Abdolmajid Liaghat

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.

Soil water retention curve (SWRC) and thermal conductivity curve (TCC) are crucial soil properties affecting water flow and plant growth in soils. This study investigated simultaneous SWRC and TCC parameter estimation using an inverse solution approach. Water and heat movement in soil were modelled in two soil column experiments, including infiltration with warm water (IWW) and evaporation with heat pulse (EHP), using the HYDRUS‐1D package. For the IWW experiment, two scenarios were considered, each based on a selection of parameters for the inverse solution. For the EHP experiment, 13 scenarios were developed by varying combinations of heat pulses and soil suction sensors as inputs. Unique solutions were obtained in the first IWW, fifth EHP, and 12th EHP scenarios. The first IWW scenario estimated two SWRC parameters (empirical shape parameters, α and n ) and three TCC parameters (empirical parameters in soil thermal conductivity function, b ₁ , b ₂ and b ₃ ) using the temperature profile and cumulative infiltration as inputs. The fifth EHP scenario estimated five SWRC parameters (saturated [ θ _s ] and residual [ θ _r ] water content, saturated hydraulic conductivity K _s , α and n ) and three TCC parameters ( b ₁ , b ₂ and b ₃ ) using three heat pulses and four tensiometers data as input to the model. The results showed both experiments could estimate SWRC and TCC, with EHP estimating up to eight parameters compared to five for IWW. The 12th EHP scenario (two heat pulses and two tensiometers) provided a unique solution using less input data, offering a more convenient approach, though with slightly wider bounds of estimated parameters.

One of the most significant, intricate, and little-discussed aspects of pressurized irrigation is cost estimation. This study attempts to model the early-stage cost of the drip irrigation system using a database of 515 projects divided into four sections the cost of the pumping station and central control system (TC_P), the cost of on-farm equipment (TC_F), the cost of installation and operation on-farm and pumping station (TC_I), and the total cost (TC_T). First, 39 environmental and management features affecting the cost of the listed sectors were extracted for each of the 515 projects previously mentioned. A database (a matrix of 515 × 43) was created, and the costs of all projects were updated for the baseline year of 2022. Then, several feature selection algorithms, such as WCC, LCA, GA, PSO, ACO, ICA, LA, HTS, FOA, DSOS, and CUK, were employed to choose the most significant features that had the biggest influence on the system cost. The selection of features was carried out for all features (a total of 39 features) as well as for easily available features (those features that existed before the irrigation system’s design phase, 18 features). Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. Support vector machine (SVM) and optimization algorithms (Wrapper) were found to be the best learner and feature selection techniques, respectively, out of all the available feature selection algorithms. The two LCA and FOA algorithms produced the best estimation, according to the evaluation criteria results. Their RMSE for all features was 0.0020 and 0.0018, respectively, and their R² was 0.94 and 0.94. For readily available features, these criteria were 0.0006 and 0.95 for both algorithms. In the part of the overall feature, the early-stage cost modeling with selected features revealed that the SVM model (with RBF Kernel) is the best model among the four cost sections discussed. Its evaluation criteria in the training stage are R² = 0.923, RMSE = 0.008, and VE = 0.082; in the testing stage, they are R² = 0.893, RMSE = 0.009, and VE = 0.102. The ANN model (MLP) was found to be the best model for a subset of features in the easily available feature part, with R² = 0.912, RMSE = 0.008, and VE = 0.083 in the training stage and R² = 0.882, RMSE = 0.009, and VE = 0.103 in the testing stage. The findings of this study can be utilized to highly accurately estimate the cost of local irrigation systems based on the recognized environmental and management parameters and by employing particular models.

The movement of solutes in soil is crucial due to their potential to cause soil and groundwater pollution. In this study, a mathematical model based on the Advection Dispersion Equation (ADE) was developed to evaluate solutions for solute transport. This equation enabled us to attain a relationship for concentrations at different locations and times, also known as the breakthrough curve. Five columns (5 cm in diameter and 30 cm in height) of soil types were prepared to check the validity of the results. An evaluation of the calculated relations showed high accuracy in estimating the breakthrough curve and the saturated hydraulic conductivity of the soil.

The assessment of sprinkler system performance is crucial in ensuring the efficient use of water resources. The commonly used indicators of the uniformity of water distribution in sprinkler systems are Christiansen's uniformity (CU) and distribution uniformity (DU) coefficients. A more comprehensive analysis of water distribution is essential in situations where the reliability of these coefficients as indicators of water distribution patterns is limited. In the present research, distribution maps of water depth were prepared from water application profiles using catch cans experiments that were carried out in the research farm of the Agriculture and Natural Resources Faculty of the University of Tehran, which is located in Alborz province. In this way, water application profile data were obtained at different operating pressures (200, 250, 300, 350, and 400 kPa). By using this data, 2D and 3D distribution maps of water depth were created due to the overlapping of sprinklers in different arrangements, spacing, and pressures. In addition, CU and DU coefficients in square, rectangular, and triangular arrangements with different spacing and pressures from 200 to 400 kPa. A total of 11,250 different simulations were calculated and analyzed. Distribution maps of water depth contribute to advancing the understanding of sprinkler irrigation system performance and aid in the optimization of water management practices.

In recent decades, groundwater overexploitation has caused an important aquifer level decline in arid zones each year. In addition to this issue, large volumes of effluent are produced each year in metropolitan areas of these regions. In this situation, an aquifer storage and recovery system (ASR) using the reclaimed domestic wastewater can be a local solution to these two challenges. In this research, a post-treatment of reclaimed municipal wastewater has been investigated through unsaturated–saturated porous media. A large-scale, L-shaped experimental model was set up near the second-stage wastewater treatment plant (WWTP) in the west of greater Tehran. The water, soil, and treated wastewater of the experimental model were supplied from the aquifer, site, and WWTP, respectively. The 13 physicochemical parameters, temperature and fecal coliform were analyzed every 10 days in seven points for a period of four months (two active periods of 40 days with a 12-h on–off rate (wet cycles) and a rest period of 40 days (dry cycle) between the two wet cycles). The results showed that the effects of the saturated zone were twice as great as those of the unsaturated zone and two-thirds of the total treatment efficiency. Furthermore, a discontinuous wet–dry–wet cycle had a significant effect on effluent treatment efficiency and contaminants’ reduction. In conclusion, an aquifer storage and recovery system using treated wastewater through the unsaturated–saturated zones is a sustainable water resource that can be used for agriculture, environmental and non-potable water demands.