University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Assessment of Climate Change Transformations and Their Impact on the Hydrological Regime of Gorgan Bay Wetland Assessment of Climate Change Transformations and Their Impact on the Hydrological Regime of Gorgan Bay Wetland 1 17 3844 10.22098/mmws.2025.17004.1568 FA Behzad Rayegani Research Group of Environmental Assessment and Risk, Research Center for Environment and Sustainable Development (RCESD), Department of Environment, Tehran, Iran Susan Barati Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran Mona Izadian Research Group of Biodiversity and Biosafety, Research Center for Environment and Sustainable Development (RCESD), Department of Environment, Tehran, Iran Farhad Hosseini Tayefeh Research Group of Biodiversity and Biosafety, Research Center for Environment and Sustainable Development (RCESD), Department of Environment, Tehran, Iran Seid Ghasem Ghorbanzadeh Zaferani Research Group of Environmental Assessment and Risk, Research Center for Environment and Sustainable Development (RCESD), Department of Environment, Tehran, Iran Siavash Shamsipour Department of Environment, Tehran, Iran Journal Article 2025 03 16 Introduction:<br /><br />Wetlands are critical natural systems that provide essential ecosystem services, including water regulation, flood control, carbon sequestration, and biodiversity conservation. However, these ecosystems are increasingly threatened by climate change and human activities. Gorgan Bay Wetland, located along the southeastern Caspian Sea, is particularly vulnerable due to its unique geographic setting and reliance on upstream freshwater inflows. Over recent decades, rising temperatures, altered precipitation patterns, reduced snow water equivalent, and increased evapotranspiration have contributed to significant declines in both the surface area and storage volume of the wetland. This study aims to assess the historical hydrological trends of Gorgan Bay Wetland and forecast its future evolution under various greenhouse gas emission scenarios. By integrating remote sensing data, field measurements, and climate modeling, the research provides a comprehensive understanding of how climatic factors drive wetland dynamics and offers valuable insights for sustainable water resource management and conservation strategies.<br /><br />Materials and Methods:<br /><br />A multi-disciplinary approach was adopted to analyze the hydrological dynamics of the wetland. High-resolution Landsat Level-2 surface reflectance imagery, covering the period from 1984 to 2022, served as the primary source for delineating wetland boundaries. Spectral indices such as the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were used to distinguish water bodies from other land cover types. These extracted boundaries were validated using ground control points (GCPs) collected along the wetland’s periphery. In October 2022, field surveys were conducted to measure water depth using differential GPS and digital depth sounders. These measurements were interpolated using the spline method to produce continuous bathymetric maps, and a Triangulated Irregular Network (TIN) model was developed to estimate the wetland’s storage volume over time.<br /><br />Climatic parameters—including temperature, precipitation, potential evapotranspiration (PET), and drought indices—were obtained from the TerraClimate database. Statistical analyses, such as Pearson correlation and regression modeling, were employed to evaluate the relationship between these climatic variables and changes in the wetland’s area and volume. To project future hydrological changes, outputs from General Circulation Models (GCMs) presented in the IPCC’s Sixth Assessment Report (AR6) were downscaled using both statistical and dynamical methods. Four emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were considered to capture a range of potential future climates, enabling robust scenario-based predictions for the wetland’s response to ongoing climatic shifts.<br /><br />Results and Discussion:<br /><br />Temporal analysis of Landsat imagery revealed that Gorgan Bay Wetland has experienced significant fluctuations in surface area and storage volume over the past few decades. A marked decline began around 2010, with the period from 2015 to 2022 showing a reduction of over 24% in surface area and more than 47% in water storage volume. These declines were strongly linked to climatic changes, particularly rising temperatures and reduced water inflows. Pearson correlation analysis indicated a statistically significant negative relationship between annual maximum temperature and both wetland area (r = -0.496, p < 0.01) and volume (r = -0.479, p < 0.01), underscoring the impact of higher temperatures on increased evaporation and reduced water retention. Conversely, a positive correlation was found between snow water equivalent and wetland area (r = 0.400, p < 0.05), emphasizing the role of snowmelt in sustaining inflows.<br /><br />Regression analysis quantified the impact of temperature increases on the wetland, showing that for each 1°C rise in annual maximum temperature, there is an approximate loss of 3,280 hectares in wetland area. Future scenario modeling projects that, under a moderate emission scenario (SSP2-4.5), maximum temperatures in the region could increase by 1.3°C to 1.8°C over the next 20 years. This temperature rise is expected to result in a loss of around 4,494 hectares of wetland area by 2040—approximately 12.8% of its current extent. Under higher emission scenarios (SSP3-7.0 and SSP5-8.5), the decline in wetland area is anticipated to be even more severe, posing substantial risks of widespread degradation.<br /><br />The integration of remote sensing, field data, and climate modeling in this study provides a detailed depiction of the interplay between climatic drivers and wetland hydrology. Despite inherent uncertainties in future climate projections and land use changes, the strong negative correlations observed reinforce the robustness of the current trends. The reduction in wetland area and volume not only threatens the ecological integrity of Gorgan Bay but also jeopardizes its ability to perform critical ecosystem functions such as flood mitigation, water purification, and habitat provision. These findings highlight the urgent need for adaptive management strategies that address both climatic and anthropogenic pressures.<br /><br />Conclusion:<br /><br />This study offers a comprehensive evaluation of the hydrological changes in Gorgan Bay Wetland driven by climate change. The integration of remote sensing, field surveys, and advanced climate modeling has revealed significant declines in both wetland area and storage volume, primarily due to rising temperatures and diminished water inflows. Future projections indicate that these trends will continue under most emission scenarios, potentially leading to severe ecological and environmental impacts. The results underscore the importance of revising water resource management policies, increasing environmental water allocations, and implementing modern irrigation practices in upstream regions to mitigate water deficits. Continued monitoring and model refinement are essential for ensuring the long-term sustainability of Gorgan Bay Wetland. Introduction:<br /><br />Wetlands are critical natural systems that provide essential ecosystem services, including water regulation, flood control, carbon sequestration, and biodiversity conservation. However, these ecosystems are increasingly threatened by climate change and human activities. Gorgan Bay Wetland, located along the southeastern Caspian Sea, is particularly vulnerable due to its unique geographic setting and reliance on upstream freshwater inflows. Over recent decades, rising temperatures, altered precipitation patterns, reduced snow water equivalent, and increased evapotranspiration have contributed to significant declines in both the surface area and storage volume of the wetland. This study aims to assess the historical hydrological trends of Gorgan Bay Wetland and forecast its future evolution under various greenhouse gas emission scenarios. By integrating remote sensing data, field measurements, and climate modeling, the research provides a comprehensive understanding of how climatic factors drive wetland dynamics and offers valuable insights for sustainable water resource management and conservation strategies.<br /><br />Materials and Methods:<br /><br />A multi-disciplinary approach was adopted to analyze the hydrological dynamics of the wetland. High-resolution Landsat Level-2 surface reflectance imagery, covering the period from 1984 to 2022, served as the primary source for delineating wetland boundaries. Spectral indices such as the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were used to distinguish water bodies from other land cover types. These extracted boundaries were validated using ground control points (GCPs) collected along the wetland’s periphery. In October 2022, field surveys were conducted to measure water depth using differential GPS and digital depth sounders. These measurements were interpolated using the spline method to produce continuous bathymetric maps, and a Triangulated Irregular Network (TIN) model was developed to estimate the wetland’s storage volume over time.<br /><br />Climatic parameters—including temperature, precipitation, potential evapotranspiration (PET), and drought indices—were obtained from the TerraClimate database. Statistical analyses, such as Pearson correlation and regression modeling, were employed to evaluate the relationship between these climatic variables and changes in the wetland’s area and volume. To project future hydrological changes, outputs from General Circulation Models (GCMs) presented in the IPCC’s Sixth Assessment Report (AR6) were downscaled using both statistical and dynamical methods. Four emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were considered to capture a range of potential future climates, enabling robust scenario-based predictions for the wetland’s response to ongoing climatic shifts.<br /><br />Results and Discussion:<br /><br />Temporal analysis of Landsat imagery revealed that Gorgan Bay Wetland has experienced significant fluctuations in surface area and storage volume over the past few decades. A marked decline began around 2010, with the period from 2015 to 2022 showing a reduction of over 24% in surface area and more than 47% in water storage volume. These declines were strongly linked to climatic changes, particularly rising temperatures and reduced water inflows. Pearson correlation analysis indicated a statistically significant negative relationship between annual maximum temperature and both wetland area (r = -0.496, p < 0.01) and volume (r = -0.479, p < 0.01), underscoring the impact of higher temperatures on increased evaporation and reduced water retention. Conversely, a positive correlation was found between snow water equivalent and wetland area (r = 0.400, p < 0.05), emphasizing the role of snowmelt in sustaining inflows.<br /><br />Regression analysis quantified the impact of temperature increases on the wetland, showing that for each 1°C rise in annual maximum temperature, there is an approximate loss of 3,280 hectares in wetland area. Future scenario modeling projects that, under a moderate emission scenario (SSP2-4.5), maximum temperatures in the region could increase by 1.3°C to 1.8°C over the next 20 years. This temperature rise is expected to result in a loss of around 4,494 hectares of wetland area by 2040—approximately 12.8% of its current extent. Under higher emission scenarios (SSP3-7.0 and SSP5-8.5), the decline in wetland area is anticipated to be even more severe, posing substantial risks of widespread degradation.<br /><br />The integration of remote sensing, field data, and climate modeling in this study provides a detailed depiction of the interplay between climatic drivers and wetland hydrology. Despite inherent uncertainties in future climate projections and land use changes, the strong negative correlations observed reinforce the robustness of the current trends. The reduction in wetland area and volume not only threatens the ecological integrity of Gorgan Bay but also jeopardizes its ability to perform critical ecosystem functions such as flood mitigation, water purification, and habitat provision. These findings highlight the urgent need for adaptive management strategies that address both climatic and anthropogenic pressures.<br /><br />Conclusion:<br /><br />This study offers a comprehensive evaluation of the hydrological changes in Gorgan Bay Wetland driven by climate change. The integration of remote sensing, field surveys, and advanced climate modeling has revealed significant declines in both wetland area and storage volume, primarily due to rising temperatures and diminished water inflows. Future projections indicate that these trends will continue under most emission scenarios, potentially leading to severe ecological and environmental impacts. The results underscore the importance of revising water resource management policies, increasing environmental water allocations, and implementing modern irrigation practices in upstream regions to mitigate water deficits. Continued monitoring and model refinement are essential for ensuring the long-term sustainability of Gorgan Bay Wetland. https://mmws.uma.ac.ir/article_3844_211a393e63ccdb04e1931c2a79fb1b74.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 International climate change law: strategies for mitigation, adaptation, and accountability International climate change law: strategies for mitigation, adaptation, and accountability 18 31 3896 10.22098/mmws.2025.17618.1610 FA Sami Najm Abed Al- Nuaimi Al-Turath University, Baghdad 10013, Iraq 0009-0008-5449-4727 Sawsan Khairy Abdullah Al-Mansour University College, Baghdad 10067, Iraq, Abbas Fadhel Eisa Muhsin Al-Mamoon University College, Baghdad 10067, Iraq Bushra Salman Husein Al-Rafidain University College Baghdad 10064, Iraq Faris Abdul Kareem Khazal Madenat Alelem University College, Baghdad 10006, Iraq Ata Amini Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran Hedieh Ahmadpari PhD Student, Hydrology of land, water resources, hydrochemistry, Russian State Hydrometeorological University, Saint Petersburg, Russia Journal Article 2025 06 08 The study of international climate change law in terms of mitigation, adaptation, compliance, and transparency charts out a complex and patchy global legal map. This study illustrates that climate strategies cannot achieve operational success without legal bases allowing for both environmental outcomes and the institutional integrity of any reporting and enforcement systems. And through its integration of emissions forecasting with adaptation performance indices and with metrics of legal accountability, the piece offers a composite picture of the way in which laws are at once governance tools and performance evaluation instruments. <br /><br />1. Mature and enforceable climate legislation, as evidenced by these findings, allows countries to be better positioned to respond to international frameworks and deliver domestic policy that effects real change. Clear legal requirements linked to in-depth institutional processes and technical dialogue potential help trigger emissions cuts and periodized adaptation approaches. In contrast, the lack of legal enforceability (or its weakness), is a major obstacle, especially in under-institutionalized countries, to both national action and international coordination. These observations lend credence to the notion that the law, when crafted and operationalized appropriately, serves not only as a procedural nicety, but rather as a strategic vehicle for long-term climate resilience and regulatory alignment.<br /><br />2. While underscoring strengths, the study also chronicles the considerable legal and structural gaps that persist, particularly in low-income and emerging economies. The wide variation in compliance scoring and transparency across countries highlights that there is an urgent need to develop harmonized legal benchmarks that can facilitate a comparative assessment, whilst still recognizing differentiated responsibilities. National contexts must also be reflected in the design of climate law, with the understanding that legal instruments must be adaptable, inclusive, and iterative so that changes in science and socio-economic realities can be acknowledged. Law-binding procedures for climate reporting and adaptation planning must become common practice, if global goals are to be met with credibility and consistency.<br /><br />3. Although the study focuses on national-level law, it also opens wider questions around the integration of non-state actors and sub-national jurisdictions into the fabric of international climate law. The contribution of cities, indigenous communities and the private sector increasingly require expanding the boundaries of legal systems to encompass multi-actor governance. The development of international legal norms, therefore, must also capture what is inherently disaggregated about potential mechanisms for climate governance today, as the national government cannot and must not be the only terrain for pursuing climate activity for both those in the countries in which they are situated and others around the world.<br /><br />4. Moving forward, the findings show that research on the intersection of legal design with financial instruments, trade policy, and human rights law should be carried out to build a more integrated legal response to the climate crisis. Further cross governance level, across economy sector and system-inspired comparative analyses could enhance understanding of the mechanisms by which climate law generates material impacts. Furthermore, enhancing legal metrics and data integration tools will be critical to further accountability, enforce legal harmonization and facilitate evidence-based policymaking going forward. The path to global climate stability will be governed by the depth, agility, and inclusivity of the legal systems that support it.<br /><br /><br /><br />The results from this study prompt a multi-dimensional assessment of how legal instruments are improving the effectiveness of international climate mechanisms on emissions mitigation, adaptive implementation, compliance and transparency fronts. Using this approach, the study underscores the importance of enforceable climate laws, transparent governance, and institutional embeds adaptation measures as necessary components to delivering the ambitions of the Paris Agreement by harmonizing them with advanced quantitative models, but with normative aspects on the function of the laws. However, this study has some limitations. While the differential emissions modelling has great sensitivity to policy variables, it relies on estimated coefficients of policy effectiveness that may vary across unmeasured contexts. This is especially relevant when comparing countries with differing governance models, population structures, and technological baselines. Second, the analysis is limited to five countries for cross-comparisons in detail, which, although being representative, cannot account for the diversity of global climate legal practices. Third, while legal indicators like NCS and LREI do enhance quantitative resolution, the scoring itself remains subjective, particularly in weighting the importance of legislative strength or adaptation. Future research could expand upon this preliminary investigation by examining a broader cross-section of all Annex I and non-Annex I countries, or by drawing on regional blocs such as the African Union or ASEAN to explore supranational coordination. Additionally, the incorporation of financial, trade, and energy sector law as variables will provide a more comprehensive view of how climate law aligns with wider sustainable development goals. Based on the results of this study, it is recommended that, to effectively address climate change, developing and enforcing comprehensive legal frameworks is essential. These frameworks should promote the sustainable management of water and soil resources, ensuring resilience and adaptation at both national and local levels. Additionally, integrating environmental, water, and soil protection laws with climate policies can enhance ecosystem health, support biodiversity, and foster long-term environmental sustainability in the face of changing climatic conditions. The study of international climate change law in terms of mitigation, adaptation, compliance, and transparency charts out a complex and patchy global legal map. This study illustrates that climate strategies cannot achieve operational success without legal bases allowing for both environmental outcomes and the institutional integrity of any reporting and enforcement systems. And through its integration of emissions forecasting with adaptation performance indices and with metrics of legal accountability, the piece offers a composite picture of the way in which laws are at once governance tools and performance evaluation instruments. <br /><br />1. Mature and enforceable climate legislation, as evidenced by these findings, allows countries to be better positioned to respond to international frameworks and deliver domestic policy that effects real change. Clear legal requirements linked to in-depth institutional processes and technical dialogue potential help trigger emissions cuts and periodized adaptation approaches. In contrast, the lack of legal enforceability (or its weakness), is a major obstacle, especially in under-institutionalized countries, to both national action and international coordination. These observations lend credence to the notion that the law, when crafted and operationalized appropriately, serves not only as a procedural nicety, but rather as a strategic vehicle for long-term climate resilience and regulatory alignment.<br /><br />2. While underscoring strengths, the study also chronicles the considerable legal and structural gaps that persist, particularly in low-income and emerging economies. The wide variation in compliance scoring and transparency across countries highlights that there is an urgent need to develop harmonized legal benchmarks that can facilitate a comparative assessment, whilst still recognizing differentiated responsibilities. National contexts must also be reflected in the design of climate law, with the understanding that legal instruments must be adaptable, inclusive, and iterative so that changes in science and socio-economic realities can be acknowledged. Law-binding procedures for climate reporting and adaptation planning must become common practice, if global goals are to be met with credibility and consistency.<br /><br />3. Although the study focuses on national-level law, it also opens wider questions around the integration of non-state actors and sub-national jurisdictions into the fabric of international climate law. The contribution of cities, indigenous communities and the private sector increasingly require expanding the boundaries of legal systems to encompass multi-actor governance. The development of international legal norms, therefore, must also capture what is inherently disaggregated about potential mechanisms for climate governance today, as the national government cannot and must not be the only terrain for pursuing climate activity for both those in the countries in which they are situated and others around the world.<br /><br />4. Moving forward, the findings show that research on the intersection of legal design with financial instruments, trade policy, and human rights law should be carried out to build a more integrated legal response to the climate crisis. Further cross governance level, across economy sector and system-inspired comparative analyses could enhance understanding of the mechanisms by which climate law generates material impacts. Furthermore, enhancing legal metrics and data integration tools will be critical to further accountability, enforce legal harmonization and facilitate evidence-based policymaking going forward. The path to global climate stability will be governed by the depth, agility, and inclusivity of the legal systems that support it.<br /><br /><br /><br />The results from this study prompt a multi-dimensional assessment of how legal instruments are improving the effectiveness of international climate mechanisms on emissions mitigation, adaptive implementation, compliance and transparency fronts. Using this approach, the study underscores the importance of enforceable climate laws, transparent governance, and institutional embeds adaptation measures as necessary components to delivering the ambitions of the Paris Agreement by harmonizing them with advanced quantitative models, but with normative aspects on the function of the laws. However, this study has some limitations. While the differential emissions modelling has great sensitivity to policy variables, it relies on estimated coefficients of policy effectiveness that may vary across unmeasured contexts. This is especially relevant when comparing countries with differing governance models, population structures, and technological baselines. Second, the analysis is limited to five countries for cross-comparisons in detail, which, although being representative, cannot account for the diversity of global climate legal practices. Third, while legal indicators like NCS and LREI do enhance quantitative resolution, the scoring itself remains subjective, particularly in weighting the importance of legislative strength or adaptation. Future research could expand upon this preliminary investigation by examining a broader cross-section of all Annex I and non-Annex I countries, or by drawing on regional blocs such as the African Union or ASEAN to explore supranational coordination. Additionally, the incorporation of financial, trade, and energy sector law as variables will provide a more comprehensive view of how climate law aligns with wider sustainable development goals. Based on the results of this study, it is recommended that, to effectively address climate change, developing and enforcing comprehensive legal frameworks is essential. These frameworks should promote the sustainable management of water and soil resources, ensuring resilience and adaptation at both national and local levels. Additionally, integrating environmental, water, and soil protection laws with climate policies can enhance ecosystem health, support biodiversity, and foster long-term environmental sustainability in the face of changing climatic conditions. https://mmws.uma.ac.ir/article_3896_558f9a7c22c86e855cf2bf2a1c412a9a.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 06 29 Projection of temperature and radiation in arid and semi-arid climates under shared socioeconomic pathways (SSP) scenarios Projection of temperature and radiation in arid and semi-arid climates under shared socioeconomic pathways (SSP) scenarios 32 48 3910 10.22098/mmws.2025.17682.1615 FA Marzieh Bagheri Khaneghahi PhD student in irrigation and drainage engineering, Department of Water Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. Abutaleb Hezarjaribi Associate Professor, Department of Water Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. Mohammad Ismaeil Kamali Assistant Professor, Soil and Water Research Department, Mazandaran Agricultural and Natural Resources Research and Education Center, AREEO, Sari, Iran. Fatemeh Zamani Assistant Professor, Artificial Intelligence Department, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran. Journal Article 2025 06 22 The phenomenon of climate change, caused by both anthropogenic and natural factors, makes the forecasting of future climate and its impact on the proper management of agriculture, water, and soil resources, as well as watershed management, the environment, and desertification control, crucial. Accordingly, this study investigates future temperature and solar radiation in arid (Mashhad and Ahvaz) and semi-arid (Kermanshah) climates in Iran. First, daily climatic data for the baseline period (1991-2020) were obtained from synoptic stations in the study areas. Subsequently, temperature and solar radiation projections were generated for the future periods of 2021-2040, 2041-2060, and 2061-2080 using LARS-WG version 8, based on the HadGEM3 climate model under the SSP scenarios. The model’s high accuracy in downscaling and its excellent performance in predicting climatic parameters across all stations were validated by high R² values (99%) and d-index scores (99%), alongside low RMSE values (less than 30%). Results indicated that, compared to the 30-year baseline period, the average maximum temperature over the next 60 years is projected to increase by 0.37, 1.48, and 2.73°C (Mashhad); 1.09, 1.47, and 2.3°C (Ahvaz); and 1.3, 1.75, and 2.65°C (Kermanshah) under the SSP126, SSP245, and SSP585 scenarios, respectively. Similarly, the average minimum temperature is expected to rise by 1.01 °C, 1.89 °C, and 2.8 °C (Mashhad); 1.57 °C, 2.17 °C, and 3.24 °C (Ahvaz); and 1.8 °C, 2.43 °C, and 3.33 °C (Kermanshah), respectively. However, changes in mean annual solar radiation did not show a consistent pattern. The monthly trends for temperature and radiation were significant at a 95% confidence level for most months. The results suggest that future temperature increases may lead to a decline in the quantity and quality of agricultural products, reduced water resources, and increased soil erosion. Future changes in solar radiation will also affect photosynthesis, evapotranspiration, energy production, and fossil fuel consumption. Therefore, to mitigate the negative impacts and adapt to future climatic conditions in the study areas, managers and planners should adopt optimal strategies. These strategies include cultivating heat- and light-resistant crops, optimizing irrigation systems, designing watershed management systems to prevent water loss, and promoting sustainable land development. The phenomenon of climate change, caused by both anthropogenic and natural factors, makes the forecasting of future climate and its impact on the proper management of agriculture, water, and soil resources, as well as watershed management, the environment, and desertification control, crucial. Accordingly, this study investigates future temperature and solar radiation in arid (Mashhad and Ahvaz) and semi-arid (Kermanshah) climates in Iran. First, daily climatic data for the baseline period (1991-2020) were obtained from synoptic stations in the study areas. Subsequently, temperature and solar radiation projections were generated for the future periods of 2021-2040, 2041-2060, and 2061-2080 using LARS-WG version 8, based on the HadGEM3 climate model under the SSP scenarios. The model’s high accuracy in downscaling and its excellent performance in predicting climatic parameters across all stations were validated by high R² values (99%) and d-index scores (99%), alongside low RMSE values (less than 30%). Results indicated that, compared to the 30-year baseline period, the average maximum temperature over the next 60 years is projected to increase by 0.37, 1.48, and 2.73°C (Mashhad); 1.09, 1.47, and 2.3°C (Ahvaz); and 1.3, 1.75, and 2.65°C (Kermanshah) under the SSP126, SSP245, and SSP585 scenarios, respectively. Similarly, the average minimum temperature is expected to rise by 1.01 °C, 1.89 °C, and 2.8 °C (Mashhad); 1.57 °C, 2.17 °C, and 3.24 °C (Ahvaz); and 1.8 °C, 2.43 °C, and 3.33 °C (Kermanshah), respectively. However, changes in mean annual solar radiation did not show a consistent pattern. The monthly trends for temperature and radiation were significant at a 95% confidence level for most months. The results suggest that future temperature increases may lead to a decline in the quantity and quality of agricultural products, reduced water resources, and increased soil erosion. Future changes in solar radiation will also affect photosynthesis, evapotranspiration, energy production, and fossil fuel consumption. Therefore, to mitigate the negative impacts and adapt to future climatic conditions in the study areas, managers and planners should adopt optimal strategies. These strategies include cultivating heat- and light-resistant crops, optimizing irrigation systems, designing watershed management systems to prevent water loss, and promoting sustainable land development. https://mmws.uma.ac.ir/article_3910_345db9a08154cdc6cd9449000a1af9f6.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Artificial Intelligence and Liability in Climate Change Arbitration Artificial Intelligence and Liability in Climate Change Arbitration 49 61 3900 10.22098/mmws.2025.17643.1611 FA Abdulmonam Yaheya Jawad Al-Turath University, Baghdad 10013, Iraq Rajha M. Shehab Al-Mansour University College, Baghdad 10067, Iraq Rafid Ali Laftah Hamad Al-Mamoon University College, Baghdad 10012, Iraq Al-Sarraf Nazar Mostafa Jawad Al-Rafidain University College Baghdad 10064, Iraq Jassim Mohamed Brieg Madenat Alelem University College, Baghdad 10006, Iraq Ata Amini Kurdistan Agriculture and Natural Resources Research and Education Center, AREEO, Iran Journal Article 2025 06 10 With its analytical capabilities over complex datasets, predictive capacity, and ability to streamline procedural tasks, artificial intelligence (AI) provides major opportunities to improve analytical capabilities over complex datasets, predictive capacity, and ability to streamline procedural tasks, artificial intelligence (AI) presents significant opportunities to enhance the efficiency of arbitration. But, the decentralized use of AI also poses fundamental questions about accountability, how to allocate liability, and transparency, for which existing legal systems However, the decentralized use of AI also poses fundamental questions about accountability, liability allocation, and transparency, for which existing legal systems are largely ill-prepared. The article explores the application of AI-based developments in the field of the arbitration of climate change liabilityexamines the application of AI-based developments in climate change liability arbitration. It looks specifically at the transformative impact of AI tools on decision-making, the extent to which liability sits with developers, users and arbitration institutions, and the ethical and regulatory implications that specifically examines the transformative impact of AI tools on decision-making, the allocation of liability among developers, users, and arbitration institutions, and the ethical and regulatory implications that arise from their interaction. The data reflects a trend of increasing usage of AI tools across reflect a trend of increasing usage of AI tools across the arbitration landscape. In the beginning, only 4–8% of cases used simple AI methods, primarily for Initially, only 4–8% of cases utilized simple AI methods, primarily for document review. With the advancements in machine learning algorithms and the advent of sophisticated legal technologies, the proportion of AI-driven cases advancements in machine learning algorithms and the emergence of sophisticated legal technologies, the proportion of AI-driven cases has climbed steadily. As of 2018–2019, almost 40% of cases had some form of predictive modeling tool incorporated, mirroring the increasing faith in AI’s capacity to detect patterns, forecast the future, and aid modeling tool incorporated, reflecting the increasing confidence in AI’s capacity to detect patterns, forecast the future, and assist arbitrators in producing fair awards. In order to enjoy the benefits of AI, stakeholders must construct transparent measures, establish international regulations for emerging liability and bias, and have clear ethical guidelines.<br /><br />Artificial intelligence (AI) offers valuable opportunities to improve analytical processing of complex datasets, enhance predictive capabilities, and streamline procedural tasks. In the field of arbitration, these advantages translate into increased efficiency, particularly in complex and data-heavy cases such as those involving climate change liability. However, the decentralized and rapidly evolving nature of AI raises critical concerns about accountability, the allocation of liability, and transparency, areas where existing legal systems are still largely unprepared. This research explores the application of AI in the arbitration of climate change liability. It focuses on the transformative impact of AI tools on decision-making processes, examines how responsibility is shared among developers, users, and arbitration institutions, and discusses the ethical and regulatory implications of AI integration. Data shows a rising trend in the use of AI in arbitration. In the early stages, only 4–8% of cases employed simple AI technologies, primarily for document review. However, with the development of advanced machine learning algorithms and legal tech platforms, the proportion of AI-assisted cases has increased significantly. By 2018–2019, around 40% of arbitration cases incorporated predictive modeling tools, reflecting growing confidence in AI’s ability to detect patterns, predict outcomes, and support arbitrators in delivering fair and informed awards. To harness AI’s benefits responsibly, stakeholders must prioritize transparency, adopt international regulatory standards, and address ethical concerns such as bias and accountability. Establishing clear guidelines for AI use in arbitration will be essential to ensure fairness, maintain public trust, and manage the evolving legal landscape surrounding artificial intelligence.<br /><br />Artificial intelligence (AI) offers valuable opportunities to improve analytical processing of complex datasets, enhance predictive capabilities, and streamline procedural tasks. In the field of arbitration, these advantages translate into increased efficiency, particularly in complex and data-heavy cases such as those involving climate change liability. However, the decentralized and rapidly evolving nature of AI raises critical concerns about accountability, the allocation of liability, and transparency, areas where existing legal systems are still largely unprepared. This research explores the application of AI in the arbitration of climate change liability. It focuses on the transformative impact of AI tools on decision-making processes, examines how responsibility is shared among developers, users, and arbitration institutions, and discusses the ethical and regulatory implications of AI integration. Data shows a rising trend in the use of AI in arbitration. In the early stages, only 4–8% of cases employed simple AI technologies, primarily for document review. However, with the development of advanced machine learning algorithms and legal tech platforms, the proportion of AI-assisted cases has increased significantly. By 2018–2019, around 40% of arbitration cases incorporated predictive modeling tools, reflecting growing confidence in AI’s ability to detect patterns, predict outcomes, and support arbitrators in delivering fair and informed awards. To harness AI’s benefits responsibly, stakeholders must prioritize transparency, adopt international regulatory standards, and address ethical concerns such as bias and accountability. Establishing clear guidelines for AI use in arbitration will be essential to ensure fairness, maintain public trust, and manage the evolving legal landscape surrounding artificial intelligence. With its analytical capabilities over complex datasets, predictive capacity, and ability to streamline procedural tasks, artificial intelligence (AI) provides major opportunities to improve analytical capabilities over complex datasets, predictive capacity, and ability to streamline procedural tasks, artificial intelligence (AI) presents significant opportunities to enhance the efficiency of arbitration. But, the decentralized use of AI also poses fundamental questions about accountability, how to allocate liability, and transparency, for which existing legal systems However, the decentralized use of AI also poses fundamental questions about accountability, liability allocation, and transparency, for which existing legal systems are largely ill-prepared. The article explores the application of AI-based developments in the field of the arbitration of climate change liabilityexamines the application of AI-based developments in climate change liability arbitration. It looks specifically at the transformative impact of AI tools on decision-making, the extent to which liability sits with developers, users and arbitration institutions, and the ethical and regulatory implications that specifically examines the transformative impact of AI tools on decision-making, the allocation of liability among developers, users, and arbitration institutions, and the ethical and regulatory implications that arise from their interaction. The data reflects a trend of increasing usage of AI tools across reflect a trend of increasing usage of AI tools across the arbitration landscape. In the beginning, only 4–8% of cases used simple AI methods, primarily for Initially, only 4–8% of cases utilized simple AI methods, primarily for document review. With the advancements in machine learning algorithms and the advent of sophisticated legal technologies, the proportion of AI-driven cases advancements in machine learning algorithms and the emergence of sophisticated legal technologies, the proportion of AI-driven cases has climbed steadily. As of 2018–2019, almost 40% of cases had some form of predictive modeling tool incorporated, mirroring the increasing faith in AI’s capacity to detect patterns, forecast the future, and aid modeling tool incorporated, reflecting the increasing confidence in AI’s capacity to detect patterns, forecast the future, and assist arbitrators in producing fair awards. In order to enjoy the benefits of AI, stakeholders must construct transparent measures, establish international regulations for emerging liability and bias, and have clear ethical guidelines.<br /><br />Artificial intelligence (AI) offers valuable opportunities to improve analytical processing of complex datasets, enhance predictive capabilities, and streamline procedural tasks. In the field of arbitration, these advantages translate into increased efficiency, particularly in complex and data-heavy cases such as those involving climate change liability. However, the decentralized and rapidly evolving nature of AI raises critical concerns about accountability, the allocation of liability, and transparency, areas where existing legal systems are still largely unprepared. This research explores the application of AI in the arbitration of climate change liability. It focuses on the transformative impact of AI tools on decision-making processes, examines how responsibility is shared among developers, users, and arbitration institutions, and discusses the ethical and regulatory implications of AI integration. Data shows a rising trend in the use of AI in arbitration. In the early stages, only 4–8% of cases employed simple AI technologies, primarily for document review. However, with the development of advanced machine learning algorithms and legal tech platforms, the proportion of AI-assisted cases has increased significantly. By 2018–2019, around 40% of arbitration cases incorporated predictive modeling tools, reflecting growing confidence in AI’s ability to detect patterns, predict outcomes, and support arbitrators in delivering fair and informed awards. To harness AI’s benefits responsibly, stakeholders must prioritize transparency, adopt international regulatory standards, and address ethical concerns such as bias and accountability. Establishing clear guidelines for AI use in arbitration will be essential to ensure fairness, maintain public trust, and manage the evolving legal landscape surrounding artificial intelligence.<br /><br />Artificial intelligence (AI) offers valuable opportunities to improve analytical processing of complex datasets, enhance predictive capabilities, and streamline procedural tasks. In the field of arbitration, these advantages translate into increased efficiency, particularly in complex and data-heavy cases such as those involving climate change liability. However, the decentralized and rapidly evolving nature of AI raises critical concerns about accountability, the allocation of liability, and transparency, areas where existing legal systems are still largely unprepared. This research explores the application of AI in the arbitration of climate change liability. It focuses on the transformative impact of AI tools on decision-making processes, examines how responsibility is shared among developers, users, and arbitration institutions, and discusses the ethical and regulatory implications of AI integration. Data shows a rising trend in the use of AI in arbitration. In the early stages, only 4–8% of cases employed simple AI technologies, primarily for document review. However, with the development of advanced machine learning algorithms and legal tech platforms, the proportion of AI-assisted cases has increased significantly. By 2018–2019, around 40% of arbitration cases incorporated predictive modeling tools, reflecting growing confidence in AI’s ability to detect patterns, predict outcomes, and support arbitrators in delivering fair and informed awards. To harness AI’s benefits responsibly, stakeholders must prioritize transparency, adopt international regulatory standards, and address ethical concerns such as bias and accountability. Establishing clear guidelines for AI use in arbitration will be essential to ensure fairness, maintain public trust, and manage the evolving legal landscape surrounding artificial intelligence. https://mmws.uma.ac.ir/article_3900_aac5f1a980965232f2ef2f1bf840c21e.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Assessing the impact of global meteorological signals on drought occurrence in Iran Assessing the impact of global meteorological signals on drought occurrence in Iran 62 87 3934 10.22098/mmws.2025.17690.1616 FA Javad Momeni Damaneh Dept. of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran Seyed Mohammad Tajbakhsh Dept. of Watershed Management, Natural Resources Faculty, University of Birjand, Birjand, Iran Ehsan Tamassoki Ph.D. in Watershed Management Science and Engineering, University of Hormozgan, Bandar Abbas, Iran Journal Article 2025 06 22 This study investigated the complex interplay between global meteorological signals—specifically the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO)—and drought occurrence in Iran. Utilizing the Standardized Precipitation Index (SPI), a widely recognized metric for characterizing drought conditions, the research analyzed rainfall data from 79 synoptic stations across Iran, spanning a three-decade period from 1988 to 2017. The primary objective was to provide a detailed understanding of the spatial and temporal patterns of drought variability nationwide and to elucidate the varying degrees of correlation between these global climatic drivers and regional drought dynamics.<br /><br />The analysis revealed intricate spatiotemporal drought patterns across Iran. Generally, the country experienced mild to moderate droughts, with a notable exception: the Lake Urmia basin, which exhibited relatively wetter conditions throughout the study period. Conversely, the Central Plateau and Eastern Border regions emerged as particularly vulnerable to drought, a vulnerability that was acutely observed during the 1998-2007 period. A deeper dive into the data, using minimum SPI analysis, suggested a country-wide susceptibility to drought. However, the mean SPI analysis highlighted the eastern border region as being consistently affected, while milder drought conditions were observed in the Central Plateau and Qarah-Qom regions.<br /><br />The study employed composite indices of SPI coupled with SOI and NAO, revealing distinct regional patterns of drought severity and sensitivity to these global climate signals. The SPI-SOI index indicated that the Caspian Sea sub-basin experienced the least severe drought, whereas the Qaraqum sub-basin suffered the most. Similarly, the SPI-NAO index showed the Lake Urmia sub-basin with the lowest drought severity and the Eastern Border sub-basin with the highest. These regional differences underscore the varied ways in which different parts of Iran respond to large-scale atmospheric forcing. Periodic indices for each decade further emphasized these regional and temporal variations, consistently showing higher drought severity in the Eastern Border and Qaraqum sub-basins, particularly during the 1998-2007 and 2008-2017 periods, which experienced more intense drought compared to 1988-1997.<br /><br />Temporal variations in drought severity were a key finding, highlighting the dynamic nature of drought and the imperative for continuous monitoring. The study identified a general trend of shifting drought patterns across the three decades. Initially (1988-1997), mild droughts were prevalent, with the Central Plateau experiencing the most severe conditions. This was followed by a significant intensification of drought (1998-2007), impacting the entire country, with the Central Plateau once again being the most affected. Subsequently, a decrease in severity was observed (2008-2017), leading to a return to mild drought conditions in most regions. This observed shift aligns with findings from other studies in the Mediterranean region, which also reported an increase in drought periods during the late 1990s and early 2000s, consistent with work by Hoerling et al. (2012) and Spinoni et al. (2015). The study period notably encompasses two of the most extensive and devastating droughts in the Mediterranean basin over the past 40 years (1999-2001 and 2007-2012), as referenced by Mathbout et al. (2021). The extended observational period to the end of 2017 in this study, while maintaining overall consistent trends, allowed for a more comprehensive temporal analysis.<br /><br />The analysis of mean SPI changes further reinforced the trend of increasing drought, particularly in the eastern border region. During the second and third decades (1998-2007 and 2008-2017), this region experienced significant drought, with the Central Plateau and Qaraqum also showing signs of mild drought. This eastward expansion of drought raises considerable concerns regarding long-term water security in Iran. Conversely, the earlier period (1988-1997) saw only mild drought in the Eastern Iranian border basin, with other regions experiencing near-normal to slightly wet conditions, suggesting a potential influence of climate change on regional drought patterns. The results, revealing a decrease in SPI values during winter, wet months, and on an annual scale, and an increase in SPI values during summer, confirm a tendency towards decreasing winter and annual precipitation and increasing summer precipitation identified in several regions of Iran (Caloiero and Veltri, 2019).<br /><br />Regarding the specific influence of global meteorological signals, the Southern Oscillation Index (SOI), which reflects the El Niño-Southern Oscillation (ENSO), was found to significantly exacerbate drought conditions, particularly in southern and southeastern Iran. The North Atlantic Oscillation (NAO), however, exhibited a more complex and regionally varying influence, with a less pronounced overall impact. These findings underscore the importance of understanding the individual and combined effects of these climate signals for accurate drought prediction and mitigation. The study's results corroborate previous research in the Mediterranean area, which often acknowledges the NAO as a primary driver of drought periods in the region (e.g., Vicente-Serrano et al., 2011). Strong positive phases of the NAO are typically associated with below-normal temperatures and precipitation in the study area, while negative phases are linked to opposite patterns (Caloiero et al., 2011). The observed dry conditions at the beginning of this century, corresponding to a positive phase of the NAO, further support this link. While ENSO's influence on the Calabria region's rainfall has been noted as weak in prior studies (Caloiero et al., 2011), its significant role as a drought driver in other regions like Turkey and China is recognized. Beyond SOI and NAO, the study also revealed a strong influence of the Mediterranean Oscillation (MO), consistent with findings from Mathbout et al. (2021). This study investigated the complex interplay between global meteorological signals—specifically the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO)—and drought occurrence in Iran. Utilizing the Standardized Precipitation Index (SPI), a widely recognized metric for characterizing drought conditions, the research analyzed rainfall data from 79 synoptic stations across Iran, spanning a three-decade period from 1988 to 2017. The primary objective was to provide a detailed understanding of the spatial and temporal patterns of drought variability nationwide and to elucidate the varying degrees of correlation between these global climatic drivers and regional drought dynamics.<br /><br />The analysis revealed intricate spatiotemporal drought patterns across Iran. Generally, the country experienced mild to moderate droughts, with a notable exception: the Lake Urmia basin, which exhibited relatively wetter conditions throughout the study period. Conversely, the Central Plateau and Eastern Border regions emerged as particularly vulnerable to drought, a vulnerability that was acutely observed during the 1998-2007 period. A deeper dive into the data, using minimum SPI analysis, suggested a country-wide susceptibility to drought. However, the mean SPI analysis highlighted the eastern border region as being consistently affected, while milder drought conditions were observed in the Central Plateau and Qarah-Qom regions.<br /><br />The study employed composite indices of SPI coupled with SOI and NAO, revealing distinct regional patterns of drought severity and sensitivity to these global climate signals. The SPI-SOI index indicated that the Caspian Sea sub-basin experienced the least severe drought, whereas the Qaraqum sub-basin suffered the most. Similarly, the SPI-NAO index showed the Lake Urmia sub-basin with the lowest drought severity and the Eastern Border sub-basin with the highest. These regional differences underscore the varied ways in which different parts of Iran respond to large-scale atmospheric forcing. Periodic indices for each decade further emphasized these regional and temporal variations, consistently showing higher drought severity in the Eastern Border and Qaraqum sub-basins, particularly during the 1998-2007 and 2008-2017 periods, which experienced more intense drought compared to 1988-1997.<br /><br />Temporal variations in drought severity were a key finding, highlighting the dynamic nature of drought and the imperative for continuous monitoring. The study identified a general trend of shifting drought patterns across the three decades. Initially (1988-1997), mild droughts were prevalent, with the Central Plateau experiencing the most severe conditions. This was followed by a significant intensification of drought (1998-2007), impacting the entire country, with the Central Plateau once again being the most affected. Subsequently, a decrease in severity was observed (2008-2017), leading to a return to mild drought conditions in most regions. This observed shift aligns with findings from other studies in the Mediterranean region, which also reported an increase in drought periods during the late 1990s and early 2000s, consistent with work by Hoerling et al. (2012) and Spinoni et al. (2015). The study period notably encompasses two of the most extensive and devastating droughts in the Mediterranean basin over the past 40 years (1999-2001 and 2007-2012), as referenced by Mathbout et al. (2021). The extended observational period to the end of 2017 in this study, while maintaining overall consistent trends, allowed for a more comprehensive temporal analysis.<br /><br />The analysis of mean SPI changes further reinforced the trend of increasing drought, particularly in the eastern border region. During the second and third decades (1998-2007 and 2008-2017), this region experienced significant drought, with the Central Plateau and Qaraqum also showing signs of mild drought. This eastward expansion of drought raises considerable concerns regarding long-term water security in Iran. Conversely, the earlier period (1988-1997) saw only mild drought in the Eastern Iranian border basin, with other regions experiencing near-normal to slightly wet conditions, suggesting a potential influence of climate change on regional drought patterns. The results, revealing a decrease in SPI values during winter, wet months, and on an annual scale, and an increase in SPI values during summer, confirm a tendency towards decreasing winter and annual precipitation and increasing summer precipitation identified in several regions of Iran (Caloiero and Veltri, 2019).<br /><br />Regarding the specific influence of global meteorological signals, the Southern Oscillation Index (SOI), which reflects the El Niño-Southern Oscillation (ENSO), was found to significantly exacerbate drought conditions, particularly in southern and southeastern Iran. The North Atlantic Oscillation (NAO), however, exhibited a more complex and regionally varying influence, with a less pronounced overall impact. These findings underscore the importance of understanding the individual and combined effects of these climate signals for accurate drought prediction and mitigation. The study's results corroborate previous research in the Mediterranean area, which often acknowledges the NAO as a primary driver of drought periods in the region (e.g., Vicente-Serrano et al., 2011). Strong positive phases of the NAO are typically associated with below-normal temperatures and precipitation in the study area, while negative phases are linked to opposite patterns (Caloiero et al., 2011). The observed dry conditions at the beginning of this century, corresponding to a positive phase of the NAO, further support this link. While ENSO's influence on the Calabria region's rainfall has been noted as weak in prior studies (Caloiero et al., 2011), its significant role as a drought driver in other regions like Turkey and China is recognized. Beyond SOI and NAO, the study also revealed a strong influence of the Mediterranean Oscillation (MO), consistent with findings from Mathbout et al. (2021). https://mmws.uma.ac.ir/article_3934_4a2abd68d035346d041a9d2fe03a2b60.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Climate Litigation and Judicial Activism in Environmental Protection Climate Litigation and Judicial Activism in Environmental Protection 88 100 3907 10.22098/mmws.2025.17674.1613 FA Haider Abdulkareem Alobaidi Al-Turath University, Baghdad 10013, Iraq Ammar Khadim Jasim Al-Mansour University College, Baghdad 10067, Iraq Ali Kareem Majeed Hadi Al-Mamoon University College, Baghdad 10067, Iraq Husam Najm Abbood Al-Bayati Al-Rafidain University College Baghdad 10064, Iraq Milad Abdullah Hafedh Madenat Alelem University College, Baghdad 10006, Iraq Ata Amini Kurdistan Agriculture and Natural Resources Research and Education Center Journal Article 2025 06 15 Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change.<br /><br />Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change.<br /><br />Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change.<br /><br />Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change. Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change.<br /><br />Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change.<br /><br />Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change.<br /><br />Climate litigation and judicial activism have become key instruments to tackle the climate crisis due to the lack of appropriate legislative or executive action. The study examines judicial activism through the lens of constitutional- and rights-related cases and its implications for some aspects of climate policies and environmental protection. The study uses both quantitative and qualitative methods to analyze more than 1,000 climates ‘litigation cases’ from a variety of jurisdictions. Judicial opinions were subjected to qualitative content analysis, and quantitative models estimated the relationship between judicial activism and emissions reductions. Data sources include judicial rulings, international agreements and interviews with legal experts. North America has the highest absolute number of cases, as well as the largest share of cases in which any of these treaties are invoked that also mention corporate defendants. Next comes Europe a country of heavy relative dependence on constitutional claims and arguments rooted in human rights. In South America and Africa there are many more recent cases involving indigenous claims and other human rights claims, perhaps suggesting increasing awareness about the climate impacts on vulnerable communities. Courts are bridging gaps in legislative and executive action and shaping policy and setting legal precedents as courts take a more prominent role in the global fight over climate change. https://mmws.uma.ac.ir/article_3907_52976bdd0aab7840835025b5eb882fee.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Sensitivity analysis of climatic factors in water yield modeling in the Talar watershed; an ecosystem service perspective Sensitivity analysis of climatic factors in water yield modeling in the Talar watershed; an ecosystem service perspective 101 120 3957 10.22098/mmws.2025.17808.1624 FA Mohsen Zabihi PhD of Watershed Management Sciences and Engineering, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran Hamidreza Moradi Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran Abdulvahed Khaledi Darvishan Associate Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran Mehdi Gholamalifard Associate Professor, Department of the Environment, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran Journal Article 2025 07 11 Understanding how climatic variables influence water yield is crucial for effective watershed management, particularly in regions that provide vital hydrological ecosystem services. This study investigates the sensitivity of key climatic factors— precipitation, reference evapotranspiration, and seasonality parameters—in modeling water yield ecosystem services using the InVEST model within the ecologically significant Talar watershed in northern Iran. Furthermore, the research aims to prioritize sub-watersheds based on their specific water yield to support ecosystem-based decision-making. Using a time series approach, water yield was modeled for the years 1989, 2000, and 2014, incorporating biophysical and climatic variables along with land use maps derived from Landsat TM and OLI imagery through SVM classification. A sensitivity analysis was conducted using the One-at-a-Time (OAT) method, with the year 2014 as the baseline and changes in each climatic factor assessed relative to 1989. Sub-watershed prioritization was carried out using specific water yield, defined as water yield per unit area. The results showed a declining trend in mean annual precipitation (from 552.6 mm in 1989 to 472.8 mm in 2014) and an increasing trend in temperature (from 8.92°C to 10.6°C), alongside a notable spatial shift in rainfall and evapotranspiration patterns. Sensitivity analysis revealed that water yield was most responsive to changes in precipitation, with a relative sensitivity index (Sr) approximately 0.42, indicating high model responsiveness. Reference evapotranspiration and seasonality parameters also exhibited a moderate influence. Prioritization results identified northern forested and agricultural sub-watersheds as having the highest specific water yields, highlighting their hydrological significance. These findings underscore the dominant role of precipitation variability in shaping regional hydrological services and emphasize the importance of spatially explicit watershed prioritization for sustainable water resource planning. The approach provides a practical framework for integrating ecosystem services into watershed management under climatic uncertainty and land use change, particularly in semi-humid regions like the Talar watershed. Understanding how climatic variables influence water yield is crucial for effective watershed management, particularly in regions that provide vital hydrological ecosystem services. This study investigates the sensitivity of key climatic factors— precipitation, reference evapotranspiration, and seasonality parameters—in modeling water yield ecosystem services using the InVEST model within the ecologically significant Talar watershed in northern Iran. Furthermore, the research aims to prioritize sub-watersheds based on their specific water yield to support ecosystem-based decision-making. Using a time series approach, water yield was modeled for the years 1989, 2000, and 2014, incorporating biophysical and climatic variables along with land use maps derived from Landsat TM and OLI imagery through SVM classification. A sensitivity analysis was conducted using the One-at-a-Time (OAT) method, with the year 2014 as the baseline and changes in each climatic factor assessed relative to 1989. Sub-watershed prioritization was carried out using specific water yield, defined as water yield per unit area. The results showed a declining trend in mean annual precipitation (from 552.6 mm in 1989 to 472.8 mm in 2014) and an increasing trend in temperature (from 8.92°C to 10.6°C), alongside a notable spatial shift in rainfall and evapotranspiration patterns. Sensitivity analysis revealed that water yield was most responsive to changes in precipitation, with a relative sensitivity index (Sr) approximately 0.42, indicating high model responsiveness. Reference evapotranspiration and seasonality parameters also exhibited a moderate influence. Prioritization results identified northern forested and agricultural sub-watersheds as having the highest specific water yields, highlighting their hydrological significance. These findings underscore the dominant role of precipitation variability in shaping regional hydrological services and emphasize the importance of spatially explicit watershed prioritization for sustainable water resource planning. The approach provides a practical framework for integrating ecosystem services into watershed management under climatic uncertainty and land use change, particularly in semi-humid regions like the Talar watershed. https://mmws.uma.ac.ir/article_3957_f91af8ce986ea9d9ee1345a2dd968d69.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Modeling streamflow dynamics under climate and land use shifts using MIKE SHE in the upper Omo Gibe catchment, Ethiopia Modeling streamflow dynamics under climate and land use shifts using MIKE SHE in the upper Omo Gibe catchment, Ethiopia 121 148 3982 10.22098/mmws.2025.17906.1632 FA Kindie Zewdie Werede PhD scholar in Hydraulic Engineering, Faculty of Hydraulic and Water Resources Engineering, Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia Tarun Kumar Lohani Professor, Faculty of Hydraulic and Water Resources Engineering, Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia Bogale Gebremariam Neka Associate Professor, Faculty of Hydraulic and Water Resources Engineering, Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia 0000-0002-0123-4437 Getachew Bereta Geremew Associate Professor, Faculty of Hydraulic and Water Resources Engineering, Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia Journal Article 2025 07 24 MIKE SHE hydrological model using three distinct scenarios was incorporated to analyze the influence of changing climatic conditions and alterations in land utilization patterns on river discharge dynamics integrating three climate data periods (1990 - 2000, 2001 - 2010, and 2011 - 2020) with three land use and land cover (LULC) maps (1990, 2005, and 2020). The model was systematically calibrated and validated, achieving Nash-Sutcliffe Efficiency (NSE) and coefficient of determination (R2) values of 0.83 and 0.82 for calibration and 0.80 and 0.81 for validation, respectively, demonstrating strong reliability in simulating the catchment’s hydrological processes. Results indicate substantial influence of both LULC and climate changes across 2001–2010 and 2011–2020. LULC alterations increased surface runoff by 10.29% and 2.38%, while subsurface flow and evapotranspiration decreased by -6.03% and -6.82%, and by 0.75% and -5.49%, respectively. Climate variations further augmented surface runoff by 2.14% and 12.72%, with corresponding reductions in subsurface flow and evapotranspiration of -7.43% and -10.40%, and -10.03% and -21.65%, respectively. Both climate and LULC changes promoted subsurface flow and evapotranspiration during 2001 - 2010, with declining trends observed during 2011 - 2020. The findings underscore that expanded settlements and reductions in forest and shrub land have intensified streamflow while lowering subsurface flow and evapotranspiration, emphasizing the need for integrated climate and land use considerations in water resource management strategies.<br /><br />Hydrological processes are driven by complex interfaces between climatic variables and land surface characteristics. Variations in climatic conditions and land utilization patterns profoundly influence hydrological systems of drainage basins, reshaping storm water discharge mechanisms, soil absorption capacities, and subsurface water replenishment rates. These alterations disrupt the long-term viability of hydrological governance, influencing basin-scale water allocation, ecosystem health, and flood risk. Even though extensive research on the changes in hydrological regimes of shifting climatic patterns and human-driven landscape alterations have been undertaken but critical gaps still remain to understand the fundamental processes governing these interactions, particularly in data-scarce regions like Ethiopia. The need for high-resolution hydrological models effectively capture surface-subsurface water interactions in improving predictions and informing adaptive water management strategies. <br /><br />Shifts in climate conditions driven by increasing greenhouse gas emissions has led to significant changes. Watershed hydrology is influenced by the uneven rainfall distribution, temperature regimes, and changes in water loss from soil and vegetation. These changes can alter streamflow patterns leading to increased duration and scale of extreme hydrological event including heavy rainfall and prolonged dry spells. The transformation of natural landscapes by deforestation, agricultural intensification, and urbanization augments disturbances in water cycles by disrupting runoff dynamics, limiting groundwater absorption, and reducing aquifer recharge rates. The convergence of shifting climate regimes and human-altered ecosystems often result in multiplicative impacts on basin stability, requiring adaptive modeling frameworks to project long-term hydrological shifts.<br /><br />Hydrologic models though extensively applied, research gaps persist in accurately representing the synergistic impacts of climate and land use transformations on streamflow. Many existing studies rely on models such as SWAT and HEC-HMS, which is rarely applicable to large-scale watershed simulations, have limitations in integrating surface and subsurface water interactions at high spatial and temporal resolutions emphasized the need for distributed models capable of capturing finer hydrological complexities in changing landscapes. The MIKE SHE model, in contrast, offers a process-based, fully distributed approach that integrates surface water, groundwater, and evapotranspiration processes, making it particularly suitable for capturing fine-scale hydrological variations and interactions.<br /><br />The Upper Omo Gibe catchment, a critical hydrological region in Ethiopia, has witnessed significant alterations in climate patterns and land utilization in past decades. Current research fully utilizes high-resolution models such as MIKE SHE to explore the synergistic impacts of these factors on streamflow. In Ethiopia, prior analyses using SWAT have emphasized broad hydrological trends driven by climate and land cover changes but overlooked localized mechanisms, such as subsurface flow dynamics that are vital for assessing fine-scale streamflow responses. Furthermore, existing research has predominantly examined either climate variability or land cover shifts as stand-alone factor, which is inadequate to capture their collective effects on hydrological processes. These linked processes is imperative for effective critical water resource planning and mitigating hydrological risks under climate change scenarios.<br /><br />This analysis addresses the limitation by using the MIKE SHE model to evaluate the combined effects of climate variability and land cover changes on streamflow in the Upper Omo Gibe catchment. Thus, this research explores the watershed hydrology by integrating high resolution climate and land use data with process based hydrological modeling.<br /><br />Daily climatological data including precipitation and temperature extremes (minimum and maximum) required for MIKE SHE model were obtained from the Ethiopian National Meteorological Agency (EMA). The data (1990 to 2020) were acquired from nine meteorological stations (1 - 9) and one hydrological station (10) to overcome problems of data continuity and delay in establishing the stations. The datasets were chosen for their completeness, long-term availability and appropriateness for model analysis. The streamflow data were collected from 1990 to 2014 from Ministry of Water and Energy (MoWE), Government of Ethiopia. The reference evapotranspiration values were computed using the Penman-Monteith method. The dataset was subjected to a thorough screening for outliers and incomplete data. Corrections were made by checking against records from neighboring stations. Catchments and monitoring stations were chosen for the completeness and quality of the available data and time series with less than 10% missing values for daily streamflow and monthly weather records. This rigorous selection and validation procedure ensured both extensive and accurate data integration MIKE SHE hydrological model using three distinct scenarios was incorporated to analyze the influence of changing climatic conditions and alterations in land utilization patterns on river discharge dynamics integrating three climate data periods (1990 - 2000, 2001 - 2010, and 2011 - 2020) with three land use and land cover (LULC) maps (1990, 2005, and 2020). The model was systematically calibrated and validated, achieving Nash-Sutcliffe Efficiency (NSE) and coefficient of determination (R2) values of 0.83 and 0.82 for calibration and 0.80 and 0.81 for validation, respectively, demonstrating strong reliability in simulating the catchment’s hydrological processes. Results indicate substantial influence of both LULC and climate changes across 2001–2010 and 2011–2020. LULC alterations increased surface runoff by 10.29% and 2.38%, while subsurface flow and evapotranspiration decreased by -6.03% and -6.82%, and by 0.75% and -5.49%, respectively. Climate variations further augmented surface runoff by 2.14% and 12.72%, with corresponding reductions in subsurface flow and evapotranspiration of -7.43% and -10.40%, and -10.03% and -21.65%, respectively. Both climate and LULC changes promoted subsurface flow and evapotranspiration during 2001 - 2010, with declining trends observed during 2011 - 2020. The findings underscore that expanded settlements and reductions in forest and shrub land have intensified streamflow while lowering subsurface flow and evapotranspiration, emphasizing the need for integrated climate and land use considerations in water resource management strategies.<br /><br />Hydrological processes are driven by complex interfaces between climatic variables and land surface characteristics. Variations in climatic conditions and land utilization patterns profoundly influence hydrological systems of drainage basins, reshaping storm water discharge mechanisms, soil absorption capacities, and subsurface water replenishment rates. These alterations disrupt the long-term viability of hydrological governance, influencing basin-scale water allocation, ecosystem health, and flood risk. Even though extensive research on the changes in hydrological regimes of shifting climatic patterns and human-driven landscape alterations have been undertaken but critical gaps still remain to understand the fundamental processes governing these interactions, particularly in data-scarce regions like Ethiopia. The need for high-resolution hydrological models effectively capture surface-subsurface water interactions in improving predictions and informing adaptive water management strategies. <br /><br />Shifts in climate conditions driven by increasing greenhouse gas emissions has led to significant changes. Watershed hydrology is influenced by the uneven rainfall distribution, temperature regimes, and changes in water loss from soil and vegetation. These changes can alter streamflow patterns leading to increased duration and scale of extreme hydrological event including heavy rainfall and prolonged dry spells. The transformation of natural landscapes by deforestation, agricultural intensification, and urbanization augments disturbances in water cycles by disrupting runoff dynamics, limiting groundwater absorption, and reducing aquifer recharge rates. The convergence of shifting climate regimes and human-altered ecosystems often result in multiplicative impacts on basin stability, requiring adaptive modeling frameworks to project long-term hydrological shifts.<br /><br />Hydrologic models though extensively applied, research gaps persist in accurately representing the synergistic impacts of climate and land use transformations on streamflow. Many existing studies rely on models such as SWAT and HEC-HMS, which is rarely applicable to large-scale watershed simulations, have limitations in integrating surface and subsurface water interactions at high spatial and temporal resolutions emphasized the need for distributed models capable of capturing finer hydrological complexities in changing landscapes. The MIKE SHE model, in contrast, offers a process-based, fully distributed approach that integrates surface water, groundwater, and evapotranspiration processes, making it particularly suitable for capturing fine-scale hydrological variations and interactions.<br /><br />The Upper Omo Gibe catchment, a critical hydrological region in Ethiopia, has witnessed significant alterations in climate patterns and land utilization in past decades. Current research fully utilizes high-resolution models such as MIKE SHE to explore the synergistic impacts of these factors on streamflow. In Ethiopia, prior analyses using SWAT have emphasized broad hydrological trends driven by climate and land cover changes but overlooked localized mechanisms, such as subsurface flow dynamics that are vital for assessing fine-scale streamflow responses. Furthermore, existing research has predominantly examined either climate variability or land cover shifts as stand-alone factor, which is inadequate to capture their collective effects on hydrological processes. These linked processes is imperative for effective critical water resource planning and mitigating hydrological risks under climate change scenarios.<br /><br />This analysis addresses the limitation by using the MIKE SHE model to evaluate the combined effects of climate variability and land cover changes on streamflow in the Upper Omo Gibe catchment. Thus, this research explores the watershed hydrology by integrating high resolution climate and land use data with process based hydrological modeling.<br /><br />Daily climatological data including precipitation and temperature extremes (minimum and maximum) required for MIKE SHE model were obtained from the Ethiopian National Meteorological Agency (EMA). The data (1990 to 2020) were acquired from nine meteorological stations (1 - 9) and one hydrological station (10) to overcome problems of data continuity and delay in establishing the stations. The datasets were chosen for their completeness, long-term availability and appropriateness for model analysis. The streamflow data were collected from 1990 to 2014 from Ministry of Water and Energy (MoWE), Government of Ethiopia. The reference evapotranspiration values were computed using the Penman-Monteith method. The dataset was subjected to a thorough screening for outliers and incomplete data. Corrections were made by checking against records from neighboring stations. Catchments and monitoring stations were chosen for the completeness and quality of the available data and time series with less than 10% missing values for daily streamflow and monthly weather records. This rigorous selection and validation procedure ensured both extensive and accurate data integration https://mmws.uma.ac.ir/article_3982_3e8d1c2238903b59f4cde6ace25d9dac.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Water resource sustainability management issues in the Sevan lake basin in the context of climate change Water resource sustainability management issues in the Sevan lake basin in the context of climate change 149 166 3983 10.22098/mmws.2025.17899.1631 FA Trahel Vardanyan Department of Physical Geography and Hydrometeorology, Yerevan State University, Yerevan, Armenia 0000-0001-9249-3372 Journal Article 2025 07 22 This work concerns water resource sustainability management issues in the Sevan lake basin in the context of climate change․ The research used the data series (1930-2020) observed by the Hydrometeorology and Monitoring Center of RA, as well as short-term field studies conducted by us. The work mainly used the methods of analysis, synthesis, dispersion, and field research. As a result of human economic activity, many water bodies of the earth have been transformed. In this regard a classic example can be Lake Sevan, which has been considered an open-air laboratory for scientific research for nearly a century. It should be noted that the use of the centuries-old resources of Lake Sevan, the inefficient management of water resources, as well as the current and expected climate change have cause irreversible losses to the ecosystem of the basin. The lake’s ecosystem has been damaged and is undergoing eutrophication. In all studied rivers of the Lake Sevan basin, a definite downward trend in maximum runoff was observed and an increase in minimum runoff (except for two). This is likely due to global climate change and changes in peak river runoff.  In other words, there is a tendency to equalize the wet and dry seasons. Studies show that in the context of climate change, a negative impact on the water resources of the lake is expected. In the worst-case scenario, river inflow of the Sevan basin will decrease by 34% (265 million m3) by 2100, and lake water evaporation will increase by 36.5% (292.6 million m3), compared to the base period (1961-1990). Consequently, the lake level is expected to drop by about 16 cm per year. in order to solve the problem, it is necessary to make legislative changes, improve the existing water legislation, as well as strengthen the institutional capacity and the water monitoring system. This work concerns water resource sustainability management issues in the Sevan lake basin in the context of climate change․ The research used the data series (1930-2020) observed by the Hydrometeorology and Monitoring Center of RA, as well as short-term field studies conducted by us. The work mainly used the methods of analysis, synthesis, dispersion, and field research. As a result of human economic activity, many water bodies of the earth have been transformed. In this regard a classic example can be Lake Sevan, which has been considered an open-air laboratory for scientific research for nearly a century. It should be noted that the use of the centuries-old resources of Lake Sevan, the inefficient management of water resources, as well as the current and expected climate change have cause irreversible losses to the ecosystem of the basin. The lake’s ecosystem has been damaged and is undergoing eutrophication. In all studied rivers of the Lake Sevan basin, a definite downward trend in maximum runoff was observed and an increase in minimum runoff (except for two). This is likely due to global climate change and changes in peak river runoff.  In other words, there is a tendency to equalize the wet and dry seasons. Studies show that in the context of climate change, a negative impact on the water resources of the lake is expected. In the worst-case scenario, river inflow of the Sevan basin will decrease by 34% (265 million m3) by 2100, and lake water evaporation will increase by 36.5% (292.6 million m3), compared to the base period (1961-1990). Consequently, the lake level is expected to drop by about 16 cm per year. in order to solve the problem, it is necessary to make legislative changes, improve the existing water legislation, as well as strengthen the institutional capacity and the water monitoring system. https://mmws.uma.ac.ir/article_3983_d294fd40101a6752a62cfaa154ee9af0.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Detection of climate warming signals in Zagros forests using cold temperature indices Detection of climate warming signals in Zagros forests using cold temperature indices 167 180 4011 10.22098/mmws.2025.18026.1640 FA Fatemeh Dargahian Associate Prof., Desert Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran Sakineh Lotfinasabasl Assistant Prof., Desert Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran Azadeh Gohardoust Researcher, Desert Department, Research Institute of Forest and Rangelands (RIFR), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran 0000-0003-1194-2581 Journal Article 2025 08 10 One of the new challenges of the last two decades is the decline of the Zagros oak forests. Numerous natural and human factors have influenced the occurrence of this phenomenon. The natural factors of climate change are decisive, which have led directly and through the superimposition of other factors to the drying out of the Zagros trees, especially the oaks. The driving forces of climate change are rising temperatures. One of the manifestations of rising temperatures is the decline in cold temperature indices. In this study, daily statistics of climate parameters in synoptic stations of the Zagros vegetation area during a complete climatic period were used to detect climate change in the Zagros oak forest ecosystem. There are 58 synoptic stations in this ecosystem. Since 30 years (1990-2019) is a climatic period, 34 stations had the appropriate statistical period length. The Expert Team on Climate Risks and Sectoral Climate Indices (ET CRSCI) method was used to identify the occurrence of climate change in the Zagros ecosystem as one of the causes of oak decline. Cold temperature indices: FD0, ID0, CSDI2, CSDI6, TN10p, and TX10p were selected from the baseline indices. The trend of changes in these indicators was extracted and zoned for the entire Zagros habitat. The results showed that the spatial distribution of cold temperature indices follows three factors: the latitude, the topographic height of the region, and the path of atmospheric currents to Zagros. All cold temperature indices showed a decreasing trend, indicating an increase in temperature and climate change in the entire ecosystem of the Zagros. Due to the temperature rise, the two indicators FD0 and CSDI6 have disappeared in the Zagros ecosystem in the last decade. This can lead to disruption of the hibernation cycle of plants and trees, changes in erosion and moisture storage, damage to soil microorganisms, and provide more suitable living conditions for pests and pathogenic fungi. One of the new challenges of the last two decades is the decline of the Zagros oak forests. Numerous natural and human factors have influenced the occurrence of this phenomenon. The natural factors of climate change are decisive, which have led directly and through the superimposition of other factors to the drying out of the Zagros trees, especially the oaks. The driving forces of climate change are rising temperatures. One of the manifestations of rising temperatures is the decline in cold temperature indices. In this study, daily statistics of climate parameters in synoptic stations of the Zagros vegetation area during a complete climatic period were used to detect climate change in the Zagros oak forest ecosystem. There are 58 synoptic stations in this ecosystem. Since 30 years (1990-2019) is a climatic period, 34 stations had the appropriate statistical period length. The Expert Team on Climate Risks and Sectoral Climate Indices (ET CRSCI) method was used to identify the occurrence of climate change in the Zagros ecosystem as one of the causes of oak decline. Cold temperature indices: FD0, ID0, CSDI2, CSDI6, TN10p, and TX10p were selected from the baseline indices. The trend of changes in these indicators was extracted and zoned for the entire Zagros habitat. The results showed that the spatial distribution of cold temperature indices follows three factors: the latitude, the topographic height of the region, and the path of atmospheric currents to Zagros. All cold temperature indices showed a decreasing trend, indicating an increase in temperature and climate change in the entire ecosystem of the Zagros. Due to the temperature rise, the two indicators FD0 and CSDI6 have disappeared in the Zagros ecosystem in the last decade. This can lead to disruption of the hibernation cycle of plants and trees, changes in erosion and moisture storage, damage to soil microorganisms, and provide more suitable living conditions for pests and pathogenic fungi. https://mmws.uma.ac.ir/article_4011_da10e3bc38485c70e582727928dc077a.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 09 23 Comparative analysis of the effects of climate change and land use on runoff and its prediction in a mountainous watershed in Northwestern Iran Comparative analysis of the effects of climate change and land use on runoff and its prediction in a mountainous watershed in Northwestern Iran 181 198 4022 10.22098/mmws.2025.18107.1644 FA Tayebeh Irani PhD graduate, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran. Hirad Abghari Associate Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran. 0000-0002-3407-3297 Ali Akbar Rasouli Professor Department Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia Journal Article 2025 08 17 Sustainable water resource management in semi-arid mountainous watersheds is increasingly critical due to the combined impacts of climate change and land use dynamics on hydrological processes. The Zolachai watershed, a vital sub-basin of the Lake Urmia watershed in northwestern Iran, spans 2,258 km² and serves as a primary water source for agricultural, urban, and ecological needs. Rapid population growth and unsustainable land use practices, particularly agricultural expansion on steep slopes, have intensified environmental pressures, resulting in forest degradation, soil erosion, reduced agricultural productivity, and compromised drinking water quality due to elevated surface runoff. This study conducts a comparative analysis of the effects of climate change and land use changes on runoff generation and water retention in the Zolachai watershed, employing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to provide actionable insights for sustainable watershed management.<br /><br /><br /><br />The study integrated multiple datasets to assess hydrological processes, including climate projections, land use data, soil hydrological properties, and runoff curve numbers (CN). Climate data were sourced from the ACCESS-CM2 model under the Coupled Model Intercomparison Project Phase 6 (CMIP6), utilizing two Shared Socioeconomic Pathways (SSP) scenarios: SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions). Precipitation data from 1966 to 2021, collected from rain gauge stations within the watershed and the Climate4impact database, were used to project precipitation patterns for 2023 and 2030. The SSP5-8.5 scenario, selected to estimate maximum runoff potential, projected an increase in heavy precipitation days by 2030, heightening flood risks, particularly in areas with low water retention capacity. This scenario underscores the potential for intensified rainfall events, which could exacerbate runoff, soil erosion, and sedimentation in downstream water bodies, threatening water security.<br /><br /><br /><br />Land use and land cover (LULC) data were derived from Sentinel-2 satellite imagery for 2016, 2020, and 2023, with a 10-meter spatial resolution. Images captured during the late June growing season, ensuring minimal cloud cover, were processed using object-based classification and the Support Vector Machine (SVM) algorithm in eCognition Developer 10.3 software. Seven land use categories were identified: water bodies, bare soil (areas with less than 5% vegetation cover), irrigated agriculture and orchards, rainfed agriculture, salt flats around Lake Urmia, rangelands, and residential areas. The classification for 2023 was validated against a predicted map, achieving high accuracy indices (Kno: 0.87, Klocation: 0.93, Klocationstrata: 0.93, Kstandard: 0.84). Land use change predictions for 2030 were generated using the Markov and CA-Markov models, with a Kappa coefficient of 0.91, indicating robust predictive accuracy. Results revealed a significant expansion of irrigated agriculture and orchards (from 221.11 km² in 2016 to 528.18 km² in 2030) and residential areas (from 9.08 km² to 24.39 km²), alongside declines in rangelands (from 857.95 km² to 724.52 km²), bare soil, and water bodies. These shifts reflect increased water consumption in upstream agricultural areas and reduced surface flows downstream, exacerbating water scarcity.<br /><br /><br /><br />Soil hydrological groups (A, B, C) were determined using field sampling data from the Soil and Water Research Institute for plain areas, supplemented by SoilGrids.org data for mountainous regions. Clay, silt, and sand percentages were integrated using SAGA GIS and ArcGIS software, based on the USDA soil texture triangle. The central watershed was dominated by groups A and B, while group C prevailed in the northwestern, southern, and western parts. Curve Number (CN) values, ranging from 36 to 93, were calculated using the USDA Soil Conservation Service method, incorporating land use and soil hydrological data. Residential areas, characterized by impervious surfaces and CN values of 70–90, exhibited the highest runoff potential (359.4–647 mm), while irrigated agriculture and orchards, with CN values of 36–80, showed the lowest runoff (137.2–333.8 mm) due to high soil permeability and vegetation cover. These CN estimates were validated against studies like Haghdadi et al. (2018) and Birhanu et al. (2019), confirming their reliability.<br /><br /><br /><br />The InVEST Water Yield model integrated these datasets to produce spatially explicit runoff and water retention maps for 2016, 2023, and 2030. Widely recognized for its simplicity and efficiency, the model has been applied in studies such as Bai et al. (2013) and Reheman et al. (2023). In the Zolachai watershed, runoff volumes ranged from 137.2–359.4 mm in 2016, 179.5–418.8 mm in 2023, and 333.8–647 mm in 2030. Residential areas and hydrological soil group C exhibited the highest runoff potential, while irrigated agriculture and orchards demonstrated superior water retention due to high infiltration rates. The central and southern watershed areas showed moderate to high runoff potential, while the northeastern parts had lower potential. Projections for 2030 indicate a decline in runoff volume (from 61.91 million m³ in 2023 to 53.59 million m³), driven by increased water retention in expanding agricultural areas.<br /><br /><br /><br />The expansion of irrigated agriculture and orchards, driven by economic incentives and modern irrigation techniques, increases water consumption, reducing surface flows and exacerbating downstream water scarcity. Climate change, under SSP5-8.5, intensifies rainfall, amplifying runoff and flood risks in residential and rangeland areas. These findings align with global studies, such as Reheman et al. (2023) in the Tian Shan Mountains, and regional research by Emlaei et al. (2021) and Azizi et al. (2022). The InVEST model’s ability to evaluate management scenarios supports prioritizing conservation in high-runoff areas and promoting sustainable practices like afforestation and drip irrigation. This study provides a robust framework for sustainable management of semi-arid mountainous watersheds, contributing to water security, improved livelihoods, and biodiversity preservation. Future research should focus on comprehensive ecosystem service assessments to refine runoff regulation strategies and address the complex interplay of climatic and anthropogenic factors. Sustainable water resource management in semi-arid mountainous watersheds is increasingly critical due to the combined impacts of climate change and land use dynamics on hydrological processes. The Zolachai watershed, a vital sub-basin of the Lake Urmia watershed in northwestern Iran, spans 2,258 km² and serves as a primary water source for agricultural, urban, and ecological needs. Rapid population growth and unsustainable land use practices, particularly agricultural expansion on steep slopes, have intensified environmental pressures, resulting in forest degradation, soil erosion, reduced agricultural productivity, and compromised drinking water quality due to elevated surface runoff. This study conducts a comparative analysis of the effects of climate change and land use changes on runoff generation and water retention in the Zolachai watershed, employing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to provide actionable insights for sustainable watershed management.<br /><br /><br /><br />The study integrated multiple datasets to assess hydrological processes, including climate projections, land use data, soil hydrological properties, and runoff curve numbers (CN). Climate data were sourced from the ACCESS-CM2 model under the Coupled Model Intercomparison Project Phase 6 (CMIP6), utilizing two Shared Socioeconomic Pathways (SSP) scenarios: SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions). Precipitation data from 1966 to 2021, collected from rain gauge stations within the watershed and the Climate4impact database, were used to project precipitation patterns for 2023 and 2030. The SSP5-8.5 scenario, selected to estimate maximum runoff potential, projected an increase in heavy precipitation days by 2030, heightening flood risks, particularly in areas with low water retention capacity. This scenario underscores the potential for intensified rainfall events, which could exacerbate runoff, soil erosion, and sedimentation in downstream water bodies, threatening water security.<br /><br /><br /><br />Land use and land cover (LULC) data were derived from Sentinel-2 satellite imagery for 2016, 2020, and 2023, with a 10-meter spatial resolution. Images captured during the late June growing season, ensuring minimal cloud cover, were processed using object-based classification and the Support Vector Machine (SVM) algorithm in eCognition Developer 10.3 software. Seven land use categories were identified: water bodies, bare soil (areas with less than 5% vegetation cover), irrigated agriculture and orchards, rainfed agriculture, salt flats around Lake Urmia, rangelands, and residential areas. The classification for 2023 was validated against a predicted map, achieving high accuracy indices (Kno: 0.87, Klocation: 0.93, Klocationstrata: 0.93, Kstandard: 0.84). Land use change predictions for 2030 were generated using the Markov and CA-Markov models, with a Kappa coefficient of 0.91, indicating robust predictive accuracy. Results revealed a significant expansion of irrigated agriculture and orchards (from 221.11 km² in 2016 to 528.18 km² in 2030) and residential areas (from 9.08 km² to 24.39 km²), alongside declines in rangelands (from 857.95 km² to 724.52 km²), bare soil, and water bodies. These shifts reflect increased water consumption in upstream agricultural areas and reduced surface flows downstream, exacerbating water scarcity.<br /><br /><br /><br />Soil hydrological groups (A, B, C) were determined using field sampling data from the Soil and Water Research Institute for plain areas, supplemented by SoilGrids.org data for mountainous regions. Clay, silt, and sand percentages were integrated using SAGA GIS and ArcGIS software, based on the USDA soil texture triangle. The central watershed was dominated by groups A and B, while group C prevailed in the northwestern, southern, and western parts. Curve Number (CN) values, ranging from 36 to 93, were calculated using the USDA Soil Conservation Service method, incorporating land use and soil hydrological data. Residential areas, characterized by impervious surfaces and CN values of 70–90, exhibited the highest runoff potential (359.4–647 mm), while irrigated agriculture and orchards, with CN values of 36–80, showed the lowest runoff (137.2–333.8 mm) due to high soil permeability and vegetation cover. These CN estimates were validated against studies like Haghdadi et al. (2018) and Birhanu et al. (2019), confirming their reliability.<br /><br /><br /><br />The InVEST Water Yield model integrated these datasets to produce spatially explicit runoff and water retention maps for 2016, 2023, and 2030. Widely recognized for its simplicity and efficiency, the model has been applied in studies such as Bai et al. (2013) and Reheman et al. (2023). In the Zolachai watershed, runoff volumes ranged from 137.2–359.4 mm in 2016, 179.5–418.8 mm in 2023, and 333.8–647 mm in 2030. Residential areas and hydrological soil group C exhibited the highest runoff potential, while irrigated agriculture and orchards demonstrated superior water retention due to high infiltration rates. The central and southern watershed areas showed moderate to high runoff potential, while the northeastern parts had lower potential. Projections for 2030 indicate a decline in runoff volume (from 61.91 million m³ in 2023 to 53.59 million m³), driven by increased water retention in expanding agricultural areas.<br /><br /><br /><br />The expansion of irrigated agriculture and orchards, driven by economic incentives and modern irrigation techniques, increases water consumption, reducing surface flows and exacerbating downstream water scarcity. Climate change, under SSP5-8.5, intensifies rainfall, amplifying runoff and flood risks in residential and rangeland areas. These findings align with global studies, such as Reheman et al. (2023) in the Tian Shan Mountains, and regional research by Emlaei et al. (2021) and Azizi et al. (2022). The InVEST model’s ability to evaluate management scenarios supports prioritizing conservation in high-runoff areas and promoting sustainable practices like afforestation and drip irrigation. This study provides a robust framework for sustainable management of semi-arid mountainous watersheds, contributing to water security, improved livelihoods, and biodiversity preservation. Future research should focus on comprehensive ecosystem service assessments to refine runoff regulation strategies and address the complex interplay of climatic and anthropogenic factors. https://mmws.uma.ac.ir/article_4022_bde8df2fc4172516dc52ae0be2673d1b.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Analysis of climate trends and change point detection in Upper Middle Olifants catchment, South Africa Analysis of climate trends and change point detection in Upper Middle Olifants catchment, South Africa 199 214 4045 10.22098/mmws.2025.18183.1656 FA Mesfin Reta Aredo Department of Civil Engineering Sciences, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Megersa Olumana Dinka Department of Civil Engineering Sciences, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Journal Article 2025 08 25 Climate variability poses a pressing shift to the hydrological cycle and diminishing water resource availability. This shift is a prevalent challenge to day-to-day activities in the Olifants River, South Africa. This study assessed rainfall, minimum and maximum temperature variability, trend analysis, and change point detection in the Olifants River using numerous statistical analysis methods, such as coefficient of variation, Kurtosis, skewness, Pettitt, Buishand, von Neumann, Standard normal homogeneity test (SNHT), Mann-Kendall, and Sen's slope tests for the period of 1988 to 2014. The result showed that annual rainfall in most stations had moderate variation, limited stations were negatively skewed, and not normally distributed. Most of the stations, such as X1E003, B5E004, and B1E003, show less variability (CV < 20), while the rest of the stations show moderate variation ranges (20 < CV < 30) for rainfall datasets. The results of kurtosis and skewness ranged from -0.41 to 1.10 and -0.12 to 0.46 for rainfall; -0.03 to 0.92 and -0.61 to -0.17 for maximum temperature; and 0.24 to 1.61 and -0.18 to 0.14 for minimum temperature, respectively. Furthermore, the majority of stations were negatively skewed for annual maximum and minimum temperatures. Unexpectedly, the homogeneity tests for annual rainfall and maximum temperature depict favorable results, while a few stations were found to be non-homogeneous for minimum temperature. Specifically, the trend analysis indicators such as Kendall's tau, S, <em>p</em>-value and Sen’s slope were results ranges from -0.03 to 0.15, -11 to 53, 0.28 to 1.0, and -1.95 to 4.98 for rainfall; 0.08 to 0.15, 29 to 51, 0.30 to 0.56, and 0.009 to 0.025 for maximum temperature; and 0.13 to 0.24, 45 to 85, 0.08 to 0.36, 0.01 to 0.014 for minimum temperature, respectively. The trend analysis result revealed that the highest percentage of stations were in increasing trend, while the magnitude varies slightly for annual rainfall, maximum, and minimum temperatures. Sustainable and innovative climate variability mitigation measures must be initiated to reduce the effects of variability in agricultural productivity and environmental changes. Future researchers can investigate the effects of natural and anthropogenic activities on water resources and their implications on water availability. Climate variability poses a pressing shift to the hydrological cycle and diminishing water resource availability. This shift is a prevalent challenge to day-to-day activities in the Olifants River, South Africa. This study assessed rainfall, minimum and maximum temperature variability, trend analysis, and change point detection in the Olifants River using numerous statistical analysis methods, such as coefficient of variation, Kurtosis, skewness, Pettitt, Buishand, von Neumann, Standard normal homogeneity test (SNHT), Mann-Kendall, and Sen's slope tests for the period of 1988 to 2014. The result showed that annual rainfall in most stations had moderate variation, limited stations were negatively skewed, and not normally distributed. Most of the stations, such as X1E003, B5E004, and B1E003, show less variability (CV < 20), while the rest of the stations show moderate variation ranges (20 < CV < 30) for rainfall datasets. The results of kurtosis and skewness ranged from -0.41 to 1.10 and -0.12 to 0.46 for rainfall; -0.03 to 0.92 and -0.61 to -0.17 for maximum temperature; and 0.24 to 1.61 and -0.18 to 0.14 for minimum temperature, respectively. Furthermore, the majority of stations were negatively skewed for annual maximum and minimum temperatures. Unexpectedly, the homogeneity tests for annual rainfall and maximum temperature depict favorable results, while a few stations were found to be non-homogeneous for minimum temperature. Specifically, the trend analysis indicators such as Kendall's tau, S, <em>p</em>-value and Sen’s slope were results ranges from -0.03 to 0.15, -11 to 53, 0.28 to 1.0, and -1.95 to 4.98 for rainfall; 0.08 to 0.15, 29 to 51, 0.30 to 0.56, and 0.009 to 0.025 for maximum temperature; and 0.13 to 0.24, 45 to 85, 0.08 to 0.36, 0.01 to 0.014 for minimum temperature, respectively. The trend analysis result revealed that the highest percentage of stations were in increasing trend, while the magnitude varies slightly for annual rainfall, maximum, and minimum temperatures. Sustainable and innovative climate variability mitigation measures must be initiated to reduce the effects of variability in agricultural productivity and environmental changes. Future researchers can investigate the effects of natural and anthropogenic activities on water resources and their implications on water availability. https://mmws.uma.ac.ir/article_4045_a037c988135f93fdd8668dd1be0947e3.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Performance of the XGBoost algorithm in downscaling temperature and relative humidity in a temperate climate: a case study in Kermanshah Performance of the XGBoost algorithm in downscaling temperature and relative humidity in a temperate climate: a case study in Kermanshah 215 232 4080 10.22098/mmws.2025.18179.1661 FA Mohammad Fouladi Nasrabad Ph.D. Candidate in Water Resources, Department of Water Engineering, University of Birjand, Birjand, Iran Mohsen Pourreza Bilondi Associate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran Mahdi Amirabadizadeh Associate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran Mahna Javaheri M.Sc student in Water Resources, Department of Water Engineering, University of Birjand, Birjand, Iran Journal Article 2025 08 29 Climate change requires a precise analysis of local data, and statistical downscaling using machine learning algorithms such as XGBoost can enhance the spatial accuracy of General Circulation Models (GCMs). This study examined the performance of XGBoost in the daily downscaling of temperature and relative humidity at the synoptic station of Kermanshah  during the period from 1990 to 2014. In order to investigate the performance of the XGBoost model in downscaling the climate variables of temperature and relative humidity, local and large-scale data were divided into two training and testing sections, so that the years 1990 to 2007 were considered as the training section and the years 2007 to 2015 were considered as the testing section. The present research showed that the XGBoost algorithm, as one of the advanced machine learning methods, performed very well in downscaling climatic parameters, especially temperature, and to some extent relative humidity. To evaluate the model's performance, the metric values were examined separately in two sections: training and testing. For temperature using ECMWF-ERA5 data, the KGE, NSE, and R² values in the training section were 0.98, 0.98, and 0.99, respectively. For temperature using MPI-ESM1-2-HR data, the values of these metrics in the training section were 0.93, 0.94, and 0.97, and in the testing section, they were 0.91, 0.88, and 0.94, respectively. Additionally, for relative humidity using ECMWF-ERA5 data, the metric values in the training section were 0.82, 0.93, and 0.97, and in the testing section, they were 0.7, 0.72, and 0.87. Finally, for relative humidity using MPI-ESM1-2-HR data, the values of these metrics in the training section were 0.72, 0.67, and 0.82, and in the testing section, they were 0.70, 0.65, and 0.81. Graphical analyses confirmed the superiority of the model in simulating intermediate and extreme temperature values, especially with ECMWF-ERA5, but limitations in reproducing extreme values were observed in relative humidity. Climate change requires a precise analysis of local data, and statistical downscaling using machine learning algorithms such as XGBoost can enhance the spatial accuracy of General Circulation Models (GCMs). This study examined the performance of XGBoost in the daily downscaling of temperature and relative humidity at the synoptic station of Kermanshah  during the period from 1990 to 2014. In order to investigate the performance of the XGBoost model in downscaling the climate variables of temperature and relative humidity, local and large-scale data were divided into two training and testing sections, so that the years 1990 to 2007 were considered as the training section and the years 2007 to 2015 were considered as the testing section. The present research showed that the XGBoost algorithm, as one of the advanced machine learning methods, performed very well in downscaling climatic parameters, especially temperature, and to some extent relative humidity. To evaluate the model's performance, the metric values were examined separately in two sections: training and testing. For temperature using ECMWF-ERA5 data, the KGE, NSE, and R² values in the training section were 0.98, 0.98, and 0.99, respectively. For temperature using MPI-ESM1-2-HR data, the values of these metrics in the training section were 0.93, 0.94, and 0.97, and in the testing section, they were 0.91, 0.88, and 0.94, respectively. Additionally, for relative humidity using ECMWF-ERA5 data, the metric values in the training section were 0.82, 0.93, and 0.97, and in the testing section, they were 0.7, 0.72, and 0.87. Finally, for relative humidity using MPI-ESM1-2-HR data, the values of these metrics in the training section were 0.72, 0.67, and 0.82, and in the testing section, they were 0.70, 0.65, and 0.81. Graphical analyses confirmed the superiority of the model in simulating intermediate and extreme temperature values, especially with ECMWF-ERA5, but limitations in reproducing extreme values were observed in relative humidity. https://mmws.uma.ac.ir/article_4080_c2d2845562140133f1693104dcae1735.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Assessment of surface runoff response to climate change in a Weyib Watershed using the WeSpass-M model Assessment of surface runoff response to climate change in a Weyib Watershed using the WeSpass-M model 233 251 4083 10.22098/mmws.2025.18248.1666 FA Mesfin Reta Aredo Department of Civil Engineering Sciences, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Megersa Olumana Dinka Department of Civil Engineering Sciences, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Journal Article 2025 09 03 Comprehending the impact of climate change on surface runoff is imperative to safeguard against excessive inundation vulnerability and management. This study estimates climate change effects on surface runoff using an ensemble of five climate models and the WetSpass-M model for the baseline period (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods. The downloaded climate models (CNRM-CM5, GFDL-ESM2M, IPSL-CM5A-MR, MPI-ESM-LR, and NorESM1-M) were downscaled by a dynamic downscaling technique and bias corrected by linear scaling. The model performance statistical indices, such as R² (0.90 and 0.85), NSE (0.95 and 0.89), and RMSE (4.19 and 9.94), were obtained by comparing the WetSpass-M model and filtered baseflow and direct runoff, respectively. The mean rainfall and temperature have been increasing compared to baseline period. The overall average monthly runoff has been rising with 8.70%, 18.22%, 6.53%, and 36.09% for RCP4.5 (MidT4.5) for the mid-term, RCP4.5 (LongT4.5) for the long-term, RCP8.5 (MidT8.5) for the mid-term, and RCP8.5 (LongT8.5) for the long-term, respectively. Seasonally, surface runoff is projected to increase throughout the entire season, except for autumn. Autumn season’s surface runoff has been dropping by 23.56%, 38.85%, 29.12%, and 43.02% for MidT4.5, LongT4.5, MidT8.5, and LongT8.5, respectively. Annually, surface runoff will increase by 8.3%, 31.20%, 1.80%, and 49.30% for the MidT4.5, LongT4.5, MidT8.5, and LongT8.5, respectively. Moreover, the findings conclusively underscored a dramatically rising surface runoff due to climate change, causing inundation in central and downstream watershed areas. Therefore, this increasing surface runoff will potentially affect daily life and weaken agricultural productivity; thus, reforestation and water conservation measures are required to lessen the adverse effects. Comprehending the impact of climate change on surface runoff is imperative to safeguard against excessive inundation vulnerability and management. This study estimates climate change effects on surface runoff using an ensemble of five climate models and the WetSpass-M model for the baseline period (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods. The downloaded climate models (CNRM-CM5, GFDL-ESM2M, IPSL-CM5A-MR, MPI-ESM-LR, and NorESM1-M) were downscaled by a dynamic downscaling technique and bias corrected by linear scaling. The model performance statistical indices, such as R² (0.90 and 0.85), NSE (0.95 and 0.89), and RMSE (4.19 and 9.94), were obtained by comparing the WetSpass-M model and filtered baseflow and direct runoff, respectively. The mean rainfall and temperature have been increasing compared to baseline period. The overall average monthly runoff has been rising with 8.70%, 18.22%, 6.53%, and 36.09% for RCP4.5 (MidT4.5) for the mid-term, RCP4.5 (LongT4.5) for the long-term, RCP8.5 (MidT8.5) for the mid-term, and RCP8.5 (LongT8.5) for the long-term, respectively. Seasonally, surface runoff is projected to increase throughout the entire season, except for autumn. Autumn season’s surface runoff has been dropping by 23.56%, 38.85%, 29.12%, and 43.02% for MidT4.5, LongT4.5, MidT8.5, and LongT8.5, respectively. Annually, surface runoff will increase by 8.3%, 31.20%, 1.80%, and 49.30% for the MidT4.5, LongT4.5, MidT8.5, and LongT8.5, respectively. Moreover, the findings conclusively underscored a dramatically rising surface runoff due to climate change, causing inundation in central and downstream watershed areas. Therefore, this increasing surface runoff will potentially affect daily life and weaken agricultural productivity; thus, reforestation and water conservation measures are required to lessen the adverse effects. https://mmws.uma.ac.ir/article_4083_b94ec66926d97f41ad5954b5f608b44c.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Statistical and multi-criteria methods for preprocessing meteorological data in reference evapotranspiration Statistical and multi-criteria methods for preprocessing meteorological data in reference evapotranspiration 252 269 4067 10.22098/mmws.2025.18259.1669 FA Laleh Parviz Associate Professor, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran Fardin Ghanbari-Maleki M.Sc Student, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran Journal Article 2025 09 06 Introduction <br /><br />Knowledge of actual evapotranspiration is valuable for assessing water availability in policy and decision-making ‌of water resources and agriculture. Despite all improvements, the measurement of actual evapotranspiration is accompanied by difficulty in some locations. In this regard, an accurate method for actual evapotranspiration estimation is linked to the reference evapotranspiration (ETo) determination as a significant component. The Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method is widely recognized for its high accuracy and making it a globally accepted standard. Despite the acceptability of the FAO Penman-Monteith method, the need for a large amount of reliable weather measurements, such as solar radiation and wind speed, has challenged the method. These data are often not available in developing countries, and the issue is related to the limited number of equipped meteorological stations or inaccuracies of measurement. Therefore, the need for an alternative ETo method seems necessary, and the efficient artificial intelligence techniques with a low number of input data can obtain accuracy equal to the FAO method. In this regard, the preprocessing step with a selection of important input data is more important. This study introduces a novel approach by systematically comparing multiple preprocessing methods for ETo estimation by integrating decision making techniques to improve data selection and model accuracy. The preprocessing methods belong to the correlation concept, regression analysis, and decision making approach, with different normalization methods. To increase the accuracy of decisions, more than one evaluation criteria were considered in the analysis.<br /><br />Materials and Methods <br /><br />The analysis of this study is focused on eleven stations (1992-2021). The station's spatial distribution consists of the North, West, North-West, East, and center of Iran. The preprocessing step in the modeling process has great importance in deriving the effective and precise factors as the input data. Several preprocessing methods were investigated in this study to identify the dominant input data for ETo estimation. They include the Pearson correlation coefficient, Kendall’s tau-b correlation coefficient, standardized Beta coefficient, stepwise regression, Shannon’s entropy, and simple additive weighting with fuzzy normalization. These methods were selected for their ability to assess important variables with data analysis from different aspects by correlation detection and data normalization, ensuring accurate ETo estimation. The Pearson correlation coefficient can distinguish the correlation between independent and dependent variables; higher values indicate higher dependency. The emphasis of stepwise regression is on the best and most impressive variables from a large set of variables. Decision making is not always between two options, and sometimes we have to make the right selection among several options. In this case, a multi-criteria decision is made, depending on the sensitivity of the problem, for which certain methods can help to reach the best option. Some methods are illustrated to solve MCDM problems, such as Shannon’s entropy. The process of entropy analysis is to assign the weights of the objective criterion. The assumption of entropy analysis is the importance of data with high-weight indicators relative to the data with low-weight indicators. The regression analysis aims to minimize the error between observed and forecasted values; this matter can be possible by SVR, which used as the model in this study. <br /><br /><br /><br />Results and Discussion <br /><br />The maximum Pearson correlation coefficient in the monthly scale is related to the solar radiation, maximum and minimum temperature in all stations. This matter was preserved by τ Kendall correlation coefficient. The derived meteorological data in the stepwise regression at the annual scale can be described as the relative humidity, wind speed, solar radiation, maximum temperature in Maku, wind speed, maximum temperature, solar radiation, sunshine hours in Yazd. Decision making analysis needs some criteria, and five criteria, RMSE, R, MAE, NSE, and GMER, were applied in Shannon’s entropy method. The selected are used to find the best solution from all data (Tmax, Tmin, RH, U, S, and R), and different combinations of data. The combination 3-7; the number of input data is equal to 3, and the data are wind speed, solar radiation, and sunshine hours, has the highest weight, pink in Maku. In the monthly scale and the combination with five input data, the RMSE of all stations related to Shannon’s entropy is higher than fuzzy normalization, except Mashhad with the same RMSE in the two methods, and Zanjan and Yazd with a low error of Shannon’s entropy. In two scales, the performance of fuzzy normalization is in a good state. In the annual scale, the Pearson correlation and stepwise regression have the same function. In the monthly scale, stepwise regression has poor performance. The selection of input data based on fuzzy normalization could decrease the error of the simulation. <br /><br />Conclusion <br /><br />The results indicated that the normalization process had better performance in the preprocessing method based on the MCDM approach relative to the other methods. The average of the criteria showed that the best method has no limitations regarding to the three types of different climates, wet, semiarid, and arid, and the fuzzy normalization had good performance. This method has no geographical limitation. Determining an efficient method for the preprocessing step has an acceptable response in all climates, which is one of the strengths and innovations of the research. One of the things that can strongly affect the preprocessing method based MCDM approach is the type of decision making method. In the decision making problem, the used method for normalization of the decision matrix has high importance in information extraction. In general, maximum temperature, relative humidity, wind speed, solar radiation, sunshine hours (annual), and minimum temperature (monthly) were introduced as the effective data. The reason for the better performance of certain data combination is related to the high dependency of these combinations with ETo variation.<br /><br />Generally, using the exact method as the preprocessing step in each climate based on the data capabilities of area and selection of the effective data can upgrade the efficiency of ETo estimation. It can led to the precise determination of water availability and strong policymaking in irrigation planning, agricultural studies. Introduction <br /><br />Knowledge of actual evapotranspiration is valuable for assessing water availability in policy and decision-making ‌of water resources and agriculture. Despite all improvements, the measurement of actual evapotranspiration is accompanied by difficulty in some locations. In this regard, an accurate method for actual evapotranspiration estimation is linked to the reference evapotranspiration (ETo) determination as a significant component. The Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method is widely recognized for its high accuracy and making it a globally accepted standard. Despite the acceptability of the FAO Penman-Monteith method, the need for a large amount of reliable weather measurements, such as solar radiation and wind speed, has challenged the method. These data are often not available in developing countries, and the issue is related to the limited number of equipped meteorological stations or inaccuracies of measurement. Therefore, the need for an alternative ETo method seems necessary, and the efficient artificial intelligence techniques with a low number of input data can obtain accuracy equal to the FAO method. In this regard, the preprocessing step with a selection of important input data is more important. This study introduces a novel approach by systematically comparing multiple preprocessing methods for ETo estimation by integrating decision making techniques to improve data selection and model accuracy. The preprocessing methods belong to the correlation concept, regression analysis, and decision making approach, with different normalization methods. To increase the accuracy of decisions, more than one evaluation criteria were considered in the analysis.<br /><br />Materials and Methods <br /><br />The analysis of this study is focused on eleven stations (1992-2021). The station's spatial distribution consists of the North, West, North-West, East, and center of Iran. The preprocessing step in the modeling process has great importance in deriving the effective and precise factors as the input data. Several preprocessing methods were investigated in this study to identify the dominant input data for ETo estimation. They include the Pearson correlation coefficient, Kendall’s tau-b correlation coefficient, standardized Beta coefficient, stepwise regression, Shannon’s entropy, and simple additive weighting with fuzzy normalization. These methods were selected for their ability to assess important variables with data analysis from different aspects by correlation detection and data normalization, ensuring accurate ETo estimation. The Pearson correlation coefficient can distinguish the correlation between independent and dependent variables; higher values indicate higher dependency. The emphasis of stepwise regression is on the best and most impressive variables from a large set of variables. Decision making is not always between two options, and sometimes we have to make the right selection among several options. In this case, a multi-criteria decision is made, depending on the sensitivity of the problem, for which certain methods can help to reach the best option. Some methods are illustrated to solve MCDM problems, such as Shannon’s entropy. The process of entropy analysis is to assign the weights of the objective criterion. The assumption of entropy analysis is the importance of data with high-weight indicators relative to the data with low-weight indicators. The regression analysis aims to minimize the error between observed and forecasted values; this matter can be possible by SVR, which used as the model in this study. <br /><br /><br /><br />Results and Discussion <br /><br />The maximum Pearson correlation coefficient in the monthly scale is related to the solar radiation, maximum and minimum temperature in all stations. This matter was preserved by τ Kendall correlation coefficient. The derived meteorological data in the stepwise regression at the annual scale can be described as the relative humidity, wind speed, solar radiation, maximum temperature in Maku, wind speed, maximum temperature, solar radiation, sunshine hours in Yazd. Decision making analysis needs some criteria, and five criteria, RMSE, R, MAE, NSE, and GMER, were applied in Shannon’s entropy method. The selected are used to find the best solution from all data (Tmax, Tmin, RH, U, S, and R), and different combinations of data. The combination 3-7; the number of input data is equal to 3, and the data are wind speed, solar radiation, and sunshine hours, has the highest weight, pink in Maku. In the monthly scale and the combination with five input data, the RMSE of all stations related to Shannon’s entropy is higher than fuzzy normalization, except Mashhad with the same RMSE in the two methods, and Zanjan and Yazd with a low error of Shannon’s entropy. In two scales, the performance of fuzzy normalization is in a good state. In the annual scale, the Pearson correlation and stepwise regression have the same function. In the monthly scale, stepwise regression has poor performance. The selection of input data based on fuzzy normalization could decrease the error of the simulation. <br /><br />Conclusion <br /><br />The results indicated that the normalization process had better performance in the preprocessing method based on the MCDM approach relative to the other methods. The average of the criteria showed that the best method has no limitations regarding to the three types of different climates, wet, semiarid, and arid, and the fuzzy normalization had good performance. This method has no geographical limitation. Determining an efficient method for the preprocessing step has an acceptable response in all climates, which is one of the strengths and innovations of the research. One of the things that can strongly affect the preprocessing method based MCDM approach is the type of decision making method. In the decision making problem, the used method for normalization of the decision matrix has high importance in information extraction. In general, maximum temperature, relative humidity, wind speed, solar radiation, sunshine hours (annual), and minimum temperature (monthly) were introduced as the effective data. The reason for the better performance of certain data combination is related to the high dependency of these combinations with ETo variation.<br /><br />Generally, using the exact method as the preprocessing step in each climate based on the data capabilities of area and selection of the effective data can upgrade the efficiency of ETo estimation. It can led to the precise determination of water availability and strong policymaking in irrigation planning, agricultural studies. https://mmws.uma.ac.ir/article_4067_8263f2d0a0a4c2bd5dab1d44463ad312.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 Special Issue : Climate Change and Effects on Water and Soil 2025 05 22 Contribution of irrigation practices for reducing farmers’ vulnerability to climate change and variability Contribution of irrigation practices for reducing farmers’ vulnerability to climate change and variability 270 293 4173 10.22098/mmws.2025.18303.1675 FA Abraham Woru Borku Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia Thomas Toma Tora Department of Geography and Environmental Studies, College of Social Science and Humanities, Arba Minch University, Arba Minch, Ethiopia Mamush Masha Department of Geography and Environmental Studies, College of Social Science and Humanities, Mettu University, Mettu, Ethiopia 0000-0002-2666-9170 Journal Article 2025 09 09 Climate change and variability pose major challenges to agricultural productivity and rural livelihoods in Ethiopia, where most smallholder farmers depend on rain-fed systems. This study investigates how small-scale irrigation (SSI) contributes to reducing farmers’ vulnerability and enhancing their adaptive capacity to climate variability in Southern Ethiopia. Using a multi-stage sampling approach, data were collected from 144 households (72 irrigation users and 72 non-users) through surveys, focus group discussions, and key informant interviews. Climate trends were analyzed using the Mann–Kendall test, Sen’s slope estimator, coefficient of variation, and Standardized Anomaly Index (SAI) from 1987 to 2022. Results revealed a significant increase in annual maximum and minimum temperatures at rates of 0.0238°C and 0.0844°C per year, respectively, and a positive annual rainfall trend (Kendall’s Tau = 0.344, p = 0.003). Vulnerability analysis using Principal Component Analysis indicated that irrigation users were less vulnerable, with indices ranging from 0 to –0.90, compared to non-users (0 to 0.75). Irrigation users demonstrated higher adaptive capacity due to improved access to water, agricultural inputs, income diversification, and enhanced awareness of climate risks. Conversely, non-irrigators remained highly sensitive to rainfall fluctuations and resource constraints. The study concludes that SSI significantly enhances farmers’ resilience by stabilizing production and income, thereby mitigating the adverse effects of climate variability. Hence, strengthening institutional support, promoting farmer-led irrigation management, and scaling up SSI technologies are recommended to improve climate adaptation and ensure sustainable rural livelihoods. Climate change and variability pose major challenges to agricultural productivity and rural livelihoods in Ethiopia, where most smallholder farmers depend on rain-fed systems. This study investigates how small-scale irrigation (SSI) contributes to reducing farmers’ vulnerability and enhancing their adaptive capacity to climate variability in Southern Ethiopia. Using a multi-stage sampling approach, data were collected from 144 households (72 irrigation users and 72 non-users) through surveys, focus group discussions, and key informant interviews. Climate trends were analyzed using the Mann–Kendall test, Sen’s slope estimator, coefficient of variation, and Standardized Anomaly Index (SAI) from 1987 to 2022. Results revealed a significant increase in annual maximum and minimum temperatures at rates of 0.0238°C and 0.0844°C per year, respectively, and a positive annual rainfall trend (Kendall’s Tau = 0.344, p = 0.003). Vulnerability analysis using Principal Component Analysis indicated that irrigation users were less vulnerable, with indices ranging from 0 to –0.90, compared to non-users (0 to 0.75). Irrigation users demonstrated higher adaptive capacity due to improved access to water, agricultural inputs, income diversification, and enhanced awareness of climate risks. Conversely, non-irrigators remained highly sensitive to rainfall fluctuations and resource constraints. The study concludes that SSI significantly enhances farmers’ resilience by stabilizing production and income, thereby mitigating the adverse effects of climate variability. Hence, strengthening institutional support, promoting farmer-led irrigation management, and scaling up SSI technologies are recommended to improve climate adaptation and ensure sustainable rural livelihoods. https://mmws.uma.ac.ir/article_4173_b91ea7cc217e9fd8d1c61ef64eb7b565.pdf