University of Tehran PressJournal of Ecohydrology2423-609811220240722Sub-watersheds Hydrological Security Prioritization of the Gorganroud Watershed based on Indicators of Hydrologic Alteration (IHA)Sub-watersheds Hydrological Security Prioritization of the Gorganroud Watershed based on Indicators of Hydrologic Alteration (IHA)1932069799110.22059/ije.2024.374860.1812FAMohammadTavosiPhD student, Department of Watershed Engineering, Faculty of Natural Resources and Marine Sciences,
Tarbiat Modares University, Noor, Iran.MehxiVafakhahProfessor and Scientific member, Department of Watershed Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, IranSeyed HamidrezaSadeghiProfessor and Scientific member, Department of Watershed Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, IranSayed M.BateniDepartment of Civil, Environmental, and Construction Engineering, and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, 96822.Journal Article20240409To do this, 31 hydrological variables of streamflow in 16 sub-watersheds of the Gorganroud watershed, which were equipped with hydrometric stations, were calculated using Indicators of Hydrological Change (IHA) software. Shannon's entropy was used to weight the hydrological variables of the streamflow. Finally, four Multi-Criteria Decision-Making Methods (MCDMs) including Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VlseKriterijumska Optimizcija I Kaompromisno Resenje (VIKOR), Simple Additive Weighting (SAW) and COmplex PRoportional Assessment (COPRAS) were implemented to prioritize the subwatersheds. The results showed that C23 (zero flow days) and C27 (high pulse count) variables received the highest and lowest weights with values of 0.057 and 0.006, respectively. The results of the subwatersheds prioritization based on the hydrological security showed that subwatershed S11 according to TOPSIS and VIKOR methods and subwatersheds S15 and S2 according to SAW and COPRAS methods have the highest hydrological security score. On the other hand, subwatershed S10 according to TOPSIS, VIKOR and COPRAS methods and subwatershed S14 according to SAW method were given the last priority. Evaluating the efficiency of MCDM methods showed that the VIKOR method provided more accurate results with the least intensity of variations.To do this, 31 hydrological variables of streamflow in 16 sub-watersheds of the Gorganroud watershed, which were equipped with hydrometric stations, were calculated using Indicators of Hydrological Change (IHA) software. Shannon's entropy was used to weight the hydrological variables of the streamflow. Finally, four Multi-Criteria Decision-Making Methods (MCDMs) including Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VlseKriterijumska Optimizcija I Kaompromisno Resenje (VIKOR), Simple Additive Weighting (SAW) and COmplex PRoportional Assessment (COPRAS) were implemented to prioritize the subwatersheds. The results showed that C23 (zero flow days) and C27 (high pulse count) variables received the highest and lowest weights with values of 0.057 and 0.006, respectively. The results of the subwatersheds prioritization based on the hydrological security showed that subwatershed S11 according to TOPSIS and VIKOR methods and subwatersheds S15 and S2 according to SAW and COPRAS methods have the highest hydrological security score. On the other hand, subwatershed S10 according to TOPSIS, VIKOR and COPRAS methods and subwatershed S14 according to SAW method were given the last priority. Evaluating the efficiency of MCDM methods showed that the VIKOR method provided more accurate results with the least intensity of variations.https://ije.ut.ac.ir/article_97991_13caafe86677d6759e57c9f4037d8878.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811220240621Investigating the Relationship Between Changes in Leaf Area Index and Soil Moisture Using Remote Sensing and Field Studies: A Case Study of the Beheshtabad WatershedInvestigating the Relationship Between Changes in Leaf Area Index and Soil Moisture Using Remote Sensing and Field Studies: A Case Study of the Beheshtabad Watershed2072229799810.22059/ije.2024.378735.1831FAElhamDavoodi,Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, IranKhodayarAbdollahiFaculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, IrandHodaGhasemiehDepartment of Nature engineering, Faculty of Natural Resources and Earth sciences, University of KashanJournal Article20240406This study examines the relationship between Leaf Area Index and soil moisture in the Beheshtabad watershed through field sampling, MODIS imagery, and black-box modeling based on various factors. For this purpose, climate data such as rainfall, evaporation, transpiration, number of rainy days, and temperature were collected from 2003 to 2015 in this watershed. Additionally, to determine the physical characteristics of the area and prepare maps of soil moisture, data on soil texture, land use, topography, geology, Digital Elevation Model, and drainage network were gathered. During the field visits in 2016 and 2017, data on soil moisture, Leaf Area Index, and vegetation characteristics were collected for the land use in the area. The findings indicate that vegetation cover requires time to respond to changes in soil moisture, with a developmental delay of approximately four months observed in the study area (coefficient of determination = 0.69). Land use, slope, and soil texture separation factors have differing impacts on the relationship between Leaf Area Index and soil moisture, which is nonlinear. This study highlights the importance of understanding the reciprocal effects between environmental factors and vegetation cover for water and soil resource management.This study examines the relationship between Leaf Area Index and soil moisture in the Beheshtabad watershed through field sampling, MODIS imagery, and black-box modeling based on various factors. For this purpose, climate data such as rainfall, evaporation, transpiration, number of rainy days, and temperature were collected from 2003 to 2015 in this watershed. Additionally, to determine the physical characteristics of the area and prepare maps of soil moisture, data on soil texture, land use, topography, geology, Digital Elevation Model, and drainage network were gathered. During the field visits in 2016 and 2017, data on soil moisture, Leaf Area Index, and vegetation characteristics were collected for the land use in the area. The findings indicate that vegetation cover requires time to respond to changes in soil moisture, with a developmental delay of approximately four months observed in the study area (coefficient of determination = 0.69). Land use, slope, and soil texture separation factors have differing impacts on the relationship between Leaf Area Index and soil moisture, which is nonlinear. This study highlights the importance of understanding the reciprocal effects between environmental factors and vegetation cover for water and soil resource management.https://ije.ut.ac.ir/article_97998_61a3a6a5270aaf50efb9a58e8159094b.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811220240621Data analysis of the role of air pollutants (nitrate and nitrite oxide) in temperature changes and precipitation of Tabriz synoptic station using multi-layer perceptron neural network machine learning and logistic regressionData analysis of the role of air pollutants (nitrate and nitrite oxide) in temperature changes and precipitation of Tabriz synoptic station using multi-layer perceptron neural network machine learning and logistic regression2232349808610.22059/ije.2024.373685.1803FABehroozSari Sarraffaculty of planning and environment sciencesuMaryamBayatikatibiuni tabrizMozaffarFarajiuni tabrizJournal Article20240408The purpose of this research is to analyze the role of air pollutants (nitrate and nitrite oxide) in the changes of 24-hour temperature and precipitation elements in Tabriz synoptic station. The materials and data used in this research are from two different sources. The temperature and precipitation data were obtained from the tabriz synoptic meteorological station hourly for a period of 31 years and the data of Tabriz air pollutants (nitrate and nitrite oxide) were obtained from Tabriz environmental organization. In connection with the air pollutant data, it can be said that these data have been simulated by the multi-layer perceptron neural network machine learning R programming language. In the logistic model, temperature and precipitation were selected as dependent variables and nitrate and nitrite oxide concentrations were selected as independent predictor variables. All data were included in the analysis and the logistic model was significant. The chi-square in nitrate and nitrite oxide was calculated as 348.01, which was significant at the error level of less than 0.05. The aforementioned independent variables have been able to correctly explain between 84 and 60 percent of the changes that led to the increase in temperature and decrease in precipitation. 78.2% of the months that had no changes were correctly classified, and 97.2% of the predictions about temperature and precipitation changes were correct. In total, 90.9% of the predictions have been estimated correctly. . The results showed that pollutants have a significant effect on temperature increase and precipitation decrease in Tabriz synoptic station. The highest and lowest levels of nitrate oxide were observed in September and March, nitrite in September and May, temperature in July and January, and precipitation in April and August, respectivelyThe purpose of this research is to analyze the role of air pollutants (nitrate and nitrite oxide) in the changes of 24-hour temperature and precipitation elements in Tabriz synoptic station. The materials and data used in this research are from two different sources. The temperature and precipitation data were obtained from the tabriz synoptic meteorological station hourly for a period of 31 years and the data of Tabriz air pollutants (nitrate and nitrite oxide) were obtained from Tabriz environmental organization. In connection with the air pollutant data, it can be said that these data have been simulated by the multi-layer perceptron neural network machine learning R programming language. In the logistic model, temperature and precipitation were selected as dependent variables and nitrate and nitrite oxide concentrations were selected as independent predictor variables. All data were included in the analysis and the logistic model was significant. The chi-square in nitrate and nitrite oxide was calculated as 348.01, which was significant at the error level of less than 0.05. The aforementioned independent variables have been able to correctly explain between 84 and 60 percent of the changes that led to the increase in temperature and decrease in precipitation. 78.2% of the months that had no changes were correctly classified, and 97.2% of the predictions about temperature and precipitation changes were correct. In total, 90.9% of the predictions have been estimated correctly. . The results showed that pollutants have a significant effect on temperature increase and precipitation decrease in Tabriz synoptic station. The highest and lowest levels of nitrate oxide were observed in September and March, nitrite in September and May, temperature in July and January, and precipitation in April and August, respectivelyhttps://ije.ut.ac.ir/article_98086_ac143ed6aa69e2ddaa8673bcd1ed5f69.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811220240621Using the deep learning approach to increase the efficiency of the alluvial aquifer vulnerability index
(Case study: Coastal aquifer: Babol-Amol)Using the deep learning approach to increase the efficiency of the alluvial aquifer vulnerability index
(Case study: Coastal aquifer: Babol-Amol)2352569816610.22059/ije.2024.373124.1801FAHadisKaramiEnvironment faculty University of TehranBahramMalekmohammadiAssociate Professor, School of Environment, College of Engineering,
University of Tehran, Tehran, IRANSamanJavadiDepartment of water engineering University of TehranJournal Article20240408This research aims to evaluate the vulnerability of aquifers by comparing two approaches of deep learning and machine learning in index calibration. Therefore, by analyzing the inherent vulnerability of the Amol-Babol aquifer with the DRASTIC index, the sensitive areas of the aquifer were identified. The results of the vulnerability index showed that the northwestern part of the aquifer is more sensitive than other areas. Examining the correlation value between nitrate concentration as an effective index with the DRASTIC vulnerability index indicates a value of 24%, which indicated the need for recalibration. Therefore, with two CNN-Harris Hawks and LSTM-MPA methods as deep learning approaches, weighting and index ranks were carried out as decision variables with the aim of maximizing the correlation of nitrate concentration and vulnerability index. The results showed that the CNN-HHO method with a correlation of 0.62 is superior to the LSTM-MPA method with a correlation of 0.59. Vulnerability zones in the assessment conditions showed that the western and northeastern parts have higher vulnerability. On the other hand, the recalibrated weights and ranks indicate an increase in all weights and ranks in recalibration conditions compared to the initial conditions, which was determined after analyzing the optimization approachesThis research aims to evaluate the vulnerability of aquifers by comparing two approaches of deep learning and machine learning in index calibration. Therefore, by analyzing the inherent vulnerability of the Amol-Babol aquifer with the DRASTIC index, the sensitive areas of the aquifer were identified. The results of the vulnerability index showed that the northwestern part of the aquifer is more sensitive than other areas. Examining the correlation value between nitrate concentration as an effective index with the DRASTIC vulnerability index indicates a value of 24%, which indicated the need for recalibration. Therefore, with two CNN-Harris Hawks and LSTM-MPA methods as deep learning approaches, weighting and index ranks were carried out as decision variables with the aim of maximizing the correlation of nitrate concentration and vulnerability index. The results showed that the CNN-HHO method with a correlation of 0.62 is superior to the LSTM-MPA method with a correlation of 0.59. Vulnerability zones in the assessment conditions showed that the western and northeastern parts have higher vulnerability. On the other hand, the recalibrated weights and ranks indicate an increase in all weights and ranks in recalibration conditions compared to the initial conditions, which was determined after analyzing the optimization approacheshttps://ije.ut.ac.ir/article_98166_3755e759cecb24a233dadcfa3792b272.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811220240621Investigation of Factors Affecting the Drying of Wetlands and Preparation of a
Cognitive Map of Factors Affecting the Dust Phenomenon in Southwest IranInvestigation of Factors Affecting the Drying of Wetlands and Preparation of a
Cognitive Map of Factors Affecting the Dust Phenomenon in Southwest Iran2572709819610.22059/ije.2024.377518.1825FANargeskhatoonDowlatabadiDepartment of Irrigation and Drainage, College of Aburaihan , University of TehranSajadNajafiDepartment of irrigation and driange college of Aburaihan. university of TehranHamidKardan MoghaddamDepartment of Water resources research, Water research institute, Ministry of energy, Tehran, IranMahsaJabbari MalayeriDepartment of Water Engineering, College of Aburaihan, University of Tehran, Iran.Seyed Mohammad SadeghEslamiCivil Engineering Department, Faculty of Engineering, Birjand of University, Birjand, IranJournal Article20240604The phenomenon of dust in the western border of the country has increased in terms of continuity and concentration, disrupting the ecological potential of this region. In addition to affecting infrastructure and causing social problems, it also adversely affects people's health. To identify the factors affecting this phenomenon, the SODA approach was used as a simple technique with a cognitive mapping approach. Cognitive mapping can show the importance of a particular factor based on the values of all factors affecting a phenomenon. In this study, seven experts in the field of water resources were interviewed, and<br />after reaching a consensus, nine factors were identified as the most effective in the occurrence of this phenomenon. The results of the analysis, using concepts related to graph theory, determined the relative activity (centrality) of nodes and the formation of a proximity matrix. This showed that the mismanagement of water resources, the lack of attention to watershed management in the region, and the unilateral development of Turkey in the area—including the construction of numerous dams upstream of the Tigris River—were identified as the most active nodes and factors. These two factors had the highest impact, with a relative weight of 0.015 in terms of prioritization. On the other hand, the analysis showed that the Iran-Iraq war and the security strategies of the Ba'athist regime in Iraq were identified as the factors with the least activity with a relative weight of 0.007 in the dust issue. Finally, this approach can lead to the extraction of practical and applied strategies to solve environmental problems such as dust.The phenomenon of dust in the western border of the country has increased in terms of continuity and concentration, disrupting the ecological potential of this region. In addition to affecting infrastructure and causing social problems, it also adversely affects people's health. To identify the factors affecting this phenomenon, the SODA approach was used as a simple technique with a cognitive mapping approach. Cognitive mapping can show the importance of a particular factor based on the values of all factors affecting a phenomenon. In this study, seven experts in the field of water resources were interviewed, and<br />after reaching a consensus, nine factors were identified as the most effective in the occurrence of this phenomenon. The results of the analysis, using concepts related to graph theory, determined the relative activity (centrality) of nodes and the formation of a proximity matrix. This showed that the mismanagement of water resources, the lack of attention to watershed management in the region, and the unilateral development of Turkey in the area—including the construction of numerous dams upstream of the Tigris River—were identified as the most active nodes and factors. These two factors had the highest impact, with a relative weight of 0.015 in terms of prioritization. On the other hand, the analysis showed that the Iran-Iraq war and the security strategies of the Ba'athist regime in Iraq were identified as the factors with the least activity with a relative weight of 0.007 in the dust issue. Finally, this approach can lead to the extraction of practical and applied strategies to solve environmental problems such as dust.https://ije.ut.ac.ir/article_98196_a58784dfb9b8c341f2230c883f89c492.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811220240621Investigating the effectiveness of data mining methods in predicting daily reference evapotranspiration (Case study: coastal strip stations in southern Iran)Investigating the effectiveness of data mining methods in predicting daily reference evapotranspiration (Case study: coastal strip stations in southern Iran)2712869819710.22059/ije.2024.375755.1816FAHalimehPiriAssociate Professor, Department of Water Engineering, Faculty of Water and Soil, University of ZabolJournal Article20240403Nonlinear relationships, inherent uncertainty, and the need for a lot of climate information in estimating evapotranspiration have made researchers use data-mining methods to estimate evapotranspiration in recent decades. The purpose of this research is to investigate the efficiency of data mining methods of support vector machine, decision tree, random forest and Gaussian process regression in forecasting the daily reference evapotranspiration of coastal strip stations in the south of the country. To do the work, daily reference evapotranspiration was calculated using 20year climatic data (2001-2021) using the Fao-Penman-Monteith method. Then, using these data as output data, 6 combined scenarios were evaluated based on the correlation between meteorological variables and reference evaporation-transpiration using data mining methods. The results of the investigations showed that all four data mining methods were able to estimate the reference evaporation-transpiration values in the studied areas.In all four stations, the Gaussian process regression method with the highest R2 value and the lowest RMSE and MAE values had a better estimate of the reference evapotranspiration values, and random forest, decision tree, and support vector machine methods were in the next ranks respectively. Gaussian process regression model in estimating reference evapotranspiration, this method is recommended for estimating reference evapotranspirationNonlinear relationships, inherent uncertainty, and the need for a lot of climate information in estimating evapotranspiration have made researchers use data-mining methods to estimate evapotranspiration in recent decades. The purpose of this research is to investigate the efficiency of data mining methods of support vector machine, decision tree, random forest and Gaussian process regression in forecasting the daily reference evapotranspiration of coastal strip stations in the south of the country. To do the work, daily reference evapotranspiration was calculated using 20year climatic data (2001-2021) using the Fao-Penman-Monteith method. Then, using these data as output data, 6 combined scenarios were evaluated based on the correlation between meteorological variables and reference evaporation-transpiration using data mining methods. The results of the investigations showed that all four data mining methods were able to estimate the reference evaporation-transpiration values in the studied areas.In all four stations, the Gaussian process regression method with the highest R2 value and the lowest RMSE and MAE values had a better estimate of the reference evapotranspiration values, and random forest, decision tree, and support vector machine methods were in the next ranks respectively. Gaussian process regression model in estimating reference evapotranspiration, this method is recommended for estimating reference evapotranspirationhttps://ije.ut.ac.ir/article_98197_e19bd1cc617621e42f1679b6a36a7f4d.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811220240621Investigating the Nexus Dynamics of Water, Energy, Food in the Hirmand Basin with the approach of Water Diplomacy under Management ScenariosInvestigating the Nexus Dynamics of Water, Energy, Food in the Hirmand Basin with the approach of Water Diplomacy under Management Scenarios2873009830910.22059/ije.2024.374822.1810FAFazelGhobaishaviPh.D. Student, Department of Agricultural Economics, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran.AliSardar ShahrakiUniversity of Sistan and Baluchestan, Faculty of Management and Economics, Zahedan, IranMahdiSafdariUniversity of Sistan and Baluchestan. Faculty of Management and Economic. Zaheddan IranNedaAli AhmadiAgricultural Economics, Faculty of Economics and Management, Sistan and Baluchestan University, Zahedan, IranJournal Article20240406The issue of water in the cross-border region of Hirmand has affected the relations between Iran and Afghanistan as a challenge, considering the wide-ranging effects on food, water, economic and environmental security. Using a proper management and policy in water diplomacy, in order to achieve security to security resources. Close to water, energy, food and the mutual influence that their combination has caused the formation of a conceptual concept called interrelated, which play an important role in achieving sustainable development. The purpose of this research is to investigate the dynamics of water, energy, and food correlation in the Hirmand watershed with water diplomacy studies under management scenarios that are not considered in other previous researches. In this research, using the dynamic system method, various relationships and feedbacks in subsystems have been defined using storages and flows. In the next step, after reviewing and evaluating the presented model, simulation has been done using statistical tests and historical scenarios related to the years 1379-1400. According to the results obtained from the ten-year simulation, by applying the scenario of increasing irrigation efficiency from 35% to 70% without increasing the cultivation area, agricultural production in Sistan region has increased compared to the base model. This increase in the production of agricultural products from 77.65 to 92.69 thousand tons shows an increase in the efficiency of water consumption. The implementation of the simulation and development of the cultivated area of Sanafeq 1404, in Hirmand catchment area, increases the security of agricultural products in 1410 compared to 1379 by the amount of 227,900 tons. Based on the results of the current research, improving water efficiency is one of the most important tools in the combined analysis of the scenario of increasing irrigation efficiency and the development of cultivated area in the horizon of 1404. Therefore, according to the obtained results, the use of water diplomacy along with the dynamic modeling of resources is related to the management of water issues and conflicts in the Hirmand watershed. Due to the Sistan region to the Hirmand Trans boundary River, the connection of water, energy and food with the strengthening of water diplomacy in order to reduce tension and sustainable management, the attention of policy makers and planners should be focused on the Hirmand watershed to protect the Sistan plain from serious damage from the future water crisis and disasters. Looked environmentally safe.The issue of water in the cross-border region of Hirmand has affected the relations between Iran and Afghanistan as a challenge, considering the wide-ranging effects on food, water, economic and environmental security. Using a proper management and policy in water diplomacy, in order to achieve security to security resources. Close to water, energy, food and the mutual influence that their combination has caused the formation of a conceptual concept called interrelated, which play an important role in achieving sustainable development. The purpose of this research is to investigate the dynamics of water, energy, and food correlation in the Hirmand watershed with water diplomacy studies under management scenarios that are not considered in other previous researches. In this research, using the dynamic system method, various relationships and feedbacks in subsystems have been defined using storages and flows. In the next step, after reviewing and evaluating the presented model, simulation has been done using statistical tests and historical scenarios related to the years 1379-1400. According to the results obtained from the ten-year simulation, by applying the scenario of increasing irrigation efficiency from 35% to 70% without increasing the cultivation area, agricultural production in Sistan region has increased compared to the base model. This increase in the production of agricultural products from 77.65 to 92.69 thousand tons shows an increase in the efficiency of water consumption. The implementation of the simulation and development of the cultivated area of Sanafeq 1404, in Hirmand catchment area, increases the security of agricultural products in 1410 compared to 1379 by the amount of 227,900 tons. Based on the results of the current research, improving water efficiency is one of the most important tools in the combined analysis of the scenario of increasing irrigation efficiency and the development of cultivated area in the horizon of 1404. Therefore, according to the obtained results, the use of water diplomacy along with the dynamic modeling of resources is related to the management of water issues and conflicts in the Hirmand watershed. Due to the Sistan region to the Hirmand Trans boundary River, the connection of water, energy and food with the strengthening of water diplomacy in order to reduce tension and sustainable management, the attention of policy makers and planners should be focused on the Hirmand watershed to protect the Sistan plain from serious damage from the future water crisis and disasters. Looked environmentally safe.https://ije.ut.ac.ir/article_98309_219478e923a9da8dfe4640b10be7e92d.pdf