University of KashanDesert Ecosystem Engineering2538-6336144620250723Identifying Factors Influencing Wildfires in Khorramabad's Natural Areas Using a Decision Tree ModelIdentifying Factors Influencing Wildfires in Khorramabad's Natural Areas Using a Decision Tree Model11611491310.22052/deej.2025.256094.1090FASoheilaNaseriradPhD student in Forest Science and Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, IranHamedNaghaviDepartment of Forestry Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, IranHamidrezaPourghasemiProfessor, Department of Engineering and Environment, Faculty of Agriculture, Shiraz University, Shiraz, IranArvinFakhriDepartment of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N Toosi University of Technology, Tehran, Iran0000-0002-4992-0487Journal Article20241231<strong>Itroduction:</strong> The frequent occurrence of wildfires in the natural landscapes of the Zagros region necessitates comprehensive research to identify contributing factors and predict future fire incidents. Accordingly, this study aims to determine the key drivers of wildfires in the Khorramabad County watershed, located in the central Zagros, using decision tree-based modeling approaches.
<strong>Material and Methods:</strong> The study examined fire-influencing factors across four categories: climate, topography, land use, and human activity. Wildfire data spanning 2011–2024 (380 fire points), obtained from the Lorestan Province General Department of Natural Resources and Watershed Management, served as the dependent variable. The dataset was split into training (70%, 266 points) and evaluation (30%, 114 points) subsets. Model performance was assessed using the ROC curve and confusion matrix for validation..
<strong>Results: </strong>Variable importance analysis revealed that distance from roads and the Normalized Difference Vegetation Index (NDVI) were among the most significant factors influencing wildfire occurrences in the study area. The results demonstrated a positive correlation between fire incidence and three key variables: (1) greater proximity to roads, (2) higher vegetation density, and (3) increased wind effects. Model validation indicated an 85% accuracy rate in correctly classifying fire events versus non-fire events. These findings provide actionable insights for identifying fire risk factors, supporting wildfire prevention strategies, and promoting sustainable land management practices in the region..
<strong>Discusssion and Conclusion: </strong>Given the critical role of forests and rangelands in water and soil conservation, as well as erosion prevention, identifying key drivers of wildfires is essential for effective land management. This study highlights distance from roads, NDVI, precipitation, wind effect, and temperature as the most influential factors affecting fire occurrences in the study area, ranked in order of significance. The decision tree model demonstrated high predictive accuracy (85%), confirming its effectiveness in identifying wildfire risk factors. These findings provide valuable insights for:Wildfire prevention strategies, Sustainable land management, and Informed decision-making for policymakers and land-use planners. Furthermore, the results serve as a foundational reference for future research on natural resource management, particularly in fire-prone ecosystems. By integrating these findings into regional planning, stakeholders can better mitigate fire risks and enhance ecosystem resilience.<strong>Itroduction:</strong> The frequent occurrence of wildfires in the natural landscapes of the Zagros region necessitates comprehensive research to identify contributing factors and predict future fire incidents. Accordingly, this study aims to determine the key drivers of wildfires in the Khorramabad County watershed, located in the central Zagros, using decision tree-based modeling approaches.
<strong>Material and Methods:</strong> The study examined fire-influencing factors across four categories: climate, topography, land use, and human activity. Wildfire data spanning 2011–2024 (380 fire points), obtained from the Lorestan Province General Department of Natural Resources and Watershed Management, served as the dependent variable. The dataset was split into training (70%, 266 points) and evaluation (30%, 114 points) subsets. Model performance was assessed using the ROC curve and confusion matrix for validation..
<strong>Results: </strong>Variable importance analysis revealed that distance from roads and the Normalized Difference Vegetation Index (NDVI) were among the most significant factors influencing wildfire occurrences in the study area. The results demonstrated a positive correlation between fire incidence and three key variables: (1) greater proximity to roads, (2) higher vegetation density, and (3) increased wind effects. Model validation indicated an 85% accuracy rate in correctly classifying fire events versus non-fire events. These findings provide actionable insights for identifying fire risk factors, supporting wildfire prevention strategies, and promoting sustainable land management practices in the region..
<strong>Discusssion and Conclusion: </strong>Given the critical role of forests and rangelands in water and soil conservation, as well as erosion prevention, identifying key drivers of wildfires is essential for effective land management. This study highlights distance from roads, NDVI, precipitation, wind effect, and temperature as the most influential factors affecting fire occurrences in the study area, ranked in order of significance. The decision tree model demonstrated high predictive accuracy (85%), confirming its effectiveness in identifying wildfire risk factors. These findings provide valuable insights for:Wildfire prevention strategies, Sustainable land management, and Informed decision-making for policymakers and land-use planners. Furthermore, the results serve as a foundational reference for future research on natural resource management, particularly in fire-prone ecosystems. By integrating these findings into regional planning, stakeholders can better mitigate fire risks and enhance ecosystem resilience.https://deej.kashanu.ac.ir/article_114913_8c5fa7e82edc90ce0285896a9c72e81a.pdfUniversity of KashanDesert Ecosystem Engineering2538-6336144620250723Analysis of Dust Aerosol Optical Depth Changes and Their Frequency in Different Areas of Jazmourian Basin Using Remote Sensing TechnologyAnalysis of Dust Aerosol Optical Depth Changes and Their Frequency in Different Areas of Jazmourian Basin Using Remote Sensing Technology173411491710.22052/deej.2025.256804.1106FAZohreEbrahimi-KhusfiDepartment of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, IranGhobadJalaliDepartment of Soil Science, Faculty of Agriculture, University of Jiroft, Jiroft, IranMahdiyehAbbasiDepartment of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, IranSarehZavariDepartment of Fisheries Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran.Journal Article20250507<strong>Introduction:</strong> In recent decades, dust phenomena have emerged as one of the most destructive natural hazards in arid and semi-arid regions, profoundly impacting various aspects of life. These regions, characterized by low humidity, sparse vegetation cover, and vulnerable ecosystems, are inherently prone to dust storms. Iran, owing to its geographical position, is no exception and frequently grapples with this phenomenon, primarily driven by factors such as drought, climatic variations, and human activities. Dust storms pose numerous adverse effects on both the environment and the economy, including air pollution, ecosystem degradation, diminished soil fertility, declining agricultural productivity, and escalating healthcare expenses. The occurrence and frequency of dust events in a given area are influenced by a range of factors, including high wind speeds, low relative humidity, exposed soil surfaces, dry land conditions, local and regional weather systems, short-term precipitation deficits, extensive land degradation, prolonged drought periods, land-use changes, and anthropogenic activities. Airborne particles, especially dust, play a vital role in the climate system, affecting both global temperatures and regional weather patterns. Aerosol Optical Depth (AOD) serves as a pivotal parameter for assessing air quality and investigating particulate pollution. It quantifies the extent to which particles in the atmosphere absorb or scatter sunlight, with higher values indicating increased concentrations of suspended particles, including dust. Particularly in arid and semi-arid zones where dust constitutes a major pollutant, AOD proves to be an effective tool for weather monitoring and air quality forecasting. Various methodologies are employed to measure AOD. Ground-based aerosol robotic networks offer precise spectral aerosol data at specific locations but lack comprehensive spatial coverage. Regional models of atmospheric particulate matter analysis are also in use. However, satellite data, with their high revisit frequency and broad spatial scope, facilitate extensive investigations of dust phenomena. Among these, the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors aboard the Terra and Aqua satellites are extensively utilized. The MCD19A2 product, with a spatial resolution of 1 kilometer, is particularly valuable for studying aerosol properties. Its accuracy is enhanced through the combined use of the Deep Blue and Dark Target algorithms, which are specifically designed to measure optical depth and retrieve particulate matter concentrations. In general, analyzing long-term trends in aerosol optical depth and the frequency of dust aerosol events is crucial for identifying vulnerable areas and devising effective mitigation strategies. While data from synoptic stations can be useful for such analyses, their sparse distribution in the Jazmurian basin limits their effectiveness. Hence, remote sensing technologies and satellite-derived products prove instrumental for comprehensive assessments. Accordingly, the primary aim of this study is to analyze the long-term (2001–2022) trends in dust aerosol optical depth and their frequency within the sub-basins of the Jazmurian basin, utilizing the MODIS MCD19A2 product. The analysis will be conducted across monthly, seasonal, and annual timescales, employing the Mann-Kendall statistical test to identify significant trends and changes over the two-decade period.<br /> <br /><strong>Materials and Methods: </strong>In this study, 15 sub-basins of the Jazmurian basin were designated as study areas to monitor variations in air particulate matter concentrations. The aerosol optical depth (AOD) product from the MODIS sensor (MCD19A2) was employed for continuous assessment of dust aerosol depths across these sub-basins. Values of optical depth exceeding 0.5 were considered significant, given their strong correlation with numerous parameters related to dust activity measurement. Data for the MCD19A2 satellite product, with a daily temporal resolution and a spatial resolution of 1 kilometer, were separately downloaded for each sub-basin through the Google Earth Engine platform, covering the period from 2001 to 2022. After extracting events where AOD > 0.5, the average AOD values were calculated at monthly, seasonal, and annual scales. To analyze the trends in temporal variations of dust aerosol optical depth, the Mann-Kendall test was employed. This non-parametric statistical method is widely used in the analysis of meteorological time series. Its advantages include its suitability for data that do not follow a specific statistical distribution and its robustness against the influence of extreme values. The null hypothesis of the Mann-Kendall test indicates the presence of randomness and no trend in the data series, while acceptance of the alternative hypothesis suggests a significant trend exists.<br /><strong>Results and Discussion</strong>: The analysis revealed that, at the monthly scale, the lowest and highest frequencies of dust events occurred in October/November and July, respectively. The minimum aerosol optical depth (AOD) values were recorded in October, while the maximum AOD was observed in January/December. Seasonally, the highest aerosol optical depth and dust occurrence frequency were associated with summer and spring, whereas the autumn season exhibited the lowest values. On an annual basis, the lowest frequency of dust events was recorded in 2002 within the Mohammadabad sub-basin, while the highest occurred in 2011 and 2012 in the Hamoun sub-basin. The minimum aerosol concentration was observed in 2002, whereas the maximum levels appeared in 2012, 2016, and 2022. Additionally, it was found that both parameters—dust event frequency and aerosol concentration—showed a decreasing trend in June, which was statistically significant for concentration values in the southeastern regions of the Jazmurian basin. Conversely, most sub-basins exhibited an increasing trend during other months. At the annual scale, over 50% of the sub-basins demonstrated an increasing trend, with this trend being statistically significant in the Rabar, Jiroft, Faryab, Dashtab, and Esfandagheh sub-basins. Seasonally, dust activity also showed an upward trend. The maximum increase in dust event frequency was observed in winter and spring, while the greatest rise in aerosol depth occurred in winter and summer. Given these findings, implementing dust control strategies and adopting improved natural resource management practices are essential—particularly in sub-basins exhibiting significant upward trends—in order to mitigate serious threats to public health and enhance the quality of life for the local population.<strong>Introduction:</strong> In recent decades, dust phenomena have emerged as one of the most destructive natural hazards in arid and semi-arid regions, profoundly impacting various aspects of life. These regions, characterized by low humidity, sparse vegetation cover, and vulnerable ecosystems, are inherently prone to dust storms. Iran, owing to its geographical position, is no exception and frequently grapples with this phenomenon, primarily driven by factors such as drought, climatic variations, and human activities. Dust storms pose numerous adverse effects on both the environment and the economy, including air pollution, ecosystem degradation, diminished soil fertility, declining agricultural productivity, and escalating healthcare expenses. The occurrence and frequency of dust events in a given area are influenced by a range of factors, including high wind speeds, low relative humidity, exposed soil surfaces, dry land conditions, local and regional weather systems, short-term precipitation deficits, extensive land degradation, prolonged drought periods, land-use changes, and anthropogenic activities. Airborne particles, especially dust, play a vital role in the climate system, affecting both global temperatures and regional weather patterns. Aerosol Optical Depth (AOD) serves as a pivotal parameter for assessing air quality and investigating particulate pollution. It quantifies the extent to which particles in the atmosphere absorb or scatter sunlight, with higher values indicating increased concentrations of suspended particles, including dust. Particularly in arid and semi-arid zones where dust constitutes a major pollutant, AOD proves to be an effective tool for weather monitoring and air quality forecasting. Various methodologies are employed to measure AOD. Ground-based aerosol robotic networks offer precise spectral aerosol data at specific locations but lack comprehensive spatial coverage. Regional models of atmospheric particulate matter analysis are also in use. However, satellite data, with their high revisit frequency and broad spatial scope, facilitate extensive investigations of dust phenomena. Among these, the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors aboard the Terra and Aqua satellites are extensively utilized. The MCD19A2 product, with a spatial resolution of 1 kilometer, is particularly valuable for studying aerosol properties. Its accuracy is enhanced through the combined use of the Deep Blue and Dark Target algorithms, which are specifically designed to measure optical depth and retrieve particulate matter concentrations. In general, analyzing long-term trends in aerosol optical depth and the frequency of dust aerosol events is crucial for identifying vulnerable areas and devising effective mitigation strategies. While data from synoptic stations can be useful for such analyses, their sparse distribution in the Jazmurian basin limits their effectiveness. Hence, remote sensing technologies and satellite-derived products prove instrumental for comprehensive assessments. Accordingly, the primary aim of this study is to analyze the long-term (2001–2022) trends in dust aerosol optical depth and their frequency within the sub-basins of the Jazmurian basin, utilizing the MODIS MCD19A2 product. The analysis will be conducted across monthly, seasonal, and annual timescales, employing the Mann-Kendall statistical test to identify significant trends and changes over the two-decade period.<br /> <br /><strong>Materials and Methods: </strong>In this study, 15 sub-basins of the Jazmurian basin were designated as study areas to monitor variations in air particulate matter concentrations. The aerosol optical depth (AOD) product from the MODIS sensor (MCD19A2) was employed for continuous assessment of dust aerosol depths across these sub-basins. Values of optical depth exceeding 0.5 were considered significant, given their strong correlation with numerous parameters related to dust activity measurement. Data for the MCD19A2 satellite product, with a daily temporal resolution and a spatial resolution of 1 kilometer, were separately downloaded for each sub-basin through the Google Earth Engine platform, covering the period from 2001 to 2022. After extracting events where AOD > 0.5, the average AOD values were calculated at monthly, seasonal, and annual scales. To analyze the trends in temporal variations of dust aerosol optical depth, the Mann-Kendall test was employed. This non-parametric statistical method is widely used in the analysis of meteorological time series. Its advantages include its suitability for data that do not follow a specific statistical distribution and its robustness against the influence of extreme values. The null hypothesis of the Mann-Kendall test indicates the presence of randomness and no trend in the data series, while acceptance of the alternative hypothesis suggests a significant trend exists.<br /><strong>Results and Discussion</strong>: The analysis revealed that, at the monthly scale, the lowest and highest frequencies of dust events occurred in October/November and July, respectively. The minimum aerosol optical depth (AOD) values were recorded in October, while the maximum AOD was observed in January/December. Seasonally, the highest aerosol optical depth and dust occurrence frequency were associated with summer and spring, whereas the autumn season exhibited the lowest values. On an annual basis, the lowest frequency of dust events was recorded in 2002 within the Mohammadabad sub-basin, while the highest occurred in 2011 and 2012 in the Hamoun sub-basin. The minimum aerosol concentration was observed in 2002, whereas the maximum levels appeared in 2012, 2016, and 2022. Additionally, it was found that both parameters—dust event frequency and aerosol concentration—showed a decreasing trend in June, which was statistically significant for concentration values in the southeastern regions of the Jazmurian basin. Conversely, most sub-basins exhibited an increasing trend during other months. At the annual scale, over 50% of the sub-basins demonstrated an increasing trend, with this trend being statistically significant in the Rabar, Jiroft, Faryab, Dashtab, and Esfandagheh sub-basins. Seasonally, dust activity also showed an upward trend. The maximum increase in dust event frequency was observed in winter and spring, while the greatest rise in aerosol depth occurred in winter and summer. Given these findings, implementing dust control strategies and adopting improved natural resource management practices are essential—particularly in sub-basins exhibiting significant upward trends—in order to mitigate serious threats to public health and enhance the quality of life for the local population.https://deej.kashanu.ac.ir/article_114917_e99eb471887a1e0d5187f53233d95c9e.pdfUniversity of KashanDesert Ecosystem Engineering2538-6336144620250723Estimation of Water Requirements for Urban Green Infrastructures in Kish IslandEstimation of Water Requirements for Urban Green Infrastructures in Kish Island354811492510.22052/deej.2025.254229.1041FAMahmoudBehrouziDepartment of Environmental Hazards - Marine Science Research Institute, University of Tehran, Tehran0000-0002-5242-7730AhmadNohegarDepartment of Accident Engineering, Education and Environmental Systems - Faculty of Environment - University of TehranPanisaHassanzadehMaster student, Environment- Education, Faculty of Environment, University of Tehran, Tehran. Iran.0009-0000-1945-0640Journal Article20240501<strong>Introduction: </strong>Since 1993 (1372 SH), Kish Island has experienced significant population growth and urban expansion, particularly in its coastal areas. This development led to the creation of extensive green infrastructure, including wide streets, neighborhood parks, and numerous gardens, primarily featuring diverse plant species. However, climate change presents substantial challenges for these urban landscapes. Rising temperatures and limited water resources intensify the need for a strategic approach to urban greenery, focusing on increasing and diversifying tree and ornamental plant species adapted to harsh urban conditions.<br />Currently, there is a paucity of information regarding the irrigation requirements of urban green spaces on Kish Island. A practical, standardized method for measuring these water needs is also lacking. This deficit in understanding creates significant challenges for both plant health and urban landscape managers, leading to inefficiencies and disruptions in water resource management. Therefore, this research aims to calculate the monthly and annual water requirements of Kish Island's urban green spaces using the WUCOLS (Water Use Classification of Landscape Species) method developed by the University of California.<br /><strong> </strong><br /><strong>Method and Materials: </strong>To determine the monthly and annual water requirements of urban green spaces on Kish Island, we first conducted field observations and utilized existing land-use maps, an AutoCAD map of Kish Island, and calculated the total area of urban green spaces. Based on these analyses, we categorized the urban green spaces into seven distinct types:<br /><br />Green spaces alongside streets, industrial estates, and the sewage treatment plant<br />Traffic islands (Refuges)<br />Green spaces within administrative and educational centers<br />Green spaces in urban squares<br />Urban-coastal parks and gardens<br />Green spaces in commercial, tourism, and sports centers, including the Olympic complex<br />Green blocks and green spaces within neighborhoods and residential complexes<br /><br />Following this classification, we calculated the water requirement for the mixed greenery of Kish Island using the guidelines proposed by the Water Use Classification of Landscape Species (WUCOLS) method from the University of California.<br /><strong> </strong><br /><strong>Results: </strong>The study found that the total annual water requirement for Kish Island's 756.6 hectares of urban green space is approximately 12.5 million cubic meters. Urban parks and gardens, along with street traffic islands (refuges), emerged as the areas with the highest water demand, each requiring over one million cubic meters annually.<br />Monthly analysis revealed that May, June, and July exhibit the highest water requirements, while December and January show the lowest, a trend that directly mirrors the island's evaporation rates. Generally, water demand begins to increase in February, coinciding with rising temperatures and evapotranspiration, and continues to climb until August. It then gradually decreases from September to January before the cycle restarts. This pattern represents the typical annual water demand cycle for Kish Island's green spaces.<br />Overall, the calculated annual water requirement for the entire green space of Kish Island stands at about 2.5 million cubic meters per year, with an estimated error of 5%.<br /> <br /><strong>Discussion and Conclusion: </strong>Urban landscape vegetation is a vital component of the urban environment, offering numerous environmental, social, and economic benefits. Environmentally, it fosters biodiversity, provides crucial habitat and food sources for urban wildlife, prevents soil erosion, and improves drainage. Green spaces also act as air purifiers and temperature regulators, effectively mitigating the urban heat island effect and reducing noise pollution. Socially, urban vegetation enhances recreational opportunities, strengthens residents' connection to nature, and significantly contributes to creating sustainable and livable cities. Economically, benefits include increased property values and, with proper design, a reduction in urban flash floods. Achieving these benefits necessitates a strong focus on sustainable water management for urban landscapes, especially in water-scarce regions where irrigation demands for urban vegetation compete with other essential water needs. A review of existing literature highlights a limited understanding of irrigation requirements for urban green spaces, posing a significant challenge to the broader adoption of green infrastructure like green walls, green roofs, rain gardens, and bio retention systems. The present study demonstrates that the WUCOLS method is a practical approach for providing an initial estimate of urban green space water demand. However, for optimal results, this estimation should ideally be refined based on the specific health and aesthetic conditions of the urban vegetation. Our findings indicate that Kish Island has approximately 756.6 hectares of urban green spaces, which, combined with natural forests (3938.5 ha), cover a total of about 4695 hectares of the island. The annual water requirement for Kish Island's urban green space, calculated using the California method, is 5.12 million cubic meters. The highest water demand occurs from May to August, decreasing thereafter until January. Water demand then rises again with increasing temperatures and plant growth, following an annual cyclical pattern.<strong>Introduction: </strong>Since 1993 (1372 SH), Kish Island has experienced significant population growth and urban expansion, particularly in its coastal areas. This development led to the creation of extensive green infrastructure, including wide streets, neighborhood parks, and numerous gardens, primarily featuring diverse plant species. However, climate change presents substantial challenges for these urban landscapes. Rising temperatures and limited water resources intensify the need for a strategic approach to urban greenery, focusing on increasing and diversifying tree and ornamental plant species adapted to harsh urban conditions.<br />Currently, there is a paucity of information regarding the irrigation requirements of urban green spaces on Kish Island. A practical, standardized method for measuring these water needs is also lacking. This deficit in understanding creates significant challenges for both plant health and urban landscape managers, leading to inefficiencies and disruptions in water resource management. Therefore, this research aims to calculate the monthly and annual water requirements of Kish Island's urban green spaces using the WUCOLS (Water Use Classification of Landscape Species) method developed by the University of California.<br /><strong> </strong><br /><strong>Method and Materials: </strong>To determine the monthly and annual water requirements of urban green spaces on Kish Island, we first conducted field observations and utilized existing land-use maps, an AutoCAD map of Kish Island, and calculated the total area of urban green spaces. Based on these analyses, we categorized the urban green spaces into seven distinct types:<br /><br />Green spaces alongside streets, industrial estates, and the sewage treatment plant<br />Traffic islands (Refuges)<br />Green spaces within administrative and educational centers<br />Green spaces in urban squares<br />Urban-coastal parks and gardens<br />Green spaces in commercial, tourism, and sports centers, including the Olympic complex<br />Green blocks and green spaces within neighborhoods and residential complexes<br /><br />Following this classification, we calculated the water requirement for the mixed greenery of Kish Island using the guidelines proposed by the Water Use Classification of Landscape Species (WUCOLS) method from the University of California.<br /><strong> </strong><br /><strong>Results: </strong>The study found that the total annual water requirement for Kish Island's 756.6 hectares of urban green space is approximately 12.5 million cubic meters. Urban parks and gardens, along with street traffic islands (refuges), emerged as the areas with the highest water demand, each requiring over one million cubic meters annually.<br />Monthly analysis revealed that May, June, and July exhibit the highest water requirements, while December and January show the lowest, a trend that directly mirrors the island's evaporation rates. Generally, water demand begins to increase in February, coinciding with rising temperatures and evapotranspiration, and continues to climb until August. It then gradually decreases from September to January before the cycle restarts. This pattern represents the typical annual water demand cycle for Kish Island's green spaces.<br />Overall, the calculated annual water requirement for the entire green space of Kish Island stands at about 2.5 million cubic meters per year, with an estimated error of 5%.<br /> <br /><strong>Discussion and Conclusion: </strong>Urban landscape vegetation is a vital component of the urban environment, offering numerous environmental, social, and economic benefits. Environmentally, it fosters biodiversity, provides crucial habitat and food sources for urban wildlife, prevents soil erosion, and improves drainage. Green spaces also act as air purifiers and temperature regulators, effectively mitigating the urban heat island effect and reducing noise pollution. Socially, urban vegetation enhances recreational opportunities, strengthens residents' connection to nature, and significantly contributes to creating sustainable and livable cities. Economically, benefits include increased property values and, with proper design, a reduction in urban flash floods. Achieving these benefits necessitates a strong focus on sustainable water management for urban landscapes, especially in water-scarce regions where irrigation demands for urban vegetation compete with other essential water needs. A review of existing literature highlights a limited understanding of irrigation requirements for urban green spaces, posing a significant challenge to the broader adoption of green infrastructure like green walls, green roofs, rain gardens, and bio retention systems. The present study demonstrates that the WUCOLS method is a practical approach for providing an initial estimate of urban green space water demand. However, for optimal results, this estimation should ideally be refined based on the specific health and aesthetic conditions of the urban vegetation. Our findings indicate that Kish Island has approximately 756.6 hectares of urban green spaces, which, combined with natural forests (3938.5 ha), cover a total of about 4695 hectares of the island. The annual water requirement for Kish Island's urban green space, calculated using the California method, is 5.12 million cubic meters. The highest water demand occurs from May to August, decreasing thereafter until January. Water demand then rises again with increasing temperatures and plant growth, following an annual cyclical pattern.https://deej.kashanu.ac.ir/article_114925_03d0178dab6fa0cd213345b102ad6802.pdfUniversity of KashanDesert Ecosystem Engineering2538-6336144620250723Interaction of Soil Salinity with Biomass of Seidlitzia rosmarinus and Nitraria schoberi in the Desert Regions of Ardestan, Qom, and KashanInteraction of Soil Salinity with Biomass of Seidlitzia rosmarinus and Nitraria schoberi in the Desert Regions of Ardestan, Qom, and Kashan496411492810.22052/deej.2025.256740.1103FAAboozarKeshavarzStudent of Desert Management and Control, Yazd UniversityHakimzadeh ArdakaniMohammad AliAssociate Professor, Faculty of Natural Resources and Desert Studies - Department of Arid and Desert Areas Management, Yazd University0000-0003-1997-238XMotaharahEsfandiariIndependent Researcher, Department of Arid and Desert Areas Management, Faculty of Natural Resources and Desert Studies, Yazd UniversityKazemKamali AliabadiAssociate Professor, Faculty of Natural Resources and Desert Studies - Department of Arid and Desert Areas Management, Yazd UniversityJournal Article20250422Introduction: Soil salinity is a major factor that limits plant growth and biomass production, particularly in arid and semi-arid regions. To effectively restore land and combat desertification in these challenging environments, it's crucial to understand how native halophyte species — plants adapted to salty conditions — respond to different levels of soil salinity. This study focused on evaluating the impact of soil salinity on the biomass production of two specific salt-tolerant plant species: Seidlitzia rosmarinus and Nitraria schoberi. We conducted our research across various sites within the central deserts of Iran, including Ardestan Plain, Fakhreh (Kashan), Gonbad Namaki (Qom), Hosseinabad, and Kashan Forest Park. To gather our data, we used a transect-plot method for vegetation sampling. For soil analysis, samples were collected at two depths: 0–20 cm and 20–40 cm, allowing us to assess vertical variations in soil properties. We measured several key physical and chemical characteristics of the soil, including electrical conductivity (EC), pH, sodium adsorption ratio (SAR), and total neutralizing value (T.N.V.). Finally, to determine biomass, we measured both aboveground components (leaves, stems, branches) and belowground biomass (roots), which involved complete root excavation and weighing after oven-drying.<br /> <br />Materials and Methods: Data analysis was performed using SPSS and Excel software. Before proceeding with statistical tests, the normality of data distribution was confirmed. To examine differences among treatments (species and sites), we used one-way analysis of variance (ANOVA). This was followed by Duncan's multiple range test at a 5% significance level (α=0.05) to pinpoint specific differences. Additionally, Pearson's correlation coefficient was calculated to assess the relationships between various biomass traits and key soil salinity indicators, specifically electrical conductivity (EC) and sodium adsorption ratio (SAR).<br /> <br />Results: We found significant differences (p<0.01 and p<0.05) in the measured traits across the five study sites. Soil properties like EC, pH, and T.N.V. varied significantly by location, indicating that local environmental factors play a big role. Interestingly, only plant available water (PAW) showed a significant variation with soil depth (p<0.05), meaning that site conditions have a stronger impact on soil quality than differences between the two depths we sampled. When looking at biomass, we saw that site conditions significantly influenced canopy cover, canopy diameter, and total biomass production (p<0.01). Plus, the interaction between the site and plant species significantly affected biomass accumulation. In highly saline environments (where EC was over 50 dS/m and SAR over 100), Nitraria schoberi performed better than Seidlitzia rosmarinus, producing higher root and shoot biomass. For example, in Qom, Nitraria schoberi had a root biomass of 4056 g and a leaf biomass of 20,250 g, while Seidlitzia rosmarinus had much lower biomass in the same conditions. Conversely, in less saline environments (EC less than 40 dS/m) like Hosseinabad, Seidlitzia rosmarinus produced nearly six times more biomass than Nitraria schoberi. This shows that Seidlitzia rosmarinus is more sensitive to extreme salinity and prefers moderately saline conditions.<br />Our correlation analysis confirmed a positive relationship between Nitraria schoberi's biomass production and increasing soil salinity. In contrast, Seidlitzia rosmarinus's biomass was negatively affected by high EC and SAR levels.<br /> <br />Discussion and Conclusion: The findings of this study underscore the significant role native halophyte species can play in restoring saline and degraded soils. Specifically, Nitraria schoberi demonstrated a remarkable ability to maintain biomass production under highly saline conditions, highlighting its potential for use in severely degraded environments. Conversely, Seidlitzia rosmarinus performed better in moderately saline environments, suggesting its suitability for areas with less extreme soil conditions. Cultivating these halophyte species offers a cost-effective and sustainable approach to land reclamation. Their presence not only contributes to soil stabilization by increasing organic matter and preventing wind erosion, but also enhances the soil's physical and chemical quality through processes such as ion uptake and organic carbon input. This biological method provides a viable alternative to expensive engineering techniques for combating desertification. Our study suggests that species selection for restoration projects should be based on detailed assessments of specific soil salinity conditions. Furthermore, adopting an intercropping system that combines multiple halophyte species with varying salinity tolerances could significantly increase the resilience and sustainability of restored ecosystems. Integrating these biological solutions with strategic water resource management, such as using treated wastewater or saline water for irrigation, can further enhance restoration success. For future research, we recommend focusing on a deeper understanding of the interactions between halophyte species and soil microbial communities, as these play a crucial role in nutrient cycling and plant health under saline conditions. Additionally, investigating the carbon sequestration potential of these species could offer new insights into their contribution to climate change mitigation strategies.Introduction: Soil salinity is a major factor that limits plant growth and biomass production, particularly in arid and semi-arid regions. To effectively restore land and combat desertification in these challenging environments, it's crucial to understand how native halophyte species — plants adapted to salty conditions — respond to different levels of soil salinity. This study focused on evaluating the impact of soil salinity on the biomass production of two specific salt-tolerant plant species: Seidlitzia rosmarinus and Nitraria schoberi. We conducted our research across various sites within the central deserts of Iran, including Ardestan Plain, Fakhreh (Kashan), Gonbad Namaki (Qom), Hosseinabad, and Kashan Forest Park. To gather our data, we used a transect-plot method for vegetation sampling. For soil analysis, samples were collected at two depths: 0–20 cm and 20–40 cm, allowing us to assess vertical variations in soil properties. We measured several key physical and chemical characteristics of the soil, including electrical conductivity (EC), pH, sodium adsorption ratio (SAR), and total neutralizing value (T.N.V.). Finally, to determine biomass, we measured both aboveground components (leaves, stems, branches) and belowground biomass (roots), which involved complete root excavation and weighing after oven-drying.<br /> <br />Materials and Methods: Data analysis was performed using SPSS and Excel software. Before proceeding with statistical tests, the normality of data distribution was confirmed. To examine differences among treatments (species and sites), we used one-way analysis of variance (ANOVA). This was followed by Duncan's multiple range test at a 5% significance level (α=0.05) to pinpoint specific differences. Additionally, Pearson's correlation coefficient was calculated to assess the relationships between various biomass traits and key soil salinity indicators, specifically electrical conductivity (EC) and sodium adsorption ratio (SAR).<br /> <br />Results: We found significant differences (p<0.01 and p<0.05) in the measured traits across the five study sites. Soil properties like EC, pH, and T.N.V. varied significantly by location, indicating that local environmental factors play a big role. Interestingly, only plant available water (PAW) showed a significant variation with soil depth (p<0.05), meaning that site conditions have a stronger impact on soil quality than differences between the two depths we sampled. When looking at biomass, we saw that site conditions significantly influenced canopy cover, canopy diameter, and total biomass production (p<0.01). Plus, the interaction between the site and plant species significantly affected biomass accumulation. In highly saline environments (where EC was over 50 dS/m and SAR over 100), Nitraria schoberi performed better than Seidlitzia rosmarinus, producing higher root and shoot biomass. For example, in Qom, Nitraria schoberi had a root biomass of 4056 g and a leaf biomass of 20,250 g, while Seidlitzia rosmarinus had much lower biomass in the same conditions. Conversely, in less saline environments (EC less than 40 dS/m) like Hosseinabad, Seidlitzia rosmarinus produced nearly six times more biomass than Nitraria schoberi. This shows that Seidlitzia rosmarinus is more sensitive to extreme salinity and prefers moderately saline conditions.<br />Our correlation analysis confirmed a positive relationship between Nitraria schoberi's biomass production and increasing soil salinity. In contrast, Seidlitzia rosmarinus's biomass was negatively affected by high EC and SAR levels.<br /> <br />Discussion and Conclusion: The findings of this study underscore the significant role native halophyte species can play in restoring saline and degraded soils. Specifically, Nitraria schoberi demonstrated a remarkable ability to maintain biomass production under highly saline conditions, highlighting its potential for use in severely degraded environments. Conversely, Seidlitzia rosmarinus performed better in moderately saline environments, suggesting its suitability for areas with less extreme soil conditions. Cultivating these halophyte species offers a cost-effective and sustainable approach to land reclamation. Their presence not only contributes to soil stabilization by increasing organic matter and preventing wind erosion, but also enhances the soil's physical and chemical quality through processes such as ion uptake and organic carbon input. This biological method provides a viable alternative to expensive engineering techniques for combating desertification. Our study suggests that species selection for restoration projects should be based on detailed assessments of specific soil salinity conditions. Furthermore, adopting an intercropping system that combines multiple halophyte species with varying salinity tolerances could significantly increase the resilience and sustainability of restored ecosystems. Integrating these biological solutions with strategic water resource management, such as using treated wastewater or saline water for irrigation, can further enhance restoration success. For future research, we recommend focusing on a deeper understanding of the interactions between halophyte species and soil microbial communities, as these play a crucial role in nutrient cycling and plant health under saline conditions. Additionally, investigating the carbon sequestration potential of these species could offer new insights into their contribution to climate change mitigation strategies.https://deej.kashanu.ac.ir/article_114928_8d42ab8790705d5effd18d2c5280074b.pdfUniversity of KashanDesert Ecosystem Engineering2538-6336144620250723Identification of Morphogenesis Regions and Rock Weathering Regimes Based on the Peltier Model (Case Study: Tajan Watershed)Identification of Morphogenesis Regions and Rock Weathering Regimes Based on the Peltier Model (Case Study: Tajan Watershed)657611493110.22052/deej.2025.255743.1083FASayed HamidSadatiPh.D., Student, Department of Watershed Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University , Sari, Iran.Seyed RamazanMousaviAssistant Professor, Department of Watershed Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University0000-0002-2157-5458GhorbanVahabzadeh KebriaAssociate Professor of Watershed Management, Department of Watershed Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, IranSayed HusseinRoshunPh.D. Graduate of Watershed Management, Department of Watershed Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran0000-0002-6922-4083Journal Article20241124<strong>Introduction: </strong>The Quaternary period stands out for its pronounced climatic fluctuations, which have profoundly impacted geological morphogenesis systems and landforms. These highly variable climatic shifts are of significant interest to geomorphologists, particularly for understanding morphoclimatic and morphodynamic transformations. Earth's surface environments are continuously sculpted by the complex interplay of climate, biological activity, and geological processes. Despite their common perception as symbols of stability, rocks progressively lose their structural integrity over time through weathering processes, whether physical, chemical, or biological. Recent research has increasingly concentrated on the influence of climate change on erosional systems and landform development. A prominent framework in this domain is Louis Peltier’s model, which systematically investigates the effects of temperature and precipitation on geomorphic processes. Peltier's work notably identified seven distinct weathering regimes, incorporating both chemical weathering and frost activity. A deeper comprehension of the drivers behind landform evolution is crucial, as it enhances our capacity to assess geomorphic hazards and environmental potentials, thus playing a vital role in both infrastructure planning and land management.
<strong>Materials and methods: </strong>This study aimed to zone rock weathering regimes within the Tajan watershed based on Peltier's model. To assess the weathering conditions and associated morphogenesis forms across the basin, we acquired climatic variables, specifically annual average temperature and precipitation data, from synoptic stations provided by the Mazandaran Meteorological Department. After addressing statistical deficiencies, including the removal of outliers and the estimation of missing data using regression methods, four synoptic stations with reliable long-term records (1983–2023) were selected for analysis. A geospatial database was subsequently developed in ArcGIS, integrating topographic maps (specifically, elevation classes and slope gradients) and fluvial erosion data. Following the analysis of temperature and precipitation trends at the selected stations, the corresponding weathering regimes were identified using Peltier's model. These regimes were then assigned weighted values, and their spatial distribution maps were generated using the Inverse Distance Weighting (IDW) interpolation method. Finally, leveraging this comprehensive database, we produced morphogenesis maps and maps illustrating the intensity and type of weathering throughout the Tajan watershed, all based on Peltier's classification system.
<strong>Result: </strong>The physical characteristics of the Tajan watershed, including elevation classes, aspect, and erosion, are pivotal in influencing the type and intensity of rock weathering. To facilitate this analysis, relevant maps depicting these variables were prepared. Climate is a central driver of geomorphic processes, hydrological conditions, vegetation cover, wildlife distribution, and human activities. Based on the patterns of temperature and precipitation, the study area is primarily divided into two main climatic types: The Caspian temperate climate and the mountainous climate. The latter further encompasses both temperate and cold subtypes.
Our findings indicate that the highest temperatures are recorded in the northwestern part of the region, correlating with lower elevations. Conversely, the lowest temperatures are observed at the Pol Sefid station, which is situated at higher altitudes. Precipitation exhibits a clear south-to-north gradient, with the highest rainfall occurring in the western part of the region during spring. The Amirabad station records the highest annual precipitation (1,025 mm), whereas the Pol Sefid station receives the lowest (210 mm). Climatically and geomorphologically, the watershed is categorized into three distinct zones: semi-arid, temperate, and savanna. Among these, the temperate zone covers the largest area, spanning 1,599 km², while the savanna zone has the smallest extent at 1,269 km². Applying Peltier’s model, three primary types of weathering regimes were identified within the region. Low mechanical weathering is the most extensive, covering an area of 2,630 km². In contrast, very low-intensity weathering is the least extensive, occupying 595 km².
<strong>Discussion and Conclusion: </strong>Weathering processes lead to the disintegration of hard, compacted rocks into fragments of varying sizes, driven by physical, chemical, or biological factors. On steep slopes, these unstable fragments rarely remain in situ; instead, they are mobilized downslope by gravity, mass weight, or transport mechanisms such as sliding, falling, and flowing, ultimately accumulating at the base of slopes. The study of weathering is critical due to its role in rock breakdown and decomposition near the Earth's surface, its intensification of erosion, its contribution to gravitational collapses, its facilitation of landform development, and its influence on mineral deposit concentration and soil formation. The Tajan watershed, our study area, is characterized as a high-rainfall basin with diverse topography, encompassing both lowland and mountainous zones. In the lowlands, minimal temperature fluctuations enhance susceptibility to chemical weathering, a finding consistent with our results. Weathering processes are influenced by a range of environmental parameters, with annual average temperature and precipitation recognized as primary drivers. Peltier’s models, which formed the basis of this study, also rely on these two fundamental climatic indicators. Our research findings indicate that the region’s temperature and precipitation patterns are largely governed by its geographic location (latitude) and topographic structure, including the orientation and alignment of mountain ranges. These factors significantly shape the spatial variability of weathering patterns across the basin. Overall, elevation and slope orientation emerge as key determinants in the formation of weathering regimes and their associated geomorphological features.<strong>Introduction: </strong>The Quaternary period stands out for its pronounced climatic fluctuations, which have profoundly impacted geological morphogenesis systems and landforms. These highly variable climatic shifts are of significant interest to geomorphologists, particularly for understanding morphoclimatic and morphodynamic transformations. Earth's surface environments are continuously sculpted by the complex interplay of climate, biological activity, and geological processes. Despite their common perception as symbols of stability, rocks progressively lose their structural integrity over time through weathering processes, whether physical, chemical, or biological. Recent research has increasingly concentrated on the influence of climate change on erosional systems and landform development. A prominent framework in this domain is Louis Peltier’s model, which systematically investigates the effects of temperature and precipitation on geomorphic processes. Peltier's work notably identified seven distinct weathering regimes, incorporating both chemical weathering and frost activity. A deeper comprehension of the drivers behind landform evolution is crucial, as it enhances our capacity to assess geomorphic hazards and environmental potentials, thus playing a vital role in both infrastructure planning and land management.
<strong>Materials and methods: </strong>This study aimed to zone rock weathering regimes within the Tajan watershed based on Peltier's model. To assess the weathering conditions and associated morphogenesis forms across the basin, we acquired climatic variables, specifically annual average temperature and precipitation data, from synoptic stations provided by the Mazandaran Meteorological Department. After addressing statistical deficiencies, including the removal of outliers and the estimation of missing data using regression methods, four synoptic stations with reliable long-term records (1983–2023) were selected for analysis. A geospatial database was subsequently developed in ArcGIS, integrating topographic maps (specifically, elevation classes and slope gradients) and fluvial erosion data. Following the analysis of temperature and precipitation trends at the selected stations, the corresponding weathering regimes were identified using Peltier's model. These regimes were then assigned weighted values, and their spatial distribution maps were generated using the Inverse Distance Weighting (IDW) interpolation method. Finally, leveraging this comprehensive database, we produced morphogenesis maps and maps illustrating the intensity and type of weathering throughout the Tajan watershed, all based on Peltier's classification system.
<strong>Result: </strong>The physical characteristics of the Tajan watershed, including elevation classes, aspect, and erosion, are pivotal in influencing the type and intensity of rock weathering. To facilitate this analysis, relevant maps depicting these variables were prepared. Climate is a central driver of geomorphic processes, hydrological conditions, vegetation cover, wildlife distribution, and human activities. Based on the patterns of temperature and precipitation, the study area is primarily divided into two main climatic types: The Caspian temperate climate and the mountainous climate. The latter further encompasses both temperate and cold subtypes.
Our findings indicate that the highest temperatures are recorded in the northwestern part of the region, correlating with lower elevations. Conversely, the lowest temperatures are observed at the Pol Sefid station, which is situated at higher altitudes. Precipitation exhibits a clear south-to-north gradient, with the highest rainfall occurring in the western part of the region during spring. The Amirabad station records the highest annual precipitation (1,025 mm), whereas the Pol Sefid station receives the lowest (210 mm). Climatically and geomorphologically, the watershed is categorized into three distinct zones: semi-arid, temperate, and savanna. Among these, the temperate zone covers the largest area, spanning 1,599 km², while the savanna zone has the smallest extent at 1,269 km². Applying Peltier’s model, three primary types of weathering regimes were identified within the region. Low mechanical weathering is the most extensive, covering an area of 2,630 km². In contrast, very low-intensity weathering is the least extensive, occupying 595 km².
<strong>Discussion and Conclusion: </strong>Weathering processes lead to the disintegration of hard, compacted rocks into fragments of varying sizes, driven by physical, chemical, or biological factors. On steep slopes, these unstable fragments rarely remain in situ; instead, they are mobilized downslope by gravity, mass weight, or transport mechanisms such as sliding, falling, and flowing, ultimately accumulating at the base of slopes. The study of weathering is critical due to its role in rock breakdown and decomposition near the Earth's surface, its intensification of erosion, its contribution to gravitational collapses, its facilitation of landform development, and its influence on mineral deposit concentration and soil formation. The Tajan watershed, our study area, is characterized as a high-rainfall basin with diverse topography, encompassing both lowland and mountainous zones. In the lowlands, minimal temperature fluctuations enhance susceptibility to chemical weathering, a finding consistent with our results. Weathering processes are influenced by a range of environmental parameters, with annual average temperature and precipitation recognized as primary drivers. Peltier’s models, which formed the basis of this study, also rely on these two fundamental climatic indicators. Our research findings indicate that the region’s temperature and precipitation patterns are largely governed by its geographic location (latitude) and topographic structure, including the orientation and alignment of mountain ranges. These factors significantly shape the spatial variability of weathering patterns across the basin. Overall, elevation and slope orientation emerge as key determinants in the formation of weathering regimes and their associated geomorphological features.https://deej.kashanu.ac.ir/article_114931_ba18dd6a12ba0d543d2546ee828d6fdb.pdfUniversity of KashanDesert Ecosystem Engineering2538-6336144620250723Assessing Soil Carbon Sequestration in Rainfed Vineyards and Diverse Land Uses in Sepidan County, Fars ProvinceAssessing Soil Carbon Sequestration in Rainfed Vineyards and Diverse Land Uses in Sepidan County, Fars Province779011494410.22052/deej.2025.256271.1095FAMehranAkbarzadehFaculty of Natural Resources and Watershed Management, Malayer University, Malayer, Hamadan, IranHamidNouriFaculty of Natural Resources and Watershed Management, Malayer University, Malayer, Hamadan, IranSamarMortazaviFaculty of Natural Resources and Environment, Malayer University, Malayer, Hamadan, Iran0000-0002-0628-4724BehnazAttaeianBehnaz attaeian, Assistant Professor, Department of Natural Resources and Watershed Management, Malayer University, Malayer, Hamadan, Iran0000-0001-6176-7756Journal Article20250313<strong>Introductio: </strong>According to annual reports from the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Framework Convention on Climate Change (UNFCCC), greenhouse gases—especially carbon dioxide—are the primary drivers of global warming and climate change. Effectively mitigating these changes requires practical and efficient strategies for reducing atmospheric carbon. One of the most promising approaches is carbon sequestration in soil and plant biomass. Soil, in particular, serves as a massive carbon reservoir, holding more carbon than the atmosphere and all terrestrial vegetation combined. The capacity of both soil and vegetation to store carbon largely depends on land use types and the associated management practices for water, soil, agriculture, and natural ecosystems. Therefore, a comprehensive evaluation of carbon sequestration across various land use systems is crucial for developing and improving carbon management strategies. This study aims to assess soil carbon sequestration in rainfed grape vineyards within Sepidan County, Fars Province. We'll compare its potential against other prevalent land use types in the region. Ultimately, this research seeks to enhance the accuracy of satellite-based estimates of soil carbon, providing more precise data for regional carbon management.<br /> <br /><strong>Materials and Methods: </strong>We conducted field surveys to pinpoint the precise spatial distribution of rainfed vineyards within the study area. From these vineyards, we collected a total of 50 soil samples. To ensure a comprehensive comparison, we also collected 50 soil samples from each of the other identified land use categories, covering both agricultural and natural land types. All soil sampling took place in 2021 (corresponding to the Iranian calendar year 1400). After collection, we analyzed various chemical and physical properties of the soil. We also generated topographic maps, including data on slope, aspect, and elevation, to account for environmental influences. We determined soil organic carbon (SOC) content using the combustion (furnace) method, a standard and reliable laboratory technique. Finally, we processed and visualized the spatial data on soil carbon sequestration by creating thematic maps derived from the SoilGrids database and the Google Earth Engine (GEE) platform.<br /> <br /><strong>Results and Discussion: </strong>Given that a significant portion of Iran lies within arid and semi-arid regions, understanding soil carbon dynamics in the context of drought is essential. Prior research consistently indicates an increasing frequency and severity of droughts, which have directly intensified carbon loss in soils across Iran. In our study, we observed the following land use distribution within the study area:<br />Pastures: 144,532 hectares (ha),<br />Forests: 66,516 ha<br />Agricultural lands: 64,486 ha<br />Orchards: 8,550 ha<br />Vineyards: 3,156 ha<br />Our results reveal distinct patterns in soil carbon storage across these land use types. Pasture lands demonstrated the highest average soil carbon storage, with an impressive 451.6 tons per hectare (t/ha). This was followed by agricultural soils at 432.6 t/ha, forest soils at 418 t/ha, and finally, vineyards with 319.9 t/ha. These findings critically underscore the profound importance of land management practices in optimizing soil carbon sequestration. The variability in carbon storage across different land uses highlights that specific land management strategies can either enhance or diminish the soil's capacity to act as a carbon sink, especially in drought-prone regions like Iran.<strong>Introductio: </strong>According to annual reports from the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Framework Convention on Climate Change (UNFCCC), greenhouse gases—especially carbon dioxide—are the primary drivers of global warming and climate change. Effectively mitigating these changes requires practical and efficient strategies for reducing atmospheric carbon. One of the most promising approaches is carbon sequestration in soil and plant biomass. Soil, in particular, serves as a massive carbon reservoir, holding more carbon than the atmosphere and all terrestrial vegetation combined. The capacity of both soil and vegetation to store carbon largely depends on land use types and the associated management practices for water, soil, agriculture, and natural ecosystems. Therefore, a comprehensive evaluation of carbon sequestration across various land use systems is crucial for developing and improving carbon management strategies. This study aims to assess soil carbon sequestration in rainfed grape vineyards within Sepidan County, Fars Province. We'll compare its potential against other prevalent land use types in the region. Ultimately, this research seeks to enhance the accuracy of satellite-based estimates of soil carbon, providing more precise data for regional carbon management.<br /> <br /><strong>Materials and Methods: </strong>We conducted field surveys to pinpoint the precise spatial distribution of rainfed vineyards within the study area. From these vineyards, we collected a total of 50 soil samples. To ensure a comprehensive comparison, we also collected 50 soil samples from each of the other identified land use categories, covering both agricultural and natural land types. All soil sampling took place in 2021 (corresponding to the Iranian calendar year 1400). After collection, we analyzed various chemical and physical properties of the soil. We also generated topographic maps, including data on slope, aspect, and elevation, to account for environmental influences. We determined soil organic carbon (SOC) content using the combustion (furnace) method, a standard and reliable laboratory technique. Finally, we processed and visualized the spatial data on soil carbon sequestration by creating thematic maps derived from the SoilGrids database and the Google Earth Engine (GEE) platform.<br /> <br /><strong>Results and Discussion: </strong>Given that a significant portion of Iran lies within arid and semi-arid regions, understanding soil carbon dynamics in the context of drought is essential. Prior research consistently indicates an increasing frequency and severity of droughts, which have directly intensified carbon loss in soils across Iran. In our study, we observed the following land use distribution within the study area:<br />Pastures: 144,532 hectares (ha),<br />Forests: 66,516 ha<br />Agricultural lands: 64,486 ha<br />Orchards: 8,550 ha<br />Vineyards: 3,156 ha<br />Our results reveal distinct patterns in soil carbon storage across these land use types. Pasture lands demonstrated the highest average soil carbon storage, with an impressive 451.6 tons per hectare (t/ha). This was followed by agricultural soils at 432.6 t/ha, forest soils at 418 t/ha, and finally, vineyards with 319.9 t/ha. These findings critically underscore the profound importance of land management practices in optimizing soil carbon sequestration. The variability in carbon storage across different land uses highlights that specific land management strategies can either enhance or diminish the soil's capacity to act as a carbon sink, especially in drought-prone regions like Iran.https://deej.kashanu.ac.ir/article_114944_0b4cab6c789bc5ae8fd4a98cfaec1577.pdfUniversity of KashanDesert Ecosystem Engineering2538-6336144620250723Zoning of Flash Flood Generation Probability Using Machine Learning Techniques (A Case Study: Karaj Dam Watershed, Iran)Zoning of Flash Flood Generation Probability Using Machine Learning Techniques (A Case Study: Karaj Dam Watershed, Iran)9110711495210.22052/deej.2025.256422.1098FAAbdolvahedKordiDepartment of Watershed Management, University of Agricultural Sciences and Natural Resources, GorganAmirSadoddinDepartment of Watershed Management, University of Agricultural Sciences and Natural Resources, Gorgan, Iran0000-0002-4192-4010GahangirMohammadiDepartment of Forest, University of Agricultural Sciences and Natural Resources, Gorgan, IranVahedBerdiSheikhDepartment of Watershed Management, University of Agricultural Sciences and Natural Resources, Gorgan, Iran.OmidAsadi NalivanDepartment of Water Engineering, Faculty of Agriculture, University of Maragheh. Iran.Journal Article20250417<strong>Introduction: </strong>Flooding stands as a major hydro-climatic extreme event and one of the most severe natural disasters, posing significant threats to socio-environmental systems. Given its multifaceted and dynamic nature, floods are influenced by numerous factors, with both natural and anthropogenic agents collectively impacting their magnitude, intensity, extent, and duration. A fundamental step in managing flood-prone areas is to identify the spatial extent of potential floods. By spatially analyzing and investigating floodplains, a risk-based management framework can be established, leading to effective preventive measures. Therefore, flood prevention and management necessitate the initial delineation of high flood-potential areas using modeling methods, followed by the identification of the underlying drivers of these events. In recent years, Iran has experienced an intensifying frequency and impact of flash floods, resulting in considerable damage. The Karaj Dam Watershed in Alborz Province, in particular, has been severely affected, creating substantial challenges for travelers, tourists, and local residents. This ongoing issue underscores the urgent need for enhanced flood management strategies. Consequently, this research focuses on the spatial analysis and susceptibility assessment of flash flood-prone areas. Our aim is to provide essential and practical insights to inform planning, decision-making, and the implementation of various flood mitigation measures by relevant authorities and stakeholders.
<strong>Materials and Methods: </strong>To investigate the probability of flash flood generation across the Karaj Dam Watershed in Alborz Province, we first compiled a comprehensive map of past flash flood events. This involved field visits, consultation with local resources, and integrating data from various sources within the ArcGIS 10.8 software environment. Based on an extensive review of relevant literature, we identified fourteen variables hypothesized to influence flash flood occurrence. To ensure an optimal combination of predictors, we applied the Variant Inflation Factor (VIF) test for feature selection, screening out redundant variables. For model development and validation, the flash flood event data was divided using a 10-fold cross-validation method, with 70% allocated for training and 30% for validation. We then processed and analyzed the spatial data, including environmental variables and hydrologic information, using three distinct machine learning algorithms: Maximum Entropy, Random Forest, and Support Vector Machine. The accuracy of each susceptibility model was rigorously evaluated using comprehensive criteria, including the Relative Operating Characteristic (ROC) curve and the Kappa index. The model demonstrating the most acceptable accuracy was subsequently selected to generate a spatial probability map of flash flood generation for the study area. The insights derived from this research offer valuable contributions to flood hazard assessment and management efforts.
<strong>Results: </strong>Our analysis revealed that the Support Vector Machine (SVM) model proved to be the most suitable algorithm for determining flash flood susceptibility within the study area. This model achieved an impressive Area Under the Curve (AUC) value of 0.88 and a Kappa coefficient of 0.68, demonstrating strong predictive capability and outperforming both the Maximum Entropy and Random Forest models. The resulting flood susceptibility map indicates that 53% of the study area, approximately 46,200 hectares, exhibits moderate, high, or very high flash flood potential. Conversely, the remaining 47% of the watershed, about 38,860 hectares, falls into the low and very low flood probability classes. Furthermore, our research identified the key contributing factors to flash flooding in the region. In order of significance, elevation, drainage density, slope, and average annual rainfall emerged as the most important drivers influencing flood occurrence.
<strong>Discussion and Conclusions: </strong>Historically, the Karaj Dam Watershed has experienced a unique interplay of climatic and geomorphological factors that amplify flash flood risk. Extreme cold conditions at higher altitudes lead to substantial winter snow accumulation. When this is followed by heavy rainfall in spring and summer, it causes a rapid increase in water flow, resulting in significant flash flooding. This aligns perfectly with the watershed's natural characteristics: its steep slopes limit the soil's capacity to absorb rainwater, and when combined with high drainage density and intense, short-duration rainfall, these factors significantly elevate the risk of flash flooding. The findings from this research are crucial for both improving our understanding of flood phenomena and for practically identifying flood-prone areas. By pinpointing the key contributing factors to flash flood occurrences, we can prioritize management measures tailored to the region's specific conditions, ultimately aiming to reduce flood-related damages. The generated flood susceptibility map serves as a vital tool, providing a roadmap for implementing effective preventive measures. Ultimately, this research offers an effective framework for developing robust flood hazard management plans. It can significantly contribute to reducing human losses and economic damages by guiding informed decision-making and planning processes. We strongly recommend that responsible organizations and agencies utilize these insights, including the detailed zoning of flood susceptibility and the identified effective factors, to optimize financial resource allocation and ensure efficient use of public resources in flood mitigation efforts.<strong>Introduction: </strong>Flooding stands as a major hydro-climatic extreme event and one of the most severe natural disasters, posing significant threats to socio-environmental systems. Given its multifaceted and dynamic nature, floods are influenced by numerous factors, with both natural and anthropogenic agents collectively impacting their magnitude, intensity, extent, and duration. A fundamental step in managing flood-prone areas is to identify the spatial extent of potential floods. By spatially analyzing and investigating floodplains, a risk-based management framework can be established, leading to effective preventive measures. Therefore, flood prevention and management necessitate the initial delineation of high flood-potential areas using modeling methods, followed by the identification of the underlying drivers of these events. In recent years, Iran has experienced an intensifying frequency and impact of flash floods, resulting in considerable damage. The Karaj Dam Watershed in Alborz Province, in particular, has been severely affected, creating substantial challenges for travelers, tourists, and local residents. This ongoing issue underscores the urgent need for enhanced flood management strategies. Consequently, this research focuses on the spatial analysis and susceptibility assessment of flash flood-prone areas. Our aim is to provide essential and practical insights to inform planning, decision-making, and the implementation of various flood mitigation measures by relevant authorities and stakeholders.
<strong>Materials and Methods: </strong>To investigate the probability of flash flood generation across the Karaj Dam Watershed in Alborz Province, we first compiled a comprehensive map of past flash flood events. This involved field visits, consultation with local resources, and integrating data from various sources within the ArcGIS 10.8 software environment. Based on an extensive review of relevant literature, we identified fourteen variables hypothesized to influence flash flood occurrence. To ensure an optimal combination of predictors, we applied the Variant Inflation Factor (VIF) test for feature selection, screening out redundant variables. For model development and validation, the flash flood event data was divided using a 10-fold cross-validation method, with 70% allocated for training and 30% for validation. We then processed and analyzed the spatial data, including environmental variables and hydrologic information, using three distinct machine learning algorithms: Maximum Entropy, Random Forest, and Support Vector Machine. The accuracy of each susceptibility model was rigorously evaluated using comprehensive criteria, including the Relative Operating Characteristic (ROC) curve and the Kappa index. The model demonstrating the most acceptable accuracy was subsequently selected to generate a spatial probability map of flash flood generation for the study area. The insights derived from this research offer valuable contributions to flood hazard assessment and management efforts.
<strong>Results: </strong>Our analysis revealed that the Support Vector Machine (SVM) model proved to be the most suitable algorithm for determining flash flood susceptibility within the study area. This model achieved an impressive Area Under the Curve (AUC) value of 0.88 and a Kappa coefficient of 0.68, demonstrating strong predictive capability and outperforming both the Maximum Entropy and Random Forest models. The resulting flood susceptibility map indicates that 53% of the study area, approximately 46,200 hectares, exhibits moderate, high, or very high flash flood potential. Conversely, the remaining 47% of the watershed, about 38,860 hectares, falls into the low and very low flood probability classes. Furthermore, our research identified the key contributing factors to flash flooding in the region. In order of significance, elevation, drainage density, slope, and average annual rainfall emerged as the most important drivers influencing flood occurrence.
<strong>Discussion and Conclusions: </strong>Historically, the Karaj Dam Watershed has experienced a unique interplay of climatic and geomorphological factors that amplify flash flood risk. Extreme cold conditions at higher altitudes lead to substantial winter snow accumulation. When this is followed by heavy rainfall in spring and summer, it causes a rapid increase in water flow, resulting in significant flash flooding. This aligns perfectly with the watershed's natural characteristics: its steep slopes limit the soil's capacity to absorb rainwater, and when combined with high drainage density and intense, short-duration rainfall, these factors significantly elevate the risk of flash flooding. The findings from this research are crucial for both improving our understanding of flood phenomena and for practically identifying flood-prone areas. By pinpointing the key contributing factors to flash flood occurrences, we can prioritize management measures tailored to the region's specific conditions, ultimately aiming to reduce flood-related damages. The generated flood susceptibility map serves as a vital tool, providing a roadmap for implementing effective preventive measures. Ultimately, this research offers an effective framework for developing robust flood hazard management plans. It can significantly contribute to reducing human losses and economic damages by guiding informed decision-making and planning processes. We strongly recommend that responsible organizations and agencies utilize these insights, including the detailed zoning of flood susceptibility and the identified effective factors, to optimize financial resource allocation and ensure efficient use of public resources in flood mitigation efforts.https://deej.kashanu.ac.ir/article_114952_c4e00a00d4ced5d89419e7e7bd2831aa.pdf