University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Futuristic research of resilience of water resources with scenario planning approach based on case study: Zayandeh Rood watershed Futuristic research of resilience of water resources with scenario planning approach based on case study: Zayandeh Rood watershed 1 19 3069 10.22098/mmws.2024.15086.1460 FA ,Mahdi Yaraghi Fard M.Sc, Department of Urban Planning, Faculty of Architecture and Urban Planning, Iran University of Science and Technology, Tehran, Iran Mohammadsaleh Shokouhibidhendi Assistant Professor, Department of Urban Planning, Faculty of Architecture and Urban Planning, Iran University of Science and Technology, Tehran, Iran Journal Article 2024 05 17 Introduction The rapid growth of the population, the development of economic activities, and human needs for natural resources, including water resources, have caused an imbalance between supply and demand, and have ultimately caused the instability and lack of resilience of these resources in most regions of the world, especially arid and semi-arid regions such as our countries. Water is the foundation of life, the foundation of nature, and the axis of economic, social, and cultural development of societies. Providing safe and sufficient water has always been one of the most important challenges of the world community in the third millennium, especially in the arid belt countries of the world and the West Asian region. The United Nations World Water Development Report (2020) shows that global water consumption has increased six-fold over the past century and continues to grow by approximately 1% annually. Therefore, an increasing share of the population will face water shortage. Achieving sustainable development of water resources is widely linked to the concept of resilience. In this research, an attempt has been made to address the various dimensions and indicators of resilience, which include social, economic, and environmental resilience, and which play an effective role in the resilience of water resources. Then we will focus on the resilience of water resources and the emergence of imaginable possibilities in case of occurrence in each scenario to prioritize environmental, economic, and social changes by considering different conditions to create a clear understanding of the change of each variable for researchers. The present study tries to apply the basic scenario planning approach with Futurology for future research by dealing with one of Iran's important central catchment areas, which is also facing various challenges. Materials and Methods The current research, with a future research approach, identifies the most important factors affecting the resilience of water resources in the Zayandeh Rood watershed. In this research, due to its exploratory nature, the environmental scanning technique was used, and due to the dominant approach of this research, which is future research, the Delphi technique was used. Because due to its specialized nature, this research cannot be done through conventional methods of surveying people. The Delphi method is a method that relies on wisdom, collective intelligence, and brainstorming to reach the consensus of experts on a specific topic so that the most appropriate answers can be obtained. The sample population of this research is a group of 50 experts, people active in the field of water, experts, who are experts in two fields: "future research approach" and "resilience of water resources". At the final stage, because the coefficient of agreement between the panel members regarding the questions of the questionnaire has increased significantly compared to the first round, the continuation of Delphi has been omitted and at this stage, the number of 33 factors has been called as the final index for structural analysis. The vision of questions and issues is designed for the next 25 years. At this stage, these 33 factors were distributed in the form of a semi-structured questionnaire among the statistical community (Delphi group consisting of experts) and they were asked to rate the variables in the framework of the cross-effects matrix based on influence and effectiveness with numbers in the range of 0 to 4. These points were entered in the cross matrix to measure the direct and indirect influence of each factor and according to the score of influence and influence of the factors, key factors are obtained. Results and Discussion In the analysis done, the dimensions of the matrix in the Mikmac software are 33 x 33 and the number of repetitions is considered 2 times. The index of filling the matrix is 91.99%, which indicates that about 92% of the cases have influenced each other. from a total of 1001 relationships; 88 relationships have cross effects, 392 relationships have cross effects 1, 497 relationships have cross effects 2, and 112 relationships have cross effects 3. These results indicate that the number of relationships with moderate impact is high compared to other relationships, and relationships with high intensity form a small percentage of the total relationships. In the following, the analysis of the direct and indirect effectiveness of the factors is discussed. What can be understood from the state of the scatter plot of variables affecting the resilience of Zayandeh Rood water resources is the state of instability of the system. After identifying the influential indicators and determining their role and importance in direct and indirect variables, the selected indicators are identified as the uncertainty of the scenarios, and the base scenario planning is formed. Therefore, 5 scenarios were prepared based on uncertainties Conclusion With the expansion of urbanization and the increase of population in metropolises, the conflict between development and the environment is gradually revealed. In the meantime, the ecological power of the regions is limited to each region. In the area of the central plateau of Iran, the amount of population settlement has never been as high as it is today. Therefore, urbanization in the central plateau of Iran has created many problems for natural resources. On the other hand, the distribution of the population in the area with natural resources, including water sources, is not consistent, because the rainfall occurs in the western area of the watershed, but the majority of the people and the population live in other parts. Therefore, the conflict of interests between citizens and villagers is observed in the use of water resources and the consumption pattern, and the need to use technology to manage and reuse water resources has become particularly important. Heterogeneous rainfall distribution within the catchment area has doubled the importance of the interaction of people groups with each other. The development of industrial and service jobs should be done with the approach of reducing jobs dependent on water resources to reduce water consumption Introduction The rapid growth of the population, the development of economic activities, and human needs for natural resources, including water resources, have caused an imbalance between supply and demand, and have ultimately caused the instability and lack of resilience of these resources in most regions of the world, especially arid and semi-arid regions such as our countries. Water is the foundation of life, the foundation of nature, and the axis of economic, social, and cultural development of societies. Providing safe and sufficient water has always been one of the most important challenges of the world community in the third millennium, especially in the arid belt countries of the world and the West Asian region. The United Nations World Water Development Report (2020) shows that global water consumption has increased six-fold over the past century and continues to grow by approximately 1% annually. Therefore, an increasing share of the population will face water shortage. Achieving sustainable development of water resources is widely linked to the concept of resilience. In this research, an attempt has been made to address the various dimensions and indicators of resilience, which include social, economic, and environmental resilience, and which play an effective role in the resilience of water resources. Then we will focus on the resilience of water resources and the emergence of imaginable possibilities in case of occurrence in each scenario to prioritize environmental, economic, and social changes by considering different conditions to create a clear understanding of the change of each variable for researchers. The present study tries to apply the basic scenario planning approach with Futurology for future research by dealing with one of Iran's important central catchment areas, which is also facing various challenges. Materials and Methods The current research, with a future research approach, identifies the most important factors affecting the resilience of water resources in the Zayandeh Rood watershed. In this research, due to its exploratory nature, the environmental scanning technique was used, and due to the dominant approach of this research, which is future research, the Delphi technique was used. Because due to its specialized nature, this research cannot be done through conventional methods of surveying people. The Delphi method is a method that relies on wisdom, collective intelligence, and brainstorming to reach the consensus of experts on a specific topic so that the most appropriate answers can be obtained. The sample population of this research is a group of 50 experts, people active in the field of water, experts, who are experts in two fields: "future research approach" and "resilience of water resources". At the final stage, because the coefficient of agreement between the panel members regarding the questions of the questionnaire has increased significantly compared to the first round, the continuation of Delphi has been omitted and at this stage, the number of 33 factors has been called as the final index for structural analysis. The vision of questions and issues is designed for the next 25 years. At this stage, these 33 factors were distributed in the form of a semi-structured questionnaire among the statistical community (Delphi group consisting of experts) and they were asked to rate the variables in the framework of the cross-effects matrix based on influence and effectiveness with numbers in the range of 0 to 4. These points were entered in the cross matrix to measure the direct and indirect influence of each factor and according to the score of influence and influence of the factors, key factors are obtained. Results and Discussion In the analysis done, the dimensions of the matrix in the Mikmac software are 33 x 33 and the number of repetitions is considered 2 times. The index of filling the matrix is 91.99%, which indicates that about 92% of the cases have influenced each other. from a total of 1001 relationships; 88 relationships have cross effects, 392 relationships have cross effects 1, 497 relationships have cross effects 2, and 112 relationships have cross effects 3. These results indicate that the number of relationships with moderate impact is high compared to other relationships, and relationships with high intensity form a small percentage of the total relationships. In the following, the analysis of the direct and indirect effectiveness of the factors is discussed. What can be understood from the state of the scatter plot of variables affecting the resilience of Zayandeh Rood water resources is the state of instability of the system. After identifying the influential indicators and determining their role and importance in direct and indirect variables, the selected indicators are identified as the uncertainty of the scenarios, and the base scenario planning is formed. Therefore, 5 scenarios were prepared based on uncertainties Conclusion With the expansion of urbanization and the increase of population in metropolises, the conflict between development and the environment is gradually revealed. In the meantime, the ecological power of the regions is limited to each region. In the area of the central plateau of Iran, the amount of population settlement has never been as high as it is today. Therefore, urbanization in the central plateau of Iran has created many problems for natural resources. On the other hand, the distribution of the population in the area with natural resources, including water sources, is not consistent, because the rainfall occurs in the western area of the watershed, but the majority of the people and the population live in other parts. Therefore, the conflict of interests between citizens and villagers is observed in the use of water resources and the consumption pattern, and the need to use technology to manage and reuse water resources has become particularly important. Heterogeneous rainfall distribution within the catchment area has doubled the importance of the interaction of people groups with each other. The development of industrial and service jobs should be done with the approach of reducing jobs dependent on water resources to reduce water consumption

https://mmws.uma.ac.ir/article_3069_9a8c773b21c3a4707c0d09c30efac0cd.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Effects of deficit irrigation combined with the use of biochar and compost on the growth, yield and water use efficiency of green pepper Effects of deficit irrigation combined with the use of biochar and compost on the growth, yield and water use efficiency of green pepper 20 36 3219 10.22098/mmws.2024.15604.1489 FA Aref Karamollacheab MSc. Irrigation and Drainage, Water Engineering and Management Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran 0009-0007-2086-0242 Seyed MAjid Mirlatifi Associate Professor, Water Engineering and Management Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran Ali Mokhtasi Bidgoli Associate Professor, Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran Journal Article 2024 08 15 Abstract Introduction Agriculture plays a crucial role in fulfilling human needs, but it is also the world's largest consumer of freshwater. This has a significant impact on water resources worldwide, particularly in arid and semi-arid regions. With the growing global population, agricultural production must be increased to meet demand. This has led researchers to explore various methods, including deficit irrigation techniques, soil nutrient enhancement, and water retention agents, to improve crop performance and productivity. Numerous studies have investigated these methods to enhance crop performance and to reduce water consumption. Previous research has also highlighted the potential benefits of biochar application at a 2% level to improve plant performance. This study aimed to evaluate the effects of continuous deficit irrigation (CDI) along with use of compost as a soil nutrient enhancer and biochar as a water retention agent on the performance, growth, and productivity of bell pepper plants. The study examined the effects of these treatments individually and in combination, as well as the combined effects of biochar and sugarcane bagasse compost at 1.5%, 2%, and 2.5% weight levels and their interactions with CDI on water savings and performance improvement of bell pepper plants in a sandy soil. Materials and Methods To investigate the research objectives, during 2023, in the research greenhouse of Tarbiat Modares University, a factorial experimental design was implemented in the form of a randomized complete block design with three factors and a total of 48 treatments in four blocks with a total of 192 pots. The first factor includes three levels of irrigation: 100% (D100), 75% (D75), and 65% (D65) of the plant's water needs. The second factor is sugarcane bagasse biochar with four levels: 0% by weight (B0), 1.5% by weight (B1.5), 2% by weight (B2), and 2.5% by weight (B2.5). The third factor is sugarcane bagasse compost with four levels: 0% by weight (C0), 1.5% by weight (C1.5), 2% by weight (C2), and 2.5% by weight (C2.5). In this research, plant growth rate and plant diameter were investigated, and fruit characteristics such as fruit drop, fruit length, fruit flesh thickness, and fruit fresh weight were investigated. In order to check the functional characteristics of the plant, the number of fruits per plant and the total weight of the fruit per plant, as well as the productivity using the ratio of the total weight of the fruit produced per plant to the volume of water used in each pot, were investigated. The watering of bell pepper plants was not like this. During the 3-day watering cycle, the amount of water used in each pot was calculated and added to the pot, and this cycle continued until the end of pepper cultivation. Results and Discussion The results showed that the application of biochar and compost at all levels along with full irrigation had a positive and significant effect on the physical characteristics of the fruit (diameter, length, flesh thickness and fresh weight) compared to the control treatment. The lowest value for fruit characteristics was observed in the control group with 65% plant water requirement. The results for the plant characteristics showed that C2B2.5, C2.5B2 and C2.5B2.5 treatments showed a significant difference in plant height compared to the control treatment. C2.5B2 and C2.5B2.5 treatments also showed the greatest increase in stem diameter and had a significant difference compared to other treatments. Also, the results for the number of fruits per plant and yield showed that C2.5B2 treatment had a significant difference compared to other treatments. It caused a 156% increase in plant yield compared to the control treatment. The lowest number of fruits and plant yield was related to the control treatment. In addition, the C2.5B2 treatment achieved an average productivity of 39.3 kg/m3, which was significantly higher than the other treatments, representing a 139% increase over the control. The control treatment had the lowest productivity with an average productivity of 16.43 kg/m3, which was significantly lower than other treatments. The results of mean square variance analysis related to fruit characteristics showed that the triple effect of biochar, compost and deficit irrigation was significant at the level of 1%, but the triple effect of biochar, compost and deficit irrigation was related to plant characteristics and functional characteristics and productivity. The plant was not significant. Conclusion The present study demonstrated that the combined application of biochar and compost in bell pepper cultivation offers substantial advantages. This innovative approach not only significantly enhances crop yield but also improves productivity by optimizing water use. Applying 2% biochar and 2.5% compost to bell pepper plants along with continuous irrigation up to 75% of the plant's water requirement can yield promising results for sandy soils. These findings are particularly crucial for arid and semi-arid regions facing water scarcity. In a world facing water shortages, this method can contribute to water conservation and sustainable agricultural development. Abstract Introduction Agriculture plays a crucial role in fulfilling human needs, but it is also the world's largest consumer of freshwater. This has a significant impact on water resources worldwide, particularly in arid and semi-arid regions. With the growing global population, agricultural production must be increased to meet demand. This has led researchers to explore various methods, including deficit irrigation techniques, soil nutrient enhancement, and water retention agents, to improve crop performance and productivity. Numerous studies have investigated these methods to enhance crop performance and to reduce water consumption. Previous research has also highlighted the potential benefits of biochar application at a 2% level to improve plant performance. This study aimed to evaluate the effects of continuous deficit irrigation (CDI) along with use of compost as a soil nutrient enhancer and biochar as a water retention agent on the performance, growth, and productivity of bell pepper plants. The study examined the effects of these treatments individually and in combination, as well as the combined effects of biochar and sugarcane bagasse compost at 1.5%, 2%, and 2.5% weight levels and their interactions with CDI on water savings and performance improvement of bell pepper plants in a sandy soil. Materials and Methods To investigate the research objectives, during 2023, in the research greenhouse of Tarbiat Modares University, a factorial experimental design was implemented in the form of a randomized complete block design with three factors and a total of 48 treatments in four blocks with a total of 192 pots. The first factor includes three levels of irrigation: 100% (D100), 75% (D75), and 65% (D65) of the plant's water needs. The second factor is sugarcane bagasse biochar with four levels: 0% by weight (B0), 1.5% by weight (B1.5), 2% by weight (B2), and 2.5% by weight (B2.5). The third factor is sugarcane bagasse compost with four levels: 0% by weight (C0), 1.5% by weight (C1.5), 2% by weight (C2), and 2.5% by weight (C2.5). In this research, plant growth rate and plant diameter were investigated, and fruit characteristics such as fruit drop, fruit length, fruit flesh thickness, and fruit fresh weight were investigated. In order to check the functional characteristics of the plant, the number of fruits per plant and the total weight of the fruit per plant, as well as the productivity using the ratio of the total weight of the fruit produced per plant to the volume of water used in each pot, were investigated. The watering of bell pepper plants was not like this. During the 3-day watering cycle, the amount of water used in each pot was calculated and added to the pot, and this cycle continued until the end of pepper cultivation. Results and Discussion The results showed that the application of biochar and compost at all levels along with full irrigation had a positive and significant effect on the physical characteristics of the fruit (diameter, length, flesh thickness and fresh weight) compared to the control treatment. The lowest value for fruit characteristics was observed in the control group with 65% plant water requirement. The results for the plant characteristics showed that C2B2.5, C2.5B2 and C2.5B2.5 treatments showed a significant difference in plant height compared to the control treatment. C2.5B2 and C2.5B2.5 treatments also showed the greatest increase in stem diameter and had a significant difference compared to other treatments. Also, the results for the number of fruits per plant and yield showed that C2.5B2 treatment had a significant difference compared to other treatments. It caused a 156% increase in plant yield compared to the control treatment. The lowest number of fruits and plant yield was related to the control treatment. In addition, the C2.5B2 treatment achieved an average productivity of 39.3 kg/m3, which was significantly higher than the other treatments, representing a 139% increase over the control. The control treatment had the lowest productivity with an average productivity of 16.43 kg/m3, which was significantly lower than other treatments. The results of mean square variance analysis related to fruit characteristics showed that the triple effect of biochar, compost and deficit irrigation was significant at the level of 1%, but the triple effect of biochar, compost and deficit irrigation was related to plant characteristics and functional characteristics and productivity. The plant was not significant. Conclusion The present study demonstrated that the combined application of biochar and compost in bell pepper cultivation offers substantial advantages. This innovative approach not only significantly enhances crop yield but also improves productivity by optimizing water use. Applying 2% biochar and 2.5% compost to bell pepper plants along with continuous irrigation up to 75% of the plant's water requirement can yield promising results for sandy soils. These findings are particularly crucial for arid and semi-arid regions facing water scarcity. In a world facing water shortages, this method can contribute to water conservation and sustainable agricultural development.

https://mmws.uma.ac.ir/article_3219_b77293dc608d3f84a7a524627fe612a4.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Evaluation of AquaCrop model in predicting rice grain yield and biomass under water stress in different years Evaluation of AquaCrop model in predicting rice grain yield and biomass under water stress in different years 37 53 3670 10.22098/mmws.2025.16123.1512 FA Hamid Mohammadi Dahrayi Department of irrigation and drainage, Lahijan Branch, Islamic Azad University, Lahijan, Iran Ebrahim Amiri Department of Water Engineering, Lahijan Branch, Islamic Azad Univ., Lahijan, Iran Mojtaba Rezaei Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran Jalal Behzadi Assistant Professor, Department of Agriculture, Lahijan Branch, Islamic Azad University, Lahijan, Iran Journal Article 2024 11 02 Introduction Rice is one of the most important cereals in providing food security and the main food of billions of people in the Asian continent and many other parts of the world. Drought is the most important factor limiting rice production in paddy fields, which affects all stages of rice growth and development. Researches has shown that avoiding the conventional flood irrigation methods in rice cultivation and using alternative methods such as intermittent irrigation have a great effect on increasing water productivity and reducing its consumption. But in determining the most suitable alternative method of irrigation in each region, it should be noted that the effects of water shortage change with changes in the intensity, duration and time of its application. Today many Crop Growth Models (CGM's) have been designed to avoid the huge costs of conducting field research, speed up finding suitable solutions, and help to better understand and solve problems related to water movement in the soil and plant growth. Studies show that CGM's are able to consider the effect of different stresses such as water stress on dry matter production and grain yield during the growth period. One of the CGM’s is the AquaCrop model developed by FAO. The presence of factors such as moderate to severe water stress causes a significant decrease in the simulation accuracy by model, which is mentioned as one of the defects of the model. Most of studies with AquaCrop model have been limited to data collected in short periods. Therefore, the aim of this research is to evaluate the efficiency and accuracy of the AquaCrop model in simulating rice grain yield and biomass under multiple water stresses and during different years. Materials and Methods In order to evaluate the accuracy of the model in predicting the grain yield and biomass of rice, data collected from several research projects carried out in different years, the model was first calibrated and validated. The Hashemi variety used in all these projects, which is the most common variety cultivated in Guilan province. All agricultural operations of planting, growing and harvesting were carried out according to regional customs and the amounts of chemical fertilizers, herbicides and pesticides based on the recommendations of experts in agriculture and herbal medicine of the rice research institute. For the present study, a total of 45 irrigation treatments were selected from the previous projects carried out in the lands of the Country Rice Research Institute, Lahijan and Soumesara, of which 31 treatments were used for the calibration section and 14 treatments were used for model validation. The model was implemented for each irrigation treatment separately and the grain yield and biomass values obtained from the simulation were analyzed with the measured values based on the statistical indicators used in this research. Results and Discussion Based on the results of calibration of the model, the range of observed grain yield was 2100 to 4870 with an average of 3765 kg.ha-1. This is while the corresponding values simulated in the calibration conditions by the model were equal to 1749 to 4704 with an average of 3748 kg.ha-1. The accuracy of the model is low at the low limit of performance, but very accurate at the high and average performance limits. This phenomenon can be attributed to the estimation error of the model in the presence of environmental stresses such as water and fertilizer stress, which has also been mentioned in the studies of other researchers. Also, the values of RMSE and NRMSE for yield simulation were equal to 309.65 kg.ha-1 and 8.22%, respectively, which indicates very good accuracy in model calibration. Also, RMSE and NRMSE values for biomass simulation in calibration conditions were equal to 596.31 kg.ha-1 and 6.41%, respectively. The values of RMSE and NRMSE for the simulation of performance in the validation conditions were equal to 168.42 kg.ha-1 and 10.30%, which indicates good accuracy in model validation. Also, the values of RMSE and NRMSE for the simulation of biomass were equal to 554.71 kg.ha-1 and 12.90%, which shows the good accuracy of the model in validation. Examining the results of the model in different water stresses showed that with the increase of water stress from permanent waterlogging to high stress with the addition of irrigation cycles, the amount of model error in yield simulation increases. Conclusion In general, the AquaCrop model has good accuracy in simulating the grain yield and biomass of Hashemi variety rice, but the more severe the amount of water stress, the accuracy of the model decreases and its error increases. This problem is attributed to the structure of the model and the mathematical equations used in it, as well as the measured data. However, the AquaCrop model has many advantages such as the need for less data, the ability to be used for a wide range of crops and the user-friendliness of the model and it is recommended to use the AquaCrop model in different irrigation managements, especially in conditions without severe water stress, where the model has very good accuracy. But it is recommended due to the advantages of the AquaCrop model, such as the need for less data, the ability to be used for a wide range of crops and the user-friendliness of the model, its use in different irrigation managements, especially in conditions without extreme water stress, where the model has very good accuracy. Introduction Rice is one of the most important cereals in providing food security and the main food of billions of people in the Asian continent and many other parts of the world. Drought is the most important factor limiting rice production in paddy fields, which affects all stages of rice growth and development. Researches has shown that avoiding the conventional flood irrigation methods in rice cultivation and using alternative methods such as intermittent irrigation have a great effect on increasing water productivity and reducing its consumption. But in determining the most suitable alternative method of irrigation in each region, it should be noted that the effects of water shortage change with changes in the intensity, duration and time of its application. Today many Crop Growth Models (CGM's) have been designed to avoid the huge costs of conducting field research, speed up finding suitable solutions, and help to better understand and solve problems related to water movement in the soil and plant growth. Studies show that CGM's are able to consider the effect of different stresses such as water stress on dry matter production and grain yield during the growth period. One of the CGM’s is the AquaCrop model developed by FAO. The presence of factors such as moderate to severe water stress causes a significant decrease in the simulation accuracy by model, which is mentioned as one of the defects of the model. Most of studies with AquaCrop model have been limited to data collected in short periods. Therefore, the aim of this research is to evaluate the efficiency and accuracy of the AquaCrop model in simulating rice grain yield and biomass under multiple water stresses and during different years. Materials and Methods In order to evaluate the accuracy of the model in predicting the grain yield and biomass of rice, data collected from several research projects carried out in different years, the model was first calibrated and validated. The Hashemi variety used in all these projects, which is the most common variety cultivated in Guilan province. All agricultural operations of planting, growing and harvesting were carried out according to regional customs and the amounts of chemical fertilizers, herbicides and pesticides based on the recommendations of experts in agriculture and herbal medicine of the rice research institute. For the present study, a total of 45 irrigation treatments were selected from the previous projects carried out in the lands of the Country Rice Research Institute, Lahijan and Soumesara, of which 31 treatments were used for the calibration section and 14 treatments were used for model validation. The model was implemented for each irrigation treatment separately and the grain yield and biomass values obtained from the simulation were analyzed with the measured values based on the statistical indicators used in this research. Results and Discussion Based on the results of calibration of the model, the range of observed grain yield was 2100 to 4870 with an average of 3765 kg.ha-1. This is while the corresponding values simulated in the calibration conditions by the model were equal to 1749 to 4704 with an average of 3748 kg.ha-1. The accuracy of the model is low at the low limit of performance, but very accurate at the high and average performance limits. This phenomenon can be attributed to the estimation error of the model in the presence of environmental stresses such as water and fertilizer stress, which has also been mentioned in the studies of other researchers. Also, the values of RMSE and NRMSE for yield simulation were equal to 309.65 kg.ha-1 and 8.22%, respectively, which indicates very good accuracy in model calibration. Also, RMSE and NRMSE values for biomass simulation in calibration conditions were equal to 596.31 kg.ha-1 and 6.41%, respectively. The values of RMSE and NRMSE for the simulation of performance in the validation conditions were equal to 168.42 kg.ha-1 and 10.30%, which indicates good accuracy in model validation. Also, the values of RMSE and NRMSE for the simulation of biomass were equal to 554.71 kg.ha-1 and 12.90%, which shows the good accuracy of the model in validation. Examining the results of the model in different water stresses showed that with the increase of water stress from permanent waterlogging to high stress with the addition of irrigation cycles, the amount of model error in yield simulation increases. Conclusion In general, the AquaCrop model has good accuracy in simulating the grain yield and biomass of Hashemi variety rice, but the more severe the amount of water stress, the accuracy of the model decreases and its error increases. This problem is attributed to the structure of the model and the mathematical equations used in it, as well as the measured data. However, the AquaCrop model has many advantages such as the need for less data, the ability to be used for a wide range of crops and the user-friendliness of the model and it is recommended to use the AquaCrop model in different irrigation managements, especially in conditions without severe water stress, where the model has very good accuracy. But it is recommended due to the advantages of the AquaCrop model, such as the need for less data, the ability to be used for a wide range of crops and the user-friendliness of the model, its use in different irrigation managements, especially in conditions without extreme water stress, where the model has very good accuracy.

https://mmws.uma.ac.ir/article_3670_3b75e3b41c40a93c9d7bb1b7157eaa47.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Analysis of Machine Learning Algorithms’ Performance in Multitemporal Image Classification for Agricultural Management (Case Study: Qazvin Plain) Analysis of Machine Learning Algorithms’ Performance in Multitemporal Image Classification for Agricultural Management (Case Study: Qazvin Plain) 54 73 3719 10.22098/mmws.2025.16425.1532 FA Masoud Soltani Assistant Professor, Water Science and Engineering Department, Agriculture and Natural Resources Faculty, Imam Khomeini International University, Qazvin, Iran Bahareh Bahmanabadi Ph.D in Irrigation and Drainage, Water Science and Engineering Department, Agriculture and Natural Resources Faculty, Imam Khomeini International University, Qazvin, Iran Journal Article 2024 12 23 Abstract Introduction Due to the world's population growth and the severe problems caused by climate change, there is an immediate demand for sustainable farming methods and effective natural resource management. Agriculture forms the foundation of economic stability and food security, so methods that lead to maximum resource utilization are needed. However, they cause minimal environmental disturbance. This goal requires the ability to carry out fast and accurate crop mapping since it is used in strategizing agricultural activities, assessing yield, and ensuring sustainability. Monitoring and mapping crop types by conventional field-based approaches have been the norm for a long time. They are primarily laborious, time-consuming, and expensive, especially on large scales. Remote sensing approaches, fueled by new-generation satellite imagery and machine-learning capabilities, offer a viable alternative to enabling large-scale monitoring and detailed classification of land cover and crop types. The most promising tools in this respect are the Sentinel-1 and Sentinel-2 satellites, with high spatial, temporal, and spectral resolution data, ideally suited for agricultural monitoring. Sentinel-1 provides radar imagery, which is especially useful for monitoring vegetation structure even in cloudy conditions, while Sentinel-2 provides high-resolution optical images with multiple spectral bands suited for vegetation study. These datasets can be combined to capture complementary information on the physical and spectral characteristics of the land surface. There are different kinds of ML algorithms. Here, the performance of three most common algorithms are compared: RF, SVM, and XGBoost. The relative strengths of each of the algorithms in classification provide insights that are critical to their suitability in diverse agricultural scenarios. Materials and Methods This study was conducted in the Qazvin Irrigation Network, an agriculturally significant area of about 80,000 hectares in Iran. The study area includes different types of land cover, such as wheat, alfalfa, fallow land, urban, and bare land, which are the most cultivated crops in the area. This heterogeneity makes classification difficult, especially in semi-arid regions where crops and other land cover classes have similar spectral signatures. The data from the Sentinel-1 and Sentinel-2 satellites were used for such challenges. The former acquires reliable radar data that captures surface roughness and soil moisture even under cloudy conditions. Meanwhile, the latter delivers high-resolution optical imagery with many spectral bands, which is excellent for studying vegetation health and structure. Several necessary data-preprocessing steps were carried out to ensure that accurate classifications were developed. Atmospheric and sensor noise reduction was accomplished via radiometric and geometric correction, respectively, for radar and optical imagery. Derived spectral indices such as NDVI, SAVI, and LAI aided the detection of vegetation characteristics under study by enhancing separability at spectral levels. Furthermore, temporal fusion was performed by combining images taken at different times to account for the phenological changes in vegetation over the growing seasons. These preprocessing steps allowed for a robust dataset representing spatial and temporal land cover changes. Finally, three machine-learning algorithms were implemented for classifying the preprocessed satellite images: RF, SVM, and XGBoost. The reasons for choosing RF, an ensemble-based approach, include its robustness to noise and its ability to handle complicated datasets. On the other hand, SVM was adopted because it optimizes classification boundaries through its kernel-based feature. XGBoost is a highly accurate advanced gradient boosting technique that can realize large-scale computing with low expenses. The dataset was then divided into a training and testing set in a 70/30 ratio to prevent overfitting and ensure the model's reliability. Classification accuracy was assessed based on overall accuracy and the kappa coefficient, while the Jeffries-Matusita (JM) test quantified spectral separability between land cover classes. Results and Discussion The results demonstrated that integrating optical and radar data significantly improved classification accuracy. Among the three algorithms, RF outperformed the others, achieving an overall accuracy of 93.98% and a kappa coefficient of 0.996. These results highlight RF's ability to handle spectrally overlapping classes and complex datasets effectively. The XGBoost algorithm also performed well, achieving an overall accuracy of 93.94%. However, its performance was slightly hindered by its inability to distinguish between classes with similar spectral characteristics, such as wheat and alfalfa. While providing reasonable results, SVM achieved a lower overall accuracy of 83.79%, mainly due to its sensitivity to spectral overlap. The JM test revealed that certain classes, such as wheat and alfalfa, exhibited low spectral separability. This limitation underscores the importance of integrating radar data and spectral indices to enhance differentiation. The study also highlighted the potential of temporal data fusion to capture phenological changes, further improving classification performance. Conclusion This study indicated the potential of integrating multi-source remote sensing data and machine learning algorithms for crop classification in semi-arid regions. The RF algorithm proved the most accurate and robust method, showing its adaptability to the heterogeneous and complicated nature of the datasets. XGBoost and SVM are also very promising, but their performance could be improved further with additional parameter optimization. Future research should investigate the application of more advanced techniques, such as CNNs and deep learning frameworks, to improve classification accuracy further. A deeper understanding of the dynamics of crops and land use changes can be achieved by including multi-temporal and multi-spectral datasets. The results of such a study can have substantial implications for sustainable agriculture and resource management. In this context, remote sensing and machine learning technologies offer means to address critical challenges related to food security and environmental conservation in the most climate-vulnerable regions. Abstract Introduction Due to the world's population growth and the severe problems caused by climate change, there is an immediate demand for sustainable farming methods and effective natural resource management. Agriculture forms the foundation of economic stability and food security, so methods that lead to maximum resource utilization are needed. However, they cause minimal environmental disturbance. This goal requires the ability to carry out fast and accurate crop mapping since it is used in strategizing agricultural activities, assessing yield, and ensuring sustainability. Monitoring and mapping crop types by conventional field-based approaches have been the norm for a long time. They are primarily laborious, time-consuming, and expensive, especially on large scales. Remote sensing approaches, fueled by new-generation satellite imagery and machine-learning capabilities, offer a viable alternative to enabling large-scale monitoring and detailed classification of land cover and crop types. The most promising tools in this respect are the Sentinel-1 and Sentinel-2 satellites, with high spatial, temporal, and spectral resolution data, ideally suited for agricultural monitoring. Sentinel-1 provides radar imagery, which is especially useful for monitoring vegetation structure even in cloudy conditions, while Sentinel-2 provides high-resolution optical images with multiple spectral bands suited for vegetation study. These datasets can be combined to capture complementary information on the physical and spectral characteristics of the land surface. There are different kinds of ML algorithms. Here, the performance of three most common algorithms are compared: RF, SVM, and XGBoost. The relative strengths of each of the algorithms in classification provide insights that are critical to their suitability in diverse agricultural scenarios. Materials and Methods This study was conducted in the Qazvin Irrigation Network, an agriculturally significant area of about 80,000 hectares in Iran. The study area includes different types of land cover, such as wheat, alfalfa, fallow land, urban, and bare land, which are the most cultivated crops in the area. This heterogeneity makes classification difficult, especially in semi-arid regions where crops and other land cover classes have similar spectral signatures. The data from the Sentinel-1 and Sentinel-2 satellites were used for such challenges. The former acquires reliable radar data that captures surface roughness and soil moisture even under cloudy conditions. Meanwhile, the latter delivers high-resolution optical imagery with many spectral bands, which is excellent for studying vegetation health and structure. Several necessary data-preprocessing steps were carried out to ensure that accurate classifications were developed. Atmospheric and sensor noise reduction was accomplished via radiometric and geometric correction, respectively, for radar and optical imagery. Derived spectral indices such as NDVI, SAVI, and LAI aided the detection of vegetation characteristics under study by enhancing separability at spectral levels. Furthermore, temporal fusion was performed by combining images taken at different times to account for the phenological changes in vegetation over the growing seasons. These preprocessing steps allowed for a robust dataset representing spatial and temporal land cover changes. Finally, three machine-learning algorithms were implemented for classifying the preprocessed satellite images: RF, SVM, and XGBoost. The reasons for choosing RF, an ensemble-based approach, include its robustness to noise and its ability to handle complicated datasets. On the other hand, SVM was adopted because it optimizes classification boundaries through its kernel-based feature. XGBoost is a highly accurate advanced gradient boosting technique that can realize large-scale computing with low expenses. The dataset was then divided into a training and testing set in a 70/30 ratio to prevent overfitting and ensure the model's reliability. Classification accuracy was assessed based on overall accuracy and the kappa coefficient, while the Jeffries-Matusita (JM) test quantified spectral separability between land cover classes. Results and Discussion The results demonstrated that integrating optical and radar data significantly improved classification accuracy. Among the three algorithms, RF outperformed the others, achieving an overall accuracy of 93.98% and a kappa coefficient of 0.996. These results highlight RF's ability to handle spectrally overlapping classes and complex datasets effectively. The XGBoost algorithm also performed well, achieving an overall accuracy of 93.94%. However, its performance was slightly hindered by its inability to distinguish between classes with similar spectral characteristics, such as wheat and alfalfa. While providing reasonable results, SVM achieved a lower overall accuracy of 83.79%, mainly due to its sensitivity to spectral overlap. The JM test revealed that certain classes, such as wheat and alfalfa, exhibited low spectral separability. This limitation underscores the importance of integrating radar data and spectral indices to enhance differentiation. The study also highlighted the potential of temporal data fusion to capture phenological changes, further improving classification performance. Conclusion This study indicated the potential of integrating multi-source remote sensing data and machine learning algorithms for crop classification in semi-arid regions. The RF algorithm proved the most accurate and robust method, showing its adaptability to the heterogeneous and complicated nature of the datasets. XGBoost and SVM are also very promising, but their performance could be improved further with additional parameter optimization. Future research should investigate the application of more advanced techniques, such as CNNs and deep learning frameworks, to improve classification accuracy further. A deeper understanding of the dynamics of crops and land use changes can be achieved by including multi-temporal and multi-spectral datasets. The results of such a study can have substantial implications for sustainable agriculture and resource management. In this context, remote sensing and machine learning technologies offer means to address critical challenges related to food security and environmental conservation in the most climate-vulnerable regions.

https://mmws.uma.ac.ir/article_3719_5d9920bd7246985194e4ffcc32983a91.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Rainfall-runoff simulation in Saqez sub-basin using artificial neural network model Rainfall-runoff simulation in Saqez sub-basin using artificial neural network model 74 87 3771 10.22098/mmws.2025.16468.1535 FA Saeed Azadi Department of Water Science Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran Amin Toranjian Department of Water and Soil Science, Faculty of Agriculture, Malayer University, Malayer, Iran Saman Mostafaei Dept. of Water and Soil Science, Faculty of Agriculture, Malayer Univ., Malayer, Iran Journal Article 2024 12 31 Introduction The rainfall-runoff process, which is affected by various hydrological parameters, is one of the most complex hydrological processes and one of the most basic hydrological topics related to understanding and predicting the processes of runoff production and transfer. It is the outlet point of the watershed. Planning and optimal utilization of runoff is one of the essential issues in watersheds. Therefore, knowing the natural capacity of runoff production and simulating rainfall-runoff is very important. Artificial intelligence and the use of neural network models are one of the methods of rainfall-runoff forecasting. An artificial neural network is a method with the ability to learn, understand, master relationships, and resist errors. Today, artificial intelligence black box methods such as self-constructing and self-learning functions have a wide ability to model and predict complex problems. Materials and Methods The purpose of this research is to evaluate the performance of the artificial neural network model for rainfall-runoff simulation in the Saghez sub-basin in Kurdistan province. To carry out this research, 18-year (2001-2018) data received daily from the Meteorological Organization and Saghez Regional Water and Hydrometry Company have been used. In this study, two types of meteorological and hydrometric data were used. The meteorological parameters used include precipitation, evaporation, average temperature, maximum and minimum temperature, and the hydrometric parameter used in this research was only discharge. In the Saghez basin, rainfall-runoff changes have always been considered one of the prominent hydrological indicators. Since the turpentine sub-basin is considered an open basin in terms of its nature, precipitation can be considered a suitable alternative for investigating discharge in the study area of this research. As a result, precipitation is selected as a potential input variable and the adequacy of the remaining variables will be tested separately for the neural network model. In this research, the meteorological parameters used include precipitation, evaporation, average temperature, and maximum and minimum temperature, and the hydrometric parameter used in this research was only Dubai. Finally, to simulate rainfall-runoff using an artificial neural network model, scenarios with different input variables were considered. To evaluate and validate the performance results of the simulated model in different scenarios of this study, using four statistical criteria of correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe index (NSE) was done. Results and Discussion Six investigated scenarios were randomly selected by combining different inputs. In the first scenario, the input variable includes precipitation and the output variable is discharge. In the second scenario, the input variables include precipitation and evaporation and the output variable is discharge. In the third scenario, the input variables include precipitation and average temperature and the output variable is discharge. In the fourth scenario, the input variables include precipitation and flow variables with a one-day delay and the output variable of flow. In the fifth scenario, the input variables include precipitation, average temperature, maximum and minimum temperature, and the output variable is discharge. In the sixth scenario, the input variables include precipitation, evaporation, average temperature, maximum and minimum temperature, and the output variable of discharge. In all six scenarios, the output variable is the flow rate. Also, in the modeling, 70% of the data for the training section and 30% of the data for the test section were examined. According to the final results, the performance of the artificial neural network model in scenario number four (input variables including rainfall and discharge with a one-day delay) among the six developed scenarios, with correlation coefficient values of 0.92, mean squared error of 6.65, the average absolute error is 2.04 and the Nash-Sutcliffe index is 0.84 in the education section with the values of 0.91, 5.34, 1.57, and respectively 0.82 selected as the best combination in the test section, and in terms of statistical performance indicators, the results of the Nash-Sutcliffe index values in the training and test section were closer to one, which indicates a good match between the observed values and It is simulated. Also, the correlation coefficient specifies the amount of agreement and distribution of observational data with the predicted results, which can be said that the error measurement indicators and data distribution in the training and test section are a favorable result for prediction. The amount of discharge in this scenario shows that it has a much better performance than the rest of the scenarios. Also, in the fourth scenario, changes in the time series of observed discharge values against the simulated values in the training and test phase were investigated in the artificial neural network model. According to this figure, compared to the observed value, the simulated flow rate had good accuracy and an acceptable error value. Conclusion The obtained results showed that for the sub-basin of turpentine, the algorithm of the artificial neural network model for simulating rainfall-runoff on a suitable daily scale has obtained suitable and acceptable results. So it can be said that artificial neural network modeling has high accuracy and low error for the study area. Also, artificial intelligence models can be used as a useful tool and a reliable approach for water resource managers. Introduction The rainfall-runoff process, which is affected by various hydrological parameters, is one of the most complex hydrological processes and one of the most basic hydrological topics related to understanding and predicting the processes of runoff production and transfer. It is the outlet point of the watershed. Planning and optimal utilization of runoff is one of the essential issues in watersheds. Therefore, knowing the natural capacity of runoff production and simulating rainfall-runoff is very important. Artificial intelligence and the use of neural network models are one of the methods of rainfall-runoff forecasting. An artificial neural network is a method with the ability to learn, understand, master relationships, and resist errors. Today, artificial intelligence black box methods such as self-constructing and self-learning functions have a wide ability to model and predict complex problems. Materials and Methods The purpose of this research is to evaluate the performance of the artificial neural network model for rainfall-runoff simulation in the Saghez sub-basin in Kurdistan province. To carry out this research, 18-year (2001-2018) data received daily from the Meteorological Organization and Saghez Regional Water and Hydrometry Company have been used. In this study, two types of meteorological and hydrometric data were used. The meteorological parameters used include precipitation, evaporation, average temperature, maximum and minimum temperature, and the hydrometric parameter used in this research was only discharge. In the Saghez basin, rainfall-runoff changes have always been considered one of the prominent hydrological indicators. Since the turpentine sub-basin is considered an open basin in terms of its nature, precipitation can be considered a suitable alternative for investigating discharge in the study area of this research. As a result, precipitation is selected as a potential input variable and the adequacy of the remaining variables will be tested separately for the neural network model. In this research, the meteorological parameters used include precipitation, evaporation, average temperature, and maximum and minimum temperature, and the hydrometric parameter used in this research was only Dubai. Finally, to simulate rainfall-runoff using an artificial neural network model, scenarios with different input variables were considered. To evaluate and validate the performance results of the simulated model in different scenarios of this study, using four statistical criteria of correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe index (NSE) was done. Results and Discussion Six investigated scenarios were randomly selected by combining different inputs. In the first scenario, the input variable includes precipitation and the output variable is discharge. In the second scenario, the input variables include precipitation and evaporation and the output variable is discharge. In the third scenario, the input variables include precipitation and average temperature and the output variable is discharge. In the fourth scenario, the input variables include precipitation and flow variables with a one-day delay and the output variable of flow. In the fifth scenario, the input variables include precipitation, average temperature, maximum and minimum temperature, and the output variable is discharge. In the sixth scenario, the input variables include precipitation, evaporation, average temperature, maximum and minimum temperature, and the output variable of discharge. In all six scenarios, the output variable is the flow rate. Also, in the modeling, 70% of the data for the training section and 30% of the data for the test section were examined. According to the final results, the performance of the artificial neural network model in scenario number four (input variables including rainfall and discharge with a one-day delay) among the six developed scenarios, with correlation coefficient values of 0.92, mean squared error of 6.65, the average absolute error is 2.04 and the Nash-Sutcliffe index is 0.84 in the education section with the values of 0.91, 5.34, 1.57, and respectively 0.82 selected as the best combination in the test section, and in terms of statistical performance indicators, the results of the Nash-Sutcliffe index values in the training and test section were closer to one, which indicates a good match between the observed values and It is simulated. Also, the correlation coefficient specifies the amount of agreement and distribution of observational data with the predicted results, which can be said that the error measurement indicators and data distribution in the training and test section are a favorable result for prediction. The amount of discharge in this scenario shows that it has a much better performance than the rest of the scenarios. Also, in the fourth scenario, changes in the time series of observed discharge values against the simulated values in the training and test phase were investigated in the artificial neural network model. According to this figure, compared to the observed value, the simulated flow rate had good accuracy and an acceptable error value. Conclusion The obtained results showed that for the sub-basin of turpentine, the algorithm of the artificial neural network model for simulating rainfall-runoff on a suitable daily scale has obtained suitable and acceptable results. So it can be said that artificial neural network modeling has high accuracy and low error for the study area. Also, artificial intelligence models can be used as a useful tool and a reliable approach for water resource managers.

https://mmws.uma.ac.ir/article_3771_25f1e817b0994dcceed8702bb51f7993.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Evaluation of Combined Models Wavelet-ARIMA-ANN and Wavelet-ARIMA-LSTM Using SRGI Index Simulation in Enhancing Drought Monitoring Evaluation of Combined Models Wavelet-ARIMA-ANN and Wavelet-ARIMA-LSTM Using SRGI Index Simulation in Enhancing Drought Monitoring 88 103 3797 10.22098/mmws.2025.16471.1536 FA Mohamadreza Sharifi Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Khuzestan, Ahvaz, Iran Mahtab Nouri Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Khuzestan, Ahvaz, Iran Md. Munir Hayet Khan Faculty of Engineering & Quantity Surveying (FEQS), INTI International University (INTI-IU), Persiaran Perdana BBN, Nilai 71800, Negri Sembilan, Malaysia Journal Article 2025 01 01 Extended Abstract Introduction Drought monitoring entails the simulation of indices which are categorized into single and combined types. Historically, simulations have predominantly relied on single indices, including Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), resulting in limited research on drought simulation using combined indices (i.e. MSPI and SPTI), particularly in conjunction with combined models. Over the years, several single models have been developed for simulating individual drought indices. For instance, the Autoregressive Integrated Moving Average (ARIMA) model has been applied to simulate drought indices like Standardized Precipitation Index (SPI) and Standard Index of Annual Precipitation (SIAP). Additionally, models such as Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) have been used for simulating indices like SPI, DI, SIAP, and SHDI. Recent studies suggest that combined models outperform single models. Wavelet ARIMA ANN (W-2A) and Wavelet ANFIS combined models to simulate the single drought index SPEI. Other researchers have developed combined models such as ARIMA-LSTM, Wavelet-ARIMA-LSTM, Wavelet-ARIMA-ANN and LSTM-CM to simulate single drought indices SPI, DI, SIAP. Despite the progress in developing drought simulation models, including single models and particularly combined models, their application has primarily focused on individual indices. Historically, simulations have predominantly relied on single indices, resulting in limited research on drought simulation using combined indices, particularly in conjunction with combined models. This study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) to test new methods of hybrid models for their ability to drought simulations based on the new combined index SRGI, employing the combined models W-AL and W-2A. Materials and Methods Drought simulated in the Alashtar sub-basin between 48, 15 east longitude and 33, 54 north latitudes, covering an area of 811 square kilometers from 1991 to 2020, utilizing individual indices such as SPI, SRI, SGI, and the combined index SRGI. The study area encompasses the Karkheh River basin. Both single models (ARIMA, LSTM, ANN) and combined models (W-AL and W-2A) were employed for this purpose. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Error (ME) were used to evaluate the performance of the models. Also, relative frequency and error distribution charts were used to evaluate and compare the results of the models. Individual indices were calculated based on fitting the best cumulative probability function to monthly precipitation, monthly discharge, and monthly water table data, respectively for indices SPI, SRI, and SGI, and then inversely transforming to a N (0,1). The SRGI index is a combination of two drought indices, SGI and SRI (Feng et al., 2020). For this purpose, the copula function is used to obtain the best joint probability distribution function governing precipitation and water table data. The selection of the best copula function was done through the Kolmogorov-Smirnov K-S test at a significant level of 5%. In the current research, four copula functions of Frank, Clayton, Gamble and Joe were used. The process of building the combined models includes the analysis of the time series of the studied drought index, using DWT and decompose into two series named approximate and partial. Then, the approximate and detail series modeled by ARIMA and ANN respectively, in W-2A model and ARIMA and LSTM, respectively, in W-AL model. Results and Discussion The results demonstrate that the combined models W-AL and W-2A exhibit higher accuracy across all indices, both individual and combined, compared to single models ARIMA, LSTM, and ANN. The RMSE ranges for the combined models were 0.44 to 0.71, while for single models, they ranged from 0.47 to 1.54. Specifically, model W- AL displayed superior accuracy across all individual indices, with RMSEs of 0.44, 0.62, and 0.59, in contrast to model W-2A, which yielded RMSEs of 0.49, 0.71, and 0.63. However, W-AL's performance lagged behind W-2A for the combined SRGI index, with respective RMSEs of 0.64 and 0.61. Thus, the simpler model yielded more acceptable results in simulating the composite index. Conclusion Among all the combined and individual models, the combined models perform better in simulating drought, based on all indices, compared to the individual models. Therefore, it can be said that combined models are more suitable for simulating and monitoring drought compared to individual models. However, the performance of the two combined models, W-2A and W-AL, in simulating the combined SRGI index is different. The performance of the simpler W-2A model is better than the more complex W-AL model, with RMSE values of 0.61 and 0.64, respectively. Therefore, in combined indices, despite the complexity of their computational process, there is not necessarily a need to use a more complex combined model. Overall, the use of combined models is recommended for monitoring various types of indices, especially drought based on combined indices such as SRGI. The major objectives of this study are: (1) to use hybrid models Wavelet-ARIMA-LSTM (W-AL) and Wavelet-ARIMA-ANN (W-2A) methods to predict monthly drought. (2) To analyze drought characteristics in Alashtar basin based on the new combined drought index, SRGI. It is expected that the research results will help to provide decision support which in turn will help in planning adaptative measures to reduce drought impacts and provide decision support for disaster prevention. Extended Abstract Introduction Drought monitoring entails the simulation of indices which are categorized into single and combined types. Historically, simulations have predominantly relied on single indices, including Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), resulting in limited research on drought simulation using combined indices (i.e. MSPI and SPTI), particularly in conjunction with combined models. Over the years, several single models have been developed for simulating individual drought indices. For instance, the Autoregressive Integrated Moving Average (ARIMA) model has been applied to simulate drought indices like Standardized Precipitation Index (SPI) and Standard Index of Annual Precipitation (SIAP). Additionally, models such as Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) have been used for simulating indices like SPI, DI, SIAP, and SHDI. Recent studies suggest that combined models outperform single models. Wavelet ARIMA ANN (W-2A) and Wavelet ANFIS combined models to simulate the single drought index SPEI. Other researchers have developed combined models such as ARIMA-LSTM, Wavelet-ARIMA-LSTM, Wavelet-ARIMA-ANN and LSTM-CM to simulate single drought indices SPI, DI, SIAP. Despite the progress in developing drought simulation models, including single models and particularly combined models, their application has primarily focused on individual indices. Historically, simulations have predominantly relied on single indices, resulting in limited research on drought simulation using combined indices, particularly in conjunction with combined models. This study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) to test new methods of hybrid models for their ability to drought simulations based on the new combined index SRGI, employing the combined models W-AL and W-2A. Materials and Methods Drought simulated in the Alashtar sub-basin between 48, 15 east longitude and 33, 54 north latitudes, covering an area of 811 square kilometers from 1991 to 2020, utilizing individual indices such as SPI, SRI, SGI, and the combined index SRGI. The study area encompasses the Karkheh River basin. Both single models (ARIMA, LSTM, ANN) and combined models (W-AL and W-2A) were employed for this purpose. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Error (ME) were used to evaluate the performance of the models. Also, relative frequency and error distribution charts were used to evaluate and compare the results of the models. Individual indices were calculated based on fitting the best cumulative probability function to monthly precipitation, monthly discharge, and monthly water table data, respectively for indices SPI, SRI, and SGI, and then inversely transforming to a N (0,1). The SRGI index is a combination of two drought indices, SGI and SRI (Feng et al., 2020). For this purpose, the copula function is used to obtain the best joint probability distribution function governing precipitation and water table data. The selection of the best copula function was done through the Kolmogorov-Smirnov K-S test at a significant level of 5%. In the current research, four copula functions of Frank, Clayton, Gamble and Joe were used. The process of building the combined models includes the analysis of the time series of the studied drought index, using DWT and decompose into two series named approximate and partial. Then, the approximate and detail series modeled by ARIMA and ANN respectively, in W-2A model and ARIMA and LSTM, respectively, in W-AL model. Results and Discussion The results demonstrate that the combined models W-AL and W-2A exhibit higher accuracy across all indices, both individual and combined, compared to single models ARIMA, LSTM, and ANN. The RMSE ranges for the combined models were 0.44 to 0.71, while for single models, they ranged from 0.47 to 1.54. Specifically, model W- AL displayed superior accuracy across all individual indices, with RMSEs of 0.44, 0.62, and 0.59, in contrast to model W-2A, which yielded RMSEs of 0.49, 0.71, and 0.63. However, W-AL's performance lagged behind W-2A for the combined SRGI index, with respective RMSEs of 0.64 and 0.61. Thus, the simpler model yielded more acceptable results in simulating the composite index. Conclusion Among all the combined and individual models, the combined models perform better in simulating drought, based on all indices, compared to the individual models. Therefore, it can be said that combined models are more suitable for simulating and monitoring drought compared to individual models. However, the performance of the two combined models, W-2A and W-AL, in simulating the combined SRGI index is different. The performance of the simpler W-2A model is better than the more complex W-AL model, with RMSE values of 0.61 and 0.64, respectively. Therefore, in combined indices, despite the complexity of their computational process, there is not necessarily a need to use a more complex combined model. Overall, the use of combined models is recommended for monitoring various types of indices, especially drought based on combined indices such as SRGI. The major objectives of this study are: (1) to use hybrid models Wavelet-ARIMA-LSTM (W-AL) and Wavelet-ARIMA-ANN (W-2A) methods to predict monthly drought. (2) To analyze drought characteristics in Alashtar basin based on the new combined drought index, SRGI. It is expected that the research results will help to provide decision support which in turn will help in planning adaptative measures to reduce drought impacts and provide decision support for disaster prevention.

https://mmws.uma.ac.ir/article_3797_1317941197580421c6b61677999f398a.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Comparative Performance Analysis of Google Earth Engine and SNAP Platforms for Estimating Water Quality Parameters in Minab Dam Reservoir Using Multi-Spectral Resolution Satellite Imagery Comparative Performance Analysis of Google Earth Engine and SNAP Platforms for Estimating Water Quality Parameters in Minab Dam Reservoir Using Multi-Spectral Resolution Satellite Imagery 104 122 3839 10.22098/mmws.2025.16743.1557 FA Fatemeh Goroee M.Sc student in Watershed Management Science and Engineering, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources Engineering, University of Hormozgan, Bandar Abbas, Iran Ommolbanin Bazrafshan Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources Engineering, University of Hormozgan, Bandar Abbas, Iran Journal Article 2025 02 06 Abstract Introduction Access to clean freshwater has become a critical global challenge due to pollution from anthropogenic activities like urbanization and intensive agriculture. These pressures have significantly degraded lake water quality, increasing the risk of harmful algal blooms—a phenomenon that is exacerbated by climate change. Lakes, as multifunctional ecosystems supporting water supply, irrigation, and energy production, are particularly vulnerable to eutrophication. This process is primarily driven by excessive aquatic plant growth, quantified through chlorophyll-a (Chl-a) concentrations. As the dominant photosynthetic pigment in phytoplankton, Chl-a serves as a sensitive biomarker for nutrient loading and lake trophic status due to its rapid response to environmental shifts. Water surface temperature (SST) and suspended sediment concentration (SSC) are equally vital water quality parameters. SST profoundly influences aquatic ecosystems' biogeochemical cycles, while turbidity—a measure of light-scattering suspended particles—reflects sediment dynamics and anthropogenic pollution. Remote sensing technologies, particularly satellite platforms like Sentinel-2, have emerged as powerful tools for synoptic water quality monitoring. This study evaluates the efficacy of Google Earth Engine (GEE) and Sentinel Application Platform (SNAP) in analyzing temporal variations of water quality parameters (Chl-a, SST, SSC) in Minab Dam Lake (2016–2023). The investigation further explores interrelationships between these variables and environmental drivers. Materials and Methods This study evaluated the water quality of Minab Esteghlal Dam Lake using remote sensing techniques, integrating field data from the Hormozgan Regional Water Department with multi-sensor satellite imagery. Sentinel-2 MSI Level-2A and Landsat-8 OLI/TIRS Collection 2 Level-2 data were acquired from the Copernicus Open Access Hub (2016–2023) to analyze Chl-a, SST, and SSC. The Normalized Difference Water Index (NDWI) was applied to delineate water boundaries, followed by atmospheric correction of Sentinel-2 data using SEN2COR. The GEE facilitated large-scale temporal processing, while the SNAP enabled advanced spectral analysis, including Chl-a estimation via OC2/OC3 algorithms and SSC quantification using red-band reflectance. Landsat-8 thermal bands were used to derive SST through radiative transfer equations. Final outputs in TIFF format were visualized in ArcGIS with standardized symbology, ensuring accurate spatial representation of water quality dynamics. This integrated approach leveraged cloud-based (GEE) and desktop (SNAP/ArcGIS) platforms to optimize efficiency and precision in monitoring the lake’s ecological parameters. Results and Discussion The analysis demonstrated a declining trend in Minab Dam Lake's water quality during the study period. Both SNAP and GEE platforms consistently revealed seasonal Chl-a dynamics, with concentrations peaking in spring/summer (attributed to elevated SST and optimal phytoplankton growth conditions) and declining in autumn/winter (due to reduced temperature and solar radiation). SNAP exhibited superior performance in atmospheric correction and spatial detail extraction, achieving 15-20% higher accuracy than GEE in Chl-a quantification, with lower estimation errors (RMSE: 3.14 vs. GEE's 4.44). This precision stems from SNAP's advanced algorithmic parameter customization capabilities, making it ideal for localized, high-resolution studies. Conversely, GEE excelled in large-scale, long-term trend analysis, processing big data 40% faster through its cloud-computing infrastructure and machine learning tools, albeit with marginally reduced precision. The SST-Chl-a relationship exhibited nonlinear seasonality, with stronger correlations observed during warmer months. While SSC generally suppressed Chl-a concentrations through light attenuation, episodic nutrient inputs from sediment loads occasionally triggered transient phytoplankton blooms. Notably, SSC impacts were more pronounced during colder months, exacerbating water quality degradation. These findings highlight the platforms' complementary strengths: SNAP for process-oriented studies requiring atmospheric precision, and GEE for synoptic, time-series investigations. The results underscore the importance of platform selection based on study objectives—whether high-precision local analysis or watershed-scale temporal monitoring. Conclusion This study demonstrates the effectiveness of remote sensing platforms in monitoring the water quality dynamics of Minab Dam Lake, revealing a clear declining trend over the 2016–2023 period. The seasonal variability of Chl-a concentrations—peaking in spring/summer due to warmer SST and favorable growth conditions, then declining in autumn/winter—was consistently captured by both SNAP and GEE. However, their complementary strengths highlight the importance of platform selection based on research objectives: SNAP proved superior for localized, high-precision analysis, excelling in atmospheric correction (15–20% greater accuracy) and spatial detail extraction. Its customizable algorithms make it ideal for mechanistic studies requiring fine-tuned parameter adjustments. GEE enabled efficient large-scale and long-term assessments, reducing processing time by 40% through cloud-based workflows. While slightly less precise, its machine learning tools and data scalability are invaluable for trend analysis. The complex interplay between environmental drivers—such as the nonlinear SST-Chl-a relationship and dual role of SSC as both light attenuators and nutrient sources—underscores the need for integrated monitoring approaches. These findings provide actionable insights for water resource management: SNAP can guide targeted mitigation of eutrophication hotspots, while GEE supports system-wide policy decisions. To enhance the monitoring and management of Minab Dam Lake, a hybrid approach combining high-precision SNAP analysis for localized eutrophication hotspots and GEE-based large-scale trend assessment is recommended. Targeted mitigation strategies, such as reducing agricultural runoff and controlling sediment inflow, should be prioritized in critical zones identified through SNAP’s detailed Chl-a mapping. Meanwhile, GEE’s rapid processing capabilities can support real-time policy adjustments by tracking seasonal water quality variations across the entire watershed. Additionally, integrating in-situ sensors with remote sensing data can improve predictive modeling, particularly under climate change-induced stressors. Finally, stakeholder collaboration between environmental agencies, local communities, and policymakers is essential to implement sustainable water management practices, ensuring long-term ecological balance in the lake ecosystem. Abstract Introduction Access to clean freshwater has become a critical global challenge due to pollution from anthropogenic activities like urbanization and intensive agriculture. These pressures have significantly degraded lake water quality, increasing the risk of harmful algal blooms—a phenomenon that is exacerbated by climate change. Lakes, as multifunctional ecosystems supporting water supply, irrigation, and energy production, are particularly vulnerable to eutrophication. This process is primarily driven by excessive aquatic plant growth, quantified through chlorophyll-a (Chl-a) concentrations. As the dominant photosynthetic pigment in phytoplankton, Chl-a serves as a sensitive biomarker for nutrient loading and lake trophic status due to its rapid response to environmental shifts. Water surface temperature (SST) and suspended sediment concentration (SSC) are equally vital water quality parameters. SST profoundly influences aquatic ecosystems' biogeochemical cycles, while turbidity—a measure of light-scattering suspended particles—reflects sediment dynamics and anthropogenic pollution. Remote sensing technologies, particularly satellite platforms like Sentinel-2, have emerged as powerful tools for synoptic water quality monitoring. This study evaluates the efficacy of Google Earth Engine (GEE) and Sentinel Application Platform (SNAP) in analyzing temporal variations of water quality parameters (Chl-a, SST, SSC) in Minab Dam Lake (2016–2023). The investigation further explores interrelationships between these variables and environmental drivers. Materials and Methods This study evaluated the water quality of Minab Esteghlal Dam Lake using remote sensing techniques, integrating field data from the Hormozgan Regional Water Department with multi-sensor satellite imagery. Sentinel-2 MSI Level-2A and Landsat-8 OLI/TIRS Collection 2 Level-2 data were acquired from the Copernicus Open Access Hub (2016–2023) to analyze Chl-a, SST, and SSC. The Normalized Difference Water Index (NDWI) was applied to delineate water boundaries, followed by atmospheric correction of Sentinel-2 data using SEN2COR. The GEE facilitated large-scale temporal processing, while the SNAP enabled advanced spectral analysis, including Chl-a estimation via OC2/OC3 algorithms and SSC quantification using red-band reflectance. Landsat-8 thermal bands were used to derive SST through radiative transfer equations. Final outputs in TIFF format were visualized in ArcGIS with standardized symbology, ensuring accurate spatial representation of water quality dynamics. This integrated approach leveraged cloud-based (GEE) and desktop (SNAP/ArcGIS) platforms to optimize efficiency and precision in monitoring the lake’s ecological parameters. Results and Discussion The analysis demonstrated a declining trend in Minab Dam Lake's water quality during the study period. Both SNAP and GEE platforms consistently revealed seasonal Chl-a dynamics, with concentrations peaking in spring/summer (attributed to elevated SST and optimal phytoplankton growth conditions) and declining in autumn/winter (due to reduced temperature and solar radiation). SNAP exhibited superior performance in atmospheric correction and spatial detail extraction, achieving 15-20% higher accuracy than GEE in Chl-a quantification, with lower estimation errors (RMSE: 3.14 vs. GEE's 4.44). This precision stems from SNAP's advanced algorithmic parameter customization capabilities, making it ideal for localized, high-resolution studies. Conversely, GEE excelled in large-scale, long-term trend analysis, processing big data 40% faster through its cloud-computing infrastructure and machine learning tools, albeit with marginally reduced precision. The SST-Chl-a relationship exhibited nonlinear seasonality, with stronger correlations observed during warmer months. While SSC generally suppressed Chl-a concentrations through light attenuation, episodic nutrient inputs from sediment loads occasionally triggered transient phytoplankton blooms. Notably, SSC impacts were more pronounced during colder months, exacerbating water quality degradation. These findings highlight the platforms' complementary strengths: SNAP for process-oriented studies requiring atmospheric precision, and GEE for synoptic, time-series investigations. The results underscore the importance of platform selection based on study objectives—whether high-precision local analysis or watershed-scale temporal monitoring. Conclusion This study demonstrates the effectiveness of remote sensing platforms in monitoring the water quality dynamics of Minab Dam Lake, revealing a clear declining trend over the 2016–2023 period. The seasonal variability of Chl-a concentrations—peaking in spring/summer due to warmer SST and favorable growth conditions, then declining in autumn/winter—was consistently captured by both SNAP and GEE. However, their complementary strengths highlight the importance of platform selection based on research objectives: SNAP proved superior for localized, high-precision analysis, excelling in atmospheric correction (15–20% greater accuracy) and spatial detail extraction. Its customizable algorithms make it ideal for mechanistic studies requiring fine-tuned parameter adjustments. GEE enabled efficient large-scale and long-term assessments, reducing processing time by 40% through cloud-based workflows. While slightly less precise, its machine learning tools and data scalability are invaluable for trend analysis. The complex interplay between environmental drivers—such as the nonlinear SST-Chl-a relationship and dual role of SSC as both light attenuators and nutrient sources—underscores the need for integrated monitoring approaches. These findings provide actionable insights for water resource management: SNAP can guide targeted mitigation of eutrophication hotspots, while GEE supports system-wide policy decisions. To enhance the monitoring and management of Minab Dam Lake, a hybrid approach combining high-precision SNAP analysis for localized eutrophication hotspots and GEE-based large-scale trend assessment is recommended. Targeted mitigation strategies, such as reducing agricultural runoff and controlling sediment inflow, should be prioritized in critical zones identified through SNAP’s detailed Chl-a mapping. Meanwhile, GEE’s rapid processing capabilities can support real-time policy adjustments by tracking seasonal water quality variations across the entire watershed. Additionally, integrating in-situ sensors with remote sensing data can improve predictive modeling, particularly under climate change-induced stressors. Finally, stakeholder collaboration between environmental agencies, local communities, and policymakers is essential to implement sustainable water management practices, ensuring long-term ecological balance in the lake ecosystem.

https://mmws.uma.ac.ir/article_3839_c8b98b15a3e5d33f3ae43b8ffd9ce605.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Investigating the accumulation of salts in the soil under sugarcane cultivation in subsurface drip irrigation Investigating the accumulation of salts in the soil under sugarcane cultivation in subsurface drip irrigation 123 139 3837 10.22098/mmws.2025.16746.1558 FA Majid Hamoodi Irrigation and Drainage, Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0009-0005-4590-8009 Abdali Naseri Irrigation and Drainage, Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran Parvaneh Tishehzan Environmental Engineering, Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Iran Fariborz Abbasi Institute of Technical and Engineering Research, Agricultural Research, Education and Extension Organization, Karaj, Iran 0000-0002-0662-7723 Amir Soltani Mohammdi Irrigation and Drainage, Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran Journal Article 2025 02 07 Abstract Introduction Soil salinity can cause the destruction of arable land and sustainable production. Increasing the concentration of salts beyond the tolerance threshold causes irreversible physiological damage to the plant. The essence of soil salinity control measures is to regulate the movement of water and the transport of salts downward and out of the area of root development and to prevent their accumulation and transport to the soil surface due to evaporation and transpiration. Subsurface drip irrigation helps improve irrigation efficiency and reduce salinity. But careful design and management are essential to control water salinity. Considering the necessity of optimal irrigation water consumption and the development of subsurface drip irrigation in sugarcane fields in Khuzestan and the important role of soil salinity, studying changes in soil salts and their distribution in different irrigation methods and irrigation management in arid and semi-arid conditions is of great importance.Therefore, this study was conducted to investigate the effect of fertilization stages in subsurface irrigation on salt accumulation, sodium absorption ratio, ammonium and nitrate levels at different soil depths in the root development zone of sugarcane under a subsurface drip irrigation system. Materials and Methods The study was conducted in the Farabi Sugarcane Agro-Industry in during the agricultural year 2021-2022, in 35 km from the Ahvaz-Abadan road, east of the Karun River. Before cultivation, the physical and chemical properties of the soil were determined. After preparing and creating the furrow and ridge, sugarcane cuttings of the CP69-1062 variety were planted in two rows with a distance of 40 cm from each other. The Water tube was placed in the middle of two rows of cuttings. In-line dripper pipes with a diameter of 20 mm, dripper spacing of 50 cm, and dripper flow rate of 2.4 L/ha were installed at a depth of 20 cm below the soil surface. The average EC of irrigation water during the research period was 3.18 dS/m. Irrigation planning and water requirements for sugarcane plant were based on the five-year average of evapotranspiration, plant coefficients, and evaporation pan coefficient, taking into account the appropriate irrigation interval. Nitrat fertilization was done at a rate of 300 kg/ha, 25 kg/ha per time. Sampling to evaluate soil salt accumulation was carried out in six stages, a one week after fertilization. EC, pH, concentrations of calcium, sodium and magnesium, ammonium and nitrate dissolved in the saturated extract were measured. In order to investigate the effect of fertilization, a factorial split-plot experimental design with three replications was used. The fertilization stages treatment consisted of six stages (T1, T2, T3, T4, T5, and T6), while the soil depth treatment included five depths (0-20, 20-40, 40-60, 60-80 and 80-100). For analyze the results was used SAS ver 9.4 software, also using the LSD test method, the means of main effects and interaction effects were compared. Results and Discussion The results showed that the highest and lowest pH measured occurred in T3 and T4 respectively.The highest electrical conductivity of 4.55 dS/m in stage T4 and the lowest electrical conductivity of 3.01 dS/m was observed in stage T1.Also, the highest EC at a depth of 20 cm is equal to 5.34 dS/m.NH4+ was highest in T1 stage at all depths compared to other fertilization stages.The highest and lowest NO3- were measured in T5 and T2equal to 30.47mg/kg and 20.11mg/kg, respectively. The upward trend in nitrate in each stage compared to previous stages is likely due to increased nitrification that occurred from the application of each fertilizer stage. In depth of 20 cm, the concentration of NO3-soil, equal to 33.54 mg/kg, is higher than at other sampled depths. This trend could be due to the subsurface drip irrigation system, where the depth of penetration of the moisture bulb is less than this range. The interaction between fertilization stages and sampling depth showed that the highest SAR was at the time of fertilization stage T4 and a depth of 20 cm from the soil surface (11.31) and the lowest SAR was at the time of fertilization stages T1 and T6 and a depth of 80 cm (4.66 and 4.57, respectively). In this study, the increase in sodium absorption ratio (SAR) was not affected by the quality of irrigation water, but rather by the type of irrigation method (subsurface drip) and the depth of drip installation. Conclusion The results of this study showed that nitrogen fertilizer application had a variable effect on soil pH during the period. This process could be due to microbial activity, mineralization, and nitrification processes. The highest pH measured was in the third stage and the lowest in the fourth stage of fertilization. The highest soil salinity is due to the interaction between fertilization stages and soil depth at the fourth fertilization stage and a depth of 20 cm, which has increased by 54.7 percent compared to the same depth in the first stage. The highest ammonium concentration was in the first stage, which was 28.3 percent higher than the lowest concentration measured in the second stage. Also soil nitrate concentration showed that the highest and lowest concentrations were in the fifth and second stages, respectively, with a difference of 67.15 percent between them. Totally, the concentration of ammonium and nitrate in the soil depth has been decreasing. The behavior of the exchange sodium adsorption ratio is similar to electrical conductivity and increased during the study, which was intensified at the soil surface due to the depth of soil moisture penetration and irrigation method. Therefore, the results of this study indicate that in order to manage salts and prevent their accumulation at the soil surface in sugarcane cultivation areas in Khuzestan using subsurface drip irrigation, it is recommended to consider flow rate and installation depth of the drippers should be considered further. Abstract Introduction Soil salinity can cause the destruction of arable land and sustainable production. Increasing the concentration of salts beyond the tolerance threshold causes irreversible physiological damage to the plant. The essence of soil salinity control measures is to regulate the movement of water and the transport of salts downward and out of the area of root development and to prevent their accumulation and transport to the soil surface due to evaporation and transpiration. Subsurface drip irrigation helps improve irrigation efficiency and reduce salinity. But careful design and management are essential to control water salinity. Considering the necessity of optimal irrigation water consumption and the development of subsurface drip irrigation in sugarcane fields in Khuzestan and the important role of soil salinity, studying changes in soil salts and their distribution in different irrigation methods and irrigation management in arid and semi-arid conditions is of great importance.Therefore, this study was conducted to investigate the effect of fertilization stages in subsurface irrigation on salt accumulation, sodium absorption ratio, ammonium and nitrate levels at different soil depths in the root development zone of sugarcane under a subsurface drip irrigation system. Materials and Methods The study was conducted in the Farabi Sugarcane Agro-Industry in during the agricultural year 2021-2022, in 35 km from the Ahvaz-Abadan road, east of the Karun River. Before cultivation, the physical and chemical properties of the soil were determined. After preparing and creating the furrow and ridge, sugarcane cuttings of the CP69-1062 variety were planted in two rows with a distance of 40 cm from each other. The Water tube was placed in the middle of two rows of cuttings. In-line dripper pipes with a diameter of 20 mm, dripper spacing of 50 cm, and dripper flow rate of 2.4 L/ha were installed at a depth of 20 cm below the soil surface. The average EC of irrigation water during the research period was 3.18 dS/m. Irrigation planning and water requirements for sugarcane plant were based on the five-year average of evapotranspiration, plant coefficients, and evaporation pan coefficient, taking into account the appropriate irrigation interval. Nitrat fertilization was done at a rate of 300 kg/ha, 25 kg/ha per time. Sampling to evaluate soil salt accumulation was carried out in six stages, a one week after fertilization. EC, pH, concentrations of calcium, sodium and magnesium, ammonium and nitrate dissolved in the saturated extract were measured. In order to investigate the effect of fertilization, a factorial split-plot experimental design with three replications was used. The fertilization stages treatment consisted of six stages (T1, T2, T3, T4, T5, and T6), while the soil depth treatment included five depths (0-20, 20-40, 40-60, 60-80 and 80-100). For analyze the results was used SAS ver 9.4 software, also using the LSD test method, the means of main effects and interaction effects were compared. Results and Discussion The results showed that the highest and lowest pH measured occurred in T3 and T4 respectively.The highest electrical conductivity of 4.55 dS/m in stage T4 and the lowest electrical conductivity of 3.01 dS/m was observed in stage T1.Also, the highest EC at a depth of 20 cm is equal to 5.34 dS/m.NH4+ was highest in T1 stage at all depths compared to other fertilization stages.The highest and lowest NO3- were measured in T5 and T2equal to 30.47mg/kg and 20.11mg/kg, respectively. The upward trend in nitrate in each stage compared to previous stages is likely due to increased nitrification that occurred from the application of each fertilizer stage. In depth of 20 cm, the concentration of NO3-soil, equal to 33.54 mg/kg, is higher than at other sampled depths. This trend could be due to the subsurface drip irrigation system, where the depth of penetration of the moisture bulb is less than this range. The interaction between fertilization stages and sampling depth showed that the highest SAR was at the time of fertilization stage T4 and a depth of 20 cm from the soil surface (11.31) and the lowest SAR was at the time of fertilization stages T1 and T6 and a depth of 80 cm (4.66 and 4.57, respectively). In this study, the increase in sodium absorption ratio (SAR) was not affected by the quality of irrigation water, but rather by the type of irrigation method (subsurface drip) and the depth of drip installation. Conclusion The results of this study showed that nitrogen fertilizer application had a variable effect on soil pH during the period. This process could be due to microbial activity, mineralization, and nitrification processes. The highest pH measured was in the third stage and the lowest in the fourth stage of fertilization. The highest soil salinity is due to the interaction between fertilization stages and soil depth at the fourth fertilization stage and a depth of 20 cm, which has increased by 54.7 percent compared to the same depth in the first stage. The highest ammonium concentration was in the first stage, which was 28.3 percent higher than the lowest concentration measured in the second stage. Also soil nitrate concentration showed that the highest and lowest concentrations were in the fifth and second stages, respectively, with a difference of 67.15 percent between them. Totally, the concentration of ammonium and nitrate in the soil depth has been decreasing. The behavior of the exchange sodium adsorption ratio is similar to electrical conductivity and increased during the study, which was intensified at the soil surface due to the depth of soil moisture penetration and irrigation method. Therefore, the results of this study indicate that in order to manage salts and prevent their accumulation at the soil surface in sugarcane cultivation areas in Khuzestan using subsurface drip irrigation, it is recommended to consider flow rate and installation depth of the drippers should be considered further.

https://mmws.uma.ac.ir/article_3837_116c2273d7d7d5ba20b1acfb83a11932.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Forecasting monthly rainfall using time series modeling and spectral analysis with Fourier model Forecasting monthly rainfall using time series modeling and spectral analysis with Fourier model 140 155 3838 10.22098/mmws.2025.16847.1563 FA Erfan Abdi PhD student, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran Yagob Dinpashoh Professor, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran Journal Article 2025 02 22 Extended Abstract Introduction Rainfall plays an important role in maintaining life on Earth and maintaining the balance of ecosystems. It is essential to understand its importance for various environmental, agricultural, and hydrological aspects. Agriculture relies heavily on rainfall for crop growth. Adequate rainfall ensures that the soil remains fertile and productive. Forecasted rainfall is critical for various sectors including disaster management and urban planning. Accurate forecasts enable individuals and organizations to make informed decisions that can reduce risks and increase productivity. This information allows them to effectively plan planting and harvesting schedules and ensure optimal crop performance. Time series models are statistical tools used to analyze and forecast data points collected over time. Common types of models include autoregressive (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models. These models use a chronological sequence of observations and allow analysts to identify patterns, trends, and seasonal changes that can provide future predictions. Therefore, the purpose of this research is to develop an integrated system to investigate and evaluate the precipitation trend and its changes in the statistical period of 24 years and use it to predict precipitation in the next 5 years. For this purpose, the rainfall data of three stations, Tabriz, Amol and Yazd, which have different climates, were used. On the other hand, for the evaluation of time series and forecasting operations, two models of Fourier series and auto-regression were used, and then the obtained results were analyzed with evaluation criteria and graphic diagrams. Materials and Methods In this study, the monthly rainfall time series of three stations in Tabriz, Amol, and Yazd during 24 years (2023-2000) was taken from the database of Mathematica software. First, the data were examined and by applying the outlier data processing, the data that had a significant difference were removed and the data that were not measured were completed with the interpolation method. After the data was completed and evaluated, it was divided into two parts, training and testing. Normally, the ratio of this division in the fields of hydrology and hydraulics is 70 to 30. For this purpose, 70% of the data (2016-2000) was assigned to training and 30% (2023-2017) to the test. Then, to predict monthly rainfall, two models, Fourier and autoregressive, which are based on time series, were used. These methods are described below. Fourier series is the mathematical representation of a periodic function as an infinite sum of sine and cosine functions. This concept is fundamental in various fields such as signal processing, physics, and engineering, and allows complex periodic signals to be analyzed. Auto-Regressive (AR) models work on the principle that the current value of a time series can be expressed as a linear combination of its past values with a random error. The performance comparison of the two models was evaluated using four criteria: root mean square error (RMSE), correlation coefficient (r), Nash Sutcliffe coefficient (NSE), and Wilmot coefficient (WI). The results showed. Results and Discussion The results showed that the Fourier model has an average error of 1.21, correlation coefficient of 0.87, Nash Sutcliffe coefficient of 0.74, and Wilmot coefficient of 0.91. The predictions made with the Fourier model were more reliable than the autocorrelation model. The graph related to the Fourier model in all three stations has almost the same trend as the real values and has less difference. But in some places, there are few fits. On the other hand, the graph of the AR model has a big difference from the real values, and in most of the points, it has a poor fit. Considering the scatter diagrams of Fourier model and AR, the scatter diagrams of Fourier model have less dispersion. Therefore, the coefficient of determination for the Fourier model in predicting the monthly rainfall of Tabriz is equal to 0.86, Amol is equal to 0.73, and Yazd is equal to 0.68. For the AR model, it is equal to 0.18 for Tabriz, 0.34 for Amol, and 0.48 for Yazd. These results show that the Fourier model has identified changes in precipitation trends better than the AR model and has provided more reliable predictions. Also, the predicted five-year trends by the Fourier model have more natural changes than the AR model, but on the contrary, the AR model has fewer fluctuations and has changes close to a straight line. Conclusion Precipitation forecasting is critical for various sectors including agriculture, natural disaster management, and climate adaptation. Accurate forecasts can significantly impact food security, infrastructure planning, and environmental conservation. Complete and abundant data has a significant impact on the accuracy of predictions and improves it. The Fourier model with the lowest error and the highest degree of correlation estimated acceptable forecasts for the precipitation of three stations, and on the other hand, the forecasts of the trend of the Fourier model for five years have acceptable changes and are somewhat similar to the trend of real precipitation. Fourier series assume that the data are periodic, which may not be true for all precipitation patterns. Many regions experience irregular rainfall distributions that do not fit well into periodic models, leading to inaccurate forecasts. Therefore, in this research, by integrating the probability distribution with the Fourier model, this problem was solved as much as possible, and accurate predictions of precipitation were made. While Fourier models can be effective for short-term forecasting, their performance degrades over longer periods. This limitation is due to the fact that the basic assumptions and periodic nature of the model are not maintained with the extension of the forecasting horizon, making it challenging to accurately predict precipitation beyond a certain time frame. Also, the effectiveness of the Fourier model depends on the quality and temporal resolution of the input data. Extended Abstract Introduction Rainfall plays an important role in maintaining life on Earth and maintaining the balance of ecosystems. It is essential to understand its importance for various environmental, agricultural, and hydrological aspects. Agriculture relies heavily on rainfall for crop growth. Adequate rainfall ensures that the soil remains fertile and productive. Forecasted rainfall is critical for various sectors including disaster management and urban planning. Accurate forecasts enable individuals and organizations to make informed decisions that can reduce risks and increase productivity. This information allows them to effectively plan planting and harvesting schedules and ensure optimal crop performance. Time series models are statistical tools used to analyze and forecast data points collected over time. Common types of models include autoregressive (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models. These models use a chronological sequence of observations and allow analysts to identify patterns, trends, and seasonal changes that can provide future predictions. Therefore, the purpose of this research is to develop an integrated system to investigate and evaluate the precipitation trend and its changes in the statistical period of 24 years and use it to predict precipitation in the next 5 years. For this purpose, the rainfall data of three stations, Tabriz, Amol and Yazd, which have different climates, were used. On the other hand, for the evaluation of time series and forecasting operations, two models of Fourier series and auto-regression were used, and then the obtained results were analyzed with evaluation criteria and graphic diagrams. Materials and Methods In this study, the monthly rainfall time series of three stations in Tabriz, Amol, and Yazd during 24 years (2023-2000) was taken from the database of Mathematica software. First, the data were examined and by applying the outlier data processing, the data that had a significant difference were removed and the data that were not measured were completed with the interpolation method. After the data was completed and evaluated, it was divided into two parts, training and testing. Normally, the ratio of this division in the fields of hydrology and hydraulics is 70 to 30. For this purpose, 70% of the data (2016-2000) was assigned to training and 30% (2023-2017) to the test. Then, to predict monthly rainfall, two models, Fourier and autoregressive, which are based on time series, were used. These methods are described below. Fourier series is the mathematical representation of a periodic function as an infinite sum of sine and cosine functions. This concept is fundamental in various fields such as signal processing, physics, and engineering, and allows complex periodic signals to be analyzed. Auto-Regressive (AR) models work on the principle that the current value of a time series can be expressed as a linear combination of its past values with a random error. The performance comparison of the two models was evaluated using four criteria: root mean square error (RMSE), correlation coefficient (r), Nash Sutcliffe coefficient (NSE), and Wilmot coefficient (WI). The results showed. Results and Discussion The results showed that the Fourier model has an average error of 1.21, correlation coefficient of 0.87, Nash Sutcliffe coefficient of 0.74, and Wilmot coefficient of 0.91. The predictions made with the Fourier model were more reliable than the autocorrelation model. The graph related to the Fourier model in all three stations has almost the same trend as the real values and has less difference. But in some places, there are few fits. On the other hand, the graph of the AR model has a big difference from the real values, and in most of the points, it has a poor fit. Considering the scatter diagrams of Fourier model and AR, the scatter diagrams of Fourier model have less dispersion. Therefore, the coefficient of determination for the Fourier model in predicting the monthly rainfall of Tabriz is equal to 0.86, Amol is equal to 0.73, and Yazd is equal to 0.68. For the AR model, it is equal to 0.18 for Tabriz, 0.34 for Amol, and 0.48 for Yazd. These results show that the Fourier model has identified changes in precipitation trends better than the AR model and has provided more reliable predictions. Also, the predicted five-year trends by the Fourier model have more natural changes than the AR model, but on the contrary, the AR model has fewer fluctuations and has changes close to a straight line. Conclusion Precipitation forecasting is critical for various sectors including agriculture, natural disaster management, and climate adaptation. Accurate forecasts can significantly impact food security, infrastructure planning, and environmental conservation. Complete and abundant data has a significant impact on the accuracy of predictions and improves it. The Fourier model with the lowest error and the highest degree of correlation estimated acceptable forecasts for the precipitation of three stations, and on the other hand, the forecasts of the trend of the Fourier model for five years have acceptable changes and are somewhat similar to the trend of real precipitation. Fourier series assume that the data are periodic, which may not be true for all precipitation patterns. Many regions experience irregular rainfall distributions that do not fit well into periodic models, leading to inaccurate forecasts. Therefore, in this research, by integrating the probability distribution with the Fourier model, this problem was solved as much as possible, and accurate predictions of precipitation were made. While Fourier models can be effective for short-term forecasting, their performance degrades over longer periods. This limitation is due to the fact that the basic assumptions and periodic nature of the model are not maintained with the extension of the forecasting horizon, making it challenging to accurately predict precipitation beyond a certain time frame. Also, the effectiveness of the Fourier model depends on the quality and temporal resolution of the input data.

https://mmws.uma.ac.ir/article_3838_067171a10789fbfd7c9e54991323a6a9.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Assessment of the Impact of Watershed Management Practices on Hydrological Variables in the Mohammadabad Watershed using the SWAT Model Assessment of the Impact of Watershed Management Practices on Hydrological Variables in the Mohammadabad Watershed using the SWAT Model 156 172 3834 10.22098/mmws.2025.16959.1566 FA Yahya Fathollahnejad Ph.D. Student, Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari 68984, Iran Ataollah Kavian Professor, Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari 68984, Iran. Leila Gholami Associated Professor, Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari 68984, Iran Journal Article 2025 03 10 Abstract Introduction Hydrological processes in watersheds are dependent on atmospheric processes and the physical characteristics of the watershed, playing a key role in the planning and management of water resources. In recent years, watershed management practices have gained attention as a managerial approach to improve ecosystem performance. These practices positively impact hydrological parameters of water bodies, including reducing flood risk, increasing soil permeability, and enhancing water quality. Additionally, by decreasing surface runoff, groundwater resources are also bolstered. Furthermore, reducing evaporation and improving water distribution throughout different seasons aids in optimizing water resource management, contributing to the sustainability of the environment and ecosystems. Therefore, assessing the impacts of these practices is essential for enhancing their efficiency. However, due to the complexity and high costs of these evaluations, the process can be challenging. Consequently, hydrological models are often utilized to simplify natural conditions and assist in water resource management, enabling the planning and prediction of changes in the hydrological cycle. In this study, the effectiveness of these practices was evaluated using the SWAT model in the Mohammadabad watershed of Golestan. Materials and Methods For this study, the required data, including climatic, physiographic, hydrological, land use, geological, and soil data, were obtained from relevant authorities and organizations. After gathering the necessary data, a database was created and data was analyzed using Excel 2019, SPSS 26, ArcGIS 10.8, and TerrSet 18.21 software. To simulate the climatic variables of the study area, daily data on rainfall, minimum and maximum temperatures, solar radiation, relative humidity, and wind speed from the Mohammadabad station for the years 1988 to 2017 were utilized. To examine the elevation of the study area, contour lines from 1:25,000 scale maps were imported into the GIS system, and a digital elevation model (DEM) with 10×10 m pixels was created, correcting any potential outlier cells using a LOW filter. The land use of the study area was initially prepared using 1:25,000 scale maps from the National Cartographic Organization, then refined and finalized using satellite images and field visits for high accuracy. For the SWAT model implementation, some necessary soil parameter data were obtained from the Golestan Natural Resources and Watershed Management Department, and due to the unavailability of some parameters, data for those parameters were sourced from FAO soil maps. Various algorithms and objective functions for calibration and validation were utilized using the SWAT-CUP software, and after evaluating the model performance in simulation and obtaining acceptable results, the model was prepared for the implementation of water and soil conservation measures. Results and Discussion The results indicate that the calibration and validation results demonstrate the acceptable performance of the SWAT model for simulating the hydrology of the Mohammadabad watershed. Specifically, the coefficient of determination during calibration and validation was found to be 0.93 and 0.98, respectively. Additionally, the Nash-Sutcliffe efficiency index was 0.90 for calibration and 0.96 for validation. Based on the findings, surface runoff would decrease significantly with the implementation of watershed management practices such as stone check dams, gabion walls, dry stone walls, afforestation, and seeding, by 36.36%, 22.73%, 18.18%, 18.18%, and 4.55%, respectively, compared to the scenario without these practices. Moreover, evaporation and transpiration were observed to increase by 85.69%, 53.87%, 29.87%, 10.11%, and 1.64% respectively when these interventions were applied. The implementation of watershed management practices in the Mohammadabad watershed has led to a reduction in peak flood discharge, decreasing from 181.5 m2 .s before these practices to 156 m2 .s per second afterward. The analysis of the 25-year flood discharge in the study watershed revealed a decrease of 13.13 m2 .s second, dropping from 93.47 m2 .s before the management practices to 80.34 m2 .s after. According to the study results, mechanical projects in the Mohammadabad watershed have successfully reduced surface runoff and flooding, subsequently decreasing soil material loss, erosion, and the transport of eroded particles from channels. This has also led to a reduction in sediment transfer from slopes to waterways, and from waterways to rivers and eventually to reservoirs and agricultural lands. In this context, the implementation of all watershed management practices in the Mohammadabad watershed has resulted in a 16.34% reduction in the 25-year flood discharge. Conclusion Overall, the findings suggest a significant positive impact of these practices within the examined watershed. The results showed the impact of watershed management measures on hydrological variables including surface runoff, peak discharge and flood volume of the Mohammadabad watershed, which will reduce the destruction and damage caused by floods in the study area. Based on the results of the watershed management operations in the study area, it was determined that biological operations are difficult to implement due to the high slope and shallow soil depth (mountainousness of the area). Therefore, since the Mohammadabad watershed is near the city of Fazelabad and has a mostly touristic aspect and the local economy is not dependent on livestock farming, therefore, management operations will be more effective. Also, mechanical operations will be prioritized over mechanical programs due to conditions such as high slope, shallow soil depth (mountainousness of the area), low concentration time, high peak discharge (basin shape coefficient) and consequently the presence of debris sediments and, most importantly, the presence of a flood history in the study watershed. Finally, it can be acknowledged that watershed management measures, in addition to helping reduce damage caused by floods and sediment, can also turn the threat of floods into an opportunity to nourish groundwater aquifers and provide water in springs, canals, and wells during droughts, at the lowest cost and in accordance with the ecological conditions of the region. Abstract Introduction Hydrological processes in watersheds are dependent on atmospheric processes and the physical characteristics of the watershed, playing a key role in the planning and management of water resources. In recent years, watershed management practices have gained attention as a managerial approach to improve ecosystem performance. These practices positively impact hydrological parameters of water bodies, including reducing flood risk, increasing soil permeability, and enhancing water quality. Additionally, by decreasing surface runoff, groundwater resources are also bolstered. Furthermore, reducing evaporation and improving water distribution throughout different seasons aids in optimizing water resource management, contributing to the sustainability of the environment and ecosystems. Therefore, assessing the impacts of these practices is essential for enhancing their efficiency. However, due to the complexity and high costs of these evaluations, the process can be challenging. Consequently, hydrological models are often utilized to simplify natural conditions and assist in water resource management, enabling the planning and prediction of changes in the hydrological cycle. In this study, the effectiveness of these practices was evaluated using the SWAT model in the Mohammadabad watershed of Golestan. Materials and Methods For this study, the required data, including climatic, physiographic, hydrological, land use, geological, and soil data, were obtained from relevant authorities and organizations. After gathering the necessary data, a database was created and data was analyzed using Excel 2019, SPSS 26, ArcGIS 10.8, and TerrSet 18.21 software. To simulate the climatic variables of the study area, daily data on rainfall, minimum and maximum temperatures, solar radiation, relative humidity, and wind speed from the Mohammadabad station for the years 1988 to 2017 were utilized. To examine the elevation of the study area, contour lines from 1:25,000 scale maps were imported into the GIS system, and a digital elevation model (DEM) with 10×10 m pixels was created, correcting any potential outlier cells using a LOW filter. The land use of the study area was initially prepared using 1:25,000 scale maps from the National Cartographic Organization, then refined and finalized using satellite images and field visits for high accuracy. For the SWAT model implementation, some necessary soil parameter data were obtained from the Golestan Natural Resources and Watershed Management Department, and due to the unavailability of some parameters, data for those parameters were sourced from FAO soil maps. Various algorithms and objective functions for calibration and validation were utilized using the SWAT-CUP software, and after evaluating the model performance in simulation and obtaining acceptable results, the model was prepared for the implementation of water and soil conservation measures. Results and Discussion The results indicate that the calibration and validation results demonstrate the acceptable performance of the SWAT model for simulating the hydrology of the Mohammadabad watershed. Specifically, the coefficient of determination during calibration and validation was found to be 0.93 and 0.98, respectively. Additionally, the Nash-Sutcliffe efficiency index was 0.90 for calibration and 0.96 for validation. Based on the findings, surface runoff would decrease significantly with the implementation of watershed management practices such as stone check dams, gabion walls, dry stone walls, afforestation, and seeding, by 36.36%, 22.73%, 18.18%, 18.18%, and 4.55%, respectively, compared to the scenario without these practices. Moreover, evaporation and transpiration were observed to increase by 85.69%, 53.87%, 29.87%, 10.11%, and 1.64% respectively when these interventions were applied. The implementation of watershed management practices in the Mohammadabad watershed has led to a reduction in peak flood discharge, decreasing from 181.5 m2 .s before these practices to 156 m2 .s per second afterward. The analysis of the 25-year flood discharge in the study watershed revealed a decrease of 13.13 m2 .s second, dropping from 93.47 m2 .s before the management practices to 80.34 m2 .s after. According to the study results, mechanical projects in the Mohammadabad watershed have successfully reduced surface runoff and flooding, subsequently decreasing soil material loss, erosion, and the transport of eroded particles from channels. This has also led to a reduction in sediment transfer from slopes to waterways, and from waterways to rivers and eventually to reservoirs and agricultural lands. In this context, the implementation of all watershed management practices in the Mohammadabad watershed has resulted in a 16.34% reduction in the 25-year flood discharge. Conclusion Overall, the findings suggest a significant positive impact of these practices within the examined watershed. The results showed the impact of watershed management measures on hydrological variables including surface runoff, peak discharge and flood volume of the Mohammadabad watershed, which will reduce the destruction and damage caused by floods in the study area. Based on the results of the watershed management operations in the study area, it was determined that biological operations are difficult to implement due to the high slope and shallow soil depth (mountainousness of the area). Therefore, since the Mohammadabad watershed is near the city of Fazelabad and has a mostly touristic aspect and the local economy is not dependent on livestock farming, therefore, management operations will be more effective. Also, mechanical operations will be prioritized over mechanical programs due to conditions such as high slope, shallow soil depth (mountainousness of the area), low concentration time, high peak discharge (basin shape coefficient) and consequently the presence of debris sediments and, most importantly, the presence of a flood history in the study watershed. Finally, it can be acknowledged that watershed management measures, in addition to helping reduce damage caused by floods and sediment, can also turn the threat of floods into an opportunity to nourish groundwater aquifers and provide water in springs, canals, and wells during droughts, at the lowest cost and in accordance with the ecological conditions of the region.

https://mmws.uma.ac.ir/article_3834_0cbd49eeaaffa2a55c832d497addf2c3.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Development of the Simplified Meta-Statistical Extreme Value (SMEV) Model for Analyzing Extreme Wind Events and Particle Transport Hazards in Eastern Lake Urmia Development of the Simplified Meta-Statistical Extreme Value (SMEV) Model for Analyzing Extreme Wind Events and Particle Transport Hazards in Eastern Lake Urmia 173 196 3863 10.22098/mmws.2025.17371.1597 FA Maryam Vasef Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran Keivan Khalili Associate Professor, Urmia Lake Research Institute, Urmia University, Urmia, Iran Majid Montaseri Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran Journal Article 2025 05 06 Introduction The ongoing desiccation of Lake Urmia in northwestern Iran has transformed its former lakebed into a significant source of airborne dust and salt particles, posing escalating environmental and public health risks. These storms pose serious environmental and health risks by elevating particulate matter concentrations (PM₁₀ and PM₂.₅), degrading air quality, and impairing agricultural productivity. Wind events exceeding 5 m/s can start wind storm mobilization and atmospheric dust generation in arid and semi-arid environments. Wind direction is important for the transport of dust to cities. The east of the Urmia Lake is more affected by wind because of the dominant wind direction in the Urmia Lake basin. This part is more important for risk assessment studies. Classical models, such as the Generalized Extreme Value (GEV) distribution, often fall short of capturing the full complexity of wind extremes under nonstationary conditions. To overcome these limitations, the Simplified Meta-Statistical Extreme Value (SMEV) model is developed and used for the first time, in this study, as a method that integrates both ordinary and extreme wind data into a unified distribution framework. This study aims to estimate return period wind speeds with SMEV and benchmarked against GEV, and evaluate wind direction probabilities for storm prediction. Results will inform regional dust storm risk management and advance extreme value modeling in the Lake Urmia basin. Materials and Methods Using three-hourly wind speed and direction data from 2005 to 2024 across four synoptic stations (Tabriz, Maragheh, Bonab, and Shabestar) in the eastern Lake Urmia Basin, SMEV was employed to estimate return period wind speeds and assess directional probabilities. In this research, the CEEMDAN method has been used as a method to remove noise and trends from wind speed data. At the stations, wind events were divided into extreme and ordinary events, based on the wind speed threshold, using the peak-over-threshold (POT) approach by applying the 90th percentile. 5, 10, 20, 50, 100, and 200 periods were chosen for the return period. The model combines a two-parameter Weibull distribution for ordinary winds with annual extreme wind counts to generate composite cumulative distribution functions (CDFs) per dominant direction sector. The bootstrap method was used for SMEV model performance evaluation. The GEV model was used as a benchmark and employed to estimate return period wind speeds, and both models were evaluated using AIC, BIC, FSE, WFSE, and leave-one-out cross-validation (LOO). Additionally, a random forest algorithm was trained to predict the likelihood of wind directions associated with dust transport. Results and Discussion SMEV predicted critical wind speeds exceeding 7 m/s with high confidence. In all 4 stations, wind speeds predicted more than 7 m/s, and wind direction analysis revealed over 70% probability of wind-driven dust transport from the southwest and south to the east, toward residential areas. The random forest method has predicted the corresponding wind directions for selected stations east of Lake Urmia. The dominant directions for extreme storm events are southwest and south for short to medium return periods. In longer time periods, the dominance of south and west continues at Shabestar and Maragheh stations, and for Tabriz and Maragheh stations, the dominant direction changes to east. GEV extreme values predicted more than 12 m/s for wind speeds. It shows the GEV overestimated. For Urmia Lake Basin, wind speeds of more than 12 happen rarely and are not common. The SMEV model outperformed the GEV model, providing more stable and realistic estimates of return-level wind speeds, particularly for long recurrence intervals. Error metrics confirmed the superiority of SMEV (FSE = 0.014; WFSE = 20.7) compared to GEV (FSE = 0.081; WFSE = 196), highlighting its improved performance in estimating environmental hazards. The advantage of this method over other classical methods is in distinguishing between extreme and normal events, as well as distinguishing extreme events with the corresponding dominant directions of extreme wind speeds. In addition, the use of a wind speed threshold limit, unlike other statistical methods such as GEV, which only focus on maximum wind speeds in the analysis of extreme events, can provide reliable accuracy for this method in estimating extreme events. Conclusion This study focused on the analysis of extreme wind speeds in the eastern part of the Urmia Lake watershed, and using a simplified metastatistical limit value model, was able to provide reliable estimates of strong winds in different return periods. The results showed that speeds exceeding 7 m/s occur with high probability in this area, and this amount is sufficient to initiate the transport of suspended particles and the formation of dust storms in the study area. In conclusion, SMEV demonstrates significant potential for use in regional wind hazard assessments, early warning systems, and dust storm risk mitigation in the Urmia Lake Basin. This model relies solely on wind speed and direction and does not consider other environmental drivers such as soil moisture, land cover, vegetation, or surface roughness that can significantly affect the potential for dust emission. This approach can also help universities, along with other tools, to identify high-risk areas susceptible to dust transport from the dry bed of Lake Urmia. Overall, this model can be used as an effective tool in analyzing climate risks associated with wind and dust storms in the region. In addition, the use of the 90th percentile threshold and 24-hour separation criteria raises statistical assumptions that more extreme events may have been identified, which has increased the accuracy of the model and, on the other hand, has made the model more sensitive to extreme phenomena. However, it is suggested that in future studies, the integration of environmental variables such as relative humidity and precipitation should be considered to improve the SMEV model. Also, combining this model with wind datasets based on satellite images can also improve the spatial representation of wind patterns. Introduction The ongoing desiccation of Lake Urmia in northwestern Iran has transformed its former lakebed into a significant source of airborne dust and salt particles, posing escalating environmental and public health risks. These storms pose serious environmental and health risks by elevating particulate matter concentrations (PM₁₀ and PM₂.₅), degrading air quality, and impairing agricultural productivity. Wind events exceeding 5 m/s can start wind storm mobilization and atmospheric dust generation in arid and semi-arid environments. Wind direction is important for the transport of dust to cities. The east of the Urmia Lake is more affected by wind because of the dominant wind direction in the Urmia Lake basin. This part is more important for risk assessment studies. Classical models, such as the Generalized Extreme Value (GEV) distribution, often fall short of capturing the full complexity of wind extremes under nonstationary conditions. To overcome these limitations, the Simplified Meta-Statistical Extreme Value (SMEV) model is developed and used for the first time, in this study, as a method that integrates both ordinary and extreme wind data into a unified distribution framework. This study aims to estimate return period wind speeds with SMEV and benchmarked against GEV, and evaluate wind direction probabilities for storm prediction. Results will inform regional dust storm risk management and advance extreme value modeling in the Lake Urmia basin. Materials and Methods Using three-hourly wind speed and direction data from 2005 to 2024 across four synoptic stations (Tabriz, Maragheh, Bonab, and Shabestar) in the eastern Lake Urmia Basin, SMEV was employed to estimate return period wind speeds and assess directional probabilities. In this research, the CEEMDAN method has been used as a method to remove noise and trends from wind speed data. At the stations, wind events were divided into extreme and ordinary events, based on the wind speed threshold, using the peak-over-threshold (POT) approach by applying the 90th percentile. 5, 10, 20, 50, 100, and 200 periods were chosen for the return period. The model combines a two-parameter Weibull distribution for ordinary winds with annual extreme wind counts to generate composite cumulative distribution functions (CDFs) per dominant direction sector. The bootstrap method was used for SMEV model performance evaluation. The GEV model was used as a benchmark and employed to estimate return period wind speeds, and both models were evaluated using AIC, BIC, FSE, WFSE, and leave-one-out cross-validation (LOO). Additionally, a random forest algorithm was trained to predict the likelihood of wind directions associated with dust transport. Results and Discussion SMEV predicted critical wind speeds exceeding 7 m/s with high confidence. In all 4 stations, wind speeds predicted more than 7 m/s, and wind direction analysis revealed over 70% probability of wind-driven dust transport from the southwest and south to the east, toward residential areas. The random forest method has predicted the corresponding wind directions for selected stations east of Lake Urmia. The dominant directions for extreme storm events are southwest and south for short to medium return periods. In longer time periods, the dominance of south and west continues at Shabestar and Maragheh stations, and for Tabriz and Maragheh stations, the dominant direction changes to east. GEV extreme values predicted more than 12 m/s for wind speeds. It shows the GEV overestimated. For Urmia Lake Basin, wind speeds of more than 12 happen rarely and are not common. The SMEV model outperformed the GEV model, providing more stable and realistic estimates of return-level wind speeds, particularly for long recurrence intervals. Error metrics confirmed the superiority of SMEV (FSE = 0.014; WFSE = 20.7) compared to GEV (FSE = 0.081; WFSE = 196), highlighting its improved performance in estimating environmental hazards. The advantage of this method over other classical methods is in distinguishing between extreme and normal events, as well as distinguishing extreme events with the corresponding dominant directions of extreme wind speeds. In addition, the use of a wind speed threshold limit, unlike other statistical methods such as GEV, which only focus on maximum wind speeds in the analysis of extreme events, can provide reliable accuracy for this method in estimating extreme events. Conclusion This study focused on the analysis of extreme wind speeds in the eastern part of the Urmia Lake watershed, and using a simplified metastatistical limit value model, was able to provide reliable estimates of strong winds in different return periods. The results showed that speeds exceeding 7 m/s occur with high probability in this area, and this amount is sufficient to initiate the transport of suspended particles and the formation of dust storms in the study area. In conclusion, SMEV demonstrates significant potential for use in regional wind hazard assessments, early warning systems, and dust storm risk mitigation in the Urmia Lake Basin. This model relies solely on wind speed and direction and does not consider other environmental drivers such as soil moisture, land cover, vegetation, or surface roughness that can significantly affect the potential for dust emission. This approach can also help universities, along with other tools, to identify high-risk areas susceptible to dust transport from the dry bed of Lake Urmia. Overall, this model can be used as an effective tool in analyzing climate risks associated with wind and dust storms in the region. In addition, the use of the 90th percentile threshold and 24-hour separation criteria raises statistical assumptions that more extreme events may have been identified, which has increased the accuracy of the model and, on the other hand, has made the model more sensitive to extreme phenomena. However, it is suggested that in future studies, the integration of environmental variables such as relative humidity and precipitation should be considered to improve the SMEV model. Also, combining this model with wind datasets based on satellite images can also improve the spatial representation of wind patterns.

https://mmws.uma.ac.ir/article_3863_6373f4b46eba542f2cbc2b7073cfc94a.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 A Hybrid SNIP-ANN Model: A Novel Approach for Accurate River Flow Prediction A Hybrid SNIP-ANN Model: A Novel Approach for Accurate River Flow Prediction 197 211 3831 10.22098/mmws.2025.17235.1585 FA Fatemeh Vosoughi Department of water engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran Mohammad Ali Ghorbani Department of water engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran Sabereh Darbandi Department of water engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran Journal Article 2025 04 21 Abstract Introduction River flow prediction is crucial for water resource management, environmental protection, and flood control, yet the nonlinear nature of hydrological data poses challenges. Previous studies using models like Random Forest and LSTM faced limitations in accuracy and computational complexity. This research introduces the hybrid SNIP-ANN model, combining the novel SNIP algorithm with Artificial Neural Networks, to enhance prediction accuracy. The study focuses on time-series analysis of monthly flows in the Columbia and Niger Rivers, vital freshwater sources. The goal is to improve forecasting precision for better water management and reduced uncertainties. Materials and Methods The SNIP-ANN model is an advanced hybrid method for analyzing river discharge time series. In this model, the SNIP algorithm first preprocesses the raw streamflow data; it separates meaningful signal components from background noise by eliminating fluctuations and enhancing significant peaks. Then, an artificial neural network (ANN), inspired by the parallel processing nature of the human brain, learns the nonlinear relationships between past and present discharge values using the processed data. In this study, a three-month lag (Q(t-3)) was selected as the optimal input. By combining SNIP’s strength in feature extraction and ANN’s ability in pattern recognition, the SNIP-ANN model demonstrated high accuracy in prediction. The model was trained and validated using historical monthly discharge data from the Columbia and Niger rivers. Evaluation metrics such as RMSE and the correlation coefficient (R) indicated high precision and low error. The model’s simplicity and use of only discharge data make it suitable for real-time water resource management and applications in other domains like meteorology and economics. Additionally, its lightweight structure and short training time allow for implementation in systems lacking advanced computational facilities. The model’s generalizability also makes it a reliable choice for time series analysis under diverse climatic conditions. Applying this approach in other data-driven fields can improve prediction accuracy while reducing computational costs. Overall, the SNIP-ANN model offers an effective, lightweight, and intelligent solution for predicting streamflow behavior in complex hydrological systems. Results and Discussion In this study, in the first stage, the temporal modeling and in the second stage, the performance evaluation of monthly streamflow prediction for the Columbia and Niger rivers during the study period was carried out using the Artificial Neural Network (ANN) model and the hybrid SNIP-enhanced ANN (SNIP-ANN) model. To model the time-dependent behavior of streamflow, three time-lagged discharge values (one-, two-, and three-month delays) were used as input parameters. Among them, the three-month lag (Q(t–3)) was selected as the optimal input based on its stronger correlation with hydrological patterns such as seasonal variability, rainfall fluctuations, and groundwater storage. Seventy percent of the data were used for training and thirty percent for testing the models. Based on the evaluation results from R, RMSE, and other metrics such as the Taylor diagram, violin plot, and observed versus predicted plots, it was found that the SNIP-ANN model significantly outperformed the standard ANN in temporal modeling. For instance, in the Columbia River, the SNIP-ANN model achieved an R value of 0.7503 and an RMSE of 865.23, while the ANN model had an R of 0.3249 and a much higher RMSE of 1325.99. Similarly, for the Niger River, the SNIP-ANN reached an R of 0.9286 and an RMSE of 231.49, surpassing the ANN model, which scored an R of 0.8014 and an RMSE of 487.88. Moreover, the scatter plots revealed that the predicted values in the SNIP-ANN model were more concentrated along the ideal line (y = x), indicating higher prediction accuracy. The violin plots further supported this finding by showing that the SNIP-ANN predictions closely matched the actual distribution of flow values, while the ANN model showed greater deviations. In general, the SNIP-ANN model demonstrated superior capability in capturing nonlinear patterns, reducing prediction error, and improving generalization performance. These results highlight the model’s potential for real-time hydrological forecasting, especially under extreme flow conditions. Future research may focus on integrating ensemble learning strategies or additional preprocessing techniques to further enhance prediction accuracy across diverse hydrological scenarios. Conclusion In this study, the hybrid SNIP-ANN model was introduced as an innovative approach for river discharge prediction and compared with the traditional ANN model. The results showed that the hybrid model significantly improved prediction accuracy compared to the ANN model. By using a three-month time delay in the input data, the model was able to effectively simulate seasonal fluctuations and periodic changes in river discharge. The correlation coefficient for the hybrid model increased from 0.3249 to 0.7503 for the Columbia River and from 0.8014 to 0.9286 for the Niger River, while the prediction error (RMSE) decreased from 1325.99 to 865.23 and from 883.487 to 497.231, respectively. This improvement in performance was particularly noticeable in simulating extreme discharge fluctuations and filtering out additional noise in the data. The results indicate that the SNIP-ANN model is an effective tool for river discharge prediction and has high potential for applications in water resource management and flood prediction. This model can be highly beneficial in long-term forecasting and addressing challenges related to climate change and hydrological fluctuations. Abstract Introduction River flow prediction is crucial for water resource management, environmental protection, and flood control, yet the nonlinear nature of hydrological data poses challenges. Previous studies using models like Random Forest and LSTM faced limitations in accuracy and computational complexity. This research introduces the hybrid SNIP-ANN model, combining the novel SNIP algorithm with Artificial Neural Networks, to enhance prediction accuracy. The study focuses on time-series analysis of monthly flows in the Columbia and Niger Rivers, vital freshwater sources. The goal is to improve forecasting precision for better water management and reduced uncertainties. Materials and Methods The SNIP-ANN model is an advanced hybrid method for analyzing river discharge time series. In this model, the SNIP algorithm first preprocesses the raw streamflow data; it separates meaningful signal components from background noise by eliminating fluctuations and enhancing significant peaks. Then, an artificial neural network (ANN), inspired by the parallel processing nature of the human brain, learns the nonlinear relationships between past and present discharge values using the processed data. In this study, a three-month lag (Q(t-3)) was selected as the optimal input. By combining SNIP’s strength in feature extraction and ANN’s ability in pattern recognition, the SNIP-ANN model demonstrated high accuracy in prediction. The model was trained and validated using historical monthly discharge data from the Columbia and Niger rivers. Evaluation metrics such as RMSE and the correlation coefficient (R) indicated high precision and low error. The model’s simplicity and use of only discharge data make it suitable for real-time water resource management and applications in other domains like meteorology and economics. Additionally, its lightweight structure and short training time allow for implementation in systems lacking advanced computational facilities. The model’s generalizability also makes it a reliable choice for time series analysis under diverse climatic conditions. Applying this approach in other data-driven fields can improve prediction accuracy while reducing computational costs. Overall, the SNIP-ANN model offers an effective, lightweight, and intelligent solution for predicting streamflow behavior in complex hydrological systems. Results and Discussion In this study, in the first stage, the temporal modeling and in the second stage, the performance evaluation of monthly streamflow prediction for the Columbia and Niger rivers during the study period was carried out using the Artificial Neural Network (ANN) model and the hybrid SNIP-enhanced ANN (SNIP-ANN) model. To model the time-dependent behavior of streamflow, three time-lagged discharge values (one-, two-, and three-month delays) were used as input parameters. Among them, the three-month lag (Q(t–3)) was selected as the optimal input based on its stronger correlation with hydrological patterns such as seasonal variability, rainfall fluctuations, and groundwater storage. Seventy percent of the data were used for training and thirty percent for testing the models. Based on the evaluation results from R, RMSE, and other metrics such as the Taylor diagram, violin plot, and observed versus predicted plots, it was found that the SNIP-ANN model significantly outperformed the standard ANN in temporal modeling. For instance, in the Columbia River, the SNIP-ANN model achieved an R value of 0.7503 and an RMSE of 865.23, while the ANN model had an R of 0.3249 and a much higher RMSE of 1325.99. Similarly, for the Niger River, the SNIP-ANN reached an R of 0.9286 and an RMSE of 231.49, surpassing the ANN model, which scored an R of 0.8014 and an RMSE of 487.88. Moreover, the scatter plots revealed that the predicted values in the SNIP-ANN model were more concentrated along the ideal line (y = x), indicating higher prediction accuracy. The violin plots further supported this finding by showing that the SNIP-ANN predictions closely matched the actual distribution of flow values, while the ANN model showed greater deviations. In general, the SNIP-ANN model demonstrated superior capability in capturing nonlinear patterns, reducing prediction error, and improving generalization performance. These results highlight the model’s potential for real-time hydrological forecasting, especially under extreme flow conditions. Future research may focus on integrating ensemble learning strategies or additional preprocessing techniques to further enhance prediction accuracy across diverse hydrological scenarios. Conclusion In this study, the hybrid SNIP-ANN model was introduced as an innovative approach for river discharge prediction and compared with the traditional ANN model. The results showed that the hybrid model significantly improved prediction accuracy compared to the ANN model. By using a three-month time delay in the input data, the model was able to effectively simulate seasonal fluctuations and periodic changes in river discharge. The correlation coefficient for the hybrid model increased from 0.3249 to 0.7503 for the Columbia River and from 0.8014 to 0.9286 for the Niger River, while the prediction error (RMSE) decreased from 1325.99 to 865.23 and from 883.487 to 497.231, respectively. This improvement in performance was particularly noticeable in simulating extreme discharge fluctuations and filtering out additional noise in the data. The results indicate that the SNIP-ANN model is an effective tool for river discharge prediction and has high potential for applications in water resource management and flood prediction. This model can be highly beneficial in long-term forecasting and addressing challenges related to climate change and hydrological fluctuations.

https://mmws.uma.ac.ir/article_3831_874e42855c806a4cc51170b28f438078.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Using Sentinel satellite images to study the June 2023 Flood and vegetation indices in the Germi and Ungut counties Using Sentinel satellite images to study the June 2023 Flood and vegetation indices in the Germi and Ungut counties 212 229 3893 10.22098/mmws.2025.17295.1592 FA Zeinab Hazbavi Associate Professor/Department of Range and Watershed Management, Faculty of Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran Marzieh Ghashamshami Former M.Sc. Student in Desertification, Department of Range and Watershed Management, Faculty of Desert Studies, Semnan University, Semnan Journal Article 2025 04 26 Introduction Flooding, one of the most common natural disasters, poses a serious threat to ecosystems and human safety, with factors such as heavy rainfall, rapid snowmelt, dam failures, and poor management of water, soil, and vegetation resources exacerbating its impacts. Over the past two decades, approximately 2.4 billion people have been affected by natural disasters, resulting in economic damages amounting to $2.97 trillion. Among these, human-related factors such as reduced permeability, construction near riverbanks, and altering water flow paths have played a greater role in intensifying flood damage than natural factors. Accurate monitoring of floods and assessing affected areas are of significant importance. Flood zoning is a practical tool that plays a crucial role in managerial planning. Today, the combined use of optical and radar imagery can provide more comprehensive information. This study aims to effectively utilize Sentinel-1 and Sentinel-2 satellite images in conjunction with the Google Earth Engine (GEE) platform for flood mapping and vegetation indices in the Germi and Ungut counties, which have been impacted by flood events in May and June 2023. This innovative approach enhances the accuracy and speed of flood detection and offers a scalable solution for managing flood risks in vulnerable areas. By leveraging the latest advancements in remote sensing and cloud computing, this research contributes to developing more resilient strategies against flooding and is essential for regions like Ardabil Province, which are prone to irregular and heavy rainfall. Materials and Methods In this study, various methods were employed to identify and analyze flood-affected areas using Sentinel-1 and Sentinel-2 satellite data. Initially, Synthetic Aperture Radar (SAR) images from Sentinel-1 in the form of GRD (Ground Range Detected) products were utilized, which can capture images in all weather conditions and at any time of day. These data were derived from the C-band and Interferometric Wide Swath (IW) mode for flood mapping. To reduce speckle noise, the Refined Lee filter was applied, which removed random noise while preserving structural details. A threshold of 1.25 was set for VV polarization (vertical transmit, vertical receive) to identify flood-prone areas, with pixels having VV values above this threshold considered as flooded regions. To avoid misinterpretation, areas with permanent water bodies were excluded using JRC Global Surface Water data. SAR images of Sentinel-1 taken before, during, and after the flood (between April 1 and August 20, 2023) were compared for change detection. Additionally, Sentinel-2 images were used to calculate the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) to assess the impact of vegetation variability from April 1, 2019, to August 20, 2023. These methods effectively identified flood-affected areas and flood impacts on the vegetation cover of the two study counties. Results and Discussion In May and June 2023, the Germi and Ungut counties witnessed unprecedented rainfall, which led to severe flooding. The results of the interpretation of Sentinel-1 SAR images showed that about 185.83 km2 were affected by flooding, which is equivalent to 9.01 % of the total study area. Before the flood (April-May 2023), the mean NDVI and EVI respectively, were 0.25 and 0.18, indicating a significant decrease in vegetation cover compared to the reference year (April-May 2019). This could be due to land use change, drought, gradual destruction of dense vegetation, or other environmental stresses before the flood. At the time of the flood (June 2023), the mean NDVI and EVI reached 0.22 and 0.15, respectively, which are the lowest levels recorded up to that time. Only seven percent of the area had healthy vegetation during this period. This decrease could be due to the inundation of plants, destruction due to the intensity of the flood flow, or the covering of the soil surface by sediments. After the flood (August 2023), a more severe decrease in vegetation status was observed. The mean NDVI decreased to 0.15 and EVI to 0.1, and only about 2% of the area remained with healthy vegetation. This decrease may be due to severe soil degradation, erosion of the fertile topsoil, reduced vegetation cover due to seasonal conditions in the area. Conclusion This study mapped the June 2023 flood and vegetation indices in two counties of Germi and Ungut, located in the northwest of Iran, using Sentinel-1 SAR and Sentinel-2 data. In adverse weather conditions, SAR technology is an effective tool for mapping and monitoring flood-prone areas. The combination of SAR data, the Refined Lee filter, and the GEE platform facilitates crisis management and mitigates flood impacts. SAR's ability to penetrate cloud cover and provide rapid data access plays a critical role in the swift detection of floods. Integrating this technology with early warning systems enables quicker responses to floods, reducing both human casualties and financial losses. Analysis of the June 2023 Flood occurred in the two counties of Germi and Ungut, with an area of 2064 km2, showed that about 185.83 km2 (equivalent to nine percent of the total area) were directly affected by the flood. From an environmental perspective, the analysis of NDVI and EVI indices in four time periods (one reference period in 2019 and three time periods related to before, during, and after the flood of 2023) indicates a sharp decrease in the density and extent of vegetation cover in the region. The amount of desirable vegetation cover decreased from approximately 269 km² in the pre-flood period to only 41 km² in the post-flood period, indicating the destructive impact of the flood on the region's ecological structure. These findings emphasize the need for integrated watershed management and strategic land-use planning. To enhance community resilience, it is recommended to prioritize and protect vulnerable areas through strategic policies. Utilizing these results can help reduce flood damage and save time and costs in foundational studies. Introduction Flooding, one of the most common natural disasters, poses a serious threat to ecosystems and human safety, with factors such as heavy rainfall, rapid snowmelt, dam failures, and poor management of water, soil, and vegetation resources exacerbating its impacts. Over the past two decades, approximately 2.4 billion people have been affected by natural disasters, resulting in economic damages amounting to $2.97 trillion. Among these, human-related factors such as reduced permeability, construction near riverbanks, and altering water flow paths have played a greater role in intensifying flood damage than natural factors. Accurate monitoring of floods and assessing affected areas are of significant importance. Flood zoning is a practical tool that plays a crucial role in managerial planning. Today, the combined use of optical and radar imagery can provide more comprehensive information. This study aims to effectively utilize Sentinel-1 and Sentinel-2 satellite images in conjunction with the Google Earth Engine (GEE) platform for flood mapping and vegetation indices in the Germi and Ungut counties, which have been impacted by flood events in May and June 2023. This innovative approach enhances the accuracy and speed of flood detection and offers a scalable solution for managing flood risks in vulnerable areas. By leveraging the latest advancements in remote sensing and cloud computing, this research contributes to developing more resilient strategies against flooding and is essential for regions like Ardabil Province, which are prone to irregular and heavy rainfall. Materials and Methods In this study, various methods were employed to identify and analyze flood-affected areas using Sentinel-1 and Sentinel-2 satellite data. Initially, Synthetic Aperture Radar (SAR) images from Sentinel-1 in the form of GRD (Ground Range Detected) products were utilized, which can capture images in all weather conditions and at any time of day. These data were derived from the C-band and Interferometric Wide Swath (IW) mode for flood mapping. To reduce speckle noise, the Refined Lee filter was applied, which removed random noise while preserving structural details. A threshold of 1.25 was set for VV polarization (vertical transmit, vertical receive) to identify flood-prone areas, with pixels having VV values above this threshold considered as flooded regions. To avoid misinterpretation, areas with permanent water bodies were excluded using JRC Global Surface Water data. SAR images of Sentinel-1 taken before, during, and after the flood (between April 1 and August 20, 2023) were compared for change detection. Additionally, Sentinel-2 images were used to calculate the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) to assess the impact of vegetation variability from April 1, 2019, to August 20, 2023. These methods effectively identified flood-affected areas and flood impacts on the vegetation cover of the two study counties. Results and Discussion In May and June 2023, the Germi and Ungut counties witnessed unprecedented rainfall, which led to severe flooding. The results of the interpretation of Sentinel-1 SAR images showed that about 185.83 km2 were affected by flooding, which is equivalent to 9.01 % of the total study area. Before the flood (April-May 2023), the mean NDVI and EVI respectively, were 0.25 and 0.18, indicating a significant decrease in vegetation cover compared to the reference year (April-May 2019). This could be due to land use change, drought, gradual destruction of dense vegetation, or other environmental stresses before the flood. At the time of the flood (June 2023), the mean NDVI and EVI reached 0.22 and 0.15, respectively, which are the lowest levels recorded up to that time. Only seven percent of the area had healthy vegetation during this period. This decrease could be due to the inundation of plants, destruction due to the intensity of the flood flow, or the covering of the soil surface by sediments. After the flood (August 2023), a more severe decrease in vegetation status was observed. The mean NDVI decreased to 0.15 and EVI to 0.1, and only about 2% of the area remained with healthy vegetation. This decrease may be due to severe soil degradation, erosion of the fertile topsoil, reduced vegetation cover due to seasonal conditions in the area. Conclusion This study mapped the June 2023 flood and vegetation indices in two counties of Germi and Ungut, located in the northwest of Iran, using Sentinel-1 SAR and Sentinel-2 data. In adverse weather conditions, SAR technology is an effective tool for mapping and monitoring flood-prone areas. The combination of SAR data, the Refined Lee filter, and the GEE platform facilitates crisis management and mitigates flood impacts. SAR's ability to penetrate cloud cover and provide rapid data access plays a critical role in the swift detection of floods. Integrating this technology with early warning systems enables quicker responses to floods, reducing both human casualties and financial losses. Analysis of the June 2023 Flood occurred in the two counties of Germi and Ungut, with an area of 2064 km2, showed that about 185.83 km2 (equivalent to nine percent of the total area) were directly affected by the flood. From an environmental perspective, the analysis of NDVI and EVI indices in four time periods (one reference period in 2019 and three time periods related to before, during, and after the flood of 2023) indicates a sharp decrease in the density and extent of vegetation cover in the region. The amount of desirable vegetation cover decreased from approximately 269 km² in the pre-flood period to only 41 km² in the post-flood period, indicating the destructive impact of the flood on the region's ecological structure. These findings emphasize the need for integrated watershed management and strategic land-use planning. To enhance community resilience, it is recommended to prioritize and protect vulnerable areas through strategic policies. Utilizing these results can help reduce flood damage and save time and costs in foundational studies.

https://mmws.uma.ac.ir/article_3893_d434e5eba12e006aba43c7196d5a6e13.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Drought Trend Analysis and Forecasting in the Moghan Plain Using the SPI Index and Cmip6 Climate Models Drought Trend Analysis and Forecasting in the Moghan Plain Using the SPI Index and Cmip6 Climate Models 230 260 3872 10.22098/mmws.2025.17356.1594 FA Mahdi Frotan Ph.D. Student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran Bromand Salahi Professor of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran. Batool Zeinali Professor of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran. Ebrahim Mesgari Department of Physical Geography, Faculty of Geography and Environmental Planning, University of Sistan and Baluchestan, Zahedan, Iran Journal Article 2025 05 02 Introduction Drought is a complex and gradual climatic phenomenon that has widespread environmental, ‎economic, and social impacts due to long-term reductions in rainfall and scarcity of water ‎resources. As global climate change intensifies, resulting in warmer and drier conditions, both the frequency and severity of ‎droughts are escalating, thereby necessitating more precise monitoring systems and comprehensive quantitative ‎analyses. Indices such as SPI are used to identify and assess the severity and ‎frequency of droughts. Droughts are typically categorized into three distinct types, meteorological, agricultural, and hydrological, each ‎of which exerts unique impacts on natural resources and anthropogenic activities. ‎Meanwhile, the Moghan Plain, as one of the country's important agricultural regions, has been ‎severely affected by recent droughts. Diminished precipitation, declining groundwater levels, and sparse vegetation cover collectively compromise the ‎livelihood sustainability of thousands of rural and nomadic households throughout the region. ‎ Materials and Methods This study used the Standard Precipitation Index (SPI) and CMIP6 climate models to predict ‎future drought in the Moghan Plain. Precipitation data from 15 meteorological and rain gauge ‎stations were received from the Iranian Meteorological Organization ‎(IRIMO.IR) and the Ardabil Regional Water Company. Then, 10 valid models from the sixth CMIP6 report, which are known for their superior performance in simulating precipitation parameters based on previous studies, were selected and their historical data were collected. The CMhyd downscaling model and four bias correction ‎methods were used to correct the data bias. In order to evaluate the efficiency of the models, the ‎corrected data were compared with observational data and the top five models were selected. ‎Then, using the Hamadi weighted average method for the top models, precipitation changes ‎were predicted for two moderate (SSP2-4.5) and pessimistic (SSP5-8.5) climate scenarios. ‎Finally, the drought situation was examined at 6 and 12-month time scales and the trend of ‎changes was analyzed using the modified Mann-Kendall test. ‎ Results and Discussion The performance of CMIP6 models with four bias correction methods showed that the linear ‎precipitation scaling method has the best results. The EC-Earth3 model was recognized as the ‎superior model, followed by the GFDL-ESM4, EC-Earth3-Veg, MIROC6, and MRI-ESM2-0 ‎models. Spatio-temporal analysis of annual precipitation in the Moghan Plain shows a decrease in ‎the precipitation pattern from the southeast to the northwest. Projections indicate an increase in annual precipitation and a decrease in precipitation in warm months under SSP2-4.5 (medium emission) and SSP5-8.5 (high emission) scenarios, which could jeopardize the sustainability of agriculture in the Moghan Plain by affecting water availability during the growing season. Analysis of the six-month SPI ‎index during the observation period shows an increasing trend and a decrease in drought ‎intensity in most stations. However, in the SSP2-4.5 scenario, Garmi and Zahra stations and in ‎SSP5-8.5, Garmi, Dasht and Parsabad stations experience a decrease in SPI and an increase in ‎drought. The results of the 12-month SPI analysis also confirm an increasing trend in most ‎stations and a decrease in drought intensity during the observation period. In SSP2-4.5, no significant changes are observed, indicating a relatively stable climate. However, under SSP5-8.5, some stations such as Agha Mohammad Biglo and Dasht show a significant decreasing trend, pointing to more severe and prolonged droughts. The analysis of the frequency of the six-month SPI index (period ‎‎1985–2014) shows that the normal condition was the most frequent, followed by drought and ‎mild and moderate wetness, while severe events were observed less frequently. In the period 2025–2050, under SSP2-4.5, the frequency of drought and mild wetness events is expected to increase, while severe events are projected to decrease. In SSP5-8.5, despite the increase in precipitation, the ‎concentration of mild and moderate droughts will increase in autumn. The climatic sequence ‎during the observation period shows that the normal class has the most stable condition and ‎droughts are mainly short-term. In the future, long-term normal sequences will continue, but ‎some areas of the Moghan Plain will face a higher risk of drought. In the statistical and spatial ‎analysis of the observation period with a 12-month scale, the normal class has the highest ‎frequency, indicating the relative stability of the climate. The stations of Qarakhan Biglo and ‎Parsabad are known as unstable centers, and the Zahra station is known as highly stable. In the ‎SSP2-4.5 scenario, the normal class continues to dominate, but seasonal fluctuations increase, ‎especially in summer. Stations such as Oslandoz and Shourestan will have more severe ‎fluctuations. In SSP5-8.5, similar conditions are also seen with greater intensity. The months of ‎June and September are relatively more stable, while March and August show the peak of ‎drought and wetness. The drought classification time series shows that the normal class tends to persist for longer periods, especially in five-month and long-term durations. Meanwhile, short-term mild and moderate droughts are frequently observed in most stations. In the future, although the pattern of frequency ‎and sequence will be relatively stable, the severity of extreme conditions will increase at some ‎stations.‎ Conclusion In this study, the linear precipitation scaling method was identified as the most effective ‎technique for bias correction of CMIP6 climate models in simulating the rainfall of the Moghan ‎Plain. The EC-Earth3 model showed superior performance and was used together with four ‎other models to build the Hamadi model. The results of the base period (1985–2014) confirmed ‎the spatial pattern of precipitation with maximum in the east and southeast and minimum in the ‎west of the region. Climate projections for the period 2025–2050 indicate an increase in annual precipitation in most areas, especially in the west of the region, while summer precipitation is expected to decrease. The Standardized Drought Index (SPI) at six-month and twelve-month scales showed ‎that in the SSP2-4.5 scenario, relative climate stability is maintained, but in SSP5-8.5, the ‎probability of more severe droughts increases. Despite the continued dominance of the normal ‎class, seasonal fluctuations and the intensity of extreme phenomena will increase at some ‎stations.‎ Introduction Drought is a complex and gradual climatic phenomenon that has widespread environmental, ‎economic, and social impacts due to long-term reductions in rainfall and scarcity of water ‎resources. As global climate change intensifies, resulting in warmer and drier conditions, both the frequency and severity of ‎droughts are escalating, thereby necessitating more precise monitoring systems and comprehensive quantitative ‎analyses. Indices such as SPI are used to identify and assess the severity and ‎frequency of droughts. Droughts are typically categorized into three distinct types, meteorological, agricultural, and hydrological, each ‎of which exerts unique impacts on natural resources and anthropogenic activities. ‎Meanwhile, the Moghan Plain, as one of the country's important agricultural regions, has been ‎severely affected by recent droughts. Diminished precipitation, declining groundwater levels, and sparse vegetation cover collectively compromise the ‎livelihood sustainability of thousands of rural and nomadic households throughout the region. ‎ Materials and Methods This study used the Standard Precipitation Index (SPI) and CMIP6 climate models to predict ‎future drought in the Moghan Plain. Precipitation data from 15 meteorological and rain gauge ‎stations were received from the Iranian Meteorological Organization ‎(IRIMO.IR) and the Ardabil Regional Water Company. Then, 10 valid models from the sixth CMIP6 report, which are known for their superior performance in simulating precipitation parameters based on previous studies, were selected and their historical data were collected. The CMhyd downscaling model and four bias correction ‎methods were used to correct the data bias. In order to evaluate the efficiency of the models, the ‎corrected data were compared with observational data and the top five models were selected. ‎Then, using the Hamadi weighted average method for the top models, precipitation changes ‎were predicted for two moderate (SSP2-4.5) and pessimistic (SSP5-8.5) climate scenarios. ‎Finally, the drought situation was examined at 6 and 12-month time scales and the trend of ‎changes was analyzed using the modified Mann-Kendall test. ‎ Results and Discussion The performance of CMIP6 models with four bias correction methods showed that the linear ‎precipitation scaling method has the best results. The EC-Earth3 model was recognized as the ‎superior model, followed by the GFDL-ESM4, EC-Earth3-Veg, MIROC6, and MRI-ESM2-0 ‎models. Spatio-temporal analysis of annual precipitation in the Moghan Plain shows a decrease in ‎the precipitation pattern from the southeast to the northwest. Projections indicate an increase in annual precipitation and a decrease in precipitation in warm months under SSP2-4.5 (medium emission) and SSP5-8.5 (high emission) scenarios, which could jeopardize the sustainability of agriculture in the Moghan Plain by affecting water availability during the growing season. Analysis of the six-month SPI ‎index during the observation period shows an increasing trend and a decrease in drought ‎intensity in most stations. However, in the SSP2-4.5 scenario, Garmi and Zahra stations and in ‎SSP5-8.5, Garmi, Dasht and Parsabad stations experience a decrease in SPI and an increase in ‎drought. The results of the 12-month SPI analysis also confirm an increasing trend in most ‎stations and a decrease in drought intensity during the observation period. In SSP2-4.5, no significant changes are observed, indicating a relatively stable climate. However, under SSP5-8.5, some stations such as Agha Mohammad Biglo and Dasht show a significant decreasing trend, pointing to more severe and prolonged droughts. The analysis of the frequency of the six-month SPI index (period ‎‎1985–2014) shows that the normal condition was the most frequent, followed by drought and ‎mild and moderate wetness, while severe events were observed less frequently. In the period 2025–2050, under SSP2-4.5, the frequency of drought and mild wetness events is expected to increase, while severe events are projected to decrease. In SSP5-8.5, despite the increase in precipitation, the ‎concentration of mild and moderate droughts will increase in autumn. The climatic sequence ‎during the observation period shows that the normal class has the most stable condition and ‎droughts are mainly short-term. In the future, long-term normal sequences will continue, but ‎some areas of the Moghan Plain will face a higher risk of drought. In the statistical and spatial ‎analysis of the observation period with a 12-month scale, the normal class has the highest ‎frequency, indicating the relative stability of the climate. The stations of Qarakhan Biglo and ‎Parsabad are known as unstable centers, and the Zahra station is known as highly stable. In the ‎SSP2-4.5 scenario, the normal class continues to dominate, but seasonal fluctuations increase, ‎especially in summer. Stations such as Oslandoz and Shourestan will have more severe ‎fluctuations. In SSP5-8.5, similar conditions are also seen with greater intensity. The months of ‎June and September are relatively more stable, while March and August show the peak of ‎drought and wetness. The drought classification time series shows that the normal class tends to persist for longer periods, especially in five-month and long-term durations. Meanwhile, short-term mild and moderate droughts are frequently observed in most stations. In the future, although the pattern of frequency ‎and sequence will be relatively stable, the severity of extreme conditions will increase at some ‎stations.‎ Conclusion In this study, the linear precipitation scaling method was identified as the most effective ‎technique for bias correction of CMIP6 climate models in simulating the rainfall of the Moghan ‎Plain. The EC-Earth3 model showed superior performance and was used together with four ‎other models to build the Hamadi model. The results of the base period (1985–2014) confirmed ‎the spatial pattern of precipitation with maximum in the east and southeast and minimum in the ‎west of the region. Climate projections for the period 2025–2050 indicate an increase in annual precipitation in most areas, especially in the west of the region, while summer precipitation is expected to decrease. The Standardized Drought Index (SPI) at six-month and twelve-month scales showed ‎that in the SSP2-4.5 scenario, relative climate stability is maintained, but in SSP5-8.5, the ‎probability of more severe droughts increases. Despite the continued dominance of the normal ‎class, seasonal fluctuations and the intensity of extreme phenomena will increase at some ‎stations.‎

https://mmws.uma.ac.ir/article_3872_074d64a729227a617643ce600ea99b0c.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Evaluation of the SEBAL for Estimating Actual Evapotranspiration in the Area of the Gareh Bygone Plain Floodwater Spreading Project Evaluation of the SEBAL for Estimating Actual Evapotranspiration in the Area of the Gareh Bygone Plain Floodwater Spreading Project 261 276 3922 10.22098/mmws.2025.17580.1607 FA Hamid Hosseinimarandi Assistant Professor, Soil Conservation and Watershed Management Research Department, Fars Agricultural Research, Education and Extension Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran Mojtaba Pakparvar Associate Professor, Soil Conservation and Watershed Management Research Department, Fars Agricultural Research, Education and Extension Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran Mirmasoud Khairkhah Zarkesh Associate Professor, Hydrology and Water Resources Development Department, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran Journal Article 2025 06 01 Abstract Introduction Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resource management, particularly in arid and semi-arid regions. This study employed the SEBAL model in conjunction with Landsat 8 and 9 satellite imagery to estimate ETa in the Gareh Bygone Plain, Fars Province, during 2018–2021. The model integrated corrected satellite data and meteorological inputs to compute key surface energy balance components, including net radiation, soil heat flux, and latent heat flux. Calibration was enhanced by incorporating wind speed data, improving the model’s accuracy. ETa values varied seasonally, ranging from 0.8 to 3.2 mm/day in colder months and 1.8 to 6.5 mm/day during warmer periods. The model’s results were validated against FAO Penman-Monteith reference ET and field measurements, confirming strong agreement. Crop coefficient (Kc) estimates highlighted significant variability based on vegetation type and growth stages. The complementary use of the SEBS model yielded ETa estimations with a low error margin (3–5%), further confirming the reliability of remote sensing-based approaches. These findings support the application of calibrated satellite models and localized parameters in optimizing irrigation strategies and addressing water scarcity in semi-arid environments. Accurate estimation of actual evapotranspiration (ETa) is crucial for water resource management and evaluating the efficiency of artificial recharge projects in arid regions. Traditional methods relying on point-based measurements often fail to represent large-scale and heterogeneous areas. Remote sensing technologies, utilizing satellite data and surface energy balance models such as SEBAL, enable precise estimation of ETa and crop coefficients (Kc) over extensive spatial and temporal scales. This study aimed to assess water consumption of various vegetation covers and the effectiveness of the flood spreading system in the Gareh Bygone plain, Fars Province, by developing improved models and analyzing the spatiotemporal distribution of ETa. Materials and Methods This study was carried out at the Kosar floodwater spreading station in the semi-arid Gareh Bygone Plain of southern Iran’s Fars Province.The Gareh Bygone Plain, with an approximate area of 192 square kilometers, is located downstream of three main watershed areas. The area features an semi-arid to arid climate, an average annual precipitation of 229 mm, and geology comprising limestone, marl, and conglomerates. Satellite data from Landsat 8 and 9, processed with geometric, radiometric, and atmospheric corrections, served as input for the SEBAL model. This model estimates actual evapotranspiration by integrating satellite imagery and meteorological data using the surface energy balance equation. Vegetation indices such as NDVI and SAVI, surface albedo, surface temperature, and other surface energy parameters were used in ETa estimation. Model results were validated with field measurements, including reference evapotranspiration, soil water balance, and estimates of return flow. Results and Discussion Satellite imagery from Landsat 8 Level 2, covering 2018–2021, was used to derive ETa maps via the SEBAL model. Due to data acquisition limitations in certain provinces, temporal coverage was incomplete. The SEBAL model, calibrated with meteorological data and incorporating wind speed as a parameter, showed good performance in estimating ETa, particularly in the Gareh Bygone plain. Comparison of SEBAL-derived ETa values with FAO Penman-Monteith reference estimates, using a crop coefficient (Kc=1.05), demonstrated the model’s reasonable accuracy. ETa values ranged from 0.8 to 3.2 mm/day during the cold season and from 1.8 to 6.5 mm/day in the warm season, reflecting a seasonal pattern consistent with the region’s climatic conditions. These results highlight the importance of using satellite data and optimizing model parameters for more accurate ETa estimation. Analysis of SEBAL model results revealed seasonal fluctuations in ETa influenced by temperature, solar radiation, and vegetation cover. ETa values ranged from 0.8 to 6.5 mm/day, peaking during the warm season. The crop coefficient (Kc) was closely linked to vegetation type and growth stage, emphasizing the need to consider these differences in water management. Field measurements of soil (moisture, texture, bulk density) in the Gareh Bygone plain indicated a progressive increase in water infiltration depth and return flow volume during the irrigation season. Comparison of SEBS model-derived ETa with field data confirmed the high accuracy of the SEBS model (error margin of 3–5%), demonstrating its superiority over traditional methods and its effectiveness for agricultural water resource management in semi-arid regions. Computational results showed an increasing trend in return flow volume throughout the 11 irrigation cycles, reaching a significant amount by the end of the season. This finding has important implications for water resource management in the region. Conclusion This study employed Landsat 8 imagery to estimate actual evapotranspiration (ETa) using the SEBAL model in the semi-arid Gareh Bygone Plain. Results indicate a strong correlation between seasonal ETa variations and temperature, solar radiation, and land surfaces vegetation activity, with peak ETa during the warm season (April-October) and minimum during the cold season (December-February). This seasonal pattern aligns with the semi-arid climate and highlights the importance of considering spatiotemporal variability in ETa estimations. Furthermore, incorporating wind speed significantly improved SEBAL model accuracy. Abstract Introduction Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resource management, particularly in arid and semi-arid regions. This study employed the SEBAL model in conjunction with Landsat 8 and 9 satellite imagery to estimate ETa in the Gareh Bygone Plain, Fars Province, during 2018–2021. The model integrated corrected satellite data and meteorological inputs to compute key surface energy balance components, including net radiation, soil heat flux, and latent heat flux. Calibration was enhanced by incorporating wind speed data, improving the model’s accuracy. ETa values varied seasonally, ranging from 0.8 to 3.2 mm/day in colder months and 1.8 to 6.5 mm/day during warmer periods. The model’s results were validated against FAO Penman-Monteith reference ET and field measurements, confirming strong agreement. Crop coefficient (Kc) estimates highlighted significant variability based on vegetation type and growth stages. The complementary use of the SEBS model yielded ETa estimations with a low error margin (3–5%), further confirming the reliability of remote sensing-based approaches. These findings support the application of calibrated satellite models and localized parameters in optimizing irrigation strategies and addressing water scarcity in semi-arid environments. Accurate estimation of actual evapotranspiration (ETa) is crucial for water resource management and evaluating the efficiency of artificial recharge projects in arid regions. Traditional methods relying on point-based measurements often fail to represent large-scale and heterogeneous areas. Remote sensing technologies, utilizing satellite data and surface energy balance models such as SEBAL, enable precise estimation of ETa and crop coefficients (Kc) over extensive spatial and temporal scales. This study aimed to assess water consumption of various vegetation covers and the effectiveness of the flood spreading system in the Gareh Bygone plain, Fars Province, by developing improved models and analyzing the spatiotemporal distribution of ETa. Materials and Methods This study was carried out at the Kosar floodwater spreading station in the semi-arid Gareh Bygone Plain of southern Iran’s Fars Province.The Gareh Bygone Plain, with an approximate area of 192 square kilometers, is located downstream of three main watershed areas. The area features an semi-arid to arid climate, an average annual precipitation of 229 mm, and geology comprising limestone, marl, and conglomerates. Satellite data from Landsat 8 and 9, processed with geometric, radiometric, and atmospheric corrections, served as input for the SEBAL model. This model estimates actual evapotranspiration by integrating satellite imagery and meteorological data using the surface energy balance equation. Vegetation indices such as NDVI and SAVI, surface albedo, surface temperature, and other surface energy parameters were used in ETa estimation. Model results were validated with field measurements, including reference evapotranspiration, soil water balance, and estimates of return flow. Results and Discussion Satellite imagery from Landsat 8 Level 2, covering 2018–2021, was used to derive ETa maps via the SEBAL model. Due to data acquisition limitations in certain provinces, temporal coverage was incomplete. The SEBAL model, calibrated with meteorological data and incorporating wind speed as a parameter, showed good performance in estimating ETa, particularly in the Gareh Bygone plain. Comparison of SEBAL-derived ETa values with FAO Penman-Monteith reference estimates, using a crop coefficient (Kc=1.05), demonstrated the model’s reasonable accuracy. ETa values ranged from 0.8 to 3.2 mm/day during the cold season and from 1.8 to 6.5 mm/day in the warm season, reflecting a seasonal pattern consistent with the region’s climatic conditions. These results highlight the importance of using satellite data and optimizing model parameters for more accurate ETa estimation. Analysis of SEBAL model results revealed seasonal fluctuations in ETa influenced by temperature, solar radiation, and vegetation cover. ETa values ranged from 0.8 to 6.5 mm/day, peaking during the warm season. The crop coefficient (Kc) was closely linked to vegetation type and growth stage, emphasizing the need to consider these differences in water management. Field measurements of soil (moisture, texture, bulk density) in the Gareh Bygone plain indicated a progressive increase in water infiltration depth and return flow volume during the irrigation season. Comparison of SEBS model-derived ETa with field data confirmed the high accuracy of the SEBS model (error margin of 3–5%), demonstrating its superiority over traditional methods and its effectiveness for agricultural water resource management in semi-arid regions. Computational results showed an increasing trend in return flow volume throughout the 11 irrigation cycles, reaching a significant amount by the end of the season. This finding has important implications for water resource management in the region. Conclusion This study employed Landsat 8 imagery to estimate actual evapotranspiration (ETa) using the SEBAL model in the semi-arid Gareh Bygone Plain. Results indicate a strong correlation between seasonal ETa variations and temperature, solar radiation, and land surfaces vegetation activity, with peak ETa during the warm season (April-October) and minimum during the cold season (December-February). This seasonal pattern aligns with the semi-arid climate and highlights the importance of considering spatiotemporal variability in ETa estimations. Furthermore, incorporating wind speed significantly improved SEBAL model accuracy.

https://mmws.uma.ac.ir/article_3922_a0f261b1966f85403eb34988fd941ea3.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Evaluating the soil retention ecosystem service using the InVEST model in Northeastern Iran Evaluating the soil retention ecosystem service using the InVEST model in Northeastern Iran 277 295 3933 10.22098/mmws.2025.17666.1612 FA Mohadese Namazi MSc in Desert Management and Control, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran Morteza Akbari Associate Professor, Department of Desert and Arid Zones Management, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran. Hadi Memarian Associate Professor, Rangeland and Watershed Management Department, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran Zahra Asadolahi Assistant Professor, Department of Environment and Fisheries, Faculty of Natural Resources, Lorestan University, Khoramabad, Iran Naser Parviyan MSc of Environment of Science, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran Journal Article 2025 06 13 Introduction Soil plays an important role in providing essential services needed by humans and other organisms and provides a variety of ecosystem services. Soil retention as an ecosystem service refers to the ability of an ecosystem to maintain soil and prevent erosion. This service is crucial as soil plays a vital role in providing essential goods for humans and other organisms, as well as a range of ecosystem services. Effective soil and water conservation management practices, particularly in enhancing vegetation cover for soil stabilization, are key. Therefore, safeguarding soil function against natural and human-induced threats is a critical regulatory function of ecosystems. In arid and semi-arid regions of Iran, ongoing degradation and significant changes in climate have impacted land cover, especially rangelands, affecting water conservation and ecosystem services provision. The Food and Agriculture Organization of the United Nations highlights that sustainable soil management can help prevent land degradation. By implementing measures such as conservation agriculture, reducing soil erosion, and enhancing water management, the risk of land degradation can be significantly reduced. Soil erosion and the consequent depletion of soil resources pose serious ecological and environmental challenges globally, hindering sustainable human development. The InVEST sediment delivery ratio model, built on the RUSLE erosion model, is a valuable tool for evaluating soil erosion, sediment export, and soil conservation across various spatial and temporal scales, widely utilized on an international scale. Materials and Methods Sarakhs Township, covering an area of approximately 5471 square kilometers, is a small part of the vast Qaraqom basin. Due to its proximity to the Qaraqom desert, this region has the potential for desert development based on its natural conditions. The cold and dry climate, low rainfall, minimal land use changes, and soil erosion have led to critical conditions for land degradation in the area. In this study, soil retention and erosion were quantified using the Sediment Retention model in the InVEST software. The necessary inputs for the model, including digital elevation model maps, land use data, rainfall erosivity, soil erodibility, and a biophysical table in raster format, were prepared in the ArcGIS 10.8 environment and inputted into the model. The DEM map of the study area was downloaded from Iran's weather and climatology website and extracted in ArcGIS version 10.8. The R factor was calculated using equation (5) based on index stations, and the R index was interpolated for the entire study area using the IDW method to create a rain erosion spatial layer in GIS. Data on the percentage of sand, silt, and clay were obtained from global soil information, converted to percentages, and used to calculate the soil erodibility index in the Raster calculator tool of the GIS environment. The NDVI index was calculated from Landsat satellite images in the Google Earth Engine platform. The P coefficient was determined using the slope-based Wenner method. A csv table with integer codes for each land use class in the land use map was required to run the model. Results and Discussion The findings indicated a wide range of soil retention levels, ranging from 0.47 to 0.53 tons per pixel per year, across different land uses. Pasture lands exhibited the highest soil retention rate at 5.98 tons per hectare per year, while residential areas had the lowest rate at 0.08 tons per pixel per year. The southwest region of the study area demonstrated the highest sediment retention capacity, likely due to the superior condition and quality of pastures in that area compared to other regions in the basin. Conversely, the central and eastern areas experienced increased erosion rates due to frequent land use changes and reduced vegetation cover. Developing a model to assess the ecosystem service of soil retention is crucial for effective ecosystem management. By utilizing spatial modeling, land managers can strategically plan to reduce sediment load at the watershed level by identifying conservation-worthy areas with high sediment retention capacity and optimizing land use practices. Conclusion To sum up, soil erosion and sediment production are significant issues in developing countries, leading to the destruction of agricultural lands, dam reservoir filling, and water pollution. This study highlights the importance of rangeland cover in soil conservation, with areas of low vegetation cover experiencing higher rates of erosion. The decline in sediment conservation in rangeland lands is attributed to reduced vegetation cover, caused by factors such as population growth, excessive livestock grazing, and prolonged land use. Land use change emerges as the primary factor influencing erosion rates and sediment conservation values. Given Sarakhs township's potential for desertification and its cold, arid climate with minimal rainfall, future studies should also assess wind erosion alongside water erosion. The results of this study can be useful in developing an ecosystem services management plan for land managers to have more effective spatial planning by identifying areas with conservation value due to the high supply of soil conservation and also paying attention to land use. Introduction Soil plays an important role in providing essential services needed by humans and other organisms and provides a variety of ecosystem services. Soil retention as an ecosystem service refers to the ability of an ecosystem to maintain soil and prevent erosion. This service is crucial as soil plays a vital role in providing essential goods for humans and other organisms, as well as a range of ecosystem services. Effective soil and water conservation management practices, particularly in enhancing vegetation cover for soil stabilization, are key. Therefore, safeguarding soil function against natural and human-induced threats is a critical regulatory function of ecosystems. In arid and semi-arid regions of Iran, ongoing degradation and significant changes in climate have impacted land cover, especially rangelands, affecting water conservation and ecosystem services provision. The Food and Agriculture Organization of the United Nations highlights that sustainable soil management can help prevent land degradation. By implementing measures such as conservation agriculture, reducing soil erosion, and enhancing water management, the risk of land degradation can be significantly reduced. Soil erosion and the consequent depletion of soil resources pose serious ecological and environmental challenges globally, hindering sustainable human development. The InVEST sediment delivery ratio model, built on the RUSLE erosion model, is a valuable tool for evaluating soil erosion, sediment export, and soil conservation across various spatial and temporal scales, widely utilized on an international scale. Materials and Methods Sarakhs Township, covering an area of approximately 5471 square kilometers, is a small part of the vast Qaraqom basin. Due to its proximity to the Qaraqom desert, this region has the potential for desert development based on its natural conditions. The cold and dry climate, low rainfall, minimal land use changes, and soil erosion have led to critical conditions for land degradation in the area. In this study, soil retention and erosion were quantified using the Sediment Retention model in the InVEST software. The necessary inputs for the model, including digital elevation model maps, land use data, rainfall erosivity, soil erodibility, and a biophysical table in raster format, were prepared in the ArcGIS 10.8 environment and inputted into the model. The DEM map of the study area was downloaded from Iran's weather and climatology website and extracted in ArcGIS version 10.8. The R factor was calculated using equation (5) based on index stations, and the R index was interpolated for the entire study area using the IDW method to create a rain erosion spatial layer in GIS. Data on the percentage of sand, silt, and clay were obtained from global soil information, converted to percentages, and used to calculate the soil erodibility index in the Raster calculator tool of the GIS environment. The NDVI index was calculated from Landsat satellite images in the Google Earth Engine platform. The P coefficient was determined using the slope-based Wenner method. A csv table with integer codes for each land use class in the land use map was required to run the model. Results and Discussion The findings indicated a wide range of soil retention levels, ranging from 0.47 to 0.53 tons per pixel per year, across different land uses. Pasture lands exhibited the highest soil retention rate at 5.98 tons per hectare per year, while residential areas had the lowest rate at 0.08 tons per pixel per year. The southwest region of the study area demonstrated the highest sediment retention capacity, likely due to the superior condition and quality of pastures in that area compared to other regions in the basin. Conversely, the central and eastern areas experienced increased erosion rates due to frequent land use changes and reduced vegetation cover. Developing a model to assess the ecosystem service of soil retention is crucial for effective ecosystem management. By utilizing spatial modeling, land managers can strategically plan to reduce sediment load at the watershed level by identifying conservation-worthy areas with high sediment retention capacity and optimizing land use practices. Conclusion To sum up, soil erosion and sediment production are significant issues in developing countries, leading to the destruction of agricultural lands, dam reservoir filling, and water pollution. This study highlights the importance of rangeland cover in soil conservation, with areas of low vegetation cover experiencing higher rates of erosion. The decline in sediment conservation in rangeland lands is attributed to reduced vegetation cover, caused by factors such as population growth, excessive livestock grazing, and prolonged land use. Land use change emerges as the primary factor influencing erosion rates and sediment conservation values. Given Sarakhs township's potential for desertification and its cold, arid climate with minimal rainfall, future studies should also assess wind erosion alongside water erosion. The results of this study can be useful in developing an ecosystem services management plan for land managers to have more effective spatial planning by identifying areas with conservation value due to the high supply of soil conservation and also paying attention to land use.

https://mmws.uma.ac.ir/article_3933_cece87c8f349c1b2fb067d2319b5d122.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Spatiotemporal variations analysis of water yield ecosystem service in the Hyrcanian region of northern Iran using the InVEST model Spatiotemporal variations analysis of water yield ecosystem service in the Hyrcanian region of northern Iran using the InVEST model 296 308 3914 10.22098/mmws.2025.17679.1614 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 06 17 Extended Abstract Introduction Ecosystems provide a wide range of benefits known as ecosystem services, which play a vital role in human livelihoods and environmental sustainability. Among these, hydrological services, particularly water yield, are essential in maintaining water security, supporting downstream ecosystems, and regulating hydrological processes. Understanding temporal and spatial variations in water yield is increasingly important in the context of land use change and climate variability. In this regard, modeling tools such as the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) have proven effective in quantifying and mapping ecosystem services, especially water yield, by integrating spatial data on land use, climate, topography, and soil properties. The rationale behind this study stems from the limited research conducted on the long-term and spatial dynamics of water yield ecosystem services in Iran, particularly in ecologically sensitive and hydrologically significant regions such as the Hyrcanian forests. Despite their rich biodiversity and critical ecological functions, these regions have received insufficient attention in ecosystem service assessments using robust modeling frameworks. Therefore, this research aims to evaluate the spatiotemporal variations in water yield services in the Talar Watershed, a representative region within the Hyrcanian forest, over a 25-year period using the InVEST model. Materials and Methods The Talar Watershed, located in Mazandaran Province in northern Iran, spans approximately 1,764 km² across the northern slopes of the Alborz Mountains. The elevation in the watershed ranges from 216 to nearly 3,980 meters, contributing to diverse microclimates and land uses. The region has a semi-humid, Mediterranean-like climate influenced by the Caspian Sea, with an average annual precipitation of 547 mm and an average annual evapotranspiration of 446 mm. The InVEST water yield model was employed to estimate annual water production based on biophysical and climatic variables for the years 1989, 2000, and 2014. The input data incuding climatic variables (precipitation, reference evapotranspiration), land use/land cover, digital elevation model (DEM), plant-available water content, root-restricting layer depth, biophysical factors (land cover condition, root depth, and evapotranspiration coefficient), seasonality parameter, watershed boundaries, and water demand for each land use class were prepared for the model in the selected years 1989, 2000, and 2014. In this regards, Annual average precipitation across the study area was estimated based on elevation gradients for the selected years. Reference evapotranspiration was calculated using the modified Hargreaves equation for studied years. Land use/land cover data were derived from Landsat satellite imagery using a supervised classification approach based on the Support Vector Machine (SVM) method in the research years. Plant-available water content was determined using soil texture characteristics and calculated as the volumetric difference between field capacity and permanent wilting point. The depth of the root-restricting layer was assumed equal to soil depth in each land unit, as no significant root-limiting layers were present. Root depth was assigned based on dominant vegetation types in each area. Land cover status was defined as either covered (1) or not covered (0), with all land use types except urban areas classified as having vegetative cover. The evapotranspiration coefficient, used to adjust reference evapotranspiration based on alfalfa as the reference crop in the InVEST model for different land use classes. The seasonality parameter, reflecting the predominantly winter rainfall pattern of the study area's climate, was set to 10. Results and Discussion Results indicate that the northern and central parts of the Talār watershed, dominated by dense forests and rangelands, produced the highest water volumes, supported by higher annual precipitation. Findings shows a 23% reduction in water provisioning service from 192.02 to 147.85 million m³ between 1989 and 2014. Validation with hydrometric data indicated a decreasing trend in the ratio of precipitation converted to water supply, likely caused by increased evapotranspiration and land use changes. Among land uses, rangelands produced the highest average annual water, while orchards produced the least. Per hectare, urban areas had the highest water production due to impervious surfaces increasing runoff, and forests had the lowest due to higher infiltration and evapotranspiration. The large extent of rangeland areas and their location in steep, slope regions, leading to reduced infiltration and increased runoff, can be considered the main reasons for the highest water yield observed in this land use type. Overall, protecting natural vegetation, especially in sloped, high-precipitation areas, is vital to maintaining watershed water production. Ecological land use planning is essential for sustainable water and soil resource management in the region. Conclusions From a policy and planning perspective, this research underscores the utility of the InVEST model as a decision-support tool for watershed managers and land use planners. The ability to quantify and map water yield variations across time provides valuable insights for identifying priority areas for conservation, designing payment for ecosystem services schemes, and implementing adaptive land management strategies. The model’s outputs can also be integrated into regional climate adaptation frameworks, particularly in semi-humid mountainous regions vulnerable to rainfall variability and water stress. Furthermore, the spatially explicit results facilitate cross-sectoral coordination among forestry, agriculture, and urban planning agencies by identifying synergies and tradeoffs in ecosystem service provision. Finally, this study contributes to the growing body of ecosystem service research in the Middle East and offers a replicable methodology for analyzing other hydrologically sensitive regions under environmental pressure. In conclusion, this research provides a comprehensive and long-term assessment of water yield ecosystem service dynamics in a critical ecological zone of Iran. The combination of empirical data, spatial analysis, and process-based modeling offers a robust foundation for evidence-based decision-making. As land use change and climate variability continue to reshape hydrological processes, integrating ecosystem service assessments into regional planning will be essential for achieving sustainable water resource management and ecological resilience in the Hyrcanian region and beyond. Extended Abstract Introduction Ecosystems provide a wide range of benefits known as ecosystem services, which play a vital role in human livelihoods and environmental sustainability. Among these, hydrological services, particularly water yield, are essential in maintaining water security, supporting downstream ecosystems, and regulating hydrological processes. Understanding temporal and spatial variations in water yield is increasingly important in the context of land use change and climate variability. In this regard, modeling tools such as the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) have proven effective in quantifying and mapping ecosystem services, especially water yield, by integrating spatial data on land use, climate, topography, and soil properties. The rationale behind this study stems from the limited research conducted on the long-term and spatial dynamics of water yield ecosystem services in Iran, particularly in ecologically sensitive and hydrologically significant regions such as the Hyrcanian forests. Despite their rich biodiversity and critical ecological functions, these regions have received insufficient attention in ecosystem service assessments using robust modeling frameworks. Therefore, this research aims to evaluate the spatiotemporal variations in water yield services in the Talar Watershed, a representative region within the Hyrcanian forest, over a 25-year period using the InVEST model. Materials and Methods The Talar Watershed, located in Mazandaran Province in northern Iran, spans approximately 1,764 km² across the northern slopes of the Alborz Mountains. The elevation in the watershed ranges from 216 to nearly 3,980 meters, contributing to diverse microclimates and land uses. The region has a semi-humid, Mediterranean-like climate influenced by the Caspian Sea, with an average annual precipitation of 547 mm and an average annual evapotranspiration of 446 mm. The InVEST water yield model was employed to estimate annual water production based on biophysical and climatic variables for the years 1989, 2000, and 2014. The input data incuding climatic variables (precipitation, reference evapotranspiration), land use/land cover, digital elevation model (DEM), plant-available water content, root-restricting layer depth, biophysical factors (land cover condition, root depth, and evapotranspiration coefficient), seasonality parameter, watershed boundaries, and water demand for each land use class were prepared for the model in the selected years 1989, 2000, and 2014. In this regards, Annual average precipitation across the study area was estimated based on elevation gradients for the selected years. Reference evapotranspiration was calculated using the modified Hargreaves equation for studied years. Land use/land cover data were derived from Landsat satellite imagery using a supervised classification approach based on the Support Vector Machine (SVM) method in the research years. Plant-available water content was determined using soil texture characteristics and calculated as the volumetric difference between field capacity and permanent wilting point. The depth of the root-restricting layer was assumed equal to soil depth in each land unit, as no significant root-limiting layers were present. Root depth was assigned based on dominant vegetation types in each area. Land cover status was defined as either covered (1) or not covered (0), with all land use types except urban areas classified as having vegetative cover. The evapotranspiration coefficient, used to adjust reference evapotranspiration based on alfalfa as the reference crop in the InVEST model for different land use classes. The seasonality parameter, reflecting the predominantly winter rainfall pattern of the study area's climate, was set to 10. Results and Discussion Results indicate that the northern and central parts of the Talār watershed, dominated by dense forests and rangelands, produced the highest water volumes, supported by higher annual precipitation. Findings shows a 23% reduction in water provisioning service from 192.02 to 147.85 million m³ between 1989 and 2014. Validation with hydrometric data indicated a decreasing trend in the ratio of precipitation converted to water supply, likely caused by increased evapotranspiration and land use changes. Among land uses, rangelands produced the highest average annual water, while orchards produced the least. Per hectare, urban areas had the highest water production due to impervious surfaces increasing runoff, and forests had the lowest due to higher infiltration and evapotranspiration. The large extent of rangeland areas and their location in steep, slope regions, leading to reduced infiltration and increased runoff, can be considered the main reasons for the highest water yield observed in this land use type. Overall, protecting natural vegetation, especially in sloped, high-precipitation areas, is vital to maintaining watershed water production. Ecological land use planning is essential for sustainable water and soil resource management in the region. Conclusions From a policy and planning perspective, this research underscores the utility of the InVEST model as a decision-support tool for watershed managers and land use planners. The ability to quantify and map water yield variations across time provides valuable insights for identifying priority areas for conservation, designing payment for ecosystem services schemes, and implementing adaptive land management strategies. The model’s outputs can also be integrated into regional climate adaptation frameworks, particularly in semi-humid mountainous regions vulnerable to rainfall variability and water stress. Furthermore, the spatially explicit results facilitate cross-sectoral coordination among forestry, agriculture, and urban planning agencies by identifying synergies and tradeoffs in ecosystem service provision. Finally, this study contributes to the growing body of ecosystem service research in the Middle East and offers a replicable methodology for analyzing other hydrologically sensitive regions under environmental pressure. In conclusion, this research provides a comprehensive and long-term assessment of water yield ecosystem service dynamics in a critical ecological zone of Iran. The combination of empirical data, spatial analysis, and process-based modeling offers a robust foundation for evidence-based decision-making. As land use change and climate variability continue to reshape hydrological processes, integrating ecosystem service assessments into regional planning will be essential for achieving sustainable water resource management and ecological resilience in the Hyrcanian region and beyond.

https://mmws.uma.ac.ir/article_3914_721a3d49be0be71cc5f047f1219be90c.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Comparative accuracy assessment of satellite (Sentinel-2, SMAP) and ground-based (TDR, PR2) sensors in soil moisture estimation Comparative accuracy assessment of satellite (Sentinel-2, SMAP) and ground-based (TDR, PR2) sensors in soil moisture estimation 309 327 3963 10.22098/mmws.2025.17736.1619 FA Jahangir Porhemmat Department of Hydrology, Soil Conservation and Watershed Management Institute, Tehran, Iran Abdolnabi Abdeh Kolahchi Department of Hydrology, Soil Conservation, and Watershed Management Research Institute, Tehran, Iran Sayed Mohammad Tajbakhsh Faculty of Natural Resources and Environment, Birjand University, Birjand, Iran Fatemehe Karimi Zafarabadi Soil Conservation and Watershed Management Research Institute, Shafie Street, Asheri Street, Km 10 of Jadeh Makhsoos, Tehran, Iran Journal Article 2025 06 30 Introduction Although soil moisture represents a small fraction of the world's available freshwater, it plays a crucial role as an effective water storage component within the hydrological cycle and is fundamentally important in hydrological, biological, and biogeochemical processes. The most critical aspect of soil moisture is the depth of water stored within the soil. Consequently, understanding the factors that influence soil moisture and its effects is essential for predicting future performance and ultimately enhancing agricultural production and food security. This understanding is also vital for optimizing irrigation water management and improving water use efficiency in agricultural fields. Therefore, various devices and equipment have been employed over the past few decades to estimate soil moisture. Their application requires consideration of several factors, including the need for calibration, accuracy of results, repeatability, spatial resolution, usability, and cost. Despite the significance of soil moisture in modeling hydrological, biogeochemical, and related dynamic processes, accurately measuring its temporal and spatial variations on a local or watershed scale presents challenges and high costs due to substantial fluctuations. Conversely, remote sensing data has provided opportunities for continuous and cost-effective monitoring of soil moisture estimates with appropriate temporal and spatial distribution, which must be controlled and calibrated with ground-based data. This study investigates and evaluates point-based sensors, including time-domain reflectometry (TDR) and the PR2 neutron probe, alongside sensors that offer suitable temporal and spatial distribution, such as Sentinel 2 and Soil Moisture Active Passive (SMAP), using weighted soil moisture measurement data. Materials and Methods In this study, soil moisture measurement as a direct method and TDR, PR2, Sentinel-2, and SMAP satellite images as indirect methods of soil moisture estimation were investigated. To assess and evaluate soil moisture sensors at the watershed scale, a part of the Telo region called Deh Sayid in Lavasanat, Tehran province, with an area of 328 hectares, was selected. Initially, due to the difficulty and high cost of measuring soil moisture by direct methods, data obtained from this method were considered the criterion for evaluating TDR and PR2 sensors. For this purpose, seven stations were established across this region in different land uses, where soil moisture monitoring was conducted through soil sampling and weight measurements in the SCWMRI laboratory, alongside simultaneous soil moisture measurements using TDR and PR2 sensors. Initially, the simultaneous data from these two sensors were evaluated against direct gravimetric observation values. Additionally, soil moisture was monitored by TDR and PR2 sensors during the passage of the Sentinel-2 and SMAP satellites from October 2020 to April 2022. During this period, TDR was used to evaluate PR2, Sentinel-2, and SMAP using statistical criteria, including correlation coefficient and percent deviation. Furthermore, considering the spatial scale of SMAP, the average of the simultaneous data from the seven stations was used for its evaluation. Results and Discussion The correlation between TDR and PR2 related to gravimetric method measurements showed that their coefficient of determination (R2) was 0.91 and 0.92, respectively, which showed a high correlation with absolute moisture values. Also, their percentage deviation was 6 and 23 percent, respectively. Therefore, TDR measured absolute soil moisture with much higher accuracy than PR2. The correlation coefficient of PR2 data with TDR varied between 0.826 and 0.933 at the monitoring stations, indicating a high correlation between them. To evaluate the moisture index of Sentinel 2 and SMAP images, simultaneous data from TDR were used due to the high accuracy of the TDR sensor. The results of the correlation analysis of the normalized differential water index (NDWI) obtained from Sentinel 2 images with TDR showed that the range of the coefficient of determination (R2) between the data of these two sensors was very high. The R² values vary from 0.003 at station P7 to 0.814 at station P2. The correlation coefficient at other stations is 0.094, 0.132, 0.587 and 0.723 at stations P3, P4, P6 and P5 respectively. However, at three stations P7, P5 and P6 the correlation coefficient is significant at the 5% level, but at three stations P2, P3 and P4 there is no significant correlation between them. The correlation coefficient of simultaneous SMAP data with the average TDR data at seven stations is 0.455 and the slope of their correlation line is 0.833. Therefore, SMAP shows more accuracy than Sentinel in estimating soil moisture Conclusion The result of this research indicated that the correlation between each of the two sensors, TDR and PR2, with gravimetric method is very high and close to each other with values of 0.956 and 0.962, respectively. Additionally, their absolute deviation is 6 and 23 percent, respectively. Consequently, TDR provides a more accurate estimate of the absolute value of soil moisture compared to PR2. Therefore, TDR is superior to PR2 as an appropriate estimator in the absence of the soil moisture data from gravimetric method in this region. Other findings from the study reveal that the indices obtained from Sentinel2 at various points yield different estimates of soil moisture; in pasture land with dense vegetation cover, the accuracy is significant, whereas in areas with tree cover, it lacks significant accuracy. Furthermore, the results demonstrated that the accuracy of Sentinel2 moisture indices is low, while SMAP offers higher accuracy for estimating moisture despite its lower spatial scale. Moreover, based on the findings of this study, the estimated absolute amount of soil moisture derived from remotely sensed data does not yet possess the necessary accuracy for practical applications, and further research is required to combine and test with other indices to enhance the accuracy of soil moisture estimates. Introduction Although soil moisture represents a small fraction of the world's available freshwater, it plays a crucial role as an effective water storage component within the hydrological cycle and is fundamentally important in hydrological, biological, and biogeochemical processes. The most critical aspect of soil moisture is the depth of water stored within the soil. Consequently, understanding the factors that influence soil moisture and its effects is essential for predicting future performance and ultimately enhancing agricultural production and food security. This understanding is also vital for optimizing irrigation water management and improving water use efficiency in agricultural fields. Therefore, various devices and equipment have been employed over the past few decades to estimate soil moisture. Their application requires consideration of several factors, including the need for calibration, accuracy of results, repeatability, spatial resolution, usability, and cost. Despite the significance of soil moisture in modeling hydrological, biogeochemical, and related dynamic processes, accurately measuring its temporal and spatial variations on a local or watershed scale presents challenges and high costs due to substantial fluctuations. Conversely, remote sensing data has provided opportunities for continuous and cost-effective monitoring of soil moisture estimates with appropriate temporal and spatial distribution, which must be controlled and calibrated with ground-based data. This study investigates and evaluates point-based sensors, including time-domain reflectometry (TDR) and the PR2 neutron probe, alongside sensors that offer suitable temporal and spatial distribution, such as Sentinel 2 and Soil Moisture Active Passive (SMAP), using weighted soil moisture measurement data. Materials and Methods In this study, soil moisture measurement as a direct method and TDR, PR2, Sentinel-2, and SMAP satellite images as indirect methods of soil moisture estimation were investigated. To assess and evaluate soil moisture sensors at the watershed scale, a part of the Telo region called Deh Sayid in Lavasanat, Tehran province, with an area of 328 hectares, was selected. Initially, due to the difficulty and high cost of measuring soil moisture by direct methods, data obtained from this method were considered the criterion for evaluating TDR and PR2 sensors. For this purpose, seven stations were established across this region in different land uses, where soil moisture monitoring was conducted through soil sampling and weight measurements in the SCWMRI laboratory, alongside simultaneous soil moisture measurements using TDR and PR2 sensors. Initially, the simultaneous data from these two sensors were evaluated against direct gravimetric observation values. Additionally, soil moisture was monitored by TDR and PR2 sensors during the passage of the Sentinel-2 and SMAP satellites from October 2020 to April 2022. During this period, TDR was used to evaluate PR2, Sentinel-2, and SMAP using statistical criteria, including correlation coefficient and percent deviation. Furthermore, considering the spatial scale of SMAP, the average of the simultaneous data from the seven stations was used for its evaluation. Results and Discussion The correlation between TDR and PR2 related to gravimetric method measurements showed that their coefficient of determination (R2) was 0.91 and 0.92, respectively, which showed a high correlation with absolute moisture values. Also, their percentage deviation was 6 and 23 percent, respectively. Therefore, TDR measured absolute soil moisture with much higher accuracy than PR2. The correlation coefficient of PR2 data with TDR varied between 0.826 and 0.933 at the monitoring stations, indicating a high correlation between them. To evaluate the moisture index of Sentinel 2 and SMAP images, simultaneous data from TDR were used due to the high accuracy of the TDR sensor. The results of the correlation analysis of the normalized differential water index (NDWI) obtained from Sentinel 2 images with TDR showed that the range of the coefficient of determination (R2) between the data of these two sensors was very high. The R² values vary from 0.003 at station P7 to 0.814 at station P2. The correlation coefficient at other stations is 0.094, 0.132, 0.587 and 0.723 at stations P3, P4, P6 and P5 respectively. However, at three stations P7, P5 and P6 the correlation coefficient is significant at the 5% level, but at three stations P2, P3 and P4 there is no significant correlation between them. The correlation coefficient of simultaneous SMAP data with the average TDR data at seven stations is 0.455 and the slope of their correlation line is 0.833. Therefore, SMAP shows more accuracy than Sentinel in estimating soil moisture Conclusion The result of this research indicated that the correlation between each of the two sensors, TDR and PR2, with gravimetric method is very high and close to each other with values of 0.956 and 0.962, respectively. Additionally, their absolute deviation is 6 and 23 percent, respectively. Consequently, TDR provides a more accurate estimate of the absolute value of soil moisture compared to PR2. Therefore, TDR is superior to PR2 as an appropriate estimator in the absence of the soil moisture data from gravimetric method in this region. Other findings from the study reveal that the indices obtained from Sentinel2 at various points yield different estimates of soil moisture; in pasture land with dense vegetation cover, the accuracy is significant, whereas in areas with tree cover, it lacks significant accuracy. Furthermore, the results demonstrated that the accuracy of Sentinel2 moisture indices is low, while SMAP offers higher accuracy for estimating moisture despite its lower spatial scale. Moreover, based on the findings of this study, the estimated absolute amount of soil moisture derived from remotely sensed data does not yet possess the necessary accuracy for practical applications, and further research is required to combine and test with other indices to enhance the accuracy of soil moisture estimates.

https://mmws.uma.ac.ir/article_3963_1211365a06b13bb1f5f00154b12a3a43.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 09 23 Identification of Flash Flood-Prone Areas in Arid and Semi-arid Regions Using Optical and Radar Imagery (Case Study: Semnan Province) Identification of Flash Flood-Prone Areas in Arid and Semi-arid Regions Using Optical and Radar Imagery (Case Study: Semnan Province) 328 350 3985 10.22098/mmws.2025.17843.1628 FA Zahra Sheikh Postdoctoral Researcher, Department of Combatting Desertification, Faculty of Desert Studies, Semnan University, Researcher in Soil Conservation and Watershed Management Research Institute (SCWMRI), Tehran, Iran 0000-0003-4271-3933 Aliasghar Zolfaghari Associate Professor, Department of Combatting Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran Journal Article 2025 07 16 Extended Abstract Introduction Flash floods are among the most destructive natural disasters, causing substantial financial and human losses worldwide each year. These phenomena primarily occur in arid and semi-arid regions and have exhibited increased frequency and intensity in recent years due to climate change and intensified human activities. The application of satellite data and imagery within advanced analytical platforms such as Google Earth Engine provides precise, near-real-time information and robust cloud computing capabilities for rapidly processing large datasets. Sentinel satellite imagery represents one of the most advanced sources of Earth observation data available. Despite the effectiveness of radar imagery, accurately distinguishing between water and sandy areas in arid regions remains challenging due to the similar backscatter signatures of these surfaces in radar wavelengths. This research aims to identify flood-prone areas in Semnan Province, located within Iran's central desert basin, using Sentinel-1, -2, and -3 imagery combined with multiple images processing techniques, including composite analysis, NDWI (Normalized Difference Water Index), automated thresholding, and band differencing, to determine the optimal approach for delineating flood zones in arid mountainous environments. Materials and Methods This study focuses on identifying and evaluating flood-affected areas following the flash flood events recorded in May 2021, using an integrated remote sensing and hydrological analysis framework. The approach was initiated with the acquisition of Sentinel-1, Sentinel-2, and Sentinel-3 satellite imagery, which facilitated the high-precision delineation of flood-prone zones. A suite of image processing techniques was subsequently applied, including Automatic Thresholding using OTSU’s method to optimize pixel classification by maximizing inter-class variance within intensity histograms. To further enhance water body detection, the Normalized Difference Water Index (NDWI) was employed, leveraging the reflectance contrast between green (560 nm) and near-infrared (842 nm) bands. Additionally, spectral band composite techniques were utilized—particularly combinations such as RGB, NIR-SWIR-Red—to improve the differentiation of land surface features including vegetation, soil moisture, water bodies, and mineralogical attributes. Complementary to the remote sensing analysis, ground-based precipitation and peak instantaneous discharge data from hydrometric stations were extracted and analyzed for validation purposes. The flood mapping results were then compared with an existing flood susceptibility map generated through machine learning models, enabling an assessment of spatial accuracy. Finally, the effectiveness of each method was evaluated to identify the most suitable approach for mapping flood extents in arid and semi-arid mountainous regions. Results and Discussion Utilizing multi-sensor satellite imagery from Sentinel-1, Sentinel-2, and Sentinel-3, this study delineated flood-affected areas within Semnan Province with high spatial and temporal precision. Sentinel-1, operating with C-band microwave radar in VH polarization, proved particularly effective for flood mapping under persistent cloud cover. Through band differencing and thresholding techniques, this sensor enabled the identification of approximately 431,835 hectares of inundated land, primarily concentrated in the northern, central, and southern parts of the province. In parallel, Sentinel-2 optical imagery, processed through rigorous cloud masking and NDWI analysis, detected around 268,000 hectares of flooding, predominantly located within depressions and low-lying terrain. Although Sentinel-3, equipped with advanced multispectral sensors such as SLSTR and OLCI, offered extensive spatial coverage—encompassing an estimated 1,005,045 hectares—its lower spatial resolution (300 m) and heightened sensitivity to clouds and atmospheric interference limited its ability to capture smaller, discrete flood patches. To enhance delineation accuracy, automated OTSU thresholding was applied to the Sentinel-1 dataset, which improved boundary detection and reduced classification noise, resulting in a refined flood estimate of approximately 467,379 hectares. Validation against hydrometric station data revealed that over 60% of flash flood events recorded during the 2020–2021 hydrological year occurred in May 2021, showing strong temporal alignment with satellite-derived flood extents. Moreover, spatial comparison with flood susceptibility maps generated from machine learning models demonstrated substantial overlap in high-risk zones, reinforcing the reliability of the remote sensing-based mapping approach. Collectively, these findings underscore the complementary strengths and inherent limitations of both radar and optical sensors in capturing flood dynamics across arid and topographically complex regions. The integration of multi-source satellite data with advanced image processing techniques significantly enhances the accuracy and credibility of flash flood detection, thereby contributing valuable insights for disaster risk mitigation in data-scarce environments. Conclusion Exacerbated extreme events such as flash floods, driven by climate variability and human activities, pose serious hazards in arid and semi-arid regions where complex environmental, climatic, and geological conditions—combined with low infiltration capacities and limited historical data—complicate hydrological assessments. In dry mountainous zones, the prevalence of gypsum and limestone soils further reduces permeability, accelerating surface runoff and intensifying flood risks. This study demonstrated that advanced cloud computing platforms such as Google Earth Engine, integrated with multispectral and radar data from Sentinel satellites, provide accurate, rapid, and cost-effective means for identifying flood-prone areas in data-scarce environments. Among the evaluated techniques, automated Otsu thresholding applied to Sentinel-1 imagery proved the most effective for delineating flood extents, yielding refined estimates of approximately 467379 hectares. Keywords: Sentinel; Image Processing; NDWI; OTSU; Semnan Extended Abstract Introduction Flash floods are among the most destructive natural disasters, causing substantial financial and human losses worldwide each year. These phenomena primarily occur in arid and semi-arid regions and have exhibited increased frequency and intensity in recent years due to climate change and intensified human activities. The application of satellite data and imagery within advanced analytical platforms such as Google Earth Engine provides precise, near-real-time information and robust cloud computing capabilities for rapidly processing large datasets. Sentinel satellite imagery represents one of the most advanced sources of Earth observation data available. Despite the effectiveness of radar imagery, accurately distinguishing between water and sandy areas in arid regions remains challenging due to the similar backscatter signatures of these surfaces in radar wavelengths. This research aims to identify flood-prone areas in Semnan Province, located within Iran's central desert basin, using Sentinel-1, -2, and -3 imagery combined with multiple images processing techniques, including composite analysis, NDWI (Normalized Difference Water Index), automated thresholding, and band differencing, to determine the optimal approach for delineating flood zones in arid mountainous environments. Materials and Methods This study focuses on identifying and evaluating flood-affected areas following the flash flood events recorded in May 2021, using an integrated remote sensing and hydrological analysis framework. The approach was initiated with the acquisition of Sentinel-1, Sentinel-2, and Sentinel-3 satellite imagery, which facilitated the high-precision delineation of flood-prone zones. A suite of image processing techniques was subsequently applied, including Automatic Thresholding using OTSU’s method to optimize pixel classification by maximizing inter-class variance within intensity histograms. To further enhance water body detection, the Normalized Difference Water Index (NDWI) was employed, leveraging the reflectance contrast between green (560 nm) and near-infrared (842 nm) bands. Additionally, spectral band composite techniques were utilized—particularly combinations such as RGB, NIR-SWIR-Red—to improve the differentiation of land surface features including vegetation, soil moisture, water bodies, and mineralogical attributes. Complementary to the remote sensing analysis, ground-based precipitation and peak instantaneous discharge data from hydrometric stations were extracted and analyzed for validation purposes. The flood mapping results were then compared with an existing flood susceptibility map generated through machine learning models, enabling an assessment of spatial accuracy. Finally, the effectiveness of each method was evaluated to identify the most suitable approach for mapping flood extents in arid and semi-arid mountainous regions. Results and Discussion Utilizing multi-sensor satellite imagery from Sentinel-1, Sentinel-2, and Sentinel-3, this study delineated flood-affected areas within Semnan Province with high spatial and temporal precision. Sentinel-1, operating with C-band microwave radar in VH polarization, proved particularly effective for flood mapping under persistent cloud cover. Through band differencing and thresholding techniques, this sensor enabled the identification of approximately 431,835 hectares of inundated land, primarily concentrated in the northern, central, and southern parts of the province. In parallel, Sentinel-2 optical imagery, processed through rigorous cloud masking and NDWI analysis, detected around 268,000 hectares of flooding, predominantly located within depressions and low-lying terrain. Although Sentinel-3, equipped with advanced multispectral sensors such as SLSTR and OLCI, offered extensive spatial coverage—encompassing an estimated 1,005,045 hectares—its lower spatial resolution (300 m) and heightened sensitivity to clouds and atmospheric interference limited its ability to capture smaller, discrete flood patches. To enhance delineation accuracy, automated OTSU thresholding was applied to the Sentinel-1 dataset, which improved boundary detection and reduced classification noise, resulting in a refined flood estimate of approximately 467,379 hectares. Validation against hydrometric station data revealed that over 60% of flash flood events recorded during the 2020–2021 hydrological year occurred in May 2021, showing strong temporal alignment with satellite-derived flood extents. Moreover, spatial comparison with flood susceptibility maps generated from machine learning models demonstrated substantial overlap in high-risk zones, reinforcing the reliability of the remote sensing-based mapping approach. Collectively, these findings underscore the complementary strengths and inherent limitations of both radar and optical sensors in capturing flood dynamics across arid and topographically complex regions. The integration of multi-source satellite data with advanced image processing techniques significantly enhances the accuracy and credibility of flash flood detection, thereby contributing valuable insights for disaster risk mitigation in data-scarce environments. Conclusion Exacerbated extreme events such as flash floods, driven by climate variability and human activities, pose serious hazards in arid and semi-arid regions where complex environmental, climatic, and geological conditions—combined with low infiltration capacities and limited historical data—complicate hydrological assessments. In dry mountainous zones, the prevalence of gypsum and limestone soils further reduces permeability, accelerating surface runoff and intensifying flood risks. This study demonstrated that advanced cloud computing platforms such as Google Earth Engine, integrated with multispectral and radar data from Sentinel satellites, provide accurate, rapid, and cost-effective means for identifying flood-prone areas in data-scarce environments. Among the evaluated techniques, automated Otsu thresholding applied to Sentinel-1 imagery proved the most effective for delineating flood extents, yielding refined estimates of approximately 467379 hectares. Keywords: Sentinel; Image Processing; NDWI; OTSU; Semnan

https://mmws.uma.ac.ir/article_3985_6a082e1041b870d896ff80605b41f8eb.pdf

University of Mohaghegh Ardabili Water and Soil Management and Modelling 2783-2546 5 3 2025 08 03 Meteorological drought monitoring based on SPI and mRAI indices in the Urmia Lake basin Meteorological drought monitoring based on SPI and mRAI indices in the Urmia Lake basin 351 373 3961 10.22098/mmws.2025.17802.1625 FA Sepideh Hadipour Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran Mahdi Erfanian Department of Range and Watershed Management, Faculty of Natural Resources Urmia University Sima Kazempour Choursi Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran Journal Article 2025 07 11 Extended Abstract Introduction Drought, a pervasive meteorological phenomenon, is driven by insufficient precipitation and linked to climatic factors. Its escalating frequency challenges natural resource management, water security, and mitigation efforts. This complex hazard is categorized into four types: meteorological, agricultural, hydrological, and socio-economic. Accurate drought monitoring relies on robust indicators, such as the Standardized Precipitation Index (SPI) and the modified Rainfall Anomaly Index (mRAI), both of which are precipitation-dependent. Acquiring reliable, spatially representative rainfall series remains a global challenge. The Global Precipitation Climatology Centre (GPCC) global dataset provides a crucial, quality-controlled, rain-gauge-based resource that addresses this data scarcity. Given Iran's limited and unevenly distributed rain gauge stations, global datasets like GPCC are indispensable. Previous research has confirmed the efficacy of the GPCC data in conjunction with the SPI and mRAI indices for comprehensive spatial and temporal drought analysis. Building on this, this research generates high-resolution drought severity maps for the Urmia Lake basin across multiple timescales using GPCC monthly precipitation data and both SPI and mRAI indices. This provides an essential tool for proactive drought monitoring in this ecologically sensitive, water-stressed region. The Urmia Lake basin, a vital ecological and economic area, has faced severe and prolonged drought, resulting in a dramatic decline in lake levels, increased dust storms, and significant socio-economic challenges. The urgent need for accurate and timely drought monitoring is thus highlighted. Effective monitoring is essential for developing sustainable water management strategies and mitigating adverse impacts on this vulnerable ecosystem. This research directly addresses this critical need by providing a substantial approach for informed management. Materials and Methods Drought analysis for the Urmia Lake Basin utilized monthly precipitation data from synoptic stations and the global GPCC dataset. The accuracy of GPCC data in estimating monthly precipitation at the studied synoptic stations was rigorously evaluated using R², RMSE, MAE, and PBIAS over the 1991–2020 period. Subsequently, raster maps of monthly precipitation for the Urmia Lake Basin were prepared using GPCC data for the same period. Drought severity maps of the Urmia Lake basin were then produced based on SPI and mRAI indices at 1-, 3-, 6-, 9-, and 12-month time scales, using GPCC precipitation data from 1991 to 2020. A comparative analysis identified critically vulnerable areas across drought severity classes (weak, moderate, severe, and very severe). Furthermore, the agreement between SPI and mRAI severity classes at different time scales was quantitatively assessed using the Kappa statistic and Cramer's V coefficient. Results and Discussion Evaluation results conclusively demonstrated the acceptable accuracy of GPCC data in estimating monthly precipitation at all studied synoptic stations (R² = 0.91, RMSE = 10.83). Analysis of the 30 years revealed consistently the highest average monthly rainfall in the western, southwestern, and southern regions of the basin across all investigated time scales. A direct comparison of drought index values at the Urmia and Tabriz stations revealed strong concordance between the SPI and mRAI indices at time scales of 3, 6, 9, and 12 months. Quantitatively, Kappa and Cramer's V coefficient values at these time scales across all stations were notably high: 0.851 and 0.837 (3 months), 0.863 and 0.847 (6 months), 0.867 and 0.850 (9 months), and 0.929 and 0.912 (12 months), respectively. These robust statistical measures confirm a significant relationship between the SPI and mRAI drought indices for assessing drought conditions at both individual and collective stations across varying time scales. Importantly, SPI-3 and mRAI-3 indices identified moderate drought affecting most areas of the Urmia Lake Basin in October 1995, 2001, and 2002; from October to December 2010; and in October and November 2019. Furthermore, based on SPI-6 and mRAI6 indices, the entire Urmia Lake basin experienced mild to severe drought, with extreme drought in localized regions, from October to November of the mentioned years (excluding December 2002). Conclusion The accuracy of GPCC data for monthly precipitation estimation at Urmia Lake Basin synoptic stations is confirmed. This validates GPCC as a foundational dataset for calculating SPI and mRAI drought indices at 3-, 6-, 9-, and 12-month time scales, enabling detailed delineation of drought severity zones across the basin (1991–2020). A critical finding is the consistent strong agreement between SPI and mRAI indices across different time scales, demonstrated by index value comparisons. High Kappa and Cramer's V coefficients further substantiate this correlation in drought class identification. Essentially, the spatial distribution of drought severity, as measured by both SPI and mRAI (at 3, 6, 9, and 12-month scales), shows a compelling agreement throughout the 1991–2020 period. Despite the utility of the GPCC data, potential estimation errors from synoptic stations necessitate rigorous calibration using advanced methods, such as linear and quantile regression. Given GPCC's 0.25-degree spatial resolution, future research should downscale this dataset, e.g., via geographically weighted regression, to identify finer-resolution drought zones, which is crucial for localized water resource management and planning in the Urmia Lake basin. Extended Abstract Introduction Drought, a pervasive meteorological phenomenon, is driven by insufficient precipitation and linked to climatic factors. Its escalating frequency challenges natural resource management, water security, and mitigation efforts. This complex hazard is categorized into four types: meteorological, agricultural, hydrological, and socio-economic. Accurate drought monitoring relies on robust indicators, such as the Standardized Precipitation Index (SPI) and the modified Rainfall Anomaly Index (mRAI), both of which are precipitation-dependent. Acquiring reliable, spatially representative rainfall series remains a global challenge. The Global Precipitation Climatology Centre (GPCC) global dataset provides a crucial, quality-controlled, rain-gauge-based resource that addresses this data scarcity. Given Iran's limited and unevenly distributed rain gauge stations, global datasets like GPCC are indispensable. Previous research has confirmed the efficacy of the GPCC data in conjunction with the SPI and mRAI indices for comprehensive spatial and temporal drought analysis. Building on this, this research generates high-resolution drought severity maps for the Urmia Lake basin across multiple timescales using GPCC monthly precipitation data and both SPI and mRAI indices. This provides an essential tool for proactive drought monitoring in this ecologically sensitive, water-stressed region. The Urmia Lake basin, a vital ecological and economic area, has faced severe and prolonged drought, resulting in a dramatic decline in lake levels, increased dust storms, and significant socio-economic challenges. The urgent need for accurate and timely drought monitoring is thus highlighted. Effective monitoring is essential for developing sustainable water management strategies and mitigating adverse impacts on this vulnerable ecosystem. This research directly addresses this critical need by providing a substantial approach for informed management. Materials and Methods Drought analysis for the Urmia Lake Basin utilized monthly precipitation data from synoptic stations and the global GPCC dataset. The accuracy of GPCC data in estimating monthly precipitation at the studied synoptic stations was rigorously evaluated using R², RMSE, MAE, and PBIAS over the 1991–2020 period. Subsequently, raster maps of monthly precipitation for the Urmia Lake Basin were prepared using GPCC data for the same period. Drought severity maps of the Urmia Lake basin were then produced based on SPI and mRAI indices at 1-, 3-, 6-, 9-, and 12-month time scales, using GPCC precipitation data from 1991 to 2020. A comparative analysis identified critically vulnerable areas across drought severity classes (weak, moderate, severe, and very severe). Furthermore, the agreement between SPI and mRAI severity classes at different time scales was quantitatively assessed using the Kappa statistic and Cramer's V coefficient. Results and Discussion Evaluation results conclusively demonstrated the acceptable accuracy of GPCC data in estimating monthly precipitation at all studied synoptic stations (R² = 0.91, RMSE = 10.83). Analysis of the 30 years revealed consistently the highest average monthly rainfall in the western, southwestern, and southern regions of the basin across all investigated time scales. A direct comparison of drought index values at the Urmia and Tabriz stations revealed strong concordance between the SPI and mRAI indices at time scales of 3, 6, 9, and 12 months. Quantitatively, Kappa and Cramer's V coefficient values at these time scales across all stations were notably high: 0.851 and 0.837 (3 months), 0.863 and 0.847 (6 months), 0.867 and 0.850 (9 months), and 0.929 and 0.912 (12 months), respectively. These robust statistical measures confirm a significant relationship between the SPI and mRAI drought indices for assessing drought conditions at both individual and collective stations across varying time scales. Importantly, SPI-3 and mRAI-3 indices identified moderate drought affecting most areas of the Urmia Lake Basin in October 1995, 2001, and 2002; from October to December 2010; and in October and November 2019. Furthermore, based on SPI-6 and mRAI6 indices, the entire Urmia Lake basin experienced mild to severe drought, with extreme drought in localized regions, from October to November of the mentioned years (excluding December 2002). Conclusion The accuracy of GPCC data for monthly precipitation estimation at Urmia Lake Basin synoptic stations is confirmed. This validates GPCC as a foundational dataset for calculating SPI and mRAI drought indices at 3-, 6-, 9-, and 12-month time scales, enabling detailed delineation of drought severity zones across the basin (1991–2020). A critical finding is the consistent strong agreement between SPI and mRAI indices across different time scales, demonstrated by index value comparisons. High Kappa and Cramer's V coefficients further substantiate this correlation in drought class identification. Essentially, the spatial distribution of drought severity, as measured by both SPI and mRAI (at 3, 6, 9, and 12-month scales), shows a compelling agreement throughout the 1991–2020 period. Despite the utility of the GPCC data, potential estimation errors from synoptic stations necessitate rigorous calibration using advanced methods, such as linear and quantile regression. Given GPCC's 0.25-degree spatial resolution, future research should downscale this dataset, e.g., via geographically weighted regression, to identify finer-resolution drought zones, which is crucial for localized water resource management and planning in the Urmia Lake basin.

https://mmws.uma.ac.ir/article_3961_b309654c21f06759c85debfdbb7694c0.pdf