University of Tehran PressJournal of Ecohydrology2423-609811320240922Consequences analysis of hydro-climatic and land use changes on Bakhtegan Lake surface changes using Landsat satellite imagesConsequences analysis of hydro-climatic and land use changes on Bakhtegan Lake surface changes using Landsat satellite images3013209885710.22059/ije.2024.373629.1802FAZahraNosarvM.S. Graduated, International Desert Research Center, University of Tehran, Karaj, IranAmirrezaKeshtkarAssociate Prof., International Desert Research Center, University of Tehran, Karaj, IranSaedHamzehAssociate Prof., Faculty of Geography, University of Tehran, Tehran, IranHamidrezaKeshtkarAssistant Prof., Faculty of Natural Resources, University of Tehran, Karaj, IranOmidKavoosiElite Senior Expert, International Desert Research Center, University of Tehran, Karaj, IranJournal Article20240630Lakes, as one of the most important aquatic ecosystems, are a coherent and interconnected set of aquatics or water-dependent plants, and the interference of natural and unnatural factors can disrupt this coherence and quality. One of the efficient methods of monitoring the changes in these ecosystems is remote sensing, which helps to monitor the changes caused by nature and human activities on lakes. The current research was conducted to study the consequences of climate and land use changes on the surface of Bakhtegan Lake over 18 years (2000 to 2017). To monitor the surface fluctuations of Bakhtegan Lake, Landsat satellite images were used in the research period, in the months with the highest surface (May and June) and the lowest surface (August and September). Lake surface and various land use areas have been extracted through a multi-band classification method and using the Support Vector Machine (SVM) method. Then, statistical methods were used to determine of impact of each variable on changes in the lake's surface. Results indicated that the surface of Bakhtegan Lake has experienced periodic fluctuations during different years and precipitation and evaporation variables have an impact on the lake surface.Lakes, as one of the most important aquatic ecosystems, are a coherent and interconnected set of aquatics or water-dependent plants, and the interference of natural and unnatural factors can disrupt this coherence and quality. One of the efficient methods of monitoring the changes in these ecosystems is remote sensing, which helps to monitor the changes caused by nature and human activities on lakes. The current research was conducted to study the consequences of climate and land use changes on the surface of Bakhtegan Lake over 18 years (2000 to 2017). To monitor the surface fluctuations of Bakhtegan Lake, Landsat satellite images were used in the research period, in the months with the highest surface (May and June) and the lowest surface (August and September). Lake surface and various land use areas have been extracted through a multi-band classification method and using the Support Vector Machine (SVM) method. Then, statistical methods were used to determine of impact of each variable on changes in the lake's surface. Results indicated that the surface of Bakhtegan Lake has experienced periodic fluctuations during different years and precipitation and evaporation variables have an impact on the lake surface.https://ije.ut.ac.ir/article_98857_34c838c814e55b84e9b211c22ff2cd36.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811320240922Water Bodies Extraction from Remote Sensing Data by Comparison of Deep Learning ModelsWater Bodies Extraction from Remote Sensing Data by Comparison of Deep Learning Models3213369885610.22059/ije.2024.378101.1829FASinaKhoshnevisanMSc Student, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, IranSaeidGharechelouAssistant Professor, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, IranRezaMortazaviAssistant Professor, School of Engineering, Damghan University, Damghan, Iran.FatemehKhakzadMSc Student, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, IranJournal Article20240708In the current century, increasing of greenhouse gases has led to significant changes in the climate. It is causing irreversible impacts on agricultural lands, food production, and drinking water supply. By using remote sensing technology and the processing of satellite data, aerial and drone imagery to collect information from the Earth surface, environmental changes monitoring and analyze water bodies has become an effective tool for planning and optimal management of water resources. Modern and interdisciplinary technologies have enabled water resource specialists to accurately identify, mapping and assess surface water resources. In this study, with the aim of identifying small water bodies using remote sensing data, four deep learning models -ENet, SegNet, SE U-Net, and DeepLabV3+EfficientNet- were trained over 50 epochs using the Binary Cross-Entropy loss function. The results showed that the DeepLabV3+EfficientNet model with a Precision of 96.09% and an IoU of 89.13%, achieved the best performance in detecting agricultural ponds. Additionally, the SegNet model with a Precision of 93.81%, and the DeepLabV3+EfficientNet model with an IoU of 85.58%, demonstrated the best performance in detecting of swimming pools. Based on these results, the DeepLabV3+EfficientNet model is recommended by this research for pools and reservoirs detection.In the current century, increasing of greenhouse gases has led to significant changes in the climate. It is causing irreversible impacts on agricultural lands, food production, and drinking water supply. By using remote sensing technology and the processing of satellite data, aerial and drone imagery to collect information from the Earth surface, environmental changes monitoring and analyze water bodies has become an effective tool for planning and optimal management of water resources. Modern and interdisciplinary technologies have enabled water resource specialists to accurately identify, mapping and assess surface water resources. In this study, with the aim of identifying small water bodies using remote sensing data, four deep learning models -ENet, SegNet, SE U-Net, and DeepLabV3+EfficientNet- were trained over 50 epochs using the Binary Cross-Entropy loss function. The results showed that the DeepLabV3+EfficientNet model with a Precision of 96.09% and an IoU of 89.13%, achieved the best performance in detecting agricultural ponds. Additionally, the SegNet model with a Precision of 93.81%, and the DeepLabV3+EfficientNet model with an IoU of 85.58%, demonstrated the best performance in detecting of swimming pools. Based on these results, the DeepLabV3+EfficientNet model is recommended by this research for pools and reservoirs detection.https://ije.ut.ac.ir/article_98856_b7bc827f1143e9511156a18cfe4dc0a8.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811320240922Improving Flow Estimation Accuracy Through the Integration of Hydrological Methods and Remote Sensing Data: Emphasizing the Role of Soil Texture and Land Use in Unguaged Sites Located Hydrometric DataImproving Flow Estimation Accuracy Through the Integration of Hydrological Methods and Remote Sensing Data: Emphasizing the Role of Soil Texture and Land Use in Unguaged Sites Located Hydrometric Data3373549923810.22059/ije.2024.383293.1842FAHafezMirzapourPhD. student of Watershed Management Engineering Faculty of Natural Resources Lorestan University, Khorram Abad, Lorestan, IranAliHaghizadehAssociate Professor, Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, IranMahdiSoleimani-MotalghAssistant Professor, Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, IranJournal Article20240722Effective water resource management in areas with limited hydrometric data requires the application of innovative and integrated methods to examine hydrological dynamics more accurately. This study investigates and analyzes how flow was estimated in the sub-basins of the Dez in Lorestan Province. Initially, Sentinel-1 and 2 satellite data were used, along with SRCI and BI indices, to extract maps of soil textures, land use, and curve number (CN). Subsequently, Relying on rainfall and discharge data from 1992 to 2023 and statistical analysis, the return period of rainfall and flow for the studied sub-basins was calculated utilizing EasyFit software. The flow for each sub-basin was estimated using the SCS method and multivariate regression. The results indicated that multivariate regression, evaluated using the Durbin-Watson statistic (1.74), the coefficient of determination (0.768), the mean squared error (17.88), and the Nash-Sutcliffe efficiency (0.758) for a 2-year return period, was the most suitable method for estimating flow at ungauged stations within the sub-basins of the Dez River. Overall, this research presents effective approaches for water resource management and the optimization of hydrological in Lorestan Province, To optimize cost and time efficiency, the use of multivariate regression for flow estimation in ungauged hydrometric sub-basins is recommended.Effective water resource management in areas with limited hydrometric data requires the application of innovative and integrated methods to examine hydrological dynamics more accurately. This study investigates and analyzes how flow was estimated in the sub-basins of the Dez in Lorestan Province. Initially, Sentinel-1 and 2 satellite data were used, along with SRCI and BI indices, to extract maps of soil textures, land use, and curve number (CN). Subsequently, Relying on rainfall and discharge data from 1992 to 2023 and statistical analysis, the return period of rainfall and flow for the studied sub-basins was calculated utilizing EasyFit software. The flow for each sub-basin was estimated using the SCS method and multivariate regression. The results indicated that multivariate regression, evaluated using the Durbin-Watson statistic (1.74), the coefficient of determination (0.768), the mean squared error (17.88), and the Nash-Sutcliffe efficiency (0.758) for a 2-year return period, was the most suitable method for estimating flow at ungauged stations within the sub-basins of the Dez River. Overall, this research presents effective approaches for water resource management and the optimization of hydrological in Lorestan Province, To optimize cost and time efficiency, the use of multivariate regression for flow estimation in ungauged hydrometric sub-basins is recommended.https://ije.ut.ac.ir/article_99238_40a3ded0ca555114bb77005aaad9175d.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811320240922The suitable method for rainfall hyetograph extracting in dry areas via hourly rainfall and under the climate change scenariosThe suitable method for rainfall hyetograph extracting in dry areas via hourly rainfall and under the climate change scenarios3553739935410.22059/ije.2024.383818.1844FARezaGhazaviProfessor, Department of Nature Engineering, Faculty of Natural Resources and Geoscience, University of Kashan, Kashan, IranZahraFarahnakianM.S. student,, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Esfahan, IranJournal Article20240723<strong>Objective</strong>: In this research, triangular (Yen and Chao) and periodic block methods were used to calculate and draw precipitation hyetograph in Kashan synoptic station as an indicator station of dry areas.<br /><strong>Method</strong>: In order to investigate the effect of climate change on precipitation in the study area, the general circulation model of the atmosphere and different climate scenarios were used. Then the frequency intensity curves for the base period (1993-2017) and the Znear future (2030-2011) and distant future (2050-2031) periods were drawn by using Kahraman-Abkhader relationship. In the following, the curves of intensity, duration, frequency of extraction and corresponding rainfall hyetograph were drawn based on triangular (Yen and Chau) and periodic block methods, and the results were compared with the rainfall patterns measured at the Kashan synoptic station.<br /><strong>Results</strong>: The results showed that in the study area, the maximum amount of precipitation occurred within 30 minutes after the beginning of the precipitation. The results showed that the precipitation hyetograph of the measured and predicted precipitation data is similar to the hyetograph drawn by the triangulation method (Yen and Chau).<br /><strong>Conclusions</strong>: According to the results, the triangular method (Yen and Chau) can be introduced as a suitable method for investigating the distribution of precipitation in dry areas.<strong>Objective</strong>: In this research, triangular (Yen and Chao) and periodic block methods were used to calculate and draw precipitation hyetograph in Kashan synoptic station as an indicator station of dry areas.<br /><strong>Method</strong>: In order to investigate the effect of climate change on precipitation in the study area, the general circulation model of the atmosphere and different climate scenarios were used. Then the frequency intensity curves for the base period (1993-2017) and the Znear future (2030-2011) and distant future (2050-2031) periods were drawn by using Kahraman-Abkhader relationship. In the following, the curves of intensity, duration, frequency of extraction and corresponding rainfall hyetograph were drawn based on triangular (Yen and Chau) and periodic block methods, and the results were compared with the rainfall patterns measured at the Kashan synoptic station.<br /><strong>Results</strong>: The results showed that in the study area, the maximum amount of precipitation occurred within 30 minutes after the beginning of the precipitation. The results showed that the precipitation hyetograph of the measured and predicted precipitation data is similar to the hyetograph drawn by the triangulation method (Yen and Chau).<br /><strong>Conclusions</strong>: According to the results, the triangular method (Yen and Chau) can be introduced as a suitable method for investigating the distribution of precipitation in dry areas.https://ije.ut.ac.ir/article_99354_bbb0f1ea6a4ef906f6821778cc249496.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811320240922Simulation of Climatic Parameters using Statistical Microscale Models of SDSM and LARS in West Azerbaijan ProvinceSimulation of Climatic Parameters using Statistical Microscale Models of SDSM and LARS in West Azerbaijan Province3743949925910.22059/ije.2024.373803.1805FAMohammad HosseinJahangirAssociate Professor, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, IranFatemehRouzbahaniM.S. graduate in Ecohydrology engineering, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, IranJournal Article20240723<strong>Objective</strong>: The objective of this study is to evaluate the performance of the statistical downscaling models SDSM and LARS-WG in simulating minimum and maximum temperatures and precipitation at four stations (Urmia, Maku, Takab, and Mahabad) in West Azerbaijan province, with data from the periods 1987-2010 and 2020-2065.<br /><strong>Method</strong>: The study used the statistical downscaling models SDSM and LARS-WG to simulate temperature and precipitation variables at the selected stations. The observed data for the period 1987-2010 and the forecasted data for 2020-2065 were compared in both models. Additionally, to validate the SDSM model, the simulated parameters were compared with NCEP data and real observed data.<br /><strong>Results</strong>: The results showed that both models were more accurate in simulating temperature than precipitation. The SDSM model performed better in simulating daily temperature compared to LARS-WG, whereas the precipitation results from the LARS-WG model were slightly more accurate than those from the SDSM model. Additionally, the RMSE values for the SDSM and LARS-WG models for precipitation were 2.84 mm and 3.4 mm, respectively, while for maximum temperature, the RMSE values were 0.02°C and 0.29°C, respectively.<br /><strong> </strong><br /><strong>Conclusions</strong>: Based on the results, the SDSM model demonstrated higher accuracy in simulating both precipitation and temperature compared to the LARS-WG model. This model can be considered a reliable tool for predicting future changes in temperature and precipitation.<strong>Objective</strong>: The objective of this study is to evaluate the performance of the statistical downscaling models SDSM and LARS-WG in simulating minimum and maximum temperatures and precipitation at four stations (Urmia, Maku, Takab, and Mahabad) in West Azerbaijan province, with data from the periods 1987-2010 and 2020-2065.<br /><strong>Method</strong>: The study used the statistical downscaling models SDSM and LARS-WG to simulate temperature and precipitation variables at the selected stations. The observed data for the period 1987-2010 and the forecasted data for 2020-2065 were compared in both models. Additionally, to validate the SDSM model, the simulated parameters were compared with NCEP data and real observed data.<br /><strong>Results</strong>: The results showed that both models were more accurate in simulating temperature than precipitation. The SDSM model performed better in simulating daily temperature compared to LARS-WG, whereas the precipitation results from the LARS-WG model were slightly more accurate than those from the SDSM model. Additionally, the RMSE values for the SDSM and LARS-WG models for precipitation were 2.84 mm and 3.4 mm, respectively, while for maximum temperature, the RMSE values were 0.02°C and 0.29°C, respectively.<br /><strong> </strong><br /><strong>Conclusions</strong>: Based on the results, the SDSM model demonstrated higher accuracy in simulating both precipitation and temperature compared to the LARS-WG model. This model can be considered a reliable tool for predicting future changes in temperature and precipitation.https://ije.ut.ac.ir/article_99259_f3652e8050a1430d7c045fff74aab8a4.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811320240922Assessing ecological niche shift for the Caspian Kutum (Rutilus frisii) in southern waters of the Caspian Sea over a decadal periodAssessing ecological niche shift for the Caspian Kutum (Rutilus frisii) in southern waters of the Caspian Sea over a decadal period3954109949310.22059/ije.2024.385183.1848FAFatehMoezziPost-doctoral Researcher, Iran’s National Elites Foundation and Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, IranSoheilEagderiAssociate Professor, Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, IranJournal Article20240723<strong>Objective</strong>: Assessment of influencing environmental fluctuations on organisms in aquatic ecosystems is of high importance in the management and conservation of them. The present study aimed to investigate ecological niche shifts of Caspian Kutum (<em>Rutilus frisii</em>) under the effects of environmental condition changes in southern waters of the Caspian Sea during a decadal period (catch seasons 2002/03 and 2011/12).<br /><strong>Method</strong>: The ecological niche modeling was applied using commercial catch data and remotely-sensed environmental data. The random forest method was used to evaluate ecological niche relationships.<br /><strong>Results</strong>: The results showed significant (P < 0.001) decreases in day-time sea surface temperature (SST) and near-surface chlorophyll-a concentration (Chl-<em>a</em>) during the study period. The importance levels of SST, slope, and distance to riverine entrance locations in defining fish ecological niche were increased over the decadal period, while Chl-<em>a</em> and particulate organic carbon (POC) content had lower importance levels at the end of the period. The estimations of optimum ecological ranges of SST indicated considerable decreases over the period, but for other parameters, there were increasing patterns of optimum levels with extending their ranges compared to the initial catch season. Also, spatial shifts were obtained in the occurrence of the ecological niche conditions over the coastal regions.<br /><strong>Conclusions</strong>: The findings of this study indicated considerable changes in the ecological niche of the Caspian Kutum during the decadal period and its spatial distribution over the southern coastal waters of the Caspian Sea.<strong>Objective</strong>: Assessment of influencing environmental fluctuations on organisms in aquatic ecosystems is of high importance in the management and conservation of them. The present study aimed to investigate ecological niche shifts of Caspian Kutum (<em>Rutilus frisii</em>) under the effects of environmental condition changes in southern waters of the Caspian Sea during a decadal period (catch seasons 2002/03 and 2011/12).<br /><strong>Method</strong>: The ecological niche modeling was applied using commercial catch data and remotely-sensed environmental data. The random forest method was used to evaluate ecological niche relationships.<br /><strong>Results</strong>: The results showed significant (P < 0.001) decreases in day-time sea surface temperature (SST) and near-surface chlorophyll-a concentration (Chl-<em>a</em>) during the study period. The importance levels of SST, slope, and distance to riverine entrance locations in defining fish ecological niche were increased over the decadal period, while Chl-<em>a</em> and particulate organic carbon (POC) content had lower importance levels at the end of the period. The estimations of optimum ecological ranges of SST indicated considerable decreases over the period, but for other parameters, there were increasing patterns of optimum levels with extending their ranges compared to the initial catch season. Also, spatial shifts were obtained in the occurrence of the ecological niche conditions over the coastal regions.<br /><strong>Conclusions</strong>: The findings of this study indicated considerable changes in the ecological niche of the Caspian Kutum during the decadal period and its spatial distribution over the southern coastal waters of the Caspian Sea.https://ije.ut.ac.ir/article_99493_71a56b9705f7eb08bae6ebebe8a836ad.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609811320240922Modeling and forecasting of climate parameters using CanESM2 model under RCP scenarios (case study: Karaj station)Modeling and forecasting of climate parameters using CanESM2 model under RCP scenarios (case study: Karaj station)4114269962110.22059/ije.2024.382370.1845FASeyyed JavadSadatinejadAssociate Professor, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, IranFarshadSoleimani SardooAssistant Professor, department of Natural Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft,IranMohammadMirzavandAssistant professor, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, IranJournal Article20240710<strong>Objective</strong>: Climate change is one of the most important challenges of this century. The consequences of these changes and how to adapt to them as well as reducing the causes of climate change are important points of this phenomenon. Currently, there is scientific and definite evidence about the warming of the earth and the unprecedented increase in temperature on the surface of the earth and the atmosphere caused by it is a human activity, it is a proof of this. The most important parameters affecting the phenomenon of climate change are precipitation and temperature.
<strong>Method</strong>: The CanESM2 model was used to predict the climatic parameters of temperature, precipitation and wind speed under the RCP to predict the climate change in Karaj station.
<strong>Results</strong>: The results showed that the average rainfall in the Karaj station was 96 mm in the historical period which is according to the scenarios RCP2.6, RCP4.5 and RCP 8.5 will decrease in the near future (2060 - 2030) and 62 % in the near future (2060 - 2030) and 81 % in the future (2070 - 2099) than the observed period (1985 - 2017). The results of the simulation of the average temperature according to RCP2.6, RCP4.5 and RCP8.5 scenarios showed that the average temperature in the future period is close to 0.53, 0.17 and 0.19% compared to the observation period (15.81 degrees Celsius) will decrease, while in the future period (2070-2100) under the RCP2.6 scenario, it will decrease by 1.11 percent and according to the RCP4.5 and RCP8.5 scenarios, it will increase by 0.39 and 2.13 percent. The average simulated wind speed showed that the wind speed was 27.89, 25.03 and 24.55% in the period from 2030 to 2060 and 34.16, 25.37 and 23.84% in the period from 207 to 2100 under the RCP scenarios compared to the value observed (2.41 m/s) will increase.
<strong>Conclusion</strong>: The evaluation results of CanESM2 model in this study can be considered as an acceptable statistical result to investigate the changes of climatic parameters. Observing the pattern of consumption and optimal use of water resources as well as preventing the increase of greenhouse gases can control the trend of increasing temperature and decreasing rainfall.<strong>Objective</strong>: Climate change is one of the most important challenges of this century. The consequences of these changes and how to adapt to them as well as reducing the causes of climate change are important points of this phenomenon. Currently, there is scientific and definite evidence about the warming of the earth and the unprecedented increase in temperature on the surface of the earth and the atmosphere caused by it is a human activity, it is a proof of this. The most important parameters affecting the phenomenon of climate change are precipitation and temperature.
<strong>Method</strong>: The CanESM2 model was used to predict the climatic parameters of temperature, precipitation and wind speed under the RCP to predict the climate change in Karaj station.
<strong>Results</strong>: The results showed that the average rainfall in the Karaj station was 96 mm in the historical period which is according to the scenarios RCP2.6, RCP4.5 and RCP 8.5 will decrease in the near future (2060 - 2030) and 62 % in the near future (2060 - 2030) and 81 % in the future (2070 - 2099) than the observed period (1985 - 2017). The results of the simulation of the average temperature according to RCP2.6, RCP4.5 and RCP8.5 scenarios showed that the average temperature in the future period is close to 0.53, 0.17 and 0.19% compared to the observation period (15.81 degrees Celsius) will decrease, while in the future period (2070-2100) under the RCP2.6 scenario, it will decrease by 1.11 percent and according to the RCP4.5 and RCP8.5 scenarios, it will increase by 0.39 and 2.13 percent. The average simulated wind speed showed that the wind speed was 27.89, 25.03 and 24.55% in the period from 2030 to 2060 and 34.16, 25.37 and 23.84% in the period from 207 to 2100 under the RCP scenarios compared to the value observed (2.41 m/s) will increase.
<strong>Conclusion</strong>: The evaluation results of CanESM2 model in this study can be considered as an acceptable statistical result to investigate the changes of climatic parameters. Observing the pattern of consumption and optimal use of water resources as well as preventing the increase of greenhouse gases can control the trend of increasing temperature and decreasing rainfall.https://ije.ut.ac.ir/article_99621_6a9315ae800f32344a2c06701c9356ff.pdf