JWSS - Journal of Water and Soil Science

In the future, the risk of land subsidence due to water resources shortage crisis and improper water resources management will become more and more dangerous. It is necessary to assess and identify areas susceptible to subsidence risk and take necessary actions to reduce risks related to land subsidence. In this study, first, the risk of land subsidence was identified and evaluated using a radar interferometry method called LiCSBAS. Then, the spatial relationship between the occurrence of land subsidence hazard and effective factors such as ground elevation, slope, slope aspect, lithology, land use, groundwater decline, distance from rivers, distance from faults, topographic moisture index, and arc curvature was investigated using the random forest (RF) model. In the end, the land subsidence hazard sensitivity map was prepared after calibrating the random forest algorithm. The analysis of LiCSBAS interferometric time series data from 2015 to 2022 showed that the center of the Marvdasht-Kharameh plain and adjacent agricultural areas are continuously subsiding and the mean deformation rate map showed a subsidence rate of 11.6 centimeters per year. The results of determining the spatial relationship between subsidence occurrence and effective factors confirmed the positive impact of distance from rivers, urban and agricultural land uses, depth of bedrock (aquifer thickness), groundwater decline, and alluvial and fine-grained formations on this phenomenon. Also, the results of subsidence modeling using the random forest algorithm showed that factors such as bedrock depth, groundwater decline, land use, and geology have the greatest impact on the potential for subsidence occurrence in the study area. Also, based on the results, about 3 to 4 percent of the areas are in the very high and extremely high-risk classes of land subsidence, especially in the center and suburbs of Mervdasht. Therefore, water resources management and control and developing a systematic program to reduce subsidence risk and aquifer recharge conservation in Merudasht-Kharameh Plain is essential.

Astragalus is the vegetation of many mountains of Iran's plateau and plays a major role in providing ecosystem services due to its pillow shape and deep rooting system, they facilitate the control and penetration of precipitation into the soil. The correlation of Astragalus ecosystems with arid and semi-arid climates has made them vulnerable to climate change. In this study, a runoff yield map based on the Budyco curve under current and future conditions of climate change (2050) was prepared using climate and temperature data from the Chelsea site (CanESM2 GCM) in TerrSet software and by using maps of sub-watersheds, annual precipitation, annual potential evapotranspiration, soil depth, plant accessible water and the current and future "Land Cover - Land Use" map, with a combination of field methods and species distribution models at the local scale of the Shur River watershed of Dehaghan (Central Zagros). Finally, the excess runoff damage produced due to climate change was estimated using the replacement cost method. The results indicated an increase in the annual runoff volume of the watershed from 70 million cubic meters to 105 million cubic meters under climate change conditions for the RCP26 scenario in 2050. Taking into account the cost of 10 million Rials for controlling 530 cubic meters of runoff through various watershed management projects, preventing the damages of excess runoff produced requires a credit amounting to 660 billion Rials based on the present value. This study proved the ability of TerrSet software to predict and produce an ecosystem service map of runoff yield under climate changes or land use changes and with the purpose of valuation on a local scale. Also, the above valuation can be the basis for planning and providing credit for the study and implementation of watershed management projects to deal with the threats of climate change.

change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.

This study investigates the impact of agricultural activities on the soils of southern Mehriz by analyzing their geochemical composition and comparing the physicochemical properties of pistachio orchard soils (agricultural soils) with those of undisturbed natural soils. The results indicate that agricultural practices have led to an increase in Total Organic Carbon (TOC), averaging 1.5%, and a reduction in soil acidity. Additionally, phosphorus concentrations have risen in agricultural soils. These soils also exhibit enrichment in elements such as cadmium (Cd), antimony (Sb), chromium (Cr), nickel (Ni), lead (Pb), scandium (Sc), and rare earth elements (REE) compared to natural soils, with a more homogenized REE distribution pattern. Although the concentrations of these trace elements remain within national environmental standards and below critical thresholds, the study highlights a significant increase in the bioavailability of heavy metals due to agricultural activity. This finding underscores a potential environmental risk if such changes are not properly managed in the future.

Soil loss and extensive degradation caused by gully erosion have always caused serious damage. Because direct field measurement and monitoring of gully erosion are costly and time-consuming, it is very difficult to determine the amount of soil loss caused by gully erosion. The present research was conducted to calculate the volume of soil loss due to gully erosion using machine learning models in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province based on field studies. Machine learning models include random forest, support vector machine, artificial neural network, and adaptive neural fuzzy inference system. The location of 68 gullies in the area was recorded. Hence, initially, digital layers of factors affecting the expansion of gullies, including topography, pedology, lithology, and hydrology, were prepared as independent variables to model soil loss caused by gullies. Then, representative gullies were selected in the studied watershed, and the volume of soil loss due to gully erosion was directly measured in the field as a dependent variable. The measured gullies were randomly divided into two training and validation groups. The results of the models were evaluated using root mean square error (RMSE) and R2, and the models were compared. According to the results, gully erosion in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province is increasing every year. Also, the amount of erosion and soil loss will increase when the amount of rainfall and the frequency of intense rainfall (≥5mm) are high. Among the machine learning models used in the present research, the random forest (RF) model was selected as the best model to predict soil loss generated by gully erosion.

Soil, as one of the vital natural resources, plays a fundamental role in ecosystem sustainability and global food security; however, degradation caused by unsustainable management, intensive agriculture, and pollution threatens its capacity. The use of organic amendments such as hydrochar is considered an innovative approach to improve soil physicochemical properties and enhance the Soil Quality Index (SQI). This study aimed to investigate the effects of different levels of hydrochar on soil properties and evaluate SQI. The treatments included control and three hydrochar levels (H10, H20, and H50). Soil properties such as pH, porosity, bulk density, electrical conductivity, organic carbon, total nitrogen, and available phosphorus were measured and normalized, and parameter weighting was conducted using entropy and principal component analysis (PCA). Results showed that nitrogen and organic carbon had the greatest importance in soil quality. The H50 treatment recorded the highest SQI (0.815), significantly greater than other treatments, while H20 (0.546) and H10 (0.336) also showed positive effects compared to the control (0.159). Hydrochar application improved organic carbon, nitrogen, and phosphorus and reduced bulk density. Although an increase in electrical conductivity was observed in H50. Overall, hydrochar application had a positive and gradual effect on SQI, with H20 recommended as an optimal level to improve fertility and reduce long-term salinity risks.

This study aimed to optimize the site selection of artificial groundwater recharge zones in the Yazd-Ardakan watershed (covering an area of 116,765 hectares) to address challenges such as water scarcity, severe groundwater depletion, and annual rainfall below 100 mm. The research integrated Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods. Seventeen influential criteria, including elevation, slope, land use, geology, soil type, climate, distance from faults, and isothermal lines were analyzed using ArcGIS 10.5. Criteria weighting was performed using the Best-Worst Method (BWM), and layer integration was achieved through the Weighted Linear Combination (WLC) approach. Results indicated that slope (weight: 0.203), elevation (correlation >0.75), and land use (correlation ≈0.5) had the highest impact on zone suitability, while climate and isothermal lines were less influential. The final suitability map (900×900 m resolution) revealed that central and southern plain areas with slopes <2%, permeable alluvial formations, flat topography, and optimal distance from faults were prioritized for artificial recharge. Sensitivity analysis identified eight key criteria (correlation >0.5), and by eliminating 53% of non-essential parameters, an efficient framework for sustainable water resource management was established. This study not only contributes to raising groundwater levels, improving soil fertility, and preserving local ecosystems but also offers a practical solution for water crisis management in arid regions. 
 

The water crisis and climatic conditions in Iran have necessitated prioritizing low water-demand crops in agricultural investment policies. Quinoa, as a pseudo-cereal with relatively low water requirements, can be considered a suitable alternative to water-intensive crops. This study was conducted to investigate the comparative advantage of quinoa production compared to water-intensive crops under water-scarce conditions in Isfahan Province. To assess comparative advantage, two indicators—Domestic Resource Cost (DRC) and Social Cost-Benefit Ratio (SCB)—were employed. Data were collected through questionnaires and governmental sources, and shadow prices of inputs and outputs were utilized in the analyses. The statistical population consisted of quinoa producers. The results indicated that quinoa possesses a comparative advantage in Isfahan Province. The breakeven yield of this crop was estimated at 653 kg/ha, meaning that production above this level indicates the existence of a comparative advantage. Furthermore, the maximum allowable water consumption to maintain this advantage was estimated at 15,792 m³/ha; water consumption exceeding this amount eliminates the comparative advantage of quinoa cultivation. Additionally, the economic water productivity of quinoa was evaluated to be higher than that of other common crops in the region. Overall, considering comparative advantage of quinoa and its higher water productivity compared to common water-intensive crops, this product has the necessary potential to be included in the cropping pattern of Isfahan Province. However, recommendations for complete substitution require targeted and multidimensional policy interventions at technological, marketing, and policy-making levels, as well as conducting complementary research on the analysis and development of the quinoa value chain and market, investigating consumer behavior, designing targeted supportive policies for the transition period, and conducting a more comprehensive analysis of the comparative advantage of other products.

A field experiment was conducted using a factorial in a randomized complete block design to evaluate the growth and yield of rice (Oryza sativa L.) cv. Hashemi in response to nitrogen (N) and phosphorus (P) fertilization. The first factor comprised four N levels (0, 46, 69, and 92 kg N ha⁻¹), and the second factor comprised three P₂O₅ levels (0, 23, and 46 kg P₂O₅ ha⁻¹), with three replications conducted over two cropping years. At the end of the growing season, growth indices, yield, and yield components of rice were measured. The results indicated that the main effects of N and P fertilization on several traits (aboveground dry matter, grain yield, and straw yield) were significant at the 1% probability level. The N × P interaction for grain yield and the main effect of year were significant for most traits at the 5% level. The highest grain yield (935.2 kg ha⁻¹) and straw yield (1455 kg ha⁻¹) occurred with 92 kg N ha⁻¹ and 46 kg P₂O₅ ha⁻¹, representing increases of 115% and 120%, respectively, relative to the control (no N or P applied). This treatment is recommended for rice production under ratoon cultivation in areas with similar climatic and soil conditions

Excessive groundwater abstraction is a major environmental and economic challenge in Iran’s arid and semi-arid regions. This study examined farmers’ perceptions of groundwater over-abstraction and its perceived socioeconomic consequences in the Siminehroud catchment. Data from 373 questionnaires were analyzed using descriptive statistics, Mann–Whitney U tests, and chi-square tests. Effect sizes were reported, and p-values were adjusted using the Benjamini–Hochberg procedure. Overall, 41.8% of respondents believed that groundwater over-abstraction was occurring in their locality. Respondents with this perception were younger (median 48.5 versus 53 years; p<0.001), and the proportion with this perception was lower among users of piped water conveyance than among users of traditional canals (24.2% versus 45.6%; p=0.001). Both associations remained significant after false-discovery-rate adjustment. The most frequently perceived consequences of groundwater-level decline were declining farm income, loss of irrigated land, changes in cropping patterns, and increased migration. The findings indicate that groundwater management in Siminehroud should incorporate aquifer monitoring, improved water conveyance, and social interventions tailored to users’ characteristics.
 

Managed Aquifer Recharge (MAR) using treated wastewater is a viable strategy for water-resource sustainability in arid regions; however, the simultaneous assessment of its hydrogeological and economic aspects remains challenging. The objective of this study was to hydro-economically evaluate the implementation of an MAR project in the Qotb-Abad Plain by integrating numerical modeling and cost-benefit analysis (CBA). The quantitative and qualitative responses of the aquifer were simulated using the MODFLOW and MT3DMS models to estimate changes in groundwater levels, nitrate concentrations, and total dissolved solids (TDS). Economic benefits, including pumping energy savings, the value of stored water, and improvements in water quality, were evaluated alongside capital and operational costs under lower- and upper-bound scenarios. Subsequently, economic indicators, including net present value (NPV), benefit-cost ratio (BCR), internal rate of return (IRR), and payback period, were calculated. The results indicated that implementing MAR with an annual treated wastewater volume of 1.898 million m³ resulted in an approximately 4.5 m rise in groundwater level, accompanied by reductions in TDS from 1859 to 1752 mg/L and in nitrate concentration from 22.2 to 15.4 mg/L by the end of the five-year simulation period. Under the upper-bound scenario, the NPV, BCR, and payback period were 43.7 billion Rials, 1.36, and 3.23 years, respectively. In contrast, the lower-bound scenario was not economically justified, as its BCR was less than 1. Overall, integrating numerical modeling with cost-benefit analysis provides a comprehensive framework for supporting groundwater-management decision-making.