https://mmws.uma.ac.ir/ Water and Soil Management and Modelling en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://mmws.uma.ac.ir/article_3796.html Extended AbstractIntroductionFlash floods are one of the most dangerous natural phenomena, causing significant loss of life and property due to their rapid and unpredictable nature. These floods claim thousands of lives each year and damage infrastructure, particularly in agricultural areas and along riverbanks. Key factors that exacerbate the damage include the lack of accurate flood hazard maps, insufficient preventive measures, dense drainage networks, and steep slopes. To mitigate the effects of floods and preserve ecosystems, it is essential to use flood models with spatial and watershed scales. Hydrological modeling, along with advanced technologies such as remote sensing and Geographic Information Systems (GIS), are powerful tools in this field. These methods provide precise spatial data and can simultaneously analyze factors such as slope, vegetation, and land use, enabling the production of more accurate flood hazard maps. Among these, numerical models like the FFPI model, based on physiographic parameters, are particularly useful in evaluating the risk of flash floods and managing crises. Numerous studies have shown that these models can effectively help identify high-risk areas and predict flood occurrences. In specific regions, such as the Samian watershed in Ardabil Province, due to its unique geographic features and climate change, accurate flood hazard assessment and mapping are crucial. This study, using the MFFPI model, analyzes factors such as slope, soil type, and vegetation, aiming to improve flood risk management and reduce potential damages. Materials and MethodsThis study's research methodology, designed to assess flood risk in the Samian watershed, is based on the analysis of geographical and hydrogeomorphological data. The Samian watershed, with an area of 4,200 square kilometers, is located in Ardabil province and includes units of the foothill plains. The data used in this study include 1:25,000 digital maps from the National Cartographic Center, a 30-meter resolution Digital Elevation Model (DEM), monthly and annual rainfall statistics from the Meteorological Organization, Landsat OLI 9 satellite images, 1:25000 soil texture maps, and 1:100,000 geological maps. The software used for data analysis includes ArcGIS 10.7, ENVI 5.3, Google Earth, SPSS, and Excel. In this study, hydrogeomorphological indices such as slope length, stream power, topographic moisture, slope curvature, and surface curvature were used to evaluate the topographical and geological features related to flood occurrence. Specifically, slope length and stream power indices were considered key factors influencing the hydrological response of the watershed. Additionally, the MFFPI model was employed to analyze the flash flood risk in the Samian watershed. This model uses parameters such as slope, stream density, soil texture, land cover, and land use. Pearson’s correlation test, applied in SPSS, was used to analyze the relationship between hydrogeomorphological indices and floods. This test examined the relationship between independent and dependent variables and analyzed the impact of each index on flood occurrence. Finally, these analyses were applied to assess the flood potential and identify flood-prone areas in the Samian watershed. Results and DiscussionThis study examined various physiographic factors influencing flash floods in the Samian watershed. The results show that land slope, flow accumulation, soil permeability, slope curvature, land use, lithological features, and vegetation significantly affect flood potential in this region. Land slope, especially in low-slope areas, is directly related to water accumulation and the likelihood of flood occurrence. Flow accumulation is also higher in areas with high stream density, such as main channels, and these areas have a higher flood potential. Soil permeability and lithological features play important roles in controlling runoff and flooding. Areas with less permeable soils and impermeable lithological features have the greatest potential for flooding. On the other hand, vegetation cover and land use, especially in residential and agricultural areas, increase runoff and decrease permeability, thereby intensifying flood risk. Correlation analysis shows that heavy rainfall (SPI) has the strongest positive relationship with flood risk. Finally, the MFFPI model was used to predict flood hazard zonation in the Samian watershed, producing a map with five flood risk classes, from low to high potential. This research emphasizes the importance of accurately analyzing physiographic factors in water resource management and flash flood prediction. Conclusion This study emphasizes the importance of hydrogeomorphological indices in flood hazard zoning for sudden floods in the Samian watershed using the MFFPI model. The model includes factors such as slope, stream density, soil permeability, land use, slope curvature, and soil texture, with flood hazard maps prepared in five risk categories. The results showed that areas with gentle slopes, poor vegetation cover, impermeable soils, and inappropriate land use are most prone to floods. Statistical analysis (Spearman and Pearson correlations) revealed that slope and soil texture had the highest significant positive impacts (0.78 and 0.70, respectively), and SPI, as the most influential index, with a coefficient of 0.64, determined flood severity. The TWI index was effective in areas with low slopes and water retention capacity, while NDVI and LSF showed no significant impact. The findings showed that SPI is useful for identifying high-risk areas in steep regions, while TWI is better suited for flatter areas. This study highlights the importance of advanced numerical models like MFFPI in flood risk management and damage reduction using GIS and remote sensing data and provides recommendations such as improving drainage infrastructure, restoring vegetation cover, and developing early warning systems. https://mmws.uma.ac.ir/article_3864.html Groundwater is a vital water resource, especially in arid and semi-arid regions such as northwestern Iran. It plays a crucial role in agriculture, drinking water supply, and industrial activities. Therefore, reliable monitoring and management of groundwater levels are essential for sustainable development. However, missing data in groundwater level time series caused by factors like equipment failure, inaccessible terrain, or extreme weather can hinder accurate analysis and prediction. To address this, interpolation techniques are used to estimate missing values based on observed data. The reliability of these techniques depends on the quantity, spatial distribution, and temporal resolution of the available data. In recent years, machine learning and deep learning methods have shown promise in handling complex, nonlinear, and high-dimensional datasets. This study evaluates the effectiveness of five interpolation methods Kriging, Inverse Distance Weighting (IDW), Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), Random Forest Spatial Interpolation (RFSI), and Deep Missing Value Imputation (DeepMVI) to reconstruct missing groundwater level data. The focus is on improving data completeness and accuracy for subsequent groundwater analyses. The case study is the Ajabshir aquifer, where long-term data from 29 piezometric wells are used. The objective is to compare the performance of traditional and modern interpolation approaches and to determine the most accurate method for handling missing groundwater level data.Materials and Methods In this study, groundwater level data from 29 piezometric wells in the Ajabshir aquifer in northwest Iran were analyzed monthly over a 17-year period (2006–2022). Due to various operational and environmental constraints, numerous gaps were observed in the dataset.To estimate the missing values, five interpolation methods were evaluated: Kriging, Inverse Distance Weighting (IDW), Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), Random Forest Spatial Interpolation (RFSI), and Deep Missing Value Imputation (DeepMVI). Kriging uses semivariograms to model spatial dependence and provides statistically unbiased estimates. IDW is a deterministic technique based on the inverse distance to known values. PCHIP maintains the monotonicity and continuity of time-series data. RFSI applies the Random Forest algorithm to capture nonlinear spatial relationships, and DeepMVI utilizes deep learning to model complex temporal and multivariate dependencies in the data.The dataset was randomly divided into training (70%) and testing (30%) subsets. The performance of each method was assessed using the correlation coefficient (R), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE).Results and Discussion The evaluation results show significant variation in model accuracy. The Kriging method, while widely used, showed poor performance in this study due to the sparse and irregular distribution of observation wells. Its results included a low correlation (R = 0.37), high RMSE (417.91), and low NSE (0.11), indicating that this method is not suitable under conditions with extensive missing data and limited spatial continuity. The IDW method improved over Kriging but still yielded moderate accuracy (R = 0.56, RMSE = 365.51, NSE = 0.30).The PCHIP method performed considerably better, reflecting its ability to handle temporal data smoothly. It achieved R = 0.89, RMSE = 7.52, and NSE = 0.72, making it the second most accurate method. The method preserved the shape of the original groundwater level trends and was effective in reconstructing long sequences of missing data. The RFSI method, which leverages machine learning, showed better accuracy than Kriging and IDW (R = 0.63, RMSE = 11.06, NSE = 0.40), although it was outperformed by PCHIP and DeepMVI. This suggests that while machine learning can improve performance, spatial interpolation with sparse data remains challenging. The DeepMVI method outperformed all other methods, achieving the highest correlation (R = 0.92), lowest RMSE (6.44), and highest NSE (0.80). Its ability to capture both spatial and temporal relationships using a hybrid deep neural architecture makes it highly effective in imputing missing groundwater data, especially when the dataset includes complex time-dependent patterns and multivariate interactions.The final comparison of time series plots across 29 piezometric wells also visually confirmed the accuracy of the DeepMVI model in maintaining original trends and minimizing noise or abrupt changes. These results demonstrate that deep learning models offer a promising approach for improving the quality and reliability of groundwater monitoring datasets.Conclusion This research evaluated the performance of five interpolation methods for reconstructing missing groundwater level data from 29 piezometric wells in the Ajabshir aquifer over a 17-year period. Among the methods tested, DeepMVI outperformed all others, providing the most accurate and reliable results. Its ability to model complex temporal and spatial dependencies makes it particularly suitable for environmental datasets with high variability and missing values. PCHIP and RFSI also performed well and could serve as viable alternatives when deep learning infrastructure is not available. Although Kriging and IDW are widely used in hydrogeological studies, their lower performance in this study suggests that their application may be limited under conditions of sparse or irregular data. The study highlights the importance of selecting appropriate interpolation methods based on data characteristics. DeepMVI, with its robust architecture, holds significant promise for future groundwater studies and can enhance the quality of groundwater monitoring systems by providing more complete and accurate datasets. This, in turn, can improve water resource management and planning in regions facing water scarcity and environmental stress. https://mmws.uma.ac.ir/article_3880.html IntroductionTraditional hydrological time series analyses often assume stationarity, particularly in the estimation of drought indices such as the Standardized Precipitation Index (SPI). However, increasing climate variability and anthropogenic influences have introduced significant non-stationarity into hydrological processes. This challenges the reliability of stationary-based assessments and highlights the need for models that can adapt to changing conditions. Generalized Additive Models for Location, Scale, and Shape (GAMLSS) offer a flexible framework for modeling such dynamics by allowing distribution parameters to vary over time or in relation to covariates. Recent studies suggest that non-stationary modeling improves drought characterization, particularly at longer time scales. Yet, findings remain mixed: while some report better accuracy with non-stationary approaches, others find stationary models still perform adequately, depending on regional and climatic factors. Given these variations, regional validation becomes essential. This study evaluates the performance of a non-stationary SPI-based index, referred to as SPIt, in comparison with the traditional stationary SPI. The case study is the Karkheh river basin in western Iran—a region with distinct climatic conditions compared to where SPIt was originally developed. Drought monitoring is conducted using monthly precipitation data from five stations, focusing on seasonal periods outside the dry summer months due to limited rainfall during that time. By comparing the two indices, the study aims to assess whether accounting for non-stationarity leads to more accurate drought representation in semi-arid climates.Materials and MethodsThe study focuses on five meteorological stations: Pol-Zal, Bostan, Pol-Kohneh, Noorabad and Halilan, spanning a historical data period ranging from 1971 to 2022 (C. E.) depending on rain gauge station availability. These stations were selected due to their diverse altitudes, geographical spread within the Karkheh basin (The latitude range is 47 to 48 degrees north and longitude 31 to 34 degrees east), and sufficiently long precipitation records, especially for the winter season (December to February), which accounts for the bulk of annual rainfall in the region. Precipitation data were analyzed for stationarity using the non-parametric Mann-Kendall trend test. Stations exhibiting significant trends were modeled using non-stationary GAMLSS, where the shape parameter of the gamma distribution was allowed to vary with time using polynomial functions optimized through the Akaike Information Criterion (AIC). The study employed a two-parameter gamma distribution to model winter precipitation in both stationary and non-stationary conditions. For drought assessment, two indices were used. SPI that Assumes stationary gamma-distributed precipitation, standardized to a normal distribution and SPIt that extends SPI by allowing the shape parameter of the gamma distribution to vary over time, thereby accommodating non-stationarity.Results and DiscussionThe Mann-Kendall test revealed significant decreasing trends in precipitation at Polzal and Bostan stations at 95% confidence level, and a similar albeit weaker trend at Norabad. No significant trends were detected at Pol-Kohneh and Holilan. The goodness-of-fit tests (Kolmogorov-Smirnov and Chi-square) confirmed that the gamma distribution was appropriate for all stations. GAMLSS modeling showed that non-stationary models outperformed stationary ones at stations with evident trends. For instance, AIC values were reduced by 5, 6, and 1 unit at Pol-Zal, Bostan, and Norabad, respectively, indicating better fit under non-stationary conditions. Time series analysis of the shape parameter in GAMLSS revealed temporal variability at all stations, supporting the hypothesis of non-stationarity. Worm plots for residual analysis confirmed model adequacy in both conditions, but improvements in model residuals under non-stationarity were evident at trend-affected stations. A comparison of SPI and SPIt indices indicated substantial differences in drought classification over time. At Polzal, years such as 1352 and 1354 showed no drought under SPI but were classified as moderate drought (D1) under SPIt. Similarly, years with similar rainfall amounts (e.g., 127 mm in 1971 vs. 125 mm in 2010) were categorized differently in SPIt, highlighting the model's sensitivity to underlying non-stationarity. At Holilan station, where no significant trend was observed, SPI and SPIt provided nearly identical results, reaffirming the utility of SPIt in trend-sensitive environments. A station-wise drought frequency comparison between SPI and SPIt further revealed that non-stationary modeling generally results in higher estimated drought frequencies at trend-affected stations. For example, the frequency of droughts at Polzal increased from 48% (SPI) to 52% (SPIt). Similar increases were noted at Bostan and Noorabad. Conversely, at Holilan and Polkohneh, where no significant trends were detected, the drought frequency remained the same or slightly decreased under SPIt. Moreover, the frequency of severe droughts (D4) decreased under the non-stationary model, with D4 events dropping from 2% to 0% at Pol-Zal, from 3% to 0% at Bostan, and from 6% to 4% at Noorabad. This suggests that the SPI may overestimate drought severity when stationarity is incorrectly assumed.ConclusionLong-term drought monitoring at various rain gauge stations highlights the importance of considering changes in precipitation when making decisions and setting policies in watersheds. When significant trends are present, drought analysis can be performed under either stationary or non-stationary assumptions, depending on the objective. If the primary concern is drought frequency, non-stationary analysis is strongly recommended. Results showed that at stations with trends such as Polzal, Bostan, and Norabad the frequency of droughts was underestimated under stationary analysis compared to non-stationary models. For example, frequencies increased from 48, 48, and 43 (stationary) to 52, 52, and 47 (non-stationary), respectively. However, in non-trending stations, stationary models may still provide reliable results for frequency estimation. In contrast, if the focus is on severe droughts, stationary models may outperform non-stationary ones at trend-affected stations. Non-stationary analysis yielded zero severe drought events, while stationary models identified 2, 3, and 6 cases in Palzal, Bostan, and Norabad, respectively. In non-trend stations like Polkohneh and Holilan, non-stationary analysis was more effective in detecting severe events. These findings align with previous research suggesting that while non-stationary models, such as those using GAMLSS, offer better parameter estimation, stationary models may sometimes better reflect reality in future projections. Therefore, although non-stationary modeling is essential under climate variability, the choice of model should depend on the monitoring goal. It is also recommended to incorporate time-varying variance and alternative probability distributions for better drought characterization under potential extreme rainfall events. https://mmws.uma.ac.ir/article_3938.html Introduction In recent years, one of the most important challenges in planning and operating reservoir systems is the estimation of actual (sufficient) environmental flows in rivers (downstream) during operation years and evaluating its effect on the performance of such reservoir systems. Failure to comply with the environmental flows of the rivers downstream has had long-term destructive consequences for socio-ecological systems. Therefore, accurate and timely estimation of environmental flow and its application in the operation of existing and new reservoir systems is of particular importance.Several studies have been conducted to assess environmental flows and the resulting changes in river ecology after the construction of reservoir dams. All studies have investigated and evaluated the flow regime before and after dam construction on rivers and have emphasized the need for timely allocation of environmental flows and changes in reservoir system operation patterns. Studies of the environmental flows of rivers have mostly focused on estimating environmental flows using various methods and comparing the river flow regime before and after dam construction in both current and ecosystem-compatible conditions in the historical period. However, the effect of timely release of environmental demand compatible with the ecosystem on the long-term behavior of various important characteristics of the storage reservoir system (such as useful volume, command curve, hydrological behavior, critical period, evaporation losses, etc.) has not been considered in previous studies, and this could be an innovative approach in this field, which has been carried out in the present study. For this purpose, in this study, a comprehensive Monte Carlo simulation approach is carried out to compare several important common methods for estimating the environmental demand of the river (dam downstream) and evaluate their effects on important design characteristics (such as useful volume) and operation (such as the command curve) in Nazlou reservoir system. The Nazlou river is one of the important rivers of the Lake Urmia basin. After providing drinking and agricultural water to the lands under its coverage, it is discharged into Lake Urmia. Materials and MethodsIn this study, the Nazlou Reservoir Dam and the Nazlou River in Urmia were selected as a case study. The Nazlou River is one of the important rivers of the Lake Urmia basin. After providing drinking and agricultural water to the lands under its coverage, it is discharged into Lake Urmia. Methods for determining environmental demands in the river downstream are classified into three groups: hydrological, hydraulic, and habitat simulation, but hydrological methods, with the availability of the required data, are used by researchers as reliable and common methods. In this study, five common hydrological methods, which are preferred by researchers, namely Desktop Reserve Model, flow duration curve, Smakhtin, Tessman, and Tennant methods, were used to estimate environmental demands. This comprehensive study is based on a Monte Carlo simulation approach with a very large process (924,000 times) of stochastic simulation of the storage system, i.e. 1,000 time series of production flow, 7 demands (DAnnual=0.1 to 0.4 MAF, step 0.05), 11 time reliability indices (Rel=0.9 to 1.0 step 0.01), 2 vulnerability indices (Vul=0.0 and 0.3), and 6 methods of determining environmental demand (the 5 above-mentioned methods + 1 Method without applying environmental demands). The aforementioned Monte Carlo simulation approach is based on a hybrid model combining the Valencia-Shaake disaggregation stochastic model and the Modified Sequent Peak Algorithm (SPA) simulation model.Results and Discussion The main results of this study are summarized as follows: a) Comparison of the environmental demand values obtained from the five applied methods showed that the environmental demand based on the Tennant and Tessman method were 20% (as minimum value) and 53% (as maximum value) of average annual flow, respectively, and the obtained values of the other three methods are approximately close to each other and are about 28% of the average annual flow. The monthly variation of the obtained environmental flow in river also indicated that the results of the three methods Tessman, FDC shifting (C) and DRM (C) had a similar behavior with the mean monthly historical flow values. b) The results showed that the performance of the used stochastic model in reproducing the statistical parameters of historical flow data at both annual and monthly levels and reproducing the correlation structure between the flows of different months and the correlation between the annual and monthly flows is quite desirable, and this plays a key role for a reservoir system simulation in real conditions. c) The results indicate a systematic long-term behavior of main reservoir storage characteristics (i.e., active storage, resiliency index, evaporation loss, and critical period) against demand for two condition, without and with including environmental flow, with the exception of the Tessman method.Conclusion The main outcome of investigation could be summarized as follows: a) The ecological system of rivers often adapts to its monthly flow regime over time, therefore, using a method of estimating the ecological demands of a river in accordance with the monthly flow regime and with an appropriate amount (30-40% of the average monthly flow) has a more relative advantage than other methods. So, FDC Shifting (C) method, with an average of 32 percent of the average monthly flow and the minimum coefficient of variation (CV=0.20), was the most appropriate method for estimating the environmental demand in tested reservoir dam. b) The combination of the annual AR(1) model and the monthly Valencia-Shaake disaggregation model has the ability to reproduce the statistical characteristics of the historical streamflow values at both annual and monthly levels sufficiently, and this provides a reliable framework with high accuracy in the stochastic analysis of a reservoir system. c) The long-term storage-demand-performance relationships of the reservoir system due to environmental demand, with the exception of the Tessman method, show a similar systematic behavior change for different methods of estimating environmental demand. d) By applying environmental demands to the analysis of storage reservoir systems, the active storage, evaporation loss, and critical period increase by 2 to 10 times. https://mmws.uma.ac.ir/article_4089.html Soil erosion constitutes a significant environmental and agricultural obstacle that jeopardizes food security throughout Nigeria. This research delves into the correlation between the intensity of soil erosion and household food security in Kano State by employing the Revised Universal Soil Loss Equation (RUSLE) and the Household Food Consumption Score (HFCS). A multistage sampling technique was used to identify 600 respondents across four Local Government Areas categorized by varying levels of erosion severity (Very High, High, Low, and Very Low). The modeling of soil erosion was accomplished in Google Earth Engine by the integration of CHIRPS rainfall data, SRTM Digital Elevation Model (DEM), FAO soil classification maps, and Landsat satellite imagery. The findings derived from the Revised Universal Soil Loss Equation (RUSLE) model indicate that more than 90% of the study area is exposed to high and very high erosion risk; The result of the One-Way ANOVA analysis showed significant differences (p < 0.001) in caloric consumption relative to erosion classifications. While 30.36% of the households situated in areas characterized by very low erosion are found to consume between 2800 and 3200 kcal/day, only 12% were found to consume between 2800 and 3200 Kcal/person/day. Similarly, the percentage of households classified as food-secure was found to be high in areas with very low erosion (72%) as against 54.67% in very high erosion areas. Crop yields revealed that cowpea and millet exhibited pronounced sensitivity to erosion, with cowpea yields diminishing by as much as 38.42% when comparing very low to very high erosion zones. This research concludes that soil erosion considerably affects agricultural productivity and food security. It calls for prompt policy measures that support agroforestry, terracing, cover cropping, and sustainable land management methodologies to alleviate erosion and boost food resilience. https://mmws.uma.ac.ir/article_3959.html IntroductionThe distribution of soil particle sizes is a key physical characteristic that affects soil structure and is closely related to soil texture and behavior. This feature enhances the exchange of matter and energy in the soil environment. The distribution of soil particle sizes not only influences soil permeability and organic matter content but also affects soil fertility, erosion susceptibility, soil conservation, moisture and nutrient movement, vegetation productivity, environmental remediation, and land degradation. On the other hand, human interventions and the improper use of soil based on its inherent potential have led to changes in various land uses, resulting in floods, erosion, sediment yield, and soil transformation. Therefore, understanding the distribution of soil particle sizes and determining the sensitivity of a region's soil to erosion can help illustrate the pattern of erosion distribution and ultimately assist managers in making decisions regarding watershed management actions. Thus, in this study, we analyze soil particle size distribution in various land uses/covers in the Vaz watershed in Mazandaran province using laser particle size analysis and statistical indices.Materials and MethodsIn the present study, the land use map of the watershed was prepared using Landsat satellite images in remote sensing software (ENVI). Then, using this map, the area of land uses and access routes to the region were determined, resulting in 47 sampling points, including 25 points for forest, 13 points for rangeland, 4 points for agriculture, and 5 points for residential areas. Samples were then collected in the field. The samples were exposed to open air to dry their moisture; subsequently, approximately 50 grams of each sample were placed in an oven for final drying after removing straw, chaff, and other waste materials for 24 hours. Finally, 30 grams of each were placed in Falcon tubes and sent to the laboratory for laser analysis. After receiving the laser particle size distribution results, these results were examined in terms of clay, silt, sand, pebble, mean particle size, sorting, skewness, kurtosis, 10d, 50d (median), and 90d. To extract particle size statistics and percentages of particle fractions, the GRADISTAT macro written in Excel was used.Results and DiscussionThe results showed that the average particle size of clay, silt, sand, and pebble in forest land use was 1.03, 51.52, 45.39, and 2.06, respectively; in rangeland use, it was 0.62, 52.41, 45.21, and 1.76; in agricultural land use, it was 1.33, 75.2, 23.39, and 0.08; and in residential land use, it was 0.57, 36.96, 54.5, and 7.97. The particle size distribution in forest and rangeland uses indicates that silt and sand fractions have the highest percentages, reflecting a balanced soil structure. This may be due to the influence of vegetation and the accumulation of organic matter. Agricultural land use has a significant percentage of silt (75.2%), indicating a finer texture, likely resulting from agricultural practices such as tillage. Residential land use shows a higher proportion of sand (54.5%) and pebble (7.97%), which is associated with construction activities in this land use. The evaluation of grain size statistics reveals that the mean particle size in residential land use is 2.70 Φ, in forest land use 3.99 Φ, in rangeland use 4.04 Φ, and in agricultural land use 5.37 Φ, corresponding to fine sand, very fine sand, silt, and silt textures, respectively. The presence of sand and pebble mines in the area and construction activities are the main reasons for the coarser mean particle size in residential land use. The sorting (standard deviation) of soil particles in agricultural land use is 1.78 Φ, in residential land use 2.21 Φ, in forest land use 2.21 Φ, and in rangeland use 2.24 Φ. According to Folk's classification, residential, forest, and rangeland uses have very poor sorting, while agricultural land use has poor sorting. The skewness of particles in agricultural land use is -0.41, in rangeland use -0.07, in forest land use -0.03, and in residential land use 0.23. Based on Folk's classification, skewness is nearly symmetrical in forest and rangeland uses, skewed toward very coarse particles in agricultural land use, and skewed toward coarse particles in residential land use.ConclusionIn general, analyzing soil particle size is crucial for identifying erosion-sensitive areas. In this study, we conducted soil particle size analysis in the Vaz watershed using sampling from various land uses/covers and laser technology. The predominance of silt and sand in the watershed indicates its sensitivity to erosion. Given that the Vaz watershed has a steep slope and considering the construction activities throughout the watershed, as well as the low presence of stable particles (coarse particle size), it is recommended that some watershed management actions be implemented in the upper part of the watershed. Also, the results obtained and the distribution the map of soil particles can assist in prioritizing soil conservation measures and controlling erosion in the Vaz watershed.______________________________________ https://mmws.uma.ac.ir/article_3964.html IntroductionSoil erosion and sedimentation are integral components of natural geomorphological cycles, playing a pivotal role in regulating watershed functionality, preserving water quality, maintaining agricultural productivity, and ensuring the long-term sustainability of ecosystems. Under natural conditions, these processes contribute to landscape evolution and nutrient redistribution. However, human activities combined with climate change have greatly accelerated erosion and sedimentation rates, causing severe environmental damage and substantial economic losses worldwide. Factors such as land use changes, deforestation, overgrazing, and improper land management have intensified soil degradation and sediment transport, seriously threatening both terrestrial and aquatic ecosystems across diverse regions. In response to these growing challenges, understanding sediment source dynamics has become critically important. Identifying main sediment contributors and accurately measuring their relative impacts are vital for developing effective conservation and management strategies. Additionally, analyzing the landscape structure through landscape metrics which assess spatial patterns, connectivity, and fragmentation of land uses offers important insights into how landscape features directly affect erosion and sediment movement processes. Integrating such comprehensive landscape level analyses significantly improves our ability to predict erosion risks and design targeted interventions to effectively protect vital soil and water resources, ultimately supporting environmental health and promoting sustainable land management practices for future generations.Materials and MethodsThis study aimed to examine the effects of landscape metrics and erosion-related factors on suspended sediment generation across different land use types in the Kasilian Watershed, located in Mazandaran Province, northern Iran. The area lies on the southern slopes of the Alborz Mountains and drains into the Caspian Sea. It features a complex land use mosaic including natural forest, agriculture, rangeland, and plantation forest, each with distinct structural and ecological characteristics affecting erosion potential. A sediment fingerprinting approach using 59 geochemical tracers was employed. These tracers were analyzed in 36 source samples and 8 suspended sediment samples collected at the watershed outlet. Discriminant function analysis was performed using the FingerPro package in R. Potassium, sodium, and lead were identified as the most effective tracers for source discrimination. These elements were then applied in a multivariate model to estimate the proportional contributions of each land use type. Simultaneously, spatial analysis using the Revised Universal Soil Loss Equation (RUSLE) was conducted. RUSLE factors included soil erodibility, rainfall erosivity, topographic slope-length and steepness, cover management, and conservation practices. These were mapped with spatial data and overlaid with land use distribution. Landscape structure was also quantified using FRAGSTATS 4.2 to calculate metrics such as patch density, mean patch size, edge contrast, and nearest neighbor distance.Results and Discussion The sediment source apportionment results revealed a striking dominance of rangelands, which contributed an estimated 76% of the suspended sediment load. In contrast, natural forests, agricultural lands, and plantation forests were responsible for only 9%, 6%, and 6% of sediment contributions, respectively. This disproportionate contribution from rangelands is particularly noteworthy given their relatively remote position from the watershed outlet. Several factors account for this observation: (1) Rangelands exhibited the highest values for the cover-management factor (C), indicating sparse and degraded vegetation cover; (2) The slope-length and steepness factor (LS) was also elevated in rangeland areas, signaling steep terrain prone to rapid runoff and detachment; (3) Soil erodibility (K) was greater in these zones, further exacerbating erosion potential. Additionally, a high mean Euclidean nearest neighbor distance (ENN_MN) in rangeland parcels suggests that these patches are more isolated and less buffered by adjacent vegetative covers, reducing their resilience to erosive forces. These results underscore the complexity of sediment production processes and highlight the importance of considering both biophysical and spatial variables in watershed-scale erosion studies. Despite the geographical distance of rangelands from the watershed outlet, their structural fragility, topographic exposure, and low vegetative cover make them particularly susceptible to erosion and major contributors to downstream sediment loads.Conclusion From a management perspective, the integration of landscape metrics with erosion related biophysical parameters provides a comprehensive framework for identifying critical sediment source areas. This approach goes beyond traditional point-based monitoring and enables a broader understanding of sediment dynamics across the landscape. The insights gained from this study offer a solid scientific basis for prioritizing conservation actions such as reforestation, rangeland restoration, slope stabilization, and the implementation of soil conservation practices tailored to specific local conditions. Moreover, this research contributes meaningfully to the broader conversation on sustainable watershed management in the face of climatic uncertainty and increasing human pressures. By combining sediment fingerprinting techniques, geospatial analysis, and landscape ecology principles, this study presents a replicable methodology that can be applied in other watersheds facing similar challenges. In conclusion, the findings emphasize the urgent need for integrated assessment frameworks that link land use, topography, and landscape structure with sediment generation and transport processes. These approaches are essential for guiding targeted, evidence-based policies and building ecological resilience. As climate change continues to intensify hydrological extremes and land degradation, proactive sediment management informed by science and supported by spatial tools will be vital to maintaining the productivity, sustainability, and environmental health of watersheds. https://mmws.uma.ac.ir/article_4023.html Introduction Climate change, one of the major challenges of the 21st century, has far-reaching impacts on natural systems and human societies. These changes, including changing precipitation patterns, increasing the intensity of droughts and floods, and changing temperatures and evaporation, pose serious uncertainties for the sustainable management of water resources. Runoff, as a key component of the hydrological cycle, plays a vital role in agricultural water supply, groundwater recharge, and river flow, and its disruption has direct consequences for aquatic ecosystems and human livelihoods. Golestan Province, and in particular the Gorganrood Basin, with its geographical and climatic diversity, is considered a region sensitive to climate change. The two sub-basins of Lazoreh and Jangaldeh are of particular importance because they provide significant surface water resources, and the economic and agricultural activities of the region depend on them. In this study, a simple and valid GR2M precipitation-runoff model was used to assess the impact of climate change on monthly runoff. This model, with minimal data required, allows for accurate simulation of hydrological processes and analysis of future scenarios. Such a level of research, focusing on sub-basins, will help policymakers and water resource managers design solutions that are adaptable to future climate conditions. These measures will not only help reduce agricultural vulnerability and ensure food security, but will also be effective in reducing social tensions caused by water scarcity. Overall, this study aims to provide a scientific and practical understanding of sustainable water resource management in the face of climate change.Materials and Methods In the present study, the GR2M precipitation-runoff model was used to simulate monthly runoff. This model is considered a suitable option for analysis at the monthly scale due to its simple structure and minimal data requirements. To predict future climate conditions (2023–2100), the outputs of the ACCESS-ESM1-5 global climate model were used under three scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. These outputs were downscaled with the help of the LARS-WG8 random weather generator to better reflect local characteristics. In order to calibrate and validate the model, daily temperature (minimum and maximum) and precipitation data from the Minudasht evapotranspiration station during 1993 to 2022, and monthly river flow data from two hydrometric stations during 2011 to 2022 were used. Also, potential evapotranspiration was estimated using the Thornthwaite method. Combining observational data, climate projections, and hydrological modeling has enabled a more detailed analysis of the impacts of climate change on water resources and provided scientific insights for sustainable water management in the region.Results and Discussion The results from the ACCESS-ESM1-5 model output and under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the future period (2023-2100) show that the average minimum and maximum temperatures in the study area in the future period (2023-2100) under all three scenarios have increased compared to the observation period (1993-2022). The results show that the average minimum temperature in the observation period (1993-2022) is 12.66 °C. While the increase in the average minimum temperature under the SSP1-2.6 scenario in the future period compared to the observation period in the time period (2077-2100) is 14.28 °C, and under the SSP2-4.5 and SSP5-8.5 scenarios, the highest increase in the minimum temperature in the time period is also in the time period (2077-2100) at 15.48 and 17.28 °C, respectively. The results show that the average maximum temperature in the observation period is 25.08 °C, but the highest increase in the maximum temperature for all three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, will occur on average in the final period (2077-2100) at 26.88, 28.07, and 29.89 °C, respectively. The results show that the minimum and maximum temperatures under all three scenarios will increase in the future period compared to the observation period. The precipitation parameter in the observation period is also 441.06 mm, with the highest increase in precipitation compared to the observation period under all three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, in the time interval (2077-2100) being 493.07, 489.53, and 513.75 mm, respectively. The output results of the GR2M model for simulating the flow of the Chehelchay and Narmab rivers at the hydrometric stations of Lazoreh and Jangaldeh during the observation period show that the model performs well in both calibration and validation periods, as shown by the Kling-Gupta values and the root mean square error. The Kling-Gupta efficiency criterion and the root mean square error in the validation period for the Lazore watershed are 0.68 and 14.95, and in the Jangaldeh watershed are 0.68 and 24.52. Therefore, the results in the observational section show that the model performs well in simulating the flow rate. The GR2M model estimated the monthly flow reasonably well, but in some months, there were differences between the observed and simulated values, indicating overestimation or underestimation. The results of future flow predictions under the SSP1-2.6 and SSP2-4.5 scenarios show that after 2040, the river flow will decrease due to increasing temperature, but in the final years, the river changes correspond well to the rainfall fluctuation pattern. In the SSP5-8.5 scenario, it is observed that in the period 2040 to 2060, increasing temperature and precipitation have largely caused the flow to increase, but then in the period 2060 to 2100, the flow velocity decreases significantly with increasing temperature and decreasing precipitation.Conclusion The results show that climate change, especially under high emission scenarios, will have a negative impact on surface water availability in the two watersheds of Lazoreh and Jangaldeh. This underscores the urgent need for adaptive water management strategies, including improved irrigation efficiency, water allocation planning, and drought preparedness. Furthermore, given the satisfactory performance of the GR2M model in this study, its simplicity, low data requirements, and ease of implementation make it a suitable tool for hydrological modeling and climate impact assessment in other data-scarce basins across Iran and similar regions. This research contributes to the growing body of evidence on climate change impacts in semi-arid regions and provides actionable insights for regional water authorities and policymakers aiming to ensure water security under future climatic uncertainty. https://mmws.uma.ac.ir/article_4027.html Extended AbstractIntroduction A major hydrological and physical event affecting surface runoff, erosion, and water infiltration is soil water repellency (SWR). Hydrophobic soils reject wetting, therefore causing water droplets to linger on the surface instead of penetrating the soil profile. Particularly in sloping environments and arid ecosystems, this disease causes more overland flow, less water retention, and higher sensitivity to soil loss. SWR is progressively seen in northern Iran, especially in the loess-derived soils of Golestan and Mazandaran provinces, as a result of a mix of climatic conditions and changes in land use. Fine silty to silty-clay textures characterize loess soils in these areas; together with environmental stressors including drought cycles, agricultural development, and deforestation, they aid in the formation and magnification of SWR. Central in the water repellency are organic compounds, especially hydrophobic plant-derived substances like waxes and lignins. Emphasizing the significance of soil chemical composition, many studies have shown that soil organic carbon is strongly positively linked to SWR intensity. Variations in clay content, pH, and electrical conductivity (EC) can also affect SWR patterns. Although SWR is very important in soil degradation processes, little research has been done employing sophisticated data-driven techniques to forecast its spatial variability. Machine learning (ML) algorithms like Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) provide strong means for modeling complicated soil behavior. Using these algorithms, the current study attempts to forecast SWR in loess soils based on a thorough set of physicochemical parameters, therefore helping to justify improved soil management and erosion control measures. Materials and Methods Northern Iran served as the location for the study, which looked at specific loess terrains in Golestan and Mazandaran provinces. From many sites including Gorgan, Maraveh Tappeh, Neka, Sari, and Amol, 45 surface soil samples (depth 0–10 cm) were gathered. While minimizing confounding effects, sampling places were chosen to record changes in topography, land use, and vegetation. Key soil physicochemical characteristics assessed were organic carbon (OC), organic matter (OM), electrical conductivity (EC), pH, mean weight diameter (MWD) of soil aggregates, and particle size distribution (sand, silt, clay). With infiltration time recorded up to 3000 seconds, WDPT tests involved dropping 50 μL distilled water droplets on air-dried soil surfaces at room temperature. WDPT values were used as the target variable in model development. Procedures used in laboratories complied with the same criteria used in earlier research. R software was used for data preprocessing. Outlier detection based on interquartile range (IQR), Z-score normalization of numerical variables, and multicollinearity analysis utilizing the Variance Inflation Factor (VIF) were included in this. Categorical variables like soil texture classes were converted to dummy variables using one-hot encoding. Three machine learning approaches—Decision Tree (CART approach), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were applied to the dataset, which was randomly separated into 70% training and 30% testing parts. Models were implemented using R packages rpart, randomForest, and xgboost. Through repeating 10-fold cross-validation, hyperparameter tuning was carried out to enhance prediction accuracy.Results and Discussion Initial model performance using default settings revealed limited predictive ability across all algorithms. The Decision Tree (DT) model yielded the weakest results with RMSE = 19.55 and R² = 0.02, indicating poor capacity to capture the variability in WDPT values. After hyperparameter optimization, both Random Forest (RF) and XGBoost (XGB) showed significant improvements. The RF model achieved RMSE = 15 and R² = 0.42, while XGB recorded RMSE = 14.7 with the same R², highlighting their comparable predictive power. Feature importance analysis revealed that organic carbon was the most influential predictor of WDPT across all models. Additional influential variables included clay content, sand fraction, EC, OM, and pH, though their relative importance varied by algorithm. In RF, organic matter and sand had high predictive value, whereas in XGB, clay and EC gained prominence. These differences reflect each model's inherent sensitivity to nonlinear interactions. Spatial analysis showed that areas with higher organic carbon content aligned with regions of higher WDPT, confirming the key role of hydrophobic organic compounds in driving soil water repellency. Uncertainty assessment using Bootstrap and Monte Carlo simulations demonstrated that RF was the most stable model, showing the lowest RMSE variability and higher resilience to noisy input data. Overall, the results confirm that machine learning algorithms, especially RF and XGB, can effectively model and interpret the complex interactions influencing soil water repellency in loess landscapes.Conclusion This study demonstrated the applicability of advanced machine learning algorithms for modeling soil water repellency (SWR) in loess-derived soils of northern Iran. Among the three tested models, Random Forest (RF) provided the most reliable and stable predictions, with optimal performance metrics (RMSE = 15, R² = 0.42) and low sensitivity to data uncertainty. XGBoost (XGB) also yielded competitive results but showed slightly lower stability under uncertain conditions. The Decision Tree (DT) model, while interpretable, lacked sufficient predictive accuracy for complex, nonlinear relationships. The results confirmed that organic carbon is the dominant driver of SWR in the study area, supporting previous findings regarding the hydrophobic nature of plant-derived organic compounds. Other variables such as clay content, sand fraction, pH, and EC also played important roles depending on the model structure. Differences in variable importance highlight the benefit of using multiple algorithms to obtain a comprehensive understanding of the underlying mechanisms. Uncertainty analysis showed that RF is less susceptible to overfitting and data noise, making it a more robust choice for environmental modeling. Spatial patterns of WDPT and key soil variables revealed strong regional correlations, suggesting the feasibility of using geospatial ML models for site-specific soil management. Future research should explore hybrid models (e.g., RF-XGB) and deep learning architectures (e.g., CNNs or ActionFormer) to enhance predictive power, particularly in dynamic or post-disturbance soil systems. Moreover, integrating multi-temporal datasets could improve the understanding of SWR variability under different environmental and management conditions. https://mmws.uma.ac.ir/article_4122.html Rapid population growth has accelerated urbanization, significantly altering land use and land cover in peri-urban areas. This study examines the urban expansion of Wolaita Sodo Town in South Ethiopia over the past two decades (2003–2023) and its socio-economic and environmental implications. A longitudinal research design was employed, combining remote sensing and GIS-based analysis of Landsat satellite imagery from 2003, 2013, and 2023 with qualitative insights from key informant interviews to assess land cover dynamics and community-level impacts. The results show that the built-up area expanded from 4,654 ha (10.8%) in 2003 to 7,914.9 ha (18.4%) in 2013 and further to 11,681.5 ha (27.2%) in 2023, while agricultural land declined from 35,891.9 ha (83.5%) in 2003 to 28,389.8 ha (66%) in 2023. Over the study period, the average annual rate of urban expansion increased from 326.09 ha/year (2003–2013) to 376.66 ha/year (2013–2023), with an overall rate of 702.75 ha/year across the 20 years. This rapid urban growth has led to large-scale land expropriations, disproportionately affecting peri-urban farmers whose agricultural lands were converted into residential, industrial, and infrastructure zones. As a result, agricultural productivity has declined, forcing many affected households to transition into low-paying informal sector jobs, contributing to economic instability and increased vulnerability. The study highlights the urgent need for integrated urban planning and sustainable land management strategies to mitigate these adverse impacts. In particular, improving compensation mechanisms for displaced communities, ensuring equitable land policies, and enhancing access to essential services are crucial for promoting resilience. The findings emphasize the importance of adopting a holistic approach to urban development that balances the needs of expanding cities with environmental conservation efforts. https://mmws.uma.ac.ir/article_4048.html IntroductionSoil degradation is closely related to increased wind erosion. Wind erosion has multifaceted and widespread effects on arid and semi-arid ecosystems. This phenomenon weakens soil structure and fertility by decreasing the soil's physicochemical quality, reducing organic matter and nutrients, and causing significant biological changes, thereby limiting the ecosystem's capacity. In the Sistan Plain, the occurrence of long and strong winds, known as the 120-day winds, is a prominent example of this erosion that has numerous consequences for the environment and human societies. These winds, by moving soil particles and reducing microbial activity, not only disrupt ecosystem performance but also threaten people's physical health through the spread of polluted dust, leading to increased respiratory problems, allergies, and chronic diseases. Moreover, continuous exposure to these conditions can create psychological and social stress, reduce agricultural productivity and quality of life, and result in economic and migration consequences. These conditions highlight the importance of carefully examining the effects of wind erosion on the physicochemical properties and microbial activities of the soil and its broad implications for ecosystem sustainability and regional challenges. Materials and MethodsIn the present study, the region was categorized into four levels of soil erosion intensity as the main treatment: no erosion, low, moderate, and severe erosion. To investigate the effects of these treatments on changes in soil properties, sampling was conducted based on a completely randomized design in the fall of 2023. At each erosion level, five homogeneous areas with similar physiographic conditions were selected, and five soil samples were collected from each area at a depth of 0 to 30 cm. The soil samples were combined using a composite sampling method. Some samples were immediately transported to the laboratory in sealed containers and stored in a refrigerator after collection to measure microbial characteristics while maintaining initial humidity. The remaining samples were air-dried and passed through a 2 mm sieve to determine physical and chemical properties. Physical characteristics assessed included soil texture, bulk density, and porosity, chemical characteristics included organic carbon, total nitrogen, available phosphorus and potassium, acidity, electrical conductivity, calcium carbonate, sodium absorption ratio. Microbial characteristics were catalase enzyme activity, basal and stimulated microbial respiration, microbial biomass carbon and nitrogen, and microbial contribution. Data were analyzed using one-way analysis of variance (ANOVA) in SPSS software version 26, and means were compared using Duncan's test at a 95% confidence level. Additionally, correlations among the studied characteristics were examined using R software. Results and Discussion The results of this study demonstrated that the intensity of wind erosion had significant and widespread effects on the physicochemical and microbial properties of the Sistan Plain soil. As erosion intensity increased, the levels of organic carbon, total nitrogen, available phosphorus and potassium, as well as soil porosity, decreased significantly. Conversely, bulk density, electrical conductivity, calcium carbonate, and sodium absorption ratio showed a significant increase. This pattern indicated the removal of fine particles rich in organic matter and nutrients from the soil surface, while coarser particles, richer in calcium carbonate and salts, remained as a result of wind erosion. Changes in surface soil pH were not significant. Additionally, the results indicated a significant decline in microbial activity and soil biological indicators. Catalase enzyme activity, basal and stimulated microbial respiration, microbial biomass carbon and nitrogen, and microbial population all decreased progressively with increasing erosion intensity. The highest values were observed in non-eroded soils, while the lowest values occurred in soils subjected to severe erosion. The ratio of microbial biomass carbon to nitrogen exhibited significant changes but no clear trend. The relative contribution of microbial carbon to total organic carbon increased at high erosion intensities, likely due to a sharp decline in total organic carbon. Correlations between erosion intensity and microbial indices were strongly negative (-0.94 to -0.99), whereas the microbial C/N ratio showed a moderate positive correlation, and the soil microbial contribution exhibited a weak negative correlation. These findings highlight that wind erosion directly limits microbial activity, organic matter decomposition, and soil nutrient cycling, posing a severe threat to the sustainability of soil fertility and ecosystem function in the Sistan Plain. Implementing appropriate soil management practices is essential to reduce erosion, preserve soil microbiota, and ensure ecosystem stability. ConclusionThe present study indicates that wind erosion not only alters the physical and chemical properties of the soil but also significantly impacts the activity and dynamics of the microbial community. The reduction of organic matter, changes in bulk density, porosity, nutrient loss, and decreased microbial activity suggest that wind erosion limits the ecosystem's capacity to maintain biological function and sustainability. These findings underscore the importance of soil resource management and the development of conservation strategies in arid regions. They can also inform the design of sustainable agricultural programs aimed at mitigating the adverse effects of wind erosion. Future research should explore the interactions between soil physicochemical and microbial changes over extended time scales, as well as the combined effects of climate change and wind erosion, to develop more effective strategies for protecting dryland ecosystems. https://mmws.uma.ac.ir/article_4096.html Introduction Climate change is intensifying global droughts, posing severe threats to the environment, agriculture, and human livelihoods. The Earth's temperature is rising at twice the global average, driving significant changes in planetary conditions. Escalating land surface temperatures have degraded ecosystems, depleted soil quality, and reduced water availability, leading to the expansion of arid regions. These shifts stress vegetation and diminish agricultural yields, exacerbating food insecurity, particularly in developing nations. Drought and water scarcity are now prominent trends in arid and semi-arid regions. Altered precipitation patterns, influenced by climate change, disrupt rainfall quantity and distribution, further straining water resources. In countries like Iran, with predominantly dry climates, these climatic shifts are especially acute. Iran ranks among the top six nations for natural disasters, with over 83 percent of its crises tied to earthquakes, floods, and droughts. Drought, a recurring issue, inflicts significant damage on Iran's society, water, and soil resources. Khuzestan Province, a key agricultural and economic hub in southwestern Iran with rich water resources, is significantly affected by climate change. Rivers like Karun, Karkheh, and Jarahi, vital for drinking water, agriculture, and industry, are impacted by these changes. This study evaluates the impact of climate change on land surface temperature and drought risk in the agricultural regions of Khuzestan Province, aiming to address these critical challenges.Materials and Methods This study utilized two primary data sources to assess climate change impacts in Khuzestan Province. Climate parameters, including precipitation, radiation, and maximum/minimum temperatures, were obtained from the Abadan synoptic station (1985–2015) from the Meteorological Organization. Additionally, satellite data for 19 stations, covering temperature, radiation, and precipitation, were sourced from the ERA5 database for 1950–2025. Due to incomplete data from 21 meteorological stations, satellite data were used for the first time in this region to model and forecast temperature and precipitation. The Mann-Kendall Z-statistic test, applied at 95 and 99 percent significance levels, analyzed trends in temperature and precipitation changes. To address the low resolution of general circulation models for regional studies, the LARS-WG8 statistical model was employed for downscaling. This model used daily time series data, including precipitation (mm), maximum/minimum temperatures (C°), and radiation (MJ/m²/day), with a base period of 1985–2015, aligned with CMIP6 models. Projections were based on the CanESM5 model under the high-emission SSP5-8.5 scenario, downscaled for short-term (2021–2040) and medium-term (2031–2050) periods. Trends in modeled data were also evaluated using the Mann-Kendall test. Model performance was assessed in Excel using percentage error, correlation coefficient (R), and root mean square error (RMSE). Additionally, satellite data from Google Earth Engine were analyzed to calculate land surface temperature (LST), normalized difference vegetation index (NDVI), standardized precipitation index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI).Results and Discussion An analysis of monthly and annual temperature trends from 1950 to 2025 across 19 stations in Khuzestan shows a significant upward trend in nearly all months. Projections using the LARS-WG8 model under the SSP585 scenario  CanESM5 indicate an approximate 3 C° increase in both the near future (2021–2040) and medium-term future (2031–2050) compared to the 1985–2015 baseline. Data from the Abadan synoptic station and satellite observations confirm this warming, with satellite data predicting even hotter summers. In contrast, precipitation is projected to decline across all months in both future periods, with December and January remaining the wettest months. The 24- year NDVI record shows substantial vegetation cover fluctuations, with the lowest values in 2000, 2008, and 2009, likely linked to low rainfall and drought. A recovery has been observed since 2010, possibly reflecting improved climatic or hydrological conditions. Land surface temperature has shown a steady rise since 2010. The Standardized Precipitation Index (SPI) indicates severe droughts in 2000–2004, 2010,  2021, and 2022. The Standardized Precipitation Evapotranspiration Index (SPEI) shows that from 1980 to 1999, conditions were generally normal with alternating mild to moderate droughts. From 2000 to 2024, mild to moderate droughts predominated, while 2008–2022 marked a shift toward severe drought conditions. According to the SPEI index, the most extreme droughts occurred in December 2010 (−2.53) and December 2021 (−2.48).Conclusion This study comprehensively analyzes the drought trend in Khuzestan Province from 1950 to 2025 using meteorological and remote sensing data. Initial findings show a significant increase in temperature in all months except November, along with a decrease in precipitation in the long term, which is consistent with the CanESM5 model predictions of a 3 C° increase in temperature and a decrease in precipitation. These results confirm that global warming and human activities, especially in industrial cities such as Ahvaz and Abadan, have exacerbated drought conditions. The SPI and SPEI indices show severe drought periods between 2000 and 2022, accompanied by a decrease in NDVI and adverse effects on agriculture and vegetation. These findings highlight the impact of non-precipitation factors, such as artificial irrigation, in mitigating the effects of drought. However, limitations include uncertainties in the SSP5-8.5 scenarios, reliance on data from specific weather stations, and weak correlations between SPI and NDVI. To address these issues, future research should use hybrid climate models, extend data collection to rural areas, and integrate artificial intelligence for advanced analysis. Practical recommendations include promoting drought-resistant crops, optimizing irrigation systems, and educating local communities. This study provides a basis for water resources management and climate policies, and emphasizes the urgent need for adaptive measures to cope with intensifying droughts. https://mmws.uma.ac.ir/article_4097.html Introduction The amount of available water in the soil for crop use, called available water (AW), is described as the capacity of the soil to retain water and make it available for root uptake. Irrigation management, agricultural projects, and soil water balance are some of the well-known practical applications related to AW. It is generally accepted that matric potentials at -33 kPa and -1500 kPa represent FC and PWP, respectively. An attractive alternative to direct and often laborious measurements of these soil hydraulic properties is their estimation using soil pedotransfer functions (PTFs). The efficiency of PTFs is not the same in different locations and conditions, so the performance of each PTFs should be evaluated according to the soil characteristics of each region. The Sistan Plain is one of the strategic border areas of eastern Iran and the edge of the desert. The main objective of this study is to evaluate the performance of 23 different PTFs models in predicting soil water retention at matric potentials of -33 kPa and-1500 kPa in the Sistan Plain. In order to determine the best PTF that is appropriate for the conditions of the study area, 100 soil samples from the Sistan region were used. Materials and Methods In order to conduct this study, 100 soil samples were collected from a part of the Sistan Plain for laboratory analysis and transported to the laboratory. Particle size distribution (sand, silt, and clay) was measured by hydrometric method, organic carbon by Walkey-Black method, and bulk density by cylinder method. Twenty-three PTFs were evaluated to predict water content (gravimetric and volumetric), including twelve functions for matric potential of -33 kPa and eleven functions for matric potential of -1500 kPa. PTFs can also be categorized based on the type of prediction, namely point and continuous PTFs, and in this study, point PTFs were evaluated. The selection of PTFs is limited to PTFs that use soil properties that are present in the data set of the present study. The sum of squared error method was used to calibrate the models under study. In this study, NRMSE, ME, r, and RES criteria were used to fully evaluate the models. Results and Discussion The results showed that the changes in the amounts of sand, silt, and clay ranged from 14 to 56, 27 to 52, and 14 to 57 percent, respectively, which resulted in the creation of textural classes of loam, clay loam, sandy clay loam, clay, silty loam, and sandy loam. The function developed by Aina and Periaswamy (1985) with NRMSE, ME, r and RES values of 0.61, 0.15, 1.48 and -0.148, respectively, had the least closeness to the measured values of θ33. The results also show that among the PTFs examined for estimating θ33, the PTF developed by dos Reis et al. (2024) with NRMSE, ME, r and RES values of 0.10, 0.01, 1.04 and -0.012, respectively, and in the next rank and with a slight difference, the PTFs developed by Oliveira et al. (2002) and Minasny and Hartemink (2011) with NRMSE, ME and r values of 0.10, 0.02 and 0.05 (same for both functions) had the best agreement with the measured values of θ33. This is while the function developed by Aina and Periaswamy (1985) with NRMSE, ME, r and RES values of 0.61, 0.15, 1.48 and -0.148 respectively had the least closeness to the measured values of θ33. The summary of the evaluation of eleven PTFs for estimating θ1500 shows that the Dijkerman (1988) and Aina and Periaswamy (1985) functions provided the best performance among the functions studied with ME of 0.00 and -0.01, NRMSE values of 0.15 and 0.16, and r of 1.02 and 0.95, respectively. The function developed by Oliveira et al. (2002) showed good performance for estimating θ33, but it was the least close to the measured values of θ1500 with NRMSE, ME, r and RES values of 0.84, 0.14, 1.83 and -0.138, respectively. Then with the aim of improvement, the high-performance economic functions were selected and recalibrated. In estimating θ33, the performance of both the dos Reis et al. (2024) and Oliveira et al. (2002) functions improved (albeit only slightly) by reducing NRMSE (0.08) and ME (0.00) to reach r of 1.00. The Dijkerman (1988) and dos Reis et al. (2024) functions also improved the estimation of θ1500 with NRMSE, ME, and r of 0.14, 0.00, and 1.00, respectively. Conclusion The results showed that among the PTFs used to estimate θ33, the functions developed by dos Reis et al. (2024) and Oliveira et al. (2002) show the best agreement with the measured values. The functions developed by Dijkerman (1988) and Aina and Periaswamy (1985) also provide the highest performance in estimating θ1500. The results of this study also show that some of the new functions presented in this field can provide good performance compared to the basic functions in predicting soil moisture content. The performance of PTFs for the study area is not affected by the number of their input components, and PTFs that require fewer inputs will not necessarily have lower performance, and this is because PTFs depend on the location where they are developed and also on the soil structure. In order to improve the results of the functions of dos Reis et al. (2024) and Oliveira et al. (2002) were recalibrated to estimate θ33 and the functions of Dijkerman (1988) and dos Reis et al. (2024) were recalibrated to estimate θ1500. The selection of the aforementioned PTFs was based on high performance and minimum required input components, or in other words, the economy of the function. Recalibration further improved the performance of the aforementioned functions. θ33, θ1500 and AW are key components in a wide range of studies such as crop modeling, hydrological modeling, water resources management, soil nutrient cycle modeling and soil pollution modeling, therefore the results presented in this study can be used in predicting soil moisture content and AW for the study area, using the soil transfer function technique as an input parameter in various modeling. https://mmws.uma.ac.ir/article_4193.html In Saharan regions, ensuring fresh water to consumers is very difficult, as the predominant source is groundwater loaded with mineral salts from reservoir rocks. The study is based on the choice of caustic soda (NaOH) as a treatment element by chemical precipitation. The protocol followed includes treatment with different doses of NaOH: (500 mg/L of NaOH), (250 mg/L of NaOH and 250 mg/L of Na2CO3 adjusting once with acetic acid CH3COOH and another time with hydrochloric acid HCl), then optimized doses of NaOH alone. The results are then examined, on the one hand, on the reduction of hardness (TH) and, on the other hand, on its impact on pH, electrical conductivity (EC) and salinity. The results indicate that the first dose significantly reduces permanent calcium and magnesium hardness (TH) from 558 mg/L exceeding (Algerian standard limited 500 mg/L) to 328 mg/L, with increases in pH exceeding the potability threshold, while treatment with sodium hydroxide and sodium carbonate are effective in reducing or even eliminating Ca2+ and Mg2+ ions, but there is still a strong increase in alkalinity. The solution is adjusted by an acid still presents additional effects such as solubility of salts and therefore the need to adjust the electrical conductivity (EC). Finally, the treatment is optimized at a low dose of NaOH (20 mg/L) without the addition of sodium carbonate. This dose has proven to be the most adequate, thus allowing a substantial reduction in TH (615 reaching 400 mg/L) while balancing the pH and electrical conductivity (EC) parameters. These results demonstrate the effectiveness of NaOH in the treatment of hard water, while keeping control of its influence on other parameters such as sodium (225.77 mg/L of Na+) where it presents an increase of up to 10%, although it is a significant increase, it is found that the waters of the region exceed this dose in their natural state. Overall, the experience still offers promising and practical solutions for domestic, agricultural and industrial applications and guaranteeing compliance with water quality standards. https://mmws.uma.ac.ir/article_4111.html Extended AbstractIntroductionVariations in snow cover, along with its phenological aspects such as the duration, onset, and cessation of snowfall, are critical to understanding mountainous ecosystems. These changes heavily influence water resource accessibility in nearby regions. Snow cover serves as a vital component in energy and temperature regulation and is intricately connected to hydrological, biological, chemical, and geological processes. Furthermore, it plays a significant role in both the hydrological cycle and the overall energy balance of the environment. Recent research indicates that mountain regions are undergoing temperature increases at twice the global average rate, with this trend intensifying at higher altitudes. As a result, mountains have come to be regarded as critical indicators for monitoring climate change over the past few decades. This study focused on examining variations in the maximum snow cover persistence in the northwest of Iran, analyzed across monthly, seasonal, and annual time scales. Materials and MethodsThis study utilized MOD10A1 and MOD10A1 version 6 products from the Terra and Aqua satellites to conduct a daily analysis of snow phenology spanning the years 2003 to 2020. These datasets, accessible as digital network data derived from the NDSI index, were retrieved through Earthdata's online portal at earthdata.nasa.gov. A threshold value of 0.1 or higher was applied for snow cover estimation to facilitate the conversion of snow cover data into binary format. All adjustments, data processing, and analytical procedures were carried out using Python. Cloudiness reduction was accomplished using data fusion algorithms, spatial neighborhood filtering, and temporal filtering techniques. By combining the Terra and Aqua databases and applying spatio-temporal filtering with a threshold range of 0.1 to 1, a binary database of daily snow cover was generated. This database served as the basis for evaluating snow cover persistence and maximum snow cover persistence parameters, which were analyzed across monthly, seasonal, and annual intervals. Results and DiscussionDuring January, the maximum consecutive snow cover pattern is marked by two prominent peaks in the Sahand and Sabalan Mountains, specifically around Ghoch-Goli Daghi and Sultan-Sabalan, with intervals spanning 20 to 30 days. In February, the MaxSCDur indicates a noticeable rise in snow cover duration, influenced by various surface roughness features and an expansion of areas showing high persistence. By March, this pattern shifts as regions of high persistence shrink and areas with low persistence expand. As the cold season concludes, the snow cover significantly decreases. The spatial pattern observed in April reveals a notable reduction in snow cover, with snow retreating from mid- and low-altitude areas while remaining concentrated on high peaks. This pattern underscores the crucial influence of altitude on the persistence of snow cover during the melting season. Between May and September, spatial patterns for MaxSCDur exhibit a gradual decline in duration, decreasing from 20.87 to 24.24 days in May to just 5.91 to 7.5 days by September. During August and September, the maximum consecutive snow cover is confined exclusively to the summit of the Sabalan Glacier. From October to December, MaxSCDur spatial patterns display an upward trend in frequency, with durations increasing from 20.84 to 23.24 days in October to approximately 30 to 31.25 days by December. The MaxSCDur pattern for October highlights a notable increase in snow cover range and duration as autumn sets in, marked by declining temperatures, especially at higher altitudes. Analysis of the spatial distribution for winter’s MaxSCDur reveals the highest snow cover duration at the summits of Sabalan and Sahand, ranging between 25.6 and 29.25 days. The spring pattern shows a reduction in MaxSCDur, decreasing progressively from southern to northern parts of the studied area when compared to winter. During summer, the maximum consecutive snowfall period is limited to 0–2 days across most regions, except for notable peaks such as Sabalan (4–20 days), Sahand, Avarin, Barda-Rash, Kale-Shin, and Qandil (2–4 days). The autumn MaxSCDur pattern records durations of 4–20 days in Sabalan and its slopes, 4–8 days in the Sahand and Bazgush mountains, and 4–6 days in Qara-Dagh, Barda-Rash, Dalampar, and Kale-Shin. On an annual scale, areas with extended MaxSCDur have shown strong regression towards central and high troughs during 2010 and 2018. Conversely, years such as 2008, 2012, and 2017 exhibit visible rock formations predominantly at higher altitudes, encompassing foothills and slopes within these geomorphologic units. ConclusionThe MaxSCDur map for February reveals that areas with high snow cover persistence have expanded and intensified when compared to January. An evaluation of the MaxSCD models for November and December indicates a marked increase in both the thickness and duration of snow cover across all snow types as the cold season advanced. December serves as the turning point when full winter conditions are established in the study area. In a seasonal context, winter is characterized by peak precipitation levels and prolonged snow cover, especially across rugged terrains throughout the region analyzed. As spring arrives, rising temperatures lead to the gradual retreat and melting of snow cover. By summer, most regions are entirely devoid of snow, with only fleeting patches visible on the highest summits. Autumn brings a shift as falling temperatures initiate the gradual accumulation of snow at elevated altitudes, resulting in a higher maximum snowfall compared to summer. These patterns reflect a transition from warmer to colder conditions and signal the region's preparation for increased winter precipitation. Historical observations show that minimum MaxSCD values occurred in 2010 and 2018, while peak values were recorded in 2008, 2012, and 2017 within the study area. https://mmws.uma.ac.ir/article_4149.html Estimating the water balance is a key factor in water resource management and environmental planning. The water balance refers to the equilibrium between water inputs and outputs in a specific area, including factors such as precipitation, evaporation, runoff, and water withdrawal. The most important elements of the water balance from the perspective of water resource management are precipitation, evapotranspiration, surface runoff, groundwater flow, and lateral flow. A correct understanding of this balance can help optimize water resource use, predict climate change, and assess human impacts on ecosystems. Given the increasing population and the growing need for water, accurate estimation of the water balance has become crucial for informed management decisions and sustainable development in the agricultural, industrial, and urban sectors. Therefore, dynamic and coherent planning, along with the implementation of appropriate management and conservation measures, is essential for the optimal use of the country's water and soil resources. In this context, employing mathematical models and field data can enhance the analysis of water resource status and future planning. Today, hydrological models are utilized to study and plan for the sustainable and effective comprehensive management of watersheds. An example of a physically based hydrological model is SWAT, which simulates large-scale processes and monitors them based on the characteristics of the watershed and its climatic conditions.Materials and Methods In this study, to model the hydrological conditions of the Kowsar Dam basin using the SWAT model, we first imported a digital elevation model with a resolution of 30 meters into the model software environment (ArcSWAT). The output location was then specified, and the watershed boundary was established. Next, we overlaid land use, soil, and slope class maps to obtain hydrological response units (HRUs) for the region. At this stage, the basin was divided into 35 sub-basins and 184 HRUs. To run the model, we utilized daily climatic data from the meteorological stations, which included precipitation, maximum and minimum temperatures, and relative humidity. The model was calibrated using the SUFI2 program, based on the data from 2007 to 2019. Initially, to identify the parameters influencing runoff in the region, a sensitivity analysis was conducted using the One Parameter at a Time (OAT) method, which helped identify the sensitive parameters for model calibration. By implementing the SUFI2 algorithm, we determined the optimal values for these sensitive parameters. Model validation was carried out using the modified parameter values obtained during the calibration stage. To evaluate model performance during both the calibration and validation phases, we used the coefficient of determination (R²) and the Nash-Sutcliffe coefficient (NS).Results and Discussion The results showed that the SWAT model simulated the water balance components of the Kowsar Dam watershed with acceptable accuracy. For this reason, the values of the R² and NSE indices were relatively high. Based on the results, it was also determined that in the studied basin, most of the precipitation occurred in the fall and winter seasons, with the maximum occurring in November (Aban) and the lowest precipitation in June (Khordad). The amount of surface runoff in November (Aban) gradually begins with the onset of autumn and winter precipitation, so that the highest amounts of surface runoff were observed in November (Aban) and February (Bahman). The temporal changes in base flow throughout the year showed that its highest amount was related to late winter and its lowest amount was related to October (Mehr). Actual evapotranspiration gradually increases from November (Aban) with the onset of precipitation, so that from late winter, as the weather warms up, the actual evapotranspiration rate increases and reaches its maximum in May (Ardibehesht). In relation to potential evapotranspiration, unlike actual evapotranspiration, this parameter will increase with increasing temperature and decreasing precipitation. The lowest potential evapotranspiration rate is in January (Day) due to the sharp decrease in temperature in this month, and the highest rate is in early summer, that is, July (Tir).Conclusion By implementing the SWAT model in the Kowsar Dam basin, we were able to simulate the monthly flow for the studied period. Statistical comparisons of this modeling demonstrated acceptable results. The comparison of the simulated and observed hydrographs showed a strong correlation according to the Nash-Sutcliffe criterion. Therefore, we can conclude that the SWAT physical model performs acceptably in the Kowsar Dam basin, based on the simulation results. By comparing the appearance and statistics of the observed hydrograph with those of the simulated hydrograph, we found a high similarity between the two during the study period. There is good agreement between the hydrographs regarding important characteristics such as peak discharge, runoff volume, and time to reach peak discharge. Overall, the results indicate that the SWAT model has the ability and acceptable accuracy to simulate the monthly runoff discharge of the Kowsar Dam watershed. In this study, the model's calibration and validation results showed its efficiency in estimating the water balance in the Kowsar Dam watershed. The final results showed that on average, about 51 percent of precipitation enters the atmosphere as evaporation and transpiration, about 21 percent as surface runoff, 5 percent as lateral flow, and 16 percent as return flow directly into waterways. In total, about 26 percent of water enters the soil layers and aquifer. The results indicate the effectiveness of the SWAT model in simulating the water balance of the Kowsar Dam watershed. https://mmws.uma.ac.ir/article_4214.html This study analyzed the morphometric characteristics of the Pambujan River Basin in Northern Samar, Philippines, to address the limited data on its linear, areal, and relief aspects essential for hydrological analysis and flood management. A Digital Elevation Model (DEM)-based morphometric analysis was conducted using a Geographic Information Systems (GIS) framework to characterize the basin and provide scientific insights for flood risk mitigation. The analysis employed Shuttle Radar Topography Mission (SRTM) DEM data, the Digital Soil Map of the World, and Sentinel-2 10-meter Land Use/Land Cover data processed in Quantum GIS to delineate watershed boundaries, extract drainage networks, and compute morphometric parameters. Results revealed that the Pambujan River Basin covers an area of 587 km², with a perimeter of 213 km and a main channel length of 139.4 km. The basin, classified as a fourth-order stream system with 94 streams totaling 498 km, exhibited an average bifurcation ratio of 4.3, indicating a dendritic and structurally undisturbed drainage pattern with moderate flood susceptibility. Areal parameters, including a low drainage density (0.75 km/km²) and stream frequency (0.16 km⁻²), suggest limited drainage efficiency and delayed hydrologic response, increasing floodplain inundation risk during extreme rainfall. The elongation ratio (0.51) characterizes the basin as elongated, implying longer concentration and lag times (17 hours) and lower but prolonged peak discharge. Relief analysis indicates a maximum basin relief of 397 m, a relief ratio of 0.0073, and a ruggedness number of 0.29, reflecting gently sloping terrain with minimal erosion potential. However, its elongated form may prolong floodwater retention during extended rainfall, requiring continuous monitoring. Upstream soil and water conservation practices such as reforestation and contour farming are recommended. The estimated lag time can guide DRRM offices and local planners in improving community-based flood management and early warning systems. Integrating morphometric results with hydrological models like HEC-HMS, alongside climate and land use data, is encouraged for better flood prediction. The study’s outcomes can support water resource planning for irrigation, domestic use, and power generation. Overall, the findings emphasize the importance of morphometric analysis in sustainable watershed management and disaster risk reduction for the Pambujan River Basin. https://mmws.uma.ac.ir/article_4153.html IntroductionWith the reduction of water resources in Iran, increasing the efficiency of water use in the agricultural sector has attracted the attention of researchers. Regarding water requirements, irrigation requirements, and crop water productivity, it is important for managers and decision-makers to adjust the cultivation information patterns of each region. In Iran, limited research has been conducted on sweet potatoes, and the extent of its production within the country remains unclear. Sweet potatoes are mostly grown in the cities of Minab, Jask, and parts of Sistan and Baluchestan province. In the study by Shamili et al. (2016), the effect of soil texture and irrigation method on improving yield and yield components of two sweet potato cultivars was investigated. Sweet potato is also used as a potential crop for animal feed and raw material in industry. Therefore, the research of Naseri et al. (2014) was conducted with the aim of cultivating sweet potato in the Minab region to produce fresh fodder, which shows the suitable ability of this plant for producing animal fodder. Given that Minab and Jask counties are significant producers of sweet potatoes in the nation, this study aimed to examine the impact of water stress on the growth and yield of sweet potato in the tropical region of Minab county.Materials and MethodsThis study was conducted in the research lysimeters of Minab Higher Education Center in 2024-2025 in a completely randomized design. The study area is located at an altitude of 29 meters above sea level at the latitude and longitude coordinates of 27° 5' 28" and 57° 5' 42", respectively. The study area is a tropical and humid region with a temperate climate in winter, and therefore most of the cultivation is done in the fall and winter. White sweet potato plant cuttings were planted at 40 cm intervals on the ridge on 2024/4/11. The lysimeters were irrigated to meet soil moisture deficiency. Irrigation treatments included (I1) 120, (I2) 100, (I3) 80, and (I4) 60 percent of the moisture requirement, and were performed in three replications. For proper establishment of the plants, the same irrigation rate was applied for all four treatments up to 40 days after planting, and then irrigation was performed based on moisture stresses. The irrigation rate was determined before irrigation and by using soil sampling from different depths. At the end of the growing season on 27/2/1404, the entire root crop as well as the plant were collected and weighed. Considering that not all produced tubers were suitable for the market and economically viable, the weight, diameter, and length of each sweet potato tuber were evaluated to determine their marketability. Those that met USDA standards were classified as marketable products. To calculate water use efficiency, the definition of irrigation water use efficiency (IWUE) was used.Results and DiscussionSome parameters such as root weight, root dry weight, and total dry matter were significantly affected by drought stress, and other parameters such as irrigation water use efficiency based on tuber and total dry matter were not significant. The weight of the harvested tubers for treatments I1 to I4 was found to be 5846, 5224, 2852, and 2069 kg ha-1, respectively. Additionally, the IWUE for all harvested tubers for treatments I1 to I4 were determined to be 1.20, 1.26, 0.97, and 0.83 kg m-3, respectively. Drought stress up to 80% moisture supply (I3) had no significant effect on yield reduction. A similar trend was observed for foliage fresh weight, with the difference that drought stress only caused a significant difference between the maximum stress (I4) and minimum stress (I1) treatments. Excessive water consumption in treatment I1 only increased the weight of foliage and had no effect on the total weight of harvested tubers. Despite the fact that water stress had no effect on the IWUE of all tubers, the IWUE value for marketable tubers was significant. According to the results, the IWUE value of treatments I1 and I2 was at the same level, but applying stress in treatments I3 and I4 significantly reduced the IWUE of marketable tubers. The amount of applied water in treatment I1 was 479 mm, treatment I2 was 408 mm, treatment I3 was 304 mm, and treatment I4 was 241 mm, which was applied based on the supply of soil moisture deficiency. The actual evapotranspiration amount during the growing season was found to be 565, 494, 390, and 334 millimeters for treatments I1 to I4, respectively.ConclusionThe effect of drought stress on the fresh weight of harvested tubers was significant. However, this effect was not significant on the IWUE. From this, it can be concluded that the sweet potato plant has the ability to adapt to drought conditions, such that with increasing drought stress, the IWUE remained constant and did not decrease. The product obtained in the present study was also evaluated in terms of marketability. The results showed that by separating the marketable product, the IWUE decreases. Also, by applying water stress, the IWUE decreases significantly. In other terms, water stress leads to a reduction in the quantity of marketable products, which has resulted in a notable decline in the Irrigation Water Use Efficiency (IWUE). Given the importance of economic issues, it is recommended that an economic analysis of this research be conducted to determine the impact of drought stress on crop profitability. Also, the response of other sweet potato cultivars to drought stress also requires a more comprehensive study. https://mmws.uma.ac.ir/article_4215.html Riverbed forms are formed by changing the power of water flow in rivers and changing the carrying capacity of sediment flows. The riverbed forms are noteworthy investigated from the hydraulic and environmental point of view. For many years, river engineers have investigated the flow structure in the presence of sandy riverbed landforms under laboratory and field conditions. Also, many laboratory studies have been conducted on two-dimensional dunes, and very few studies have been conducted on three-dimensional dunes, which have been conducted in field conditions and with very limited capabilities. It can be safely stated that this is a major gap in river engineering science. Due to the limitations in laboratory and field studies, including the difficulties of Hydraulic data collection in field conditions and the inability to create a variety of hydraulic and geometric conditions in controlled laboratory conditions, numerical methods have been considered and can accurately examine the flow structure on river bed forms. Hence, to fill the gap in previous research, the main target of this research is the investigation of the various geometric conditions of three-dimensional dunes and their effects on the structure of turbulent flow passing through these three-dimensional bedforms. In this research, simulations on three-dimensional dunes (Lobe and Saddle) were performed using computational fluid mechanics (CFD). The experimental geometry included a laboratory channel with a length of 15.75 m, a width of 90 cm, and a height of 60 cm, as well as three-dimensional dunes built in the channel bed. Hydraulic conditions and boundary conditions were created in OpenFoam software, and meshing was also created using the block-Mesh file in the same software. Simulations were performed in OpenFoam software. First, the validation was carried out with experiments conducted in the laboratory channel of Isfahan University of Technology. At this stage, the optimal mesh was selected. Coarse meshing led to faster simulation convergence, but due to the coarseness of the cells, the simulation results were not reliable. On the other hand, finer meshing gave more accurate results but increased the simulation time. By changing the meshing and validating the simulation results with laboratory results, the optimal mesh was selected. It should be noted that the simulations were performed using the supercomputer system of Isfahan University of Technology. With the aim of examining the intended objectives, the effect of changes in three parameters, including bed form angle, bed form wavelength, and the curvature of the three-dimensional dune crest line, was investigated. It should be noted that as the angle changes, the wavelength remains constant, which inevitably increases the height of the 3D dune. The results showed that for the lobe bed form, with the decrease in the exit angle of the bedform, the velocity and Reynolds shear stress increased. Meanwhile, for the saddle bedform, the velocity increased and the Reynolds shear stress decreased with the decrease of the exit angle. For both the Lobe and Saddle bed forms, negative velocities were observed near the bed and four selected profiles, indicating the occurrence of flow separation near the bed. The results showed that by increasing the exit angle of the 3D bed form in both the Lobe and Saddle 3D bed forms, the thickness of the flow separation zone increased. On the other hand, the decrease in the wavelength of the three-dimensional lobe and saddle dunes led to a decrease in the velocity and an increase in the Reynolds shear stress. In this section, the results showed that the thickness of the flow separation zone increased with decreasing wavelength. Also, with the increase in the crest line curvature in the 3D lobe bed form, the velocity increased in the first half of the bed form wavelength. Although in the second half of the bed form wavelength, the increased velocity with increasing crest line curvature in the outer layer of the flow was clear, in the inner layer, the velocity difference was not significant, and the velocity profiles overlapped over a large part of the depth. The results for the lobe bed form showed that with increasing crest line curvature, the Reynolds shear stress decreased throughout the bed wavelength. Meanwhile, for another 3D dune bed form, the saddle, increasing crest line curvature led to a decrease in velocity. Also, a comparison of Reynolds shear stress values for the 3D saddle dune bed form showed that with increasing crest line curvature, Reynolds shear stress increased in most cases. In many previous studies, the turbulent flow structure for the two-dimensional dunes has been investigated in the laboratory and in the field, and three-dimensional dunes have been studied to a limited extent in field conditions. Given this strong need to identify the flow structure on three-dimensional dunes, the effect of changing the geometric parameters of three-dimensional lobe and saddle dunes on the flow structure was investigated in this study. The results showed that for the lobe bed form, with a decrease in the exit angle of the bed, the velocity and Reynolds shear stress increased, and the thickness of the flow separation zone decreased. Meanwhile, for the saddle bed form, with a decrease in the exit angle, the velocity increased, and the Reynolds shear stress decreased. Therefore, despite the increase in velocity, an increase in the exit angle can reduce the flow turbulence zone and have a positive effect on the aquatic habitat in the river. Also, a decrease in the wavelength of the three-dimensional lobe and saddle dunes led to a decrease in velocity and an increase in the thickness of the flow separation zone. An increase in the curvature of the crest line in the lobe bed form resulted in an increase in velocity and a decrease in shear stress. Meanwhile, for the saddle bed shape, increasing the crest line curvature has led to a decrease in velocity and, in most cases, an increase in Reynolds shear stress. Therefore, in general, it can be concluded that increasing the exit angle and wavelength can have positive effects on the river environment. https://mmws.uma.ac.ir/article_4194.html  Extended AbstractIntroduction Introduction The increasing pressure of population growth has placed significant constraints on finite land and water resources. In arid and semi-arid regions like Iran, the combination of recurrent droughts and decades of groundwater over-extraction has led to a severe water crisis. The Minab plain in Hormozgan province, a critical agricultural hub, exemplifies this challenge. It has experienced a drastic groundwater level drop of nearly 14 meters, resulting in land subsidence and water quality deterioration. To address this crisis, the “Groundwater Resources Revival and Balancing Plan” was initiated to enforce strict limits on water extraction. However, a critical gap exists between this top-down policy and on-farm implementation. The primary novelty of this study lies in bridging this gap. While numerous studies have applied Linear Programming (LP) for general crop optimization, this research specifically utilizes the LP model as a practical tool to operationalize the water balancing policy. It addresses the critical question of how farmers can adapt their cropping patterns to comply with the new legal water withdrawal limits while simultaneously maximizing their economic returns. This research, therefore, provides a scientifically-grounded strategy that aligns the goals of environmental sustainability with the economic viability of agriculture in the Minab plain. Accordingly, the following sections detail the materials and methods employed, present and discuss the results from the optimization model, and provide concluding remarks and policy recommendations. Materials and Methods The optimization problem was formulated as a Linear Programming (LP) model. The objective function was designed to maximize the total net profit (Z) from all agricultural activities, defined as: Maximize Z = Σ (BCi * Xi) where Xi are the decision variables representing the area (ha) allocated to each crop i, and BCi is the net profit per hectare of that crop. This maximization is subject to three key constraints: (1) Water Availability: Total annual water consumption (Σ Vi * Xi) must not exceed the sum of available surface water and the legally mandated groundwater limit of 57.92 MCM. (2) Land Availability: The total cultivated area (Σ Xi) must be less than or equal to the total arable land. (3) Cultivation Bounds: The area for each crop (Xi) must remain within its observed historical minimum and maximum levels to ensure market stability and reflect practical farming conditions.This study was conducted in the Minab plain, located in southern Iran, which covers an area of approximately 652 km². The region’s water supply is derived from both surface sources, primarily the Esteghlal Dam on the Minab River, and groundwater extracted from the alluvial aquifer. To develop the optimization model, a comprehensive dataset was compiled for twenty major crops cultivated in the area, covering grains, vegetables, melons, and forage crops.This data, sourced from the Jihad-e-Agriculture Organization, the Regional Water Authority, and national statistical databases, included historical cultivation areas (minimum and maximum bounds), crop yield, production costs, and market prices. Crop water requirements were calculated using the NetWat software, which considers local climatic conditions and specific plant characteristics. A Linear Programming (LP) model was formulated to solve the resource allocation problem. The model’s objective function was designed to maximize the total net profit, calculated as the sum of net returns from all cultivated crops. The model was subjected to a set of critical constraints: (1) Water resource availability, limiting total water consumption to the sum of surface water allocation and the permissible groundwater withdrawal of 57.92 million cubic meters (MCM) as stipulated by the balancing plan; (2) Land availability, ensuring the total cultivated area does not exceed the available arable land; (3) Crop area limits, restricting the cultivation area for each crop to be within its observed historical minimum and maximum levels to ensure market stability and crop diversity. To evaluate the impact of water management practices, the model was executed under two distinct irrigation efficiency scenarios: Scenario 1 with 53% efficiency, representing the current situation, and Scenario 2 with 65% efficiency, representing an improved condition achievable through modern irrigation technologies. Results and Discussion The results of the Linear Programming model provided distinct optimal cropping patterns for the two irrigation efficiency scenarios. Under Scenario 1 (53% efficiency), the optimal solution allocated a total of 13,949 hectares for cultivation, generating a maximum net profit of 8.37 trillion Rials. In this scenario, the cultivation areas for most crops were set at their minimum allowable limits, with the exception of highly profitable crops like leafy vegetables, which were allocated a larger area. This indicates that under current efficiency levels, water scarcity is the primary limiting factor for agricultural expansion. In contrast, under Scenario 2 (65% efficiency), the model recommended a significantly larger total cultivation area of 21,382 hectares, resulting in an increased net profit of 10.55 trillion Rials. This represents a 53% increase in cultivated land and a 26% rise in profit compared to the baseline scenario. The improved water use efficiency allowed for the expansion of high-value crops such as green beans, peppers, cucumbers, and leafy vegetables to their maximum allowable cultivation limits. A crucial finding is that both optimal scenarios successfully operated within the mandated groundwater withdrawal limit of 57.92 MCM. This is a substantial reduction compared to the average historical consumption of approximately 65.9 MCM between 2011 and 2014. Furthermore, the net profits generated under both optimized scenarios were significantly higher than the profits recorded in those historical years. This demonstrates that a scientifically planned cropping pattern can simultaneously achieve the dual goals of reducing groundwater over-extraction and enhancing farmers’ economic returns. ConclusionsThis study successfully demonstrated the effectiveness of Linear Programming as a powerful tool for optimizing cropping patterns to address the intertwined challenges of water scarcity and economic viability in the Minab plain. The findings conclusively show that it is possible to increase farmers’ net profits while adhering to strict groundwater withdrawal limits necessary for aquifer restoration. The research highlights that improving irrigation efficiency is the most critical lever for unlocking the region’s agricultural potential. A transition from the current 53% efficiency to a more achievable 65% can substantially increase both the cultivated area and farmer income, creating a strong economic incentive for adopting sustainable practices. The practical implications of this work are significant; the developed model serves as a robust decision-support tool for policymakers and water resource managers. It is recommended that local agricultural authorities promote the outputs of this model and facilitate the adoption of modern irrigation technologies through supportive policies and subsidies. By aligning the financial interests of farmers with the long-term goal of water resource sustainability, this approach offers a tangible and effective pathway to reviving the Minab aquifer while ensuring a prosperous agricultural future for the region. https://mmws.uma.ac.ir/article_4221.html Extended Abstract The Caspian Sea, the largest enclosed inland body of water on Earth, is bordered by five countries: Russia, Kazakhstan, Turkmenistan, Iran, and Azerbaijan. It has a unique geographical setting with a surface area of approximately 371,000 square kilometers and a maximum depth of about 1,025 meters. The climate around the Caspian Sea varies significantly, with the northern part experiencing cold winters and hot summers, while the southern part has milder winters and hotter summers. The general wind patterns and atmospheric systems affecting the Caspian Sea include the Siberian High, which brings cold air masses, and the Azores High, which influences the summer weather. The overall water circulation in the Caspian Sea is cyclonic, and wave conditions are influenced by wind patterns and the basin's morphology. The southern coast of the Caspian Sea is characterized by diverse bathymetric features, with depths ranging from shallow coastal areas to deeper offshore regions. The coastal morphology is influenced by sediment deposition and erosion processes, which are driven by wave and current dynamics. The general circulation of water in the southern Caspian Sea is influenced by wind-driven currents and the basin's topography, leading to complex flow patterns. Wave conditions in this region are primarily affected by local wind patterns and can vary significantly depending on seasonal changes. Field measurements of wave and current parameters are crucial in oceanographic studies as they provide essential data for understanding the physical dynamics of marine environments. These measurements help assess the impact of climatic changes on ocean circulation, wave patterns, and coastal erosion. Accurate field data are necessary for validating numerical models and improving the predictability of oceanographic phenomena, which is vital for coastal management and marine resource exploitation. Despite its significance, the Caspian Sea lacks comprehensive oceanographic data, particularly regarding wave and current measurements. This scarcity of data hampers the ability to fully understand the sea's dynamic processes and their implications for the surrounding environment. The limited availability of observational data is a significant challenge for researchers, making it difficult to develop accurate models and forecasts for the region. Recent studies have utilized Acoustic Doppler Current Profilers (ADCP) to measure wave and current parameters in the Caspian Sea. In 2010, Ghaffari and Chegini conducted a study titled "Acoustic Doppler Current Profiler Observations in the Southern Caspian Sea: Shelf Currents and Flow Field off Feridoonkenar Bay, Iran." This research involved offshore bottom-mounted ADCP measurements and wind records to characterize current fields in the continental shelf and offshore deeper regions in the southern Caspian Sea. The results indicated that long-period waves dominate the current field in the continental shelf off Feridoonkenar Bay. The study found that the prevailing wind patterns significantly influence the current profiles observed during the measurements. In 2014, Firoozfar and Neshaei researched sediment deposition and erosion processes along the southern coast, showing that local wave patterns significantly impact coastal morphology. In 2024, Zavialov and Kostianoy conducted a study on the Kazakhstan shelf of the Caspian Sea, revealing that the currents were predominantly along the shore but simultaneously variable in direction. The results also indicated that the along-shore wind stress significantly influenced the wave and current dynamics. In 2019, Masoud et al. conducted a study titled "Low-Frequency Variations in Currents on the Southern Continental Shelf of the Caspian Sea." This research evaluated wind-induced currents along the southern Caspian Sea, revealing that low-frequency variations in currents were significantly influenced by wind patterns. In this study, considering the importance of field measurements in oceanography and the lack of this type of information in the Caspian Sea, wave and current information was recorded at seven nearshore stations (five 10-meter stations and two 30-meter stations) on the southern coast of the Caspian Sea in Iran over more than a year. This information was recorded in different water column layers, which in this study considered surface and bottom layer information. Then, the recorded information was analyzed and examined temporarily and spatially. For this purpose, various diagrams were used, including wind rose, wave rose, scatter diagram, and radar diagram. The results confirmed the counterclockwise circulation of the Caspian Sea's currents. On the southern coasts, the predominant current direction aligns with this general circulation, except at the Roudsar stations, where local eddies reverse the flow. Although the overall pattern was consistent, significant spatial and seasonal variability was observed. At Amirabad and Anzali, reversing currents differed due to wind-driven water level fluctuations and coastal morphology. Among all stations, Anzali exhibited the highest energy levels regarding wave and current activity. Additionally, seasonal variations were observed, with winter recording the most intense currents and highest waves at most stations. Wave direction also varied by location and season. At western stations, the most frequent and substantial waves originated from the north and northeast, while at eastern stations, they came from the north and northwest. The central station at Noshahr predominantly recorded waves from the north. These patterns were influenced by regional wind systems, including the Siberian High and Azores High, which affect seasonal weather and wave formation. Although the counterclockwise circulation was dominant, the presence of reversing currents at specific stations—particularly Roudsar—highlighted the complexity of local hydrodynamic processes. These reverse flows, shaped by topographic features and localized eddies, underscore the need for site-specific analysis in coastal modeling. The observed differences between surface and bottom currents, as well as the stratification of energy levels, further emphasize the importance of vertical profiling in understanding marine dynamics. https://mmws.uma.ac.ir/article_4227.html IntroductionClimate change is one of the most pressing challenges for global agriculture, particularly in arid and semi-arid regions such as southwestern Iran. Increases in temperature, alterations in rainfall distribution, and the rise in atmospheric CO₂ concentrations strongly affect crop growth, yield formation, and water use efficiency (WUE). Rapeseed (Brassica napus L.), as a C₃ plant and a major oilseed crop, is highly sensitive to both climatic fluctuations and water availability. In Khuzestan province, rapeseed has become increasingly important in recent years, but its productivity is constrained by limited water resources and the growing threat of climate stress. Therefore, it is crucial to evaluate the combined effects of cultivar choice and irrigation strategies under both current and projected climate conditions to support sustainable rapeseed production. This study aimed to assess the performance of various rapeseed cultivars and irrigation regimes under future climate conditions. Materials and MethodThe research was conducted using the APSIM model to simulate the growth and yield of rapeseed at five sites in Khuzestan Province (Shush, Shushtar, Izeh, Dezful, and Behbahan) under both baseline and future periods. Climate projections for the future periods were obtained from five Global Circulation Models (GCMs: MPI-ESM1-2-LR, ACCESS-ESM1-5, MRI-ESM2-0, HadGEM3-GC31-LL, and CNRM-CM6-1) under three SSP scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The LARS-WG software (version 8) was employed for statistical downscaling to generate future climate data. Three widely cultivated rapeseed cultivars, namely Hyola401, Hyola308, and RGS003, together with five irrigation treatments, were considered to simulate their responses to future climate conditions in terms of yield, WUE, ETc, and to identify the optimal combinations for the study sites. Irrigation treatments were defined based on soil field capacity (FC) levels, including FC50, FC60, FC70, FC80, and FC90. These treatments corresponded to the replenishment of soil moisture whenever the available water content was depleted to 50%, 60%, 70%, 80%, and 90% of field capacity, respectively. Statistical analyses, including regression analysis and Duncan’s multiple range test, were applied at the 0.05 and 0.01 probability levels.Results and DiscussionThe results showed that, under the baseline period, the cultivar Hyola401 achieved the highest grain yield in the FC90 treatment (an average of 4631 kg ha⁻¹ across all regions). The highest WUE was obtained by Hyola401 under the FC50 treatment (1.1 kg m⁻³) in Izeh. Regression analysis revealed a positive and significant relationship between grain yield and WUE (R² = 0.91; p-value < 0.05). During the future period, mean temperature increased by 1.53 °C compared with the baseline, while the length of the growing season decreased by about 5%, leading to reductions in both yield and WUE in most regions. For instance, in Izeh, a slight improvement in yield was observed for the cultivar RGS003 under FC50 and FC60 irrigation (by 0.65% and 0.34%, respectively) under the SSP1-2.6 scenario. In Shushtar, however, yield improvements were associated with the cultivar Hyola401 across all scenarios and irrigation treatments, with yield increases ranging from 2.5% to 17%. The results highlight the trade-off between grain yield and water use efficiency under different irrigation levels. While FC90 maximized grain yield, it also increased ETc beyond 6000 m3, resulting in lower WUE. Conversely, deficit irrigation (FC50) improved WUE but caused a significant yield reduction. The optimal balance was achieved at intermediate irrigation levels (FC60–70), particularly when combined with high-performing cultivars such as Hyola401. Future climate scenarios suggested that the negative effects of increased temperature and shorter growth duration would outweigh the positive effects of CO₂ fertilization in most warm regions. The increase in temperature and its negative association with grain yield were observed in in regression analyses, where the relationship between mean temperature and grain yield was significantly negative (R² = -0.68; p-value < 0.05). The largest yield reductions were predicted in Shush, confirming that heat stress during the reproductive stage is the most critical constraint. Nonetheless, in cooler regions such as Izeh the positive impact of elevated CO₂ partially offset the yield decline, confirming the spatial heterogeneity of climate change impacts. These findings are consistent with previous reports indicating that elevated CO₂ may mitigate some of the negative consequences of rising temperatures in C₃ crops, but cannot fully compensate for severe heat stress.Conclusion This study demonstrated that both cultivar selection and irrigation management are key determinants of sustainable rapeseed production under climate change. Under baseline conditions, Hyola401 × FC90 achieved the highest grain yield, while Hyola401 × FC50 achieved the highest WUE. However, under future climate conditions, yield reductions of up to -9% were projected, particularly in warmer areas such as Shush under SSP5-8.5. In contrast, yield and WUE improvements were observed in Izeh and Shushtar, largely due to the positive effects of increased atmospheric CO₂. Overall, intermediate irrigation strategies (FC60–70) combined with resilient cultivars such as Hyola401 and Hyola308 were found to offer the most sustainable balance between yield and WUE under future climate conditions. These results emphasize the importance of climate-smart strategies, including adaptive irrigation management, the selection of stress-tolerant cultivars, and the adjustment of planting schedules, to mitigate the adverse impacts of climate change and ensure sustainable rapeseed production in Khuzestan. https://mmws.uma.ac.ir/article_4231.html IntroductionThe Iranian plateau, especially in arid and semi-arid regions, has sensitive and fragile conditions due to its specific climatic characteristics. In these regions, soil erosion and desertification are among the most important factors that severely threaten water and soil resources. In such conditions, the restoration of degraded rangelands through revegetation with compatible species such as Haloxylon spp. can play an effective role in reducing soil erosion, improving soil quality, and increasing carbon and nitrogen stocks. Due to its high resistance to drought and salinity, this species is one of the key options for stabilizing mobile sands and restoring desert ecosystems in Iran. In addition to its ecological role, the establishment of Haloxylon not only helps to increase soil organic carbon content and reduce atmospheric carbon dioxide but also can strengthen the livelihoods of local communities by improving soil conditions and providing economic opportunities. However, the dynamics of carbon and nitrogen stocks over time and with increasing plantation age are still not fully understood and require careful scientific investigation. Therefore, the present study aimed to investigate the effect of Haloxylon plantation age on soil carbon and nitrogen stocks in arid regions, to provide a basis for the optimal management of dryland restoration and the development of carbon sequestration schemes by explaining the relationships between soil properties and vegetation age. Materials and MethodsThis study aimed to evaluate the effect of Haloxylon plantation age on soil carbon and nitrogen stocks in dry lands, in three areas located in the Merak area of Birjand County (South Khorasan province). The study areas included two Haloxylon plantations with ages of 34 and 26 years and a control area without Haloxylon cultivation, which are located about 10 km northeast of Birjand city. The altitude of the area varies between 1610 and 1810 meters above sea level, and its climate is dry and desert. Sampling operations were carried out in June 2024 using a systematic random method. In each area, six replicates were collected from three soil depths (0–15, 15–30, and 30–45 cm), and a total of 54 soil samples were used for physical and chemical analyses. Physicochemical properties, including pH, electrical conductivity, porosity, bulk density, organic carbon, and total nitrogen were measured, and soil carbon and nitrogen stocks were also determined. After checking for normality and homogeneity of variances, the resulting data were analyzed using a factorial design and one-way analysis of variance (ANOVA) in SPSS software. Also, the relationships between variables were examined using Pearson's correlation coefficient in R software. Results and Discussion The results of the analysis of variance showed that the effects of both area (plantation age) and soil depth on physicochemical properties, including pH, electrical conductivity, bulk density, organic carbon, and total nitrogen were statistically significant (p<0.01). Comparison of means showed that with increasing age of the Haloxylon plantation, pH decreased slightly, and electrical conductivity was higher in the 26-year-old site. The decrease in bulk density and increase in soil porosity indicated improved soil structure and greater activity of roots and soil organisms. One-way analysis of variance of carbon and nitrogen stocks at each depth showed that these variables were significantly higher in the 34- and 26-year-old Haloxylon plantation sites than in the control area. The age of Haloxylon planting had a significant effect on soil carbon and nitrogen accumulation, with this effect varying by soil depth. In the 0–15 cm layer, the highest carbon stocks were observed in the 26-year-old site (4.46 t ha⁻¹), while in the 15–30 and 30–45 cm layers, the 34-year-old site exhibited the highest stocks (5.78 and 3.35 t ha⁻¹, respectively), indicating the role of vegetation growth stage and microbial activity in organic matter accumulation and stabilization, and highlighting the importance of long-term management and drought-resistant species in enhancing soil nutrient storage capacity. Principal component analysis (PCA) and correlation coefficients also showed that organic carbon, total nitrogen, and nutrient stocks are co-directional and correlated with each other, while bulk density has an inverse relationship. These findings emphasize that over time, Haloxylon cultivation improves the physical and chemical quality of the soil and increases the nutrient storage capacity and environmental sustainability of the soil in the drylands of the Merak region of Birjand County. ConclusionThe findings of this study indicate that the age of Haloxylon plantation plays a decisive role in improving soil quality and increasing carbon and nitrogen stocks in the drylands of Birjand County. Over time, the gradual accumulation of carbon and nitrogen in the surface and subsurface soil layers increased, and bulk density decreased, indicating improved soil structure, increased porosity, and higher activity of roots and soil microorganisms. Principal component analysis and correlation coefficients confirmed that increased soil organic matter is the main driver of the simultaneous increase in carbon and nitrogen stocks, and that the surface layers show the greatest sensitivity to management changes. These results indicate that long-term Haloxylon plantation can enhance soil ecological processes, including carbon and nitrogen cycling, and increase the environmental sustainability of drylands. From a management perspective, the results of the study indicate that selecting appropriate areas for Haloxylon plantation and continuously monitoring the age of vegetation cover can help increase soil fertility and improve nutrient storage capacity. Therefore, Haloxylon plantation is not only a soil restoration strategy, but with proper management, it plays an important role in the sustainable development of drylands. https://mmws.uma.ac.ir/article_4233.html Introduction This study aimed to investigate the factors influencing farmers’ participation in the implementation of modern irrigation systems and to identify the key determinants of their involvement in such projects. The significance of this research lies in the crucial role of farmers’ active cooperation in achieving sustainable water resource management, particularly in agricultural regions such as Aq Qala, located in Golestan Province. The primary objective was to examine individual, economic, and institutional characteristics that affect farmers’ willingness or reluctance to participate in these initiatives. Key factors considered included education, economic status, land ownership, access to water sources, farm size, and familiarity with participatory concepts and local institutions. By modeling these factors using the C5 decision tree algorithm, the study sought to provide a reliable predictive tool for policymakers and agricultural planners to design targeted interventions, enhance sustainable water resource management, and promote effective and enduring farmer participation. Additionally, the research aimed to offer practical recommendations to increase farmer engagement in modern irrigation programs, ultimately contributing to improved water management, enhanced agricultural productivity, and long-term resilience of local farming communities. The insights gained from this study may also inform future strategies for the adoption of innovative agricultural technologies in similar socio-economic and environmental contexts.Materials and Methods This research is applied in nature and employs a survey-based approach for data collection. For modeling and analysis, the C5 decision tree algorithm was used. The classification method of this algorithm is based on dividing data into smaller and more homogeneous subsets. Its advantages include simplicity, high interpretability, and the use of a small number of parameters, which enhances its efficiency and computational performance. The implementation of the C5 decision tree algorithm in this study was carried out using the RapidMiner software environment. After designing and distributing the questionnaire among farmers, agricultural experts, and relevant authorities, the collected data were analyzed using the C5 decision tree modeling method in RapidMiner. This approach was employed to identify the key factors influencing farmers’ willingness or unwillingness to participate in the implementation of modern irrigation systems. The research sample consisted of 68 farmers from the Aqqala region in Golestan Province, Iran. Their responses to the questionnaire were analyzed to determine the most significant factors affecting their decision to participate in modern irrigation projects. The validity of the questionnaire was confirmed by academic supervisors, professional experts, and officials from the Golestan Regional Water Company. The reliability of the questionnaire was also verified through Cronbach’s alpha coefficient, which was calculated using SPSS version 22. The independent variables used in this study included literacy level, education, age, occupation, economic status, cultivated land area, land ownership type (agricultural or orchard), water sources, familiarity with the concept of participation, and awareness of local institutions and organizations. The dependent variable was defined as the farmers’ willingness or unwillingness to participate in the implementation of modern irrigation projects.  Results and Discussion The results revealed that farmers’ willingness to participate in modern irrigation systems is shaped by a combination of economic, social, and institutional factors. Key individual and economic drivers included literacy, economic status, land ownership, and familiarity with participatory practices. Educated, financially secure, and land-owning farmers exhibited higher participation, whereas illiterate, low-income, or tenant farmers were more cautious. Institutionally, access to legal water sources and trust in governmental Authorities, along with economic incentives such as free installation and after-sales service guarantees, were significant motivators. The interaction between age and education showed that younger farmers and educated older farmers were more actively engaged. Conversely, distrust of governmental institutions and complex water regulations acted as barriers. The C5 decision tree effectively modeled these complex relationships and quantified the predictive accuracy of farmers’ participation willingness. Without economic support, it is difficult to achieve sustainable participation, especially among low-income groups. In the social dimension, age and education level play a reinforcing role, and the younger generation and the educated elderly have shown a greater willingness to participate in modern irrigation system projects. On the other hand, distrust in government institutions and the complexity of laws related to water resources are considered to be factors inhibiting effective participation. Accordingly, promoting farmer participation requires a combination of economic support, legal facilitation, targeted education, and institutional trust-building.Conclusion Enhancing farmer participation in modern irrigation systems requires a multifaceted strategy encompassing economic support, targeted education, legal facilitation, and institutional trust-building. Financial status and governmental support emerged as the strongest predictors of participation, indicating that sustainable engagement is unlikely without economic backing. Social factors such as age and education play a supplementary role, with literate populations, particularly youth, more inclined to participate. Distrust of authorities and regulatory complexity remain significant obstacles. Policy recommendations include providing low-interest financial support, implementing targeted training and outreach programs, and establishing sustainable post-implementation technical services. Such measures are expected to transform minimal or conditional participation into active, voluntary, and enduring engagement, promoting optimal and sustainable water resource management in agriculture. https://mmws.uma.ac.ir/article_4279.html Extended AbstractIntroductionSoil moisture or water content, and the fraction of that water available to plants, are among the most critical aspects of soil water management. The concept of plant-available water was first introduced nearly a century ago by Veihmeyer and Hendrickson (1927), derived from the difference between Field Capacity (FC) and Permanent Wilting Point (PWP). The concepts of the Unrestricted Water Content Range and the Minimum Restricted Water Content Range were proposed by DaSilva et al. (1994). In this framework, in addition to the two moisture limits (FC and PWP), soil aeration and the effect of soil penetration resistance on water availability to the plant are considered using simple relationships. A limitation or defect of the LLWR concept is that it treats the boundary values for aeration porosity, penetration resistance, and soil water potential as abrupt or discontinuous in restricting water availability. In an effort to overcome the shortcomings of these preceding concepts, Minasny and McBratney (2003) proposed the Soil Water Integral Energy (IE) as a criterion for estimating plant-available water in soil, replacing the focus on soil moisture content. Soil water integral energy is a measure of the energy required to extract water from the soil over a specified range of soil water content. Under this concept: firstly, the plant-available water is not solely confined to the PWP and FC range; secondly, the effect of rapid drainage at high moisture contents, which reduces the opportunity for soil water supply to the plant, is taken into account; and thirdly, the limitation imposed by soil hydraulic conductivity at low moisture contents on water flow towards the root and subsequent absorption is incorporated. They utilized various weighting functions across a wide range of soil water potentials, encompassing the potential effect of all limiting physical characteristics on soil water availability. The most significant limitation in employing this index is the time-consuming and costly process of obtaining the soil moisture characteristic curve, the soil penetration resistance curve, and the accuracy or reliability of the coefficients used in defining the proposed weighting functions. Furthermore, in addition to time and expense, errors present in soil sampling and measurement can impose constraints on the application of IE (Integral Energy). Materials and MethodsThe study area includes a part of the Tabriz plain. For estimation of IE using the deep learning method, Artificial Neural Network (ANN), and Multiple Linear Regression (MLR), Mathematica Wolfram software version 14.1.0 was utilized. The input features for all three models (MLR, ANN, and Deep ANN) were identical. The data were randomly divided into two groups: training (67 data points) and testing (30 data points). The input features for the models included: 1- Percentage of water-stable aggregates 2- Soil bulk density 3- Porosity 4- Saturated hydraulic conductivity of the soil 5- Percentage of soil texture particles 6- Equivalent calcium carbonate 7- Penetration resistance at saturated moisture 8- Saturated moisture. Results and DiscussionThe created models were evaluated using the evaluation statistics of the coefficient of determination R2, the adjusted coefficient of determination R2adj, the root mean square error RMSE, the relative error RMSEr, the model efficiency coefficient NSE, and the average percentage of relative error RME.The results showed that the deep learning method with the highest adjusted coefficient of determination (training: 0.998, test: 0.661) and the lowest root mean square error (training: 15.943, test: 118.593), the artificial neural network method (training: 0.945, test: 0.514) and root mean square error (training: 45.347, test: 139.267), and the linear multivariate regression method (training: 0.544, test: 0.317) and root mean square error (training: 126.955, test: 239.264), respectively, provide the best estimate of the IE index. ConclusionThis study underscores the importance of soil water management and the precise assessment of Plant Available Water (PAW). Given the limitations of traditional concepts like PAW and LLWR, particularly their reliance on discontinuous boundaries, the newer the soil water Integral Energy (IE) criterion is adopted as a more accurate measure for estimating plant water availability in soil.The results demonstrated that IE can be effectively and accurately estimated in the studied area (the Tabriz plain) using deep learning techniques (Artificial Neural Networks), relying on a comprehensive set of key soil properties, including the percentage of water-stable aggregates, bulk density, porosity, and saturated hydraulic conductivity. These findings pave the way for applying advanced, data-driven modeling approaches to optimize soil water resource management in arid and semi-arid regions. https://mmws.uma.ac.ir/article_4251.html Evapotranspiration (ET) is the second most important element of the hydrological cycle after rainfall. Despite rising attention in hydrological responses to environmental change, limited extensive evaluations of AET have been conducted in the study watershed that integrates the combined influences of LULC  and climate change. Previous research has largely focused on broader areas, such as the LTSB and the Abbay Basin, offering a limited understanding of localized relations between these factors. Therefore, this study investigates the synergistic impacts of LULC dynamics and climate change on AET within the Guna Tana Watershed (GTW) using the physically based MIKE SHE hydrological model, aiming to improve understanding of watershed-scale hydrological responses under future environmental conditions. ENVI 5.3 and QGIS 2.18.15 were used to assess the LULC classification and prediction, respectively. Ensembles of GCM were used after bias correction, and calibration of the model was done using streamflow. Agriculture was expanded from 2047.02 km2 to 2268.82 km2, whereas forest will decline to 103.38 km2 from 127.64 km2 in the 1991-2021 period. Built-up showed the least amount of coverage (0.02%, 0.11%, and 0.31%). The results of the calibration and validation show that MIKE SHE is capable of modeling the AET effectively. Excellent results were indicated in two watersheds by both calibration and validation (R =0.87-0.94). The rise in AET may be detrimental to the watersheds because it reduces streamflow and groundwater recharge.  Moreover, soil moisture stress increases the risk of drought. Projected changes in AET relative to the baseline period indicate increasing trends in both the Gumara and Ribb watersheds under future climate scenarios. In the Gumara watershed, mean annual AET is expected to rise moderately, with increases of 3.25% and 1.19% in the 2020s and 2050s under SSP2-4.5, and 5.09% and 8.01% under SSP5-8.5. The Ribb watershed shows a stronger response, with AET increasing by 16.92% and 19.30% under SSP2-4.5, and 14.13% and 22.07% under SSP5-8.5. All of this presents problems for the environment and water balance downstream, such as Lake Tana. Future research should include additional climate models and ground truth data regarding plant characteristics to increase model accuracy and reduce uncertainty. https://mmws.uma.ac.ir/article_3998.html AbstractIntroductionThe escalating global demand for water, driven by population growth, urbanization, climate change, and excessive use of fertilizers and pesticides, has significantly impacted groundwater levels, leading to soil salinization and desertification. Continuous groundwater extraction exacerbates aquifer depletion, increasing pumping costs and limiting production capacity. Accurate groundwater level estimation is critical for effective water resource management, especially in arid and semi-arid regions like South Khorasan, Iran, where water scarcity is pronounced. This study aims to address the challenge of predicting monthly groundwater level fluctuations in the Birjand plain using advanced modeling techniques. Traditional physical and numerical models, while reliable, face limitations such as high computational demands, data dependency, and inability to handle nonlinear conditions effectively. In contrast, data-driven and artificial intelligence-based models offer simplicity, speed, and reasonable accuracy, particularly when historical data is available (Norouzi Khatiri et al., 2023). Fuzzy logic-based models, especially nonlinear fuzzy regression, excel in handling uncertainty and complex relationships in hydrological systems (Asadollahi, 2023). This research evaluates three fuzzy-based models—nonlinear fuzzy support vector regression (NLF-SVR), fuzzy nonlinear autoregressive regression (FNAR), and fuzzy linear least squares regression (FLSR)—using climatic variables (temperature, precipitation, humidity, and evapotranspiration) to enhance prediction accuracy and support sustainable groundwater.Materials and MethodsThe study was conducted in the Birjand plain, South Khorasan, Iran, a region characterized by an arid climate with an average annual rainfall of 169 mm in the plain and 216 mm in the highlands. The Birjand aquifer, spanning 3155 km², is heavily exploited, making it a critical case study for groundwater management. A comprehensive dataset covering daily climatic variables—mean air temperature (Tave), precipitation (Prc), relative humidity (RH), and evapotranspiration (ETo)—from April 1998 to March 2017 was compiled from regional meteorological and water authority stations. After quality control and preprocessing, daily data were aggregated into monthly values. The dataset was split into 70% for model training and 30% for validation. Three fuzzy-based models were developed: (1) Fuzzy Linear Least Squares Regression (FLSR), which extends classical regression to handle fuzzy data; (2) Nonlinear Fuzzy Support Vector Regression (NLF-SVR), combining fuzzy logic with support vector machines for nonlinear relationships; and (3) Fuzzy Nonlinear Autoregressive Regression (FNAR), designed for multi-variable fuzzy predictions. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency (NSE). Fuzzy numbers, particularly triangular fuzzy numbers, were used to model uncertainty, with Gaussian kernels applied for FNAR and NLF-SVR. Parameter optimization was achieved using grid search and generalized cross-validation (GCV).Results and DiscussionThe analysis revealed that the NLF-SVR model outperformed both FNAR and FLSR in predicting monthly groundwater levels in the Birjand plain, achieving an average RMSE of 0.15 m, MAE of 0.37 m, and NSE of 0.99. The model effectively captured complex, nonlinear relationships between climatic variables (Tave, Prc, RH, ETo) and groundwater levels, particularly during warmer months (July–September), where regular water consumption and evapotranspiration patterns enhanced predictability. In contrast, performance dipped in colder months (November–February) due to irregular precipitation and delayed groundwater recharge, aligning with findings by Zhang et al. (2022). The FNAR model showed acceptable performance, with higher sensitivity to seasonal climatic shifts, while FLSR struggled to model nonlinear dynamics, exhibiting higher errors and lower NSE. Nonlinear fuzzy models demonstrated robustness against outliers and noisy data, maintaining approximately 85% of their initial accuracy even with 30% noise, compared to a 60% accuracy drop in classical models. These findings align with studies by Sahoo et al. (2017) and Kumar et al. (2023), confirming the superiority of fuzzy-based approaches in handling hydrological uncertainties. Limitations include the study’s focus on a single region and reliance on climatic inputs alone, suggesting future inclusion of factors like land use and geological characteristics. The NLF-SVR model’s high accuracy supports its application in early.ConclusionThis study demonstrates that nonlinear fuzzy models, particularly NLF-SVR, provide superior accuracy for predicting monthly groundwater levels in the Birjand plain, with RMSE of 0.15 m and NSE of 0.99, outperforming FNAR and FLSR. The ability of NLF-SVR to model complex, nonlinear relationships and handle data uncertainties makes it a robust tool for hydrological forecasting in arid climates. Key findings include the models’ enhanced performance during warmer months and reduced accuracy in colder seasons due to variable precipitation patterns. The robustness of fuzzy models against outliers and noisy data eliminates extensive preprocessing, preserving valuable information. Practically, NLF-SVR supports sustainable groundwater management through applications like drought warning systems and optimized irrigation scheduling. However, limitations such as region-specific data and the exclusion of non-climatic factors (e.g., land use, geology) suggest caution in generalizing results. Future research should test these models across diverse hydrogeological settings, integrate deep learning techniques, and incorporate additional variables like climate indices and satellite data. Developing multi-scale models to capture both short- and long-term fluctuations and creating decision-support systems for real-time water management are recommended. This study underscores the potential of fuzzy intelligent systems to enhance groundwater management in water-scarce regions, paving the way for advanced hydrological modeling. https://mmws.uma.ac.ir/article_4261.html Accurate significant wave height (SWH) prediction is essential for improving the safety and efficiency of maritime operations. Thus, our study develops the Gaussian data augment (GDA) technique- Meerkat optimization algorithm (MOA)- variational mode decomposition (VMD)- complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)- bidirectional long short-term memory neural network model (BILSTM)- attention mechanism (AT)- gated recurrent unit (GRU) model to accurately predict SWH and overcome the limitations of the GRU model. First, the GDA method addresses the problem of data scarcity by providing new data points. Next, MOA is used to adjust the parameters of the components of the hybrid model. The VMD method then reduces the intricacy of the time series by converting them into subseries with lower complexity named intrinsic mode functions (IMFs). However, as the first IMF retains the complex characteristics of the original time series, the CEEMDAN method is applied to decompose it into secondary IMFs with reduced complexity. Subsequently, the BILSTM model extracts forward and backward temporal features from the secondary IMFs and the initial remaining IMFs. An attention mechanism is then applied to assign the attention weights to the extracted features. Each attention weight indicates the importance of a feature, enabling the GRU model to identify the most important time series features for predicting SWH. Finally, the weighted features are fed into the GRU model to predict SWH accurately. Our study also couples the kernel density estimation method with the GDA- MOA-VMD-CEEMDAN-BILSTM- attention mechanism-GRU (GMVCBAG) model to quantify the uncertainty of the model outputs. The new model is benchmarked against multiple predictive models. Our study also uses various performance metrics to evaluate the accuracy of predictions. Our findings indicate that Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), standard deviation of the relative error (STDRE), and explained variance of the GMVCBAG model are 0.973, 0.245, 1.245, and 0.899, respectively. Results indicate that GMVCBAG provides reliable SWH predictions. Moreover, the outputs of the new model have a lower uncertainty than those of the other predictive models. Thus, GMVCBAG is a suitable model for predicting SWH in the different regions of the world.   https://mmws.uma.ac.ir/article_4271.html Increasing anthropogenic pressures have intensified contamination in river ecosystems, highlighting the urgent need for comprehensive environmental evaluations. This study was designed to evaluate the ecological quality of the Qara-Su River in Ardabil Province, Iran, using a combination of biotic and abiotic metrics. Macroinvertebrate sampling was conducted across four stations from June 2021 to April 2022 using a Surber sampler, yielding a total of 5,092 specimens representing ten taxonomic orders. Water and sediment samples were analyzed for lead and cadmium concentrations, and macroinvertebrate communities were assessed to compute diversity indices (Shannon, Simpson, evenness, dominance) and biotic indices (HFBI, BMWP). Additional evaluations included bioconcentration (BCF), biota–sediment accumulation (BSAF), and contamination indices (Igeo, Er, RI, HPI). Correlation analysis was used to explore relationships between biotic and abiotic variables. The results revealed that the Pb and Cd content were elevated in both water and biota, particularly in Hydropsychidae, and exceeded permissible limits at downstream sites. Seasonal water-quality patterns showed higher nutrient loads and lower dissolved oxygen during warmer periods, along with consistently greater pollution at downstream stations exposed to cumulative agricultural, domestic, and aquaculture inputs. The strong correlations between abiotic and biotic indices confirmed the reliability of macroinvertebrate-based assessment. The combination of biotic and abiotic indicators revealed spatial variation in ecological health along the Qara-Su River, highlighting localized pollution risks masked by average conditions. These findings emphasize the importance of integrating multiple assessment tools to support targeted river management and mitigation strategies. https://mmws.uma.ac.ir/article_4286.html Soil and water resources are the foundation of life on Earth, serving as the most essential natural resources for sustaining agricultural productivity, ecological balance, and human well-being. They form the basis for food security, biodiversity, and environmental sustainability. However, in many developing countries, including Ethiopia, soil and water resources are under severe pressure due to both natural and human-induced factors. Unsustainable land use, rapid population growth, deforestation, and poor management practices have significantly accelerated the rate of soil degradation and water scarcity. As a result, the productivity of agricultural lands has declined, threatening livelihoods that depend heavily on these natural resources. In response, numerous soil and water conservation (SWC) measures have been introduced over the past decades, both by farmers through indigenous knowledge and by government and development agencies through modern interventions. Despite these efforts, the rate of adoption of SWC practices among smallholder farmers remains uneven and often limited by socio-economic, institutional, and environmental constraints.The present study was therefore conducted to assess farmers’ practices and identify the key factors influencing the adoption of soil and water conservation measures in the study area. The primary goal was to generate a comprehensive understanding of how farmers manage soil and water resources, what motivates or discourages their adoption of conservation techniques, and how different socio-economic variables interact to shape these decisions. Understanding these dynamics is essential for designing effective policies and interventions aimed at promoting sustainable land management and improving agricultural productivity in erosion-prone areas.To achieve these objectives, a cross-sectional survey design was employed. The study used a mixed research approach, specifically a concurrent triangulation strategy, which allowed the integration of both quantitative and qualitative data collected simultaneously. This approach enabled the researcher to validate and enrich the findings through the combination of statistical analysis and narrative insights. A total of 341 farm households were selected using a simple random sampling technique to ensure representativeness of the population and to minimize bias. Data collection instruments included structured questionnaires, key informant interviews (KIIs), and focus group discussions (FGDs). The combination of these tools provided a holistic understanding of both the statistical trends and the underlying reasons behind farmers’ decisions regarding SWC adoption.Quantitative data were analyzed using descriptive and inferential statistical methods. Descriptive statistics such as frequency, percentage, mean, and standard deviation were used to summarize and describe farmers’ demographic and socio-economic characteristics, as well as their perceptions and practices regarding soil and water conservation. Inferential analysis, particularly the binary logistic regression model, was employed to identify and quantify the factors influencing the likelihood of adopting soil and water conservation practices among households. This model was suitable because the dependent variable—whether a farmer adopted SWC measures—was dichotomous (adopted or not adopted). Additionally, qualitative data obtained from interviews and group discussions were transcribed, narrated, and thematically analyzed to complement and validate the quantitative findings.The results of the study revealed that deforestation, steep topography, erratic and erosive rainfall, land fragmentation, overgrazing, weak management systems, and improper farming practices are the major drivers of soil degradation in the study area. Continuous cultivation without sufficient fallow periods and limited use of organic or chemical fertilizers have further exacerbated soil nutrient depletion. Farmers reported that soil erosion and loss of fertility were among the most pressing challenges, often leading to reduced crop yields and food insecurity. The physical nature of the landscape, characterized by steep slopes and shallow soils, further intensified the problem, particularly during heavy rainfall seasons when surface runoff and sediment loss are high.Despite these challenges, farmers in the area have developed and maintained a range of indigenous soil conservation practices that have been passed down through generations. These include crop rotation, contour plowing, fallowing, mulching, manuring, and the construction of traditional cut-off drains. These practices play an important role in minimizing soil erosion, maintaining soil fertility, and improving water infiltration. In recent years, however, the introduction of modern soil and water conservation measures has been encouraged by local government offices and development partners. The most commonly adopted modern measures include soil bunds, vetiver grass strips, agroforestry systems, hillside terracing, and micro-basins. The integration of indigenous knowledge with modern conservation technologies has shown promising results in reducing erosion and improving soil structure and productivity.The binary logistic regression analysis identified several key socio-economic and institutional variables that significantly influenced the adoption of SWC measures. Gender, for instance, had a positive and significant effect on adoption, indicating that male-headed households were more likely to adopt conservation practices, possibly due to greater access to labor, land, and information. Age of the household head also showed a positive relationship, suggesting that experience accumulated over time enhances awareness and appreciation of the long-term benefits of conservation. Educational status emerged as another important factor, as literate farmers were more likely to adopt improved SWC technologies due to better understanding of training materials and extension messages.Moreover, access to credit was found to have a positive and significant influence on adoption. Farmers with access to financial resources were more capable of covering the initial costs of implementing conservation structures and maintaining them over time. Similarly, landholding size had a positive association, implying that households with larger plots had more flexibility to allocate portions of their land for conservation without compromising food production. In contrast, distance to farm plots exhibited a negative and significant relationship, meaning that the farther the farmland was from the homestead, the less likely the farmer was to adopt SWC measures. This is likely due to the increased labor and transportation burden associated with managing distant fields.Qualitative findings further supported these results. Farmers emphasized that the success of SWC adoption depends not only on economic and biophysical conditions but also on the level of community participation, local leadership, and extension support. In areas where local extension workers were active and community-based organizations were functional, adoption rates were notably higher. Conversely, in places with weak institutional linkages and poor follow-up, conservation structures often deteriorated or were abandoned after implementation. https://mmws.uma.ac.ir/article_4308.html Extended Abstract Introduction Soil erosion is one of the most critical environmental challenges in semi-arid regions worldwide, particularly in landscapes dominated by loessic deposits, where weak physical and mechanical characteristics significantly increase susceptibility to subsurface erosion and the development of piping features. Piping often initiates and progresses covertly during its early stages, eventually leading to sudden surface collapse, topographic instability, accelerated sediment delivery, reduced land productivity, and disruption of hydrological and ecological systems. Despite extensive research on soil erosion, most existing studies have adopted static or single-temporal approaches, and substantial scientific gaps still remain regarding multi-temporal and spatial monitoring of piping frequency, distribution, density, and evolutionary trends. Furthermore, the combined and interactive influences of topography, vegetation cover, and land use patterns on piping development in loess-derived terrains are not yet adequately understood, posing challenges for soil and water conservation planning, bioengineering practices, and watershed management decisions. Accordingly, the present study aims to monitor four-year temporal changes in the number, location, and evolution of piping features, and to evaluate the controlling roles of slope, elevation, vegetation cover, and land use using ultra-high-resolution UAV data combined with multi-temporal Digital Elevation Model of Difference (DoD) analysis. This approach enables the assessment of spatial patterns of erosion and deposition in areas with and without piping and the estimation of annual erosion-sedimentation rates, thereby improving the identification of high-risk zones and supporting evidence-based management and mitigation strategies in loess environments. Materials and Methods Initially, two sub-watersheds with different proportions of rangeland and agricultural land use were selected. To ensure accurate detection and temporal monitoring of piping development, the spatial location of all piping features within both sub-watersheds was recorded using GPS during the 2019 and 2023 survey campaigns. Multi-temporal UAV surveys were conducted under comparable illumination and meteorological conditions using a Phantom 4Pro UAV, and the acquired high-resolution imagery was processed using a photogrammetric workflow in ContextCapture to generate three-dimensional point clouds and high-precision Digital Elevation Models (DEMs) with a spatial accuracy of approximately 5 cm. To quantify volumetric topographic changes, the DoD approach was applied within ArcGIS, resulting in spatially explicit erosion deposition maps as well as annual mean volumetric change estimates (expressed as tons per hectare per year) for each sub-watershed. Land use classification was carried out through visual interpretation of UAV imagery combined with extensive field verification. Slope and elevation layers were extracted from the DEM using ArcGIS to examine topographic control on piping distribution. Density plots generated in R software were used to statistically explore the relationships between piping occurrence, slope gradient, and elevation range. Finally, temporal variations in piping frequency, spatial displacement, initiation, expansion, or disappearance were compared between the two sub-watersheds to identify dominant geomorphic and land-management drivers of piping dynamics. Results and Discussion The findings indicated that in Sub-watershed 1, with 85% agricultural land, the piping density was only 10%, of which 2 occurred in croplands and 18 in rangelands. In Sub-watershed 2, with 70% rangeland, the density was considerably higher at 55%, with 198 cases occurring in rangelands. Piping mainly occurred at lower elevations (370–410 m and 300–340 m in Sub-watersheds 1 and 2), on steep slopes (25–35°), and weak vegetation. DoD analysis over the period 2019–2023 revealed that in agricultural lands, deposition was the dominant process, whereas in rangelands, erosion  were more pronounced; in Sub-watershed 1, 72% of the area experienced deposition and 28% erosion, while in Sub-watershed 2, 77% erosion and 23% deposition were recorded. Annual rates were ±5 t/ha/yr in Sub-watershed 1 and 15–25 t/ha/yr in Sub-watershed 2. Over four years, two agricultural piping features were lost, but two new features formed in Sub-watershed 1 and ten in Sub-watershed 2. The main advantage of this study lies in the integration of real UAV data, precise DoD analysis, pixel-based monitoring of erosion, and piping relocation, enabling identification of high-risk areas and prioritization for management interventions. Conclusion Based on the findings of this research, Steeper slopes, lower elevations, reduced vegetation density and land-use type were identified as the primary environmental factors controlling the initiation and development of piping in semi-arid loess landscapes. Moreover, the integration of multi-temporal UAV data with the DoD technique enabled accurate detection of morphological evolution and delineation of susceptible areas over time. According to the results, Sub-watershed 1, dominated by agricultural land use, was mainly characterized by depositional processes, and the total number of piping features remained constant during the four-year monitoring period. In contrast, Sub-watershed 2, where rangelands are dominant, experienced severe erosion, resulting in the formation of eight new piping. This discrepancy can be attributed to contrasting land management practices: agricultural operations such as tillage and crop cultivation may lead to the infilling or concealment of existing pipes, whereas terrain forms, overgrazing and vegetation degradation in rangelands facilitate accelerated piping expansion. Field observations also revealed a dual functional role of piping features. While they intensify subsurface discharge, soil erosion, and desertification, their internal cavities may also serve as favorable microhabitats for the establishment of drought-resistant plant species such as wild pomegranate and Amygdalus scoparia. The outcomes of this research can directly support soil and water conservation planning, particularly for prioritizing preventive measures in fragile dryland environments. Enhancing deep-rooted vegetation, regulating grazing patterns, and applying bio-engineering strategies around piping zones are recommended for controlling further degradation. Future studies are advised to integrate UAV observations with seasonal satellite datasets and high-resolution DEM modeling while assessing climate-change-driven rainfall scenarios to better predict long-term piping dynamics. https://mmws.uma.ac.ir/article_4309.html Development of Multiple Linear Regression Models for Annual Reference Evapotranspiration Estimation under Limited Data Conditions Accurate estimation of reference evapotranspiration (ET₀) is essential for agricultural water management, particularly in regions with limited data availability. The aim of this study was to evaluate multiple linear regression (MLR) models to estimate ET₀ at the annual scale. Meteorological data from the Kuhdasht synoptic station, Iran for a 25-year period (1998–2022) were used. ET₀ was calculated using the FAO-56 Penman-Monteith method implemented through the CROPWAT 8.0 software. A total of 31 MLR models were developed using the Regression option from the Analysis ToolPak of Microsoft Excel 2019 to quantify the relationship between ET₀ and climatic variables. Seven statistical indices were used to evaluate the performance of the MLR models in estimating ET₀. Results showed that 16 models achieved very high accuracy, with coefficients of determination (R²) greater than 0.92. Among single-variable models, wind speed (MLR4) was the most significant predictor of ET₀ (R² = 0.92, P-value = 0), followed by minimum temperature (MLR1, R² = 0.39, P-value = 0) and maximum temperature (MLR2, R² = 0.39, P-value = 0). Relative humidity (MLR3, R² = 0.1, P-value = 0.12) and sunshine (MLR5, R² = 0, P-value = 0.79) were not statistically significant predictors. Several two-variable models achieved R² = 0.92 to 0.96, and most three-variable models reached R² = 0.93 to 0.97. Four-variable models also performed strongly (R² ≈ 0.95 to 0.97), while the five-variable model yielded R² ≈ 0.97, similar to simpler models. Wind speed emerged as the most influential factor, highlighting that well-chosen two- or three-variable models can estimate ET₀ as effectively as more complex alternatives. Development of Multiple Linear Regression Models for Annual Reference Evapotranspiration Estimation under Limited Data Conditions Accurate estimation of reference evapotranspiration (ET₀) is essential for agricultural water management, particularly in regions with limited data availability. The aim of this study was to evaluate multiple linear regression (MLR) models to estimate ET₀ at the annual scale. Meteorological data from the Kuhdasht synoptic station, Iran for a 25-year period (1998–2022) were used. ET₀ was calculated using the FAO-56 Penman-Monteith method implemented through the CROPWAT 8.0 software. A total of 31 MLR models were developed using the Regression option from the Analysis ToolPak of Microsoft Excel 2019 to quantify the relationship between ET₀ and climatic variables. Seven statistical indices were used to evaluate the performance of the MLR models in estimating ET₀. Results showed that 16 models achieved very high accuracy, with coefficients of determination (R²) greater than 0.92. Among single-variable models, wind speed (MLR4) was the most significant predictor of ET₀ (R² = 0.92, P-value = 0), followed by minimum temperature (MLR1, R² = 0.39, P-value = 0) and maximum temperature (MLR2, R² = 0.39, P-value = 0). Relative humidity (MLR3, R² = 0.1, P-value = 0.12) and sunshine (MLR5, R² = 0, P-value = 0.79) were not statistically significant predictors. Several two-variable models achieved R² = 0.92 to 0.96, and most three-variable models reached R² = 0.93 to 0.97. Four-variable models also performed strongly (R² ≈ 0.95 to 0.97), while the five-variable model yielded R² ≈ 0.97, similar to simpler models. Wind speed emerged as the most influential factor, highlighting that well-chosen two- or three-variable models can estimate ET₀ as effectively as more complex alternatives. Development of Multiple Linear Regression Models for Annual Reference Evapotranspiration Estimation under Limited Data Conditions Accurate estimation of reference evapotranspiration (ET₀) is essential for agricultural water management, particularly in regions with limited data availability. The aim of this study was to evaluate multiple linear regression (MLR) models to estimate ET₀ at the annual scale. Meteorological data from the Kuhdasht synoptic station, Iran for a 25-year period (1998–2022) were used. ET₀ was calculated using the FAO-56 Penman-Monteith method implemented through the CROPWAT 8.0 software. A total of 31 MLR models were developed using the Regression option from the Analysis ToolPak of Microsoft Excel 2019 to quantify the relationship between ET₀ and climatic variables. Seven statistical indices were used to evaluate the performance of the MLR models in estimating ET₀. Results showed that 16 models achieved very high accuracy, with coefficients of determination (R²) greater than 0.92. Among single-variable models, wind speed (MLR4) was the most significant predictor of ET₀ (R² = 0.92, P-value = 0), followed by minimum temperature (MLR1, R² = 0.39, P-value = 0) and maximum temperature (MLR2, R² = 0.39, P-value = 0). Relative humidity (MLR3, R² = 0.1, P-value = 0.12) and sunshine (MLR5, R² = 0, P-value = 0.79) were not statistically significant predictors. Several two-variable models achieved R² = 0.92 to 0.96, and most three-variable models reached R² = 0.93 to 0.97. Four-variable models also performed strongly (R² ≈ 0.95 to 0.97), while the five-variable model yielded R² ≈ 0.97, similar to simpler models. Wind speed emerged as the most influential factor, highlighting that well-chosen two- or three-variable models can estimate ET₀ as effectively as more complex alternatives. https://mmws.uma.ac.ir/article_4310.html Extended AbstractIntroductionLarge quantities of wastewater containing complex organic compounds, lignin, phenolics, suspended solids, and various toxic substances are generated by the pulp and paper industry. Effluents with elevated BOD, COD, TSS, and TDS pose serious environmental and public-health risks if discharged without adequate treatment. Black liquor produced from soda pulping particularly when using non-wood raw materials such as wheat straw also contains high levels of silica, making conventional treatment methods expensive, inefficient, and in many cases impractical.Biological treatment using microorganisms has therefore gained attention as a more eco-friendly and cost-effective option, capable of degrading complex organic pollutants while reducing the need for chemicals and energy. However, free microbial cells often experience problems such as washout, gradual loss of activity, and sensitivity to toxic components. Immobilizing biomass on porous supports, especially polyurethane foam, helps retain the cells, stabilize enzyme activity, increase tolerance to environmental fluctuations, and enable repeated use. In this study, the performance of the white-rot fungus P. chrysosporium in both free and immobilized forms was evaluated for the treatment of soda black liquor. The focus was on reducing major pollution indicators, including COD, BOD, TDS, and TSS, to provide a sustainable approach for managing industrial wastewater.Materials and Methods Black liquor was obtained from laboratory-scale soda pulping of wheat straw. A 50-g oven-dry sample of wheat straw was cooked in a batch digester at 160 °C for 30 minutes using an active alkali charge of 16% NaOH based on oven-dry straw. After washing the pulp, the resulting black liquor was collected, filtered, and stored at 4 °C until needed. Before biological treatment, the liquor was diluted tenfold with distilled water. Fungal treatment was carried out at 30 °C using free and immobilized P. chrysosporium cells, with polyurethane foam (PUF) serving as the immobilization matrix. Experiments were conducted under near-optimal pH conditions (6.5–7) over treatment periods of 0, 1, 3, 7, 11, and 14 days. Pollution parameters COD, BOD, TDS, and TSS were measured at each interval. All experiments were performed in triplicate. Statistical analyses were conducted using SPSS software. Independent-samples t-tests were used to determine significant differences between treatment groups, F-tests were applied for variance analysis, and Duncan’s multiple range test was employed for comparing mean values.Results and Discussion In soda black liquor, both free and immobilized cells of P. chrysosporium substantially reduced the organic and dissolved solids load, but the immobilized fungus consistently showed superior performance for all monitored parameters. By day 14, the immobilized biomass achieved reductions of 78.03% in COD, 87.54% in BOD and 74.89% in TDS, whereas the free-cell system resulted in 58.05%, 71.54% and 56.22% reduction, respectively. The highest degradation rates for both systems occurred during the early stages of treatment, particularly up to day 7, after which the removal efficiency increased more slowly. This decline in the rate of pollutant removal can be attributed to the depletion of readily biodegradable organic matter, gradual limitation of nutrients and oxygen within the biomass, and partial autolysis or aging of fungal cells. The consistently higher performance of the immobilized fungus indicates that attachment on polyurethane foam improves contact between the biomass and soluble substrates, enhances local retention of enzymes and metabolites, and protects the cells against hydraulic wash-out and fluctuations in wastewater composition. The three-dimensional structure and high porosity of the carrier likely facilitate better mass transfer and provide additional active sites for adsorption and subsequent enzymatic degradation. Overall, the results demonstrate that immobilization not only increases the extent of COD, BOD and TDS removal, but also stabilizes fungal activity over time, thereby improving the robustness and overall efficiency of biological treatment for soda black liquor.Conclusion This study demonstrates that P. chrysosporium particularly in immobilized form on polyurethane foam—is an efficient, stable, and environmentally sound option for the primary biotreatment of soda black liquor. Both free and immobilized systems reduced COD, BOD, TDS, and TSS under controlled laboratory conditions, but the immobilized fungus consistently outperformed free cells. By day 14, the immobilized system achieved reductions of 78.03% (COD), 87.54% (BOD), and 74.89% (TDS), compared to 58.05%, 71.54%, and 56.22% for free cells, highlighting the improved treatment efficiency. The highest removal rates occurred during the first week, after which the process slowed, likely due to nutrient depletion and partial saturation of the immobilization matrix. These findings confirm the strong potential of P. chrysosporium to degrade complex organic pollutants in black liquor through its active ligninolytic enzymes. Immobilization enhances biomass stability, increases resilience to environmental changes, and improves enzymatic performance by ensuring sustained substrate access. Accordingly, immobilization on carriers such as polyurethane foam offers a cost-effective, environmentally friendly, and operationally stable strategy for primary treatment of effluents from the pulp and paper industry. Moreover, this approach can serve as an effective pretreatment before secondary processes such as activated sludge or advanced treatment, thereby supporting the development of semi-industrial and industrial-scale biotreatment systems based on immobilized microorganisms. Overall, the study highlights immobilized white-rot fungi as a scalable and sustainable alternative to conventional chemical or thermal treatment methods for industrial wastewater management. https://mmws.uma.ac.ir/article_4328.html Introduction Drought is a climatic anomaly that results from long-term disruptions in components of the water balance (Wang et al., 2021; Portner et al., 2022; Zhang et al., 2023; Kartal, 2024; Tareke, 2025). This phenomenon has both direct and indirect adverse impacts, with water resources being the most significantly affected (Balooei et al., 2024). Water scarcity and its associated challenges are recognized as among the most critical and urgent global crises (Zarei et al., 2019a). Although drought cannot be prevented, understanding its nature and characteristics enhances the potential for partial prediction and, through preparedness and planning, helps reduce—and, where possible, control—its detrimental effects (Rezaei et al., 2024). Therefore, greater attention must be paid to drought and to identifying the key factors influencing it across different regions, particularly in vulnerable countries such as Iran, which face growing water scarcity. The increasing need to understand drought and its consequences has motivated extensive global research aimed at developing various drought indices. Among these, the Reconnaissance Drought Index (RDI), introduced by Tsakiris et al. (2007), is one of the most notable. Materials and Methods In this study, daily observational data from nine synoptic stations covering a 30-year period (1991–2020) were obtained from the Iran Meteorological Organization to estimate potential evapotranspiration and the RDI index. These stations were selected so that each represents one of Iran’s major climatic groups. In this research, the performance of six temperature-based models and three radiation-based models for estimating potential evapotranspiration was evaluated. The primary goal of the analysis is to identify which of these simplified approaches provides results most consistent with the FAO Penman–Monteith (FAO-56 PM) model, which is widely recognized as the standard reference method (Allen et al., 1998). Results and Discussion Evaluation of Daily Potential Evapotranspiration Models The results indicated that the performance of evapotranspiration models is strongly influenced by the climatic conditions of each region. For example, the Blaney–Criddle model performed best in certain climates, while the same model showed lower accuracy in others. This finding is consistent with previous studies, including Eghtedarnezhad et al. (2016), which emphasized the role of climatic factors in drought monitoring. Moreover, the variability in model performance across different regions further underscores the need to evaluate and select models that are appropriate for the specific climatic conditions of each area. This observation also aligns with the findings of Lehner et al. (2020) and Beobide-Arsuaga et al. (2021), who stressed the importance of model adaptability to local conditions. Overall, it can be concluded that choosing the appropriate model for estimating evapotranspiration is a crucial step in drought studies and water resource management, and must be carried out with careful consideration of each region’s climatic characteristics. Evaluation of Monthly Potential Evapotranspiration Models Overall, the results demonstrate that, similar to the daily scale, temperature-based models do not exhibit uniform behavior across different climates at the monthly scale. A model may perform exceptionally well in one climate while ranking among the weakest in another. These differences highlight the necessity of considering climate type, geographical characteristics, the study period, and careful model selection in climatological research (Latrech et al., 2024). Assessment of Drought Using the 6- and 12-Month RDI Indices for Optimal Models at Selected Stations The overall findings of this study indicate that at the 6-month timescale, the frequency of drought and wet periods is higher, whereas at the 12-month timescale, their frequency decreases but their persistence increases. This result is in agreement with the study by Ahrari and Raja (2025), who examined meteorological, agricultural, and hydrological drought indices in the Mahabad plain. Furthermore, based on the results of the present study, the 12-month RDI was identified as a more suitable timescale for monitoring drought and wet periods, which is consistent with the findings of Nouri and Homaee (2020), Torabinejad et al. (2023), and Rezaei et al. (2024). Conclusion Frequent drought events and the significant damages they cause in various sectors, including agriculture, the environment, socio-economic, and other areas, have made this phenomenon one of the fundamental challenges in different regions of the world. The present study aimed to evaluate the performance of different potential evapotranspiration models and their influence on the RDI drought index across diverse climatic zones, using 30 years (1991–2020) of data from nine synoptic stations representing nine distinct climate types in Iran (BSh, BSk, BWh, BWk, Cfa, Csa, Csb, Dsa, Dsb). The evaluation of nine evapotranspiration models showed that their performance is strongly affected by the climatic characteristics of each region. For example, the Droogers–Allen model performed best in cold semi-arid (BSk), Mediterranean temperate with warm summers (Csb), and cold climates with dry, warm summers (Dsb), whereas the same model exhibited poor performance in hot semi-arid climates (BSh) and several other regions. In the drought analysis section, comparison of the 6- and 12-month RDI indices revealed that although both timescales confirm the occurrence of frequent droughts during most of the study period, the 12-month RDI was identified as the more suitable scale for drought monitoring and analysis in Iran. This is due to its ability to filter out short-term fluctuations and better reflect the persistence of drought periods. https://mmws.uma.ac.ir/article_4342.html Amidst intensifying climatic and management pressures on water resources in Iran, this research focuses on exploring the desirable and effective future of water use in agriculture, with a case study of maize in Khuzestan Province. The LARS-WG 8 was used to project climate data up to the horizon year 2040. The biophysical crop yield was examined using AquaCrop 7.1. According to the results, the LARS-WG model generated temperature data (NRMSE≈1%) with greater accuracy than precipitation (NRMSE≤13%) at Ahvaz and Dezful stations. In addition, the AquaCrop model (R²=0.96, RMSE<0.5 t/ha, NSE≈0.98) confirmed the high accuracy of maize yield simulation. Moreover, structural scenarios developed with the ScenarioWizard software and the MICMAC matrix included 13 significant drivers from the policy, technology, and climate domains. The findings indicate that the effect of climate change on water productivity is incremental, and shaping a desirable future is largely influenced by management. The results showed that, when moving from SSP1-2.6 to SSP5-8.5, grain maize yield and water productivity increased in both spring and summer maize. In spring, yield increased from 6.26 t/ha in SSP1-2.6 to 6.79 t/ha in SSP5-8.5, and water productivity increased from 1.13 to 1.22 kg/m3. In summer, this trend was more pronounced, with yield rising from 8.32 to 9.15 t/ha and water productivity from 1.29 to 1.42 kg/m3. These results indicated that under the more severe climate change scenario (SSP5-8.5), crop growth and yield were more affected, especially in summer. Furthermore, this study provides a picture of the desired future of water use in agriculture. According to the Total Impact Score in ScenarioWizard, (381, 405, 408) a desirable and effective future was identified when political and technological measures were taken at a high level of intervention. According to these results, achieving a desirable future depends on the full implementation of decentralization policies, data transparency, and the use of advanced statistical systems. https://mmws.uma.ac.ir/article_4356.html Optimal crop patterns improve both profitability and sustainability in land resource management. This study assessed current crop patterns in the Baliqlu Chay River Basin using water productivity, efficiency, labor, and net profit indices. Data from Ardabil, Nir, and Sareyn (2022–2023) show that potato is the most water- and labor-intensive crop (~6,000 m³/ha and >30 person-days/ha) but yields the highest net profit (~2.67 billion IRR/ha). Wheat has the lowest profit (0.5–0.6 billion IRR/ha) due to lower input requirements. Barley, alfalfa, and canola are more suitable for water-limited conditions. Spatially, Ardabil accounts for 91.8% of basin profits, while Nir and Sareyn contribute less than 5%, indicating strong regional disparities. The results show that expanding cultivated area alone does not ensure higher returns; instead, adaptive water management and efficient use of inputs are crucial. Crop performance was further assessed using water productivity indicators, including PWP, GEWP, and NEWP. Despite its relatively high water requirement, potato exhibits the highest water productivity among the studied crops, with PWP values ranging from approximately 5.0 to 5.8 kg/m³, GEWP from 454 to 527 thousand IRR/m³, and NEWP from 348 to 526 thousand IRR/m³. Wheat, although characterized by lower physical productivity (PWP≈1.1-1.9 kg/m³), remains a strategic staple crop with comparatively favorable economic water productivity (NEWP≈133-250 thousand IRR/m³). In contrast, barley, canola, and particularly alfalfa demonstrate lower water productivity levels, with alfalfa exhibiting the lowest net economic water productivity (NEWP≈38-79 thousand IRR/m³). This lower efficiency indicates alfalfa’s agro-ecological role in fodder production and soil improvement rather than economic water productivity. The results support adaptive cropping systems that combine economic and hydrological indicators to reduce water stress while improving watershed-scale sustainability. https://mmws.uma.ac.ir/article_4380.html Extended Abstract Introduction Heavy metal contamination in terrestrial ecosystems has become a major environmental and public health concern worldwide, particularly in regions exposed to intensive industrial operations, agricultural inputs, and land-use alterations. Soil acts as both a reservoir and a medium for the transport of potentially toxic elements (PTEs), and therefore the evaluation of its contamination status is essential for sustainable land management and ecosystem protection. The Shazand region in Markazi Province, central Iran, hosts several heavy industries—including the Imam Khomeini Oil Refinery, a petrochemical complex, a large thermal power plant, and numerous mining activities, which have previously been reported as major pollution sources. Nevertheless, earlier investigations have predominantly focused on downstream plains and industrial zones, with limited knowledge regarding the contamination status of upstream sub-watersheds that are assumed to be less affected by anthropogenic pressures. The present study aims to fill this critical gap by providing a multi-index assessment of heavy metal contamination in the upstream Pakal sub-watershed in Shazand County, with an emphasis on how different land-use types (rangeland, cultivation, and orchards) influence the distribution, enrichment, and ecological risks of seven key heavy metals: Pb, Cd, Cu, Zn, Ni, Mn, and Fe.   Materials and Methods A total of 32 composite soil samples were collected from surface layers (0–30 cm), following a systematic sampling design based on land units that integrated slope, land use, and lithology. The fine fraction (<0.063 mm) of the soils was analyzed using a near-total four-acid digestion method, and metal concentrations were determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS), ensuring high analytical accuracy for trace and ultra-trace elements. To comprehensively evaluate contamination status, multiple geochemical and ecological indices, including the Contamination Factor (CF), Degree of Contamination (Cd), Modified Degree of Contamination (mCd), Pollution Load Index (PLI), Geo-accumulation Index (Igeo), Enrichment Factor (EF), and Potential Ecological Risk (Eri and RI)—were employed. Moreover, multivariate statistical analyses, including Principal Component Analysis (PCA) with Varimax rotation and Hierarchical Cluster Analysis (HCA), were conducted to distinguish between natural (geogenic) and anthropogenic sources.   Results and Discussion The results of one-way ANOVA indicated that none of the measured heavy metals exhibited statistically significant differences among the three land-use categories (P > 0.05), suggesting that spatial variation is primarily governed by natural geochemical controls rather than recent anthropogenic inputs. This finding was strongly supported by PCA and HCA. PCA extracted three principal components that together explained 69.59% of the total variance. The second component, dominated by Fe and Mn with extremely high loadings, clearly represented geogenic contributions associated with parent material and mineralogical composition. In contrast, the first component (characterized by Cd, Cu, and Pb) and the third component (Zn, Ni, and Fe) reflected mixed or anthropogenic influences, likely originating from agricultural activities such as phosphate fertilizers, pesticides, and machinery emissions. HCA further confirmed these groupings by separating the elements into two distinct clusters: a geogenic cluster (Fe and Mn) and an anthropogenic/mixed cluster (Pb, Cd, Cu, Zn, and Ni). Despite the statistical homogeneity implied by ANOVA, contamination indices revealed noteworthy evidence of cumulative anthropogenic enrichment. CF values showed that Pb, Cu, Zn, Ni, Mn, and Fe had moderate contamination levels (CF > 1) across most land uses. Pb exhibited the highest CF value, particularly in agricultural soils (2.31), highlighting its elevated sensitivity to anthropogenic activities. Cadmium, although present in low absolute concentrations, demonstrated substantial ecological importance due to its high toxic response factor. The Degree of Contamination (Cd) ranged from 7.93 to 9.77, classifying all land uses as moderately contaminated. Notably, the Pollution Load Index exceeded unity for rangeland (1.05) and cultivated land (1.15), indicating cumulative pollution, while orchards (0.94) remained below the contamination threshold. This discrepancy between ANOVA and PLI highlights that while spatial variation is not statistically significant, long-term pollutant accumulation has occurred. Geochemical indices further corroborated the dominance of natural sources. EF values for most metals fell within the “no enrichment” to “minor enrichment” categories (EF ≤ 3), confirming minimal anthropogenic addition. Only Pb showed consistent minor enrichment across land uses, particularly in orchards and agricultural soils, aligning with patterns typically associated with the historical deposition of lead-containing particulates and agrochemical inputs. Similarly, Igeo values for all metals, except Pb, were negative, indicating unpolluted conditions. Pb in cultivation and orchard soils was classified as “unpolluted to moderately polluted” (0 < Igeo ≤ 1), marking it as the only element with a detectable anthropogenic signal. Ecological risk assessment showed that individual ecological risk values (Eri) for all metals fell within the low-risk category. However, Cd accounted for the highest proportion of ecological risk due to its high toxicity, despite its relatively low concentration. The integrated ecological risk index (RI) ranged from 32.30 to 44.08 for the three land uses—well below the threshold of 150—indicating a low overall ecological threat in the upstream Pakal sub-watershed.   Conclusions In conclusion, although geogenic factors remain the primary determinant of heavy metal distribution in the upstream Pakal sub-watershed, pollution indices reveal subtle yet meaningful anthropogenic contributions, particularly from agricultural activities. The slight enrichment of Pb and Cu, the moderate contamination levels indicated by CF and Cd, and the PLI values exceeding unity in cultivated and rangeland soils collectively demonstrate the early stages of cumulative pollution in areas traditionally considered pristine. These findings underscore the necessity of continuous monitoring, stricter management of agricultural inputs, and preventive measures to mitigate further contamination and potential ecological risks. Given the proximity to major industrial sources and expanding agricultural practices, upstream sub-watersheds like Pakal serve as critical zones for early detection of contamination trends that may escalate if left unaddressed. https://mmws.uma.ac.ir/article_4381.html Extended Abstract Introduction Groundwater resources play a vital role in sustaining socio-economic development, particularly in arid and semi-arid regions where surface water is scarce. In recent decades, these resources have come under increasing stress due to climate change, prolonged droughts, and unsustainable abstraction. The vulnerability of aquifers to drought has therefore become a central issue in water resources management and policy-making. The resilience, reliability, and vulnerability (RRV) framework provides a robust means to assess the performance and sustainability of groundwater systems under hydrological stress. This study focuses on evaluating the groundwater resources of the Dehgolan plain, located in Kurdistan Province, Iran, to determine their vulnerability, reliability, and resilience in the face of recurrent droughts. The Dehgolan plain is one of the most important agricultural zones in the region, yet it has experienced significant groundwater decline over the past few decades. Continuous extraction for irrigation, coupled with reduced precipitation and recharge, has led to critical drops in groundwater levels and deterioration in aquifer storage. Using long-term hydrological and meteorological data, this research aims to (1) quantify the temporal and spatial variations of drought intensity and duration, (2) assess groundwater system behavior during drought events, and (3) evaluate overall aquifer performance through the RRV indices. The results provide a scientific foundation for improving groundwater management strategies in drought-prone regions. Materials and Methods The study area encompasses the Qorveh–Dehgolan plain in eastern Kurdistan Province, bounded by latitudes 34°56′ to 35°02′ N and longitudes 47°07′ to 47°24′ E. The region is characterized by a cold semi-arid climate with an average annual precipitation of approximately 350 mm and average temperatures ranging from –23°C to 41°C. The study utilized data from seven meteorological stations and 54 observation wells covering the 1977–2022 period. Drought assessment was performed using two major indicators: The Standardized Precipitation Index (SPI) for meteorological drought and the Groundwater Resource Index (GRI) for hydrogeological drought. SPI values were calculated for 6- and 12-month timescales using gamma probability distribution fitting. GRI was derived by standardizing deviations of monthly groundwater levels from their long-term means, thereby reflecting groundwater storage anomalies. Reliability (Rel) represents the probability of the system being in a satisfactory state, Resiliency (Res) measures the speed of recovery after a drought, and Vulnerability (Vul) quantifies the magnitude of failure when the system departs from acceptable conditions. Results and Discussion The SPI analysis revealed alternating wet and dry cycles, with severe droughts during 1977–1981, 1987–1992, and 1997–2001. SPI values ranged from –1.8 to +1.8, indicating alternating meteorological extremes and a general decline in precipitation after 1990. The GRI results showed continuous groundwater depletion, particularly in the central and eastern zones of the plain, where water-level declines exceeded 20 m. Average drought duration varied between 1.5 and 3.5 months, while 16–32% of the observation wells experienced recurrent droughts. RRV indicators quantified system performance under drought stress. Reliability values ranged from 0.91 to 0.94 (mean 0.92), showing that the aquifer remained in a satisfactory state most of the time. Resilience ranged between 0.72 and 0.95 (mean 0.82), reflecting moderate recovery capacity after drought. Vulnerability values varied from 0.23 to 0.39 (mean 0.31), signifying moderate failure magnitude. The composite RRV index averaged 0.62 based on SPI data and 0.68 using GRI data, representing a moderately stable yet declining system. Spatially, higher RRV values occurred in northern sectors, while central and southern zones exhibited reduced resilience and higher vulnerability. Overall, the findings indicate that although the Dehgolan aquifer retains moderate reliability, its recovery capacity has weakened due to prolonged overexploitation and limited recharge. These results align with previous research in western Iran, confirming that unsustainable groundwater abstraction combined with persistent droughts is reducing aquifer stability and resilience. Conclusion The integrated analysis of meteorological and groundwater droughts in the Dehgolan plain demonstrates that the aquifer system is under increasing pressure, primarily driven by climatic variability and excessive abstraction. The combined use of SPI, GRI, and RRV indices proved effective in identifying spatial and temporal patterns of groundwater vulnerability and in quantifying the system’s performance under drought stress. The mean reliability value (≈0.92) indicates that the groundwater system generally maintains acceptable performance, but the moderate resilience (≈0.82) and vulnerability (≈0.31) highlight limitations in recovery capacity and the potential for further degradation if current extraction rates persist. The RRV index values (0.62–0.68) collectively suggest a moderately stable yet increasingly fragile system. To enhance groundwater sustainability, it is essential to implement adaptive management strategies such as controlled abstraction, artificial recharge, improved irrigation efficiency, and continuous monitoring of groundwater levels. The results of this study provide a quantitative foundation for policymakers and regional planners to develop drought mitigation frameworks and long-term groundwater management plans aimed at preserving aquifer resilience and reliability under future climatic uncertainties. https://mmws.uma.ac.ir/article_4382.html Cross-sectional geometry has a fundamental impact on the hydraulic behavior of the Shatt al-Arab River, an important waterway providing the water life supply to almost 4.5 million people in Basra City, southern Iraq. Using 200 cross sections at 1-km spacing through a 200-km hydrodynamic modeling framework that is employed in HEC-RAS, the analysis combines Sentinel-2 and Landsat-9 satellite imagery, 30-m DEM datasets, and in situ discharge measurements to quantify the impact of channel width on flow velocity, tidal intrusion, and sediment dynamics. Here, we find an inverse relationship between channel width and flow velocity (R² = 0.92). In this way, expansion of 150 to 350 m of channel increases discharge capacity by 125% but at the same time, flow velocity decreases by 57%, increasing tidal penetration by half while increasing the sediment deposition by 50%. In contrast, channel narrowing results in high water levels with a rise in flood risk of up to 1.8 m. An optimal width range of 280–300 m is determined which provides a balanced hydraulic performance in terms of discharge capacity, approximately 5,900 m³/s capacity, seawater intrusion by ~25 km, and sediment build-up by nearly 35% as opposed to the wide channel segments. The results suggest that this width of channel should be considered an optimal width range for river rehabilitation and management, with dredging of high-sedimentation reaches (100–150 km; >12 cm/year) be in the priority mode. The continued and real-time hydrological monitoring application is further recommended in order to assure sustainable water security, with long-term river operation. https://mmws.uma.ac.ir/article_4387.html Evapotranspiration is one of the main sources of water loss in agricultural lands, and its accurate estimation plays a key role in reducing water wastage in the agricultural sector. In this study, several empirical equations, including FAO Penman–Monteith, Blaney–Criddle, Hargreaves–Samani, Irmak, Dalton, Romanenko, and Jensen–Haise, were used to estimate monthly evapotranspiration at two meteorological stations in Iran: Ardabil (semi-arid climate) and Zabol (arid climate). To improve the accuracy of these equations, both linear regression and nonlinear optimization methods were applied for calibration. In addition to the empirical equations, data-driven models, including a multilayer perceptron artificial neural network (MLP) and a hybrid MLP model combined with the ant colony optimization algorithm (MLP–ACO), were also developed. Model performance was evaluated using the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and general performance index (GPI). The results indicated that calibration using both linear and nonlinear methods significantly reduced RMSE (96.46% to 98.08%) and MAE (96.78% to 98.27%) values and increased R² (up to 5.49%) for all equations at both stations. The nonlinear optimization method showed greater performance improvement compared to linear regression. Among the empirical equations, the calibrated Blaney–Criddle equation exhibited the best performance. Furthermore, the MLP–ACO model outperformed the standalone MLP model and the non-calibrated empirical equations. Overall, the results demonstrated that equation calibration was highly effective, as the calibrated empirical equations outperformed both standalone and hybrid intelligent models in most cases under both climatic conditions. https://mmws.uma.ac.ir/article_4388.html Extended Abstract Introduction Rafsanjan Plain, an arid region of Iran, has experienced groundwater depletion and significant hydrological changes in recent decades. This study applied the semi-distributed, process-based SWAT model to simulate hydrological processes and estimate the water balance. Spatial inputs (DEM, land use, soil type, slope) and daily climate data (precipitation, temperature, humidity, wind, solar radiation) for 2002–2024 were used. The watershed was divided into sub-basins and HRUs, and the model was calibrated and validated using SUFI-2 in SWAT-CUP, with sensitivity and uncertainty analyses conducted. Results indicated that curve number, saturated hydraulic conductivity, and baseflow parameters most influenced streamflow simulation. Model performance was acceptable (P-factor 0.42, R-factor 2.62 in calibration; 0.42 and 0.39 in validation). Over 60% of annual precipitation was lost via evapotranspiration, with surface runoff contributing less than 0.1%. These findings demonstrate SWAT’s effectiveness for water resources management and climate impact assessment in arid regions. Materials and Methods The Rafsanjan Plain, encompassing 12,513 km² within the Kavir-e Dranjir-Saghand basin, was delineated into 12 sub-basins using a 30-meter Digital Elevation Model. The model configuration incorporated land use/land cover maps (derived from Landsat imagery), soil classification data from the Iranian Soil and Water Research Institute, and slope categories to generate Hydrological Response Units (HRUs) through an overlay process. Daily climatic inputs spanning 2002-2024 included precipitation, maximum/minimum temperature, and relative humidity from the Rafsanjan synoptic station; wind speed and solar radiation were obtained from NASA's POWER database to fill data gaps. Monthly discharge observations from two hydrometric stations facilitated model calibration (2002-2017) and independent validation (2018-2024). The SUFI-2 algorithm in SWAT-CUP performed automated calibration through iterative Latin Hypercube sampling, accounting for parameter uncertainty by bracketing observations within 95% prediction uncertainty bounds. Sensitivity analysis employed the t-test method to rank 27 parameters related to runoff generation, soil water movement, and groundwater flow. Performance evaluation utilized the P-factor (percentage of observations within uncertainty band) and R-factor (average width of uncertainty band normalized by standard deviation), supplemented by coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), and root mean square error (RMSE). The water balance equation in SWAT quantified precipitation partitioning into evapotranspiration, surface runoff, lateral flow, baseflow, and deep aquifer recharge components. Results and Discussion Global sensitivity analysis identified 27 parameters significantly influencing streamflow simulation, with the curve number (CN2), saturated hydraulic conductivity (SOL_K), and baseflow recession constant exhibiting the highest sensitivity based on t-statistics and p-values (<0.05). Calibration achieved P-factor=0.42 and R-factor=2.62, while validation yielded P-factor=0.42 and R-factor=0.39, indicating acceptable model performance according to ASABE guidelines. Statistical metrics demonstrated strong agreement (R²≈0.91, NSE=0.84) between simulated and observed monthly discharge, though the model overestimated peak flows in extreme years (2006, 2014) due to limited availability of sub-daily precipitation data and simplified representation of runoff generation during high-intensity events. Water balance analysis revealed that 63.2% of mean annual precipitation (142 mm) was lost through actual evapotranspiration, 28.4% contributed to deep aquifer recharge, 7.8% generated surface runoff, and return flow constituted merely 0.08%, characterizing typical hyper-arid hydrology. Baseflow dominated river discharge during dry months (June-September), comprising 85% of total flow, while snowmelt contributed significantly to spring peaks. Uncertainty analysis demonstrated that parameters controlling runoff partitioning and soil water retention (CN2, SOL_K, soil available water capacity) contributed 68% of total prediction uncertainty. Seasonal patterns showed that precipitation and runoff peaked in March-April, while potential evapotranspiration reached maximum values during June-August. The model's performance in simulating baseflow recession was superior to its representation of quickflow response, reflecting its conceptual structure and parameterization limitations in capturing rapid runoff processes. Conclusion This study successfully calibrated and validated the SWAT model for the Rafsanjan Plain, demonstrating its capability to simulate hydrological processes in data-scarce arid environments with acceptable uncertainty levels. The identification of 27 sensitive parameters, particularly CN2 and SOL_K, highlights that accurate characterization of soil hydraulic properties is critical for reducing simulation uncertainty and improving prediction reliability. Quantification of water balance components revealed severe water loss through evapotranspiration (>60%) and minimal groundwater recharge, emphasizing the unsustainable nature of current water use practices and the urgent need for demand management strategies. While the model effectively reproduced seasonal flow patterns and baseflow dynamics (R²≈0.91), overestimation of peak flows indicates limitations in representing extreme rainfall-runoff events, attributable to coarse temporal resolution of precipitation data and simplified infiltration processes. These findings provide a robust scientific foundation for evaluating climate change scenarios, land use change impacts, and water management interventions such as deficit irrigation and artificial recharge. Future research should integrate SWAT with groundwater quality modules to address salinization, incorporate higher-resolution meteorological forcing data, and couple with optimization algorithms to support multi-objective water allocation decisions. The established parameter ranges and methodological framework offer transferable guidance for hydrological modeling in similar arid watersheds, ultimately supporting evidence-based policies for sustainable water resource management. https://mmws.uma.ac.ir/article_4446.html Extended Abstract Introduction Drought is one of the most significant natural hazards in arid and semi-arid regions, affecting water resources, agriculture, vegetation, and human health. Iran, located in the arid belt of the world, frequently experiences severe and prolonged droughts, which have intensified in recent decades due to climate change and precipitation variability. Assessing drought characteristics and monitoring is essential for effective water resource management and risk reduction. Drought can be classified as meteorological, agricultural, or hydrological, depending on the component of the hydrological cycle affected. Traditional drought monitoring relies on sparse ground-based station data, which often has limited coverage and spatial resolution. High-resolution gridded climate datasets, such as TerraClimate, provide long-term monthly data on precipitation, temperature, evapotranspiration, and other hydrological variables, overcoming the limitations of sparse station networks. The Standardized Precipitation Evapotranspiration Index (SPEI), a widely used meteorological drought index, integrates precipitation and potential evapotranspiration to quantify drought intensity and duration more realistically, particularly under changing climatic conditions. Event-based approaches, such as the Runs Theory, enable the identification and characterization of drought episodes, including their duration, intensity, magnitude (severity), and interevent intervals. This study applies SPEI and the runs theory to high-resolution TerraClimate data (1985–2024) to assess drought characteristics across Iran. At the national scale, this framework enables detailed spatiotemporal analysis of short-, medium-, and long-term droughts, providing valuable information for water management, agricultural planning, and climate adaptation strategies. Materials and Methods TerraClimate gridded data (1985–2024), comprising monthly precipitation and potential evapotranspiration at a spatial resolution of 1/24° (approximately 4 km), were employed. The SPEI at pixel level was computed at 3-, 9-, and 12-month timescales to evaluate short-, medium-, and long-term drought events. Calculation involved: (1) derivation of the monthly climatic water balance (precipitation minus potential evapotranspiration); and (2) standardisation using a three-parameter log-logistic probability distribution and transferring the probability value to a normal distribution. Drought events were delineated using the run theory, with monthly percentile thresholds applied to account for seasonal variability and consecutive drought periods. Principal drought characteristics included duration, magnitude or severity, intensity, inter-event intervals, and event frequency over the period 1985–2024. Trend analysis used the modified Mann-Kendall test to identify significant spatiotemporal changes in SPEI, with serial correlation adjusted for. All analyses were performed in Python, using a raster-based dataset to ensure comprehensive spatial coverage and to detect localized patterns that are often undetected by station networks. This integrated approach—combining multi-timescale drought assessment, event-based characterisation, and trend detection—provides a thorough evaluation of drought risk and dynamics across Iran's arid, semi-arid, and relatively humid regions. Results and Discussion Drought conditions in Iran intensified in duration, severity, and spatial extent from 1985 to 2024, exhibiting considerable regional heterogeneity. Seasonal or short-term (3-month) droughts occurred frequently in northern Iran, whereas the central, eastern, and southeastern arid regions experienced longer and more intense droughts. At the 9-month timescale, droughts extended regionally, revealing persistent water deficits in the central and eastern areas. Annual or long-term (12-month) droughts affected nearly the entire country, sparing only narrow northern coastal zones, and underscoring widespread hydrological stress. Analyses of cumulative severity and intensity indicated disproportionate impacts in central and southeastern Iran, aligning with prior reports of elevated drought risk in these zones. Event frequency revealed that arid regions experienced fewer but more severe and persistent droughts, suggesting delayed recovery and accumulated hydrological deficits. The modified Mann-Kendall test detected significant negative trends in SPEI across more than 95% of the country at the 3-month timescale, over 99% at the 9-month timescale, and nationwide at the 12-month scale. These trends reflect a progression from localized seasonal droughts to pervasive national-scale phenomena, extending even to historically wetter northern areas. High-resolution gridded datasets demonstrated clear advantages over traditional station-based methods in resolving fine-scale and regional drought patterns. The combination of SPEI and run theory provides a robust framework for characterising drought properties, temporal evolution, and spatial variability, offering essential insights for water resource management and climate adaptation. Conclusion This analysis provides a comprehensive evaluation of drought characteristics and trends in Iran from 1985 to 2024, based on high-resolution TerraClimate data, SPEI, and run theory. Results show increased drought duration, intensity, and spatial extent, particularly in central, eastern, and southeastern regions, with a shift from seasonal events to persistent nationwide hydrological stress. Run theory enabled precise quantification of duration, severity, intensity, and inter-event intervals, highlighting limitations of station-based monitoring in resolving fine-scale dynamics. Nationwide significant negative SPEI trends underscore escalating hydrological drought and the need for multi-timescale, data-informed management approaches. The framework serves as an operational tool for early warning, climate adaptation, agricultural planning, and water allocation, with potential application to other arid and semi-arid regions worldwide. Integration of high-resolution gridded data, multi-timescale indices, and event-based analysis enhances resilience to climate variability and supports evidence-based policy for sustainable water and agricultural management. This transferable methodology facilitates broader national and regional drought risk assessment. https://mmws.uma.ac.ir/article_4447.html Extended Abstract Introduction The efficient management of vital soil and water resources requires a deep and precise understanding of the complex mechanisms of sedimentation and runoff formation in various ecosystems. This understanding must encompass the variable conditions of topography, land slope, and surface cover, as effective erosion control, especially on steep slopes prone to degradation, is considered the cornerstone of sustainable development in environmental and agricultural sectors. The intensity of soil erosion is a function of the complex interaction of numerous factors such as regional climate, inherent soil characteristics, topographic features, land use type; These factors collectively determine the final fate of eroded sediments, which may lead to their drainage from the system or storage in lower points of the watershed. Meanwhile, surface runoff acts as the primary driver for soil particle detachment. Key hydrological processes, including runoff generation, water infiltration, and ultimately sediment transport, are strongly influenced by the physical characteristics of the soil surface. Specifically, surface roughness, or microtopography—which involves small elevation changes (on the scale of 2 to 25 cm)—plays a pivotal role. This roughness, influenced by agricultural activities and vegetation type, directly affects the intensity of erosion process and the overall sediment transport rate by creating resistance to or guiding the flow. Materials and Methods This study investigated the effect of surface roughness on sediment heterogeneity using an artificial rainfall simulator. This system, operating at a height of 2.5 meters with a droplet spray mechanism, was installed over a plot measuring approximately 0.9 m×0.5 m to replicate natural rainfall conditions at a small scale. The experimental variables included several key components for system operation and control: an electric motor to supply the necessary power, a computerized control unit for precise nozzle management, a water reservoir, a pump, and a pressure gauge for regulating water flow and pressure. Rainfall intensities (45,60, and 70 mm/h and slopes10%, 20%, and 30%), selected based on the erosivity limits of the study area. Experiments were conducted on both bare and vegetated soil conditions. Each treatment was replicated three times, with each test run lasted for 60 minutes and was divided into six 10‑minute intervals. to allow for the collection and measurement of runoff and sediment yield. The influence of surface roughness on sediment heterogeneity was assessed using indices for intra-cluster and inter-cluster heterogeneity. Total heterogeneity was defined as the algebraic sum of these two components: intra-cluster heterogeneity indicating internal variation within blocks of a cluster, and inter-cluster heterogeneity representing the differences between neighboring clusters (based on rainfall intensity). Furthermore, a two-way Analysis of Variance (ANOVA) was employed to evaluate the main and interactive effects of rainfall intensity and slope on the resulting sediment yield. Results and Discussion The highest sedimentation without roughness at an intensity of 45 mm/h was related to a 30% slope, which increased with increasing slope due to increased shear energy and runoff. At an intensity of 60 mm/h, the highest sedimentation without roughness was related to a 20% slope, which indicates the complex effect of slope angle on surface layer protection and the strong role of runoff volume in this rainfall intensity. At an intensity of 70 mm/h, the highest sedimentation without roughness was observed at a 20% slope and the lowest at a 30% slope. In all intensities, the presence of surface roughness generally affected the sedimentation rate. The results also showed that surface roughness is an effective and acceptable factor for adjusting the volume of surface runoff and weakens the effect of runoff washing on different slopes. In general, surface roughness plays an effective role in reducing sedimentation and increases water and soil protection. Vegetation acts as a barrier to runoff, increases infiltration time, and also reduces the kinetic energy of raindrops before they directly hit the soil surface. According to two-way analysis of variance, rainfall intensity and slope are factors affecting sediment production. Increasing flow intensity and kinetic energy of rain make soil particles more likely to be transported. The results also showed that rainfall intensity of 60 mm/h and steep slopes create the greatest heterogeneity (variability) in sedimentation, which is due to the complex interaction of dynamic and physical environmental factors. Conclusion This study investigated the effect of surface roughness heterogeneity on sediment yield under different slope gradients and rainfall intensities using a rainfall simulator. The results indicated that at a rainfall intensity of 45 mm h⁻¹, surface roughness significantly reduced sediment yield across all slopes. On smooth surfaces, the highest sediment yield occurred at a 30% slope, while on rough surfaces it appeared at 20%. Roughness reduced flow velocity and enhanced infiltration, thereby limiting sediment transport and controlling erosion. At 60 mm h⁻¹, the maximum sediment yield was recorded on a 20% slope (smooth surface) and the minimum on a 10% slope, which may be attributed to thinner surface soil layers and changes in infiltration capacity. Under a rainfall intensity of 70 mm h⁻¹, the lowest sediment yield occurred on the steepest slope (30%), likely due to increased infiltration resulting from exposure of subsurface pores after the surface layer was eroded by raindrop impact. Two-way ANOVA confirmed that both rainfall intensity and slope gradient had significant effects on sediment yield, with the highest variability observed at 60 mm h⁻¹ and on steep slopes. Overall, findings highlight the critical role of surface roughness in reducing runoff and mitigating soil erosion. Despite experimental limitations such as calibration precision of the rainfall simulator, wind effects, and difficulties in instrument setup on different slopes, the study provides valuable insight into the interactions among rainfall intensity, slope gradient, surface roughness, and sediment generation processes. https://mmws.uma.ac.ir/article_4457.html Accurate estimation of reference evapotranspiration (ETo) is indispensable for precision irrigation and sustainable water resource management, yet the lack of physical interpretability in advanced machine learning models limits their operational adoption. This study proposes a systematic framework integrating the state-of-the-art categorical boosting (CatBoost) algorithm, Bayesian hyperparameter optimization, and SHapley Additive exPlanations (SHAP) to predict daily ETo across three contrasting climatic classifications in Iran: arid, semi-arid, and humid. By benchmarking CatBoost against extreme gradient boosting (XGBoost) and Random Forest under various sensor-availability scenarios, we demonstrated the superior robustness and generalization capability of the gradient boosting framework (CatBoost achieved R2 > 0.99 and RMSE ranging from 0.06 to 0.13 mm/day across all climates), particularly in capturing peak evaporative demands. Beyond mere prediction, the integration of explainable AI revealed a distinct climatic divergence in hydrological drivers; while aerodynamic forces, specifically wind speed, act as the primary accelerator of ETo in arid environments, the process is predominantly energy-limited and driven by temperature and solar radiation in humid regions. Furthermore, the study identified critical non-linear environmental thresholds that trigger rapid escalations in water demand, a dynamic often missed by linear empirical equations. Uncertainty analysis using Quantile Regression further confirmed the model's reliability in handling stochastic climatic extremes (achieving a Prediction Interval Coverage Probability of 88.1-91.4% and narrow interval widths). Practically, our findings offer a cost-effective roadmap for agricultural monitoring, suggesting that while low-cost, temperature-based sensors suffice for humid and semi-arid regions, the inclusion of aerodynamic sensors is non-negotiable for accurate irrigation scheduling in arid zones. This research contributes to bridging the gap between predictive accuracy and physical interpretability, offering a methodological blueprint for optimizing hydro-meteorological networks in data-scarce regions. https://mmws.uma.ac.ir/article_4458.html The serious health risks associated with brominated disinfection by-products necessitate robust control strategies for bromide precursors in drinking water. Studies have shown that conventional coagulation process using commonly applied coagulants are often insufficient for removing monovalent anions such as bromide. This limitation is particularly pronounced under alkaline conditions. Accordingly, to overcome this issue, the present study investigates the application of polymeric coagulants as an alternative strategy. For a more focused evaluation, PAC was selected as the inorganic polymeric coagulant and PM-667 as the organic polymeric coagulant. To comprehensively assess process performance, key operational parameters including pH, coagulant dosage, and initial bromide concentration were examined. Response Surface Methodology (RSM), implemented via Design-Expert software, was employed to model the complex interactions between these parameters and to determine the optimal operational conditions. Optimization analysis revealed distinct coagulation behaviors: PM-667 achieved a maximum bromide removal efficiency of 88.90% under near-neutral conditions (pH 7.3), whereas PAC reached its peak efficiency of 82.72% under mildly acidic conditions (pH 6.5). Importantly, under alkaline conditions (pH 8.5) characteristic of the study area (Ardabil drinking water), PAC demonstrated superior resilience, maintaining a removal efficiency of 55.86%, compared to 43.09% for PM-667 and 36.57% for ferric chloride (FeCl₃). These findings provide a data-driven framework for coagulant selection. They also offer practical guidance for water treatment plant operators to adjust coagulant type and dosage according to raw water pH, thereby enhancing treatment efficiency and reducing operational costs. https://mmws.uma.ac.ir/article_4461.html Extended Abstract Introduction Dust storms, which are particularly prevalent in arid and semi-arid regions such as Sistan and Baluchestan Province in Iran, pose significant environmental and health challenges. These storms are influenced by climatic factors and large-scale atmospheric patterns known as teleconnections, which modulate dust activity by affecting wind patterns, precipitation, and temperature. This study investigates the relationship between teleconnection indices and Aerosol Optical Depth (AOD) at two stations, Iranshahr and Zabol, aiming to improve the understanding and prediction of dust storms in the region. By leveraging machine learning models, the research seeks to identify key climatic drivers and develop accurate predictive tools for dust storm management. Materials and Methods The study utilized meteorological and climatic data from local weather stations, satellite sources (e.g., MODIS), and global teleconnection indices obtained from NOAA’s Physical Sciences Laboratory. Data preprocessing involved normalization and standardization to enhance model performance. Relationships between teleconnection indices and AOD were examined using Pearson correlation analysis. Feature selection was performed with the Boruta method, followed by the application of five machine learning algorithms Bagged CART, LightGBM, Gradient Boosting, Random Forest, and XGBoost for AOD prediction. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Furthermore, Shapley values, Sobol sensitivity analysis, and Partial Dependence Plots (PDPs) were employed to assess variable importance and interpret model behavior. Results and Discussion Correlation analysis revealed distinct patterns between teleconnection indices and Aerosol Optical Depth (AOD) at the two study stations. At Iranshahr, a strong negative correlation (-0.437) was observed with the Atlantic Meridional Mode (AMM), while the North Atlantic Oscillation (NAO) showed a positive correlation (0.236). In contrast, the most influential indices at Zabol were the Trans-Niño Index (TNI) and the Western Hemisphere Warm Pool (WHWP).Feature selection identified AMM, WHWP, and the Tropical Northern Atlantic index (TNA) as critical drivers for Iranshahr, whereas TNI and WHWP emerged as dominant predictors for Zabol. The applied machine learning models demonstrated strong predictive performance for AOD, with XGBoost and Gradient Boosting achieving the highest accuracy (R²=1 for Iranshahr and R²=0.99  for Zabol). Sensitivity analyses confirmed nonlinear and threshold-dependent relationships between teleconnection indices and AOD. Both Shapley and Sobol analyses highlighted AMM as the dominant factor, particularly at short-term lags, while Partial Dependence Plots (PDPs) further corroborated the threshold-dependent and nonlinear nature of these interactions. Conclusion The analysis of results from the Iranshahr and Zabol stations indicates that teleconnection indices significantly influence Aerosol Optical Depth (AOD) variations in these regions. This influence stems from the indices' impact on atmospheric circulation patterns, dust transport pathways, and regional moisture conditions. While similar general patterns have been observed elsewhere, the intensity and direction of these relationships vary due to the unique geographical characteristics of each location. Pacific Ocean indices dominate AOD variations at Zabol, with increasing influence over longer lags, whereas Atlantic indices are the primary drivers at Iranshahr, due to distinct local wind and geographical conditions. From a modeling perspective, boosting-based algorithms (e.g., XGBoost, Gradient Boosting) outperformed bagging models, demonstrating higher efficiency in capturing the nonlinear relationships between climatic indices and AOD. This study advances the understanding of AOD control mechanisms by identifying key teleconnection drivers and developing accurate predictive models. It also accounts for spatial variations in influential factors, which can support the design of region-specific early warning systems for dust storms. The identification of threshold-dependent relationships and critical behavioral thresholds in the indices can significantly improve the accuracy of both short-term and long-term AOD predictions. Furthermore, these results provide a robust scientific foundation for adaptive management planning in sectors such as water resources, agriculture, public health, and transportation. By leveraging this enhanced understanding of regional climatic mechanisms, policymakers and planners can develop more targeted and effective strategies to mitigate the impacts of dust storms. https://mmws.uma.ac.ir/article_4462.html Extended Abstract Introduction Culverts are among the most common and important transitional structures used to convey water, materials, or buried facilities beneath the ground. Therefore, the structures in question should be designed and constructed to maintain their safety, durability, and efficiency in a variety of environmental conditions. Several factors, including the flow pattern at the structure site, maximum instantaneous flood discharge, backflow due to blockage, morphological stability of the river, and erosion and scour effects, influence the hydraulic performance of crossing structures. Therefore, considering various influencing factors in the design and location of these structures to improve their performance is essential. On the other hand, hydrologic models are effective tools for simulating surface and subsurface hydrologic processes in watersheds and are widely used to enhance water resource management. Prediction of flood events and simulation of hydrological processes in watersheds are two fundamental applications of rainfall-runoff models that play a crucial role in water resource planning and management. Accordingly, this research was conducted using the HEC-HMS model to examine the impact of and performance of road-crossing water on flood crises in several selected watershed in the Yazd province.   Materials and Methods The present study was conducted due to the presence of multiple intersecting structures and the history of damage during the floods of 2022 in six selected watersheds in the counties of Taft, Ashkazar, and Mehriz, including the Khamsian, Darbe Raz, Dashtak, Roobaz, Ghavam-Abad, and Konj-Kuh watersheds in Yazd province. The aim was to gather and obtain necessary information about corresponding rainfall-runoff events from relevant sources and extract precise details of intersecting structures such as culverts and small bridges to gain a comprehensive view of the physical, hydraulic, and structural conditions. This serves as the basis for evaluating capacity, analyzing hydraulic performance during floods, and assessing the efficiency of the studied route's drainage system. After identifying the number of culverts in the watersheds, the HEC-HMS software was used to determine the volume and amount of flood. To determine the compatibility of their flow capacity with the flood discharge using the HEC-HMS model, hydrological parameters were first extracted from the watershed and then, using numerical hydraulic models, the flow behavior at culvert sections was analyzed.   Results and Discussion The results of comparing the hydraulic capacities of selected culverts with the design flood discharge at various return periods revealed that the hydraulic behavior of structures in response to increased return periods is non-linear and highly sensitive; in most cases, as intensity and frequency of rainfall increase, ratio (Qc/Qd) decreases rapidly, leading the structure into the critical. The watersheds of Khamsian, Dashtak, and Roobaz have minimum relative capacities and enter an unstable state from 5 to 10-year periods. A Qc/Qd <0.5 in these areas severe hydraulic section deficiencies, significant energy at the culvert outlet, and the risk of overflow from the culvert. Such conditions are mainly observed in structures with reduced hydraulic performance due toation, increased roughness, and geometric shapes. In the Darbe Raz and Dashtk watersheds, the structure's condition is acceptable up to 10-25-year return periods, but beyond 25 years, there is a likelihood of flooding and upstream reversal. The rate of capacity reduction to Qd decreasing from 0.09 to below 0.5 the 25-50- range indicates that the flood is entering unstable state and the outlet is starting saturate. In the Ghavam-Abad watershed, the ratio (Qc/Qd) above 1 in all return periods, making it the only structure evaluated as from a hydraulic design perspective. Ac/Qd value of 4.1 indicates a significant excess capacity resulting the larger outlet dimensions and the suitable longitudinal slope of the inlet channel. However, for extreme events above 500 years, this ratio decreases to around 1, indicating that the flow has reached the threshold of the final capacity. Given the potential for severe rainfall events with return periods exceeding 500 years in 2022, it necessary to reassess the design range for this structure as well. Conversely, the Konj-Kuh culvert exhibits the poorest performance, entering a critical state even at the 2-year return period, meaning that normal annual rainfall could trigger overflows.   Conclusion In view of the flood rainfall in April 2022 in Yazd province, as one of the most intense recorded rainfall events in the contemporary statistical period; the analysis of ratios (Qc/Qd) showed that with an increase in the return period from 5 to 500 years, the average ratio of design capacity to flow rate from around 0.75 to less than 0.20 percent. Among the existing structures in the studied watersheds, only 1 structure was evaluated safe (Ghavam-Abad) and 8 structures are in critical conditions in one of the return periods less than an equal 25 years. This pattern that the initial design of culverts based on return periods lower than the of publication 415 (1 to 25 years). https://mmws.uma.ac.ir/article_4466.html Extended Abstract Introduction Complete and continuous climatic datasets are fundamental for reliable analyses in hydrology, climate change assessment, water resources management, and environmental modeling. However, observational climate records frequently suffer from missing values due to instrument malfunction, station relocation, data transmission errors, or long-term interruptions in measurements. If not appropriately addressed, missing data can introduce bias, reduce statistical power, and compromise the reliability of subsequent modeling and decision-making processes. This challenge is particularly critical in regions with complex climatic variability and environmental sensitivity, such as the Lake Urmia Basin in northwestern Iran. Traditional approaches for handling missing climatic data, including listwise deletion or simple statistical substitution (e.g., mean or median imputation), are computationally convenient but often distort the statistical structure of the data and fail to capture inter-variable dependencies. In response to these limitations, advanced multivariate and machine-learning-based imputation methods have gained increasing attention. Among them, Multiple Imputation by Chained Equations (MICE) has emerged as a robust framework that accounts for uncertainty and exploits relationships among multiple variables. Recent studies suggest that integrating MICE with machine learning algorithms can further enhance imputation accuracy, particularly for non-linear and highly interdependent climatic variables. Nevertheless, comprehensive evaluations comparing different MICE-based hybrid models across multiple climatic variables and stations remain limited. Therefore, this study aims to systematically assess and compare the performance of standard MICE and four hybrid approaches MICE-Linear Regression (MICE–LR), MICE- Decision Tree (MICE–DT), MICE-K-Nearest Neighbor (MICE–KNN), and MICE- Support Vector Machine (MICE–SVM), across a wide range of climatic variables and meteorological stations within the Lake Urmia Basin. Materials and Methods This study was conducted using daily climatic data from six synoptic meteorological stations located in the Lake Urmia Basin. The dataset includes a diverse set of climatic variables representing thermal conditions, atmospheric moisture, cloudiness, wind characteristics, radiation and energy balance, and sea-level pressure. To ensure consistency and robustness, all variables were preprocessed through quality control procedures, including outlier detection and temporal consistency checks. Missing data were reconstructed using five imputation approaches: standard MICE and four hybrid MICE-based models (MICE-LR, MICE-DT, MICE-KNN, and MICE-SVM). The imputation procedure was implemented iteratively within the chained equations framework to ensure convergence and stability of the reconstructed values. Model performance was evaluated using multiple complementary statistical metrics, including the coefficient of determination (R²), normalized root means square error (NRMSE), Kling–Gupta Efficiency (KGE), and percent bias (PBIAS). These metrics collectively assess accuracy, variability representation, correlation structure, and systematic bias. In addition to predictive performance, computational efficiency was assessed by measuring the average execution time of each model. The evaluation framework was designed to enable comparisons from three perspectives: climate-variable-based, model-based, and station-based analyses. Results and Discussion The comparative analysis revealed substantial differences in imputation performance among the evaluated models, depending on the type of climatic variable and station characteristics. Overall, hybrid MICE-based models demonstrated superior performance compared to the standard MICE approach, particularly for temperature-related variables and atmospheric moisture parameters. Among the hybrid models, MICE–DT achieved comparatively higher KGE values for several variables, highlighting its ability to model non-linear interactions. Nevertheless, both MICE–DT and MICE–LR provided a more balanced trade-off between reconstruction accuracy and computational efficiency. In contrast, MICE–KNN and MICE–SVM exhibited variable performance, with notable sensitivity to station-specific conditions and variable type. While these models performed reasonably well for certain variables, their performance deteriorated for others, especially in cases involving higher variability or weaker spatial coherence. Standard MICE and MICE–LR showed comparable results, suggesting that linear assumptions may be insufficient for fully representing the dynamics of complex climatic systems. The station-based analysis highlighted spatial heterogeneity in model performance, emphasizing the influence of local climatic and topographic conditions. Furthermore, the computational analysis indicated that while hybrid models generally required longer execution times than standard MICE, MICE–DT provided a favorable balance between accuracy and computational efficiency. These findings underscore the importance of selecting imputation methods based on both data characteristics and practical constraints. Conclusion This study provides a comprehensive evaluation of standard and hybrid MICE-based imputation methods for reconstructing missing climatic data in a multi-variable and multi-station framework. The results demonstrate that incorporating machine learning algorithms within the MICE framework substantially improves reconstruction accuracy, particularly for variables characterized by non-linear behavior. Among the evaluated models, MICE–DT emerged as the most robust and efficient approach, offering consistently high performance across different climatic variables and stations. Despite these strengths, certain limitations were identified. The performance of some hybrid models, particularly MICE–KNN and MICE–SVM, showed sensitivity to station-specific conditions and increased computational demand, which may limit their applicability in large-scale studies. These findings suggest that no single imputation method is universally optimal, and model selection should be tailored to the characteristics of the dataset and research objectives. From a practical perspective, the proposed framework provides valuable guidance for researchers and practitioners seeking reliable methods for handling missing climatic data. The results have direct implications for hydrological modeling, climate trend analysis, and environmental impact assessments in data-scarce regions. Future research should explore the integration of deep learning approaches within the MICE framework and assess model performance under varying missing-data scenarios and spatial scales. https://mmws.uma.ac.ir/article_4495.html This study proposes a reproducible and GIS-based methodology for digital soil texture mapping by integrating geostatistical interpolation with deterministic decision tree classifiers (DTCs) derived from the United States Department of Agriculture (USDA) soil texture classification system. A total of 68 topsoil samples (0–20 cm) were collected across the irrigated area of northern Biskra province (southeastern Algeria) and analyzed for sand, silt, and clay contents. Among the most commonly applied interpolation techniques, ordinary kriging (OK), simple kriging (SK), and inverse distance weighting (IDW) were tested to generate continuous spatial distribution maps of soil particle fractions. Since the objective of this research was methodological demonstration rather than comprehensive benchmarking of interpolation algorithms, the method showing slightly better cross-validation (LOOCV) performance was selected. OK produced marginally lower RMSE values (15.93% for sand and 13.11% for silt) and satisfactory coefficients of determination (R²=0.758 for sand and 0.687 for silt) and was therefore adopted. To preserve the compositional constraint (sand + silt + clay=100%), clay content was derived from interpolated sand and silt maps. Four deterministic DTCs were implemented within the GIS environment to convert particle fraction rasters into continuous USDA texture classes. The final texture map demonstrated an almost perfect agreement with observed classifications (Kappa coefficient=0.898). The proposed framework emphasizes methodological simplicity, transparency, and applicability under moderate sampling density without reliance on auxiliary environmental covariates or complex machine learning models. Although interpolation uncertainty may influence classification near texture boundaries, the approach provides a practical and scientifically robust solution for soil texture mapping in data-limited regions.