دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Application of GIS and MCDA in flood susceptibility modeling in Jigawa State, Nigeria Application of GIS and MCDA in flood susceptibility modeling in Jigawa State, Nigeria 1 14 4095 10.22098/mmws.2025.18160.1653 FA Muttaka Idris Assistant Lecturer, Department of Geography, Faculty of Earth and Environmental Science, Aliko Dangote University of Science and Technology, Wudil, Nigeria Adamu Mustapha Professor, Department of Geography, Faculty of Earth and Environmental Science, Aliko Dangote University of Science and Technology, Wudil, Nigeria Akinola Adesuji Komolafe Associate professor, Department of Remote Sensing and Geoscience Information System, School of Earth and Mineral Science, Federal University of Technology, Akure, Nigeria 0000-0003-0202-0518 Journal Article 2025 08 23 Flood disaster is considered as one of the devastating and destructive in the world. Due to its high concern, predicting areas with varying susceptibility is crucial. This study aims at identifying and mapping areas and extent of varying flood susceptible areas in Jigawa State. multi criteria decision Analytical Hierarchy Process (AHP) and geographic information science (GIS) were used. Distance to river, Elevation, Flow accumulation, Lineament density, Slope, Geology, Soil and Land use were used as explanatory parameters to develop the model.  The raster data parameters were analyzed using ArcGIS 10.1 and AHP was calculated. The model ranked distance to river high followed by elevation, flow-accumulation, lineament, land use and land cover, slope, geology, soil. Area covered by very high and high susceptibility was 27.68% and 29.20% respectively. The use of analytical hierarchy process, a multi criteria process has modeled the susceptibility to flood in Jigawa state accurately. Among the 23 Local Government Areas in the study area, 12 fell within very high and high susceptibility areas covering 56.88%. The study recommends adopting early warning system strategies to prevent the future flood disaster. Flood disaster is considered as one of the devastating and destructive in the world. Due to its high concern, predicting areas with varying susceptibility is crucial. This study aims at identifying and mapping areas and extent of varying flood susceptible areas in Jigawa State. multi criteria decision Analytical Hierarchy Process (AHP) and geographic information science (GIS) were used. Distance to river, Elevation, Flow accumulation, Lineament density, Slope, Geology, Soil and Land use were used as explanatory parameters to develop the model.  The raster data parameters were analyzed using ArcGIS 10.1 and AHP was calculated. The model ranked distance to river high followed by elevation, flow-accumulation, lineament, land use and land cover, slope, geology, soil. Area covered by very high and high susceptibility was 27.68% and 29.20% respectively. The use of analytical hierarchy process, a multi criteria process has modeled the susceptibility to flood in Jigawa state accurately. Among the 23 Local Government Areas in the study area, 12 fell within very high and high susceptibility areas covering 56.88%. The study recommends adopting early warning system strategies to prevent the future flood disaster. https://mmws.uma.ac.ir/article_4095_61f073af5499773f33ea8163dffb3275.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Influence of land use and management practices on soil physicochemical properties across slope gradients Influence of land use and management practices on soil physicochemical properties across slope gradients 15 27 4150 10.22098/mmws.2025.18301.1674 FA Awoke Abebaw MSc in Soil Science, Department of Natural Resources Management, College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Ethiopia Walle Jemberu Professor, Department of Natural Resources Management, College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Ethiopia 0009-0003-7320-7408 Mekonnen Getahun PhD in Soil Science, Amhara Design and Supervision Works Enterprise, Bahir Dar, Ethiopia Wasihun Mengiste Assistant Professor, Department of Soil Resource and Watershed Management, College of Agriculture and Natural Resource, Gambella University, Gambella, Ethiopia Aweke Endalew MSc in Soil Science, Department of Soil Resource and Watershed Management, College of Agriculture and Natural Resource, Gambella University, Gambella, Ethiopia Journal Article 2025 09 08 The decline in soil fertility is being intensified by both human activities and natural phenomena. Therefore, in order to realize the concerns of the agricultural revolution and sustainable land productivity. It is crucial to examine the influence of land use and land management practices on soil properties across various slope gradients. The research aimed to examine how different land use and management techniques influence selected physicochemical properties of the soil across various slope classes. The study employed an experimental design that included three types of land use and two slope classes, with samples collected from managed and unmanaged areas. Finally, 36 samples were obtained at a depth 0-20 cm. Three-way ANOVA was used for statistical analysis. The results revealed that most soil physicochemical properties were significantly (P < 0.05) influenced by land use type management practice, and slope gradient. Forest land exhibitted the highest values for clay (44.25%), TP (59.03%), pH (5.73), OM (5.03%), TN (0.16%), Av. P (12.19 ppm), CEC (31.60 cmol(+) kg-1) and exchangeable bases: Ca²+ (8.94 cmol(+) kg-1), Mg²+ (3.10 cmol(+) kg-1), K+ (0.62 cmol(+) kg-1) and Na+ (0.61 cmol(+) kg-1). In contrast, cultivated land showed the highest sand content (33.42%) and bulk density (1.25 g cm-3), while grazing land recorded the highest silt content (31.67%). Managed areas recorded the highest values for clay (43.11%), TP (57.57%), pH (5.65), OM (4.35%), TN (0.15%), Av. P (11.14 ppm), CEC (29.25 cmol(+) kg-1) and exchangeable bases: Ca^²+ (8.37 cmol(+) kg-1), Mg²+ (2.48 cmol(+) kg-1), K+ (0.59 cmol(+) kg-1) and Na⁺ (0.52 cmol(+) kg-1) compared to unmanaged areas. With respect to slope gradient, it was generally noted that most of the studied soil properties increased with decreasing slope gradient. Generally, the sources of variations in the soils physicochemical properties were land use type, management practice, and slope. Therefore, there is need for appropriate and integrated land management techniques, such as agroforestry, contour plowing, terracing, conservation tillage, mulching, crop rotation, cover cropping, controlled grazing, reforestation, and the use of organic amendments, which aim to improving the physicochemical properties of soils to the different land use and slope classes in Burat Watershed. The decline in soil fertility is being intensified by both human activities and natural phenomena. Therefore, in order to realize the concerns of the agricultural revolution and sustainable land productivity. It is crucial to examine the influence of land use and land management practices on soil properties across various slope gradients. The research aimed to examine how different land use and management techniques influence selected physicochemical properties of the soil across various slope classes. The study employed an experimental design that included three types of land use and two slope classes, with samples collected from managed and unmanaged areas. Finally, 36 samples were obtained at a depth 0-20 cm. Three-way ANOVA was used for statistical analysis. The results revealed that most soil physicochemical properties were significantly (P < 0.05) influenced by land use type management practice, and slope gradient. Forest land exhibitted the highest values for clay (44.25%), TP (59.03%), pH (5.73), OM (5.03%), TN (0.16%), Av. P (12.19 ppm), CEC (31.60 cmol(+) kg-1) and exchangeable bases: Ca²+ (8.94 cmol(+) kg-1), Mg²+ (3.10 cmol(+) kg-1), K+ (0.62 cmol(+) kg-1) and Na+ (0.61 cmol(+) kg-1). In contrast, cultivated land showed the highest sand content (33.42%) and bulk density (1.25 g cm-3), while grazing land recorded the highest silt content (31.67%). Managed areas recorded the highest values for clay (43.11%), TP (57.57%), pH (5.65), OM (4.35%), TN (0.15%), Av. P (11.14 ppm), CEC (29.25 cmol(+) kg-1) and exchangeable bases: Ca^²+ (8.37 cmol(+) kg-1), Mg²+ (2.48 cmol(+) kg-1), K+ (0.59 cmol(+) kg-1) and Na⁺ (0.52 cmol(+) kg-1) compared to unmanaged areas. With respect to slope gradient, it was generally noted that most of the studied soil properties increased with decreasing slope gradient. Generally, the sources of variations in the soils physicochemical properties were land use type, management practice, and slope. Therefore, there is need for appropriate and integrated land management techniques, such as agroforestry, contour plowing, terracing, conservation tillage, mulching, crop rotation, cover cropping, controlled grazing, reforestation, and the use of organic amendments, which aim to improving the physicochemical properties of soils to the different land use and slope classes in Burat Watershed. https://mmws.uma.ac.ir/article_4150_5f9a4fd133c5eb95283cbc95a1646ce1.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Integrating geodetic technology with legal governance for global monitoring of water and soil dynamics: Kalman filter and inverse modeling approaches Integrating geodetic technology with legal governance for global monitoring of water and soil dynamics: Kalman filter and inverse modeling approaches 28 40 4012 10.22098/mmws.2025.17992.1638 FA Wasan Maki Mohammed Al-Turath University, Baghdad 10013, Iraq Sabah M. Kallow Al-Mansour University College, Baghdad 10067, Iraq Deena Waleed Hameed Jawad Al-Mamoon University College, Baghdad 10012, Iraq Jassim Kadhi Kabrch Al-Rafidain University College Baghdad 10064, Iraq Khdier Salman Madenat Alelem University College, Baghdad 10006, Iraq Ata Amini Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran Journal Article 2025 08 05 The need for accurate, reliable geodetic data has never been greater, particularly as water and soil systems face growing threats from global challenges such as agricultural development, land deformation, and flood vulnerability. This study examines the integration of advanced geodetic systems with international legal frameworks to support collaborative and cross-border environmental monitoring, with specific implications for water and soil management. An innovative five-component methodology was developed, comprising data acquisition, standardization, sharing, analytical processing, and model integration aimed at enhancing data accuracy and interoperability for hydrological and soil-related applications. Techniques such as Kalman filtering, inverse modeling, covariance analysis, and PSD-based spectral decomposition were employed to reduce uncertainty and isolate critical environmental signals. Kalman filtering–augmented time series analysis reduced uncertainty in sea-level estimates by 38%, achieving millimeter-level precision. Tectonic frequencies down to 0.05 Hz were detected, enhancing the monitoring of land deformation processes. Inverse modeling produced a 42% reduction in uncertainty for glacial and groundwater mass loss estimates, directly informing water resource assessments. GNSS displacement vectors improved land deformation risk models by 53%, while altimetry-based sea-level trends increased forecasting accuracy by 50%, benefiting coastal water management. Standardization mitigated data variability, and international legal mechanisms including agreements and licensing protocols, enabled broader data access and institutional collaboration. Elevation data strengthened flood vulnerability assessments by 52%, and cryosphere modeling experienced a 53% error reduction in mass balance estimates. The Combined Earth System Model, integrating global geodetic inputs, achieved a maximum accuracy gain of 54%. This study highlights that embedding legal infrastructures within scientific geodetic modeling enhances the governance, reliability, and equity of water and soil monitoring systems across national boundaries.<br /><br /><br /><br />The need for accurate, reliable geodetic data has never been greater, particularly as water and soil systems face growing threats from global challenges such as agricultural development, land deformation, and flood vulnerability. This study examines the integration of advanced geodetic systems with international legal frameworks to support collaborative and cross-border environmental monitoring, with specific implications for water and soil management. An innovative five-component methodology was developed, comprising data acquisition, standardization, sharing, analytical processing, and model integration aimed at enhancing data accuracy and interoperability for hydrological and soil-related applications. Techniques such as Kalman filtering, inverse modeling, covariance analysis, and PSD-based spectral decomposition were employed to reduce uncertainty and isolate critical environmental signals. Kalman filtering–augmented time series analysis reduced uncertainty in sea-level estimates by 38%, achieving millimeter-level precision. Tectonic frequencies down to 0.05 Hz were detected, enhancing the monitoring of land deformation processes. Inverse modeling produced a 42% reduction in uncertainty for glacial and groundwater mass loss estimates, directly informing water resource assessments. GNSS displacement vectors improved land deformation risk models by 53%, while altimetry-based sea-level trends increased forecasting accuracy by 50%, benefiting coastal water management. Standardization mitigated data variability, and international legal mechanisms including agreements and licensing protocols, enabled broader data access and institutional collaboration. Elevation data strengthened flood vulnerability assessments by 52%, and cryosphere modeling experienced a 53% error reduction in mass balance estimates. The Combined Earth System Model, integrating global geodetic inputs, achieved a maximum accuracy gain of 54%. This study highlights that embedding legal infrastructures within scientific geodetic modeling enhances the governance, reliability, and equity of water and soil monitoring systems across national boundaries.<br /><br /><br /><br />The need for accurate, reliable geodetic data has never been greater, particularly as water and soil systems face growing threats from global challenges such as agricultural development, land deformation, and flood vulnerability. This study examines the integration of advanced geodetic systems with international legal frameworks to support collaborative and cross-border environmental monitoring, with specific implications for water and soil management. An innovative five-component methodology was developed, comprising data acquisition, standardization, sharing, analytical processing, and model integration aimed at enhancing data accuracy and interoperability for hydrological and soil-related applications. Techniques such as Kalman filtering, inverse modeling, covariance analysis, and PSD-based spectral decomposition were employed to reduce uncertainty and isolate critical environmental signals. Kalman filtering–augmented time series analysis reduced uncertainty in sea-level estimates by 38%, achieving millimeter-level precision. Tectonic frequencies down to 0.05 Hz were detected, enhancing the monitoring of land deformation processes. Inverse modeling produced a 42% reduction in uncertainty for glacial and groundwater mass loss estimates, directly informing water resource assessments. GNSS displacement vectors improved land deformation risk models by 53%, while altimetry-based sea-level trends increased forecasting accuracy by 50%, benefiting coastal water management. Standardization mitigated data variability, and international legal mechanisms including agreements and licensing protocols, enabled broader data access and institutional collaboration. Elevation data strengthened flood vulnerability assessments by 52%, and cryosphere modeling experienced a 53% error reduction in mass balance estimates. The Combined Earth System Model, integrating global geodetic inputs, achieved a maximum accuracy gain of 54%. This study highlights that embedding legal infrastructures within scientific geodetic modeling enhances the governance, reliability, and equity of water and soil monitoring systems across national boundaries.national boundaries.national boundaries. The need for accurate, reliable geodetic data has never been greater, particularly as water and soil systems face growing threats from global challenges such as agricultural development, land deformation, and flood vulnerability. This study examines the integration of advanced geodetic systems with international legal frameworks to support collaborative and cross-border environmental monitoring, with specific implications for water and soil management. An innovative five-component methodology was developed, comprising data acquisition, standardization, sharing, analytical processing, and model integration aimed at enhancing data accuracy and interoperability for hydrological and soil-related applications. Techniques such as Kalman filtering, inverse modeling, covariance analysis, and PSD-based spectral decomposition were employed to reduce uncertainty and isolate critical environmental signals. Kalman filtering–augmented time series analysis reduced uncertainty in sea-level estimates by 38%, achieving millimeter-level precision. Tectonic frequencies down to 0.05 Hz were detected, enhancing the monitoring of land deformation processes. Inverse modeling produced a 42% reduction in uncertainty for glacial and groundwater mass loss estimates, directly informing water resource assessments. GNSS displacement vectors improved land deformation risk models by 53%, while altimetry-based sea-level trends increased forecasting accuracy by 50%, benefiting coastal water management. Standardization mitigated data variability, and international legal mechanisms including agreements and licensing protocols, enabled broader data access and institutional collaboration. Elevation data strengthened flood vulnerability assessments by 52%, and cryosphere modeling experienced a 53% error reduction in mass balance estimates. The Combined Earth System Model, integrating global geodetic inputs, achieved a maximum accuracy gain of 54%. This study highlights that embedding legal infrastructures within scientific geodetic modeling enhances the governance, reliability, and equity of water and soil monitoring systems across national boundaries. https://mmws.uma.ac.ir/article_4012_3fbee27db754e707b18cc6844b2ea6f8.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Estimating soil loss rates and land capability classification based on erosion severity in the Womba Watershed, Southern Ethiopia Estimating soil loss rates and land capability classification based on erosion severity in the Womba Watershed, Southern Ethiopia 41 64 4121 10.22098/mmws.2025.18258.1667 FA Dessalegne Chanie Haile Assistant Professor, Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia Mulugeta Debele Bedhane Associate Professor, Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia 0009-0002-4671-0123 Wakshum Shiferaw Associate Professor, Department of Natural Resource Management of Agricultural Science, College Agriculture, Arba Minch University, Arba Minch, Ethiopia Gebremedhin Chameno Chalite MSc, Department of Natural Resource Management of Agricultural Science, College of Agriculture, Arba Minch University, Arba Minch, Ethiopia Daniel Azazie Birra MSc, Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia Zeleke Dosa Morgamo MA, Department of Marketing Management, College of Commerce, Arba Minch University, Arba Minch, Ethiopia Zerihun Ayalew Gebre PhD Candidate, Department of Psychology, College of Education and Behavioral Science, Arba Minch University, Arba Minch, Ethiopia Solomon Tesfamariam Assistant Professor, Department of Geography and Environmental Studies., College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia Journal Article 2025 09 05 Land degradation, mainly through soil erosion and nutrient depletion, threatens agricultural sustainability in Sub-Saharan Africa, particularly in Ethiopia. This study focuses on estimating annual soil loss rates and evaluating land capability in the Womba watershed to inform conservation strategies. Key factors considered include rainfall erosivity, soil erodibility, slope, land use, and management practices. The study uses ArcGIS 10.8 tools and RUSLE equation methods soil loss within the study area. Soil loss in the watershed, utilizing Digital Elevation Models (DEM) for slope calculations. Annual soil loss ranged from 2.18 t ha⁻¹ yr⁻¹ (slightly severe) to 163.58 t ha⁻¹ yr⁻¹ (highly severe), with an average rate of 10.84 t ha⁻¹ yr⁻¹, totaling 28,552.96 tons annually. While soil loss in less severe classes was within tolerable limits, highly severe categories exceeded acceptable thresholds. The watershed is classified into four erosion classes, with 1,697.33 ha (64.3%) in Class I and 675.13 ha (25.6%) in Class II, suitable for agriculture and annual crops. About 208.02 ha (7.8%) of the watershed is classified as Class IV, suitable for perennial crops, urban development, and grazing, while 55.52 ha (2.3%) is Class VI, suitable for forest development and wildlife conservation. Despite their small area, both classes face significant soil erosion, highlighting the need for strategies from the government and local stakeholders to prevent further soil loss. Land degradation, mainly through soil erosion and nutrient depletion, threatens agricultural sustainability in Sub-Saharan Africa, particularly in Ethiopia. This study focuses on estimating annual soil loss rates and evaluating land capability in the Womba watershed to inform conservation strategies. Key factors considered include rainfall erosivity, soil erodibility, slope, land use, and management practices. The study uses ArcGIS 10.8 tools and RUSLE equation methods soil loss within the study area. Soil loss in the watershed, utilizing Digital Elevation Models (DEM) for slope calculations. Annual soil loss ranged from 2.18 t ha⁻¹ yr⁻¹ (slightly severe) to 163.58 t ha⁻¹ yr⁻¹ (highly severe), with an average rate of 10.84 t ha⁻¹ yr⁻¹, totaling 28,552.96 tons annually. While soil loss in less severe classes was within tolerable limits, highly severe categories exceeded acceptable thresholds. The watershed is classified into four erosion classes, with 1,697.33 ha (64.3%) in Class I and 675.13 ha (25.6%) in Class II, suitable for agriculture and annual crops. About 208.02 ha (7.8%) of the watershed is classified as Class IV, suitable for perennial crops, urban development, and grazing, while 55.52 ha (2.3%) is Class VI, suitable for forest development and wildlife conservation. Despite their small area, both classes face significant soil erosion, highlighting the need for strategies from the government and local stakeholders to prevent further soil loss. https://mmws.uma.ac.ir/article_4121_5d87d02b987486f9bad226136f0d61c7.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Green synthesis of silver nanoparticles using Chromolaena odorata leaf extract for adsorptive removal of heavy metals and textile dyes from aqueous systems Green synthesis of silver nanoparticles using Chromolaena odorata leaf extract for adsorptive removal of heavy metals and textile dyes from aqueous systems 65 77 4013 10.22098/mmws.2025.18099.1645 FA John Paul Purigay Teacher II, Science Department, Nueva Vizcaya General Comprehensive High School, Bayombong, Philippines Journal Article 2025 08 18 Background<br /><br />Water pollution by heavy metals and synthetic dyes is a critical global environmental challenge with severe consequences for ecosystems and human health. Pollutants such as lead (Pb²⁺), copper (Cu²⁺), cobalt (Co²⁺), iron (Fe²⁺), and textile dyes including methyl orange, methyl red, methyl blue, and Congo red are commonly released into water bodies from industrial effluents, textile manufacturing, mining, and agricultural runoff. These contaminants are persistent, bioaccumulative, and toxic, with risks including oxidative stress, neurodevelopmental damage, and ecosystem disruption. Conventional remediation methods such as chemical precipitation, ion exchange, membrane filtration, and activated carbon adsorption have been widely applied but remain constrained by cost, secondary waste generation, and operational complexity.<br /><br />Green nanotechnology has emerged as a sustainable alternative, particularly the biosynthesis of silver nanoparticles (AgNPs) using plant extracts as reducing and stabilizing agents. Chromolaena odorata (locally known as Hagonoy) contains phytochemicals such as flavonoids, phenolic acids, terpenoids, and saponins that enable the ecofriendly synthesis of stable nanoparticles. While prior studies have reported antimicrobial and biomedical applications of C. odorata-mediated AgNPs, their environmental application in the removal of both heavy metals and dyes has received limited exploration. This study addresses this gap by synthesizing AgNPs via C. odorata leaf extracts and systematically assessing their adsorption performance under controlled laboratory conditions.<br /><br />Methods<br /><br />Fresh C. odorata leaves were shade-dried, powdered, and subjected to aqueous extraction. The extract was mixed with AgNO₃ solution to induce nanoparticle synthesis. Nanoparticle formation was confirmed by ultraviolet–visible (UV–Vis) spectroscopy, which exhibited a sharp surface plasmon resonance (SPR) peak at 428 nm, indicating stable AgNP synthesis.<br /><br />Batch adsorption experiments were carried out using 50 mL of contaminant solution with concentrations ranging from 10–50 mg/L, treated with 0.1 g of AgNPs under agitation at 150 rpm. Removal efficiency was quantified using UV–Vis spectrophotometry, while adsorption capacity was calculated by mass balance. Kinetic modeling was conducted using pseudo-first-order (PFO) and pseudo-second-order (PSO) equations. Adsorption isotherms were fitted to Langmuir, Freundlich, and Temkin models, and thermodynamic parameters (ΔG°, ΔH°, ΔS°) were derived using the van ’t Hoff equation at temperatures of 25°C, 35°C, and 45°C.<br /><br />Results<br /><br />Nanoparticle characterization. The UV–Vis spectrum displayed an SPR peak at 428 nm, characteristic of spherical, monodispersed AgNPs stabilized by phytochemicals in the C. odorata extract.<br /><br />Adsorption efficiency. AgNPs exhibited high removal efficiencies across contaminants. For heavy metals, removal efficiency followed the order Pb²⁺ (92.3%) > Fe²⁺ (88.5%) > Cu²⁺ (85.1%) > Co²⁺ (81.6%). For dyes, removal efficiency ranked as methyl orange (89.7%) > Congo red (86.4%) > methyl red (84.2%) > methyl blue (77.5%). Optimal performance was observed at near-neutral pH (6–7) for metals and slightly acidic pH (5–6) for dyes. Equilibrium was reached within 90–120 minutes.<br /><br />Kinetics. Adsorption data aligned closely with the PSO model (R² > 0.99), confirming chemisorption as the primary mechanism. Metals generally achieved equilibrium faster (90 minutes) than dyes (100 minutes).<br /><br />Isotherms. Langmuir modeling best described the adsorption process, suggesting monolayer adsorption. Maximum adsorption capacities (qmax) were 50 mg/g (Pb²⁺), 46 mg/g (Fe²⁺), 43 mg/g (Cu²⁺), and 44.5 mg/g (Co²⁺). Dyes exhibited slightly lower qmax values (40–42 mg/g). RL values of 0.36–0.50 indicated favorable adsorption. Freundlich constants (KF = 9.8–12.5) reflected moderate surface heterogeneity, while Temkin parameters confirmed significant adsorbate–adsorbent interactions.<br /><br />Thermodynamics. Negative ΔG° values confirmed spontaneous adsorption. Positive ΔH° values (15–18 kJ/mol) indicated endothermic processes, and positive ΔS° values (118–140 J/mol·K) reflected increased randomness at the solid–solution interface. These thermodynamic properties are consistent with entropy-driven adsorption involving release of bound water molecules during adsorption.<br /><br />Discussion<br /><br />The superior removal of Pb²⁺ compared with other metals can be explained by its larger ionic radius and higher affinity for surface functional groups, which promote strong binding. In contrast, the relatively lower adsorption of methyl blue may result from steric hindrance and electrostatic repulsion, limiting access to active sites. The PSO kinetic model’s strong fit suggests chemisorption mechanisms, while Langmuir isotherm modeling confirms uniform monolayer adsorption. The thermodynamic data affirm that adsorption is spontaneous, feasible at higher temperatures, and enhanced by entropy-driven mechanisms.<br /><br />These findings highlight the potential of C. odorata-mediated AgNPs as sustainable adsorbents, not only because of their efficiency but also because of their ecological implications. C. odorata is an invasive species in the Philippines and other tropical regions; using its leaves for nanoparticle synthesis provides a dual benefit by controlling an invasive weed while creating value-added applications in environmental remediation.<br /><br />Conclusion<br /><br />This study demonstrates the potential of Chromolaena odorata-mediated AgNPs as effective, ecofriendly adsorbents for removing heavy metals and synthetic dyes from aqueous solutions. High removal efficiencies, favorable adsorption kinetics, and thermodynamically feasible processes underscore their viability for wastewater treatment. Beyond laboratory findings, practical application requires addressing scalability, nanoparticle aggregation, and performance in complex wastewater matrices. Economic feasibility also depends on estimating how many kilograms of C. odorata leaves are required to synthesize sufficient AgNPs for treating several liters of wastewater in real-world settings.<br /><br />Overall, this research advances green nanotechnology for environmental applications, aligning with global sustainability goals, particularly Sustainable Development Goal 6 (Clean Water and Sanitation). Future work should include pilot-scale validation, regeneration studies, and integration into hybrid treatment systems to ensure cost-effectiveness and long-term applicability. Heavy metal and dye pollution in water resources presents a pressing global challenge due to its adverse impacts on environmental quality and human health. Conventional treatment methods, while effective, are often costly, energy-intensive, and generate secondary waste. In this study, silver nanoparticles (AgNPs) were synthesized via an ecofriendly green method using Chromolaena odorata leaves collected from Nueva Vizcaya, Philippines. The phytochemical-rich extract served as both a reducing and stabilizing agent. The synthesized nanoparticles were characterized by ultraviolet–visible (UV–Vis) spectroscopy, with a distinct surface plasmon resonance (SPR) peak observed at 428 nm, confirming nanoparticle formation and stability. Adsorption experiments were conducted to evaluate the removal efficiency of AgNPs against selected heavy metals (Pb2+, Fe2+, Cu2+, Co2+) and textile dyes (methyl orange, methyl red, methyl blue, Congo red) using simulated wastewater prepared from analytical-grade reagents. Results revealed high removal efficiencies, with Pb2+ (92.3%) and methyl orange (89.7%) exhibiting the highest adsorption under optimal conditions, while other contaminants ranged between 74.5% and 86.8%. Kinetic analysis demonstrated that adsorption followed a pseudo-second-order model (R² > 0.99), indicating chemisorption as the dominant mechanism, with equilibrium reached within 90–100 minutes. Isotherm modeling confirmed monolayer adsorption, with Pb2+ showing the highest maximum adsorption capacity (50 mg/g), followed by Fe2+ (46 mg/g), Cu2+ (43 mg/g), and Co2+ (44.5 mg/g). Dye adsorption capacities ranged from 40 to 42 mg/g. Thermodynamic parameters revealed negative Gibbs free energy (–18 to –25.5 kJ/mol), positive enthalpy (15–18 kJ/mol), and positive entropy (118–140 J/mol·K), confirming that the process was spontaneous, endothermic, and entropy-driven. These findings highlight the potential of C. odorata-derived AgNPs as sustainable and effective adsorbents for wastewater remediation. However, limitations include testing under controlled laboratory conditions, warranting further studies in real wastewater systems and assessments of nanoparticle reusability and scalability. Overall, this study advances green nanotechnology for environmental applications and supports the development of cost-effective, ecofriendly water treatment strategies. https://mmws.uma.ac.ir/article_4013_3f85ccd7229a6cde46b54ae8936bd68c.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Adoption of small-scale irrigation pathway for rural food security: Key determinants and coping strategies in Ethiopia Adoption of small-scale irrigation pathway for rural food security: Key determinants and coping strategies in Ethiopia 78 94 4114 10.22098/mmws.2025.18304.1676 FA Mamush Masha Department of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia 0000-0002-2666-9170 Abraham Woru Borku Department of Geography and Environmental Studies, College of Social Science and Humanities, Debark University, Debark, Ethiopia Journal Article 2025 09 09 Food insecurity remains a critical challenge in Sub-Saharan Africa despite favorable natural conditions. In Ethiopia, rural households continue to face persistent food shortages, yet evidence on how small-scale irrigation affects food security at household level remains limited. This study assessed the food security status and coping strategies of households in the Damota area of Wolaita Zone, southern Ethiopia. Primary data were collected from 130 households through a cross-sectional survey and analyzed using descriptive statistics and a Binary Logistic Regression model. Results showed that 56.15% of households were food insecure, while 43.85% were food secure. Household food security was significantly influenced by family size, age of household head, access to markets, education level, and livestock ownership. Coping strategies varied by severity: at initial stages, households relied on labor migration, social support, credit purchases, and asset sales, while at severe stages; they turned to food-for-work programs, distress livestock sales, school dropout, food aid, and sale of production equipment. The study contributes localized evidence on the role of irrigation in enhancing food security and reducing reliance on negative coping strategies. Findings highlight the need for policies that promote small-scale irrigation adoption, strengthen market access, and support diversified livelihood strategies for rural poor households. Food insecurity remains a critical challenge in Sub-Saharan Africa despite favorable natural conditions. In Ethiopia, rural households continue to face persistent food shortages, yet evidence on how small-scale irrigation affects food security at household level remains limited. This study assessed the food security status and coping strategies of households in the Damota area of Wolaita Zone, southern Ethiopia. Primary data were collected from 130 households through a cross-sectional survey and analyzed using descriptive statistics and a Binary Logistic Regression model. Results showed that 56.15% of households were food insecure, while 43.85% were food secure. Household food security was significantly influenced by family size, age of household head, access to markets, education level, and livestock ownership. Coping strategies varied by severity: at initial stages, households relied on labor migration, social support, credit purchases, and asset sales, while at severe stages; they turned to food-for-work programs, distress livestock sales, school dropout, food aid, and sale of production equipment. The study contributes localized evidence on the role of irrigation in enhancing food security and reducing reliance on negative coping strategies. Findings highlight the need for policies that promote small-scale irrigation adoption, strengthen market access, and support diversified livelihood strategies for rural poor households. https://mmws.uma.ac.ir/article_4114_19bcc61be66ea6f8229ef0aaa71be0bb.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Integration of conceptual hydrological and machine learning models via output augmentation for enhanced streamflow prediction Integration of conceptual hydrological and machine learning models via output augmentation for enhanced streamflow prediction 95 115 4035 10.22098/mmws.2025.18128.1648 FA Bewketu Mulu PhD Candidate in Geoinformation and Earth Observation for Hydrology, Faculty of Meteorology and Hydrology, Arba Minch Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia Fasikaw Zimale Associate Professor, Faculty of Civil and Water Resources Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia Mulugeta Kebede Assistant Professor, Institute of Geophysics, Space Science, and Astronomy, Atmospheric and Oceanic Sciences Unit, Addis Ababa University, Addis Ababa, Ethiopia Journal Article 2025 08 20 Quantifying water resources is essential for developing evidence-based management strategies. Hydrological models play a great role in estimating streamflow, particularly in regions with limited flow measurement infrastructure. This study evaluates the integration of the GR4J conceptual hydrological model with Machine Learning (ML) techniques, Random Forest (RF), Extreme Learning Machine (ELM), eXtreme Gradient Boosting (XGB), and Long Short-Term Memory (LSTM) networks to improve daily streamflow prediction in the Bilate River watershed. Though GR4J captures general hydrological trends, its limitations in modeling nonlinear dynamics and extreme flows necessitate advanced approaches by augmenting GR4J’s simulated outputs with climate input features to train the ML models. The integrated models GR4J-RF, GR4J-ELM, GR4J-XGB, and GR4J-LSTM combine GR4J’s physical interpretability with ML’s capability to capture complex and nonlinear relationships, addressing the shortcomings of both the conceptual and ML methods. Findings of the study demonstrate significant improvements over standalone GR4J, with GR4J-LSTM and GR4J-XGB achieving the highest test performance (NSE of 0.77, KGE of up to 0.86), GR4J-RF excelling in training fit (train NSE of 0.87) with gaps in generalization, and GR4J-ELM offering computational efficiency with comparable performance (test NSE of 0.74). These findings highlight the potential of integrated modeling to improve streamflow prediction in data-limited regions, supporting applications such as flood prediction and drought monitoring. Quantifying water resources is essential for developing evidence-based management strategies. Hydrological models play a great role in estimating streamflow, particularly in regions with limited flow measurement infrastructure. This study evaluates the integration of the GR4J conceptual hydrological model with Machine Learning (ML) techniques, Random Forest (RF), Extreme Learning Machine (ELM), eXtreme Gradient Boosting (XGB), and Long Short-Term Memory (LSTM) networks to improve daily streamflow prediction in the Bilate River watershed. Though GR4J captures general hydrological trends, its limitations in modeling nonlinear dynamics and extreme flows necessitate advanced approaches by augmenting GR4J’s simulated outputs with climate input features to train the ML models. The integrated models GR4J-RF, GR4J-ELM, GR4J-XGB, and GR4J-LSTM combine GR4J’s physical interpretability with ML’s capability to capture complex and nonlinear relationships, addressing the shortcomings of both the conceptual and ML methods. Findings of the study demonstrate significant improvements over standalone GR4J, with GR4J-LSTM and GR4J-XGB achieving the highest test performance (NSE of 0.77, KGE of up to 0.86), GR4J-RF excelling in training fit (train NSE of 0.87) with gaps in generalization, and GR4J-ELM offering computational efficiency with comparable performance (test NSE of 0.74). These findings highlight the potential of integrated modeling to improve streamflow prediction in data-limited regions, supporting applications such as flood prediction and drought monitoring. https://mmws.uma.ac.ir/article_4035_36b440dbdf08b1105a1cf86cd5229de6.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Socio-economic and biophysical determinants of landscape restoration adoption among smallholder farmers Socio-economic and biophysical determinants of landscape restoration adoption among smallholder farmers 116 142 4113 10.22098/mmws.2025.18362.1680 FA Dessalegne Chanie Haile Assistant Professor, Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia Mulugeta Debele Bedhane Associate Professor, Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia 0009-0002-4671-0123 Wakshum Shiferaw Associate Professor, Department of Natural Resource Management of Agricultural Science, College of Agriculture, Arba Minch University, Arba Minch, Ethiopia Daniel Azazie Birra M.Sc, Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia Zeleke Dosa Morgamo M.A, Department of Marketing Management, College of Commerce, Arba Minch University, Arba Minch, Ethiopia Gebremedhin Chameno Chalite M.Sc, Department of Natural Resource Management of Agricultural Science, College of Agriculture, Arba Minch University, Arba Minch, Ethiopia Zerihun Ayalew Gebre Ph.D Candidate, Department of Psychology, College of Education and Behavioral Science, Arba Minch University, Arba Minch, Ethiopia Journal Article 2025 09 16 Land degradation significantly threatens global food security and ecosystems, necessitating effective landscape restoration measures, particularly among smallholder farmers in vulnerable areas like the Womba watershed. This study investigates the localized socio-economic and physical factors influencing the adoption of landscape restoration practices within the Womba watershed. Utilizing data from 337 randomly selected household heads, along with focus group discussions and key informant interviews, the data were analyzed using descriptive and multivariate probit modeling estimation.The findings reveal that physical restoration practices, biological land management practices, and agronomic measures are prevalent among farmers. Key factors influencing adoption include gender dynamics, with male-headed households favoring physical interventions, while female-headed households prefer agronomic approaches. Age negatively impacts the adoption of physical and biological practices, while education correlates positively with agronomic methods. Family size enhances agronomic adoption, and access to credit significantly increases the likelihood of implementing diverse restoration strategies. Village memberships are positively associated with four landscape restoration practices at a 1% significance level. Notably, while farmers recognize the ecological benefits of these practices, their perceptions of socio-economic advantages remain limited. To promote broader adoption of restoration initiatives, policymakers should enhance educational outreach on the long-term socio-economic benefits and improve access to credit and extension services. Integrating these dimensions into policy frameworks will foster greater participation from both male and female farmers, ultimately supporting sustainable development in the Womba watershed and beyond. Land degradation significantly threatens global food security and ecosystems, necessitating effective landscape restoration measures, particularly among smallholder farmers in vulnerable areas like the Womba watershed. This study investigates the localized socio-economic and physical factors influencing the adoption of landscape restoration practices within the Womba watershed. Utilizing data from 337 randomly selected household heads, along with focus group discussions and key informant interviews, the data were analyzed using descriptive and multivariate probit modeling estimation.The findings reveal that physical restoration practices, biological land management practices, and agronomic measures are prevalent among farmers. Key factors influencing adoption include gender dynamics, with male-headed households favoring physical interventions, while female-headed households prefer agronomic approaches. Age negatively impacts the adoption of physical and biological practices, while education correlates positively with agronomic methods. Family size enhances agronomic adoption, and access to credit significantly increases the likelihood of implementing diverse restoration strategies. Village memberships are positively associated with four landscape restoration practices at a 1% significance level. Notably, while farmers recognize the ecological benefits of these practices, their perceptions of socio-economic advantages remain limited. To promote broader adoption of restoration initiatives, policymakers should enhance educational outreach on the long-term socio-economic benefits and improve access to credit and extension services. Integrating these dimensions into policy frameworks will foster greater participation from both male and female farmers, ultimately supporting sustainable development in the Womba watershed and beyond. https://mmws.uma.ac.ir/article_4113_2a998e1bb05e1201c2608e143b09e72b.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Hydrological regime shifts and functional river typologies in South-Western Nigeria Hydrological regime shifts and functional river typologies in South-Western Nigeria 143 158 4037 10.22098/mmws.2025.18030.1641 FA Ayoola Awode PhD of Department of Civil Engineering, Federal University of Technology, Akure, Nigeria James Adewumi Professor, Department of Civil Engineering, Federal University of Technology, Akure, Nigeria Obinna Obiora-Okeke Associate Professor, Department of Civil Engineering, Federal University of Technology, Akure, Nigeria Akinola Komolafe Associate Professor, Department of Remote sensing and Geo-informatics, Federal University of Technology, Akure, Nigeria 0000-0003-0202-0518 Journal Article 2025 08 10 Rivers in tropical regions and specifically West Africa are experiencing core hydrological changes under the twin-impacts of climate variability and intense land use change. This study analysed daily discharge for six rivers in South-Western Nigeria (Oba, Ogbese, Ogun, Osun, Owena, Yewa) over 1983–2023 (~14,600 observations per river). Methods combined Mann–Kendall with Sen’s slope (annual means), flow-duration curves (Q10/Q50/Q90 and log-slope), descriptive statistics, percentile-based extremes (Q95/Q5), Weibull flood-frequency, Colwell’s predictability (P, C, M), and PCA–k-means clustering. No river showed a significant monotonic trend (p > 0.05), yet variability was large (CV range ≈ 1.03–2.10). Ogun carried the largest high flows (Q10 = 488 m³ s⁻¹) and, with Yewa (CV ≈ 1.03), exhibited more baseflow support; Oba and Ogbese were flashy (CV = 2.10 and 1.37). Predictability was low across basins (P ≤ 0.246; C = 0), indicating unstable seasonality. Extremes were frequent: each river recorded ~296–749 flood days and a similar number of drought days over the record; 100-year peaks in Ogun exceeded 3,000 m³ s⁻¹. PCA–k- means separated Ogun from the other five rivers, supporting a functional typology for management. These findings argue for regime-oriented, nonstationary planning: linking operations, urban green infrastructure, and monitoring to variability, thresholds, and detected shifts rather than historical means. Rivers in tropical regions and specifically West Africa are experiencing core hydrological changes under the twin-impacts of climate variability and intense land use change. This study analysed daily discharge for six rivers in South-Western Nigeria (Oba, Ogbese, Ogun, Osun, Owena, Yewa) over 1983–2023 (~14,600 observations per river). Methods combined Mann–Kendall with Sen’s slope (annual means), flow-duration curves (Q10/Q50/Q90 and log-slope), descriptive statistics, percentile-based extremes (Q95/Q5), Weibull flood-frequency, Colwell’s predictability (P, C, M), and PCA–k-means clustering. No river showed a significant monotonic trend (p > 0.05), yet variability was large (CV range ≈ 1.03–2.10). Ogun carried the largest high flows (Q10 = 488 m³ s⁻¹) and, with Yewa (CV ≈ 1.03), exhibited more baseflow support; Oba and Ogbese were flashy (CV = 2.10 and 1.37). Predictability was low across basins (P ≤ 0.246; C = 0), indicating unstable seasonality. Extremes were frequent: each river recorded ~296–749 flood days and a similar number of drought days over the record; 100-year peaks in Ogun exceeded 3,000 m³ s⁻¹. PCA–k- means separated Ogun from the other five rivers, supporting a functional typology for management. These findings argue for regime-oriented, nonstationary planning: linking operations, urban green infrastructure, and monitoring to variability, thresholds, and detected shifts rather than historical means. https://mmws.uma.ac.ir/article_4037_6db66807183d36a38e694411079d7ab7.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Determinants of farmers’ use of small-scale irrigation practice in the Achewa area, Gambella, Southwestern Ethiopia Determinants of farmers’ use of small-scale irrigation practice in the Achewa area, Gambella, Southwestern Ethiopia 159 173 4049 10.22098/mmws.2025.18243.1665 FA Wasihun Mengiste PhD Scholars, Department of Soil and Water Management, College of agricultural science, Arba Minch University, Arba Minch, Ethiopia Dereje Tsegaye Associate Professor, Department of Plant science, College of agricultural science, Arba Minch University, Arba Minch, Ethiopia Samuel Dagalo Associate Professor, Department of Irrigation Engineering, Faculty of Water Resources and Irrigation Engineering, Arba-Minch University, Arba Minch, Ethiopia Teshome Yitbareke Associate Professor, Department of Natural Resource Management, Wolkite University, Wolkite, Ethiopia Journal Article 2025 09 02 Small-scale irrigation (SSI) has emerged as a vital strategy for enhancing food security, increasing agricultural productivity, and building resilience to climate change. This study examined the determinants of SSI use among farmers in the Achewa area, Gambella, Southwestern Ethiopia. A cross-sectional research design was employed, using a two-stage stratified random sampling approach to select 294 household heads (192 irrigation users and 102 non-users). Data collection involved household surveys, focus group discussions, and key informant interviews. Binary logistic regression analysis identified several significant factors affecting the adoption of small-scale irrigation (SSI). Notably, male-headed households were 12.85 times more likely to adopt SSI than female-headed households, underscoring a marked gender disparity in adoption patterns. Access to credit increased the likelihood of use by 2.98 times, while access to training raised it by 4.64 times. Larger livestock holdings (measured in Tropical Livestock Units) were associated with a 1.60 times higher probability of SSI use. Additionally, households with higher education levels and larger family sizes were more likely to be SSI users, with education and family size positively influencing adoption. These findings emphasize the importance of targeted interventions to promote equitable access to training and financial resources, particularly for female-headed households. Policies should prioritize empowering marginalized groups, such as female-headed households and older farmers, to enhance the adoption of small-scale irrigation (SSI). By addressing the barriers they face, these policies can expand SSI use, improve agricultural productivity, and uplift rural livelihoods in the region. Small-scale irrigation (SSI) has emerged as a vital strategy for enhancing food security, increasing agricultural productivity, and building resilience to climate change. This study examined the determinants of SSI use among farmers in the Achewa area, Gambella, Southwestern Ethiopia. A cross-sectional research design was employed, using a two-stage stratified random sampling approach to select 294 household heads (192 irrigation users and 102 non-users). Data collection involved household surveys, focus group discussions, and key informant interviews. Binary logistic regression analysis identified several significant factors affecting the adoption of small-scale irrigation (SSI). Notably, male-headed households were 12.85 times more likely to adopt SSI than female-headed households, underscoring a marked gender disparity in adoption patterns. Access to credit increased the likelihood of use by 2.98 times, while access to training raised it by 4.64 times. Larger livestock holdings (measured in Tropical Livestock Units) were associated with a 1.60 times higher probability of SSI use. Additionally, households with higher education levels and larger family sizes were more likely to be SSI users, with education and family size positively influencing adoption. These findings emphasize the importance of targeted interventions to promote equitable access to training and financial resources, particularly for female-headed households. Policies should prioritize empowering marginalized groups, such as female-headed households and older farmers, to enhance the adoption of small-scale irrigation (SSI). By addressing the barriers they face, these policies can expand SSI use, improve agricultural productivity, and uplift rural livelihoods in the region. https://mmws.uma.ac.ir/article_4049_f9c443cd43ed9e038b8eb1256f948e14.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Estimating water productivity of center-pivot irrigation systems using the WaPOR (Case study: Moghan plain) Estimating water productivity of center-pivot irrigation systems using the WaPOR (Case study: Moghan plain) 174 190 4001 10.22098/mmws.2025.17934.1635 FA Javanshir AziziMobaser Associate Professor, Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran 0000-0001-7801-2720 Mahsa Heydari Ali Kamar PhD student, Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Arash Amirzadeh Ph.D. Student., Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Mohammad Reza Kohan PhD student in Water Science and Engineering, Faculty of Agriculture and Natural Resources, Mohaghegh Ardabili University, Ardabil, Iran Ali Rasoulzadeh Professor, Department of Water Engineering and and Member of Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Majid Raoof Professor, Department of Water Engineering and and Member of Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Javad Ramezani Moghadam Associate Professor, Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2025 07 29 InField-based measurements rely on direct records of irrigation water depth, cultivated area, and actual harvested yield (Table 3), thereby reflecting localized agronomic management such as crop rotation, irrigation frequency, and fertilization practices. For example, field Q, which consistently showed the highest water productivity, followed a canola–maize rotation—a practice widely recognized to improve soil health and enhance nutrient and water uptake. Such agronomic details are rarely captured in remote sensing–based models like WaPOR. Conversely, WaPOR relies on medium-resolution satellite imagery and bio-physical modeling, which inherently apply spatial and temporal averaging. While this ensures regional consistency and enables large-scale assessments, it often overlooks farm-level management heterogeneity, leading to underestimation of actual water productivity. The discrepancy may also be exacerbated by differences in evapotranspiration estimation techniques, the use of generalized crop coefficients, and the static nature of some WaPOR input layers. Notably, temporal trends of field-based GBWP and NBWP values remained relatively stable across the five-year period, while WaPOR-derived values exhibited a more variable and increasing trend. This divergence could result from annual updates in WaPOR algorithms or climatic changes influencing satellite-based evapotranspiration estimates. plot P, a uniform irrigation depth of 55.24 mm per 10-day period appears adequate and well-aligned with crop water requirements during early development (vegetative and rapid leaf expansion stages, mid-June to early July). This is supported by high ETIa (41.97 mm) and Water Stress Coefficients (WSC) values (>1.5), which resulted in a favorable GBWP of 1.97 kg/m³ in 2020. However, as the season progressed into July–August (tasseling and ear formation), crop water demand gradually declined, yet the irrigation rate remained unchanged. With evapotranspiration (ETc) slightly dropping and day temperatures rising (above 24.5 °C), this resulted in marginal over-irrigation during late reproductive stages, possibly increasing non-beneficial losses and contributing to a GBWP drop in 2021 to 1.50 kg/m³ despite consistent irrigation. In contrast, plot Q received only 23.47 mm of irrigation per 10 days—sufficient for early growth but inadequate during mid-to-late season, especially during the reproductive phase when ETc surpassed 50 mm. This under-irrigation, combined with elevated temperatures (~25 °C in July 2021), likely limited photosynthetic efficiency and biomass accumulation. As a result, ETIa dropped below 20 mm in several decades, and GBWP declined to 1.54 kg/m³ in 2021, while NBWP followed a slower recovery trend despite improved weather conditions in subsequent years. Plot R, irrigated with 26.83 mm per 10-day period, exhibited similar constraints. While this amount may have met early-season requirements, it failed to match peak ETc demands during tasseling and grain-filling stages (July–August), which coincided with hot and dry weather. Consequently, actual crop water uptake (ETIa) remained suboptimal, particularly in 2021, where NBWP dropped to its minimum (2.12 kg/m³), indicating acute water stress. Recovery in GBWP and NBWP was observed in 2023, likely due to enhanced rainfall (60 mm) and moderate temperatures (~23 °C), which helped mitigate irrigation shortfalls.<br /><br />A key observation is that none of the plots implemented stage-specific irrigation. Fixed irrigation rates across all decades ignored the bell-shaped curve of crop water demand, leading to inefficiencies—over-irrigation during physiological maturity (e.g., September) and under-irrigation during peak demand periods (mid-season). The absence of flexible scheduling likely suppressed yield potential and water productivity, especially under heat stress conditions where crops require tightly controlled moisture regimes to maintain stomatal function and assimilate production. <br /><br />This study conducted a comprehensive comparative analysis of Net and Gross Biomass Water Productivity (NBWP and GBWP) across three agricultural fields (P, Q, and R) over five growing seasons (2020–2024), incorporating both ground-based measurements and WaPOR remote sensing estimates. The findings reveal clear inter-annual and spatial variability in water productivity, closely influenced by irrigation scheduling, rainfall patterns, temperature fluctuations, and crop management practices such as rotation. While NBWP showed a consistent upward trend in all fields—especially in Field Q, where advanced irrigation techniques and a canola–maize rotation contributed to sustained gains—GBWP was more sensitive to climatic extremes and non-optimized water use. The year 2021 emerged as a critical turning point marked by simultaneous declines in both GBWP and NBWP due to limited rainfall and elevated temperatures. Conversely, 2023 presented optimal climatic conditions, leading to productivity recovery, particularly in Fields Q and R. The comparison between WaPOR estimates and field-derived data highlighted a significant water productivity gap, with satellite-derived GBWP and NBWP values consistently underestimating actual productivity by up to 50% or more. This discrepancy is primarily attributed to the coarse spatial resolution, static model assumptions, and inability of WaPOR to capture localized agronomic nuances, such as stage-specific irrigation or soil fertility variations. Nonetheless, WaPOR’s consistent structure offers valuable insights for regional-scale assessments and long-term monitoring. Moreover, the analysis of decade-wise irrigation depth emphasized the limitations of uniform irrigation scheduling. Fixed irrigation rates failed to meet dynamic crop water requirements, leading to either over-irrigation in late-season stages or water stress during peak demand phases (tasseling and grain filling), especially under high-temperature conditions. These mismatches likely suppressed both biomass production and water use efficiency. In conclusion, this study underlines the critical need for integrated irrigation scheduling, climate-adaptive management, and field-calibrated remote sensing approaches to bridge the productivity gap and enhance sustainable water use. Future strategies should prioritize the adoption of precision irrigation technologies and stage-based water allocation to improve both the accuracy of water productivity assessments and the resilience of agroecosystems in semi-arid regions such as Moghan. Water productivity is essential for sustainable agriculture, especially in semi-arid regions with limited water resources. This study evaluates Net Biomass Water Productivity (NBWP) and Gross Biomass Water Productivity (GBWP) in three agricultural fields (P, Q, and R) cultivating silage maize under center pivot irrigation from 2020 to 2024. Ground measurements of irrigation depth, crop yield, and evapotranspiration, combined with temperature and precipitation data, were analyzed to understand temporal variations and the impact of environmental and management factors. Results showed a consistent increase in NBWP across all fields, with Field Q achieving the highest gain (39%), likely due to advanced irrigation techniques and better adaptation to climatic conditions. GBWP, however, fluctuated more significantly, with declines in 2021 coinciding with severe drought and elevated temperatures, highlighting maize sensitivity to water and heat stress. Field R was most affected during this period, reflecting the importance of targeted drought mitigation. Comparison between field data and WaPOR satellite-based estimates revealed systematic underestimation by the portal, attributed to its coarse spatial resolution and inability to capture localized agronomic practices, such as crop rotation and irrigation scheduling. The study also identified uniform irrigation rates applied throughout the crop cycle, ignoring the dynamic water demands during different growth stages. This led to over-irrigation during maturity and under-irrigation during critical reproductive phases, exacerbating water stress under high temperatures. The findings emphasize the necessity of integrating precise field measurements with remote sensing data for accurate water productivity assessment. Implementing stage-specific irrigation management can optimize water use efficiency and maintain crop biomass production under varying climatic conditions. This research provides valuable insights for improving irrigation strategies and water resource management, contributing to agricultural resilience in water-scarce semi-arid environments facing climate variability. https://mmws.uma.ac.ir/article_4001_13973bb7f56fe8fc8c7a7dd1b8810e13.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Biophysical factors determining adoption of soil and water conservation measures in small holder farming system Biophysical factors determining adoption of soil and water conservation measures in small holder farming system 191 210 4081 10.22098/mmws.2025.18269.1670 FA Mamush Masha Department of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia 0000-0002-2666-9170 Abraham Woru Borku Department of Geography and Environmental Studies, College of Social Science and Humanities, Debark University, Debark, Ethiopia Journal Article 2025 09 06 Ethiopia’s highlands are severely affected by moderate to severe soil erosion due to high population density, intense rainfall, and rugged, long-cultivated topography. The landscape, including steep hills, valleys, and dissected plains, contributes to soil acidity and environmental degradation. Settlement expansion, inappropriate land use, land-use changes, and construction activities further exacerbate land degradation, often resulting in sheet and rill erosion during the rainy season. Despite local governments promoting soil and water conservation (SWC) programs, implementation remains inconsistent, and adoption among farm households is limited. This study investigates household perceptions toward SWC adoption and identifies critical factors influencing the implementation of measures such as soil bunds, stone bunds, and fanya juu structures. Data were analyzed using descriptive statistics and multinomial logistic regression. Results revealed clear differences in perceptions: adopters exhibited medium to high recognition of benefits in soil fertility, erosion control, and productivity, whereas non-adopters showed low perception, indicating limited awareness and skepticism. Multinomial logistic regression identified that male-headed households, larger family size, greater farm size, livestock ownership, secure land tenure, education, and extension service contact significantly increased the likelihood of adoption. Conversely, greater farmland distance from home reduced adoption. The study recommends strengthening awareness programs, securing land tenure, and adapting SWC technologies to local biophysical conditions to improve adoption rates and promote sustainable land management in Southern Ethiopia. Ethiopia’s highlands are severely affected by moderate to severe soil erosion due to high population density, intense rainfall, and rugged, long-cultivated topography. The landscape, including steep hills, valleys, and dissected plains, contributes to soil acidity and environmental degradation. Settlement expansion, inappropriate land use, land-use changes, and construction activities further exacerbate land degradation, often resulting in sheet and rill erosion during the rainy season. Despite local governments promoting soil and water conservation (SWC) programs, implementation remains inconsistent, and adoption among farm households is limited. This study investigates household perceptions toward SWC adoption and identifies critical factors influencing the implementation of measures such as soil bunds, stone bunds, and fanya juu structures. Data were analyzed using descriptive statistics and multinomial logistic regression. Results revealed clear differences in perceptions: adopters exhibited medium to high recognition of benefits in soil fertility, erosion control, and productivity, whereas non-adopters showed low perception, indicating limited awareness and skepticism. Multinomial logistic regression identified that male-headed households, larger family size, greater farm size, livestock ownership, secure land tenure, education, and extension service contact significantly increased the likelihood of adoption. Conversely, greater farmland distance from home reduced adoption. The study recommends strengthening awareness programs, securing land tenure, and adapting SWC technologies to local biophysical conditions to improve adoption rates and promote sustainable land management in Southern Ethiopia. https://mmws.uma.ac.ir/article_4081_ff9b344c13522332ff8a1abf1b42f48d.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Application of light concrete balls as floatation balls to reduce evaporation from free water surfaces Application of light concrete balls as floatation balls to reduce evaporation from free water surfaces 211 222 4110 10.22098/mmws.2025.18231.1672 FA Jahangir Abedi Koupai Professor, Department of Water Sciences and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran Maryam Peiravi 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 09 07 Evaporation is a significant climatic phenomenon, particularly in arid and semi-arid regions, where it contributes to considerable water loss from open water bodies such as reservoirs, dams, and agricultural water storage ponds. Efficient management of evaporation is essential to conserve water resources and enhance irrigation sustainability. Although various methods have been proposed to reduce surface water loss, economic feasibility and ease of application remain key factors in their adoption. This study investigates an affordable and practical solution by employing floating cement balls made from low-cost and locally available materials to minimize evaporation from open water surfaces. To evaluate evaporation rates, several empirical models were tested under the climatic conditions of the Zayandeh Rood Dam reservoir. Among them, the Linacre, Ivanov, and DeBruin methods were identified as the most suitable for estimating daily and monthly evaporation in the study area. Additionally, laboratory experiments were conducted using different types of floating concrete balls. The results indicated that floating balls made of perlite, Lika-4-10, and Lika-2-4 reduced evaporation from the tank surface by 33.9, 38.5, and 42.7 percent, respectively. Therefore, Leca 2-4 concrete balls were the most effective, reducing evaporation compared to the uncovered surface. The outcomes of this research suggest that the proposed method offers a cost-effective and scalable approach for reducing evaporation losses, particularly in agricultural ponds and small-scale reservoirs in water-scarce regions Evaporation is a significant climatic phenomenon, particularly in arid and semi-arid regions, where it contributes to considerable water loss from open water bodies such as reservoirs, dams, and agricultural water storage ponds. Efficient management of evaporation is essential to conserve water resources and enhance irrigation sustainability. Although various methods have been proposed to reduce surface water loss, economic feasibility and ease of application remain key factors in their adoption. This study investigates an affordable and practical solution by employing floating cement balls made from low-cost and locally available materials to minimize evaporation from open water surfaces. To evaluate evaporation rates, several empirical models were tested under the climatic conditions of the Zayandeh Rood Dam reservoir. Among them, the Linacre, Ivanov, and DeBruin methods were identified as the most suitable for estimating daily and monthly evaporation in the study area. Additionally, laboratory experiments were conducted using different types of floating concrete balls. The results indicated that floating balls made of perlite, Lika-4-10, and Lika-2-4 reduced evaporation from the tank surface by 33.9, 38.5, and 42.7 percent, respectively. Therefore, Leca 2-4 concrete balls were the most effective, reducing evaporation compared to the uncovered surface. The outcomes of this research suggest that the proposed method offers a cost-effective and scalable approach for reducing evaporation losses, particularly in agricultural ponds and small-scale reservoirs in water-scarce regions https://mmws.uma.ac.ir/article_4110_c5ed4eb5ecccd9ca09b76de0552701c9.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Farmers’ willingness to use and pay for treated wastewater in the context of untreated wastewater availability Farmers’ willingness to use and pay for treated wastewater in the context of untreated wastewater availability 223 239 4044 10.22098/mmws.2025.18124.1646 FA Yashar Naderi Graduated of Agricultural Management, Department of Water Engineering and Agricultural Management, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran 0009-0006-4820-9390 Asghar Bagheri Professor, Department of Water Engineering and Agricultural Management, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Ali Rasoulzadeh Professor, Department of Water Engineering and Agricultural Management, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Zohreh Deh-Haqi Graduated of Agricultural Management, Department of Water Engineering and Agricultural Management, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Mousa Akbari Niari PhD of Water Engineering, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran Journal Article 2025 08 19 This study investigated farmers’ willingness to use (WTU) and to pay (WTP) for recycled water for irrigation in a water-scarce region of Ardabil province, Iran, where untreated sewage is available and partly used for irrigation. A sample of 261 farmers was selected for data collection and the necessary data were gathered through face-to-face interviews The study utilized Contingent Valuation Method (CVM) and binary Probit model. Respondents were proposed with four different qualities of treated sewage and three price options for the treated outflow from the treatment plant. Results indicated that a majority of farmers (76.2%) were willing to use treated sewage for irrigation. The WTU increased significantly with treated effluent quality, rising from 11.5% to 52.5% to 97.7%, before stabilizing at 84.3%; a perception that the highest proposed quality is suitable to drink may have tempered adoption at the upper end. More than half of the farmers (57.9%) expressed willingness to pay the same price as freshwater (150000 Rials per hour, Rls/h) for recycled water. Only 23.4% were willing to pay a higher price (187500 Rls/h), while 83.9% were willing to pay the lowest price (112500 Rls/h). Among the variables studied, environmental concern had the most significant influence on WTU, whereas management-related factors most strongly affected WTP. Effective incentives, such as reducing the price of treated wastewater in relation to its quality, training on management, health, and safety aspects of treated wastewater use, and promoting farmers' confidence in water quality can improve both WTU and WTP for treated wastewater. This study investigated farmers’ willingness to use (WTU) and to pay (WTP) for recycled water for irrigation in a water-scarce region of Ardabil province, Iran, where untreated sewage is available and partly used for irrigation. A sample of 261 farmers was selected for data collection and the necessary data were gathered through face-to-face interviews The study utilized Contingent Valuation Method (CVM) and binary Probit model. Respondents were proposed with four different qualities of treated sewage and three price options for the treated outflow from the treatment plant. Results indicated that a majority of farmers (76.2%) were willing to use treated sewage for irrigation. The WTU increased significantly with treated effluent quality, rising from 11.5% to 52.5% to 97.7%, before stabilizing at 84.3%; a perception that the highest proposed quality is suitable to drink may have tempered adoption at the upper end. More than half of the farmers (57.9%) expressed willingness to pay the same price as freshwater (150000 Rials per hour, Rls/h) for recycled water. Only 23.4% were willing to pay a higher price (187500 Rls/h), while 83.9% were willing to pay the lowest price (112500 Rls/h). Among the variables studied, environmental concern had the most significant influence on WTU, whereas management-related factors most strongly affected WTP. Effective incentives, such as reducing the price of treated wastewater in relation to its quality, training on management, health, and safety aspects of treated wastewater use, and promoting farmers' confidence in water quality can improve both WTU and WTP for treated wastewater. https://mmws.uma.ac.ir/article_4044_d9467c5c7bb1805511a23b3f3b9e9260.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Soil and water conservation practices: effectiveness and determinants of adoption among smallholder farmers Soil and water conservation practices: effectiveness and determinants of adoption among smallholder farmers 240 253 4103 10.22098/mmws.2025.18283.1673 FA Mamush Masha Assistant Professor, Department of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia 0000-0002-2666-9170 Abraham Woru Borku Assistant Professor, Department of Geography and Environmental Studies, College of Social Science and Humanities, Debark University, Debark, Ethiopia Wasihun Mengiste Ph.D candidate in Soil and Water Management, Department of Soil Resource and Watershed Management, College of Agriculture and Natural Resource Management, Gambella University, Gambella, Ethiopia Journal Article 2025 09 07 This research scrutinized the effectiveness of soil-water conservation (SWC) techniques and the determinants of their adoption by smallholder farmers in Gesha District, Southwest Ethiopia. A total of 36 soil samples were collected from preserved and non-preserved plots, and a household survey was conducted with 332 randomly selected respondents. Soil physical and chemical properties were analyzed using standard laboratory techniques, while mean differences were tested through one-way ANOVA. In addition, binary logistic regression was employed to identify factors influencing the adoption of SWC practices. Results revealed that preserved plots had higher soil fertility indicators associated to non-preserved plots, including soil pH (6.17 vs. 5.83), organic carbon (1.85% vs. 1.77%), available phosphorus (10.92 ppm vs. 9.93 ppm), and cation exchange capacity (37.3 vs. 30.3 cmol (+)/kg), while bulk density was lower (0.42–0.69 g/cm³ vs. 1.22 g/cm³). Adoption rates, however, remained limited to 53.9% of households. Regression results showed that adoption was positively influenced by education, farm size, livestock ownership, land slope, and farmers’ perception of erosion, while land tenure insecurity and credit access negatively affected adoption. The findings underscore that although SWC practices significantly improve soil fertility, socio-economic and institutional constraints hinder their wider uptake. Strengthening extension services, providing tenure security, and designing targeted interventions are recommended to enhance sustainable adoption. This research scrutinized the effectiveness of soil-water conservation (SWC) techniques and the determinants of their adoption by smallholder farmers in Gesha District, Southwest Ethiopia. A total of 36 soil samples were collected from preserved and non-preserved plots, and a household survey was conducted with 332 randomly selected respondents. Soil physical and chemical properties were analyzed using standard laboratory techniques, while mean differences were tested through one-way ANOVA. In addition, binary logistic regression was employed to identify factors influencing the adoption of SWC practices. Results revealed that preserved plots had higher soil fertility indicators associated to non-preserved plots, including soil pH (6.17 vs. 5.83), organic carbon (1.85% vs. 1.77%), available phosphorus (10.92 ppm vs. 9.93 ppm), and cation exchange capacity (37.3 vs. 30.3 cmol (+)/kg), while bulk density was lower (0.42–0.69 g/cm³ vs. 1.22 g/cm³). Adoption rates, however, remained limited to 53.9% of households. Regression results showed that adoption was positively influenced by education, farm size, livestock ownership, land slope, and farmers’ perception of erosion, while land tenure insecurity and credit access negatively affected adoption. The findings underscore that although SWC practices significantly improve soil fertility, socio-economic and institutional constraints hinder their wider uptake. Strengthening extension services, providing tenure security, and designing targeted interventions are recommended to enhance sustainable adoption. https://mmws.uma.ac.ir/article_4103_b5966b6a8a8bb8afc087c7aaaec1b5f3.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 A systematic review of performance assessment in canal irrigation systems: Integrating socio-technical, remote sensing, and AI-driven approaches for a climate-resilient future A systematic review of performance assessment in canal irrigation systems: Integrating socio-technical, remote sensing, and AI-driven approaches for a climate-resilient future 254 276 4115 10.22098/mmws.2025.18343.1683 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 09 17 This systematic review investigates the evolution of performance assessment in canal irrigation systems globally, drawing evidence from Asia, Africa, and Latin America. Adhering to PRISMA guidelines, it synthesized 98 peer-reviewed studies and key organizational reports published between 1990 and 2025, primarily from Scopus and Web of Science. The analysis reveals a clear methodological progression from direct measurements to remote sensing (RS) and agro-hydrological modeling, with Artificial Intelligence (AI) now evidenced as an applied tool in some assessments, not merely a future prospect. A critical insight, however, is that despite these technical advancements, persistent underperformance is primarily rooted in deep-seated non-technical (financial, institutional, social) barriers. The current review highlights a significant gap: the absence of a unified framework systematically integrating these technical and socio-institutional dimensions with forward-looking climate resilience. Our primary contribution is a novel, integrated socio-technical assessment framework designed to bridge this divide. Distinct from previous reviews, the proposed framework explicitly combines the methodological triad, comprehensive socio-institutional analysis, quantifiable climate resilience metrics, and mechanisms to ensure social equity in AI-driven management. This adaptable, multi-scale diagnostic tool offers an actionable blueprint, applicable from local canal management to national policy levels, that accounts for diverse regional data limitations. By enabling more effective problem diagnosis and intervention design, proposed framework provides significant analytical value and actionable lessons for enhancing the productivity, equity, and climate resilience of canal irrigation systems, thereby directly advancing Sustainable Development Goals 2 and 6. This systematic review investigates the evolution of performance assessment in canal irrigation systems globally, drawing evidence from Asia, Africa, and Latin America. Adhering to PRISMA guidelines, it synthesized 98 peer-reviewed studies and key organizational reports published between 1990 and 2025, primarily from Scopus and Web of Science. The analysis reveals a clear methodological progression from direct measurements to remote sensing (RS) and agro-hydrological modeling, with Artificial Intelligence (AI) now evidenced as an applied tool in some assessments, not merely a future prospect. A critical insight, however, is that despite these technical advancements, persistent underperformance is primarily rooted in deep-seated non-technical (financial, institutional, social) barriers. The current review highlights a significant gap: the absence of a unified framework systematically integrating these technical and socio-institutional dimensions with forward-looking climate resilience. Our primary contribution is a novel, integrated socio-technical assessment framework designed to bridge this divide. Distinct from previous reviews, the proposed framework explicitly combines the methodological triad, comprehensive socio-institutional analysis, quantifiable climate resilience metrics, and mechanisms to ensure social equity in AI-driven management. This adaptable, multi-scale diagnostic tool offers an actionable blueprint, applicable from local canal management to national policy levels, that accounts for diverse regional data limitations. By enabling more effective problem diagnosis and intervention design, proposed framework provides significant analytical value and actionable lessons for enhancing the productivity, equity, and climate resilience of canal irrigation systems, thereby directly advancing Sustainable Development Goals 2 and 6. https://mmws.uma.ac.ir/article_4115_ef6b3e4656b31edad8a06309db4f7f36.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Indirect estimation of Arvand River discharge using numerical modeling and remote sensing: A novel approach in water resources management Indirect estimation of Arvand River discharge using numerical modeling and remote sensing: A novel approach in water resources management 277 300 4112 10.22098/mmws.2025.18232.1662 FA Abbas Einali Assistant Professor of Physical Oceanography, Faculty of Environmental and Marine Sciences, University of Mazandaran, Mazandaran, Iran Masoud Sadrinasab Associate Professor of Physical Oceanography, Department of Environmental Design, University of Tehran, Tehran, Iran Mohammad Akbarinasab Associate Professor of Physical Oceanography, Faculty of Environmental and Marine Sciences, University of Mazandaran, Mazandaran, Iran Jafar Azizpour Assistant Professor of Physical Oceanography, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran 0000-0003-2121-1520 Journal Article 2025 09 01 The river discharge is the most critical parameter in the hydrologic cycle, and its measurement is vital considering climate change and water resource management. Due to local problems, the discharge of the Arvand River located in the Middle East (hot-dry climate) has not yet been measured directly. The Arvand River is considered the main source of freshwater inflow in the Persian Gulf and plays an essential environmental role in the northwest coastal zones of the Persian Gulf. For this reason, an indirect method was derived and used for the Arvand River discharge in this study. This method estimates the river discharge based on the river plume dimension. For this purpose, numerical modeling extracted the relationship between river discharge and river plume area in the first part. Thus, the Persian Gulf's temperature, salinity, and water circulation were modeled using FVCOM. In the following, the sensitivity of the river plume to the discharge and wind was investigated more accurately by applying fourteen different wind modes plus eight different discharge modes to the model. The numerical model results indicate that the river plume of Arvand is a "surface-advected plume" with a high sensitivity to wind fluctuations. Numerous experiments extracted the mathematical relation between the Plume Area and the River Discharge (PA-RD) within various wind conditions. A surface salinity of 37 psu determined the river plume border. The second step extracted the Arvand River plume (salinity plume) area using remote sensing techniques. The linear relationship between the sea surface salinity in-situ measurements and surface reflectance (SSS-SR) of Landsat TM5 satellite bands was obtained using a regression model at the river mouth in 1992. The surface salinity pattern at the Arvand River mouth was revealed by applying the SSS-SR relation to all of the Landsat pixels. Eventually, in 1992, the river plum (salinity plume) area was extracted, and then by substituting it in the PA-RD relation, the river discharge was estimated at 540 m³.s^-1. The present work is the first serious step toward studying the Arvand River discharge. The river discharge is the most critical parameter in the hydrologic cycle, and its measurement is vital considering climate change and water resource management. Due to local problems, the discharge of the Arvand River located in the Middle East (hot-dry climate) has not yet been measured directly. The Arvand River is considered the main source of freshwater inflow in the Persian Gulf and plays an essential environmental role in the northwest coastal zones of the Persian Gulf. For this reason, an indirect method was derived and used for the Arvand River discharge in this study. This method estimates the river discharge based on the river plume dimension. For this purpose, numerical modeling extracted the relationship between river discharge and river plume area in the first part. Thus, the Persian Gulf's temperature, salinity, and water circulation were modeled using FVCOM. In the following, the sensitivity of the river plume to the discharge and wind was investigated more accurately by applying fourteen different wind modes plus eight different discharge modes to the model. The numerical model results indicate that the river plume of Arvand is a "surface-advected plume" with a high sensitivity to wind fluctuations. Numerous experiments extracted the mathematical relation between the Plume Area and the River Discharge (PA-RD) within various wind conditions. A surface salinity of 37 psu determined the river plume border. The second step extracted the Arvand River plume (salinity plume) area using remote sensing techniques. The linear relationship between the sea surface salinity in-situ measurements and surface reflectance (SSS-SR) of Landsat TM5 satellite bands was obtained using a regression model at the river mouth in 1992. The surface salinity pattern at the Arvand River mouth was revealed by applying the SSS-SR relation to all of the Landsat pixels. Eventually, in 1992, the river plum (salinity plume) area was extracted, and then by substituting it in the PA-RD relation, the river discharge was estimated at 540 m³.s^-1. The present work is the first serious step toward studying the Arvand River discharge. https://mmws.uma.ac.ir/article_4112_4f1212c2619f5d66a7aaae076e0b1fcd.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Land use/land cover dynamics and their impact on selected soil properties in an Ethiopian highland ecosystem Land use/land cover dynamics and their impact on selected soil properties in an Ethiopian highland ecosystem 301 322 4158 10.22098/mmws.2025.18381.1687 FA Sisay Moges MSc in Soil Science, Estie District Office of Agriculture, Amhara National Regional State, South Gondar Zone, Debre Tabor, Ethiopia Eyayu Molla Associate Professor, Department of Natural Resources Management, College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Ethiopia Mulatie Mekonnen Associate Professor, Department of Natural Resources Management, College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Ethiopia Aweke Endalew MSc in Soil Science, Department of Soil Resource and Watershed Management, College of Agriculture and Natural Resource, Gambella University, Gambella, Ethiopia Endalew Tasew MSc in Watershed Management and Soil and Water Conservation, Department of Soil Resource and Watershed Management, College of Agricultural Sciences, Bole Hora University, Bole Hora, Ethiopia 0009-0008-2950-9878 Journal Article 2025 09 17 Change in land use/land cover can impact soil quality either negative or positive effects. Therefore, it is crucial to evaluate these changes and their impact on soil properties for sustainable soil management practices. Thus, the objectives of this study were to analyze land use/land cover dynamics over 30 years and to investigate the impact of land use types on soil properties in the Shemit watershed, northwestern Ethiopia. ERDAS Imagine 2010 software was utilized to conduct LULC classification and image analysis on Landsat satellite images from 1989, 2004, and 2019. Four land use types (plantation forest, cultivation land, natural forest, and grazing land) were chosen to evaluate the impact of  land use types on soil properties. 24 soil samples were gathered from three representative sampling plots across each land use type at two soil depths (0-20 and 20-40 cm), and examined their soil properties. The study found that cultivated land and settlement increased by 3.65% and 6.33%, respectively, while grazing and forest lands declined by 7.23% and 2.76% from 1989 to 2019. Soil analysis revealed that, clay (54.33%), pH (6.34), total nitrogen (0.28%), soil organic matter (5.32%), available phosphorus (6.24 mg kg^-1), exchangeable basic cations, and cation exchange capacity (28.77 cmol_ckg^-1) were higher on the natural forest than on other land use types. In the same manner, clay (49.33%), bulk density (1.29 g cm^-3), pH (5.97), and exchangeable basic cations increased with depth across all types of land use. In general, land use/land cover dynamics have adversely affected soil qualities owing to population growth, deforestation, and the expansion of settlement and cultivated areas. These transformations have degraded soil fertility and natural ecosystems. Promoting sustainable practices such as agroforestry, mixed farming, and the use of plant residues can help restore soil health, enhance carbon storage, and mitigate climate change. Immediate adoption of integrated land management and soil fertility strategies is essential to rehabilitate degraded lands and ensure sustainable agricultural productivity. Change in land use/land cover can impact soil quality either negative or positive effects. Therefore, it is crucial to evaluate these changes and their impact on soil properties for sustainable soil management practices. Thus, the objectives of this study were to analyze land use/land cover dynamics over 30 years and to investigate the impact of land use types on soil properties in the Shemit watershed, northwestern Ethiopia. ERDAS Imagine 2010 software was utilized to conduct LULC classification and image analysis on Landsat satellite images from 1989, 2004, and 2019. Four land use types (plantation forest, cultivation land, natural forest, and grazing land) were chosen to evaluate the impact of  land use types on soil properties. 24 soil samples were gathered from three representative sampling plots across each land use type at two soil depths (0-20 and 20-40 cm), and examined their soil properties. The study found that cultivated land and settlement increased by 3.65% and 6.33%, respectively, while grazing and forest lands declined by 7.23% and 2.76% from 1989 to 2019. Soil analysis revealed that, clay (54.33%), pH (6.34), total nitrogen (0.28%), soil organic matter (5.32%), available phosphorus (6.24 mg kg^-1), exchangeable basic cations, and cation exchange capacity (28.77 cmol_ckg^-1) were higher on the natural forest than on other land use types. In the same manner, clay (49.33%), bulk density (1.29 g cm^-3), pH (5.97), and exchangeable basic cations increased with depth across all types of land use. In general, land use/land cover dynamics have adversely affected soil qualities owing to population growth, deforestation, and the expansion of settlement and cultivated areas. These transformations have degraded soil fertility and natural ecosystems. Promoting sustainable practices such as agroforestry, mixed farming, and the use of plant residues can help restore soil health, enhance carbon storage, and mitigate climate change. Immediate adoption of integrated land management and soil fertility strategies is essential to rehabilitate degraded lands and ensure sustainable agricultural productivity. https://mmws.uma.ac.ir/article_4158_28edf090392bf272b06cadf66e4a5c15.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Predicting the electrical conductivity of water using the Hicking CEEMD-LSSVM optimization algorithm Predicting the electrical conductivity of water using the Hicking CEEMD-LSSVM optimization algorithm 323 348 4076 10.22098/mmws.2025.18187.1657 FA Elham Ghanbari-Adivi Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran Jalil Kermannezhad Chaharmahal & Bakhtiari Water and Waste Water Company, Shahrekord, Iran Ali Raeisi Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran Journal Article 2025 08 25 Accurate prediction of electrical conductivity (EC) concentrations in river water is essential for effective water quality management and environmental protection. This study develops a novel hybrid model, named HOA-CEEMD-LSSVM, that integrates the hiking optimization algorithm (HOA), complementary ensemble empirical mode decomposition (CEEMD), and least square support vector machine (LSSVM) to forecast daily EC concentrations in the Aidoghmoush River, Iran. HOA simultaneously optimizes key parameters of CEEMD and LSSVM to enhance prediction accuracy. CEEMD decomposes complex time series into intrinsic mode functions (IMFs) with more predictable patterns, which serve as inputs to the LSSVM predictor. The model’s performance is evaluated through multiple metrics, demonstrating significant improvements over benchmark models in terms of R² and Kling-Gupta Efficiency (KGE). The proposed model enhances the R² and KGE values of other prediction models by 1%-10 % and 3.17%-17%, respectively. Our findings show that the HAO-CEEMD-LSSVM model can precisely forecast EC concentration. This approach provides a robust framework for capturing the nonlinear, nonstationary characteristics of EC time series data. The model is applicable in water resource planning, pollution control, and river ecosystem management. While showing high forecasting accuracy, its computational complexity and black-box nature present limitations. Future work should explore parallel computing and explainable artificial intelligence techniques to enhance efficiency and interpretability. Accurate prediction of electrical conductivity (EC) concentrations in river water is essential for effective water quality management and environmental protection. This study develops a novel hybrid model, named HOA-CEEMD-LSSVM, that integrates the hiking optimization algorithm (HOA), complementary ensemble empirical mode decomposition (CEEMD), and least square support vector machine (LSSVM) to forecast daily EC concentrations in the Aidoghmoush River, Iran. HOA simultaneously optimizes key parameters of CEEMD and LSSVM to enhance prediction accuracy. CEEMD decomposes complex time series into intrinsic mode functions (IMFs) with more predictable patterns, which serve as inputs to the LSSVM predictor. The model’s performance is evaluated through multiple metrics, demonstrating significant improvements over benchmark models in terms of R² and Kling-Gupta Efficiency (KGE). The proposed model enhances the R² and KGE values of other prediction models by 1%-10 % and 3.17%-17%, respectively. Our findings show that the HAO-CEEMD-LSSVM model can precisely forecast EC concentration. This approach provides a robust framework for capturing the nonlinear, nonstationary characteristics of EC time series data. The model is applicable in water resource planning, pollution control, and river ecosystem management. While showing high forecasting accuracy, its computational complexity and black-box nature present limitations. Future work should explore parallel computing and explainable artificial intelligence techniques to enhance efficiency and interpretability. https://mmws.uma.ac.ir/article_4076_2f085093e462c6541c6a06f02fabf0f4.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 5 4 2025 10 23 Digital transformation in environmental parameter measurement and monitoring: transitioning from traditional methods Digital transformation in environmental parameter measurement and monitoring: transitioning from traditional methods 349 364 3958 10.22098/mmws.2025.17790.1623 FA Abdulqadous Abdullah Al-Turath University, Baghdad 10013, Iraq, Suzan Mohammed Jawad Alkazraji Al-Mansour University College, Baghdad 10067, Iraq Ayah Ahmed Jasim Al-Mamoon University College, Baghdad 10012, Iraq Aseel Ibraheem Muhsin Al-Rafidain University College Baghdad 10064, Iraq, Ola Janan Madenat Alelem University College, Baghdad 10006, Iraq, Somaye Allahvaisi Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Sanandaj, Iran Journal Article 2025 07 08 Traditional environmental monitoring, which relies on manual sampling and laboratory analysis, often suffers from slow response times, high operational costs, and limited spatial or temporal resolution. These constraints hinder timely and informed decision-making, particularly in the face of accelerating environmental change. This study investigates the potential of digital technologies—primarily Internet of Things (IoT) sensors and Artificial Intelligence (AI)—to modernize environmental monitoring systems focused on air quality, water, and soil. A comparative design was employed to evaluate traditional methods against digital systems, incorporating IoT-enabled data collection and AI-driven analytics, supported by big data infrastructure. Key environmental indicators included PM2.5 concentration, soil moisture, water pH, temperature, and carbon emissions. The results showed significant improvements: measurement accuracy increased by approximately 20%, response time was reduced by 97.9%, and data processing speed surged by more than 19,900%, effectively reducing processing durations from several hours to near real-time. Operational costs decreased by over 50%. Additionally, predictive models powered by AI allowed for early warnings, while real-time data acquisition through IoT improved responsiveness to environmental threats. Although blockchain was not used directly for measurement or analysis, it played a critical role in ensuring data integrity, transparency, and traceability—factors essential to building trust in digital monitoring frameworks. Despite ongoing challenges such as scalability, energy consumption, and connectivity in rural regions, the findings highlight the potential of integrated digital tools to create more adaptive, efficient, and sustainable environmental management systems. These smart technologies present a path toward proactive governance and resilient ecosystem stewardship.The objective of this study is to investigate how the integration of advanced digital technologies such as IoT, AI, big data, and cloud computing, can revolutionize environmental monitoring and management. By assessing these tools’ potential to enhance data accuracy, responsiveness, and stakeholder collaboration, the research aims to develop proactive, transparent, and cost-effective strategies that address the complex challenges of ecological resilience and sustainable resource use in both urban and rural settings. The objective of this study is to investigate how the integration of advanced digital technologies such as IoT, AI, big data, and cloud computing, can revolutionize environmental monitoring and management. By assessing these tools’ potential to enhance data accuracy, responsiveness, and stakeholder collaboration, the research aims to develop proactive, transparent, and cost-effective strategies that address the complex challenges of ecological resilience and sustainable resource use in both urban and rural settings. <br /><br /><br /><br />This study aimed to investigate the role of digital technologies in enhancing the effectiveness of environmental monitoring systems, particularly in relation to air, water, and soil quality. The results demonstrate that integrating IoT-based sensors, AI-driven analytics, cloud computing, and blockchain infrastructure can substantially improve measurement accuracy, reduce operational delays, and support faster and more informed decision-making. Real-time monitoring of parameters such as PM2.5 concentration, water pH, and soil moisture content proved notably more precise and reliable than traditional sampling methods, offering practical benefits for both environmental assessment and resource management. In particular, the ability to detect fluctuations in soil moisture and water quality at higher temporal resolution enabled quicker response to environmental risks, which is critical in ecosystems sensitive to drought, contamination, or land degradation. The automation of data collection and processing also led to significant gains in cost efficiency and processing speed, further confirming the operational advantages of digital transformation in environmental systems. Moreover, the application of AI-based predictive models supported proactive intervention, allowing environmental authorities to anticipate potential hazards and take early action before adverse impacts escalate. Nevertheless, the implementation of such technologies remains dependent on infrastructure readiness, reliable network connectivity, and energy efficiency—factors that may limit scalability in certain rural or underdeveloped areas. While tested in Iraq, these findings are applicable to other regions with similar environmental challenges, pending infrastructure upgrades. For example, in Iraq’s rural regions, limited broadband infrastructure, frequent network disruptions, and inconsistent mobile coverage posed significant challenges to continuous data transmission from IoT devices. These connectivity issues resulted in occasional data loss and reduced the overall effectiveness of real-time monitoring efforts. Furthermore, as digital systems become more deeply embedded in environmental governance, considerations around data ownership, system interoperability, and long-term sustainability will need to be addressed. Based on the findings, future research should explore strategies for optimizing low-power digital monitoring frameworks, enhancing sensor durability in diverse terrain, and developing governance mechanisms that ensure data transparency and equitable access. Such efforts are essential for building resilient, responsive, and inclusive systems capable of supporting long-term environmental stewardship.<br /><br />Based on the comparative performance analysis, IoT-based real-time sensing combined with AI-powered predictive analytics proved to be the most effective in improving measurement accuracy and response time. These tools are highly recommended for environmental monitoring applications, particularly in water and soil resource management. Blockchain, while essential for ensuring data transparency and integrity, had a relatively lower direct impact on measurement accuracy and operational efficiency, and thus is recommended primarily as a supplementary tool for secure data governance rather than for core monitoring tasks Traditional environmental monitoring, which relies on manual sampling and laboratory analysis, often suffers from slow response times, high operational costs, and limited spatial or temporal resolution. These constraints hinder timely and informed decision-making, particularly in the face of accelerating environmental change. This study investigates the potential of digital technologies—primarily Internet of Things (IoT) sensors and Artificial Intelligence (AI)—to modernize environmental monitoring systems focused on air quality, water, and soil. A comparative design was employed to evaluate traditional methods against digital systems, incorporating IoT-enabled data collection and AI-driven analytics, supported by big data infrastructure. Key environmental indicators included PM2.5 concentration, soil moisture, water pH, temperature, and carbon emissions. The results showed significant improvements: measurement accuracy increased by approximately 20%, response time was reduced by 97.9%, and data processing speed surged by more than 19,900%, effectively reducing processing durations from several hours to near real-time. Operational costs decreased by over 50%. Additionally, predictive models powered by AI allowed for early warnings, while real-time data acquisition through IoT improved responsiveness to environmental threats. Although blockchain was not used directly for measurement or analysis, it played a critical role in ensuring data integrity, transparency, and traceability—factors essential to building trust in digital monitoring frameworks. Despite ongoing challenges such as scalability, energy consumption, and connectivity in rural regions, the findings highlight the potential of integrated digital tools to create more adaptive, efficient, and sustainable environmental management systems. These smart technologies present a path toward proactive governance and resilient ecosystem stewardship. https://mmws.uma.ac.ir/article_3958_a5006072df1a12534fc2fed203d81dff.pdf