https://mmws.uma.ac.ir/ Water and Soil Management and Modelling en daily 1 Fri, 22 May 2026 00:00:00 +0330 Fri, 22 May 2026 00:00:00 +0330 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_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_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_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_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_4221.html Extended AbstractThe 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_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_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_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_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_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_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_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_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. https://mmws.uma.ac.ir/article_4546.html Effective water resource management and canal performance analysis in semi-arid regions are fundamentally reliant on precise estimates of crop water demand, which are typically derived from accurate, up-to-date crop inventories. For command areas in semi-arid India, this vital information is a primary input for agro-hydrological models used to assess irrigation efficiency and plan water allocation. However, the inherent complexity of these landscapes characterized by small, fragmented landholdings introduces substantial uncertainty into remote sensing based crop classification, threatening the reliability of subsequent management decisions. This study systematically addresses this input uncertainty by performing a comprehensive, multi-factorial sensitivity analysis using multi-temporal Sentinel-1 (SAR) and Sentinel-2 (optical) data. We investigated the combined effects of four multi-sensor data fusion strategies, six Machine Learning (ML) classifiers, three feature selection techniques, and five training/testing data split ratios. The findings of the study provide crucial operational insights for modelers and managers. The synergistic fusion of Sentinel-1 and Sentinel-2 data was identified as the single most critical factor for achieving high accuracy. Furthermore, classification performance showed high sensitivity to training data volume, with an optimal threshold observed at an 80/20 train/test split. The Extreme Gradient Boosting (XGBoost) classifier, coupled with Backward Elimination feature selection, emerged as the superior strategy, achieving a maximum overall accuracy of 98.4%. By identifying this optimized workflow, this research provides a robust and scalable method for generating highly reliable spatial input data, thereby minimizing uncertainty in crop water requirement calculations and significantly enhancing the predictive capacity and practical utility of agro-hydrological models for sustainable water management. https://mmws.uma.ac.ir/article_4308.html Extended AbstractIntroduction 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_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_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_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_4496.html Identification and analysis of changes in hydrological indices of river flow play a key role in assessing the impacts of climate change and human interventions on river regimes. This study investigates the long-term trends of hydrological indices in the Samian watershed, located in Ardabil province. Daily river discharge data from 1973 to 2019 were analyzed using the Indicators of Hydrologic Alteration (IHA) approach. The results revealed that river flow values in the fall and winter months, particularly from October to February, exhibited a significant decreasing trend (P-value < 0.01). Additionally, minimum flows (1 to 90-day periods), base flow indices, and the timing of minimum flows showed significant decreasing trends, indicating reduced flow stability during dry periods and heightened water stress. The highest dispersion coefficients (over 4) were associated with low-flow events in May, June, and July, indicating high variability. In contrast, indices related to short-term and intense floods showed lower dispersion. The median of maximum flows (1 to 7 days) was estimated between 27 and 40 cms, while minimum flows were much lower (below 0.1) and exhibited considerable variability. EFC Low Flow indices in most months showed low medians and high dispersion, indicating the vulnerability of riverine ecosystems. Long-term flow trend analysis enables the identification of critical dry periods and hydrological regime changes, aiding decision-making in water resource allocation, and the protection of aquatic ecosystems. Furthermore, these results provide a scientific basis for developing climate adaptation strategies at the watershed scale and optimizing surface water resource management. Identification and analysis of changes in hydrological indices of river flow play a key role in assessing the impacts of climate change and human interventions on river regimes. This study investigates the long-term trends of hydrological indices in the Samian watershed, located in Ardabil province. Daily river discharge data from 1973 to 2019 were analyzed using the Indicators of Hydrologic Alteration (IHA) approach. The results revealed that river flow values in the fall and winter months, particularly from October to February, exhibited a significant decreasing trend (P-value < 0.01). Additionally, minimum flows (1 to 90-day periods), base flow indices, and the timing of minimum flows showed significant decreasing trends, indicating reduced flow stability during dry periods and heightened water stress. The highest dispersion coefficients (over 4) were associated with low-flow events in May, June, and July, indicating high variability. In contrast, indices related to short-term and intense floods showed lower dispersion. The median of maximum flows (1 to 7 days) was estimated between 27 and 40 cms, while minimum flows were much lower (below 0.1) and exhibited considerable variability. EFC Low Flow indices in most months showed low medians and high dispersion, indicating the vulnerability of riverine ecosystems. Long-term flow trend analysis enables the identification of critical dry periods and hydrological regime changes, aiding decision-making in water resource allocation, and the protection of aquatic ecosystems. Furthermore, these results provide a scientific basis for developing climate adaptation strategies at the watershed scale and optimizing surface water resource management. Identification and analysis of changes in hydrological indices of river flow play a key role in assessing the impacts of climate change and human interventions on river regimes. This study investigates the long-term trends of hydrological indices in the Samian watershed, located in Ardabil province. Daily river discharge data from 1973 to 2019 were analyzed using the Indicators of Hydrologic Alteration (IHA) approach. The results revealed that river flow values in the fall and winter months, particularly from October to February, exhibited a significant decreasing trend (P-value < 0.01). Additionally, minimum flows (1 to 90-day periods), base flow indices, and the timing of minimum flows showed significant decreasing trends, indicating reduced flow stability during dry periods and heightened water stress. The highest dispersion coefficients (over 4) were associated with low-flow events in May, June, and July, indicating high variability. In contrast, indices related to short-term and intense floods showed lower dispersion. The median of maximum flows (1 to 7 days) was estimated between 27 and 40 cms, while minimum flows were much lower (below 0.1) and exhibited considerable variability. EFC Low Flow indices in most months showed low medians and high dispersion, indicating the vulnerability of riverine ecosystems. Long-term flow trend analysis enables the identification of critical dry periods and hydrological regime changes, aiding decision-making in water resource allocation, and the protection of aquatic ecosystems. Furthermore, these results provide a scientific basis for developing climate adaptation strategies at the watershed scale and optimizing surface water resource management. Identification and analysis of changes in hydrological indices of river flow play a key role in assessing the impacts of climate change and human interventions on river regimes. This study investigates the long-term trends of hydrological indices in the Samian watershed, located in Ardabil province. Daily river discharge data from 1973 to 2019 were analyzed using the Indicators of Hydrologic Alteration (IHA) approach. The results revealed that river flow values in the fall and winter months, particularly from October to February, exhibited a significant decreasing trend (P-value < 0.01). Additionally, minimum flows (1 to 90-day periods), base flow indices, and the timing of minimum flows showed significant decreasing trends, indicating reduced flow stability during dry periods and heightened water stress. The highest dispersion coefficients (over 4) were associated with low-flow events in May, June, and July, indicating high variability. In contrast, indices related to short-term and intense floods showed lower dispersion. The median of maximum flows (1 to 7 days) was estimated between 27 and 40 cms, while minimum flows were much lower (below 0.1) and exhibited considerable variability. EFC Low Flow indices in most months showed low medians and high dispersion, indicating the vulnerability of riverine ecosystems. Long-term flow trend analysis enables the identification of critical dry periods and hydrological regime changes, aiding decision-making in water resource allocation, and the protection of aquatic ecosystems. Furthermore, these results provide a scientific basis for developing climate adaptation strategies at the watershed scale and optimizing surface water resource management. https://mmws.uma.ac.ir/article_4515.html Extended Abstract Introduction Heatwaves are among the most hazardous extreme climate phenomena, during which air temperatures persistently exceed reference climatic thresholds for several consecutive days. These events have significant impacts on agriculture, ecosystems, energy resources, and human health). The frequency and intensity of heatwaves have increased over recent decades across different regions of the world, with numerous events reported in Europe, North America, Asia, and Oceania. Consequently, understanding this phenomenon is essential for vulnerable regions. Numerous studies have investigated heatwaves both globally and in Iran. However, despite Iran's high vulnerability and the frequent occurrence of heatwaves, a comprehensive study that analyzes the seasonal intensity of extreme heatwaves using different indices is lacking. Therefore, the main objective of this study is to conduct a seasonal analysis of extreme heatwave intensity in Iran using four indices (HWMId, HEATcum, AVI, and AVA) and to compare their spatiotemporal patterns in order to identify the most suitable index for assessing heatwaves in arid and semi-arid regions of Iran. The results of this study can be applied in water and soil resource management and in planning for climate change adaptation. Materials and Methods This study used daily maximum temperature data from 112 synoptic stations across Iran for a 33-year period (1991–2023). Missing data were reconstructed following the World Meteorological Organization guidelines, and their quality was validated using the cross-validation method. A heatwave was defined based on the 90th percentile threshold of daily maximum temperatures within a 15-day moving window (7 days before, the day itself, and 7 days after), with a minimum duration of three consecutive days. Heatwave analysis was conducted separately for each season, and a 5-month window was considered for each season to account for cross-seasonal heatwaves. Four heatwave intensity indices were calculated: the daily Heat Wave Magnitude Index (HWMId), cumulative heat (HEATcum), average intensity (AVI), and average anomaly (AVA). HWMId was calculated by standardizing temperature anomalies using the 25th and 75th percentiles. HEATcum and AVA represent the sum and mean of temperature anomalies relative to the 90th percentile threshold, respectively, while AVI is based on the mean of absolute daily temperatures. Results and Discussion Seasonal Analysis of Maximum Temperature Distribution and Annual Average of Heatwaves The results showed that the highest maximum temperatures occur in summer across central, southern, and southeastern regions, while the lowest occur in winter across northwestern and northern regions. The highest annual average of heatwaves is observed in the northwest of the country. The highest and lowest heatwave values belong to summer and autumn, respectively. The spatial distribution pattern of maximum temperature is influenced by local factors (latitude and altitude) and external factors (general atmospheric circulation elements). Heatwave Intensity Indices The HWMId index showed its highest values in spring at Rasht, Abadan, and Baft stations (mainly in 2023). Autumn had the highest heatwave intensity (Hamedan, 2019), while in summer and winter, northern regions exhibited the highest intensity, which is attributed to the percentile-based nature of this index. The HEATcum index showed its highest values in spring in northern regions (Gorgan, Babolsar, Rasht stations) between 2007 and 2011, with winter having the highest cumulative heat; areas with maximum values of this index are concentrated in northeastern, northern, and northwestern regions. The AVI index showed its highest values in spring in southeastern, central, and southwestern regions, and its lowest at Abali station (2019); it expanded across the entire country in summer and showed a significant increase in recent years (especially 2023); its distribution follows a similar pattern in spring, autumn, and winter. The AVA index showed its highest values in spring along the northern strip of the country (Babolsar, Nowshahr, Ramsar, Bandar Anzali) and Bandar Mahshahr between 2007 and 2011, with winter showing the highest values nationwide; the maximum of this index occurs at higher latitudes. Comparison of Different Heatwave Intensity Indices Comparison of the four indices revealed that the AVI index, based on absolute temperature, exhibits a completely different pattern from the three anomaly-based indices (HWMId, HEATcum, and AVA) and is unsuitable for comparing regions with different climates. HWMId and HEATcum showed similar patterns, both identifying northwestern and western Iran as areas with the highest heatwave intensity. Regarding the year of occurrence, HWMId and HEATcum had similar temporal patterns, with recent years recorded as the period of most intense heatwaves. Overall, anomaly-based indices are more suitable for identifying and analyzing heatwaves in Iran, and among them, cumulative-based indices (HWMId and HEATcum) are more effective for detecting the most intense heatwaves due to their greater sensitivity to short-lived, severe anomalies. Conclusion The present study aimed to conduct a seasonal analysis of extreme heatwave intensity in Iran using data from 112 synoptic stations and four indices (HWMId, HEATcum, AVI, and AVA) over the period 1991–2023. The results showed that the highest maximum temperatures are observed in southeastern, southern, and southwestern regions, while the highest annual average of heatwaves occurs in the northwest of the country. The highest and lowest heatwave values belong to summer and autumn, respectively. Due to its percentile-based nature, the HWMId index revealed a different pattern of heatwave distribution, with higher intensity in northern regions. The HEATcum index showed its highest values in northeastern, northern, and northwestern regions, particularly during winter and spring. The AVI index exhibited a similar pattern in spring, autumn, and winter and showed an increasing trend in recent years. The AVA index recorded its maximum at higher latitudes. Comparison of the four indices indicated that anomaly-based indices (HWMId, HEATcum, and AVA) are more suitable for identifying heatwaves in Iran than the absolute-temperature-based index (AVI). Among them, cumulative-based indices (HWMId and HEATcum) are more effective due to their greater sensitivity to short-lived, severe anomalies. It is recommended that future research on heatwaves in Iran examine quantitative and synoptic dimensions as well. https://mmws.uma.ac.ir/article_4529.html Extended Abstract Introduction Flooding is a major natural disaster, according to the UN, endangering lives, property, and societies more than any other. Mapping floodplains and modeling floods in mountain basins is essential for development projects, helping identify critical areas and control damage. A key 21st-century tool is satellite imagery, which provides valuable flood-related data. By processing these images, various information such as flooded areas, vegetation, lithology, slope, soil moisture, etc. can be calculated and estimated. In addition, machine learning algorithms have made it possible to estimate very complex relationships between various parameters and floods. However, these models require complex calibration and extensive data. Recently, many flood susceptibility models have been developed. Combining statistical and decision-making models with remote sensing and GIS has gained attention for improving predictive ability. Today, machine learning algorithms such as artificial neural networks, generalized linear algorithms, support vectors, and random forest models are used.Machine learning models are used in two aspects;one is to process and identify flooded areas, and the other is to zone and examine the importance of flood-intensifying parameters. Materials and Methods In recent decades, new methods have been used to identify the risk of flooding in basins and prepare maps of sensitivity to its occurrence, such as the use of multivariate statistical models, data mining, random forest methods, and machine learning methods. This study aimed to evaluate the performance of five machine learning models including random forest model, support vector model, generalized ensemble model, generalized linear model, classification and regression tree, and augmented tree regression (RF, SVM, BRT, CART, and GLM) in modeling flood probability in the northern mountainous basin of Khuzestan province. Also, to increase the stability and accuracy of the models, four ensemble methods were used, including simple mean, weighted mean, committee mean, and median. in the first step, thirteen different parameters were used as factors affecting the flood phenomenon, and using the collinearity test between the parameters, it was ensured that there was no strong relationship between each of them and other parameters. The factors studied are distance from the river, distance from the dam lake, curvature of the longitudinal profile of the waterway, curvature of the waterway plan, shape factor, river density, basin area, geology, vegetation, erosion factor, SPI index, TWI index, and curve number value. In this regard, the digital elevation model (DEM) of the region with an accuracy of 30 meters was extracted from the USGS website.   Results and Discussion The AUC values ​​for RF, BRT, SVM, CART and GLM models were estimated to be 0.932, 0.929, 0.885, 878 and 0.855, respectively. The results indicate that the random forest (RF) model has higher accuracy than other models in predicting flood risk in the study area. The results showed that all the models used showed acceptable performance; however, tree-based models had a significant advantage over linear and SVM models. In particular, the random forest (RF) model achieved the best overall performance in predicting flood occurrence in the region, with the highest AUC value of 0.932. The boosted tree regression (BRT) model was followed by the least accurate model with an AUC of 0.929. In contrast, the generalized linear model (GLM) had the lowest accuracy among the individual models with an AUC of 0.855. In addition, the results from ensemble methods also showed that the values of AUC, TPR, and FPR parameters of these four methods are in the ranges of 0.919 to 0.926, 0.826 to 0.857, and 0.072 to 0.079. Among the methods studied, the average and weighted average methods have higher accuracy than the other two methods. Conclusion These findings emphasize the need to pay special attention to the spatial and hydrological characteristics of the basin in flood risk management planning, and introduce the RF model and microaggregate approaches as effective strategies for preparing more accurate zoning maps. The use of four ensemble methods (Mean, WMean, Median, and Committee Averaging) resulted in more stable risk maps. Overall, the performance of these methods, effectively reduced the uncertainty resulting from the selection of a single model. The simple mean (Mean) and weighted mean (WMean) methods provided more favorable results than the other ensemble methods due to their high accuracy (AUC = 0.926) and rapid convergence of results. This indicates that combining the outputs, with or without appropriate weighting (WMean), resulted in a more robust and repeatable estimate. This is consistent with similar studies that confirm the effectiveness of averaging methods in improving the performance of classification models. https://mmws.uma.ac.ir/article_4585.html Extended Abstract Introduction Soil erosion is one of the most serious and pervasive environmental challenges worldwide, directly and significantly threatening the stability of natural ecosystems, the quality of drinking water and agricultural resources, and long-term food security. In many mountainous regions of Iran, the combination of climatic conditions, steep slopes, geological instability, and increasing anthropogenic pressures has exacerbated this process. The Chehelchay watershed, located in Golestan Province, is a prime example of a watershed susceptible to erosion. This basin, covering an area of 25683 hectares and receiving an average annual rainfall of 766 mm, has witnessed extensive land-use changes, with a significant portion of its natural forest cover being converted into agricultural lands. Despite numerous studies conducted in this region and similar areas in the past, limited efforts have been made to integrate empirical models (such as RUSLE) with process-based or distributed models (such as SEDD) for the simultaneous estimation of the spatial pattern of soil erosion and sediment transfer, using reliable spatial data. Therefore, this study aims (i) to accurately quantify the spatial distribution of soil erosion and (ii) to estimate sediment transfer rates, and to identify priority and critical erosion areas within the Chehelchay watershed, by integrating the Revised Universal Soil Loss Equation (RUSLE) model and the Sediment Delivery Distributed (SEDD) model. Few studies have simultaneously mapped soil erosion and sediment delivery using integrated empirical and distributed models in data‑limited mountainous watersheds. This integrated approach, which simultaneously accounts for processes of surface sediment generation and its transfer along the river network, provides a robust and comprehensive framework for supporting conservation and management planning in data-limited watersheds. Materials and Methods To estimate surface erosion and sediment yield in the Chehelchay watershed, the RUSLE and the SEDD model were employed. For estimating surface erosion, the RUSLE model was utilized, incorporating five factors: Rainfall erosivity (R), Soil erodibility (K), Slope-Length (LS), Land cover and management (C), and Support practices (P). Spatial input maps were generated for each RUSLE factor in ArcGIS. The R-factor map was extracted using long-term daily precipitation data and the modified Fournier index. Physicochemical properties of the soil formed the basis for preparing the K-factor map. The LS factor map was calculated by processing the Digital Elevation Model (DEM) in a GIS environment. The interpretation of Landsat 8 satellite imagery and vegetation cover maps enabled the preparation of the C-factor map. The P-factor was determined based on land use type and farming practices. After preparing the RUSLE parameter layers, the annual water erosion map was generated by multiplying these layers in ArcGIS software. Subsequently, the SEDD model was used to quantitatively estimate sediment yield and its transfer ratio to the watershed outlet. This model, by spatially calculating the Sediment Delivery Ratio (SDR), establishes a link between the erosion calculated by RUSLE and the sediment data measured at the Lezoreh hydrometric station. Suspended sediment data from the Lezoreh station were analyzed using the sediment rating curve method and an intermediate data approach. The accuracy of the curve calibration was confirmed with a coefficient of determination R² = 0.79. Finally, the sediment yield, SDR, and erosion maps derived from the models were integrated and spatially analyzed to identify priority areas with severe erosion and high sediment yield potential. Results and Discussion The RUSLE-based assessment indicated that the average annual soil erosion rate in the Chehelchay watershed is approximately 4 t ha⁻¹ yr⁻¹. However, its spatial distribution exhibited considerable heterogeneity and was found to be significantly controlled by topographic factors, particularly the LS factor (slope and slope length), and land use type. The highest erosion rates were observed in agricultural fields situated on steep slopes; this pattern is primarily attributed to minimal soil conservation measures and the direct exposure of soil to intense rainfall events. In contrast, forested areas displayed strong protective effects, substantially reducing surface erosion due to dense vegetation cover and soil structural stability. These findings underscore the critical importance of preserving and expanding forest cover in mountainous regions. Based on data collected from the Lezoreh sediment monitoring station, the average annual sediment yield was estimated at 1.78 t ha⁻¹ yr⁻¹. The overall watershed SDR was calculated to be approximately 44%. This relatively high SDR indicates the presence of efficient sediment transfer pathways, largely facilitated by steep slopes and discontinuous vegetative cover across the watershed. Integrated mapping of erosion potential and sediment delivery ratios revealed that areas with the highest erosion potential and sediment generation are primarily concentrated in the middle and upper sections of the watershed, where slope gradients exceed 30% and agricultural expansion has replaced natural forest cover. The spatial correspondence between high erosion potential and elevated SDR highlights the crucial importance of simultaneously considering both sediment generation (erosion) and sediment transport (SDR) processes when designing management interventions. Conclusion Ultimately, the outcomes of this research can serve as a practical guide for developing and implementing sustainable water and soil resource management plans in the Chehelchay watershed and in other regions with comparable ecological and data conditions. The overall study results emphasize the pivotal role of topographic factors and land use; specifically, terrain slope, land use type, and vegetation condition were identified as the most determinant factors of erosion intensity. Notably, the negative consequences of land use changes, particularly the degradation of forest cover and its conversion to agricultural or other uses on steep slopes, lead to a significant increase in soil erosion and, consequently, a higher volume of sediment transported downstream. This study affirms the critical importance of principled land use planning, taking into full account the inherent topographical constraints of mountainous regions. The effective implementation of conservation measures, including the preservation and restoration of natural vegetation and the application of sustainable management practices in agricultural lands, is deemed essential for controlling and reducing sediment transport in priority areas. Despite the satisfactory accuracy of the employed models, limitations such as sensitivity to the quality and precision of input data and the lack of comprehensive field data for complete validation still persist. These limitations create valuable opportunities for future research to significantly improve prediction accuracy by integrating extensive field sampling, high-resolution soil data, and the application of more advanced process-based modeling approaches. https://mmws.uma.ac.ir/article_4606.html This study investigates the impacts of climate variability and urbanization on vegetation dynamics in Diyala City, Iraq, using remote sensing data and deep learning techniques. Multi-source satellite and climate datasets covering the period from 2014 to 2024 were processed and analyzed using Google Earth Engine (GEE). Sentinel-2, Landsat-8/9, MODIS, and CHIRPS datasets were utilized to derive Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). In addition, Dynamic World land use/land cover (LULC) data were employed to quantify temporal changes in vegetation cover, croplands, and built-up areas. A Long Short-Term Memory (LSTM) deep learning model was developed to forecast NDVI variations based on historical environmental data. The results revealed a substantial expansion of built-up areas by approximately 61.3% between 2017 and 2024, accompanied by a 15.4% increasing in crop-cover areas and a 72.7% decrease in high-vegetation-density classes. Furthermore, low and very low NDVI classes are significantly higher in central urbanised regions, while NDBI and LST analyses indicated increasing urban density and surface temperature patterns. The LSTM model demonstrated strong predictive capability, achieving validation RMSE and MAE values of 0.047 and 0.034, respectively, indicating reliable performance in forecasting vegetation dynamics. The findings demonstrate the effectiveness of integrating remote sensing technologies, Google Earth Engine, and deep learning models for environmental monitoring and vegetation prediction. This study provides valuable insights for sustainable urban planning, environmental management, and long-term land-use monitoring in semi-arid regions. https://mmws.uma.ac.ir/article_4613.html Extended Abstract Introduction Floods are among the most complex hydrological phenomena which are known to be the main factor of natural disasters and costs in many river basins, especially in regions with arid and semi-arid climate. Due to the nonlinear nature, high intensities and low durations of floods, there are large uncertainties in their predicting, forecasting, modeling, and managing. Therefore it is necessary to have measured characteristics of flood flows occurred in the river to calibrate and validate mathematical and hydraulic models. However, measuring river flow during floods and inundation of floodplains is very difficult, costly, and dangerous. For this reason, methods based on extrapolation of stage-discharge rating curve are usually used to estimate river flood discharge. Different methods can be used for this purpose, most of which require a lot of field data. For example, in most of these methods, the Manning roughness coefficients of the main channel and floodplains must be known, which has many limitations in this regard. In this study, a simple but practical method has been used to estimate flood discharge of compound river channels, which, while having appropriate accuracy, requires minimal gauging data. It only requires that the river channel geometry, flood stage, and the river stage-discharge rating curve be known.   Materials and Methods To estimate flood discharges from stage–discharge curve, three methods were evaluated and compared. The first is the rating curve extension, which fits Q=a(h−h0​)b to non‑flood measurements data and then extrapolated to higher water levels. This method, introduces considerable error in rivers with wide and rough floodplains. The second method is the conveyance-slope approach, which is specifically developed for compound sections. The cross‑section is divided into the main channel and floodplains and then the conveyance factor Ki=AiRi2/3/ni is computed for each water level. The river energy slope Sf=(Q/K)2 is then derived and extrapolated to flood stages. A major limitation of this method is the need for reliable Manning’s roughness coefficients for both the main channel and the floodplains. To obtain these coefficients, the quasi‑2D model of Shiono and Knight (1991) was employed. The third method is the alpha (α) method, which combines energy slope and Manning’s n into one parameter as α=Q/(AR2/3). The Manning formula is usually face with many uncertainties in riverbed Manning roughness coefficient and energy slope, especially for compound channels with natural features or dense vegetation. This study discusses flood discharge estimation using a calibrated α parameter for alluvial rivers in Iran (Golestan province) and England. Parameter α accounts for the simultaneous effects of energy slope and Manning’s roughness. The method computes α from measured discharges and geometric properties (area A, hydraulic radius R), then fits a regression curve to α-R. From this curve, α is predicted for any flood stage, and flood discharge is subsequently calculated.   Results and Discussion To evaluate three flood discharge extrapolation methods, some statistical metrics (RMSE, MAE, and R²) were used. For the new alpha method, the mean absolute errors were 7.4%, 12.4%, and 5.2% at Arazkooseh, Aghghala, and River Severn, respectively, which are quite acceptable for flood control engineering applications. The conveyance slope method yields MAE values of 20.7%, 42.1%, and 20.8%, while the stage-discharge curve extension method gives 26.3%, 6.0%, and 10.3% for these three river stations. The alpha coefficient method performs more accurately than the other two methods. Its superiority is confirmed by higher R² and lower RMSE error measure, demonstrating better reproduction of actual stage-discharge behavior under floods. The high errors of the conveyance slope method reflect its inability to capture nonlinearity during high flows and floodplain inundation. The rating curve extension method works acceptably only at some stations (e.g., Aghghala). From a hydraulic perspective view, the alpha parameter simultaneously considers variations in discharge (Q), cross-sectional area (A), and hydraulic radius (R), providing a more consistent physical description. Its superiority comes from incorporating concurrent changes in A, R and the nonlinear Q–h curve. When the floodplains become inundated, abrupt changes in velocity distribution and effective roughness occur. Methods relying on slope of rating curve or energy slope extension cannot fully represent this nonlinearity. The alpha parameter accounts for the rate of change of Q with respect to hydraulic cross-section characteristics, offering greater flexibility in modeling flow regime transitions. Therefore, the alpha-based method is proposed as a reliable framework for extrapolating stage-discharge curves, particularly for flood-prone rivers.   Conclusion (1) In this study, a simple method proposed for flood discharge prediction in alluvial rivers with floodplains conveying flood flows which overtax the main river channel. The main reason behind the development of this method is its requirement for minimum river data recorded at the time of river flood flows, including river bathymetry (even before flood passage), flood stage and the common stage-discharge rating curve (and independent to riverbed Manning roughness coefficients of main channel and floodplains). (2) The obtained results of flood flow discharges based on the new developed method are quite satisfactory for all three selected gauge stations in comparison with the classic available methods for flood prediction.(3) The proposed method may faces with physical limitations or problem in rivers with prismatic or manmade main channel and in these cases, at least one data or record of flood flow (stage and discharge) should be given.(4) The implication of this new method is recommended for flood flow prediction in meandering compound river channels and also in multi-stage compound channels which have been recently become a topic of interest to researchers.   https://mmws.uma.ac.ir/article_4664.html This review synthesizes the strategic implications of physical, chemical, and biological evaporation suppression methods, integrating quantitative and qualitative impact assessments with a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. To bridge the gap between technical efficacy and practical implementation, this study uniquely employs a detailed quantitative SWOT analysis for each method, offering novel insights into their strategic positioning. Physical covers, despite their inherent internal weaknesses such as high initial and maintenance costs, installation complexities, and vulnerability to environmental factors, also face significant external threats. Their placement in the WT (Weaknesses-Threats) quadrant mandates a defensive strategy focused on mitigating these challenges. chemical covers, while offering lower initial costs and ease of application, are also positioned in the ST quadrant due to threats such as environmental health concerns, the need for repeated applications, and reduced effectiveness in windy conditions; thus, competitive strategies involving biodegradable compounds and improved formulations are essential. In contrast, biological covers, characterized by environmental compatibility and natural regeneration, fall into the SO (Strengths-Opportunities) quadrant. This unique positioning necessitates an aggressive strategy that leverages growing public environmental awareness and regulatory support by investing investments in optimizing suitable plant/microbial species and developing intelligent monitoring systems, while managing inherent challenges like growth uncertainty and continuous oversight. This integrated analysis underscores the imperative for developing balanced solutions that judiciously balance efficiency, water quality, economic feasibility, and environmental sustainability. https://mmws.uma.ac.ir/article_4665.html Introduction Salinity accumulation and the consequent deterioration of water quality in reservoir systems represent a critical challenge in water resources management, particularly in arid and semi-arid regions where evaporation rates are high and freshwater availability is limited. In such environments, even moderate increases in salinity can significantly reduce the usability of stored water for agricultural, domestic, and industrial purposes. The problem becomes more severe in reservoirs located downstream of evaporite geological formations, where continuous dissolution of salt-bearing strata introduces persistent and often difficult-to-control saline inflows. These processes not only degrade water quality but also complicate reservoir operation, ecological stability, and downstream water allocation. The Gotvand Dam reservoir in southwestern Iran is a well-documented and particularly severe example of this phenomenon. Since its design and construction phases, the reservoir has been recognized as vulnerable to salinity intrusion due to its proximity to the Gachsaran Formation, specifically the Anbal salt section. Following impoundment, rapid salinization of reservoir water confirmed concerns raised in early feasibility studies, making it one of the most significant salinity-impacted reservoirs in the region. Previous studies have primarily focused on identifying salinity sources and simulating its distribution; however, there remains a lack of long-term, field-based evaluations of salinity control strategies. This study aims to address this gap by investigating the temporal evolution of salinity within the reservoir and evaluating the effectiveness of outlet structures, particularly the bottom outlet and GRP pipe, in controlling salinity over a 13-year operational period. By integrating extensive field measurements with mass balance and stratification analyses, this research aims to provide a comprehensive understanding of both natural processes and operational interventions influencing reservoir salinity dynamics. Materials and Methods The study was based on an extensive and continuous dataset collected over 13 years from August 2011 to October 2024, comprising more than 1,400 sampling days. The collected data included inflow and outflow discharge measurements, salinity concentrations of released water, vertical salinity profiles at multiple depths, reservoir water level fluctuations, and detailed operational records of outlet structures. Salinity observations were primarily conducted at the deepest point near the dam body, which was confirmed through preliminary analysis to be representative of the overall vertical salinity structure of the reservoir. This location provided a reliable proxy for assessing stratification dynamics and the effectiveness of withdrawal operations across different depth layers. The reservoir system receives its primary inflow from regulated releases of the upstream Masjed Soleyman Dam, supplemented by contributions from intermediate sub-basin areas. Outflows from the reservoir occur through four main structures: the hydropower intake, the spillway, a bottom outlet located at an elevation of 123 meters, and a GRP pipe installed at an elevation of 90 meters. To analyze salinity behavior, the study employed a combination of time-series analysis, vertical stratification assessment, and salt mass balance modeling. Stratification assessments were used to evaluate vertical salinity gradients and the stability of density-driven layering under different hydrological and thermal conditions. The mass balance approach quantified salt inputs, outputs, and storage variations within the reservoir system, enabling a system-scale understanding of salinity accumulation and removal processes. Furthermore, the performance of outlet structures was evaluated under different operational scenarios by correlating changes in salinity profiles with discharge regimes. Results and Discussion The results indicated that reservoir salinity dynamics were governed by a combination of hydrological, thermal, and operational factors. Water level fluctuations, inflow characteristics, and seasonal temperature variations significantly influenced salinity stratification. During warm periods, inverse stratification was observed in upper layers due to evaporation and inflow of warmer saline layers, whereas colder seasons exhibited more uniform salinity profiles near the surface. Flood events played a dual role by both inducing vertical mixing and introducing additional salt loads, particularly from intermediate sub-basin interacting with saline formations. However, the intensity of salt dissolution from the Anbal formation appeared to decrease over time, suggesting a reduction in readily soluble salt sources. A key finding of this study was the differentiated performance of outlet structures in salinity management. The bottom outlet had proven highly effective in removing saline water from intermediate layers (approximately 120–160 m elevation), thereby reducing salinity gradients and preventing upward migration of saline layers toward the power intake level. Sustained operation of this outlet (even at moderate discharges of 5–10 m³/s) significantly stabilized salinity conditions in these layers. In contrast, the GRP pipe, designed to evacuate highly saline water from deeper layers, exhibited limited effectiveness due to its low discharge capacity. While it can locally reduce salinity near its intake elevation (90–100 m), its overall impact on reservoir-scale salinity control was negligible. Moreover, its operation contributed to increased salinity in downstream systems. The salt mass balance analysis further corroborated these findings. While the total salt load entering the reservoir from upstream flows was substantial, its concentration remained relatively low. Conversely, salt inflow from the Gachsaran Formation showed a decreasing trend over time, except during major flood events. The total salt storage in the reservoir increased sharply during the initial impoundment phase but stabilized in subsequent years, reflecting the effectiveness of controlled outlet operations, particularly after 2019. Conclusion This study provided robust, long-term empirical evidence on salinity dynamics and management in a reservoir affected by evaporite formations. The findings demonstrated that while natural factors such as hydrology and temperature influenced salinity distribution, operational strategies played a decisive role in its control. Among the evaluated measures, selective withdrawal through the bottom outlet emerged as the most effective strategy for managing salinity, particularly in intermediate layers. In contrast, the GRP pipe had limited practical utility despite its intended purpose. The results highlighted that optimizing the operational regime of the bottom outlet was essential for sustainable salinity management in the Gotvand Dam reservoir. Furthermore, the study underscored the importance of continuous monitoring and improved data availability, especially for intermediate inflows, to enhance the reliability of future assessments and support informed reservoir management decisions. https://mmws.uma.ac.ir/article_4677.html Introduction According to United Nations reports, Iran is among the ten countries experiencing the highest levels of water stress worldwide, with annual per capita renewable water resources falling below 1,000 cubic meters, thereby exceeding the critical threshold. Excessive extraction of water resources, declining precipitation caused by climate change, and rapid urban and agricultural population growth have intensified the water crisis across many regions of the country. Furthermore, climate change impacts—particularly the increasing intensity and frequency of droughts—have significantly increased the recurrence of critical conditions and complicated long-term water resource forecasting. Reduced access to water resources not only exacerbates poverty and livelihood vulnerability but also contributes to forced migration, especially from rural to urban areas, thereby deepening regional inequalities. Empirical evidence suggests that water scarcity and its unequal distribution disproportionately affect low-income populations, intensifying social and economic disparities at both local and global scales. In Ardabil, the water crisis is not limited to quantitative shortages; water quality has also become a serious concern. In addition to natural processes, human activities—particularly those associated with the agricultural sector—have contributed to the contamination of water resources with heavy metals and other pollutants. Materials and Methods This study is applied in terms of purpose and descriptive–analytical in nature, employing a futures studies approach. The primary objective is to develop plausible scenarios for the future of the water crisis in Ardabil, considering developments and uncertainties up to the horizon year 2035. The research methodology is based on a combination of the modified Delphi method and cross-impact analysis, with data collected through expert questionnaires. The study population consisted of 30 experts in the fields of water resource management, urban planning, environmental science, agriculture, and climatology, selected through purposive sampling. In the first phase, a two-round modified Delphi method was employed to identify the variables influencing the water crisis. During the first round, a semi-structured questionnaire was distributed among experts, including sections related to environmental, economic, social, and managerial variables, while also allowing respondents to introduce additional factors through open-ended responses. The purpose of this stage was to extract an initial set of influential variables. In the second round, the identified variables were presented to the experts, who were asked to evaluate their importance and influence using a Likert scale. Based on the aggregated responses, the final set of variables was selected. Subsequently, these variables were analyzed using a cross-impact matrix to determine their levels of influence and interdependence. Finally, Scenario Wizard software was utilized to process the data and generate plausible future scenarios. Results and Discussion Based on the outputs generated by scenario wizard, six robust scenarios were identified for the future of the water crisis in Ardabil. Among these, scenario 1 represents the most favorable and optimal condition. The analysis indicates that the future of Ardabil’s water system can be categorized into three distinct yet interconnected groups, each reflecting different levels of governance effectiveness, infrastructural capacity, and the interaction between water supply and demand. The first group, which includes scenarios 1 and 2, represents conditions of relative to optimal stability. In these scenarios, integrated water resource management, targeted infrastructure investment, and increased public awareness and participation contribute to a relative balance between available resources and consumption. Under such circumstances, the system demonstrates sufficient resilience to cope with climatic fluctuations and maintain stability through adaptive mechanisms. In contrast, the second group, represented by Scenario 3, reflects a transitional phase toward instability. In this scenario, early signs of water stress—such as reduced precipitation, declining infrastructure efficiency, and weak institutional coordination—gradually emerge. Although the system remains functional, the accumulation of these pressures increases vulnerability and reduces adaptive capacity over time. The third group, consisting of scenarios 4, 5, and 6, represents critical and highly unstable conditions. These scenarios are characterized by severe water shortages, deteriorating infrastructure performance, ineffective governance, increasing demand pressures, and declining institutional capacity. Under such circumstances, the imbalance between water supply and demand intensifies, leading to heightened social, economic, and environmental challenges. Conclusion The findings indicate that the future of Ardabil’s urban water system is shaped by a complex interaction of managerial, infrastructural, social, and climatic factors and cannot be attributed solely to natural variations such as precipitation levels. Even under conditions of relatively improved rainfall, weaknesses in governance, inefficient infrastructure, and limited managerial capacity may still intensify the crisis. This suggests that the roots of the water crisis lie more in structural and institutional deficiencies than in absolute resource scarcity. Furthermore, the developed scenarios demonstrate that the future trajectory of the system is non-linear and highly dependent on governance quality, institutional coordination, investment levels, and consumption patterns. In the sustainable scenarios, integrated management, strengthened infrastructure, and enhanced social participation contribute to balancing supply and demand and improving system resilience. Conversely, in the critical scenarios, accumulated inefficiencies, poor planning, and inadequate demand management result in persistent water shortages, even when water resources are relatively abundant. Scenario 3, representing the transitional phase, is particularly significant as a strategic turning point, providing opportunities for effective intervention at lower cost and with greater impact. Once the system enters more advanced stages of crisis, the complexity of the situation increases, making a return to stable conditions considerably more difficult. In conclusion, the future of Ardabil’s water resources depends more on governance quality and managerial capacity than on climatic conditions alone. Achieving sustainability therefore requires the adoption of integrated water resource management approaches, the strengthening of technical infrastructure, improved institutional coordination, increased social participation, and the reform of consumption patterns. Additionally, incorporating foresight and scenario-based approaches into decision-making processes can facilitate the timely identification of emerging threats, prevent the system from shifting toward critical scenarios, and support sustainable urban development. https://mmws.uma.ac.ir/article_4678.html Introduction Coastal sediment transport, particularly longshore sediment transport (LST), governs shoreline evolution and port sedimentation. Accurate prediction requires separate quantification of bedload and suspended load—a capability absent in traditional single-chamber streamer traps, which merge both phases into one sample. This technological gap prevents independent calibration of two-phase numerical models, introducing significant uncertainty into coastal management decisions. This study addresses the gap by developing and field-testing a novel Biphasic Portable Streamer Trap (BPST) equipped with an internal horizontal baffle that physically separates bedload from suspended load at the point of capture. The BPST provides, for the first time, phase-resolved field data essential for advancing sediment transport modeling and enabling evidence-based Integrated Coastal Zone Management (ICZM). Materials and Methods The BPST was developed by modifying the full-depth streamer trap design with a stainless-steel horizontal baffle installed 150 mm above the base plate, creating independent lower (bedload) and upper (suspended load) compartments. The 150-mm height was determined from bedload layer thickness theory. A 100-µm polyester mesh covers the frame. An extended-handle configuration with a boat-based mooring system was developed for deployments deeper than 1.0 m. Field tests were conducted over one year (December 2022–December 2023) on the sandy southern Caspian coast near Nowshahr Port, Iran. The bimodal wave climate comprises calm conditions (Hs < 0.5 m, 85% of the year) and storm events (Hs > 0.5 m, 15%). Twenty tests were performed at depths of 0.3–1.3 m. Samples from both compartments were separately collected, desalinated, oven-dried, and weighed to 0.1 g precision. Wave, current, and wind data were obtained from a nearshore ADCP and ERA5 reanalysis. Results and Discussion The BPST successfully separated the two transport phases across all 20 field tests. The mean total captured sediment was 1665 g per deployment, comprising 1543 g bedload (92.7%) and 122 g suspended load (7.3%). Decisive proof of selective performance was the simultaneous recording of substantial bedload (up to 4890 g) with zero suspended load during calm conditions, confirming complete physical separation. During storm events, suspended load increased significantly, reaching up to 560 g, demonstrating that high-energy waves drive sediment suspension. The extended-handle configuration performed stably in water depths up to 1.3 m, overcoming traditional depth limitations. Analysis of phase-resolved data against synchronous wave measurements confirmed that suspended sediment mobilization occurs primarily during storms, while bedload dominates the annual transport budget. These phase-resolved datasets provide the missing empirical foundation for independently calibrating bedload and suspended load components in two-phase sediment transport models. Conclusion This study successfully developed and validated the Biphasic Portable Streamer Trap as a reliable instrument for phase-resolved measurement of coastal sediment transport. The internal baffle design proved effective in physically separating bedload and suspended load, verified by the selective capture of bedload during calm conditions. On the studied coast, bedload accounts for approximately 93% of total annual LST, with suspended load mobilized primarily during storm events. The BPST bridges the critical gap between two-phase numerical models and field observations by delivering the first phase-resolved datasets for this environment. Minor limitations include the fixed 150-mm baffle height, which may be exceeded by saltating grains in extreme plunging breakers. The BPST offers a robust, cost-effective monitoring platform for Iranian coastal waters and supports evidence-based ICZM. Future work should deploy BPST arrays across broader spatiotemporal scales to construct regional phase-resolved sediment transport atlases and systematically integrate these data into numerical model calibration routines. https://mmws.uma.ac.ir/article_4692.html Introduction The rapid expansion of arid and semi-arid regions, driven by climate change and unsustainable land management, has led to severe soil degradation, fertility loss, and increasing threats to global food security. In this challenging context, biochar, as a highly stable carbon-based soil amendment, holds significant potential for improving critical soil chemical properties. However, most existing research focuses on short-term effects, and the long-term temporal dynamics of key soil indicators following a single biochar application in semi-arid ecosystems remain poorly understood. Current evidence suggests that initial agronomic benefits may gradually decline over time due to surface aging processes and nutrient leaching. Therefore, determining the optimal application rate and effective time horizon to sustain soil health is crucial for sustainable land restoration. This study aimed to monitor the three-year effects of a single application of poplar wood biochar at 25 and 50 t/ha on soil organic matter, total nitrogen, carbon-to-nitrogen ratio, pH, and electrical conductivity under semi-arid field conditions. The main hypothesis was that soil chemical responses to biochar would follow a non-linear trajectory, with higher application rates demonstrating greater functional stability over the three-year monitoring period. This targeted approach provides a robust scientific framework for cost-effective and ecologically sound decision-making in degraded land restoration programs worldwide.   Materials and Methods This field experiment was conducted at the Urmia University research farm (37°39′N, 44°58′E; 1362 m a.s.l.) under a cold semi-arid climate with ~340 mm annual rainfall and 11.5°C mean temperature. A completely randomized design with three replications evaluated single applications of poplar wood biochar at 0 (control), 25, and 50 t/ha across nine 10-m² plots. Biochar was produced via slow pyrolysis at 450°C (120 min, 10–15°C/min heating rate), sieved to 0.5–1.0 mm, and uniformly incorporated into the top 25 cm of loam-clay soil (texture verified by hydrometer) at trial initiation, with no further amendments over three years. Composite soil samples were collected annually at the end of each cropping cycle. Soil organic matter was determined via the Walkley-Black wet oxidation method (Van-Bemmelen conversion factor 1.724), total nitrogen by standard Kjeldahl digestion-distillation-titration, and the C/N ratio was subsequently calculated. Soil pH and electrical conductivity were measured in saturated paste extracts following established laboratory protocols. Data normality and variance homogeneity were confirmed using Shapiro-Wilk and Levene’s tests. Temporal and treatment effects were analyzed through repeated-measures ANOVA, with mean separations performed using the LSD test at p≤0.05 in SPSS v27. All experimental procedures strictly followed standardized agronomic and soil analytical guidelines.   Results and Discussion Repeated measures ANOVA revealed significant effects of biochar application and time on all soil chemical properties (p<0.05). Organic matter increased significantly in both biochar treatments: at 25 t/ha, values were 197%, 120%, and 95% higher than control in years one, two, and three, respectively, rising from 2.88% to 1.77% (w/w); at 50 t/ha, increases reached 267%, 195%, and 151%, with values increasing from 3.56% to 2.29% (w/w). Total nitrogen showed significant increases only at 50 t/ha across all three years (0.26%, 0.22%, 0.20%; representing 108%, 97%, and 67% above control), whereas the 25 t/ha rate yielded smaller gains of 65%, 42%, and 22%. The C/N ratio increased by 75%, 53%, and 62% at 25 t/ha and by 71%, 46%, and 52% at 50 t/ha relative to control, with no significant difference between application rates. pH increased modestly by 2.3%, 1.5%, and 1.6% at 25 t/ha and by 4.3%, 2.9%, and 2.2% at 50 t/ha versus control, converging by year three. Electrical conductivity rose within the non-saline range: at 25 t/ha, values increased from 1.37 to 1.08 dS/m, representing a decline in relative enhancement from 40% to 5.2% above control; at 50 t/ha, values increased from 1.65 to 1.11 dS/m, with relative enhancement decreasing from 70% to 8.1%. The significant treatment×time interaction for organic matter and electrical conductivity indicates temporal sensitivity, whereas non-significant interactions for nitrogen, C/N ratio, and pH suggest stable, uniform effects. These quantitative patterns demonstrate that 25 t/ha suffices for structural and regulatory improvements, while 50 t/ha is necessary for sustained nitrogen retention in semi-arid soils over a three-year horizon.   Conclusion The findings demonstrate that biochar’s influence on soil chemical parameters follows a dynamic, non-linear trajectory over a three-year period. Initial application yields peak improvements, gradually transitioning toward surface aging and functional equilibrium by year three. While soil organic matter maintained significant enhancements at both 25 and 50 t/ha, long-term total nitrogen stabilization exclusively required the higher rate. Conversely, the C/N ratio, pH, and electrical conductivity reached practical saturation thresholds by the final year, indicating that doubling the application from 25 to 50 t/ha offered no additional agronomic benefit for these indicators. This divergent response highlights the necessity of a targeted, objective-driven biochar strategy in semi-arid agroecosystems. Farmers prioritizing structural improvement, chemical buffering, and runoff mitigation can confidently adopt 25 t/ha as a cost-effective solution. In contrast, low-input systems requiring sustained nitrogen autonomy must apply 50 t/ha to maintain adequate nutrient reservoirs across multiple cropping cycles. Uniform application guidelines without specific restoration objectives risk significant economic inefficiencies. Given the study’s constraints to a single feedstock, pyrolysis temperature, and loam-clay texture, future research should emphasize multi-year monitoring, isotopic tracing of nutrient fluxes, and integration with complementary nature-based practices. These efforts will refine stability models and establish optimized, site-specific protocols for resilient dryland agriculture. Adaptive management frameworks remain essential for scaling implementation.   https://mmws.uma.ac.ir/article_4693.html Introduction Rainfed wheat is one of the most important strategic crops in arid and semi-arid regions, where crop production is highly dependent on rainfall variability and environmental conditions. In these systems, spatial and temporal differences in soil properties, vegetation dynamics, and topographic conditions can significantly affect crop growth and final yield. Soil physical and chemical characteristics influence water retention, nutrient availability, aeration, and root development, while vegetation indices derived from satellite imagery can reflect crop vigor and seasonal growth conditions. Similarly, topographic variables such as elevation, slope, and moisture-related indices affect runoff generation, soil moisture distribution, erosion processes, and local microclimatic conditions. In semi-arid regions of Iran, including Semnan Province, identifying the dominant environmental factors controlling rainfed wheat yield is essential for improving agricultural management and reducing production risks. Previous studies have often investigated soil, vegetation, or topographic factors separately; however, fewer studies have simultaneously evaluated their combined effects and relative importance under field conditions. In addition, the interrelationships among environmental variables may complicate the interpretation of their independent effects on yield. Therefore, the present study was conducted in the Kalpoosh Plain to investigate the relationships among soil properties, NDVI, topographic indices, and rainfed wheat yield using correlation analysis and Boruta feature selection. The study also evaluated multicollinearity among environmental variables to improve the interpretation of variable importance and environmental interactions. Materials and Methods This study was conducted in rainfed wheat fields of the Kalpoosh Plain located in northeastern Mayami County, Semnan Province, Iran. Data were collected from 112 wheat fields during the growing season. Wheat yield was measured using 5 × 5 m plots, and soil samples were collected from the 0–30 cm layer at the center of each plot. Geographic coordinates of sampling points were recorded using GPS. Soil analyses included texture fractions (sand, silt, and clay), bulk density (BD), pH, electrical conductivity (EC), equivalent calcium carbonate (TNV), organic carbon (OC), total nitrogen (N), soluble potassium (K), sodium (Na), calcium plus magnesium (Ca+Mg), and sodium adsorption ratio (SAR). Laboratory analyses were performed using standard procedures. Monthly NDVI values from April to July were extracted from Sentinel-2 imagery with 10 m spatial resolution after atmospheric correction and cloud removal. Topographic variables including elevation, slope, topographic wetness index (TWI), LS-factor, plan curvature, profile curvature, valley depth, and relative slope position (RSP) were derived from the 30 m SRTM digital elevation model. Pearson correlation analysis was used to investigate relationships among environmental variables and wheat yield. Correlation matrices and heatmaps were generated for graphical interpretation. Because strong correlations among predictor variables can influence statistical interpretation, multicollinearity was evaluated using Variance Inflation Factor (VIF) and Tolerance (TOL) indices. Variable importance and sensitivity analysis were performed using the Boruta algorithm based on Random Forest in the R-Studio environment. Results and Discussion The results showed that many soil properties were strongly interrelated, whereas their direct relationships with wheat yield were generally weak. Among soil variables, TNV (r = −0.25) and soil pH (r = −0.21) showed the relatively weak to moderate negative correlations with yield. Soil texture fractions also exhibited very strong interrelationships, particularly between sand and silt (r = −0.97), while their correlations with yield were not significant. NDVI values across all months showed positive correlations with yield. The highest correlation was observed in May (r = 0.53), followed by June (r = 0.46), July (r = 0.43), and April (r = 0.42), highlighting the importance of vegetation conditions during stem elongation and reproductive growth stages. Strong correlations among monthly NDVI values also indicated strong temporal continuity in crop growth dynamics. Among topographic variables, elevation showed the strongest (though weak) negative correlation with yield (r = −0.29), whereas TWI showed a weak positive correlation (r = 0.24). The negative effect of elevation may be associated with lower temperatures and shorter growing periods at higher altitudes. Multicollinearity analysis indicated that most variables did not exhibit serious collinearity; however, sand, clay, soluble sodium, and NDVI (month 2) showed high VIF values, suggesting the presence of multicollinearity among these predictors. Therefore, these variables should be interpreted with caution in model-based inference due to potential redundancy and overlapping information. Boruta analysis identified NDVI, elevation, TNV, pH and Ca+Mg the most influential variables affecting rainfed wheat yield. Conclusion This study identifies four key factors affecting rainfed wheat yield: altitude as a microclimatic factor, TNV, pH, and Ca+Mg as the main soil chemical constraints, and NDVI as the optimal remote sensing indicator for the critical growth stage. The insignificant contribution of soil texture and most topographic indicators highlights the nonlinear and interactive nature of these environmental factors, which can complicate linear modeling approaches. Theoretically, these findings emphasize the improvement of the efficiency of the feature selection algorithm (Boruta). In practical terms, targeted monitoring of the four identified variables (altitude, TNV, pH, Ca+Mg and NDVI) can support sustainable management through methods such as soil amendment to reduce lime and salinity, combined with seasonal remote sensing monitoring. In general, in semi-arid regions, rainfed wheat yield depends more on a few dominant environmental factors than on a wide range of variables. Future research should integrate dynamic climate data and use advanced models (e.g., neural networks) for yield prediction. Such approaches will help reduce yield variability, enhance food security, and support climate adaptation strategies in rainfed agriculture. https://mmws.uma.ac.ir/article_4695.html Introduction Accurate prediction of root water uptake under salinity stress contributes to efficient water management in arid and semi-arid regions. Salinity caused by irrigation is a major limiting factor for crop production in these dry environments. Despite water scarcity, the amount of irrigation water must exceed evapotranspiration to leach excess salts and prevent further soil salinization. To minimize water consumption while avoiding yield reduction, precise prediction of root water uptake under salinity stress is essential. Macroscopic root water uptake models, which employ stress response functions describing the dependence of reduction coefficients on matric or osmotic potential at each soil depth, are widely used in soil water and solute transport simulation models such as HYDRUS and SWAP. Materials and Methods In this study, an optimization approach was used to determine the root water uptake parameters within a macroscopic model, and the corresponding root water uptake was quantified. The experimental work was conducted in a greenhouse using potato plants. Six pots were prepared for cultivation, three of which were subjected to salinity stress, while the remaining three were used to measure potential transpiration. Each pot was equipped with two moisture sensors installed at different depths to monitor soil moisture and electrical conductivity. Two potato seed tubers were planted in each pot on September 1, 2018, and ten days later (after germination), the number of plants was reduced to one per pot. To prevent soil evaporation, the pot surfaces were covered during the daytime. The pots were weighed manually on a daily basis to determine the actual transpiration rate. The drought stress period began on December 13, 2018, after the volumetric water content had been reduced to 0.35. Salinity stress was applied to the relevant treatments immediately after the onset of drought stress in two stages. In the first stage, irrigation was performed using a 3000‑ppm NaCl solution starting on December 13, 2018. Since no reduction in plant transpiration was observed by December 23, 2018, a 5000‑ppm NaCl solution was applied on December 23, marking the second stage of salinity stress. The experiments were continued until the relative transpiration (the ratio of actual to potential transpiration) dropped below 0.5. After completing the experiments and following full plant development, all pots were emptied. At the end of the experiment and after the salinity stress period, the root density distribution was determined by harvesting the plants. The root uptake parameters were estimated inversely by minimizing the sum of squared differences between the observed and simulated daily transpiration rates. Finally, root water uptake at each depth and time was calculated by substituting the linearly interpolated osmotic potential into the stress response function. Results and Discussion The results showed that the optimized daily transpiration agreed well with the observed values. In addition, the deviations in the three optimized stress response functions were small under low to moderate stress levels, indicating the reliability of the method. In the non-stress treatments, most of the root volume was located in the first and fourth soil quartiles. The high root volume in the fourth quartile (the lower part of the soil profile) was due to the limitation of root penetration by the impermeable bottom of the pots. In the salinity-stress treatments, no specific trend was observed, which indicates that the plant was showed no consistent pattern in root development and water uptake due to the presence of osmotic stress. A comparison between the calculated and measured transpiration values around the 1:1 line shows that, in both the non-stress and salinity-stress treatments, most points were located below the 1:1 line and tended toward the calculated transpiration values. This indicates that the transpiration equation is overestimated and can be corrected by a coefficient for potato. In the salinity-stress treatments, transpiration values were much lower than those in the non-stress treatments. The average value of parameter P2 (the exponent of the water uptake reduction coefficient equation) was obtained as 4.98. The average value of parameter ho50 (the osmotic potential at which root water uptake reaches 50% of its potential uptake) was obtained as 4244 cm of water. A comparison of the ho50​ values shows that potato is less tolerant to salinity than canola and more tolerant than bean. In the non-stress treatments, since irrigation was not carried out with saline water, no significant reduction in the uptake reduction coefficient was observed; therefore, the uptake reduction coefficient (α) can be considered equal to unity in this case. In all three salinity-stress treatments, as the salt concentration in the soil solution increased (due to irrigation with saline water and the reduction in soil moisture caused by root water uptake), the uptake reduction coefficient decreased. In the salinity-stress treatments, as the osmotic potential increased from 100 to 10,000 cm, the root uptake coefficient decreased from 1 to zero. Conclusion As time progressed, and particularly during the mid-growth stage of the potato plants, the transpiration values in the stressed treatments became closer to the mean transpiration of the non-stressed treatments. In the late growth stage, the application of salinity stress in the salinity-stressed treatments led to a decrease in both transpiration and relative transpiration values. This indicates a reduction in the root water uptake capacity in the pots subjected to salinity stress, resulting from an increase in osmotic potential. In the non-stressed treatments, since irrigation was not carried out with saline water, no considerable reduction in the uptake reduction coefficient was observed; therefore, the uptake reduction coefficient (α) may be considered equal to unity in this case. In all three salinity-stressed treatments, as the salt concentration in the soil solution increased due to irrigation with saline water and the reduction in soil moisture caused by root water uptake, the uptake reduction coefficient decreased. In the salinity-stressed treatments, as the osmotic potential increased from 100 to 10,000 cm, the root uptake coefficient decreased from 1 to zero. https://mmws.uma.ac.ir/article_4696.html Introduction Soil moisture in the vadose zone is a fundamental variable controlling key hydrological and agro-environmental processes, including actual evapotranspiration, root water uptake, deep percolation, and crop productivity. In irrigated agriculture, accurate characterization of soil water dynamics is essential for improving water use efficiency and minimizing deep losses. However, continuous field-scale monitoring of soil moisture is often constrained by high costs, spatial heterogeneity, sensor calibration requirements, and limited temporal and depth coverage. As a result, simulation models have become indispensable tools for reconstructing soil water dynamics and evaluating water balance components. Among these models, Hydrus-1D and Aquacrop represent two fundamentally different modeling philosophies. Hydrus-1D is a physically based numerical model that solves the Richards equation to simulate water flow in saturated and unsaturated porous media, explicitly accounting for hydraulic gradients and nonlinear soil hydraulic properties. In contrast, Aquacrop is a lumped root-zone, crop-oriented model that simulates soil water dynamics through a water balance approach linked to crop development and yield response to water. These conceptual differences may lead to significant discrepancies in model outputs, particularly under conditions where nonlinear flow processes and vertical gradients are dominant. Therefore, the objective of this study was to compare the performance of Hydrus-1D and Aquacrop in simulating soil moisture, actual evapotranspiration, and deep percolation in an irrigated wheat field and to identify the structural causes of differences between the two models. Materials and Methods The study was conducted in a 20-hectare irrigated wheat field located at the research farm of Ferdowsi University of Mashhad, Iran. Field measurements were collected throughout the growing season and included soil moisture, irrigation amounts, daily meteorological data, and soil physical and hydraulic properties. Volumetric soil water content was measured using a PR2 profile probe at multiple depths and monitoring stations to capture spatial and temporal variability. Meteorological data, including minimum, mean, and maximum air temperature, rainfall, relative humidity, and solar radiation, were obtained from a nearby weather station. Soil properties such as texture, bulk density, saturated hydraulic conductivity, and characteristic water contents (saturation, field capacity, and permanent wilting point) were obtained from a previous study conducted at the same site. In Hydrus-1D, water flow was simulated by numerically solving the Richards equation using the van Genuchten–Mualem hydraulic functions. Soil hydraulic parameters were calibrated using inverse modeling with the Levenberg–Marquardt optimization algorithm, while atmospheric boundary conditions incorporating irrigation, rainfall, and evapotranspiration were applied. In AquaCrop, model inputs included climate data, soil characteristics, irrigation schedules, and crop parameters. Key crop and soil parameters were calibrated using field observations of soil moisture and canopy development. Model performance was evaluated using statistical indicators including root mean square error (RMSE), Pearson correlation coefficient (r), and Nash–Sutcliffe efficiency (NSE). The analysis focused on both statistical agreement and structural interpretation of model behavior.     Results and Discussion The results showed that both models were able to reproduce the general temporal pattern of soil moisture with acceptable accuracy. This was evidenced by Pearson correlation coefficients (r) ranging from 0.76 to 0.97 and RMSE values between 0.9% and 4.55%. Specifically, the NSE was 0.56 for Aquacrop, while for Hydrus-1D, it ranged from 0.52 to 0.90 across the four monitored depths, indicating a satisfactory to very good fit for both models. Hydrus-1D demonstrated a higher capability in capturing depth-dependent variations and short-term fluctuations, particularly at the 10 and 40 cm depths where $r$ exceeded 0.95. This superiority is due to its physically based formulation and explicit representation of unsaturated hydraulic conductivity. In contrast, Aquacrop, due to its lumped root-zone structure, represented soil moisture dynamics as an averaged response, resulting in smoother temporal variations and a slightly higher RMSE of 4.55%. Differences were also evident in the simulation of actual evapotranspiration. Aquacrop provided a more management-oriented estimation by incorporating crop coefficients and canopy cover, leading to a more realistic estimation of seasonal water consumption. Hydrus-1D, on the other hand, estimated actual evapotranspiration based on root water uptake and soil hydraulic limitations, making it more sensitive to soil drying. The most significant divergence between the two models was observed in deep percolation. In Aquacrop, deep percolation occurs only when soil water content exceeds field capacity, whereas in Hydrus-1D, it is governed by hydraulic gradients and can occur even under unsaturated conditions. Additionally, the atmospheric boundary conditions in Hydrus-1D impose limitations on infiltration rates, while Aquacrop incorporates irrigation water more directly into the soil water balance. These findings indicate that differences between the models are primarily structural rather than parametric. Conclusion The findings of this study indicate that Hydrus-1D is more suitable for process-based analysis of soil water movement, as it provides a physically rigorous representation of water flow in the soil profile based on the Richards equation and nonlinear hydraulic functions. It is particularly effective for analyzing vertical gradients, layered soil behavior, and transient flow processes such as infiltration and redistribution. In contrast, Aquacrop is more appropriate for irrigation management, crop performance assessment, and applications under data-limited conditions, as it provides a simplified yet robust representation of root-zone water balance and crop response to water. However, each model has inherent limitations. Hydrus-1D requires detailed soil hydraulic data, accurate boundary condition definition, and careful calibration, and it may exhibit high sensitivity to parameter uncertainty. Aquacrop, due to its simplified structure, has limited capability in representing vertical flow processes and nonlinear hydraulic behavior. From a theoretical perspective, the study highlights the importance of considering structural differences between differential and reservoir-based models when interpreting simulation results. From a practical standpoint, model selection should be based on study objectives, spatial scale, data availability, and required accuracy. The development of integrated modeling frameworks that combine the physical rigor of Hydrus-1D with the crop growth and management capabilities of Aquacrop could significantly improve the simulation of soil–water–plant systems and enhance decision-making in agricultural water management. https://mmws.uma.ac.ir/article_4700.html Introduction Facing the drought crisis and water resource depletion is a major global challenge, and this crisis is expected to intensify due to overexploitation of water resources, especially in agriculture. Agriculture in Iran consumes approximately 90% of water resources. However, frequent groundwater withdrawals in Iran and inefficient irrigation methods have exacerbated the negative effects of water resource depletion more than ever before. This situation requires a comprehensive understanding of the factors that determine farmers’ willingness to engage in water conservation behaviors. In recent years, Ardabil County has faced an increasing acceleration of drought and water resource depletion. Despite the expansion of various types of deep and semi-deep wells (authorized and unauthorized), the prevalence of water-intensive crops and reliance on traditional irrigation systems have led to over-extraction of surface and groundwater. In the face of increasing drought and water resource depletion, improving farmers’ behavioral practices plays a decisive role. In this context, conservation motivation theory is one of the most important theories on improving protection intention and behaviors, which sometimes enhance its behavioral effectiveness through developmental constructs. Therefore, the main aim of this research is to explain the predictive constructs of farmers' behavior in Ardabil County in protecting water resources in ten hypotheses by expanding the conservation motivation theory (developmental constructs of moral norms and technical knowledge).  Materials and Methods The present study was a descriptive-analytical and applied research in terms of its purpose, which was carried out in the field. The statistical population of this study included all active farmers with irrigated agriculture (18,582 people) in Ardabil city, which was determined to be 274 people based on the Cochran formula and the multi-stage random sampling method with proportional assignment. Therefore, from the three parts of the County, four selected rural districts were selected: Sardabeh (4 villages with 95 people as the statistical sample), East Rural District (4 villages with 86 people as the statistical sample), Hir Rural District (2 villages with 49 people as the statistical sample), and West Rural District (2 villages with 44 people as the statistical sample). The data collection tool in this study included a questionnaire containing personal-professional characteristics and the main part that included scales related to measuring health motivation theory (perceived sensitivity in 7 items, perceived intensity in 5 items, self-efficacy in 5 items, response effectiveness in 5 items, response cost in 5 items, protection intention in 6 items, and water conservation behavior in 8 items) and the developmental and supplementary constructs of moral norm (in 5 items) and technical knowledge about water resource conservation (in 5 items). The behavior leveling formula was used to estimate the status of farmers' water conservation behavior. Also, structural equation modeling (SEM) was used to analyze the hypotheses, fit the initial theory, and develop the conservation motivation theory with the help of SmartPLS software. Results and Discussion According to the findings, most farmers were at a relatively unfavorable level in terms of applying water conservation methods. The findings of the structural equation model showed that the developed model (extended model) improved the variance in the original model and the explanatory power of the model increased by 10.9%. As a result, the modified model explains 69.3% of the variance in farmers' water conservation behavior. Technical knowledge and moral norms show their influence through the desire and intention to conserve water to express water conservation behavior; therefore, it challenges the assumption that having sufficient technical knowledge or merely having a moral norm in accordance with water conservation directly leads to the occurrence of behavior. Other findings showed that response effectiveness and response cost were positively and significantly affected water protection intention and behavior in both models. Therefore, when farmers strongly believe that new behaviors can increase their performance and the related actions are not too costly, complicated, and laborious, they develop a positive tendency towards adoption. According to the results, the construct of perceived intensity and then perceived sensitivity were determined to be the strongest constructs affecting the intention to conserve water resources in both models. Also, all research hypotheses were confirmed except for the two hypotheses of the direct effect of technical knowledge and moral norms on water conservation behavior. Conclusion Since most farmers were at a relatively unfavorable level in terms of the level of application of water conservation methods, in this context, a program to expand pressurized irrigation methods in the region, create and develop financial support (in the form of loans and credits and agricultural and rural funds) and technical support (the presence of technical water experts in the region) is suggested as the first step. Other results showed that the perceived severity construct was determined to be the strongest influential construct on the intention to conserve water resources in both models. In this regard, the use of posters and local media to express the severity of deficiencies in water resources (improving perceived severity) can be effective. The findings of the fit of the extended model also showed that the inclusion of the two constructs of technical knowledge and moral norm in the initial conservation motivation model has increased the explanatory power of the model. Agricultural and rural planners can improve the moral norm of farmers to prevent overconsumption of water resources to some extent by attracting the opinions of opinion leaders. To maximize the effectiveness of technical education, training based on experiential learning, holding local exhibitions and visiting model farms, and implementing the Farmers-Field-School (FFS) approach can play an effective role in improving technical knowledge and restoring the ethical norms of farmers in order to protect the region's water resources. https://mmws.uma.ac.ir/article_4702.html Introduction Sustainable ecosystems fundamentally support biogeochemical cycles and maintain terrestrial equilibrium. However, extensive conversion of rangelands to rainfed agriculture has severely compromised soil surface integrity, triggering accelerated degradation trajectories. Biological soil crusts (biocrusts) function as critical ecological engineers, yet their integrated biological and physical proxies remain inadequately monitored across sequential land-use transitions. Elucidating the precise responses of these functional components to anthropogenic disturbances is paramount for designing targeted rehabilitation interventions. Consequently, this investigation specifically evaluated the sensitivity of six key biocrust proxies, encompassing exopolysaccharide concentrations, basal microbial respiration rates, biocrust thickness, gravimetric moisture content, mean weight diameter, and geometric mean diameter of soil aggregates, across three distinct land-use categories: natural rangeland, actively cultivated rainfed fields, and recently abandoned rainfed land. Situated within a representative cold semi-arid watershed, the research tested the primary hypothesis that conversion to conventional rainfed farming precipitates rapid biocrust collapse, whereas short-term passive abandonment fails to catalyze substantial functional recovery. By systematically quantifying these highly responsive indicators, the study establishes a rigorous diagnostic protocol for early-stage soil degradation assessment. Ultimately, these findings will inform evidence-based land management frameworks and promote sustainable restoration paradigms in ecologically fragile dryland environments globally.   Materials and Methods The field investigation was executed within the Khanqah Sorkh watershed, a cold semi-arid region experiencing pronounced seasonal moisture deficits and alkaline loam-clay soils. A systematic random-compound sampling design was implemented to capture spatial heterogeneity across three land-use types: reference rangeland, active rainfed agriculture, and two-to-three-year abandoned fields. Twelve independent composite samples per land-use category were meticulously extracted from the upper two-centimeter biocrust horizon using sterilized tools to prevent cross-contamination. Exopolysaccharides were quantified via the phenol-sulfuric acid colorimetric assay, with absorbance measured at 490 nanometers. Basal microbial respiration was determined through closed-system alkali absorption, wherein evolved carbon dioxide was trapped in sodium hydroxide and subsequently titrated with standardized hydrochloric acid. Biocrust thickness was recorded using a high-precision digital caliper across five micro-sites per plot. Soil moisture was established gravimetrically after oven-drying at 105 degrees Celsius for twenty-four hours. Aggregate stability was assessed via dry sieving through a nested series of standard sieves, enabling precise computation of mean weight diameter and geometric mean diameter. Data normality and homogeneity of variance were rigorously verified prior to statistical processing. One-way analysis of variance coupled with Tukey’s honest significant difference test was employed at a ninety-five percent confidence level to detect inter-treatment disparities. All laboratory procedures adhered strictly to internationally recognized soil analytical protocols to ensure reproducibility. Quality control measures included duplicate analyses for ten percent of samples and calibration against certified reference materials. Statistical computations were performed using specialized analytical software, guaranteeing robust parameter estimation. This methodological framework effectively isolates land-use effects from extraneous environmental noise factors.   Results and Discussion One-way ANOVA demonstrated highly significant land-use effects (p<0.001) on all six biocrust proxies, with F-values ranging from 27.71 (biocrust thickness) to 101.51 (GMD). Natural rangelands exhibited maximum values across all indicators: gravimetric moisture (9.39±1.78%), exopolysaccharide concentrations (0.15±0.02 mg g⁻¹), basal microbial respiration (0.21±0.02 mg CO₂ g⁻¹ day⁻¹), biocrust thickness (11.42±4.14 mm), mean weight diameter (1.69±0.08 mm), and geometric mean diameter (1.14±0.03 mm). Conversion to active rainfed agriculture precipitated substantial declines: exopolysaccharides decreased 27% (to 0.11±0.01 mg g⁻¹), respiration 29% (to 0.15±0.02 mg CO₂ g⁻¹ day⁻¹), thickness 55% (to 5.16±1.17 mm), MWD 17% (to 1.41±0.07 mm), and GMD 15% (to 0.97±0.03 mm). Abandoned rainfed lands (2–3 years post-cultivation) showed negligible recovery: exopolysaccharides (0.07±0.02 mg g⁻¹), respiration (0.11±0.01 mg CO₂ g⁻¹ day⁻¹), and thickness (4.23±1.15 mm) remained statistically equivalent to active rainfed soils (Tukey's HSD, p>0.05). Pearson correlation analysis revealed strong interdependencies among proxies: EPS–BMR (r=0.866), EPS–moisture (r=0.844), BMR–MWD (r=0.676), and MWD–GMD (r=0.880; all p<0.001). These quantitative patterns confirm that passive abandonment fails to restore biocrust functionality within short management timeframes. The integrated assessment of these highly responsive indicators provides a robust diagnostic framework for early detection of soil surface degradation and for evaluating restoration efficacy in semi-arid landscapes where moisture limitation and anthropogenic pressures converge.   Conclusion This investigation conclusively demonstrates that sequential land-use transitions from intact rangelands to cultivated fields and subsequently abandoned plots trigger profound, multi-dimensional degradation of biological and physical soil surface proxies. All measured indicators exhibited extreme sensitivity to agricultural conversion, with active cultivation causing immediate functional collapse across exopolysaccharide concentrations, microbial respiration, crust thickness, moisture retention, and aggregate stability metrics. Critically, two to three years of passive abandonment failed to initiate meaningful ecological recovery, as structural indices and metabolic activities remained statistically indistinguishable from actively managed degraded soils. This evidence unequivocally confirms that natural succession processes are insufficient to restore biocrust-mediated ecosystem services in semi-arid environments within short, management-relevant timeframes. Strong functional correlations among proxies highlight the mechanistic coupling between biological activity and physical structure in surface soil communities. Consequently, land management paradigms must urgently shift from passive conservation toward active ecological engineering approaches. We recommend implementing targeted biotechnological interventions, particularly in recently abandoned fields, to stimulate polymeric secretion and accelerate structural rehabilitation. Additionally, integrating these six validated proxies into routine soil monitoring protocols will enable early detection of degradation thresholds and precise evaluation of restoration efficacy. Future research should prioritize long-term field trials assessing intervention scalability and economic viability to establish standardized guidelines for sustainable dryland management worldwide. https://mmws.uma.ac.ir/article_4703.html Introduction Rice is a strategic cereal crop that secures food security for millions of people in Iran and worldwide. However, traditional flooding irrigation, which is still the dominant practice in Iranian rice paddies, consumes approximately 1.5 times more water than the global average – an unsustainable practice under the severe water scarcity conditions prevailing in most parts of the country. Dry seeding technology eliminates the nursery and transplanting stages, thereby saving significant amounts of irrigation water and labor. Nevertheless, this system increases crop sensitivity to drought stress and nitrogen deficiency during the early growth stages because the roots establish in non-saturated soil. Drip irrigation, especially when combined with plastic mulch, has demonstrated promising water savings in various field crops, while nitrogen remains the most limiting nutrient for rice yield. There is a strong interactive effect between water and nitrogen management on crop growth and productivity. However, to date, no comprehensive field study has investigated the interactive effects of irrigation method and nitrogen application rate on grain yield and water productivity of the local rice cv. Anbarboo under the specific agro-climatic conditions of Ilam province, western Iran. Therefore, this study was conducted to determine the optimal combination of irrigation method and nitrogen application rate for maximizing grain yield and water productivity under dry seeding conditions.Materials and Methods A two-year field experiment was conducted during the 2023 and 2024 growing seasons at the research farm of Ilam University, Ilam province, Iran. The experimental design was a split-plot arrangement based on randomized complete blocks with three replications. Four irrigation methods were assigned to main plots: (1) drip tape, (2) drip tape with plastic mulch, (3) furrow irrigation, and (4) furrow irrigation with plastic mulch. Three nitrogen application rates were assigned to sub-plots: 50%, 75%, and 100% of the recommended nitrogen rate (100 kg ha⁻¹ pure N based on soil test results). Rice (Oryza sativa L. cv. Anbarboo) was direct-seeded into dry soil. Measured traits included plant height, number of panicles per plant, number of grains per panicle, 1000-grain weight, above-ground biomass, grain yield, harvest index, and water productivity. Combined analysis of variance was performed across the two experimental years, and means were compared using Fisher's Least Significant Difference (LSD) test at the 5% probability level.Results and Discussion The combined ANOVA revealed that grain yield was significantly affected by year, irrigation method, and nitrogen rate (p < 0.01). Furthermore, all double and triple interactions among the experimental factors (year × irrigation method, year × nitrogen, irrigation method × nitrogen, and year × irrigation method × nitrogen) were also significant (p < 0.01), indicating the complex dependency of rice yield on the combined management of water and nitrogen. The highest grain yield (4724 kg ha⁻¹), harvest index (48%), and water productivity (0.74 kg m⁻³) were obtained in the second year of the experiment from the combination of drip tape with plastic mulch and 100% nitrogen application. In contrast, the lowest values for these traits (1347 kg ha⁻¹, 29%, and 0.14 kg m⁻³, respectively) were recorded under furrow irrigation with 50% nitrogen application. The superior performance of mulch treatments can be attributed to reduced evaporation from the soil surface, improved soil moisture retention, and enhanced water and nutrient uptake efficiency by the root system. Increasing nitrogen application up to the full recommended rate (100%) significantly improved yield components, including panicle number, grains per panicle, and 1000-grain weight. The significant triple interaction among year, irrigation method, and nitrogen rate indicated that the optimal treatment combination strongly depends on the prevailing climatic conditions of each growing season.Conclusion Based on the two-year experimental results, drip tape irrigation combined with plastic mulch and full (100%) nitrogen application was identified as the superior treatment, positively improving all measured growth and yield traits. The better performance of drip tape with plastic mulch in increasing grain yield and water productivity is due to the elimination of unproductive evaporation, maintenance of soil moisture in the root zone, and increased root length density and root activity. Plastic mulch effectively reduced unproductive evaporation, improved soil moisture retention, and enhanced both water and nutrient use efficiency. Full nitrogen application improved all yield components, leading to higher grain yield and harvest index. This integrated management system is strongly recommended for dry-seeded rice cultivation in arid and semi-arid regions to simultaneously improve grain yield and water productivity. A limitation of this study is that the results are based on only two years of data collected under specific climatic conditions of Ilam province; therefore, generalization to other regions and growing seasons requires further research under different climatic and soil conditions. Future research should evaluate the performance of this system across different rice varieties and diverse geographical regions. Additionally, a comprehensive economic feasibility analysis of plastic mulch and drip tape installation should be conducted to facilitate adoption by local farmers. https://mmws.uma.ac.ir/article_4713.html Extended AbstractIntroduction The shape of breakthrough curves (BTCs) is governed by two simultaneous and often competing sets of processes: hydraulic parameters dictating the flow regime and kinetic parameters controlling interaction rates (e.g., Ka and Kd). Temperature, acting as a key thermodynamic variable, exerts profound and complex effects on these parameters and, consequently, on the overall shape of the BTCs. Elevated temperatures alter unsaturated hydraulic functions by reducing viscosity and surface tension; concurrently, they influence the equilibrium between mobile and immobile colloid phases during attachment and detachment processes by modifying energy barriers. A fundamental challenge in prior studies lies in the failure to decouple these interacting mechanisms, which can lead to ambiguous interpretations and compromise the generalizability of predictive models under non-isothermal conditions. The majority of existing research has evaluated the lumped effect of temperature without isolating the specific contributions of the hydraulic and kinetic components. Accordingly, the primary objective of the present study is to introduce a sequential modeling approach designed to decouple the hydraulic and kinetic effects of temperature on colloid transport under unsaturated conditions. To this end, employing inverse modeling in HYDRUS-1D and maintaining baseline kinetic coefficients constant, the temperature effect on hydraulic parameters is first evaluated independently; subsequently, BTCs are reproduced at varying temperatures. This approach not only provides a deeper mechanistic understanding but also enhances the reliability of numerical models for simulating colloid transport.Materials and Methods To decouple the thermal effects on the hydraulic and kinetic processes governing colloid transport, unsaturated column experiments were conducted utilizing a custom-designed automated temperature-control system (Abadis). Washed gravelly porous media (d50=3.6 mm) and commercial SiO2 colloids (d50=240 nm) were employed. Transport experiments were executed under three isothermal conditions (15, 30, and 45 °C). Initially, a conservative tracer (KNO3) was applied to evaluate hydrodynamic dispersion, followed by a colloid pulse to derive the breakthrough curves (BTCs). The baseline Soil Water Retention Curve (SWRC) was experimentally determined at 30 °C, from which the van Genuchten-Mualem parameters were fitted.Numerical modeling was performed via HYDRUS-1D, coupling the advection-dispersion equation with a one-site kinetic attachment model. To isolate temperature effects, a sequential optimization approach was adopted. Baseline kinetic coefficients were extracted from the 30 °C BTCs. Assuming constant kinetic properties and boundary water contents, the van Genuchten shape parameters (α and n) for 15 °C and 45 °C were inversely estimated. Utilizing these adjusted parameters alongside experimentally measured saturated hydraulic conductivities, temperature-specific SWRC and unsaturated hydraulic conductivity functions were reconstructed. Ultimately, forward simulations incorporating the modified hydraulic parameters and fixed baseline kinetic coefficients were executed. The deviations between the simulated and observed BTCs systematically delineate the isolated contribution of temperature-induced hydraulic variations to the overall colloid transport behavior.Results and Discussion Investigation into the effect of temperature on flow hydraulics revealed that an increase in heat from 15 to 45 °C enhances the saturated hydraulic conductivity (Ks) by 20% following a linear trend (Ks=0.8965T+123.27), which is directly attributed to the reduction in both the dynamic and kinematic viscosity of the fluid. The inverse modeling results of the soil water retention curve (SWRC) indicated a downward and leftward shift of the curve at higher temperatures; such that parameter α experienced a 52% increase (from 0.023 to 0.035 cm−1) and parameter n encountered a 26% decrease (from 5.73 to 4.24). Examination of the unsaturated hydraulic conductivity (K(h)) revealed a crossover point, indicating a shift in the dominant mechanism from viscosity (at near-saturation moisture levels) to surface tension and capillary forces (at higher suctions).Evaluation of the breakthrough curves demonstrated that elevated temperatures significantly enhance colloid retention. This phenomenon was accompanied by a 78% increase in the attachment coefficient (Ka) and a 93% drop in the detachment coefficient (Kd). Despite the increased flow velocity at higher temperatures, the persistence of curve tailing signifies the definitive dominance of kinetic mechanisms over the hydrodynamic flushing process.Ultimately, sequential simulation with the decoupling of hydraulic and kinetic effects proved that thermal fluctuations (in addition to direct kinetic impacts) influence the transport and retention patterns of colloids in the porous medium solely by modifying the soil water retention curve and altering the flow regime. Neglecting this temperature dependence in unsaturated modeling leads to the error of parameter compensation; a phenomenon in which the model falsely attributes structural (hydraulic) inadequacies to kinetic mechanisms. Therefore, incorporating the thermal dynamics of hydraulics is an essential prerequisite for the reliable modeling of colloid transport.Conclusion This study demonstrated that disregarding the temperature dependence of hydraulic and kinetic parameters may introduce structural model errors. Such errors are often artifactually compensated for through the calibration of kinetic parameters, thereby constraining the predictive capability of the model under varying thermal conditions.The findings suggest that the effects of temperature on kinetic and hydraulic processes operate in a competitive manner. Temperature-induced alterations in the fluid’s hydraulic properties led to flow acceleration and reduced residence times for both the fluid and the contaminant, as corroborated by the behavior of the conservative tracer (nitrate). However, the pronounced decline in the concentration peak and the diminished recovery observed in the colloidal breakthrough curves at elevated temperatures indicate the predominance of kinetic effects over hydraulic processes. Recognizing this distinction is a crucial prerequisite for the accurate modeling of colloid transport under non-isothermal conditions within coarse-grained gravelly media.Although the current findings provide key insights into these competitive thermal mechanisms, they are predicated on a specific particle size distribution and a steady moisture regime. Since medium texture (governing specific surface area and pore distribution) and varying moisture levels can significantly alter hydraulic-kinetic interactions, future research should evaluate this decoupling approach across diverse soil gradations and dynamic moisture conditions. Such investigations will ultimately facilitate the development of more robust and generalizable models for predicting contaminant transport. https://mmws.uma.ac.ir/article_4734.html Introduction Soil erosion and sediment transport are among the most significant natural processes contributing to land degradation and the deterioration of soil and water resources within watershed systems. These processes are inherently dynamic and are influenced by a complex interplay of natural and anthropogenic factors, including topography, land use/land cover, rainfall variability, and vegetation conditions. Understanding the mechanisms governing sediment generation, transport, and connectivity within a watershed is therefore essential for effective soil and water conservation planning and management. In recent years, the concept of sediment connectivity has emerged as an efficient framework for describing the degree of linkage between sediment source areas and watershed outlets. The Index of Connectivity (IC) provides a quantitative measure of sediment transfer potential based primarily on topographic characteristics derived from Digital Elevation Models (DEMs). Nevertheless, recent studies have demonstrated that sediment connectivity is not solely controlled by topography, but is also affected by other factors such as surface roughness, land use patterns, and vegetation dynamics. Vegetation cover plays a critical role in reducing runoff velocity, mitigating raindrop impact energy, and regulating sediment transport processes. Consequently, incorporating dynamic vegetation indicators such as the Normalized Difference Vegetation Index (NDVI) can substantially improve the accuracy of monthly sediment modeling and connectivity assessments. Despite these advances, most existing studies have approached sediment connectivity from a structural or static perspective, with limited attention given to seasonal and monthly variability. At the same time, the Revised Universal Soil Loss Equation (RUSLE) has been widely applied for estimating soil erosion; however, its relationship with sediment connectivity at monthly temporal scales has received comparatively little attention. Accordingly, the present study aims to investigate the relationship between sediment connectivity and soil erosion at a monthly scale in the Kasilian Watershed, while also evaluating the effectiveness of incorporating NDVI into sediment connectivity analyses and monthly sediment estimation. Materials and Methods This study was conducted in the Kasilian Watershed, located in Mazandaran Province, northern Iran. The watershed is characterized by complex topography, a semi-humid to humid climate, and diverse land use/land cover types, including forestlands, rangelands, agricultural areas, residential zones, and rock outcrops. These characteristics make the region a suitable environment for investigating erosion and sediment transport processes. To evaluate sediment connectivity, the IC was employed. Initially, the annual IC was calculated for the year 2021 based on topographic attributes derived from the DEM. Subsequently, monthly IC values were estimated by incorporating monthly NDVI data as a weighting factor within the connectivity model. NDVI data for the Kasilian Watershed were extracted from MODIS satellite imagery and normalized to a range between 0 and 1 prior to model implementation. The relationship between sediment connectivity and monthly soil erosion was then assessed by comparing IC results with monthly soil erosion estimates derived from the Revised Universal Soil Loss Equation (RUSLE). Correlation analyses between monthly sediment connectivity and monthly soil erosion were performed at three analytical levels: land use/land cover classes, slope categories, and working units. Monthly mean values were extracted using GIS-based spatial analysis tools. After testing data normality using the Shapiro–Wilk test, Pearson or Spearman correlation coefficients were applied in SPSS software depending on the distribution characteristics of the variables. Results and Discussion The results revealed a significant relationship between sediment connectivity and soil erosion at the monthly scale. Incorporating monthly NDVI-based vegetation dynamics into the calculation of the sediment connectivity index substantially improved the correlation between sediment connectivity and soil erosion compared with the conventional annual IC approach. The correlations between the results of monthly and annual sediment connectivity calculations with monthly and annual erosion of the Kasilian watershed was investigated in slope, land use/land cover, and work unit classes showed the significant role of vegetation cover using the NDVI in the sediment connectivity index calculations to increase the correlation between sediment connectivity and soil erosion in the monthly time interval. This finding highlights the critical role of vegetation dynamics in regulating sediment transfer processes at monthly scale. The highest correlation between soil erosion and monthly sediment connectivity was observed in January and in slope classes (-0.85, sig. 0.02), while the lowest correlation was observed in October in land use/land cover (0.14, non-significant) and also in March in work units (0.17, non-significant). The results also showed that the combination of slope class and forest land use/land cover in two work units 2 and 4 caused a significant decrease in monthly soil erosion in these two units in April (0.01 t ha-1). Also, working unit 4 has the lowest monthly sediment attachment rate in January (-5.64) and October (-5.54). The strongest correlations were observed during periods of dense vegetation cover across the watershed, particularly from May to November. In contrast, during periods characterized by sparse or absent vegetation cover, topographic conditions and land use/land cover factors exerted a stronger influence on both erosion estimates and sediment connectivity patterns, emphasizing the dominant role of these controls in the absence of sufficient vegetation protection. Conclusion The findings of this study demonstrated that soil erosion and sediment connectivity are interrelated and highly dynamic processes that vary considerably at the monthly temporal scale. Incorporating vegetation-cover information and dynamic remote sensing approaches can substantially enhance the accuracy of sediment connectivity modeling. The results further indicated that vegetation cover plays a key role in regulating the relationship between topography and sediment transport. Under conditions of sparse or absent vegetation cover, slope gradient and land use/land cover emerged as the dominant controlling factors, whereas under dense vegetation conditions, the protective effect of vegetation weakened the direct relationship between topography and sediment transfer processes. From a practical perspective, integrating remote sensing data with sediment connectivity models provides a more accurate and efficient framework for watershed management. This approach improves the identification of erosion-prone areas and supports more informed decision-making in soil and water resources management. Overall, the monthly sediment connectivity approach proposed in this study demonstrated clear advantages over the conventional annual topography-based method, providing a more realistic representation of erosion and sediment transport dynamics for watershed managers and environmental planners. https://mmws.uma.ac.ir/article_4735.html Introduction Surface water resources in the form of lakes, rivers, reservoirs, wetlands, snow, and glaciers play an essential role in various aspects of life on Earth. These valuable resources influence ecosystems, hydrology, and climate while providing numerous benefits, including urban, agricultural, and industrial water supply, supporting wildlife and fisheries, and creating recreational opportunities for humans. Among these, reservoirs regardless of their size are of great importance in the integrated management of water resources in watersheds. Large and medium reservoirs play a vital role in flood control and hazard mitigation, water supply and irrigation, hydropower generation, and runoff regulation. Small reservoirs, despite their large numbers, play an irreplaceable role in ensuring drinking water and food security, aquaculture, and water resource allocation. Reservoir storage capacity is an important indicator of its healthy functioning, which is closely related to regional climate; therefore, regular and accurate monitoring of reservoir water volume is essential for the protection and rational exploitation of water resources and the formulation of related policies. This dynamic monitoring is particularly important in arid regions for water resource assessment, hydropower generation, and irrigation. However, existing methods for calculating reservoir water levels are mainly based on field measurements, which limits their application in data-sparse regions. Unlike previous studies, the present study provides a comprehensive framework for monitoring the water level of the Dez Dam reservoir using Sentinel-2 imagery and the SRTM digital elevation model within the Google Earth Engine platform. In this framework, the support vector machine model, uncertainty analysis, and sensitivity analysis are all implemented in an integrated framework.   Materials and Methods In this study, water level variations of the Dez Dam reservoir were analyzed using satellite data from 2018 to 2024. Sentinel-2 surface reflectance products from the COPERNICUS collection were utilized, employing bands B3, B8, and B11. The Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) were calculated to separate water surface from surrounding dry land. Water body boundaries were extracted, and a one-pixel buffer around the water boundary was created to extract elevation values from the SRTM digital elevation model (30 m resolution), with the mean elevation along this boundary considered as the reservoir water level. For each observational date, five input variables were extracted: satellite-derived water level, mean NDWI, mean MNDWI, reservoir surface area, and the 5‑day lagged observed water level. An ensemble-based support vector machine regression model (SVM) with ten independent models was developed using random sampling of the training dataset, and the average prediction of all models was considered as the final output. The ensemble approach, by averaging ten independently trained models, effectively reduced variance and improved generalization compared to a single SVM model. For uncertainty estimation, conformal prediction was applied without assuming any specific statistical distribution for the errors. This method generated prediction intervals by calculating nonconformity scores on a calibration set, and then deriving a threshold at the 90th percentile. The resulting intervals were designed to provide distribution-free uncertainty bounds with a target 90% coverage for new observations. Permutation importance was used for sensitivity analysis to quantify the contribution of each input parameter to the model's predictive performance.   Results and Discussion The SVM model was evaluated under two scenarios. In the first scenario, four satellite-derived variables, including satellite-derived water level, reservoir surface area, NDWI, and MNDWI, were used as model inputs. This scenario yielded RMSE values of 2.29 m for training and 2.64 m for validation, with corresponding (R2) values of 0.96 and 0.94, respectively. Error analysis showed slight overestimation at low water levels (below 320 m) and underestimation at high water levels (above 345 m), indicating reduced accuracy in reproducing extreme hydrological conditions such as drought and flood peaks. In the second scenario, the 5-day lagged water level was added to the four satellite-derived inputs, resulting in improved model performance. RMSE decreased to 1.36 m for training and 1.97 m for validation. Although validation R² and NSE remained nearly unchanged between the two scenarios, the lower RMSE, and narrower prediction intervals indicate a more accurate and stable model. Uncertainty analysis using conformal prediction further showed that the prediction interval width decreased from 6.81 m in the first scenario to 4.24 m in the second scenario, although conformal coverage remained slightly below the target 90% in both scenarios (84% and 85%, respectively). These results indicate that incorporating antecedent water-level information substantially improves both predictive accuracy and uncertainty performance of the SVM model. Permutation importance analysis further showed that the 5-day lagged water level was the most influential predictor (ΔRMSE = 4.05 m), followed by satellite-derived water level (2.85 m) and reservoir surface area (0.94 m), whereas NDWI and MNDWI exhibited comparatively minor direct contributions to model performance.   Conclusion This study developed an integrated framework within Google Earth Engine to estimate the water level of the Dez Dam reservoir from 2018 to 2024 using Sentinel-2 imagery, SRTM digital elevation data, and an SVM model. Two modeling scenarios were evaluated. In the first scenario, four satellite-derived variables, were used as model inputs, yielding a validation RMSE of 2.64 m. In the second scenario, the 5-day lagged water level was added to these variables, improving model performance and reducing the validation RMSE to 1.97 m. Uncertainty analysis using conformal prediction showed that the prediction interval width decreased from 6.81 m in the first scenario to 4.24 m in the second scenario, indicating reduced predictive uncertainty when antecedent water-level information was incorporated. Nevertheless, because the first scenario relies solely on remotely sensed variables and does not require ground-based observational data, it provides a practical solution for reservoir monitoring in ungauged or data-scarce regions, with lower accuracy than the hybrid scenario. Overall, the proposed framework offers a reproducible and automated approach for water-level estimation, while future studies may further improve performance by incorporating additional hydrological variables such as inflow, outflow, and evaporation, as well as radar observations (e.g., Sentinel-1) to enhance temporal coverage under cloudy conditions. https://mmws.uma.ac.ir/article_4736.html Introduction Today, one of the major problems in the watersheds is soil erosion and sediment production, which threatens the sustainable development in these areas. Erosion not only leads to soil degradation and decreasing fertility, but also to pollution of Water Resources and decreased water quality. One of the methods to prevent the loss of water and soil resources is the usage of biological conditioners to control erosion and soil conservation. Organic conditioners can play the more effective role in reducing runoff and sediment. As an organic conditioner, especially in forest areas, Moss is a suitable conservation cover that reduces runoff and soil loss by directly Collision of rainfall droplets. The present study aimed to examination the conservation effects of moss as a type of biological soil conditioner with two planting pattern forms, compared to a plot without conditioner (control) at different slopes (20%, 30% and 40%) with three replications at the plot scale under a rainfall of 120 mm h-1 using a rainfall simulator.   Materials and Methods In order to control erosion in forest areas, natural mulch that is native to the region should be used because the usage of non-native materials such as chemical mulch, including polymers and polyacrylamide, which are long-lasting, if lost, does not play the role in soil enrichment and can even pollute the environment and cause the disrupting natural habitats. In this study, the effects of forest moss on runoff and sediment components were investigated in the rainfall simulator laboratory of the Faculty of Natural Resources, Sari Agricultural and Natural Resources Sciences University. The studied soil in this research was collected from the Darabkola forest located in Mazandaran Province. The transported soil was dried and passed through a 4 mm-sieve for relative stability of soil aggregates. In the present study, two types of soil cultivation patterns were used under laboratory conditions to investigation the conservation effects of moss on runoff and sediment. The conservation treatments including moss cover with planting patterns of clumped, strip and control treatment (without cover) on three different slopes of 20%, 30%, and 40% under intensity of 120 mm h-1 and with three replications in plot scale with dimensions of 1 × 0.5 m2 in depth of 0.2 m. Then, runoff and sediment samples were collected for duration of 10 min for each experiment. The collected sediment samples, after being left to settle for 24 h were dried at 1050C for 24 h, and finally their weight was measured.   Results and Discussion A total of 27 rainfall simulation experiments were conducted. Compared with the control treatment, runoff volume was reduced by 20.24% and 33.83% under the clumped and strip planting patterns, respectively, on the 20% slope. At the 30% slope, runoff volume decreased by 19.56% and 33.46%, while corresponding reductions on the 40% slope were 13.30% and 31.05%, respectively. Soil loss decreased under both planting patterns. The clumped and strip patterns reduced, in slop of 20 percent measured with rates of 28.35% and 51.33% (for planting patterns of clumped and strip); 36.67% and 53.33% (for slope of 30 percent); 2984% and 46.40% (for slope of 40 percent), respectively. Sediment concentration also decreased under both planting patterns. The clumped and strip patterns reduced sediment concentration by 14.35% and 23.13% at the 20% slope, 17.53% and 26.78% at the 30% slope, and 19.12% and 23.62% at the 40% slope, respectively. Likewise, soil loss was substantially reduced under both planting patterns, with the strip arrangement consistently providing greater protection than the clumped pattern. Statistical analysis indicated that moss planting pattern had a highly significant effect (P < 0.01) on runoff volume, soil loss and sediment concentration. Also the separately effect of slope on runoff and soil loss was significant on at level of 99 percent. However, the interaction effects between slope and planting pattern were not significant for all measured variables.   Conclusion In general, the results of the present study showed that the usage of cultivation patterns of clumped and strips significantly reduced the runoff volume and sediment and prevents soil loss. In other words, moss with reducing the flow velocity, caused to increasing infiltration and reduced the separation of soil particles by raindrops. Moss are very effective in increasing water retention capacity at soil, and in fact, the moss surface is like a soft, porous sponge, and rainfall quickly is absorbed by moss, and this conservation treatment traps the caused soil particles by water erosion. So the moss cover is one of the important factors in the management and conservation of water and soil Resources. Given the importance of water and soil conservation and the high cost and environmental inappropriate impacts of mechanical methods over biological methods of water and soil conservation, the application of this conservation cover can be useful in the programs development of soil erosion control. https://mmws.uma.ac.ir/article_4737.html Introduction Forests are among the most valuable natural ecosystems on Earth, providing a wide range of provisioning, regulating, supporting, and cultural ecosystem services. Despite their ecological and socio-economic importance, many ecosystem services are not traded in conventional markets and are therefore overlooked in land-use planning and policy decisions. Economic valuation of ecosystem services provides an effective framework for incorporating environmental benefits into natural resource management and sustainable development strategies. The Fandoghloo Forest, located in Ardabil Province in northwestern Iran, is one of the country's most important hazelnut forest ecosystems and forms part of the Hyrcanian Forest landscape. Besides supplying timber and non-timber forest products, the forest plays a vital role in water regulation, soil conservation, and carbon sequestration. However, increasing anthropogenic pressures and land-use changes threaten these ecosystem functions. Therefore, quantifying and economically valuing these services is essential for supporting sustainable forest management. Accordingly, this study aimed to evaluate the economic value of major provisioning and regulating ecosystem services of the Fandoghloo Forest, including timber production, non-timber forest products, water yield, soil conservation, and carbon storage.   Materials and Methods The study was conducted in the Fandoghloo Forest within the Samian watershed, Ardabil Province, Iran. Three forested sub-watersheds (Aladizgeh, Soha, and Kalesar) were selected for analysis. Forest inventory data were collected using systematic random sampling. Fifteen 10 × 10 m plots were established in each sub-watershed, where all trees with DBH > 2.5 cm and height >1.3 m were measured. Aboveground biomass was estimated using species-specific allometric equations and converted to economic values using the market price method. Non-timber forest products were assessed based on hazelnut production and local market prices. Regulating ecosystem services were quantified using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Water yield was estimated using the Budyko water balance approach, soil conservation was evaluated using the Sediment Delivery Ratio (SDR) model based on the Revised Universal Soil Loss Equation (RUSLE), and carbon storage was calculated from four carbon pools including aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter. Economic valuation was performed using different approaches depending on the ecosystem service. Timber and non-timber forest products were valued using the market price method, whereas water yield, soil conservation, and carbon storage were valued using the replacement cost approach. Carbon storage was converted to CO₂ equivalent using a carbon price of USD 53 per ton of CO₂.   Results and Discussion Among the provisioning services, the Soha sub-watershed exhibited the highest economic value of timber production, estimated at approximately 3.09 billion IRR ha⁻¹, followed by Aladizgeh with 2.86 billion IRR ha⁻¹ and Kalesar with 1.83 billion IRR ha⁻¹. The superior timber value in Soha is mainly attributable to its greater forest density, larger standing biomass, and a higher proportion of mature forest stands. These characteristics enhance wood volume per unit area and consequently increase the market value of timber resources. In contrast, the lower timber value observed in Kalesar reflects its relatively lower forest biomass and a greater proportion of other land-use types. The valuation of non-timber forest products demonstrated that Kalesar generated the highest economic return, with hazelnut production valued at approximately 896 million IRR ha⁻¹ yr⁻¹, whereas Aladizgeh produced an estimated 176 million IRR ha⁻¹ yr⁻¹. No commercial hazelnut production was recorded in the Soha sub-watershed. These findings highlight the considerable contribution of non-timber forest products to local livelihoods and rural economies. The assessment of regulating ecosystem services further demonstrated the critical environmental functions of the forest ecosystem. Water yield analysis using the InVEST model indicated annual economic values of approximately 169.6 million IRR ha⁻¹ for Aladizgeh, 169.2 million IRR ha⁻¹ for Soha, and 154.4 million IRR ha⁻¹ for Kalesar. The relatively similar values among the sub-watersheds indicate comparable climatic conditions, while differences are primarily associated with variations in land cover, vegetation density, and evapotranspiration characteristics. Soil conservation analysis revealed substantial differences among the sub-watersheds. The highest economic value of soil conservation was estimated for Soha (174.5 million IRR ha⁻¹ yr⁻¹), followed by Kalesar (160.5 million IRR ha⁻¹ yr⁻¹) and Aladizgeh (76.3 million IRR ha⁻¹ yr⁻¹). These results emphasize the effectiveness of well-preserved forest cover in minimizing soil erosion and sediment transport. Carbon storage assessment showed that forest land contained the highest carbon stock among all land-use classes, whereas residential areas stored the least carbon. Forest ecosystems stored substantially larger amounts of carbon in aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter than agricultural or rangeland ecosystems. The estimated economic value of carbon storage was approximately 4.2 billion IRR ha⁻¹ in all three sub-watersheds because identical carbon density coefficients were assigned to each land-use category in the InVEST model. Conclusion The findings demonstrate that the Fandoghloo Forest provides substantial provisioning and regulating ecosystem services with significant economic value. Among the studied sub-watersheds, Soha exhibited the highest overall ecosystem service value, emphasizing its priority for conservation and sustainable management. Integrating ecosystem service valuation into forest management and land-use planning can improve decision-making and strengthen conservation policies. Although the InVEST model proved effective for quantifying ecosystem services, the results are subject to uncertainties associated with model parameterization, the use of standard coefficients, and limitations in field validation. Future studies should incorporate locally calibrated parameters, consistent economic valuation baselines, and additional ecosystem services to improve the accuracy and comprehensiveness of ecosystem service assessments.