https://ijswr.ut.ac.ir/ Iranian Journal of Soil and Water Research en daily 1 Fri, 20 Feb 2026 00:00:00 +0330 Fri, 20 Feb 2026 00:00:00 +0330 https://ijswr.ut.ac.ir/article_106005.html Sandy soils, commonly found in arid and semi-arid regions, offer poor growing conditions for crops like wheat owing to their inherently low ability to retain moisture and nutrients. Amending these soils with stable and eco-friendly conditioners is therefore essential to enhance their productivity. This study aimed to investigate the effects of bentonite and nanobentonite application, including 30 tonnes of bentonite with 0.75 tonnes of nanobentonite (B30NB0.75), 30 tonnes of bentonite with 2.5 tonnes of nanobentonite (B30NB2.5), and 60 tonnes of bentonite (B60), as well as positive and negative controls, on the physical properties of sandy soil and the physiological parameters of Triticum aestivum L. (Sirvan cultivar) under greenhouse conditions. The experiments were conducted in a completely randomized block design with three replications, and the results were analyzed using cluster analysis. The findings showed that the applications of bentonite and nanobentonite in sandy soil increased soil saturation water content (47%), decreased bulk density (15%), and improved soil aggregate stability (260%). The improvement of soil physical properties following bentonite and nanobentonite applications led to increased physiological parameters of wheat, including biomass weight, grain weight, grain number, spike length, and plant height. Cluster analysis indicated that the physiological parameters measured in wheat under the B30NB2.5 treatments were closely related to those observed in the positive control, while the B60 treatment exhibited a similar effect to the negative control. These results highlight the key role of nanobentonite in improving wheat yield in sandy soil. The mechanism of nanobentonite effectiveness is attributed to its ability to increase cation exchange capacity, enhance soil structure, improve water and nutrient retention, and reduce nutrient leaching. Overall, the combined application of bentonite and nanobentonite, particularly at the B30NB2.5 level, can be recommended as an effective and eco-friendly approach to improving the fertility of sandy soils and increasing wheat production in arid and semi-arid regions. https://ijswr.ut.ac.ir/article_106006.html Monitoring the morphological changes of rivers (especially narrow-width rivers) has always been challenging. In this study, after preparing and preprocessing Sentinel-2 satellite images, the performance of two machine learning methods—supervised Support Vector Machine (SVM) classification and unsupervised K-means clustering—for extracting the centerline of the Atrak River in Golestan province was evaluated. The spectral indices NDWI, MNDWI, and AWEIsh, along with spectral bands, were used to train the models. The accuracy of the SVM model was assessed using the Kappa coefficient and IoU metrics. The river centerlines for both methods were extracted using QGIS software, and the accuracy of the results was evaluated based on RMSE, percentage length difference, and spatial agreement (using a 10-meter buffer zone). Finally, the centerline extracted by the superior model was used to calculate geometrical parameters and meander migration rates. The accuracy assessment results clearly demonstrated the superiority of the SVM method. For this method, the Overall Accuracy, Kappa coefficient, and IoU for 2016 were 96.7%, 0.9333, and 0.9354, respectively, and for 2021 were 95%, 0.9, and 0.9045, respectively. Furthermore, the RMSE of the SVM method (3.82 m and 3.35 m for 2016 and 2021, respectively) was significantly lower than that of the K-means method (5.11 m and 4.58 m, respectively). The analysis of morphological changes indicated a very high and varying migration rate of the Atrak River in different meanders, with the highest meander migration rate calculated as 39.7 meters per year. The dominant patterns of these changes were rotation and extension. https://ijswr.ut.ac.ir/article_106007.html This study investigates upper-level atmospheric circulation over Iran's North-Western region, centered on Tabriz, from 1948 to 2010. The core of the research is the creation of a Daily Cyclonicity Index (DCI) for the 500 hPa and 700 hPa levels, designed to quantify the influence of cyclonic troughs and anticyclonic ridges, utilizing 23011 daily geopotential height maps from the NCEP/NCAR Reanalysis. The study classifies circulation into five distinct types: Trough Line (TL), Trough Edge (TE), Geopotential Col (COL), Ridge Edge (RE), and Ridge Line (RL). The analysis of mean annual frequencies at 500 hPa reveals a pronounced dominance of high-pressure systems. Ridge patterns (RL and RE) collectively influence the East Azerbaijan area for 53% of the year, while trough patterns (TL and TE) occur only 22% of the time. This anticyclonic dominance is especially strong in the warm season, governed by the subtropical high-pressure belt. A more balanced distribution emerges in the cold season, with high, col, and low systems occurring 43%, 28%, and 29% of the time, respectively. The long-term trend assessment using the Mann-Kendall test identifies a significant shift in circulation patterns over the 63-year period. The study documents a statistically significant decrease in the frequency of low-pressure systems and a concurrent increase in high-pressure systems. This trend provides a clear signal of changing atmospheric dynamics in the region, with important implications for local climate, including potential impacts on precipitation amount and patterns and drought frequency in Iran's North-West. https://ijswr.ut.ac.ir/article_106008.html Due to complexity of the evapotranspiration process and the interactive effects of climatic variables, conducting sensitivity analysis is essential for identifying the most influencing parameters and reducing uncertainties in ETp estimation models. Sensitivity analysis is particularly important under climate change conditions, as it provides insights into how ETp responds to variations in temperature, radiation, humidity, and wind speed. This study evaluates the sensitivity of (ETp) to mean, minimum, and maximum temperature, wind speed, net radiation, and relative humidity across eight selected stations in Iran during the baseline (2001–2024) and the future (2025–2100) period. The global Sobol sensitivity analysis method, based on variance decomposition, was applied to quantify both the individual and interaction effects of input parameters on model outputs. Baseline climate data were obtained from IRIMO and Future climatic data were obtained from the CNRM-ESM2-1 and INM-CM5-0 climate models under the SSP2-4.5 and SSP5-8.5 scenarios of CMIP6. ETp was calculated using three methods FAO-56 Penman–Monteith, Romanenko and Thom-Oliver using the PyET package. Results indicated that Tmax and RH are the most influential variables in most stations. In the future period, the influence of temperature-related variables increases, while the role of radiation and wind decreases, confirming global warming and the reduction in atmospheric humidity effects on ET. A shift from radiation-dominated to temperature-dominated control was observed in humid northern regions, whereas increased sensitivity to relative humidity and mean temperature was evident in central arid regions. These findings suggest that revising water management strategies as a result of altered energy–moisture balance is essential, https://ijswr.ut.ac.ir/article_106009.html This study aimed to investigate the effects of polyethylene microplastics and the application of an efficient bio-recombinant agent composed of bacterial and fungal strains to mitigate the adverse impacts of microplastic particles on various properties of a soil–plant system. To achieve this objective, a factorial experiment was conducted in a completely randomized design, incorporating two types of polyethylene microplastics, low-density polyethylene (LDPE) and high-density polyethylene (HDPE), at three concentration levels (0%, 1%, and 2%), with and without inoculation of the bio-recombinant agent. The experiment was carried out in a sandy loam soil with three replications per treatment, following a 135-day incubation period and subsequent cultivation of sunflower plants over a 60-day growth cycle. The results revealed that in treatments without the bio-recombinant agent, the presence of LDPE and HDPE microplastics in the soil, particularly at higher concentrations, led to a significant increase in total soil organic carbon. Specifically, LDPE treatments showed increases of 0.35% in P1 and 0.55% in P2 compared to the control, while HDPE treatments exhibited increases of 0.23% in P1 and 0.51% in P2. Moreover, the type of microplastic influenced the extent of organic carbon accumulation; LDPE in the P1 treatment resulted in a 13% higher increase in total organic carbon compared to HDPE. Additionally, the findings demonstrated that the presence of both types of microplastics, when combined with bio-recombinant inoculation, significantly enhanced total organic carbon and water-soluble organic carbon, particularly the fraction soluble in hot water, compared to the control and other non-inoculated treatments. Notably, the impact of higher concentrations of LDPE was more pronounced than HDPE, with increases of 32.1%, 18.1%, and 35.1%, respectively. https://ijswr.ut.ac.ir/article_106010.html The present study investigated the effects of irrigation with industrial wastewater and the application of sewage sludge on heavy metal bioavailability and accumulation in purslane (Portulaca oleracea L.). A pot experiment was conducted in a completely randomized design with treatments including municipal water, industrial wastewater, contaminated and uncontaminated soils, with or without sewage sludge application. Soil characteristics (pH, EC, CEC, and available heavy metals) and as well as heavy metal contents in the plant were measured. The results indicated that industrial wastewater, with high nitrogen and phosphorus and moderate EC, could be used for irrigation; however, long-term increases in EC and SAR may negatively affect soil structure. Application of sewage sludge significantly increased the availability of Cd, Cu, Ni, and Zn. Heavy metal accumulation in purslane followed Zn > Cu > Ni > Cd, predominantly in the aerial parts. Translocation factor (TF) of Cd (3.19), Ni (1.37) and Cu (1.91) was particularly enhanced in contaminated soil irrigated with municipal water, showing the highest accumulation and translocation to shoots. In this study, the calculation of the bioconcentration factor (BCF) was carried out based on the amount of bioavailable heavy metals present in the soil. Due to the high BCF for studies heavy metals in most treatments, purslane can be used as a monitor plant. Hazard quotient for Cd exceeded the permissible limit, indicating a potential risk to food safety. Overall, wastewater irrigation and sewage sludge use improved soil fertility but increased heavy metal accumulation, emphasizing the need for continuous monitoring. https://ijswr.ut.ac.ir/article_106011.html Drought stress is one of the major limiting factors for plant growth and productivity, posing serious challenges to agricultural production. Nutrient management, particularly the supply of key elements such as phosphorus and zinc, can play a significant role in enhancing plant resistance to drought conditions. This study aimed to investigate the effect of phosphorus and zinc application on the drought tolerance of two wheat cultivars (Tajan and Heydari) during the summer of 2024 in the research greenhouses of the Soil and Water Research Institute in Alborz Province, Iran. The experiment was conducted as a factorial design within a completely randomized layout, including four factors: method of phosphorus and zinc application (soil and seed priming), irrigation levels (100% and 50% of field capacity), and wheat cultivar (Tajan, as drought-sensitive; Heidari, as drought-resistant). Results indicated that drought stress negatively affected morphological and performance traits of wheat. However, soil application of phosphorus (12 mg/kg) and zinc (2 mg/kg) under drought stress significantly increased dry weight of shoots and roots (up to 500%), plant height and root length (up to 130%), and relative water content (by up to 200%). Moreover, soil-applied zinc played a crucial role in rhizosheath formation as a defense mechanism against drought. Overall, simultaneous soil application of phosphorus and zinc proved more effective than seed priming in improving wheat tolerance to drought stress, leading to better biomass allocation and enhanced plant resilience. https://ijswr.ut.ac.ir/article_106012.html Soil salinity stress is a major environmental challenge in arid and semi-arid regions. Cultivating salt-tolerant plants is a sustainable approach for livestock feed production and soil conservation. This study aimed to investigate the effects of irrigation water salinity on biomass production and morpho-physiological traits of two-year-old vetiver grass (Chrysopogon zizanioides) seedlings.The experiment included six levels of water salinity (city water (as the control), 4, 8, 16, 32, and 64 dS/m), arranged in a completely randomized block design with four replications over ten months.Results showed that from the seventh month onward, plant height, fresh and dry weight of underground organs, leaf relative water content, and proline concentration were significantly affected by salinity, whereas crown diameter, fresh and dry weight of aerial parts and leaves, aboveground biomass, and photosynthetic pigments were not significantly affected. The lowest plant height occurred at 64 dS/m, while the highest was observed at 32 dS/m, representing a 57% increase compared to the control. Fresh and dry weight of roots and leaf relative water content exhibited significant changes at higher salinity levels. Furthermore, contrary to expectations, proline content decreased at 4 dS/m. A transfer factor (TF) below 1 indicates that vetiver grass acts as a salt-excluding plant. Overall, vetiver grass demonstrated high tolerance to salinity; even at 64 dS/m, aerial biomass and crown development were not significantly affected. These findings support the use of this species for forage production and soil protection in saline rangeland restoration projects. https://ijswr.ut.ac.ir/article_106013.html Objective: The primary aim of this study was to calibrate and validate the Rothamsted Carbon model (RothC) and to simulate the effects of future climate scenarios on soil respiration flux from Tamarix, Haloxylon, Atriplex, and Nitraria trees and their understory rangelands in the Meighan Kavir. Method: Following RothC model validation, the impacts of a baseline scenario without climate change and three projected climate scenarios (RCP2.6, RCP4.5, and RCP8.5) were simulated across three periods (2025–2040, 2041–2060, and 2061–2080) to estimate soil respiration fluxes from the different vegetation covers. Results: Model validation demonstrated high accuracy of RothC in reproducing observed SOC data in the Meighan Kavir, with a model performance efficiency (EF) of 0.96. Compared to the baseline scenario, the highest soil respiration fluxes were predicted in 2025–2040 under RCP4.5 and RCP8.5 (up to a 36% increase), and in 2041–2060 under RCP2.6 (up to a 30% increase). Across all vegetation types, soil respiration fluxes exhibited a decreasing trend over time under all climate scenarios. After 2061, the decline slowed and approached a relatively stable state, though minor variations among vegetation types persisted. Conclusions: Considering the lower soil respiration fluxes from Nitraria trees and their understory rangelands, combined with the species’ native status, Nitraria is recommended for cultivation and restoration in the Meighan Kavir to mitigate desertification. https://ijswr.ut.ac.ir/article_106014.html Rising temperatures and altered precipitation patterns driven by global warming pose a serious threat to the sustainability of vegetation and agriculture in arid and semi-arid regions such as the Karun River basin. This study investigates the impacts of climate change on the Normalized Difference Vegetation Index (NDVI) and monitors agricultural drought using the Vegetation Condition Index (VCI) under the IPCC’s SSP3 and SSP5 scenarios. Climate model outputs for a historical period (1991–2014) and three future intervals (2020–2045, 2046–2072, 2073–2099) were analyzed at five synoptic stations (Borujerd, Boroujen, Abadan, Kouhrang, and Yasuj). Results for the baseline period indicate that the climate models simulate temperature with high accuracy (R² > 0.95 and RMSE in the range of ~1.5–2.2 °C). A nonparametric Theil–Sen regression model demonstrated satisfactory performance in estimating NDVI from temperature, with R² values between 0.49 and 0.77 and p-values < 0.05. Analyses reveal an increase in NDVI in colder regions (up to 68.56% in Kouhrang under SSP5) and a more limited decline (up to 14% in Abadan) in warmer areas. VCI-based results indicate an increased frequency of severe droughts (up to 11% in Kouhrang) and a reduction in wet conditions. These trends are amplified under SSP5, particularly in the 2073–2099 interval, where winter VCI declines of up to 40% indicate intensifying moisture stress. The findings underscore the necessity of adaptation measures (such as integrated water resources management and crop pattern adjustments) and provide an evidence base for resilient planning in the face of climate change. https://ijswr.ut.ac.ir/article_106015.html Soil erosion prediction at the watershed scale remains a critical challenge in sustainable land and water resource management. Although many studies have applied various empirical and statistical models, most have not adequately integrated long-term sediment records with a regional approach that accounts for both environmental and hydrological variability.The aim of this study is to develop regional models for predicting soil erosion rates through the integration of long-term sediment monitoring data and advanced statistical techniques, including Factor Analysis (FA), Principal Component Analysis (PCA), and Clustering methods, across the Urmia Lake watershed. Sediment data from 30 selected gauging stations within the major sub-basins were collected over a 20-year period and correlated with hydrological, topographic, geological, climatic, and land-use parameters of the study area. Initially, PCA was applied to reduce multicollinearity among input variables and to identify the principal factors controlling sediment transport. Subsequently, using hierarchical clustering based on hydrological behavior and sediment load, the sub-basins were divided into two distinct groups. Finally, the results of the factor analysis were employed as optimized inputs for multivariate regression modeling, leading to the development of regional predictive models. The validation results demonstrated that the developed models possess a high explanatory capacity, with an average coefficient of determination (R²) of 0.89 at the regional scale. The findings indicated that three primary factors — agricultural land area, erosion-prone lithological formations, and mean annual discharge — exerted the greatest influence on variations of the Sediment Delivery Ratio (SDR) across the sub-basins. The developed regional models provide strong potential for rapid assessment of erosion risk in newly monitored sub-basins without the need for extensive field measurements, offering an effective decision-support tool for sustainable land management, soil erosion control, and watershed policy planning within the Urmia Lake basin. https://ijswr.ut.ac.ir/article_106016.html Climate change has profound impacts on hydrological processes, with evaporation and wind patterns playing key and determining roles. In this study, using the outputs of 18 climate models from the CMIP6 project under four emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the future changes in evaporation and wind over the Caspian Sea during the period 2021–2100 were investigated. In the first step, the models’ performance was evaluated against observational data for the baseline period (1980–2020), and error analysis was conducted. Subsequently, two models with the best performance, MPI-ESM1-2-HR and MIROC6, were selected for the final analysis. The results indicate that the annual average evaporation will increase in all scenarios compared to the baseline period, with an increase of approximately 11% in SSP1-2.6, 23% in SSP2-4.5, 26% in SSP3-7.0, and 31% in SSP5-8.5. This increase is more pronounced during warm seasons, especially in summer and early autumn, with monthly growth reaching up to 60% relative to the baseline. Moreover, wind patterns exhibit remarkable changes; the average wind speed in the future is projected to vary between 2 and 6 m/s, while in winter, under the influence of the Siberian high-pressure systems, values exceeding 6 m/s are recorded. These changes, by intensifying evaporation through the replacement of saturated air with dry air, will have significant consequences for hydrodynamics, circulation patterns, water balance, and vulnerable ecosystems such as the Gorgan Bay and Kara-Bukaz-Gol. The findings suggest that the combined effects of increased evaporation and altered wind regimes may seriously threaten the water level and ecological stability of the Caspian Sea. Therefore, considering these developments in regional water resource management and policy-making is essential. https://ijswr.ut.ac.ir/article_106020.html Eutrophication, resulting from excessive inputs of phosphorus and nitrogen into surface waters, is recognized as a major global environmental challenge. This phenomenon, through the occurrence of extensive algal blooms and the proliferation of invasive aquatic plants, disrupts the balance of aquatic ecosystems, degrades water quality, and threatens biodiversity. This article first examines the eutrophication process and distinguishes it from natural eutrophication, and then outlines the main management strategies at three levels: source control, in-transit control, and end-of-pipe treatment. In addition, biomanipulation is introduced as a complementary approach to improve water quality by regulating food-web structure, including classical methods such as the removal of planktivorous fish and stocking of piscivorous fish, as well as newer approaches involving phytoplanktivorous fish, bivalves, and submerged macrophytes. Emerging technologies, including phosphorus-adsorbing nanomaterials and photoactive lighting systems, are also analyzed as novel solutions for the direct suppression of algal blooms. The novelty of this study lies in proposing an integrated and adaptive framework that combines ecological, biological, and technological approaches, while explicitly considering climatic and ecosystem-specific differences among lakes, thereby enabling the design of sustainable and site-specific eutrophication management strategies. The results indicate that integrating biological and technological methods, tailored to the ecological characteristics of each lake, can play a key role in the effective and sustainable management of eutrophication and in preventing the spread of invasive aquatic plants. https://ijswr.ut.ac.ir/article_106021.html A greenhouse experiment was conducted using a completely randomized factorial design with three replications to evaluate the effects of different levels of amino acid–rich soluble organic fertilizers enriched with various iron sources on maize growth and soil enzymatic activity in calcareous soils. Fertilizer type was considered as the first factor at eight levels, and fertilizer application rate as the second factor at two levels. The treatments included: control (C); 3% iron-containing ferrous sulfate solution (S); organic fertilizer without iron enrichment (O); separate application of organic fertilizer without iron and 3% ferrous sulfate (OS); organic fertilizer enriched with 3% iron from ferrous sulfate (A); organic fertilizer enriched with 1.5% iron from Fe-EDTA and 1.5% from ferrous sulfate (AE); organic fertilizer enriched with 1.5% iron from Fe-DTPA and 1.5% from ferrous sulfate (AD); and organic fertilizer enriched with 1.5% iron from Fe-EDDHA and 1.5% from ferrous sulfate (AH). These fertilizers were applied via fertigation at two levels, 50 and 100 L ha⁻¹, corresponding to 0.08 and 0.16 g per 3 kg of soil, respectively. The results showed that all fertilizer treatments increased fresh and dry weights of shoots and roots, plant height, and soil enzymatic activity compared to the control. The AH treatment at 100 L ha⁻¹ (AH100) exhibited the highest yield, increasing dry shoot and root weights and plant height by 34%, 31%, and 36.9%, respectively, compared to the control, as well as enhancing alkaline phosphatase, catalase, and urease activities by 23.2%, 22.3%, and 21.5%, respectively. Application of the organic fertilizer containing Fe-EDDHA significantly improved maize growth and physiological status, which was attributed to enhanced iron nutrition, chlorophyll content, and soil biological activity. https://ijswr.ut.ac.ir/article_101026.html In this study hydraulic jump (HJ) in a rectangular channel with a sudden divergence and the use of an intersecting beam system as roughness to stabilize the asymmetric hydraulic jump was simulated using Flow3D . The characteristics of the hydraulic jump were investigated using three different configurations of the intersecting beam system. The number and thickness of the beams at different percentages of the reference tailwater depth (hs) were examined . The results showed that use of the intersecting beam system leads to stability and elimination of asymmetric waves and return flow in the asymmetric S-type jump and significantly reduces the jump length. The maximum reduction in the length of the asymmetric S-type HJ was observed for configuration 3 and 0.9hs at 78.02%. To evaluate the optimal configuration for a Fr of 9.5, which was measured using a 3D EMV velocity meter, it was found that the use of intersecting beams in all cases of tailwater depth and configurations reduces the jump length. The trend of changes in the dimensionless length of the basin as an indicator of length reduction as well as the roller length from 0.7hs to hs was decreasing in such a way that on average, the dimensionless roller length in 0.7hs tailwater conditions with a value of 19.65 has the highest and hs of 11.63 has the lowest value and compared to the reference test value for a Fr of 9.5 showed a decrease of 54.77 and 73.23 percent, respectively. https://ijswr.ut.ac.ir/article_105694.html With growing global concerns about soil and water pollution and the escalating impacts of climate change, there is an urgent need for innovative technologies capable of simultaneously achieving bioremediation and clean energy generation. Microbial fuel cells (MFCs) and their sediment- or soil-based variants (SMFCs) have emerged as promising bioelectrochemical systems that convert the chemical energy of organic matter into electrical energy through the metabolic activity of electrogenic microorganisms, while concurrently degrading resistant pollutants. Microbial communities residing in sediments and soil layers, particularly those forming anodic biofilms, play a crucial role in system performance by facilitating direct and mediated electron transfer. However, the open and dynamic nature of SMFC environments leads to complex microbial succession, influencing the electrochemical stability and long-term efficiency of the system. Despite significant progress, challenges such as limited diversity of efficient electroactive species, competition between non-electrogenic and electrogenic microorganisms, and biofilm instability continue to restrict large-scale deployment. This review focuses on the microbial and electrochemical aspects of MFCs and SMFCs, discussing electron transfer mechanisms, microbial community dynamics in anodic and cathodic zones, and the influence of electrochemical parameters on system performance. Future perspectives include the development of engineered microbial consortia with complementary functionalities, integration of biostimulation strategies to regulate microbial succession, and optimization of operational conditions to enhance both power generation and bioremediation efficiency. The insights presented in this review may facilitate the design of more sustainable and efficient systems for environmental management and renewable energy production. https://ijswr.ut.ac.ir/article_105755.html This study aims to identify flood‑prone areas and assess the performance of two statistical models—Shannon Entropy and the Evidential Belief Function (EBF)—for flood‑hazard zoning in the Kela Rud watershed, located in Babol County, Mazandaran Province, northern Iran. A GIS‑based spatial integration framework was applied using nine conditioning factors: elevation, slope, aspect, land use, soil type, distance from rivers, distance from roads, drainage density, and the topographic wetness index (TWI). Core datasets were derived from a 30‑m DEM, thematic layers, and Sentinel‑2 imagery (2023). The Shannon Entropy model quantified the informational contribution of each factor, showing that elevation (0.1983), aspect (0.1517), and slope (0.1423) were the most influential variables. In the EBF model, spatial flood probability was reconstructed using belief (Bel), disbelief (Dis), and uncertainty (Unc) indices, allowing more reliable representation of uncertain and incomplete data. Model performance was evaluated through the area under the ROC curve (AUC) and root mean square error (RMSE). The EBF model achieved higher predictive accuracy (AUC = 0.83; RMSE = 0.219) compared to the Shannon Entropy model (AUC = 0.71; RMSE = 0.293). High‑hazard zones were mainly located at elevations of 700–1300 m and slopes of 30–45°, particularly around the villages of Shiadeh, Anjilak, and Lamsukola. The superior performance of the EBF model reflects its capacity to synthesize heterogeneous evidence and manage uncertainty in complex hydrological environments. The results provide a robust scientific basis for watershed management, flood‑risk monitoring, and sustainable land‑use planning in similar mountainous forested regions. https://ijswr.ut.ac.ir/article_105886.html This study aimed to assess the sensitivity of rainfed wheat yield to climatic indices over the period 1990–2023 across three representative stations in Zanjan Province, Iran —Zanjan, Khodabandeh, and Khorramdarreh. The Extreme Gradient Boosting (XGBoost) algorithm, interpreted via SHapley Additive exPlanations (SHAP) values, was employed to identify the most influential climatic variables and to determine their optimal and yield-limiting thresholds. The methodological framework integrated four key approaches that have been seldom considered simultaneously in previous research: (i) estimating the length of the growing period (LGP) based on updated FAO guidelines, (ii) aligning climatic data with the actual crop growing period, (iii) focusing on the temporal distribution patterns of climatic indices rather than solely on their cumulative or seasonal means, and (iv) performing interactive and interpretable climatic analysis. The results indicated increasing trends in both yield and LGP across all three sites, although the LGP trend in Zanjan was statistically non-significant (P > 0.1). SHAP analysis revealed that moisture-related variables were the primary determinants of yield in all sites. Specifically, effective rainfall (ERGP: 1.8–2.9 mm/day) in Khodabandeh, the number of precipitation days (N_pr^GP: 4–31 days) in Zanjan, and uniform rainfall distribution (URGP: 11.2–31 mm) in Khorramdarreh emerged as the most influential positive drivers. Conversely, yield limitations were associated with shortened growing periods (LGP: 50–68 days) in Khodabandeh, poorly distributed rainfall (UR/ER: 5.4–10 mm) in Khorramdarreh, and a low number of precipitation days in Zanjan. https://ijswr.ut.ac.ir/article_105892.html Land subsidence, as a consequence of the excessive groundwater extraction, poses a serious threat to infrastructure and environment. In the current research, the land subsidence caused by groundwater withdrawal was investigated and modeled using a probabilistic analysis approach. A computer program was developed in MATLAB based on the modified Budryk–Knothe influence function and several copula functions were employed to model the dependence among input parameters. Given the significance of incorporating uncertainty in subsidence modeling, two approaches were considered and compared including the application of temporal random variables and the random variables related to mechanical soil properties. A case study was conducted in the Foomanat region of Guilan Province, characterized by silty clay soil. The results demonstrated that when the temporal random variables were considered, the variations in the range of calculated subsidence gradually decreased over time, indicating a more stable subsidence model. In contrast, incorporating the uncertainties associated with mechanical soil properties led to an increased scattering in subsidence values with time. Accordingly, the findings suggest that for short-term subsidence assessments, greater emphasis should be placed on soil mechanical properties, whereas temporal uncertainties become more dominant in long-term analyses. Generally, for the silty clay soil of the study area, the Frank copula was identified as the most appropriate function for modeling the joint probability distributions when the temporal random variables and random soil properties are considered. https://ijswr.ut.ac.ir/article_105930.html This study was conducted in two parts to investigate the ability of actinomycete strains to increase potassium solubility and improve soil properties and barley growth. In the first part, an experiment was conducted using a completely randomized design with 29 actinomycete strains in Alexandrov medium, with three replications, to evaluate the strains' ability to dissolve potassium and alter the pH. In the second part, barley was cultivated under four treatments (control (C), chemical fertilizer (CF), and two strains, S3C and S5A) with three replications in a completely randomized block design. Some soil characteristics, growth traits, and plants' nutrition were evaluated. In Alexandrov medium, the lowest pH value (3.23 and 4.25) and the highest amount of soluble potassium (4.83 and 4.73 mg/L) were observed in the S3C and S5A strains, respectively. The lowest pH value and the highest amount of soil potassium were observed in the two treatments containing actinomycete. The amount of available soil phosphorus in the S3C treatment and the amount of available nitrogen and iron in the S5A treatment were the highest. The amount of plant potassium in the S3C and S5A treatments increased 3 times compared to the control. The highest amount of plant phosphorus and iron was observed in the S3C and S5A, respectively. Since these microorganisms, in addition to releasing potassium into the soil, also enhance the overall nutrient status of the soil, their use as a biofertilizer, as demonstrated by field experiments, can increase crop productivity and support the development of sustainable agriculture. https://ijswr.ut.ac.ir/article_105931.html Lead contamination of water resources as an environmental challenge requires the development of highly efficient decontamination methods such as the use of efficient, non-toxic and low-cost adsorbents. This study was conducted to compare the performance of rice husk biochar (RHB) and layered double hydroxides (Mg/Al-LDH) in removing lead from aqueous solutions. In this study, biochar was prepared from the pyrolysis of rice straw and stubble residues at 500°C under limited oxygen conditions and magnesium/aluminum layered double hydroxides at a ratio of 2:1 by co-precipitation method. The evaluation of parameters including adsorption kinetics, isotherm, and the initial solution pH on lead adsorption was performed. According to the results, kinetic studies showed that lead adsorption by RHB and LDH reached equilibrium in 60 and 240 min, respectively and the pseudo-second-order kinetic model showed high ability to predict the adsorption kinetics. with the increasing of initial solution pH, lead adsorption by both adsorbents increased. The Langmuir models showed high ability to predict the lead adsorption behavior by the adsorbents. The maximum lead adsorption capacity by RHB and LDH was 297.83 and 173.46 mg/g, respectively. The results of this study confirm that rice husk biochar can be considered a superior, low – cost, non – toxic and environmentally friendly option for the decontamination of lead from aquatic environments due to the high accessibility of active sites and stable performance over a wide pH range. https://ijswr.ut.ac.ir/article_105994.html Soil water retention characteristics, such as field capacity (FC) and permanent wilting point (PWP), are vital for efficient water management, yet their direct measurement is often challenging. This study aimed to estimate FC and PWP using visible-near infrared (Vis-NIR) spectral data and soil physicochemical properties through random forest (RF) and multiple linear regression (MLR) models. A total of 130 soil samples were collected from five provinces in Iran. Spectral and physicochemical properties were analyzed, and the dataset was divided into training (90) and testing (40) subsets. Eleven pedotransfer functions (PTFs) were developed using three modeling steps. Spectral preprocessing methods, including multiplicative scatter correction (MSC), first and second derivatives with Savitzky–Golay filtering (FD–SG, FD–SG2), and standard normal variate (SNV), were compared with no-preprocessing (NP). The RF model (RMSE = 0.050) outperformed MLR (RMSE = 0.057) for FC prediction. For PWP, RF produced slightly better results across most PTFs, with significant improvement for PTF2 (AIC = −264.3). During training, PTF11 achieved the best performance for FC (AIC = −540.2), while PTF7 showed the highest accuracy for PWP (AIC = −612.4). PTF3, based on sand, clay, and organic matter, was the most accurate estimator of FC (AIC = −553.3), and PTF6, using sand, clay, organic matter, and total porosity, was most effective for PWP (AIC = −616.2). Principal component analysis identified key wavelengths at 409 nm for FC and 1414, 1912, and 2150 nm for PWP. Integrating spectral and soil data with machine learning improved prediction accuracy over spectral-only models.