modify it according to the specifics of the region and its contamination load. The aim of this study is to investigate a suitable DRASTIC modification for vulnerability assessment by changes in its rating and weighting systems. The goal is to explore and compare the impact of both objective and subjective weighting methods in the vulnerability assessment of a smaller aquifer situated beneath agricultural land. The frequency ratio (FR) method is used to adjust the DRASTIC index rates based on nitrate contamination as the main contamination from fertilizers extensively used in the study area. The DRASTIC parameters' weights are determined using two objective and subjective methods, including Shannon entropy and single-parameter sensitivity analysis (SPSA), respectively. In total, five frameworks, including FR-DRASTIC, DRASTIC-Entropy, DRASTIC-SPSA, FR-SPSA, and FR-Entropy are developed and evaluated. We validate these proposed vulnerability indices based on the nitrate concentrations in 14 samples. The results show that the vulnerability map obtained from the FR-Entropy framework is superior, showing a 0.85 correlation with nitrate concentrations. Notably, Shannon entropy as an objective weighting method outperformed the subjective SPSA approach. [ABSTRACT FROM AUTHOR]

Determining optimal exploitation from aquifers is always a major challenge, especially for aquifers facing a drop in their groundwater level. In aquifers with artificial recharge, more complex algorithms are required to determine the optimal exploitation amount. Therefore, in this study, the optimal amount of harvest from the exploitation wells has been determined using a combined simulation–optimization model considering the artificial recharge in Yasouj aquifer in Iran. The model is based on a combination of MODFLOW code and gene expression programming (GEP) simulator tool to simulate the aquifer and particle swarm optimization (PSO) to maximize the total exploitation from the aquifer. The simulation results showed that the artificial recharge was ineffective in maximum exploitation from the aquifer. As a result, considering several constraints, including the maximum pumping rate from the aquifer and the permissible drop in the groundwater level, the maximum exploitation from the aquifer was defined as the objective function. The optimization results showed that the optimal exploitation rate is equal to 8.84 million cubic meters (MCM) per year, and only 74% of the water from artificial recharge can be used based on this amount. Additionally, the most appropriate locations to exploit this amount of water are the northwest and east of the aquifer. According to the findings, it is suggested to ban exploitation from the central and southern parts of the aquifer due to the low groundwater level. The results of the sensitivity analysis show that the reduction in the maximum exploitation rate along with a 50% drop in the groundwater level play an effective role in decreasing the optimal exploitation amount. [ABSTRACT FROM AUTHOR]

Assessing the status of water resources is essential for long-term planning related to water and many other needs of a country. According to climate reports, climate change is on the rise in all parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects. [ABSTRACT FROM AUTHOR]

The assessment and prediction of the groundwater resource quality are required for the sustainable management of this crucial resource. This study develops a new index for assessing and a model for predicting the quality of groundwater resources. The groundwater quality index (GWQI), the Shannon entropy method, was used to determine the weight of parameters, and the complex proportional assessment multi‐decision criteria method was used to score the GWQI. Water quality parameters, including TDS, EC, TH, Mg+2, Na+, SO42−, pH, K+, Cl−, HCO3− and Ca+2, were used as decision criteria. The support vector regression–particle swarm optimization)SVR‐PSO(simulation–optimization model is developed to predict new GWQI (C‐GWQI) of the aquifer. The development of this new index called C‐GWQI is one of the innovations of this article. Based on these approaches, the index is used to determine three water quality classes (optimum, permissible, and impermissible) for drinking water following World Health Organization (WHO) criteria. The distribution of C‐GWQI shows that groundwater quality in most of the Zanjan aquifer of Iran was in the optimum range. Still, it is deteriorating into the permissible range due to pollution from urban areas during some periods. The hybrid SVR‐PSO model can predict the groundwater quality with sufficient accuracy with a Mean Absolute Relative Error (MARE) of 1.5% and 0.88% in training and testing phases, respectively. Results show that temperature, precipitation, evaporation, returned water and groundwater level did not significantly affect groundwater quality prediction. In contrast, the previous month's C‐GWQI, recharge, and discharge were most influential in predicting groundwater quality. [ABSTRACT FROM AUTHOR]