Bijan Nazari

Climate change in hot and arid regions like Abadan poses a serious threat to food security and water resources. Accurate projections of future conditions are essential for developing effective adaptation strategies. In this study, the LARS-WG 8 model was applied using meteorological data from Abadan station (Iran) under three climate scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, for the year 2040. Interpretation of the climate data for 2023 and 2024 compared to future scenarios for 2040 (SSP1-2.6, SSP2-4.5, SSP5-8.5) at Abadan station reveals a clear trend of warming and seasonal shifts in precipitation. In July, maximum temperature rises from 47.2℃ (2024) to 48.7℃ (SSP5-8.5), while evapotranspiration drops from 393.5 mm to around 291–294 mm, a 25% reduction. December precipitation increases dramatically from 0.8 mm to 25.7 mm in SSP5-8.5, over 30 times higher. In contrast, rainfall in June remains zero across all scenarios. March precipitation declines from 45.0 mm to 19.1 mm in SSP2-4.5 (a 58% drop), while minimum temperature rises from 13.9℃ to 16.0–16.5℃ (about 15% increase). These shifts indicate higher temperatures, reduced summer evapotranspiration, and a concentration of rainfall in cooler months, directly affecting crop water needs and irrigation scheduling. Based on the results obtained, it is recommended that agricultural planning in Abadan move towards climate change-resistant cropping patterns, improve irrigation efficiency, and revise water allocation plans to take into account the increasing temperature and increasing concentration of precipitation in the cold months of the year.

Climate change in hot and arid regions like Abadan poses a serious threat to food security and water resources. Accurate projections of future conditions are essential for developing effective adaptation strategies. In this study, the LARS-WG 8 model was applied using meteorological data from Abadan station (Iran) under three climate scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, for the year 2040. Interpretation of the climate data for 2023 and 2024 compared to future scenarios for 2040 (SSP1-2.6, SSP2-4.5, SSP5-8.5) at Abadan station reveals a clear trend of warming and seasonal shifts in precipitation. In July, maximum temperature rises from 47.2℃ (2024) to 48.7℃ (SSP5-8.5), while evapotranspiration drops from 393.5 mm to around 291–294 mm, a 25% reduction. December precipitation increases dramatically from 0.8 mm to 25.7 mm in SSP5-8.5, over 30 times higher. In contrast, rainfall in June remains zero across all scenarios. March precipitation declines from 45.0 mm to 19.1 mm in SSP2-4.5 (a 58% drop), while minimum temperature rises from 13.9℃ to 16.0–16.5℃ (about 15% increase). These shifts indicate higher temperatures, reduced summer evapotranspiration, and a concentration of rainfall in cooler months, directly affecting crop water needs and irrigation scheduling. Based on the results obtained, it is recommended that agricultural planning in Abadan move towards climate change-resistant cropping patterns, improve irrigation efficiency, and revise water allocation plans to take into account the increasing temperature and increasing concentration of precipitation in the cold months of the year.

This study utilizes a system dynamics approach (SD) to assess the effects of water productivity improvement strategies on the Qazvin plain, Iran, and the uncertainty of the individual and interactive effects. The key indicators included in the important strategies are cropping pattern scenarios (CPS), deficit irrigation (DI), and modern irrigation systems development (MISD). Plain-scale results show that CPS 7, CPS 4, and CPS 8 had the highest physical water productivity (WP_p) at 2.11, 1.99, and 1.95 kg/m3, respectively, representing a 21, 14, and 12 percent increase over CPS 1. Compared with CPS 1, CPS 4, CPS 6, and CPS 8 showed the highest values of WP_e (5678, 5568, and 5503 Rials/m³, respectively). At the field scale, under DI, WP_p increased for all crops (except corn, which was the most sensitive), but WP_e is only increased for tomato, canola, pea, and barley and reduced for corn, potato, beans, lentils, and sugar beet. The WP_e was affected by the DI, the irrigation system type, and the CPS. CPS 7 and CPS 6 had the highest and lowest water requirements, respectively, with 11,699 and 8207 m3/ha. Volume decline in aquifers is significantly affected by both CPS and DI. The CPS6, CPS8, and CPS2 were better than other scenarios. By modifying the cropping pattern, it is possible to prevent aquifer decline, thus improving the aquifer status (CPS5). MISD increased both field and plain WP_p for all crops. The MISD improved groundwater resources and reduced demand by increasing efficiency to improve aquifer condition.

Background: Due to the complexity and interdependence of water resources, the evaluation of the efficiency of water resources management is difficult. So, it is necessary to use effective modeling methods to study and recognize the feedback loops available in water resources systems. Regarding the importance of domestic water for the ...

Increasing groundwater salinity has recently raised severe environmental and health concerns around the world. Advancement of the novel methods for spatial salinity modeling and prediction would be essential for effective management of the resources and planning mitigation policies. The current research presents the application of machine learning (ML) models in groundwater salinity mapping based on the dichotomous predictions. The groundwater salinity is predicted using the essential factors (i.e., identified by the simulated annealing feature selection methodology) through k-fold cross-validation methodology. Six ML models, namely, flexible discriminant analysis (FDA), mixture discriminant analysis (MAD), boosted regression tree (BRT), multivariate adaptive regression spline (MARS), random forest (RF), support vector machine (SVM), were employed to groundwater salinity mapping. The results of the modeling indicated that the SVM model had superior performance than other models. Variables of soil order, groundwater withdrawal, precipitation, land use, and elevation had the most contribute to groundwater salinity mapping. Results highlighted that the southern parts of the region and some parts in the north, northeast, and west have a high groundwater salinity, in which these areas are mostly matched with soil order of Entisols, bareland areas, and low elevations.