دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Linking soil erosion and food security in Kano State, Nigeria: A geospatial assessment using RUSLE and household surveys Linking soil erosion and food security in Kano State, Nigeria: A geospatial assessment using RUSLE and household surveys 1 20 4089 10.22098/mmws.2025.18151.1650 FA Abdulazeez Hudu Wudil Senior Lecturer, Department of Agricultural Economics and Agribusiness, Faculty of Agriculture, Federal University Dutse, Jigawa State, Dutse, Nigeria Muttaka Idris Ph.D Candidate of Remote Sensing and GIS, Department of Remote Sensing and Geo-Science Information System, School of Earth and Mineral Sciences, Federal University of Technology Akure, Akure, Nigeria Akinola Adesuji Komolafe Associate Professor, Department of Remote Sensing and Geo-Science Information System, School of Earth and Mineral Sciences, Federal University of Technology Akure, Akure, Nigeria 0000-0003-0202-0518 Journal Article 2025 08 22 <span style="font-size: 10.0pt;">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.</span> <span style="font-size: 10.0pt;">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.</span> https://mmws.uma.ac.ir/article_4089_3b86a50c1f0cf51b321694256d631aa7.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Land cover dynamics and urbanization in peri-urban areas: Assessing the socio-economic and environmental consequences of rapid urban expansion Land cover dynamics and urbanization in peri-urban areas: Assessing the socio-economic and environmental consequences of rapid urban expansion 21 35 4122 10.22098/mmws.2025.18305.1677 FA Abraham Woru Borku Department of Geography and Environmental Studies, College of Social Science and Humanities, Debark University, Debark, Ethiopia Mamush Masha Department of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia 0000-0002-2666-9170 Alemayehu Abera Department of Geography and Environmental Studies., College of Social Science and Humanities, Mettu University, Mettu, Ethiopia Journal Article 2025 09 09 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. 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_4122_727f152d07b6765b93f218f046ba4866.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Effectiveness of reducing Ca–Mg hardness using NaOH precipitation, in Ghardaïa groundwater Effectiveness of reducing Ca–Mg hardness using NaOH precipitation, in Ghardaïa groundwater 36 51 4193 10.22098/mmws.2025.18308.1679 FA Kheira Bouamer Laboratory of Materials, Energy Systems Technology and Environment, Université de Ghardaia, Ghardaia, Algeria Hamed Boukhari Laboratory of Materials, Energy Systems Technology and Environment, Université de Ghardaia, Ghardaia, Algeria Khaled Mansouri Department of Process Engineering, Faculty of Science and Technology, Mathematics and Applied Science, Ghardaïa University, Ghardaïa, Algeria 0009-0004-0181-533X Journal Article 2025 09 14 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. 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_4193_0e46b3a0b17e4c7f541e4e061dd4b752.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Digital elevation model based-morphometric characterization of Pambujan River Basin in Northern Samar, Philippines Digital elevation model based-morphometric characterization of Pambujan River Basin in Northern Samar, Philippines 52 66 4214 10.22098/mmws.2025.18366.1692 FA Deodatus G. Sagadal Assistant Professor, Department of Agricultural and Biosystems Engineering, College of Engineering, University of Eastern Philippines, Catarman, Northern Samar, Philippines Felix S. Licas Associate Professor, Department of Civil Engineering, College of Engineering, University of Eastern Philippines, Catarman, Northern Samar, Philippines Eladio B. Jao Jr. Assistant Professor, Department of Agricultural and Biosystems Engineering, College of Engineering, University of Eastern Philippines, Catarman, Northern Samar, Philippines Journal Article 2025 09 27 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. 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_4214_191ae18976a5ab079ff28ccc664f63e9.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Simulating and investigating the impact of bedform geometric features on flow structure in three-dimensional dunes Simulating and investigating the impact of bedform geometric features on flow structure in three-dimensional dunes 67 88 4215 10.22098/mmws.2025.18557.1705 FA Masoumeh Ghodousi Former M.Sc. Student, Department of Water Sciences and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran Elham Fazel Najafabadi Assistant Professor, Department of Water Sciences and Engineering. College of Agriculture, Isfahan University of Technology, Isfahan, Iran Journal Article 2025 10 16 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. The river bed forms are noteworthy investigated from the hydraulic and environmental point of view. For many years, river engineers have researched the flow structure in the presence of sandy river bedforms. Due to the limitations in laboratory and field studies, numerical methods can accurately examine the flow structure of river bedforms. In this research, flow simulation on three-dimensional dunes was performed using computational fluid dynamics (CFD). Also, the effect of changes in bed form angle, bed form wavelength, and the curvature of the three-dimensional dune crest line was investigated. The results showed that in the lobe-shaped dune at the centerline channel, the maximum stream-wise velocity was 20cm/s; however, for the saddle-shaped dune, a similar value was 31cm/s. Also, the separation zone is 7cm and 30cm for the lobe- and saddle-shaped bedforms, respectively. Also, increasing the lee-side angle from 15 to 30 degrees caused the 20% velocity reduction at the dune crest, for the lobe- and saddle-shaped bedform; however, 50% and 10% reduction in the stream-wise velocity was illustrated at the lee-side, for the lobe- and saddle-shaped bedform, respectively. Meanwhile, for the saddle-shaped bedform, the velocity increased and the Reynolds shear stress decreased with the decrease of the exit angle. Increasing the wavelength from 25 to 96 cm showed the 20% stream-wise velocity reduction at the dune crest, for the lobe- and saddle-shaped bedform; however, 40% and 10% reduction in the stream-wise velocity was illustrated at the lee-side, for the lobe- and saddle-shaped bedform, respectively. Decreasing the curvature crest line showed a 15% reduction and 20% increase in stream-wise velocity at the dune crest, for the lobe- and saddle-shaped bedform, respectively; however, a 50% reduction and 20% increase in the stream-wise velocity was illustrated at the lee-side, for the lobe- and saddle-shaped respectively. Meanwhile, for the saddle-shaped bedform, the increase in the curvature of the crest line has led to a decrease in velocity and, in most cases, an increase in Reynolds shear stress. https://mmws.uma.ac.ir/article_4215_0d32d4de7911a40f3d8db5c716f959aa.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Analysis of hydrodynamic patterns in the coastal waters of the Caspian Sea using field measurements Analysis of hydrodynamic patterns in the coastal waters of the Caspian Sea using field measurements 89 120 4221 10.22098/mmws.2025.18495.1698 FA Abbas Einali Assistant Professor, Faculty of Environmental and Marine Sciences, University of Mazandaran, Mazandaran, Iran Mohammad Akbari Nasab Associate Professor of Physical Oceanography, Faculty of Environmental and Marine Sciences, University of Mazandaran, Mazandaran, Iran Mohammad Hossein Nemati MSc. in Physical Oceanography, Port and Maritime Organization, Tehran, Iran Journal Article 2025 10 05 Extended Abstract<br /><br />The Caspian Sea, the largest enclosed inland body of water on Earth, is bordered by five countries: Russia, Kazakhstan, Turkmenistan, Iran, and Azerbaijan. It has a unique geographical setting with a surface area of approximately 371,000 square kilometers and a maximum depth of about 1,025 meters. The climate around the Caspian Sea varies significantly, with the northern part experiencing cold winters and hot summers, while the southern part has milder winters and hotter summers. The general wind patterns and atmospheric systems affecting the Caspian Sea include the Siberian High, which brings cold air masses, and the Azores High, which influences the summer weather. The overall water circulation in the Caspian Sea is cyclonic, and wave conditions are influenced by wind patterns and the basin's morphology.<br /><br />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.<br /><br />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.<br /><br />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.<br /><br />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.<br /><br />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.<br /><br />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.<br /><br />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. This study investigates the wave and current dynamics of the Caspian Sea, the world’s largest enclosed inland water body, with a focus on its southern coast. The Caspian’s meridional axis, diverse climatic conditions, and complex coastal morphology contribute to highly variable hydrodynamic behavior, yet oceanographic data in the region remain scarce. To address this gap, wave and current measurements were conducted over more than a year at seven nearshore stations—five at 10 meters and two at 30 meters depth—spanning east to west along Iran’s coastline. Data were collected using Acoustic Doppler Current Profilers (ADCP), including AWAC and AquaDopp systems, configured for high-resolution profiling of surface and bottom layers. All measurements underwent multi-layered quality control using PMODynamics software, and descriptive statistical indicators such as mean, maximum, variance, and standard deviation were calculated to assess seasonal and spatial variability. The results confirmed the Caspian Sea’s counterclockwise circulation, with eastward currents prevailing in central and eastern stations (Noshahr, Anzali, Amirabad), and southward flows dominating western stations (Astara). Roudsar showed localized eddy activity, reversing the dominant flow. Seasonal analysis revealed that winter produced the most intense hydrodynamic conditions, with surface current speeds reaching up to 1.15 m/s at Amirabad and standard deviations peaking at 0.16 m/s in autumn. Bottom currents remained more stable, with mean speeds below 0.13 m/s and minimal variance. Wave conditions also varied significantly across stations and seasons. Anzali recorded the most intense wave regime, with significant wave heights frequently ranging between 0.8 and 1.2 meters, especially during autumn and winter. In contrast, spring exhibited the lowest variability, with standard deviations under 0.35 meters at most stations. These patterns reflect the influence of seasonal wind forcing and coastal morphology on wave behavior. These findings provide essential baseline data for coastal management, sediment transport modeling, and infrastructure design in a region increasingly affected by climate change and water level fluctuations https://mmws.uma.ac.ir/article_4221_5160da7e49afd42b6927f162b1b8a9fe.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Synergistic effect of land use and climate change on evapotranspiration Synergistic effect of land use and climate change on evapotranspiration 121 139 4251 10.22098/mmws.2025.18634.1710 FA Damte Tegegne Fetene Ph.D. scholar of Hydraulic Engineering, Faculty of Hydraulic & Water resources Engineering, Arba Minch University, Arba Minch, Ethiopia Tarun Kumar Lohani Professor, Faculty of Hydraulic & Water resources Engineering, Arba Minch University, Arba Minch, Ethiopia Abdella Kemal Mohammed Associate Professor, Faculty of Hydraulic & Water resources Engineering, Arba Minch University, Arba Minch, Ethiopia Journal Article 2025 10 26 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. 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_4251_43c45cb5e88ff5c771e7ec628ad68033.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Water wave height prediction using a novel hybrid deep learning model with output uncertainty quantification Water wave height prediction using a novel hybrid deep learning model with output uncertainty quantification 140 170 4261 10.22098/mmws.2025.18652.1709 FA Elham Ghanbari-Adivi Associate Professor, Department of the Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran Mohammad Ehteram Ph.D. Department of Hydraulic Structures, Semnan University, Semnan, Iran Journal Article 2025 10 25 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.   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_4261_811902e02f067e6236608de309c95bda.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Factors influencing household-level adoption of soil and water conservation practices among smallholder farmers: Application of Binary Logistic Regression Factors influencing household-level adoption of soil and water conservation practices among smallholder farmers: Application of Binary Logistic Regression 171 192 4286 10.22098/mmws.2025.18639.1707 FA Alemayehu Abera Department of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia Wasihun Mengiste Department of Soil Resource and Watershed Management, College of Agriculture and Natural Resource Management, Gambella University, Gambella, Ethiopia Melese Reda Department of Soil Resource and Watershed Management, College of Agriculture and Natural Resource Management, Gambella University, Gambella, Ethiopia Mohamed Abdu Department of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia Muhammed Adem Department of Geography and Environmental Studies, College of Social Science and Humanities, Debark University, Debark, Ethiopia Abraham Woru Borku Department of Geography and Environmental Studies, Arba Minch University, Arba Minch, Ethiopia Biniyam Assefa Department of Soil Resource and Watershed Management, College of Agriculture and Natural Resource Management, Gambella University, Gambella, Ethiopia Shibiru Masha Departments of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia 0000-0002-2666-9170 Journal Article 2025 10 23 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.<br /><br /><br /><br />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.<br /><br /><br /><br />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.<br /><br /><br /><br />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.<br /><br /><br /><br />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.<br /><br /><br /><br />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.<br /><br /><br /><br />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.<br /><br /><br /><br />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.<br /><br /><br /><br />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. The study examined the practices and factors affecting the adoption of soil and water conservation (SWC) measures in Mettu district. To achieve this, a cross-sectional survey design with a mixed-methods approach, specifically concurrent triangulation, was employed. A total of 341 households were selected using simple random sampling. Quantitative data were analyzed through descriptive and inferential statistics and a binary logistic regression model, while qualitative data were summarized, narrated, and interpreted. The findings revealed that the primary drivers of soil degradation in the area were deforestation (47.4%), steep slopes (79%), irregular and erosive rainfall (26.2%), land fragmentation (72%), overgrazing (88.2%), weak management practices (58.5%), and improper farming techniques (63.8%). Among these, overgrazing, steep slopes, and land fragmentation were the most influential factors. Indigenous soil conservation practices widely employed by farmers included crop rotation (85.5%), contour plowing (74.2%), fallowing (51%), mulching (77%), manuring (56.7%), and traditional cut-off drains (78Among the introduced soil and water conservation measures, vetiver grass (91.3%) emerged as the most widely adopted practice, followed by soil bunds (70%), hillside terraces (69.5%), agroforestry (40.5%), and micro-basins (38.2%). The binary logistic regression results revealed that gender, household age, education level, access to credit, and landholding size positively and significantly influenced farmers’ decisions to adopt SWC practices. In contrast, longer distances between homes and farm plots significantly reduced the likelihood of adoption. Overall, strengthening farmers’ awareness supported by coordinated efforts from relevant stakeholders is essential to advancing sustainable soil and water conservation in the district. https://mmws.uma.ac.ir/article_4286_8cf62ee19658f3360292569e801a6b79.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Integrated biotic and abiotic indicators for evaluating ecosystem health in the Qara-Su River, Iran Integrated biotic and abiotic indicators for evaluating ecosystem health in the Qara-Su River, Iran 193 208 4271 10.22098/mmws.2025.18793.1725 FA Ehsan Asadisharif Offshore Fisheries Research Centre, Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Chabahar, Iran Shima Rahim Pouran Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Amin Salimi Godeh Kahriz Department of Biology, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran 0009-0007-7749-0406 Abolfazl Bayrami Department of Biology, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2025 11 09 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 (I_geo, 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. 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 (I_geo, 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_4271_d156eec980b639fc7775c877f6e450ee.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions 209 231 4309 10.22098/mmws.2025.18810.1726 FA Hedieh Ahmadpari Ph.D. Candidate, Hydrology of Land, Water Resources, Hydrochemistry, Russian State Hydrometeorological University, Saint Petersburg, Russia Vitaly Khaustov Candidate of Technical Sciences, Associate Professor at the Department of Engineering Hydrology of the RSHU, Saint Petersburg, Russia Ata Amini Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran Journal Article 2025 11 11 Development of Multiple Linear Regression Models for Annual Reference Evapotranspiration Estimation under Limited Data Conditions <br /><br />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.<br /><br />Development of Multiple Linear Regression Models for Annual Reference Evapotranspiration Estimation under Limited Data Conditions <br /><br /><br /><br />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.<br /><br />Development of Multiple Linear Regression Models for Annual Reference Evapotranspiration Estimation under Limited Data Conditions <br /><br /><br /><br />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. 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 ing up to 92% of ET₀ variability. Several two-variable models achieved R² = 0.92–0.96, and most three-variable models reached R² = 0.93–0.97. Four-variable models also performed strongly (R² ≈ 0.95–0.97), 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_4309_fafdde1ce3c5151e24f540eaf69349cf.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Future-oriented agricultural water management with scenario-based evaluation: Case study of Maize in Khuzestan Future-oriented agricultural water management with scenario-based evaluation: Case study of Maize in Khuzestan 232 251 4342 10.22098/mmws.2025.18953.1740 FA Tahmine Dehghani Ph.D. Candidate in Irrigation and Drainage, Department of Irrigation and Reclamation, University of Tehran, Karaj, Iran Bijan Nazari Associate Professor, Department of Irrigation and Reclamation, University of Tehran; and Faculty Member at Imam Khomeini International University, Qazvin, Iran Abdolmajid Liaghat Professor, Department of Irrigation and Reclamation, University of Tehran, Karaj, Iran Journal Article 2025 12 01 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. 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_4342_30cdb787ccac07899f059044322c8350.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Assessing current cropping patterns in a semi-arid basin using cost-benefit and water productivity indicators Assessing current cropping patterns in a semi-arid basin using cost-benefit and water productivity indicators 252 266 4356 10.22098/mmws.2026.18950.1739 FA Seyedsaeid Nabavi Ph.D. Candidate of Watershed Management Sciences and Engineering, Department of Reclamation of Arid & Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran 0000-0002-4168-7027 Arash Malekian Professor, Department of Reclamation of Arid & Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran Naser Mashhadi Assistant Professor, International Desert Research Center, University of Tehran, Karaj, Iran Khaled Ahmadaali Associate Professor, Department of Irrigation and Reclamation, Faculty of Agriculture, University of Tehran, Karaj, Iran Raoof Mostafazadeh Associate Professor, Department of Natural Resources and Member of Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Ali Shahbazi Ph.D. of Watershed Management Sciences and Engineering, Natural Resources and Watershed Management Organization of Alborz Province, Karaj, Iran Journal Article 2025 12 05 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. 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_4356_76f8da21e8f81d747de4999d87af7f97.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Optimal channel geometry for balancing discharge and tidal resistance: insights from the Shatt al-Arab River Optimal channel geometry for balancing discharge and tidal resistance: insights from the Shatt al-Arab River 267 286 4382 10.22098/mmws.2026.19094.1754 FA Razzaq Jawad Karrar Ph.D. student of Watershed Management Sciences and Engineering, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran Hirad Abghari Associate Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran 0000-0002-3407-3297 Journal Article 2025 12 20 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. 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_4382_94ca9cc67279570535c0e71612438359.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Soil texture mapping: a novel approach combining interpolation techniques and decision tree classifiers Soil texture mapping: a novel approach combining interpolation techniques and decision tree classifiers 287 302 4495 10.22098/mmws.2026.19187.1767 FA Samir Boudibi Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria Zineeddine Benguega Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria, and Department of Agronomic Sciences, Laboratory of Saharan Bioresources: Preservation and Valorization, Kasdi Merbah University, Ouargla, Algeria Haroun Fadlaoui Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria Zine-eddine Khomri Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria Azeddine Aissaoui Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria Bachir Sakaa Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria Tarik Otmane Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria Narimen Bouzidi Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria Journal Article 2026 01 01 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. 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_4495_4f2d7cf8417f5bcae45560419f486fe4.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 2 2026 05 22 Optimizing machine learning to reduce crop classification uncertainty in semi-arid Canal command areas Optimizing machine learning to reduce crop classification uncertainty in semi-arid Canal command areas 303 325 4546 10.22098/mmws.2026.18944.1745 FA Mohansing Rajaput Ph.D. Scholar, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru-575025, Karnataka, India Abhilash Ramadasa Scientist ‘C’, National Institute of Hydrology, Hard Rock Regional Centre, Visvesvaraya Nagar, Belagavi-590019, Karnataka, India 0000-0002-7505-5263 Basavanand M. Dodamani Professor, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru-575025, Karnataka, India Journal Article 2025 12 05 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. 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_4546_eafde0e1d6610d18ea992a70862ed7a6.pdf