https://ijswr.ut.ac.ir/ Iranian Journal of Soil and Water Research en daily 1 Wed, 23 Sep 2026 00:00:00 +0330 Wed, 23 Sep 2026 00:00:00 +0330 https://ijswr.ut.ac.ir/article_108828.html Sustainable water and soil resource management is a strategic cornerstone for ensuring food security and achieving sustainable agricultural development amidst climate change, erratic precipitation, and intensifying pressure on natural resources. Despite its humid climate and fertile soils, Guilan Province faces rising water demand, inefficient resource use, soil degradation, and environmental pollution, necessitating the development of prioritized, practical, and feasible strategies for effective management of basic resources. The present study aimed to employ the analytic hierarchy process (AHP) as a multi-criteria decision-making (MCDM) method to identify and rank effective management strategies for the optimal utilization of water and soil resources in the agricultural sector of Guilan Province. To this end, the study's criteria and sub-criteria were selected through a systematic review of scientific literature and subsequently refined via expert consultation. The required data were collected using pairwise comparison questionnaires and analyzed with Expert Choice software. The findings revealed that the criterion of water productivity held the highest priority with a relative weight of 0.367, followed by environmental sustainability (0.278) and soil resource productivity (0.160). At the sub-criteria level, irrigation efficiency and reduction of water loss, along with reducing agricultural operation pollution and optimal fertilizer use, were accorded greater significance. The prioritization of management strategies identified the implementation of modern pressurized irrigation systems as the most effective option, succeeded by the development of conservation agriculture and farmer education and extension programs. Finally, for a more comprehensive assessment, integrating MCDMs with spatial data and emphasizing stakeholder education and active participation is recommended. https://ijswr.ut.ac.ir/article_108829.html Climate change is one of the major drivers altering the hydrological regime of watersheds and intensifying extreme events such as floods. This study aimed to assess the impacts of climate change on flood frequency in the Poldokhtar Basin, Iran. To this end, precipitation data from CMIP6 climate models under the SSP126, SSP245, SSP370, and SSP585 scenarios were extracted and bias-corrected using the XGBoost algorithm. The results indicated that MAE decreased from 2.1–2.4 mm to 1.1–1.5 mm, while RMSE was reduced from 6.2–7 mm to 4–4.8 mm after bias correction. Furthermore, the calibration results of the MME-XGBoost model demonstrated satisfactory performance, with correlation coefficients ranging from 0.78 to 0.84 and Nash–Sutcliffe Efficiency (NSE) values between 0.58 and 0.70. Daily streamflow was subsequently simulated using the ANFIS-PSO model, which achieved coefficients of determination (R²) of 0.84 and 0.74 during the training and testing phases, respectively, with normalized root mean square errors (NRMSE) of 0.35 and 0.40. Flood frequency analysis indicated that the impacts of climate change become more pronounced with increasing return periods. During the 2076–2100 period, the 50- and 100-year flood discharges are projected to increase by approximately 50% and 68%, respectively, relative to the baseline period (1985–2014). The highest frequency of flood events was observed in March. The findings emphasize the necessity of revising hydraulic structure design criteria and flood risk management strategies under future climate conditions. https://ijswr.ut.ac.ir/article_108830.html This study evaluated the sensitivity of various soil organic carbon (SOC) fractions, compared and ranked carbon pools based on their responsiveness to combined chemical and organic fertilizer applications, and identified the most sensitive SOC fraction for monitoring SOC dynamics under semi-arid conditions. The experiment was conducted at the Soil and Water Research Institute of Iran's research station in Meshkin Dasht, Karaj, using a randomized complete block design with three replications. Over seven years (2017–2024), six fertilizer treatments were continuously applied: an unfertilized control (CO), chemical NPK fertilizer, cattle manure (MF), municipal waste compost (CF), cattle manure combined with 75% of the recommended nitrogen rate (MNF), and municipal waste compost combined with 75% of the recommended nitrogen rate (CNF). In 2024, after the wheat harvest, soil samples were collected from a depth of 0–30 cm to measure active and passive fractions, as well as microbial biomass carbon. Results showed that organic fertilizers and their combined use with chemical nitrogen fertilizers significantly increased various SOC fractions (P ≤ 0.01). Integrated treatments (especially CNF and MNF) exhibited the highest sensitivity indices (SI), calculated as the percentage increase in each carbon fraction relative to the reference soil (RE), while the control treatment showed the lowest values. The relative sensitivity of SOC fractions followed this order: dissolved organic carbon (DOC) (3.93) > particulate organic carbon (POC) (1.94) > light fraction organic carbon (LFOC) (1.61) > passive carbon (PC) (1.08) > microbial biomass carbon (MBC) (0.70) > permanganate-oxidizable carbon (POXC) (0.61). This demonstrates the efficiency of dynamic indices for short-term monitoring of fertilizer management effects. Therefore, dissolved organic carbon can be considered a rapid and cost-effective indicator for assessing changes in SOC dynamics within crop rotation systems. https://ijswr.ut.ac.ir/article_108831.html This study aimed to evaluate the effects of pit-seeding on vegetation cover indices and soil properties in the Tang-Sorkh rangelands of Kohgiluyeh and Boyer-Ahmad Province. The research was conducted as a comparative study between pit-seeded and control areas. Vegetation attributes were assessed using 1 m² quadrats established along transects, and soil samples were collected from two depths (0–15 and 15–30 cm). The results indicated that pit-seeding induced significant changes in most of the studied variables. Vegetation cover (79.15%) and litter cover (16.44%) increased significantly, whereas bare soil (4.12%) and gravel cover (4.26%) decreased. Regarding soil physical properties, pit-seeding significantly increased clay content while reducing sand content and mean particle diameter (p<‌0.01), with no significant effect on silt content. In terms of soil chemical characteristics, significant treatment effects were observed for soil pH, organic matter (OM), cation exchange capacity (CEC), phosphorus (P), potassium (K), calcium (Ca), and lime content. Soil biological attributes exhibited the strongest response, with basal respiration (BR), substrate-induced respiration (SIR), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and the microbial biomass C:N ratio being significantly affected by pit-seeding (p< 0.01). The highest values of BR (1.949 mg CO₂ kg⁻1 soil h⁻1), SIR (2.136 mg CO₂ kg⁻1 soil h⁻1), MBC (260.00 mg kg-1), and MBN (13.85 mg kg-1) were recorded in the pit-seeded area within the 0–15 cm soil layer. https://ijswr.ut.ac.ir/article_108832.html Nutrient deficiencies and imbalances are among the major factors reducing the productivity of fruit trees in calcareous soils. This study was conducted in Sahneh County, Kermanshah Province, to evaluate the nutritional responses of apple and peach trees to two-year integrated nutrient management and to identify yield-limiting nutrients using compositional nutrient diagnosis (CND). In each of three apple orchards and three peach orchards, 30 trees were evaluated in a randomized complete block design with two treatments, namely the control and integrated nutrient management, and three replications. The nutrient management program included the integrated application of organic, chemical, and biological fertilizers through deep placement, fertigation, and foliar spraying from winter 2024 to summer 2025. The results showed that this program significantly increased the concentrations of most nutrients in leaves and fruits. In apple, leaf nitrogen, iron, and zinc concentrations increased by 10.6–19.6%, 37.4–45.6%, and 41.3–53.0%, respectively, while fruit nitrogen, calcium, and zinc concentrations increased by 21.5–29.8%, 43.2–77.4%, and 51.0–77.3%, respectively, compared with the control. In peach, leaf nitrogen, potassium, and magnesium concentrations increased by 17.4–23.0%, 38.5–49.7%, and 37.5–55.3%, respectively, whereas fruit nitrogen, calcium, and iron concentrations increased by 45.4–56.7%, 31.3–36.6%, and 25.1–39.1%, respectively. Critical yields were determined to be 109–98 kg tree⁻¹ in apple orchards and 51–20 kg tree⁻¹ in peach orchards. The CND indices identified zinc, calcium, and phosphorus in apple and calcium, magnesium, and potassium in peach as potentially yield-limiting nutrients. https://ijswr.ut.ac.ir/article_108833.html The conventional three-stage tillage system in paddy fields, with high water and fuel consumption, increased production costs, and soil compaction caused by frequent heavy machinery traffic, threatens rice production through increased greenhouse gas emissions and delayed planting. This study aimed to evaluate the feasibility of subtituting the conventional system with a more efficient “single-tillage” system by investigating the effects of both tillage methods on specific soil physical and chemical properties. The experiment was conducted on a clay soil that had been without cultivation and agricultural operations for a long period, using two treatments: conventional tillage and single-tillage. Soil sampling was performed at depths of 0–15 and 15–30 cm with three replications. Indicators such as pH, EC, Eh, OC, BD, MWD, and Ks were measured. The results showed that bulk density in the topsoil significantly decreased in both methods compared to the control, by 23.23% and 19.56% for conventional and single-tillage, respectively. In the topsoil, MWD decreased by 47.06% and 36.64% in the single-tillage and conventional systems, respectively, and a significant difference was observed between the two tillage methods; however, no difference was noted in the sub-surface layer. Rice yield was similar in both methods, with no significant difference. In conclusion, the initial design of the single-tillage system, by maintaining yield and reducing operation time, along with a 30% saving in fuel and water, is evaluated as a suitable option for improving paddy field preparation. https://ijswr.ut.ac.ir/article_108834.html Heavy metals are among the most persistent soil contaminants, posing serious threats to environmental health, soil quality, and food security. The application of biochar– mineral composites has attracted considerable attention as a novel strategy for the immobilization of heavy metals in contaminated soils because of the synergistic interactions between their constituent components. In this study, the effects of two types of biochar (wheat straw biochar and common bean (Phaseolus vulgaris L.) straw and pod biochar), three mineral materials (diatomite, triple superphosphate, and zeolite), and six biochar– mineral composites produced through a synergistic approach on the immobilization of lead (Pb), zinc (Zn), and cadmium (Cd) in a heavy metal-contaminated soil were evaluated under controlled conditions. The biochars were produced at 400 °C with a residence time of 3 h, mixed with the mineral materials at a 1:1 ratio (50:50, w/w), and applied to the contaminated soil at rates of 0, 1.25, 2.5, and 5% (w/w). The results showed that all treatments significantly (P< 0.05) reduced the bioavailable concentrations of Pb, Zn, and Cd; however, their effectiveness depended on the type of biochar– mineral composite. The biochar– mineral composites were more effective in immobilizing heavy metals than the individual sorbents applied separately. The lowest DTPA- extractable Pb, Zn, and Cd concentrations were obtained with the common bean biochar– triple superphosphate composite applied at 5% (w/w), with values of 0.913, 27.83, and 3.7 mg kg-1 soil, respectively, corresponding to reductions of 99.70, 86.10, and 86.78%, respectively, compared with the control. This treatment also enhanced maize growth and reduced heavy metal concentrations in the shoots. Overall, biochar– mineral composites, owing to their synergistic effects involving surface adsorption, ion exchange, precipitation, and stable complex formation, represent a promising approach for the immobilization of heavy metals and the reduction of their transfer to plants in contaminated soils. https://ijswr.ut.ac.ir/article_108875.html The restrictive policies regarding timber harvesting from natural forests, compounded by critical water scarcity, have necessitated the exploration of sustainable alternatives for industrial wood production. This study investigates the feasibility of using treated wastewater for irrigation in commercial-scale plantations (2 ha) in Mahdasht, Karaj, Iran. The economic and environmental performance of three fast-growing species—Populus spp., Eucalyptus spp., and Paulownia spp.—was evaluated. Economic viability was determined using Net Present Value (NPV), Internal Rate of Return (IRR), and Benefit–Cost Ratio (BCR), alongside sensitivity analyses. Our findings indicate significant inter-species variations in financial performance. Paulownia demonstrated the highest economic potential (NPV: 2.48 billion Tomans; IRR: 28.8%; BCR: 1.56), followed by Eucalyptus and Populus. Sensitivity analysis identified timber market price as the primary driver of long-term profitability; notably, Populus showed higher vulnerability to land-cost fluctuations, while Eucalyptus was sensitive to irrigation expenditures. Crucially, this study demonstrates that reliance solely on financial metrics is insufficient for species selection. While Paulownia offered superior returns, it entails higher ecological and management risks. Populus, despite lower economic gains, exhibited the highest environmental sustainability and ecological compatibility, aligning better with national conservation objectives. We conclude that a multi criteria decision-making (MCDM) framework—balancing economic profitability, environmental sustainability, and implementation feasibility—is essential for the development of resilient plantation forestry in water-limited arid and semi-arid regions. https://ijswr.ut.ac.ir/article_108876.html Groundwater contamination caused by urban and industrial activities is considered one of the most important environmental threats to the coastal plains of northern Iran. The aim of the present study is to model and assess groundwater quality risk in the Mazandaran Plain using the XGBoost machine learning algorithm integrated with a Geographic Information System (GIS). In this study, groundwater quality data including nitrate, chloride, total dissolved solids (TDS), and electrical conductivity (EC) were collected from monitoring wells across the study area. In addition, a set of topographic and climatic variables, remote sensing indices (NDVI, NDBI, NDWI, and LST), and indicators related to human activities were incorporated into the model as predictor variables. The XGBoost algorithm was trained to classify groundwater quality risk into three classes: low, moderate, and high. Model performance was evaluated using overall accuracy and confusion matrix analysis. The results showed that the XGBoost model has a suitable capability for identifying spatial patterns of contamination, and variables such as elevation, distance from urban and industrial areas, and the NDBI index had the greatest influence on predicting contamination risk. The results of model evaluation in the testing phase indicated that the XGBoost algorithm achieved the highest accuracy in estimating electrical conductivity (R² = 0.486, RMSE = 280.31 µS/cm) and showed the lowest absolute error for chloride, while the coefficient of determination for nitrate reached R² = 0.412. The risk zoning maps indicate that high risk areas are mainly concentrated in low elevation zones and in regions adjacent to urban and industrial pollution sources. The results of this study demonstrate that the integration of XGBoost and GIS can be used as an effective tool for the management and protection of groundwater resources in environmentally sensitive areas. https://ijswr.ut.ac.ir/article_108877.html Due to the high level of water consumption in the agricultural sector, improving water use efficiency is considered a key factor in achieving agricultural development goals. Kurdistan Province, located in western Iran, despite having a cold climate, has faced water scarcity and an imbalance between water supply and demand over the past two decades, particularly in its eastern plains of Qorveh and Dehgolan (water code 1308). Given that agriculture is the primary water-consuming sector, this study was conducted with the aim of proposing strategies to improve water use and reduce the impacts of water scarcity on agricultural lands, using a qualitative approach and the SWOT technique. The required data were collected through questionnaires administered to 75 local experts from governmental and private organizations. To assess the internal and external environment of water consumption management in the study area, 98 sub-components were identified and analyzed as strengths, weaknesses, opportunities, and threats. After calculating the relative importance, assigning weights to each sub-component, and scoring the evaluation matrix of internal and external factors, the findings indicated that the primary and priority strategy for improving water consumption falls within the ST (competitive strategy) category. To prioritize the identified strategies, opinions were obtained from ten experts. The results show that weaknesses and threats in the internal and external environments outweigh strengths and opportunities; however, overall, the positive strategic space is greater than the negative space. Among the 14 extracted strategies, seven were identified as top priorities for improving agricultural water resources and consumption in the study area: implementation of an adaptive cropping pattern, formulation of a comprehensive water plan, reform of the water allocation model, development of modern training programs, on-farm water storage, improvement of farm management, and smart irrigation. Therefore, the adoption of adaptive cropping patterns, managerial interventions in farm administration, and reform of water allocation management are strongly recommended. https://ijswr.ut.ac.ir/article_108878.html This study was conducted to investigate the effects of microbial contamination and heavy metals resulting from wastewater effluent irrigation on soil and barley in Isfahan. To statistically evaluate the effects of wastewater on soil and barley plants in terms of heavy metals and microbial contamination, composite soil and plant samples were collected at harvest time for each crop under wastewater and well-water irrigation conditions, with ten samples taken from each treatment and sent to the laboratory for analysis. Statistical analysis of soil and plant results using a paired-samples t-test showed that the concentrations of cadmium, nickel, chromium, cobalt and arsenic in soil and plants did not show significant differences compared with well-water irrigation. The enrichment factor for lead and cadmium in plants was greater than 2, while other metals showed weak enrichment for transfer to roots and aerial plant parts. The bioconcentration factor for lead, cadmium, nickel, chromium, cobalt, and arsenic in aerial plant parts was also less than 1, while the translocation factor for lead was greater than 1 and for other heavy metals under both wastewater and well-water irrigation conditions was less than 1. Microbial contamination, including total coliforms and fecal coliforms in soil and plant samples collected twenty-one days after the last irrigation, showed no significant differences between wastewater and well-water treatments, indicating that microbial contamination was reduced to levels comparable with well-water-irrigated lands after twenty-one days. Therefore, it is recommended that seed harvesting should be carried out at least twenty-one days after the final irrigation event. https://ijswr.ut.ac.ir/article_108880.html Contamination of calcareous soils with selenium (Se) significantly enhances its bioavailability, resulting in growth inhibition and the disruption of essential nutrient balance within plant tissues. Therefore, implementing effective immobilization strategies is crucial to mitigate Se toxicity and its adverse impacts on crop production. This study aimed to evaluate the remediation efficiency of carboxymethyl chitosan (CMCh), magnetite (Fe3O4), and a magnetite–carboxymethyl chitosan composite (Fe3O4/CMCh) for Se immobilization, assessing their effects on growth performance, nutrient uptake, and distribution in maize (Zea mays L.). A greenhouse factorial experiment was conducted using a completely randomized design. Soils were artificially contaminated with Se at concentrations of 0, 2, 4, 8, 16, and 32 mg kg⁻¹. Following an equilibration period, amendments were applied at rates of 0, 0.25, 0.50, and 1% (w/w). After soil equilibration, maize seedlings were cultivated for 60 days. Subsequently, plant growth parameters and concentrations of Se, phosphorus (P), potassium (K), iron (Fe), and zinc (Zn) were determined in roots and shoots. Results indicated that increasing Se levels significantly reduced maize growth and decreased the translocation of P and Fe to aboveground tissues. Conversely, plant growth, as well as K and Zn concentrations, reached their maximum at 2 mg Se kg⁻¹, declining at higher levels. The amendments effectively reduced Se bioavailability; however, the Fe3O4/CMCh composite showed the highest efficiency by immobilizing approximately 90% of bioavailable Se. While Fe3O4/CMCh is an effective amendment for contaminated soils, its application in uncontaminated soils requires careful management to avoid excessive immobilization of essential micronutrients. 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_108262.html Quantitative land suitability assessment for dryland crops in semi-arid regions requires methods with acceptable accuracy in yield prediction. In this study, the capability of two approaches, AEZ and the AquaCrop model, was compared for predicting dryland wheat yield (cv. Azar 2) in the Halab region (Zanjan province). Climatic data from two growing seasons (2022–2023 and 2023–2024), soil properties of 11 profiles, and actual wheat yield were measured. Attainable yield using the AEZ method was estimated through calculation of potential yield, application of water stress, and the edaphic index. The AquaCrop model was also calibrated and executed. Accuracy evaluation was performed using MAE, RMSE, nRMSE, r², and R² indices. The results showed that the AquaCrop model (MAE=0.155, RMSE=0.189 t/ha, nRMSE=10.57%, r²=0.777, and R²=0.657) had higher accuracy compared to the AEZ method (MAE=0.178, RMSE=0.235 t/ha, nRMSE=13.17%, r²=0.728, and R²=0.544). The superiority of AquaCrop is due to its dynamic daily simulation of water stress with separation of evaporation from transpiration. Land suitability classification showed that although estimates differed between the two models, all land units fell into class S2, and the predicted yield of all units was above the critical yield (412 kg/ha), indicating the economic justification of cultivation in the region. The AquaCrop model is more suitable for field-scale yield prediction and detailed land suitability assessment under dryland conditions, while the AEZ has greater applicability for regional macro-planning. The development of hybrid models that integrate dynamic water stress simulation with nutrient uptake sub-models is recommended to increase prediction accuracy in future studies. https://ijswr.ut.ac.ir/article_108263.html برای این منظور از دو نوع مصالح درشت‌دانه گردگوشه و تیزگوشه، با نفوذپذیری‌هایی به‌ترتیب 39/0 و 43/0 متر بر ثانیه تحت اثر شیب ملایم 4 درصد و در شرایط مرزی تراز آب بالادست و پایین‌دست مختلف محاسبات انجام شد و در نهایت با مدل آزمایشگاهی صحت‌سنجی گردید. بررسی پروفیل جریان درون مصالح درشت‌دانه نشان داد، با افزایش نفوذپذیری مقدار خطای حل تحلیلی این پژوهش کمتر شده و در مقابل خطای حل تحلیلی Jager (1956) افزایش یافته است. حداقل و حداکثر درصد خطای حل تحلیلی این پژوهش در شیب 4 درصد گردگوشه نسبت به مدل آزمایشگاهی در سه حالت تراز آب بالادست و پایین‌دست مختلف به-ترتیب 73/5 و 75/12 درصد حاصل شده، این در حالی است که مقدار خطا برای حل تحلیلی Jager (1956) دارای مقدار حداقل و حداکثر 7/11 و 17/13 درصد به‌دست آمده است. در شیب 4 درصد تیزگوشه میزان خطا در حل تحلیلی این پژوهش به میزان 6/5 و 09/10 درصد کاهش یافته است و در مقابل درصد خطای حل Jager (1956) به میزان 75/14 و 33/15 درصد افزایش یافته، که این موضوع دلیل محکمی برای پذیرش این حل می‌تواند باشد زیرا که اساس فرض این حل تحلیلی، برای جریانات با نفوذپذیری بالا یا به اصطلاح سرعت زیاد (جریان‌های غیردارسی) مطرح شده است. بنابراین نتایج حاصل از این پژوهش نشان داد حل تحلیلی ارائه شده بر اساس فرضیات اشاره شده در بالا نتایج خوبی ارائه می‌نماید که در مقایسه با حل تحلیلی دیگر پژوهشگران به مراتب دقیق‌تر می‌باشد. https://ijswr.ut.ac.ir/article_108382.html Precipitation is one of the most important climatic elements and a key component of the hydrological cycle, playing a vital role in supplying water resources in mountainous regions. Understanding the spatial and temporal patterns of precipitation and their relationship with elevation is essential for sustainable water resource management. The present study aimed to investigate seasonal precipitation patterns across different elevation classes in the Zagros Mountains. For this purpose, MSWEP satellite-based precipitation data for the period 1990–2024 and the ALOS PALSAR Digital Elevation Model (DEM) were utilized. The study area was classified into five elevation zones: below 1,000 m, 1,000–1,500 m, 1,500–2,000 m, 2,000–2,500 m, and above 2,500 m. Temporal trends in precipitation were analyzed using the Mann–Kendall test and Sen’s slope estimator. In addition, the relationship between precipitation and elevation was assessed using Spearman’s correlation coefficient, while differences in mean precipitation among elevation classes were evaluated through one-way analysis of variance (ANOVA) and Scheffé’s post hoc test. The results indicated that the highest annual mean precipitation occurred in the 1,000–1,500 m elevation class, whereas the lowest values were observed at elevations above 2,500 m. A negative relationship between precipitation and elevation was detected during autumn and winter, while the highest precipitation amounts in spring were recorded at mid-elevations. The findings suggest that precipitation patterns in the Zagros Mountains are not solely controlled by elevation but are also influenced by topographic conditions and the geographical position relative to moisture sources. These results can contribute to improved water resource management in Zagros. https://ijswr.ut.ac.ir/article_108383.html Snow cover area (SCA) and snow water equivalent (SWE) are critical variables for understanding water resource status and river flows from snowmelt in mountainous regions. This study investigated the long-term spatial and temporal variations of SCA and SWE over the 2000–2025 period in Razavi Khorasan Province, Iran, utilizing MODIS satellite data and the FLDAS in the Google Earth Engine (GEE). To evaluate the accuracy of the results, the estimated data were validated against field measurements from snow-gauge stations. The satellite-derived estimates were evaluated using statistical metrics, including linear correlation (R), root mean square error (RMSE), mean absolute error (MAE), and bias. Furthermore, the SWE-to-precipitation ratio (SPR) index was calculated to investigate long-term changes in the contribution of snowfall to total precipitation. The annual Standardized Precipitation Index (SPI) was also employed to analyze the relationship between SCA and SWE, with drought and wet events. The results indicated that the maximum snow cover areas occurred in 2005 and 2008, 10775.7 km2 and 6697.7 km2, respectively. Conversely, the minimum snow cover area was recorded in 2018 at 632.4 km2. The overall trend of both SCA and SWE in the study area was downward. The negative slope of trend revealed a significant annual decrease of approximately -31.9 km2/year in snow cover area (at the 5% statistical significance level). The long-term SPR index also exhibited a decreasing trend with a slope of -0.0004/year, indicating a declining share of snowfall relative to total precipitation in the region. https://ijswr.ut.ac.ir/article_108386.html Soil erodibility is one of the key indicators of soil quality and is directly influenced by soil physical properties. This study aimed to Assessment soil physical quality indices and identify the most sensitive indicators across different soil erodibility classes in the semi-arid region of Zanjan Province, Iran. For this purpose, 20 soil physical properties were measured at 68 sampling points, including rangeland (45 points), cropland (13 points), orchard (8 points), and forest (2 points) land uses. Soils were classified into three erodibility classes based on the Williams erodibility factor. Differences among physical properties across erodibility classes were assessed using one-way ANOVA. Soil physical quality indices (SQI) were calculated using linear and nonlinear scoring functions with both simple and weighted approaches for the total dataset (TDS) and minimum dataset (MDS), and were compared using discriminant analysis. PCA identified three main components explaining 83% of the total variance. In the TDS, sand content, mean weight diameter of primary particles (MWDp), and mean weight diameter of dry aggregates (MWDdry) had the highest weights (W = 0.12, p < 0.01). In the MDS, sand content was the most important factor (W = 0.57, p < 0.01), followed by wet mean geometric diameter of aggregates (GMDwet) and MWDdry (W = 0.32 and 0.10, respectively). Discriminant analysis indicated that the best soil physical quality index for erodibility assessment was the weighted linear scoring method based on the TDS (SQI-2). Overall, soil structure stability and PSD were identified as effective tools for monitoring soil physical quality. https://ijswr.ut.ac.ir/article_108387.html Crude oil contamination of soil is a complex environmental challenge requiring novel remediation approaches. This study designed a synergistic system combining electrokinetics (0, 1.5, and 3 V/cm), phytoremediation with Bermuda grass, and bioremediation using a bacterial–algal consortium (Pseudomonas fluorescens and Chlorella) at moisture levels of 60%, 80%, and 100% of field capacity. The system was evaluated for remediating soil contaminated with 1.5%, 3%, and 4.5% crude oil. Response Surface Methodology (RSM) was used to optimize and model treatment effects on plant growth indices (fresh and dry weight, height, leaf area, and greenness index). RSM analyses showed complex interactions between electric field intensity, contamination level, and the biological consortium, with models achieving high R² values (0.82–0.99). Under optimal conditions, all plant indices increased with the application of the integrated electrokinetic-bioremediation system, and the greatest improvement in the studied variables was observed in samples containing the algal–bacterial consortium, representing the innovative aspect of this research. The maximum values of fresh and dry weight, greenness index, plant height, and leaf area were observed in treatment No. 22 (1.5% crude oil contamination, electric current of 1.5 V/cm, biological treatment with 3% algal (Chlorella)-bacterial (Pseudomonas fluorescens) consortium, and soil moisture at 80% of field capacity), reaching 50.10 and 20.67 g/pot, 70.50 and 45.50 cm, and 4.90 cm²/pot, respectively. The results demonstrated that employing an integrated electrokinetic–biological–phytoremediation system alongside modeling approaches (e.g., RSM) can provide an effective and scalable strategy for managing oil-contaminated soils and enhancing plant resilience in such environments. https://ijswr.ut.ac.ir/article_108388.html The aim of this study was to determine the efficiency of a Leonardite-based slow-release organic-mineral fertilizer for corn nutrition in comparison with common fertilizers in two experiments, with a completely randomized block design. The first experiment included corn fertilizer requirement plus (B=0, T1=300, T2=500, and T3=800 kg/ha) of slow-release fertilizer. The second experiment included 500 kg/ha of slow-release fertilizer plus (B+=0, R1=50, R2=75, and R3=100) percent of corn NPK requirement, in 4 replications and a total of 32 plots in the field. The results showed that the efficiency of nitrogen, phosphorus, and potassium in the second experiment was higher than their efficiency in the first experiment. The higher efficiency of nitrogen, phosphorus, and potassium when using slow-release fertilizer, led to increase the end- season yield. The use of slow-release fertilizer, in addition to increasing the efficiency of NPK and yield, also caused a significant decrease in pH, which probably helped improve the availability of trace nutrients. The efficiency of nitrogen, phosphorus, and potassium in the mid season of both experiments, when the plant is not fully established, was very low, but in the end season, the efficiency of nitrogen, phosphorus, and potassium increased sharply. Overall, the highest efficiency of nitrogen (N=66.68%) was obtained in the R3, phosphorus (P=85.19%) in the B+, and potassium in the R3 (K=8.87%) treatments of the second experiment. As a general conclusion, it can be stated that the tested slow-release fertilizer can be used as a complete or auxiliary fertilizer in corn nutrition with appropriate yield. https://ijswr.ut.ac.ir/article_108389.html The utilization of treated municipal wastewater for forage maize irrigation represents an unavoidable strategy for mitigating water scarcity crises. However, this practice may entail the risk of heavy metal accumulation in plant tissues. The objective of this study was to determine the optimal blending ratio of well water and wastewater through a multi-objective optimization framework to simultaneously achieve maximum forage maize yield and minimum concentrations of nickel (Ni), zinc (Zn), cadmium (Cd), and lead (Pb). Three-year field data (2020–2022) were obtained from a lysimetric experiment conducted in Babolsar, Iran, comprising five blending treatments (0 to 100% wastewater). In the initial phase, a full interactions regression model was developed, attaining an average adjusted coefficient of determination of 0.9968 between six water quality parameters (EC, Na⁺, SAR, Ca²⁺, Mg²⁺, and N) and the five objectives. Subsequently, the NSGA-II multi-objective genetic algorithm, configured with a population size of 500 and 1,000 generations, generated a Pareto front consisting of 500 non-dominated solutions. Combined sensitivity analysis identified sodium concentration as the most influential variable governing the yield-toxicity trade-off, exhibiting a sensitivity coefficient of -0.232. In contrast, the sodium adsorption ratio (SAR) ranked second in importance with a coefficient of +0.207. This solution achieved a grain yield of 24.54 ton/ha (0.33% higher than the pure wastewater treatment) while substantially reducing lead concentration from 46.85 to 14.19 µg/g and cadmium concentration from 8.39 to 0.96 µg/g (corresponding to 70% and 89% reductions, respectively). https://ijswr.ut.ac.ir/article_108390.html Visible and Near-Infrared (Vis–NIR) spectroscopy has emerged as a rapid, non-destructive, and cost-effective technique for estimating soil properties and supporting digital soil mapping. This review critically evaluates the performance and limitations of Vis–NIR spectroscopy for predicting soil properties in arid and semi-arid regions by synthesizing findings from studies conducted in Iran and elsewhere. The review covers the fundamental principles of soil spectroscopy, sample preparation, spectral preprocessing techniques, multivariate modeling approaches, and recent advances in multi-source data fusion. The reviewed studies demonstrate that Vis–NIR spectroscopy provides satisfactory predictions for several soil properties, including soil organic matter, texture, moisture, electrical conductivity, and calcium carbonate, particularly when coupled with advanced machine learning algorithms. However, predictive performance varies widely in arid and semi-arid soils due to the complex effects of high carbonate, gypsum, and salinity contents, mineralogical heterogeneity, and differences in spectral preprocessing, calibration strategies, and modeling techniques. The review further indicates that variations in model performance are primarily associated with the quality and representativeness of reference datasets, the availability of regional spectral libraries, and site-specific calibration rather than the choice of prediction algorithm alone. Consequently, developing regional spectral libraries, standardizing spectral measurement protocols, and integrating Vis–NIR data with remote sensing and environmental covariates are identified as key priorities for improving model transferability and practical implementation. Overall, Vis–NIR spectroscopy has considerable potential to support precision agriculture, sustainable soil management, and digital soil mapping in arid and semi-arid environments, provided that region-specific calibration and robust validation procedures are adopted. https://ijswr.ut.ac.ir/article_108391.html The increasing entry of silver nanoparticles (Ag-NPs) into soil ecosystems threatens biochemical health and biogeochemical cycles. This study evaluated the ecotoxicity and temporal dynamics (aging) of Ag-NPs on soil enzyme activities across two contrasting soil textures. A laboratory incubation experiment was conducted as a factorial based on a completely randomized design (CRD). Treatments included soil texture (sandy loam and clay), Ag-NP concentration (0, 100, 500, and 1000 mg/kg), and incubation time (7, 24, and 72 days). Urease, acid phosphatase, and alkaline phosphatase activities were measured as biomarkers. Results showed that Ag-NP inhibition strongly depended on soil texture and time. In sandy loam soil, urease was highly vulnerable; 1000 mg/kg reduced its activity by 54% on days 7 and 24 compared with the control. In contrast, clay soil showed greater buffering capacity. A pronounced hormesis effect occurred for alkaline phosphatase in clay soil: 100 mg/kg on day 7 increased activity by 109% relative to the control, and stimulation stabilized at a persistent 21% increase on days 24 and 72. For acid phosphatase in clay soil, initial inhibition at 1000 mg/kg almost fully recovered by day 24 (8% decline), but a 16% decline reappeared by day 72. Clay colloids and organic carbon likely mitigated Ag-NP toxicity through chelation and reduced bioavailability, whereas light soils remained highly vulnerable without this protective barrier. These findings highlight the importance of soil properties in determining nanoparticle ecological impacts. Nevertheless, temporal monitoring showed that pollutant aging remains a silent long-term ecological risk, even in colloid-rich soils. https://ijswr.ut.ac.ir/article_108392.html Predicting coupled ocean–atmosphere teleconnections is inherently difficult because oceanic thermal memory and atmospheric chaos impose markedly different limits on forecast skill. This study reconstructed and forecast the Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO) through the integrated, data-driven RIMEF framework using 912 monthly observations from January 1950 to December 2025. Sea-surface temperature data were obtained from ERSSTv6, whereas sea-level pressure and 500-hPa geopotential-height fields were derived from ERA5. AMO was reconstructed over 0–70°N using latitude-dependent cosine weighting and removal of the contemporaneous global-warming component. NAO was extracted through empirical orthogonal function analysis of pressure anomalies across 20°–80°N and 90°W–40°E. The predictor space comprised one- to six-month lags, rolling means, phase and trend components, and the AO, PDO, and NINO3.4 indices. RidgeCV, ElasticNetCV, Extra Trees, Random Forest, and HistGradientBoosting were evaluated at 1-, 3-, 6-, and 12-month horizons through leakage-free rolling-origin validation. ElasticNetCV produced the lowest one-month AMO error (RMSE=0/621), whereas RidgeCV performed best at three months (RMSE=0/772). For NAO, the optimal RMSE values were 1/032 and 1/059 at one and three months, respectively. Phase accuracy declined from 0/592 to 0/515, revealing rapidly deteriorating atmospheric predictability. What distinguishes this study is its integration of raw-field index reconstruction, multiscale feature engineering, temporally rigorous validation, SHAP-based explain ability, and uncertainty quantification within a unified architecture. The findings support regularized models for AMO and probabilistic, uncertainty-aware forecasting for NAO. Such a bifurcated strategy can improve climate services while preventing deterministic NAO forecasts from being interpreted beyond their empirically demonstrated predictability threshold practice. https://ijswr.ut.ac.ir/article_108447.html Chemical and polymeric additives can improve the behavior of clayey soils by altering strength characteristics and refining microstructure. In this work, a recycled glass powder–based geopolymer was employed to modify the strength and structural response of an expansive clay. The tested soil was a bentonite whose dominant clay mineral was montmorillonite. Stabilized samples were prepared with varying geopolymer contents by dry weight and cured for 7 and 28 days. Both microstructural and macrostructural analyses were carried out. The findings indicated that the optimal recycled glass powder content was 15% of the dry soil weight. Samples containing 40% geopolymer exhibited the most pronounced formation of amorphous phases and changes in X‑ray diffraction patterns. The results are applicable to the stabilization of expansive or problematic clay soils. Results showed a non-monotonic strength response: UCS increased from 2.1 kg/cm² (untreated) to 23.19 kg/cm² at 15% RGP, dropped to 12.43 kg/cm² at 20%, then surged to 63.22 kg/cm² at 40% RGP (28-day values). Strength gains were consistently greater at 28 days than at 7 days, and samples shifted from ductile to brittle behavior with increasing RGP content. From environmental and economic standpoints, the outcomes are of particular interest due to the use of eco‑friendly materials and waste‑derived components. https://ijswr.ut.ac.ir/article_108448.html In semi-arid regions, shrublands are one of the most important types of vegetation and play a fundamental role in the sustainability of soil nutrient cycling. Despite numerous studies on the effects of vegetation on soil, information on shrubland degradation is limited. In this study, the physical, chemical, and biological properties of the soil in the shrubland habitat and its conversion uses (30 years ago) including grassland, wheat field, and alfalfa were investigated. Soil sampling was performed at three depths of 0-10, 10-20, and 20-30 cm, and 36 samples were collected from each habitat.The results showed that the destruction of shrubland increased bulk density, sand, silt, temperature and decreased porosity, soil aggregate stability and soil moisture. Also, the values of pH, electrical conductivity, carbon, nitrogen, phosphorus, potassium, calcium and magnesium available in grassland and cropland were significantly lower than in shrubland. While the highest C/N ratio was observed in the hayfield. The results showed that in semi-arid regions, the destruction of shrub cover and its replacement with barley croplands is accompanied by an intensification of degradation processes and a significant reduction in soil yield. In this regard, the protection of these habitats should be a priority in natural resource management. At the same time, in degraded lands, the replacement of less stressful agricultural uses such as wheat fields with barley fields can partially help improve soil properties and reduce the severity of its quality. https://ijswr.ut.ac.ir/article_108472.html Actual evapotranspiration (ETa) is the important component of the water balance in arid and semi-arid basins, and its quantitative estimation is essential for sustainable water resources management. In this study, the spatiotemporal distribution of ETa across the Hirmand (Helmand) River Basin (~280,000 km²) was estimated for 2023 using the Surface Energy Balance Algorithm for Land (SEBAL) implemented within the Google Earth Engine (GEE) cloud computing platform. Input datasets comprised MODIS surface reflectance (MOD09GA), land surface temperature (MOD11A1), and ERA5-Land atmospheric reanalysis products. Mean annual ETa across the basin was estimated at 670 mm/year, following a clear decreasing gradient from the mountainous headwaters (>1,100 mm/year) to the arid lowland plains (<100 mm/year). Seasonal ETa peaked in spring (745 mm/season) and approached zero in winter. Correlation analysis revealed that net radiation (R = +0.703) and land surface temperature (R = −0.933) exhibited the strongest positive and negative associations with ETa, respectively, while NDVI (R = +0.372) and surface albedo (R = −0.053) showed comparatively weaker relationships. Validation against the MOD16A2GF product yielded a correlation of r = 0.858 and a coefficient of determination of R² = 0.736, confirming the reliability of the spatial distribution pattern. The systematic overestimation of SEBAL relative to MOD16 (PBIAS = +283.9%) is attributed to differences in the computational approaches of the two models and to the well-documented tendency of MOD16 to underestimate ETa in arid environments. The findings of study provide a robust quantitative framework for water resources management and transboundary water diplomacy in the Hirmand Basin. https://ijswr.ut.ac.ir/article_108473.html Background and Objective: The diversity of reproductive traits in woody species from arid regions provides key insights into regeneration potential and population persistence under climate change. However, little is known about this diversity in wild almond (Amygdalus scoparia) in Iran. This study is the first to investigate the phenotypic diversity of reproductive traits in six natural populations of this species in western Qom province. Materials and Methods: Seven reproductive traits were measured along with seven environmental variables across 72 plots. One way ANOVA, Tukey HSD test, Principal Component Analysis, and Pearson correlation were used for data analysis. Results: The highest reproductive trait diversity was observed in Dastjerd and Zavarian populations. A strong positive correlation was observed between flower length and flower number (r = 0.738, p < 0.001). However, correlations between precipitation and reproductive traits were weak and non-significant (r = -0.116 for flower length and r = 0.142 for flower number, p > 0.05). Soil variables, particularly electrical conductivity (EC) and potassium, played a more prominent role. The first two principal components accounted for 95.44% of the total variance (PC1: 83.15%, PC2: 12.29%). High altitude populations exhibited larger flowers but fewer flowers per inflorescence. Conclusion: The high phenotypic diversity of reproductive traits in Dastjerd and Zavarian populations makes them conservation priorities and suitable candidates for future genetic studies. Soil variables including electrical conductivity (EC), potassium, and nitrogen were more determinant than precipitation. https://ijswr.ut.ac.ir/article_108474.html In order to investigate the effect of irrigation levels, humic, and salicylic acid on some morphophysiological traits and sunflower yield, an experiment was conducted as a split-factorial design based on randomized complete block design with three replications at the research farm of Islamic Azad University, Birjand Branch. In this study, irrigation was considered as the main factor at two levels (120 and 240 mm pan evaporation), humic acid at three levels (0, 20, and 40 liters per hectare), and salicylic acid at two levels (0 and 0.75 mM) as sub-factors. Traits such as plant height, stem diameter, number of leaves per plant, head diameter, chlorophyll meter reading at flowering time, stomatal conductance at flowering time, seed yield, and seed water use efficiency were studied. The results showed that the effect of irrigation on plant height, head diameter, stomatal conductance, and seed yield was significant. The highest plant height (88 cm), head diameter (87.8 cm), stomatal conductance at flowering time (103.6 mmol m⁻² s⁻¹), and seed yield (1126.9 kg ha⁻¹) were obtained from the 120 mm pan evaporation irrigation level. The effect of salicylic acid on the chlorophyll meter reading at flowering time was significant, and the highest value was obtained in the control treatment (44). Based on the results of this research, to achieve maximum sunflower seed yield in the Birjand region, irrigation treatment after 120 mm cumulative pan evaporation, without the use of humic acid and salicylic acid, can be used. https://ijswr.ut.ac.ir/article_108536.html Improving the yield and quality of maize forage is of great importance in livestock nutrition. This study investigated the effects of different ammonium-to-nitrate ratios (0:100, 12.5:87.5, 25:75, 37.5:62.5, and 50:50) on the yield and forage quality of two maize cultivars (Single Cross 704 and 410) grown under soilless culture conditions. The results revealed significant interactions between ammonium-to-nitrate ratio and cultivar for most of the traits studied. The highest shoot nitrogen concentration was observed in cultivar 704 at the 37.5:62.5 ratio, showing a 72% increase compared to the control. The highest phosphorus concentration (2.3 times the control) was recorded in cultivar 704 at the 50:50 ratio, with no significant difference from cultivar 410 at the same ratio. The increasing trend of iron concentration continued up to the 25:75 ratio. The highest shoot dry weight (168.90 g plant⁻¹) was obtained at the 25:75 ratio, and the highest crude protein content (16.56%) and total chlorophyll (61% increase) were recorded in cultivar 704 at the 37.5:62.5 ratio. The highest activities of catalase and guaiacol peroxidase were observed at the 50:50 ratio. At the 37.5:62.5 ratio, neutral detergent fiber, crude fat, and ash content increased by 43.2%, 81%, and 33.3%, respectively, while water-soluble carbohydrates increased by 4.6 times compared to the control. Cultivar 704 outperformed cultivar 410 in most yield and quality traits. Overall, the 37.5:62.5 ammonium-to-nitrate ratio in combination with cultivar 704 appeared to provide the best balance between forage yield and quality in soilless culture systems. https://ijswr.ut.ac.ir/article_108561.html Given spatial variability of soil organic carbon (SOC) as a key indicator of soil quality, its precise regional mapping is essential. This study compared random forest (RF), regression kriging (RK), and extreme gradient boosting (XGBoost) algorithms to identify the most influential factors controlling SOC distribution in the Karun-3 watershed. 158 composite surface soil samples (0–5 cm) were collected using a conditioned Latin Hypercube Sampling (cLHS) design. Standard laboratory methods measured SOC, sand, silt, clay, electrical conductivity (EC), pH, calcium equivalent carbonate (CCE), and bulk density (BD). Additionally, 31 auxiliary variables—topographic, spectral, climatic indices, and soil characteristics—were used to model SOC spatial variability. These variables were pre-processed by removing those with near-zero variance and those highly intercorrelated (r ≥ 0.8) with a variance inflated factor (VIF) > 10. The XGBoost algorithm outperformed the others, demonstrating the highest coefficient of determination (R2 = 0.628), the lowest error metrics (RMSE = 0.473 and MAE = 0.316), and the highest ratio of performance to interquartile range (RPIQ = 1.98). BD, CCE, Carbonate Index (Carb.Ind), Normalized Difference Moisture Index (NDMI), and Length-Slope Factor (LS-Factor) contributed most significantly to SOC distribution. SOC distribution maps indicated that the northern and northwestern parts of the area contain the highest levels, while the central, southern, and southeastern parts have the lowest. Uncertainty maps revealed that predictive map values are less reliable in parts of the northwestern, eastern, and southern study area. Overall, simultaneous application of predictive and uncertainty maps is necessary to understand both predicted values and their reliability. https://ijswr.ut.ac.ir/article_108562.html Water scarcity which is a major challenge for agricultural production in rainfed lands of semi-arid regions can be affected by management methods. This study was conducted to investigate the effect of land use change on soil structure and soil water- retention capacity in some semi-arid areas. The experiment was conducted in seven areas with two types of land uses (pasture and cultivated land). In total twenty one soil samples (0-30 cm depth) were collected from study area and soil physical properties consist of particle size distribution, aggregate size and stability, bulk density, macroporosity, and hydraulic conductivity were determined in the lab. Rainwater retention was measured in erosion flumes (0.5 m × 1 m) under simulated rainfalls with 50 mm h-1 in intensity for 60 min. Based on the results, the soil textures were and there was seven textures (clay loam, silty clay loam, sandy clay loam, silt loam, loam, sandy loam, and loamy sand. Land use change led to the loss of soil structure properties and the reduction of rainwater retention, especially in medium-textured soils such as loam, silty loam, and sandy loam. The lowest variation in rainwater retention was in loamy sand soil (0.1%) and the highest value was in silty loam soils (5.4%). There was a direct relationship between soil hydraulic conductivity and aggregate size (r= 0.44). This research showed that converting pastures to rainfed lands degrades soil structure and decreases soil infiltration as well as rainwater retention in semi-arid areas, particularly with medium soil textures. https://ijswr.ut.ac.ir/article_108707.html Fire is one of the most important ecological disturbances that can significantly affect the physical, chemical, and biological properties of soil. The intensity, duration, and frequency of fire, are effective on soil properties. In Khuzestan Province, intentional and managed fires are widely practiced in agricultural lands, especially after sugarcane harvest. In order to conduct this study, three land uses including sugarcane, wheat and virgin shrubland were selected and the effect of fire on four land treatments including sugarcane (SAH), wheat with residues (WCR), wheat without residues (WRR) and shrubland (BSA) was investigated. Soil sampling was carried out from a depth of 0 to 10 cm, before and after the fire. Also, temperature changes during and after the fire were monitored at surface up to 60 cm. The data were analyzed using a paired t-test. In addition, the DNDC model was used to evaluate soil carbon and nitrogen cycles. The results showed that the highest temperature increase occurred at the soil surface and decreased with increasing depth. In the WCR treatment, fire caused a significant increase in electrical conductivity and a significant decrease in the final infiltration rate and microbial respiration. In the BSA treatment, more severe changes were observed; organic carbon, total nitrogen, electrical conductivity, wilting point moisture, final infiltration rate, and microbial respiration were significantly reduced, while bulk density showed a significant increase. The DNDC model results also showed a significant decrease in soil organic carbon and organic nitrogen in the BSA and WCR treatments in the long term. https://ijswr.ut.ac.ir/article_108771.html Performance assessment of surface-water distribution systems in irrigation networks, particularly under water shortage and reduced water-supply reliability, requires a framework capable of jointly analyzing performance variations across spatial, scenario-based, and multi-indicator dimensions. However, in many operational assessments, the integration of performance indicators and the identification of critical irrigation units are often carried out with limited attention to spatial heterogeneity and changes in water-shortage severity. This study aims to develop and apply a spatial and scenario-based multi-criteria assessment framework for integrating operational performance indicators in the Jarghuyeh–Mahyar irrigation network. For this purpose, the performance of 675 independent irrigation units was evaluated based on adequacy, dependability, and efficiency indicators under eight operational scenarios, including normal conditions and seven water-shortage levels. After directional harmonization and normalization of the indicators, their weights were calculated using the objective Entropy and Principal Component Analysis methods, and an Integrated Performance Index was determined for each unit and scenario. The results showed that the Entropy method was more sensitive to increasing water shortage; the weight of the adequacy indicator increased from 0.44 under the normal scenario to 0.95 under the unprecedented water-shortage scenario, whereas the PCA weights showed more limited variations. The mean IPI decreased from 62.83 to 8.64 under the Entropy method, while it changed from 61.13 to 54.52 under PCA. Spatial zoning also indicated that, under severe scenarios, the Entropy method represented the expansion of the critical class more explicitly, whereas PCA reflected this expansion in a more moderated manner. https://ijswr.ut.ac.ir/article_108772.html Due to the heterogeneous distribution of discharge–sediment concentration data in most rivers, fitting a single linear sediment rating curve is often inadequate. Consequently, different sediment rating curves are commonly fitted to datasets representing different flow magnitudes. Accordingly, this study aimed to determine the threshold discharge using Bayesian and Genetic Algorithm (GA) approaches and to compare their performance with the median discharge method at the Tamar and Ramian hydrometric stations located in Golestan Province, Iran. After preparing the discharge–sediment datasets, a Bayesian segmented linear regression model was developed and implemented in the WinBUGS software environment. For the Genetic Algorithm approach, the search range for the threshold discharge at both study stations was defined as the interquartile range (25th–75th percentiles) of the observed discharge values. The GA parameters were set to an initial population of 20 chromosomes, a crossover rate of 0.70, and a mutation rate of 0.30. The results demonstrated that, for both calibration and validation datasets, the Genetic Algorithm outperformed the Bayesian and median discharge methods. At the Tamar station, the GA achieved RMSE, Nash–Sutcliffe Efficiency (NSE), and correlation coefficient values of 2443, 0.16, and 0.46, respectively. At the Ramian station, the corresponding values were 1782, 0.35, and 0.77, respectively. Comparison with the Bayesian approach revealed that the threshold discharge estimated by the Genetic Algorithm was located between the 75th and 97.5th percentiles of the observed discharge distribution at the Tamar station, whereas at the Ramian station it was situated between the 2.5th and 25th percentiles. https://ijswr.ut.ac.ir/article_108864.html Co-composting with various additives can improve the quality of the resulting compost. This study aimed to investigate the effect of six selected co-composts on pH, electrical conductivity (EC), and the available concentrations of phosphorus, potassium, iron, zinc, copper, and manganese in two calcareous and acidic soils during a four month incubation period. The treatments included municipal solid waste compost (MSW-compost), compost containing 1-2 mm biochar (B1–2 mm, 10%), NaOH-activated biochar co-compost (NaOH-AB, 5%), H₂SO₄-activated biochar co-compost (H₂SO₄-AB, 10%), leonardite co-compost (10%), and MgSO₄-modified zeolite co-compost (MgSO₄-zeolite, 10%), each applied at three levels (0, 2, and 4% w/w). The results showed that in calcareous soil, compost application decreased pH and increased EC, whereas in acidic soil, pH increased and EC showed slight changes. The MSW compost treatment resulted in the highest concentrations of P, Fe, Zn, and Cu in both soils. The highest K concentration in both soils was observed in the MgSO₄ zeolite 10% treatment, indicating the high efficiency of this additive in K retention. The co composts played a dual role depending on the soil type. In calcareous soil, they significantly increased Fe and Mn concentrations, while in acidic soil, they reduced the available concentrations of these elements, which may indicate a mitigation of their toxicity. Overall, the application of a 4% level of activated biochar containing co composts and modified zeolite co compost, depending on the soil type, can effectively address nutritional limitations and serve as a sustainable strategy for managing the fertility of calcareous and acidic soils. https://ijswr.ut.ac.ir/article_109024.html The response of saffron (Crocus sativus L.) to fertilizer inputs varies markedly with soil properties, climatic conditions, corm vigor, and field-management practices. This variability is particularly pronounced in calcareous, low-organic-matter soils of arid and semi-arid regions, where the optimal type, rate, timing, and combination of fertilizer inputs remain insufficiently defined. This review comparatively evaluates the effects of chemical, organic, bio-fertilizers, integrated nutrient management (INM), and nano-fertilizers on saffron growth, yield, stigma quality, and production sustainability. Evidence indicates that balanced application of nitrogen, phosphorus, and potassium fertilizers can improve corm traits, flower production, and stigma yield; however, excessive or unbalanced use may reduce nutrient-use efficiency and adversely affect soil health. Organic and bio-fertilizers can improve soil physical, chemical, and biological properties, enhance nutrient availability, and contribute to yield and quality improvement, although their effects depend on amendment characteristics, soil conditions, climate, and management. Overall, the available evidence suggests that INM, through the complementary use of chemical, organic, and biological inputs, can improve nutrient-use efficiency, maintain soil fertility, and support sustainable saffron production more effectively than sole fertilizer application. Nano-fertilizers also show potential to enhance nutrient efficiency, but evidence regarding their optimum rates, field performance, and long-term environmental implications in saffron cultivation remains limited. Therefore, saffron fertilizer programs should be developed on a site-specific basis using soil-test results, corm status, climatic conditions, and production objectives. Further long-term field studies are needed to optimize integrated nutrient strategies and evaluate their economic and environmental outcomes. https://ijswr.ut.ac.ir/article_109025.html ABSTRACT Introduction Camelina is a low-input, high-oil crop, yet the effects of anti-transpirants on it under varying sowing dates remain unexplored. This study investigated the impact of anti-transpirants on biochemical traits, antioxidant activity, and oil quantity/quality of Camelina across different sowing dates.Method The experiment used a split-plot randomized complete block design with three replications. Main factors were four sowing dates (April 4, April 19, May 5, May 20), and sub-factors were five anti-transpirant treatments (salicylic acid, zinc sulfate, control, paclobutrazol, atrazine).Results Results indicated a significant interaction between sowing date and anti-transpirants on seed yield. The highest yield (1207.5 kg/ha) occurred on April 19 with salicylic acid, 43% higher than the lowest yield (710.9 kg/ha) on May 20 with atrazine. Zinc sulfate and salicylic acid consistently improved yield across all dates. Furthermore, the interaction effect on proline content was significant; the highest proline (2.69 µmol/g fresh weight) was observed on May 20 with zinc sulfate, 42% higher than the control on April 4. Anti-transpirants also significantly affected carotenoid content, peaking with salicylic acid (5.37 mg/g) and lowest with atrazine (4.96 mg/g).Conclusion Late sowing reduced Camelina’s quantitative and qualitative yields due to end-of-season heat stress. However, applying anti-transpirants, particularly zinc sulfate and salicylic acid, mitigated this stress by enhancing enzymatic activity (e.g., catalase), thereby improving seed and biological yields. https://ijswr.ut.ac.ir/article_109026.html Oil refinery activities are considered to be one of the most important sources of heavy metal emissions in the environment and can cause soil pollution and increase ecological and health risks. The present study was conducted with the aim of assessing the status of heavy metal pollution, determining ecological and human health risks, and identifying pollution hotspots in soils located near the wastewater discharge route of the Baiji Oil Refinery in Iraq. For this purpose, 15 soil samples were taken from the wastewater discharge route to the Tigris River and the concentrations of eight heavy metals were measured using an ICP-MS device. The examination of pollution indices showed that the overall condition of the region was in the low to moderate pollution range, however, Cd and Hg contributed the most to the increase in ecological risk. The PERI index showed moderate risk in a significant part of the sites, and sites 6, 9, 14 and 15 were identified as hotspots of contamination. The results also showed that organic matter and clay percentage are the most important factors affecting the accumulation of heavy metals in the soil. The health risk assessment showed that although the non-carcinogenic risks for adults were within acceptable limits, children faced unacceptable risks at 100% of the sites. The ELCR values at all sites for both age groups were above the standard range. Overall, the findings indicate that continued input of pollutants could lead to increased environmental and health risks in the region in the future. https://ijswr.ut.ac.ir/article_109068.html Given the adverse consequences of wind erosion, the lack of long-term, spatially gridded data in this field has hindered sustainable land management in arid and semi-arid regions, including Iran. Field-based methods for mapping wind erosion are time-consuming and costly. In the present study, the Revised Wind Erosion Equation (RWEQ) was integrated with the Google Earth Engine platform (GEE-RWEQ) to examine the spatiotemporal variations of soil wind erosion potential (SWEP) across Iran from 2014 to 2024. The results revealed a significant correlation between SWEP and aerosol optical depth (r = 0.59, p < 0.01), as well as the absorbing aerosol index (r = 0.64, p < 0.01). This correlation confirms the relative validity of the GEE-RWEQ model. According to the GEE-RWEQ model, wind erosion, with a mean annual rate of 4.75 t ha⁻¹, affects approximately 53% of the country's area. The largest wind erosion hotspot (exceeding 150 t ha⁻¹) was located in the southeast (including the Sistan plains and the areas surrounding Hamoun) and the northeast of the country. Summer was also identified as the most critical season in terms of wind erosion intensity. Overall, the combination of inherently high soil erodibility with erosive winds and sparse vegetation cover, particularly during the warm season, plays a key role in the formation of wind erosion hotspots in Iran. It can therefore be concluded that the GEE-RWEQ model provides an efficient, scalable, and cost-effective approach for national-scale wind erosion monitoring and for informing soil conservation policies in Iran. . https://ijswr.ut.ac.ir/article_109121.html In arid and semi-arid regions, salinity stress is considered one of the major limiting factors for sugar beet production. This study was conducted to investigate the interactive effects of salinity and potassium sulfate application on antioxidant enzyme activities and selected quantitative and qualitative traits of sugar beet (Shokoufa cultivar) under greenhouse conditions. The experiment was conducted as a factorial arrangement in a randomized complete block design with three replications. Treatments consisted of five levels of irrigation water salinity (0, 5, 10, 15, and 20 dS m⁻¹) and two levels of potassium sulfate (0 and 50 kg ha⁻¹). The results showed that increasing salinity significantly affected the activities of catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), polyphenol oxidase (PPO), and phenylalanine ammonia-lyase (PAL). In addition, increasing salinity stress intensity resulted in greater accumulation of the compatible osmolytes proline and glycine betaine, indicating the activation of plant defense mechanisms and osmotic adjustment in response to salinity stress. Application of potassium sulfate at 50 kg ha⁻¹, compared with no potassium sulfate application, alleviated some of the adverse effects of salinity and improved the biochemical status and selected quantitative and qualitative traits of the plant. Overall, the results indicated that adequate potassium supply can enhance sugar beet tolerance to salinity stress by strengthening antioxidant defense mechanisms and osmotic adjustment. Therefore, appropriate potassium nutrition management can be considered an effective strategy for mitigating the adverse effects of salinity and maintaining sugar beet yield and quality under saline conditions. https://ijswr.ut.ac.ir/article_109122.html This study investigates the influence of ensemble size on the performance of machine learning models for monthly rainfall forecasting using the CHIRPS satellite precipitation dataset. A diverse set of machine learning algorithms, including tree-based, regression-based, kernel-based, instance-based, and ensemble models, was employed to generate independent rainfall predictions. The outputs of these models were then combined using a weighted ensemble approach. For all possible ensemble combinations ranging from 2 to 15 models, optimal weights were determined through a Brute Force search, and the best ensemble was selected based on the Nash–Sutcliffe Efficiency (NSE) criterion. The main objective was to determine the optimal ensemble size while considering both predictive accuracy and computational cost. Results showed a nonlinear relationship between ensemble size and forecasting performance. The best performance was achieved by a four-model ensemble consisting of SMOreg, AdditiveRegression, DecisionTable, and M5Rules, which produced an NSE of 0.88 and an RMSE of 21.10 mm. This ensemble provided the best balance between prediction accuracy, stability, and computational efficiency. Increasing the number of ensemble members beyond four did not improve performance significantly. Instead, greater redundancy and higher correlation among model errors increased computational cost without meaningful accuracy gains. The findings indicate that structural diversity among base learners is more important than simply increasing ensemble size. The proposed framework can be adapted to other regions with similar climatic conditions after retraining with local datasets. https://ijswr.ut.ac.ir/article_109123.html This study aimed to evaluate the effects of Khorramabad River water quality on the drip irrigation system and olive fruit quality in the Khorramabad Olive Orchard. Water samples were taken from the Khorramabad River across four seasons—summer, autumn, winter (2024), and spring (2025)—at three locations: the river pumping site, before, and after the irrigation filtration system. Thirty water quality parameters were analyzed, including temperature, salinity, pH, EC, TDS, TH, cations, anions, dissolved oxygen, biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), nitrate, phosphate, and heavy metals (Cd, As, Cr, Pb, Hg, Ni, Al, B, V, Mn). To assess the effect of river water quality on olive fruits, two series of olive samples were collected—table olives (November 2024) and oil-producing olives (December 2024)—and their heavy metal concentrations were measured. The Langelier Saturation Index (LSI) indicated that Khorramabad River water tends to be scaling in summer and highly corrosive in winter. Sodium Adsorption Ratio (SAR) values were below 1 for all seasons, classifying the water as excellent (S1). Heavy metal assessment showed that lead concentrations in olive oil from three cultivars (Mari, Roghni, and Shengeh) exceeded permissible limits, while cadmium and arsenic concentrations in both table and oil olives were below the tolerance thresholds. https://ijswr.ut.ac.ir/article_109181.html Heavy metal transport from the soil surface by runoff is an important pathway for pollutant entry into surface water resources. This study investigated the effects of land use, rainfall intensity, and duration on the concentration and load of lead (Pb) and cadmium (Cd) and their relationships with runoff and sediment in the Kuhdasht watershed, Lorestan Province, Iran. Treatments included three land uses (agricultural land, rangeland, and forest), three rainfall intensities (50, 70, and 90 mm h⁻¹), and three durations (5, 10, and 15 min), with three replications. 81 rainfall-simulation experiments were conducted. Runoff volume, sediment yield, and Pb and Cd concentrations in runoff were measured, and metal loads were calculated from concentration and runoff volume. Land use and rainfall intensity significantly affected the concentrations and loads of both metals, with different interaction patterns. Mean Cd and Pb loads in forest land were 5.60 and 2.79 mg m⁻², respectively, exceeding those in rangeland and agricultural land (p < 0.05). The highest Cd (9.97 mg m⁻²) and Pb (4.07 mg m⁻²) loads occurred under forest land use at 90 mm h⁻¹. Both metal loads increased with rainfall intensity and were positively correlated with runoff volume and sediment yield. Rainfall intensity and land use affected Pb and Cd transport, and evaluating transported loads alongside concentrations provided a better assessment of metal loss from the soil surface. From a management perspective, runoff and erosion control, vegetation cover maintenance, and buffer strips, particularly under high-intensity rainfall, can reduce Pb and Cd transport to surface waters. https://ijswr.ut.ac.ir/article_109233.html Water infiltration into soil plays an important role in runoff generation, soil erosion, and irrigation planning. Accurate estimation of infiltration is essential for effective soil and water management. This study investigated temporal variations in water infiltration and evaluated the performance of four infiltration models, namely Philip, Kostiakov, Kostiakov–Lewis, and Horton, in eight watersheds of Ardabil Province, Iran, including Aladizgeh, Iril, Sula, ShamsAbad, Amuqin, Namin, Nouran, and Nir. Infiltration experiments were conducted using a double-ring infiltrometer. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). Relationships between model parameters and soil properties were examined using Pearson correlation analysis. The results showed that the Philip model performed best in six watersheds (Aladizgeh, Iril, Sula, Amuqin, Namin, and Nouran), while the Kostiakov and Kostiakov–Lewis models performed best in ShamsAbad and Nir, respectively. Overall, the Philip model was the most suitable model, with mean R² and NSE values of 0.975 and 0.927, respectively. Correlation analysis indicated that the Philip capillary sorptivity coefficient (S) was significantly positively correlated with clay content (r = 0.82) and negatively correlated with sand content (r = −0.76) at the 5% significance level. These findings demonstrate that infiltration model performance depends on local soil conditions and that model selection should consider soil properties and multiple performance criteria. The Philip model is therefore recommended for most semi-arid watersheds in Ardabil Province.