University of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Determining the best volume of potato irrigation water in tape drip irrigation system using by WOFOST modelDetermining the best volume of potato irrigation water in tape drip irrigation system using by WOFOST model115299010.22098/mmws.2024.14880.1446FAHamidNeysi.Sc. Student of Irrigation and drainage, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, IranAslanEgdernezhadAssistant professor, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, IranSaloomehSepehri SadeghiyanAssistant professor of Irrigation and Drainage Engineering, Agricultural Engineering Research Institute (AERI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, IranJournal Article20240408Introduction <br /><br />Determining the appropriate amount of water for potato cultivation has been investigated by various researchers around the world and based on various factors such as irrigation methods, available technology, local climate and other management factors, various amounts have been presented. Based on these studies, the optimal amount of irrigation water for each region should be determined by field experiments. But doing it requires spending a lot of time and money. On the other hand, Iran's facing drought conditions in the coming decades and the need to optimize water consumption in the agricultural sector increases the importance of determining the optimal amount of irrigation water for potato production. To solve this problem and increase the speed of decision-making, plant models have been developed. The WOFOST model is one of the plant models introduced by Wageningen University in the Netherlands to simulate the growth and yield of agricultural plants. Although the WOFOST model has been widely used by researchers to simulate agricultural plants; But its use for potatoes has been less interesting to researchers. On the other hand, the optimal amount of potato irrigation water has been suggested by researchers around the world in a wide range, and it is necessary to determine and optimize these amounts in each region. Based on this importance, the present research was conducted to determine the optimal limits of potato irrigation water with the aim of achieving optimal yield and high water productivity. WOFOST model was used to simulate different irrigation scenarios.<br /><br /><br /><br />Materials and Methods <br /><br />This research was conducted in two crop years in a research farm located in Kermanshah city in the form of complete random blocks. The investigated treatments included the supply of irrigation water at the levels of 100 (T1), 75 (T2) and 50 (T3) percent of the potato plant's water requirement in T-Tape drip irrigation system. Irrigation treatments were the same until the 5-leaf stage, and after that, irrigation treatments were applied. In order to determine the amount of irrigation water in each treatment, volumetric meters were used. The amount of readily available water and soil residue (W2) and the percentage of usable water discharge or Re in the plant root zone were calculated using equations (1) and (2). <br /><br />(1) <br /><br /><br /><br />(2) <br /><br /><br /><br />The WOFOST model is a plant growth simulation model based on the carbon cycle and has a complex structure. This model simulates plant growth in three conditions of no limiting factor, water limitation and food limitation. In fact, in the WOFOST model, crop growth is simulated based on eco-physiological processes. Before recalibration and validation of the WOFOST model, sensitivity analysis was performed based on equation (3):<br /><br />(3) <br /><br /><br /><br />In this regard, Sc is the dimensionless sensitivity coefficient, Pm is the estimated value of the desired parameter based on adjusted input data, and Pb is the estimated value of the desired parameter based on the basic input data. After the sensitivity analysis, the WOFOST model was recalibrated using the data of the first year and validated using the data of the second year of cultivation.<br /><br /><br /><br />Results and Discussion <br /><br />The statistical results of observed and simulated yield and water productivity by WOFOST model are shown in Table (1). According to these results, in the calibration phase, the WOFOST model had an underestimation error. The accuracy of this model based on the NRMSE statistic was in the excellent category. The error rate of WOFOST model for determining yield and water productivity was equal to 1.6 tons per hectare and 0.17 kg/m3, respectively. Based on two statistics, EF and d, the efficiency of this model to determine crop yield was better than water productivity.<br /><br /><br /><br />Table 1- Statistical results of observed and simulated yield and water productivity by the WOFOST model in the calibration stage<br /><br />MBE RMSE NRMSE EF D Unit Parameter<br /><br />0.5- 1.6 0.06 0.93 0.99 Tons/ha Yield<br /><br />0.06- 0.17 0.04 0.04- 0.99 kg/m3 Water productivity<br /><br /><br /><br />The statistical results of yield and water productivity in the validation stage are shown in table (2). Based on the MBE statistic, the WOFOST model had an overestimation error in the performance simulation and an underestimation error in the water productivity simulation. Based on the NRMSE statistic, the accuracy of this model was excellent for determining both parameters. As in the calibration phase, the efficiency of the WOFOST model was better in simulating performance than water productivity.<br /><br /><br /><br />Table 2- Statistical results of observed and simulated yield and water productivity by the WOFOST model in the validation stage<br /><br />MBE RMSE NRMSE EF D Unit Parameter<br /><br />0.1 0.93 0.03 0.97 0.99 Tons/ha Yield<br /><br />0.04- 0.14 0.03 0.40- 0.99 kg/m3 Water productivity<br /><br /><br /><br />In general, to determine the optimal scenario, it should be kept in mind that the difference between potato yield in two irrigation depths of 634 and 487 mm was greater than other depths, while the difference between water productivity between these two depths was only 5%. Therefore, the irrigation water depth of 634 mm is chosen as the optimal depth for irrigation. These results are close to the values suggested by Doornbos and Kassam (1979) in FAO publication No.33.<br /><br /><br /><br />Conclusion <br /><br />The results showed that the WOFOST model had the necessary accuracy (NRMSE<0.1) and efficiency (d>0.99) to simulate the yield and water productivity of potato in Kermanshah region. Based on these results, different irrigation water management scenarios were investigated on the yield and water productivity of this crop. The difference between potato yield in two irrigation depths of 634 and 487 mm was greater than other irrigation intervals. For this reason, the yield and water productivity values were compared with each other in this interval. Based on all the results, the depth of 634 mm was determined as the optimal value for potato cultivation. Potato yield at this depth is about 25 tons per hectare and water productivity is about 2.4 kg/m3. These values decreased and increased by 7.3 tons per hectare and 0.8 kg/m3 compared to providing 100% of the plant's water needs (975 mm of irrigation water).Introduction <br /><br />Determining the appropriate amount of water for potato cultivation has been investigated by various researchers around the world and based on various factors such as irrigation methods, available technology, local climate and other management factors, various amounts have been presented. Based on these studies, the optimal amount of irrigation water for each region should be determined by field experiments. But doing it requires spending a lot of time and money. On the other hand, Iran's facing drought conditions in the coming decades and the need to optimize water consumption in the agricultural sector increases the importance of determining the optimal amount of irrigation water for potato production. To solve this problem and increase the speed of decision-making, plant models have been developed. The WOFOST model is one of the plant models introduced by Wageningen University in the Netherlands to simulate the growth and yield of agricultural plants. Although the WOFOST model has been widely used by researchers to simulate agricultural plants; But its use for potatoes has been less interesting to researchers. On the other hand, the optimal amount of potato irrigation water has been suggested by researchers around the world in a wide range, and it is necessary to determine and optimize these amounts in each region. Based on this importance, the present research was conducted to determine the optimal limits of potato irrigation water with the aim of achieving optimal yield and high water productivity. WOFOST model was used to simulate different irrigation scenarios.<br /><br /><br /><br />Materials and Methods <br /><br />This research was conducted in two crop years in a research farm located in Kermanshah city in the form of complete random blocks. The investigated treatments included the supply of irrigation water at the levels of 100 (T1), 75 (T2) and 50 (T3) percent of the potato plant's water requirement in T-Tape drip irrigation system. Irrigation treatments were the same until the 5-leaf stage, and after that, irrigation treatments were applied. In order to determine the amount of irrigation water in each treatment, volumetric meters were used. The amount of readily available water and soil residue (W2) and the percentage of usable water discharge or Re in the plant root zone were calculated using equations (1) and (2). <br /><br />(1) <br /><br /><br /><br />(2) <br /><br /><br /><br />The WOFOST model is a plant growth simulation model based on the carbon cycle and has a complex structure. This model simulates plant growth in three conditions of no limiting factor, water limitation and food limitation. In fact, in the WOFOST model, crop growth is simulated based on eco-physiological processes. Before recalibration and validation of the WOFOST model, sensitivity analysis was performed based on equation (3):<br /><br />(3) <br /><br /><br /><br />In this regard, Sc is the dimensionless sensitivity coefficient, Pm is the estimated value of the desired parameter based on adjusted input data, and Pb is the estimated value of the desired parameter based on the basic input data. After the sensitivity analysis, the WOFOST model was recalibrated using the data of the first year and validated using the data of the second year of cultivation.<br /><br /><br /><br />Results and Discussion <br /><br />The statistical results of observed and simulated yield and water productivity by WOFOST model are shown in Table (1). According to these results, in the calibration phase, the WOFOST model had an underestimation error. The accuracy of this model based on the NRMSE statistic was in the excellent category. The error rate of WOFOST model for determining yield and water productivity was equal to 1.6 tons per hectare and 0.17 kg/m3, respectively. Based on two statistics, EF and d, the efficiency of this model to determine crop yield was better than water productivity.<br /><br /><br /><br />Table 1- Statistical results of observed and simulated yield and water productivity by the WOFOST model in the calibration stage<br /><br />MBE RMSE NRMSE EF D Unit Parameter<br /><br />0.5- 1.6 0.06 0.93 0.99 Tons/ha Yield<br /><br />0.06- 0.17 0.04 0.04- 0.99 kg/m3 Water productivity<br /><br /><br /><br />The statistical results of yield and water productivity in the validation stage are shown in table (2). Based on the MBE statistic, the WOFOST model had an overestimation error in the performance simulation and an underestimation error in the water productivity simulation. Based on the NRMSE statistic, the accuracy of this model was excellent for determining both parameters. As in the calibration phase, the efficiency of the WOFOST model was better in simulating performance than water productivity.<br /><br /><br /><br />Table 2- Statistical results of observed and simulated yield and water productivity by the WOFOST model in the validation stage<br /><br />MBE RMSE NRMSE EF D Unit Parameter<br /><br />0.1 0.93 0.03 0.97 0.99 Tons/ha Yield<br /><br />0.04- 0.14 0.03 0.40- 0.99 kg/m3 Water productivity<br /><br /><br /><br />In general, to determine the optimal scenario, it should be kept in mind that the difference between potato yield in two irrigation depths of 634 and 487 mm was greater than other depths, while the difference between water productivity between these two depths was only 5%. Therefore, the irrigation water depth of 634 mm is chosen as the optimal depth for irrigation. These results are close to the values suggested by Doornbos and Kassam (1979) in FAO publication No.33.<br /><br /><br /><br />Conclusion <br /><br />The results showed that the WOFOST model had the necessary accuracy (NRMSE<0.1) and efficiency (d>0.99) to simulate the yield and water productivity of potato in Kermanshah region. Based on these results, different irrigation water management scenarios were investigated on the yield and water productivity of this crop. The difference between potato yield in two irrigation depths of 634 and 487 mm was greater than other irrigation intervals. For this reason, the yield and water productivity values were compared with each other in this interval. Based on all the results, the depth of 634 mm was determined as the optimal value for potato cultivation. Potato yield at this depth is about 25 tons per hectare and water productivity is about 2.4 kg/m3. These values decreased and increased by 7.3 tons per hectare and 0.8 kg/m3 compared to providing 100% of the plant's water needs (975 mm of irrigation water).https://mmws.uma.ac.ir/article_2990_edc29701ab6aefc13fd538ebb7b60635.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Estimation of surface net water flow and its relationship with hydrological and ecological parameters in the Urmia Lake basinEstimation of surface net water flow and its relationship with hydrological and ecological parameters in the Urmia Lake basin1633300510.22098/mmws.2024.14929.1450FAMohammad SadeghTahmouresiM.Sc., Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, IranMohammad HosseinNiksokhanProfessor, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, IranAmir HoushangEhsaniAssociate Professor, Department of Environmental Design, Faculty of Environment, University of Tehran, Tehran, IranJournal Article20240420Introduction<br /><br />The dynamics of hydrological fluxes are integral to maintaining the delicate balance within the Earth's ecosystems. As the primary conduits of water, energy, and biogeochemical cycles, these fluxes not only support the fundamental processes of life but also act as critical indicators of environmental health and sustainability. The surface net water flux (NWF), a crucial parameter emerging from the interplay between infiltration and evapotranspiration, acts as a barometer for the underlying hydrological processes that dictate the recharge rates of vital groundwater reserves. The essence of understanding NWF lies in its direct correlation with the overall water availability within a region, which is instrumental for agricultural productivity, ecosystem vitality, and human consumption. In the face of expanding urbanization, intensive agricultural practices, and the unpredictable swings of climate change, the hydrological fluxes are subject to significant perturbations. These disruptions, often stemming from anthropogenic influences, have the potential to profoundly alter the natural cycles, thereby necessitating advanced methodologies for accurate monitoring and predictive modeling. While the impact of such changes might be localized in nature, their cumulative effect can have far-reaching implications for regional water security and ecological integrity (Sadeghi et al., 2019). Groundwater, the unseen treasure beneath our feet, is under increasing pressure as global populations soar and demands for water surge. This silent crisis of groundwater depletion, marked by falling water levels and deteriorating quality, poses one of the most significant challenges for contemporary water management (Moore & Fisher, 2012; Richey et al., 2015). Studies have underlined the importance of robust monitoring systems, combining field data, remote sensing, and sophisticated modeling, as pivotal for diagnosing the health of our hydrological systems. The National Research Council (2012) has particularly emphasized the critical role of such integrated approaches in addressing water resource management's vulnerabilities and challenges. In this study, we will dissect the mechanisms of NWF and its profound influence on the ecological and hydrological parameters within the Urmia Lake Basin. <br /><br /><br /><br />Materials and Methods <br /><br />Situated in northwest Iran, the Urmia Lake Basin spans approximately 51,801 square kilometers, surrounded by the northern Zagros mountains, the southern slopes of Mount Sabalan, and the northern, western, and southern flanks of Mount Sahand. This diverse landscape features the sentinel Lake Urmia, positioned 1276 meters above sea level, covering about 5750 square kilometers and surrounded by 16 wetlands, illustrating the region's ecological vulnerability. This study focuses on soil moisture dynamics using L4 data from the SMAP satellite, emphasizing its critical role in hydrological and climatic processes such as runoff, flood modeling, and drought monitoring. Additional data from the CHIRPS dataset and MODIS products enrich our understanding of the hydrological interactions within the basin, with groundwater volume derived from the GLDAS-2.2 product and water levels of Lake Urmia from local databases. The analytical model for estimating surface net water flux (NWF), developed by Sadeghi et al. (2019), uses direct soil moisture data and applies Warrick's (1975) solution to Richards' equation (1931), which describes soil moisture dynamics due to infiltration and gravitational water flow in variably saturated soils. This model predicts NWF accurately without the need for calibration, providing a computational advantage and facilitating its application in environmental studies and water resource management.<br /><br /><br /><br />Results and Discussion<br /><br />The results and discussion section of the research conducted on the Urmia Lake Basin, spanning 2015 to early 2021, highlights significant fluctuations in the surface net water flux (NWF), suggesting a solid seasonal pattern influenced by the hydrological conditions of the basin. Analyzing the temporal dynamics, periods of increased NWF correlate with rainy seasons, leading to heightened surface and subsurface water flows towards Lake Urmia, while decreases during dry seasons coincide with elevated evaporation and transpiration, reducing basin water inputs. The overall trend suggests that these variations do not follow a long-term increase or decrease but are predominantly driven by annual and seasonal factors. Annual and seasonal box plots reveal stable medians amidst more comprehensive ranges of data variability across different years, with 2018 and 2020 marked by significant variability likely due to climatic or land-use changes. This period analysis underlines the NWF's sensitivity to environmental and anthropogenic factors. The multifaceted analysis further includes a correlation heatmap showing solid relationships between vital environmental variables such as soil moisture, vegetation indices, and lake water levels, underscoring the interconnectedness of the ecosystem's water dynamics. These insights are crucial for strategic water resource management and highlight the need for comprehensive data and consideration of water use policies to ensure informed decision-making. This analysis exemplifies how integrated data-driven approaches can enhance the understanding and managing of water resources in ecologically sensitive regions. <br /><br /><br /><br />Conclusion <br /><br />In this study, after estimating the surface net water flux (NWF) in the Urmia Lake Basin, we examined its impact on the hydrological and ecological parameters within the basin. Utilizing soil moisture data from the SMAP satellite and the analytical model developed by Sadeghi et al. (2019), the surface NWF was estimated from 2015 to 2020. The results indicated that the surface NWF, which results from the difference between infiltration and evapotranspiration, plays a crucial role in maintaining the water balance and, consequently, the water level of Lake Urmia. This study revealed that seasonal and annual variations in the surface NWF significantly affect soil moisture, precipitation, evaporation, and underground water storage. Additionally, correlation analyses demonstrated significant relationships between the surface NWF and environmental parameters such as soil moisture, the Normalized Difference Vegetation Index (NDVI), and land surface temperature (LST). The analyses conducted on satellite data and hydrological models highlight that effective water resource management in the Urmia Lake Basin requires a thorough understanding of the interactions between hydrological and ecological parameters. This understanding can lead to more effective management decisions to conserve water resources and associated ecosystems. Ultimately, this study underscores the importance of utilizing satellite data and advanced models to analyze and manage water resources. Given the increasing challenges posed by climate change and human activities, developing methods for sustainable study and management of water resources is essential to maintaining ecosystem balance and meeting the water needs of human communities.Introduction<br /><br />The dynamics of hydrological fluxes are integral to maintaining the delicate balance within the Earth's ecosystems. As the primary conduits of water, energy, and biogeochemical cycles, these fluxes not only support the fundamental processes of life but also act as critical indicators of environmental health and sustainability. The surface net water flux (NWF), a crucial parameter emerging from the interplay between infiltration and evapotranspiration, acts as a barometer for the underlying hydrological processes that dictate the recharge rates of vital groundwater reserves. The essence of understanding NWF lies in its direct correlation with the overall water availability within a region, which is instrumental for agricultural productivity, ecosystem vitality, and human consumption. In the face of expanding urbanization, intensive agricultural practices, and the unpredictable swings of climate change, the hydrological fluxes are subject to significant perturbations. These disruptions, often stemming from anthropogenic influences, have the potential to profoundly alter the natural cycles, thereby necessitating advanced methodologies for accurate monitoring and predictive modeling. While the impact of such changes might be localized in nature, their cumulative effect can have far-reaching implications for regional water security and ecological integrity (Sadeghi et al., 2019). Groundwater, the unseen treasure beneath our feet, is under increasing pressure as global populations soar and demands for water surge. This silent crisis of groundwater depletion, marked by falling water levels and deteriorating quality, poses one of the most significant challenges for contemporary water management (Moore & Fisher, 2012; Richey et al., 2015). Studies have underlined the importance of robust monitoring systems, combining field data, remote sensing, and sophisticated modeling, as pivotal for diagnosing the health of our hydrological systems. The National Research Council (2012) has particularly emphasized the critical role of such integrated approaches in addressing water resource management's vulnerabilities and challenges. In this study, we will dissect the mechanisms of NWF and its profound influence on the ecological and hydrological parameters within the Urmia Lake Basin. <br /><br /><br /><br />Materials and Methods <br /><br />Situated in northwest Iran, the Urmia Lake Basin spans approximately 51,801 square kilometers, surrounded by the northern Zagros mountains, the southern slopes of Mount Sabalan, and the northern, western, and southern flanks of Mount Sahand. This diverse landscape features the sentinel Lake Urmia, positioned 1276 meters above sea level, covering about 5750 square kilometers and surrounded by 16 wetlands, illustrating the region's ecological vulnerability. This study focuses on soil moisture dynamics using L4 data from the SMAP satellite, emphasizing its critical role in hydrological and climatic processes such as runoff, flood modeling, and drought monitoring. Additional data from the CHIRPS dataset and MODIS products enrich our understanding of the hydrological interactions within the basin, with groundwater volume derived from the GLDAS-2.2 product and water levels of Lake Urmia from local databases. The analytical model for estimating surface net water flux (NWF), developed by Sadeghi et al. (2019), uses direct soil moisture data and applies Warrick's (1975) solution to Richards' equation (1931), which describes soil moisture dynamics due to infiltration and gravitational water flow in variably saturated soils. This model predicts NWF accurately without the need for calibration, providing a computational advantage and facilitating its application in environmental studies and water resource management.<br /><br /><br /><br />Results and Discussion<br /><br />The results and discussion section of the research conducted on the Urmia Lake Basin, spanning 2015 to early 2021, highlights significant fluctuations in the surface net water flux (NWF), suggesting a solid seasonal pattern influenced by the hydrological conditions of the basin. Analyzing the temporal dynamics, periods of increased NWF correlate with rainy seasons, leading to heightened surface and subsurface water flows towards Lake Urmia, while decreases during dry seasons coincide with elevated evaporation and transpiration, reducing basin water inputs. The overall trend suggests that these variations do not follow a long-term increase or decrease but are predominantly driven by annual and seasonal factors. Annual and seasonal box plots reveal stable medians amidst more comprehensive ranges of data variability across different years, with 2018 and 2020 marked by significant variability likely due to climatic or land-use changes. This period analysis underlines the NWF's sensitivity to environmental and anthropogenic factors. The multifaceted analysis further includes a correlation heatmap showing solid relationships between vital environmental variables such as soil moisture, vegetation indices, and lake water levels, underscoring the interconnectedness of the ecosystem's water dynamics. These insights are crucial for strategic water resource management and highlight the need for comprehensive data and consideration of water use policies to ensure informed decision-making. This analysis exemplifies how integrated data-driven approaches can enhance the understanding and managing of water resources in ecologically sensitive regions. <br /><br /><br /><br />Conclusion <br /><br />In this study, after estimating the surface net water flux (NWF) in the Urmia Lake Basin, we examined its impact on the hydrological and ecological parameters within the basin. Utilizing soil moisture data from the SMAP satellite and the analytical model developed by Sadeghi et al. (2019), the surface NWF was estimated from 2015 to 2020. The results indicated that the surface NWF, which results from the difference between infiltration and evapotranspiration, plays a crucial role in maintaining the water balance and, consequently, the water level of Lake Urmia. This study revealed that seasonal and annual variations in the surface NWF significantly affect soil moisture, precipitation, evaporation, and underground water storage. Additionally, correlation analyses demonstrated significant relationships between the surface NWF and environmental parameters such as soil moisture, the Normalized Difference Vegetation Index (NDVI), and land surface temperature (LST). The analyses conducted on satellite data and hydrological models highlight that effective water resource management in the Urmia Lake Basin requires a thorough understanding of the interactions between hydrological and ecological parameters. This understanding can lead to more effective management decisions to conserve water resources and associated ecosystems. Ultimately, this study underscores the importance of utilizing satellite data and advanced models to analyze and manage water resources. Given the increasing challenges posed by climate change and human activities, developing methods for sustainable study and management of water resources is essential to maintaining ecosystem balance and meeting the water needs of human communities.https://mmws.uma.ac.ir/article_3005_8e39f8cca4c4504f0d2e1c2deb901567.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Evaluation of thermal and combined methods in determining the water requirement of plants using moving correlationEvaluation of thermal and combined methods in determining the water requirement of plants using moving correlation3450337310.22098/mmws.2024.15744.1491FAAHADMOLAVI, Department of Water Science and Engineering, Ta.C., Islamic Azad University, Tabriz, IranJournal Article20240830Introduction <br /><br />Drought and limited water resources in Iran, especially in Azerbaijan, require preventing the wastage of existing water resources and making optimal use of them. One of the main indicators for estimating the water needed by plants (crops) and optimal design of irrigation and drainage networks is determining the amount of water evaporated and transpired from the desired area. The more accurately the estimation of this factor is done, the more the efficiency of agricultural products and the reduction of irrigation system costs and the reduction of excess water consumption will increase, so determining the amount of ETP should always be considered by researchers as one of the important and effective factors in agricultural studies. Based on the climate and weather conditions of the regions, several relationships have been presented by different researchers to estimate the amount of potential evapotranspiration, and in each of these relationships, a number of climatic factors have been used as the most important effective factors in this process. Considering the recent droughts in the northwest of the country and considering that this region is one of the agricultural poles of the country, it is inevitable to conduct such a research in a part of this region. The purpose of this research is to estimate and localize the potential evapotranspiration estimation models in Pars Abad Moghan synoptic station and finally determine the most accurate method for the above location.<br /><br />Materials and Methods <br /><br />The area studied in this research is Pars Abad Moghan synoptic station, this station is located at 39 degrees and 39 minutes north latitude and 47 degrees and 55 minutes east longitude from the Greenwich meridian. The height of this station is about 45.4 meters above sea level. Parsabad Mughan is located in Moghan plain. As the northernmost city of Iran, this city is located on the southern banks of Aras River and borders with the Republic of Azerbaijan. In this regard, several climatic factors were used in the calculation of potential evapotranspiration with the combined methods of Penman, Penman Monteith, Penman Wright and Van Bavel, as well as thermal methods including Blaney Criddle, Thornthwaite, Linacre and Hargreaves. Visual Basic programming language was used to calculate potential evapotranspiration with eight methods and also to determine moving correlation coefficients. Among the climatic factors, monthly evaporation rate from the evaporation pan, monthly average temperature, monthly maximum relative humidity, monthly average relative humidity, monthly minimum relative humidity, air pressure, wind speed, percentage of sunny hours, extraterrestrial radiation and of synoptic meteorology of Parsabad Moghan, has been used in the calculation of monthly potential evapotranspiration, as well as their correction. Statistical methods of linear correlation and power were used to modify and regionalize potential evapotranspiration estimation models. The performance of each used method was evaluated using correlation coefficient, root mean square error and t test.<br /><br />Results and Discussion <br /><br />The monthly potential evapotranspiration was estimated from the eight mentioned methods. The results showed that in the combined methods, the potential evapotranspiration calculated from April to June and in some of them until July have higher values than the evaporation pan method, and after that it is the opposite until December. Potential evapotranspiration values obtained from the Linacre method indicate that in all months these values are proportionally higher than the values obtained from the evaporation pan method, which shows a high correlation with the evaporation pan method. The results of the Thornthwaite method for all months have almost uniformly lower values than the values of the evaporation pan, which indicates the high agreement of the above values with the values of the evaporation pan method. Correlation coefficients in all thermal methods were higher than combined methods. The methods of Linacre with a correlation coefficient of 0.883 and Thornthwaite with a correlation coefficient of 0.874 have the highest correlation coefficient with the values obtained from the evaporation pan method. Its climate and the fact that thermal methods work better in areas with high temperatures are not far from expected. Linacre's method has been confirmed in several researches, including Benzaghna and Aniadik in calculating potential evapotranspiration in Libya and West Africa.<br /><br />Conclusion <br /><br />According to the statistical analysis table, the lowest RMSE value is related to the Penman Monteith method, which indicates the lower error of the results of this method compared to the values obtained from the evaporation pan method. After Penman Monteith method with RMSE 48.71 mm per month, Penman method with RMSEof 49.65 mm per month, Penman Wright with 51.39 mm per month and Thornthwaite with 53.42 mm per month, had the lowest root mean square error value. In all methods, at the confidence level of 99%, hypothesis one is rejected (H1: µ1≠µ2) and null hypothesis (H0: µ1=µ2) is confirmed. The lowest correlation coefficient is related to the combined method of Van Bavel. Potential evaporation and transpiration values calculated by the Hargreaves method do not have a particular trend and agreement with the values of the evaporation pan method, which shows the lower correlation of the above method with the evaporation pan method. Also, due to the high correlation coefficients of the methods of Linacre, Thornthwaite, these methods are proposed after applying correction coefficients to estimate the potential evapotranspiration of Parsabad Moghan and these methods are suitable and valid alternatives for potential evaporation and transpiration values for the evaporation pan method.Introduction <br /><br />Drought and limited water resources in Iran, especially in Azerbaijan, require preventing the wastage of existing water resources and making optimal use of them. One of the main indicators for estimating the water needed by plants (crops) and optimal design of irrigation and drainage networks is determining the amount of water evaporated and transpired from the desired area. The more accurately the estimation of this factor is done, the more the efficiency of agricultural products and the reduction of irrigation system costs and the reduction of excess water consumption will increase, so determining the amount of ETP should always be considered by researchers as one of the important and effective factors in agricultural studies. Based on the climate and weather conditions of the regions, several relationships have been presented by different researchers to estimate the amount of potential evapotranspiration, and in each of these relationships, a number of climatic factors have been used as the most important effective factors in this process. Considering the recent droughts in the northwest of the country and considering that this region is one of the agricultural poles of the country, it is inevitable to conduct such a research in a part of this region. The purpose of this research is to estimate and localize the potential evapotranspiration estimation models in Pars Abad Moghan synoptic station and finally determine the most accurate method for the above location.<br /><br />Materials and Methods <br /><br />The area studied in this research is Pars Abad Moghan synoptic station, this station is located at 39 degrees and 39 minutes north latitude and 47 degrees and 55 minutes east longitude from the Greenwich meridian. The height of this station is about 45.4 meters above sea level. Parsabad Mughan is located in Moghan plain. As the northernmost city of Iran, this city is located on the southern banks of Aras River and borders with the Republic of Azerbaijan. In this regard, several climatic factors were used in the calculation of potential evapotranspiration with the combined methods of Penman, Penman Monteith, Penman Wright and Van Bavel, as well as thermal methods including Blaney Criddle, Thornthwaite, Linacre and Hargreaves. Visual Basic programming language was used to calculate potential evapotranspiration with eight methods and also to determine moving correlation coefficients. Among the climatic factors, monthly evaporation rate from the evaporation pan, monthly average temperature, monthly maximum relative humidity, monthly average relative humidity, monthly minimum relative humidity, air pressure, wind speed, percentage of sunny hours, extraterrestrial radiation and of synoptic meteorology of Parsabad Moghan, has been used in the calculation of monthly potential evapotranspiration, as well as their correction. Statistical methods of linear correlation and power were used to modify and regionalize potential evapotranspiration estimation models. The performance of each used method was evaluated using correlation coefficient, root mean square error and t test.<br /><br />Results and Discussion <br /><br />The monthly potential evapotranspiration was estimated from the eight mentioned methods. The results showed that in the combined methods, the potential evapotranspiration calculated from April to June and in some of them until July have higher values than the evaporation pan method, and after that it is the opposite until December. Potential evapotranspiration values obtained from the Linacre method indicate that in all months these values are proportionally higher than the values obtained from the evaporation pan method, which shows a high correlation with the evaporation pan method. The results of the Thornthwaite method for all months have almost uniformly lower values than the values of the evaporation pan, which indicates the high agreement of the above values with the values of the evaporation pan method. Correlation coefficients in all thermal methods were higher than combined methods. The methods of Linacre with a correlation coefficient of 0.883 and Thornthwaite with a correlation coefficient of 0.874 have the highest correlation coefficient with the values obtained from the evaporation pan method. Its climate and the fact that thermal methods work better in areas with high temperatures are not far from expected. Linacre's method has been confirmed in several researches, including Benzaghna and Aniadik in calculating potential evapotranspiration in Libya and West Africa.<br /><br />Conclusion <br /><br />According to the statistical analysis table, the lowest RMSE value is related to the Penman Monteith method, which indicates the lower error of the results of this method compared to the values obtained from the evaporation pan method. After Penman Monteith method with RMSE 48.71 mm per month, Penman method with RMSEof 49.65 mm per month, Penman Wright with 51.39 mm per month and Thornthwaite with 53.42 mm per month, had the lowest root mean square error value. In all methods, at the confidence level of 99%, hypothesis one is rejected (H1: µ1≠µ2) and null hypothesis (H0: µ1=µ2) is confirmed. The lowest correlation coefficient is related to the combined method of Van Bavel. Potential evaporation and transpiration values calculated by the Hargreaves method do not have a particular trend and agreement with the values of the evaporation pan method, which shows the lower correlation of the above method with the evaporation pan method. Also, due to the high correlation coefficients of the methods of Linacre, Thornthwaite, these methods are proposed after applying correction coefficients to estimate the potential evapotranspiration of Parsabad Moghan and these methods are suitable and valid alternatives for potential evaporation and transpiration values for the evaporation pan method.https://mmws.uma.ac.ir/article_3373_5fc3a1c727afd0345c68cc27b6b6efa0.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622The effect of biochar-modified layered double hydroxide (LDH) on uptake of heavy metals from landfill leachate and industrial wastewater by basil plantThe effect of biochar-modified layered double hydroxide (LDH) on uptake of heavy metals from landfill leachate and industrial wastewater by basil plant5168342810.22098/mmws.2024.15824.1497FAُSeyed MostafaEmadiAssociate Professor, Department of Soil Science and Engineering, Faculty of Crop Sciences, Sari Agriculture Sciences and Natural Resources University, Sari, IranMohammad AliBahmanyarProfessor, Department of Soil Science and Engineering, Faculty of Crop Sciences, Sari Agriculture Sciences and Natural Resources University, Sari, Iran0000-0001-8804-8479Seyed MostafaEmadi BaladehiFormer M.Sc. Student, Department of Soil Science and Engineering, Faculty of Crop Sciences, Sari Agriculture Sciences and Natural Resources University, Sari, IranJournal Article20240912Introduction <br /><br />The increasing use of effluents for irrigation, particularly in regions facing water scarcity, poses a substantial threat to agricultural sustainability due to the high level of heavy metals. These metals can accumulate in soil and plants, posing risks to food safety and human health. To address this issue, researchers have explored various remediation techniques, including the use of layered double hydroxides (LDHs). The LDHs can potentially capture the heavy metals through multiple mechanisms. These include ion exchange, where positively charged layers attract and bind negatively charged metal ions. Surface complexation involves the formation of complexes between metal ions and hydroxyl groups on the LDH surface. The intercalation allows larger metal ions to enter the interlayer space and interact with anions. In addition, the precipitation of metal hydroxides within the interlayer space can further immobilize heavy metals. These mechanisms, acting synergistically, contribute to the remarkable adsorption capabilities of LDHs for heavy metal removal. However, the efficiency of LDHs can be further enhanced by incorporating biochar into their structure. Biochar, a carbonaceous material produced from the pyrolysis of biomass, possesses a high surface area and porosity, enhancing the adsorption capacity of LDHs. The combination of LDHs and biochar (BCLDH) creates a synergistic effect, resulting in a more efficient and sustainable remediation approach. Therefore, the aim of this study is to investigate the effect of LDH and BCLDH on reducing the uptake of some heavy metals, including lead, cadmium, nickel, and zinc, in basil plants during irrigation with landfill leachate and industrial wastewater. <br /><br />Materials and Methods<br /><br />The LDH and BCLDH were synthesized using the co-precipitation method. The landfill leachate was sampled from the Sari landfill site, and the industrial wastewater was collected from Sari Industrial Park No. 1. A split-plot design with 3 replications was used for this study, where the main factor was the type of irrigation water, including two types: landfill leachate and industrial wastewater. The sub-factor consisted of treatments with LDH and BCLDH amendments, arranged in a completely randomized design with 7 treatments as follows: 1- Control, 2- LDH 0.25%, 3- LDH 0.5%, 4- LDH 1%, 5- BCLDH 0.25%, 6- BCLDH 0.5% , and 7- BCLDH 1%. After mixing various amounts of LDH and BCLDH with 6 kg of dry soil, 10 basil seeds were planted in each pot. The irrigation method was based on weight loosing, meaning that before planting the seeds, the pots were first saturated with tap water according to their pore volume, and the weight of each pot after draining excess water was recorded as the initial weight. Two weeks after seed germination, irrigation with landfill leachate and industrial wastewater was conducted over 8 weeks based on the plants' evapotranspiration and the reduction in pot weight (after reducing the available water up to 75%). Finally, after harvesting the plants, the levels of heavy metals in the soil and plants were measured. <br /><br />Results and Discussion<br /><br />The results indicated an increase in soil pH up to 7.8 following the application of 1% BCLDH, probobly due to the effective role of biochar in raising soil pH. Morever, the results showed that the LDH and BCLDH amendments in irrigation with industrial wastewater had no significant effect on the levels of heavy metals in the soil and the basil in the roots, and shoots. In contrast, both application of LDH and BCLDH on irrigation of landfill leachate successfully minimized the metal levels in the soil and plant organs, with rate of BCLDH 1% showing the best performance. The lowest concentrations of lead, cadmium, and nickel in the soils were measured in the BCLDH 1% treatment, with values of 2.44, 0.18, and 1.64 mg/kg, respectively. Moreover, this treatment in landfill leachate irrigation reduced the concentrations of lead, nickel, and zinc in the shoots by 75.5%, 62%, and 36.4%, respectively. This effect is attributed to the clear role of LDH and its contribution in increasing soil pH, cation exchange, electrostatic adsorption, and the precipitation of metals into insoluble compounds in the soil. In addition, the modification of LDH with biochar improved its structure, increased its surface area, and enhanced its porosity, thereby reducing pore diameter. This led to the increased surface adsorption capacity, enhanced electrostatic adsorption, and more effective removal of heavy metals, which reached its peak in the BCLDH 1% treatment.<br /><br />Conclusion <br /><br />The present study focused on the beneficial role of LDH and BCLDH amendments in reducing the uptake of heavy metals (lead, cadmium, nickel, and zinc) by basil plants under irrigation with landfill leachate and industrial wastewater. The results do not support the hypotheis on use of these amendments for industrial wastewater irrigation, which is likely due to the inefficiency of amendments at low concentrations of heavy metals. On the other hand, the results demonstrated the high efficiency of BCLDH in reducing the uptake of heavy metals by basil during landfill leachate irrigation. The lowest levels of heavy metals were observed in the BCLDH 1% treatment in soil, roots, and shoots. This outcome highlights the successful synthesis of biochar on LDH, which improved the structure and increased the metal adsorption capacity of BCLDH. . In addition, the BCLDH 1% treatment increased soil pH, electrostatic adsorption, and cation exchange, thereby the reducing metal uptake by basil. Given that the uncontrolled use of effluents may lead to excessive accumulation of metals in the edible parts of plants and heightened concerns about human health, the application of BCLDH emerges as a effective approach for significantly improving plant performance and reducing heavy metal uptake and its associated consequences. However, before making the final recommendations, futhur studies should be conducted at the field scale, over multiple years, and using different plant species to ensure the effective and reliable outcomes.Introduction <br /><br />The increasing use of effluents for irrigation, particularly in regions facing water scarcity, poses a substantial threat to agricultural sustainability due to the high level of heavy metals. These metals can accumulate in soil and plants, posing risks to food safety and human health. To address this issue, researchers have explored various remediation techniques, including the use of layered double hydroxides (LDHs). The LDHs can potentially capture the heavy metals through multiple mechanisms. These include ion exchange, where positively charged layers attract and bind negatively charged metal ions. Surface complexation involves the formation of complexes between metal ions and hydroxyl groups on the LDH surface. The intercalation allows larger metal ions to enter the interlayer space and interact with anions. In addition, the precipitation of metal hydroxides within the interlayer space can further immobilize heavy metals. These mechanisms, acting synergistically, contribute to the remarkable adsorption capabilities of LDHs for heavy metal removal. However, the efficiency of LDHs can be further enhanced by incorporating biochar into their structure. Biochar, a carbonaceous material produced from the pyrolysis of biomass, possesses a high surface area and porosity, enhancing the adsorption capacity of LDHs. The combination of LDHs and biochar (BCLDH) creates a synergistic effect, resulting in a more efficient and sustainable remediation approach. Therefore, the aim of this study is to investigate the effect of LDH and BCLDH on reducing the uptake of some heavy metals, including lead, cadmium, nickel, and zinc, in basil plants during irrigation with landfill leachate and industrial wastewater. <br /><br />Materials and Methods<br /><br />The LDH and BCLDH were synthesized using the co-precipitation method. The landfill leachate was sampled from the Sari landfill site, and the industrial wastewater was collected from Sari Industrial Park No. 1. A split-plot design with 3 replications was used for this study, where the main factor was the type of irrigation water, including two types: landfill leachate and industrial wastewater. The sub-factor consisted of treatments with LDH and BCLDH amendments, arranged in a completely randomized design with 7 treatments as follows: 1- Control, 2- LDH 0.25%, 3- LDH 0.5%, 4- LDH 1%, 5- BCLDH 0.25%, 6- BCLDH 0.5% , and 7- BCLDH 1%. After mixing various amounts of LDH and BCLDH with 6 kg of dry soil, 10 basil seeds were planted in each pot. The irrigation method was based on weight loosing, meaning that before planting the seeds, the pots were first saturated with tap water according to their pore volume, and the weight of each pot after draining excess water was recorded as the initial weight. Two weeks after seed germination, irrigation with landfill leachate and industrial wastewater was conducted over 8 weeks based on the plants' evapotranspiration and the reduction in pot weight (after reducing the available water up to 75%). Finally, after harvesting the plants, the levels of heavy metals in the soil and plants were measured. <br /><br />Results and Discussion<br /><br />The results indicated an increase in soil pH up to 7.8 following the application of 1% BCLDH, probobly due to the effective role of biochar in raising soil pH. Morever, the results showed that the LDH and BCLDH amendments in irrigation with industrial wastewater had no significant effect on the levels of heavy metals in the soil and the basil in the roots, and shoots. In contrast, both application of LDH and BCLDH on irrigation of landfill leachate successfully minimized the metal levels in the soil and plant organs, with rate of BCLDH 1% showing the best performance. The lowest concentrations of lead, cadmium, and nickel in the soils were measured in the BCLDH 1% treatment, with values of 2.44, 0.18, and 1.64 mg/kg, respectively. Moreover, this treatment in landfill leachate irrigation reduced the concentrations of lead, nickel, and zinc in the shoots by 75.5%, 62%, and 36.4%, respectively. This effect is attributed to the clear role of LDH and its contribution in increasing soil pH, cation exchange, electrostatic adsorption, and the precipitation of metals into insoluble compounds in the soil. In addition, the modification of LDH with biochar improved its structure, increased its surface area, and enhanced its porosity, thereby reducing pore diameter. This led to the increased surface adsorption capacity, enhanced electrostatic adsorption, and more effective removal of heavy metals, which reached its peak in the BCLDH 1% treatment.<br /><br />Conclusion <br /><br />The present study focused on the beneficial role of LDH and BCLDH amendments in reducing the uptake of heavy metals (lead, cadmium, nickel, and zinc) by basil plants under irrigation with landfill leachate and industrial wastewater. The results do not support the hypotheis on use of these amendments for industrial wastewater irrigation, which is likely due to the inefficiency of amendments at low concentrations of heavy metals. On the other hand, the results demonstrated the high efficiency of BCLDH in reducing the uptake of heavy metals by basil during landfill leachate irrigation. The lowest levels of heavy metals were observed in the BCLDH 1% treatment in soil, roots, and shoots. This outcome highlights the successful synthesis of biochar on LDH, which improved the structure and increased the metal adsorption capacity of BCLDH. . In addition, the BCLDH 1% treatment increased soil pH, electrostatic adsorption, and cation exchange, thereby the reducing metal uptake by basil. Given that the uncontrolled use of effluents may lead to excessive accumulation of metals in the edible parts of plants and heightened concerns about human health, the application of BCLDH emerges as a effective approach for significantly improving plant performance and reducing heavy metal uptake and its associated consequences. However, before making the final recommendations, futhur studies should be conducted at the field scale, over multiple years, and using different plant species to ensure the effective and reliable outcomes.https://mmws.uma.ac.ir/article_3428_1f35943064e22fc882fd299c8a706652.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Modeling Greenhouse Cucumber Evapotranspiration Using Machine Learning: A Random Forest Approach Versus Traditional and Non-linear Crop CoefficientsModeling Greenhouse Cucumber Evapotranspiration Using Machine Learning: A Random Forest Approach Versus Traditional and Non-linear Crop Coefficients6987355410.22098/mmws.2024.16164.1516FAMortezaKhoshsimaie ChenarPhD Student in Irrigation and Drainage Engineering, Department of Irrigation and Reclamation Engineering, Faculty of Agricultural, College of Agriculture and Natural Recourses, University of Tehran, Karaj, IranArashTaftehAssociate Professor of Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.NiazaliEbrahimipakAssociate Professor of Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.Journal Article20241110Extended Abstract<br /><br />Introduction<br /><br />Accurate estimation of crop evapotranspiration (ETc) is fundamental for the development of efficient irrigation strategies in greenhouse systems, where environmental conditions differ significantly from open-field farming. In Iran, greenhouse agriculture, particularly for cucumbers (Cucumis sativus L.), has expanded considerably, making irrigation optimization a critical priority. The specific microclimate within greenhouses, including controlled humidity, temperature, and solar radiation levels, affects plant water needs, requiring tailored approaches to predict ETc. Traditional models, like the FAO 56 crop coefficient (Kc) method, provide a standardized way to estimate ETc but are generally suited to field crops under variable outdoor conditions. The limitations of fixed Kc values in capturing the complexity of greenhouse environments have prompted the exploration of alternative models. In recent years, machine learning (ML) techniques, especially ensemble methods like the Random Forest (RF) algorithm, have emerged as promising tools for ETc modeling due to their capacity to manage non-linear interactions among meteorological variables and enhance model flexibility. This study evaluates the performance of three ETc estimation approaches for greenhouse-grown cucumber: the conventional FAO 56 Kc method, a non-linear Kc model using a third-degree polynomial, and direct ETc prediction through the RF algorithm. These methods are assessed across two growth cycles, autumn-winter (A-W) and spring-summer (S-S), to capture seasonal differences in crop water requirements.<br /><br />Materials and Methods<br /><br />The study was conducted in a research greenhouse located at the College of Agriculture and Natural Recourses, University of Tehran, focusing on daily ETc of cucumber over two distinct growth periods. Environmental parameters were measured both inside and outside the greenhouse, including maximum, minimum, and average temperatures, relative humidity, and solar radiation. Reference evapotranspiration inside the greenhouse (EToG) was derived using a micro-lysimeter installed with a turfgrass surface, while daily ETc was measured using a soil water balance method, where soil moisture content was monitored daily across three experimental plots to ensure precision. ETc calculations were performed through three modeling approaches. In the first approach, the FAO 56 Kc model estimated ETc by applying fixed crop coefficients and multiplying them by EToG. Although this method has been widely applied in field conditions, its applicability to greenhouses is limited due to fixed Kc assumptions. In the second approach, a non-linear Kc model was developed using third-degree polynomial regression on Kc values calculated as the ratio of ETc to EToG, capturing growth-stage specific variations. In the final approach, the RF model directly predicted ETc based on a broad range of meteorological inputs. To optimize the RF model, hyperparameters were tuned using Python’s GridSearchCV tool, and data were split into training (70%) and testing (30%) sets to validate model performance. After initial RF modeling, a feature selection process using Permutation Feature Importance (PFI) was applied to identify the most influential variables, refining the RF model to the top four parameters.<br /><br />Results and Discussion<br /><br />The results highlighted seasonal variability in cumulative ETc, with the S-S period exhibiting nearly double the ETc of the A-W period due to higher ambient temperature and increased solar radiation. These findings underscore the necessity of dynamic ETc models that can accommodate seasonal and environmental variations. The FAO 56 Kc method produced a mean RMSE of 0.915 mm/day across both growth cycles, demonstrating limitations in fixed Kc approaches under greenhouse conditions. The non-linear Kc model, with an average RMSE of 0.64 mm/day, provided improved accuracy by adjusting Kc values across different growth stages, especially during mid-growth when water demand peaks. This improvement aligns with the premise that non-linear models can better capture the ETc variability within controlled environments. The RF algorithm demonstrated superior accuracy and flexibility, outperforming both Kc-based models with R² values of 0.96 for the A-W period and 0.94 for the S-S period in training datasets, and with respective RMSE values of 0.365 mm/day and 0.57 mm/day in testing datasets. These results illustrate the RF model’s capacity to accurately model ETc by capturing complex, non-linear interactions among variables such as maxTG (maximum temperature inside the greenhouse), meanRHG (average relative humidity inside the greenhouse), and RadiationG (solar radiation inside the greenhouse) during the A-W period, with RadiationG and EToG emerging as key variables during the S-S period. By emphasizing critical seasonal drivers of ETc, the RF model offers a robust alternative that adjusts to environmental changes without relying on static Kc values. This adaptability supports RF’s potential as a powerful tool for ETc estimation, accurately reflecting seasonal influences on greenhouse crop water needs.<br /><br />Conclusion<br /><br />The findings from this study demonstrate that the RF algorithm, when applied to ETc modeling in greenhouse conditions, provides a flexible, high-accuracy alternative to traditional Kc methods. Unlike the FAO 56 Kc and non-linear Kc models, which rely on predefined or growth-stage specific coefficients, the RF approach enables direct ETc prediction using real-time meteorological data. By optimizing input variables through feature selection, RF efficiently reduced the model complexity, focusing on the top four influential parameters while retaining high predictive accuracy. This reduction not only streamlines data collection requirements but also enhances the model's applicability in practical greenhouse operations. The results indicate that RF's capacity to model complex relationships among variables makes it especially suited for greenhouse environments, where precision irrigation is crucial for sustainable water management. Ultimately, this research underscores the importance of integrating machine learning techniques in ETc estimation, providing greenhouse operators with adaptive, resource-efficient tools for managing water use in controlled agricultural settings.Extended Abstract<br /><br />Introduction<br /><br />Accurate estimation of crop evapotranspiration (ETc) is fundamental for the development of efficient irrigation strategies in greenhouse systems, where environmental conditions differ significantly from open-field farming. In Iran, greenhouse agriculture, particularly for cucumbers (Cucumis sativus L.), has expanded considerably, making irrigation optimization a critical priority. The specific microclimate within greenhouses, including controlled humidity, temperature, and solar radiation levels, affects plant water needs, requiring tailored approaches to predict ETc. Traditional models, like the FAO 56 crop coefficient (Kc) method, provide a standardized way to estimate ETc but are generally suited to field crops under variable outdoor conditions. The limitations of fixed Kc values in capturing the complexity of greenhouse environments have prompted the exploration of alternative models. In recent years, machine learning (ML) techniques, especially ensemble methods like the Random Forest (RF) algorithm, have emerged as promising tools for ETc modeling due to their capacity to manage non-linear interactions among meteorological variables and enhance model flexibility. This study evaluates the performance of three ETc estimation approaches for greenhouse-grown cucumber: the conventional FAO 56 Kc method, a non-linear Kc model using a third-degree polynomial, and direct ETc prediction through the RF algorithm. These methods are assessed across two growth cycles, autumn-winter (A-W) and spring-summer (S-S), to capture seasonal differences in crop water requirements.<br /><br />Materials and Methods<br /><br />The study was conducted in a research greenhouse located at the College of Agriculture and Natural Recourses, University of Tehran, focusing on daily ETc of cucumber over two distinct growth periods. Environmental parameters were measured both inside and outside the greenhouse, including maximum, minimum, and average temperatures, relative humidity, and solar radiation. Reference evapotranspiration inside the greenhouse (EToG) was derived using a micro-lysimeter installed with a turfgrass surface, while daily ETc was measured using a soil water balance method, where soil moisture content was monitored daily across three experimental plots to ensure precision. ETc calculations were performed through three modeling approaches. In the first approach, the FAO 56 Kc model estimated ETc by applying fixed crop coefficients and multiplying them by EToG. Although this method has been widely applied in field conditions, its applicability to greenhouses is limited due to fixed Kc assumptions. In the second approach, a non-linear Kc model was developed using third-degree polynomial regression on Kc values calculated as the ratio of ETc to EToG, capturing growth-stage specific variations. In the final approach, the RF model directly predicted ETc based on a broad range of meteorological inputs. To optimize the RF model, hyperparameters were tuned using Python’s GridSearchCV tool, and data were split into training (70%) and testing (30%) sets to validate model performance. After initial RF modeling, a feature selection process using Permutation Feature Importance (PFI) was applied to identify the most influential variables, refining the RF model to the top four parameters.<br /><br />Results and Discussion<br /><br />The results highlighted seasonal variability in cumulative ETc, with the S-S period exhibiting nearly double the ETc of the A-W period due to higher ambient temperature and increased solar radiation. These findings underscore the necessity of dynamic ETc models that can accommodate seasonal and environmental variations. The FAO 56 Kc method produced a mean RMSE of 0.915 mm/day across both growth cycles, demonstrating limitations in fixed Kc approaches under greenhouse conditions. The non-linear Kc model, with an average RMSE of 0.64 mm/day, provided improved accuracy by adjusting Kc values across different growth stages, especially during mid-growth when water demand peaks. This improvement aligns with the premise that non-linear models can better capture the ETc variability within controlled environments. The RF algorithm demonstrated superior accuracy and flexibility, outperforming both Kc-based models with R² values of 0.96 for the A-W period and 0.94 for the S-S period in training datasets, and with respective RMSE values of 0.365 mm/day and 0.57 mm/day in testing datasets. These results illustrate the RF model’s capacity to accurately model ETc by capturing complex, non-linear interactions among variables such as maxTG (maximum temperature inside the greenhouse), meanRHG (average relative humidity inside the greenhouse), and RadiationG (solar radiation inside the greenhouse) during the A-W period, with RadiationG and EToG emerging as key variables during the S-S period. By emphasizing critical seasonal drivers of ETc, the RF model offers a robust alternative that adjusts to environmental changes without relying on static Kc values. This adaptability supports RF’s potential as a powerful tool for ETc estimation, accurately reflecting seasonal influences on greenhouse crop water needs.<br /><br />Conclusion<br /><br />The findings from this study demonstrate that the RF algorithm, when applied to ETc modeling in greenhouse conditions, provides a flexible, high-accuracy alternative to traditional Kc methods. Unlike the FAO 56 Kc and non-linear Kc models, which rely on predefined or growth-stage specific coefficients, the RF approach enables direct ETc prediction using real-time meteorological data. By optimizing input variables through feature selection, RF efficiently reduced the model complexity, focusing on the top four influential parameters while retaining high predictive accuracy. This reduction not only streamlines data collection requirements but also enhances the model's applicability in practical greenhouse operations. The results indicate that RF's capacity to model complex relationships among variables makes it especially suited for greenhouse environments, where precision irrigation is crucial for sustainable water management. Ultimately, this research underscores the importance of integrating machine learning techniques in ETc estimation, providing greenhouse operators with adaptive, resource-efficient tools for managing water use in controlled agricultural settings.https://mmws.uma.ac.ir/article_3554_5d845e3e90ed91d4d9574877c87ea1fb.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Investigating the effect of biodiesel on diesel engine corrosion and durability with an environmental assessment: Modeling the impact on water resources with machine learningInvestigating the effect of biodiesel on diesel engine corrosion and durability with an environmental assessment: Modeling the impact on water resources with machine learning88106377410.22098/mmws.2025.16378.1526FARaminMeshkabadiAssociate professor, Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Ardabil, Iran.0000-0001-8329-6190SinaArdabiliAssistant professor, Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Ardabil, IranAzizBabapoorProfessor, Department of Chemical Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, IranMohammadAhmadiAssociate professor, Department of Science and Wood and Paper Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.0000-0002-9238-0631AlirezaToloueiPhD student, Department of Science and Wood and Paper industries, Faculty of Natural Resources, University of Tehran, Karaj, IranJournal Article20241215Introduction<br /><br />Because of the increasing trend towards the adoption of biodiesel-a veritable renewable alternative to fossil fuel-for purposes of transport, acceleration of research has been initiated over performance and durability of the engine. One major challenge identified can be termed as biodiesel as corrosive while in contact with metallic surfaces of diesel engines. Studies have indicated that biodiesel induces faster corrosion rates in metals like copper, brass, and carbon steel-associated with fuel systems and engine components. Corrosive behavior of biodiesel, being derived factors from its higher porosity, electrical conductivity, and presence of organic acids-forming when subjected to oxidation processes, are some of the factors that induce corrosion of parts in the engine which, in turn, are likely to stimulate higher maintenance costs and reduced life of engines at the end.<br /><br />Biodiesel corrosion and engine endurance can be assessed using this study as a pioneering attempt to understand the manner in which biodiesel interacts with all metal components in diesel engines and its environmental effects in water pollution using advanced modeling techniques. The thorough investigation of multiple metal types regarding biodiesel combustion presents an exhaustive life cycle assessment-from-their-staged influence of these metals on water pollution. Such a multidisciplinary investigative activity advances materials degradation understanding and pushes the development of enduring engine components that can negotiate the peculiarities of biofuels within their operating environments. Additionally, machine-learning models are among the most significant advances toward predicting corrosion performance from different biodiesel compositions and environmental conditions. Traditional approaches rely on spending a large amount of time conducting experiments, performing static immersion tests, and suffering delays in the delivery of data. Machine learning will help leverage the large data sets for pattern recognition and correlations to help ensure a better selection of materials and fuel formulations.<br /><br />Materials and Methods<br /><br />The steps of this research were carried out in four steps. In the first step, biodiesel was produced through the transesterification process of waste cooking oil in the presence of 1.1% by weight of sodium hydroxide (as a catalyst) and methanol with an alcohol to oil ratio of six to one and a mixing intensity of 710 rpm. The production temperature was kept constant in the range of the boiling point of methanol. Then, test fuel samples labeled B0 (pure diesel as a control fuel), B2 (diesel fuel containing two percent biodiesel), B5 (diesel fuel containing five percent biodiesel), and B10 (diesel fuel containing ten percent biodiesel) were prepared with a volume of one liter. Next, the engine test was performed using a single-cylinder diesel engine. Next, engine oil sampling was carried out in volumes of 150 ml at working intervals of 48, 96, and 144 hours. Atomic absorption spectroscopy was used to atomize metal elements in the presence of an oxyacetylene flame. In this process, the digested solution is converted into free atoms containing vapor. Using the calibration and extrapolation method, the obtained absorption value is used to determine the element concentration in the presence of control solutions. In the next step, the life cycle assessment began with the preparation of a life cycle inventory focusing on the inputs related to fuel sample preparation and its effects on the corrosion rate in a diesel engine based on engine operating hours. The IMPACT2002+ method was used to perform the production inventory assessment. The next step included modeling and determining the effective parameter using the support vector machine method (a method of machine learning). In this step, 60% of the data was used as data for the model training stage and 40% as data for the test stage. The root mean square error parameter and the correlation coefficient were used to evaluate the model. The recursive feature removal method was used to perform sensitivity analysis and select the independent variable affecting the dependent variables.Text<br /><br />Results and Discussion<br /><br />According to the results, it was concluded that increased biodiesel concentration has a pronounced effect on the indicators, with B10 being the highest level considered for acidification and eutrophication due to increased nitrogen oxides stemming from biodiesel combustion. It thus calls for optimizing combustion processes coupled to nitrogen oxide emission reductions in order to mitigate adverse ecological impacts. Coupled with this, there was also a support vector machine (SVM) modeling approach to predict these water quality parameters, which showed high accuracy and reliability in being modeled. Sensitivity analysis determined relevant independent variables. The analysis was performed on six independent parameters and showed that they affected much of the environment-related indicators to water resources. The critical factor, biodiesel percentage in fuel, emerged as a significant factor affecting all the indicators for environment impact (percentage basis: 0.55 for eutrophication, 0.81 for acidification, and 0.76 for ecotoxicity). Such a trend indicated that changes in biodiesel content would play an important role in incurring-reducing or increasing impacts on the environment. Engine operational hours had a relatively low impact on eutrophication (0.45) and acidification (0.61) with an even smaller impact on ecotoxicity (0.38). This indicates that engine hours do act towards deteriorating the environment but are less efficient than other parameters. Biodiesel production schemes highly impact the water eutrophication.<br /><br />Conclusion<br /><br />This operation period extremely matters because, from the analysis of the engine oils, it says that the amount of metal will be highly influenced by operation time. This was further brought about by oxidation or corrosion over time on the aluminum, chromium, copper, and iron content in this trend of attempting to use a higher percentage of biodiesel to achieve a significant increase in welding and corrosion affecting engine parts. Besides these two, acidification and also eutrophication of water resources have also been worsened by the use of biofuels, more especially B10. Excess nitrogen oxides emitted through burning biodiesel act as nutrients and favor algal blooms that ultimately acidify waters and could eventually be destructive to aquatic ecosystems. Hence nitrogen oxide emissions shall be strictly controlled, and one must install after-treatment systems for catalytic mitigation of this kind of fuels on the adverse effects the environment has to face with such fuels.Introduction<br /><br />Because of the increasing trend towards the adoption of biodiesel-a veritable renewable alternative to fossil fuel-for purposes of transport, acceleration of research has been initiated over performance and durability of the engine. One major challenge identified can be termed as biodiesel as corrosive while in contact with metallic surfaces of diesel engines. Studies have indicated that biodiesel induces faster corrosion rates in metals like copper, brass, and carbon steel-associated with fuel systems and engine components. Corrosive behavior of biodiesel, being derived factors from its higher porosity, electrical conductivity, and presence of organic acids-forming when subjected to oxidation processes, are some of the factors that induce corrosion of parts in the engine which, in turn, are likely to stimulate higher maintenance costs and reduced life of engines at the end.<br /><br />Biodiesel corrosion and engine endurance can be assessed using this study as a pioneering attempt to understand the manner in which biodiesel interacts with all metal components in diesel engines and its environmental effects in water pollution using advanced modeling techniques. The thorough investigation of multiple metal types regarding biodiesel combustion presents an exhaustive life cycle assessment-from-their-staged influence of these metals on water pollution. Such a multidisciplinary investigative activity advances materials degradation understanding and pushes the development of enduring engine components that can negotiate the peculiarities of biofuels within their operating environments. Additionally, machine-learning models are among the most significant advances toward predicting corrosion performance from different biodiesel compositions and environmental conditions. Traditional approaches rely on spending a large amount of time conducting experiments, performing static immersion tests, and suffering delays in the delivery of data. Machine learning will help leverage the large data sets for pattern recognition and correlations to help ensure a better selection of materials and fuel formulations.<br /><br />Materials and Methods<br /><br />The steps of this research were carried out in four steps. In the first step, biodiesel was produced through the transesterification process of waste cooking oil in the presence of 1.1% by weight of sodium hydroxide (as a catalyst) and methanol with an alcohol to oil ratio of six to one and a mixing intensity of 710 rpm. The production temperature was kept constant in the range of the boiling point of methanol. Then, test fuel samples labeled B0 (pure diesel as a control fuel), B2 (diesel fuel containing two percent biodiesel), B5 (diesel fuel containing five percent biodiesel), and B10 (diesel fuel containing ten percent biodiesel) were prepared with a volume of one liter. Next, the engine test was performed using a single-cylinder diesel engine. Next, engine oil sampling was carried out in volumes of 150 ml at working intervals of 48, 96, and 144 hours. Atomic absorption spectroscopy was used to atomize metal elements in the presence of an oxyacetylene flame. In this process, the digested solution is converted into free atoms containing vapor. Using the calibration and extrapolation method, the obtained absorption value is used to determine the element concentration in the presence of control solutions. In the next step, the life cycle assessment began with the preparation of a life cycle inventory focusing on the inputs related to fuel sample preparation and its effects on the corrosion rate in a diesel engine based on engine operating hours. The IMPACT2002+ method was used to perform the production inventory assessment. The next step included modeling and determining the effective parameter using the support vector machine method (a method of machine learning). In this step, 60% of the data was used as data for the model training stage and 40% as data for the test stage. The root mean square error parameter and the correlation coefficient were used to evaluate the model. The recursive feature removal method was used to perform sensitivity analysis and select the independent variable affecting the dependent variables.Text<br /><br />Results and Discussion<br /><br />According to the results, it was concluded that increased biodiesel concentration has a pronounced effect on the indicators, with B10 being the highest level considered for acidification and eutrophication due to increased nitrogen oxides stemming from biodiesel combustion. It thus calls for optimizing combustion processes coupled to nitrogen oxide emission reductions in order to mitigate adverse ecological impacts. Coupled with this, there was also a support vector machine (SVM) modeling approach to predict these water quality parameters, which showed high accuracy and reliability in being modeled. Sensitivity analysis determined relevant independent variables. The analysis was performed on six independent parameters and showed that they affected much of the environment-related indicators to water resources. The critical factor, biodiesel percentage in fuel, emerged as a significant factor affecting all the indicators for environment impact (percentage basis: 0.55 for eutrophication, 0.81 for acidification, and 0.76 for ecotoxicity). Such a trend indicated that changes in biodiesel content would play an important role in incurring-reducing or increasing impacts on the environment. Engine operational hours had a relatively low impact on eutrophication (0.45) and acidification (0.61) with an even smaller impact on ecotoxicity (0.38). This indicates that engine hours do act towards deteriorating the environment but are less efficient than other parameters. Biodiesel production schemes highly impact the water eutrophication.<br /><br />Conclusion<br /><br />This operation period extremely matters because, from the analysis of the engine oils, it says that the amount of metal will be highly influenced by operation time. This was further brought about by oxidation or corrosion over time on the aluminum, chromium, copper, and iron content in this trend of attempting to use a higher percentage of biodiesel to achieve a significant increase in welding and corrosion affecting engine parts. Besides these two, acidification and also eutrophication of water resources have also been worsened by the use of biofuels, more especially B10. Excess nitrogen oxides emitted through burning biodiesel act as nutrients and favor algal blooms that ultimately acidify waters and could eventually be destructive to aquatic ecosystems. Hence nitrogen oxide emissions shall be strictly controlled, and one must install after-treatment systems for catalytic mitigation of this kind of fuels on the adverse effects the environment has to face with such fuels.https://mmws.uma.ac.ir/article_3774_1bcf791e394bd2c02bbd4e6bea4c8759.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Evaluation of Actual Evapotranspiration Estimations of GLEAM Model and GRACE Satellite in the Gharehsu-Gorganrud basinEvaluation of Actual Evapotranspiration Estimations of GLEAM Model and GRACE Satellite in the Gharehsu-Gorganrud basin107123322110.22098/mmws.2024.15502.1482FAAmirhoseinPirmoonDepartment of Irrigation and Reclamation Engineering, College of Agriculture, University of Tehran, Karaj, IranNozarGhahremanDepartment of Irrigation and Reclamation Engineering, College of Agriculture, University of Tehran, Karaj, IranJournal Article20240724Introduction<br /><br />Improving water efficiency in agriculture especially in the face of global warming, requires an accurate of evapotranspiration. The Gharehsu-Gorganrud Watershed with a complex topography is located in Golestan province,north of Iran. Remote sensing methods can provide acceptable estimation of ET in larfe areas with inadequate ground observations. However, these methods have lower accuracy compared to ground-based techniques and require regional validation using water balance or lysimeter approaches. Selecting suitable satellite datasets for water management planning in a specific study area is a fundamental challenge that needs validation through physical methods and ground data. Previous studies show that the GLEAM model wwhich is based on satellite data provides reliable outputs for the Karkheh basin, west of Iran and can be used as an alternative to empirical and conventional methods for estimating crop water requirements. Gharekhani et al. (2020) investigated the uncertainty of actual evapotranspiration in the Gharehsu-Gorganrud basin using two climate databases and a remote sensing-based model. The study demonstrated that the ERA-Interim, GLEAM, and ETPT-JPL databases performed well in reducing uncertainty. Another study by Hafezparast et al. (2022) utilized GRACE satellite data to monitor changes in groundwater levels in the Mianrahān aquifer, revealing critical conditions in some aquifers.Overall, the research aimed to investigate the uncertainty in actual evapotranspiration estimates using GRACE satellite data and climate databases in the Gharehsu-Gorganrud Basin.<br /><br />Materials and Methods <br /><br />The study area is Gharehsu-Gorganrud basin which is a sub- basin of the main Caspian Sea Basin..The meteorological data used in this study were collected from synoptic stations of Iran meteorological organization. These data included average, minimum and maximum temperature, relative humidity, precipitation, wind speed, and total sunshine hours. The satellite-derived data provides estimates of actual evapotranspiration, as key variable for this study. However, to compare these estimates with the Penman-Monteith equation (FAO 56 PM) and determine the potential evapotranspiration, we need to consider the vegetation cover factor specific to the study crop of wheat. Hence, remote sensing techniques was employed to retrieve and acquire satellite images exclusively for the wheat fields, allowing us to accurately calculate the potential evapotranspiration by multiplying the actual evapotranspiration by the corresponding vegetation cover factor.The Global Land Evaporation Amsterdam Model (GLEAM) is an algorithm that estimates various components of evaporation and transpiration using satellite observations. The model outputs include potential evaporation, root zone soil moisture, surface soil moisture, and evaporative stress.This model utilizes solar radiation and temperature data to calculate potential evapotranspiration and multiplies it by the evaporative stress to obtain actual evaporation. The data is available on a daily, monthly, and yearly basis,and the grids are divided into 0.25-degree geographical resolution.GRACE satellite data is obtained from the GRACE spacecraft,measuring changes in Earth's gravity field due to water variations. These data, along with ground-based information like precipitation and runoff, enable the calculation of actual evapotranspiration. By assuming a water balance for a specific watershed and utilizing variables such as precipitation, runoff, and ΔS from GRACE, actual evapotranspiration can be determined.<br /><br />Results and Discussion <br /><br />Based on the comparisons, the best performance GLEAM model was obtained in Rezvan station, with an elevation of 1447 meters, dominant agricultural land use pattern, with statistical metrics of RMSE=0.32, MAE=0.30, R2 of 0.78, and MAPE =13.67.The lowest agreement was related to the "Kalaleh" station, with an elevation of 127 meters, non-agricultural land use pattern, and statistical indices of RMSE =0.77, MAE =0.60, R2=0.49, and MAPE =18.05. Overall, the results indicate that estimating evapotranspiration using the Penman-Monteith FAO equation performs better in high-elevation areas with agricultural land use patterns, while it yields less reliable results in low-elevation areas with non-agricultural land use patterns. The study by Gharekhani et al. (2020) also showed that the GLEAM model exhibits less uncertainty at elevations between 1400 and 1800 meters above sea level and in areas with agricultural land use patterns. For more precise explanations, further examination of the environment and comparison with field data is required. Based on the conducted comparisons, the best GRACE performance is associated with the "Rezvan" station, which has a drier climate compared to other stations, and statistical indices of 0.41 RMSE,0.38 MAE, 0.66 R2, and 17.46 MAPE. The worst performance is related to the "Kalaleh" station, with an elevation of 127 meters, non-agricultural land use pattern, and statistical indices of 0.91 RMSE, 0.77 MAE,0.45 R2, and 23.17 MAPE.<br /><br /><br /><br />Conclusions<br /><br />The use of satellite imagery can provide broader insights into various topics. In this study, the estimation of actual evapotranspiration was conducted using GRACE satellite data and the GLEAM model in the Gharehsu-Gorganrud region of Golestan province. The FAO Penman-Monteith equation was employed for evapotranspiration calculation. The results indicated that the best estimations belongs to Rezvan station, while the worste case performance was observed in Kalaleh station in estimating evapotranspiration based on the FAO Penman-Monteith equation measure and GLEAM data.More precise informatin on land cover maps in the region for ET estimation using vegetation cover dependent coefficents is necessary.Introduction<br /><br />Improving water efficiency in agriculture especially in the face of global warming, requires an accurate of evapotranspiration. The Gharehsu-Gorganrud Watershed with a complex topography is located in Golestan province,north of Iran. Remote sensing methods can provide acceptable estimation of ET in larfe areas with inadequate ground observations. However, these methods have lower accuracy compared to ground-based techniques and require regional validation using water balance or lysimeter approaches. Selecting suitable satellite datasets for water management planning in a specific study area is a fundamental challenge that needs validation through physical methods and ground data. Previous studies show that the GLEAM model wwhich is based on satellite data provides reliable outputs for the Karkheh basin, west of Iran and can be used as an alternative to empirical and conventional methods for estimating crop water requirements. Gharekhani et al. (2020) investigated the uncertainty of actual evapotranspiration in the Gharehsu-Gorganrud basin using two climate databases and a remote sensing-based model. The study demonstrated that the ERA-Interim, GLEAM, and ETPT-JPL databases performed well in reducing uncertainty. Another study by Hafezparast et al. (2022) utilized GRACE satellite data to monitor changes in groundwater levels in the Mianrahān aquifer, revealing critical conditions in some aquifers.Overall, the research aimed to investigate the uncertainty in actual evapotranspiration estimates using GRACE satellite data and climate databases in the Gharehsu-Gorganrud Basin.<br /><br />Materials and Methods <br /><br />The study area is Gharehsu-Gorganrud basin which is a sub- basin of the main Caspian Sea Basin..The meteorological data used in this study were collected from synoptic stations of Iran meteorological organization. These data included average, minimum and maximum temperature, relative humidity, precipitation, wind speed, and total sunshine hours. The satellite-derived data provides estimates of actual evapotranspiration, as key variable for this study. However, to compare these estimates with the Penman-Monteith equation (FAO 56 PM) and determine the potential evapotranspiration, we need to consider the vegetation cover factor specific to the study crop of wheat. Hence, remote sensing techniques was employed to retrieve and acquire satellite images exclusively for the wheat fields, allowing us to accurately calculate the potential evapotranspiration by multiplying the actual evapotranspiration by the corresponding vegetation cover factor.The Global Land Evaporation Amsterdam Model (GLEAM) is an algorithm that estimates various components of evaporation and transpiration using satellite observations. The model outputs include potential evaporation, root zone soil moisture, surface soil moisture, and evaporative stress.This model utilizes solar radiation and temperature data to calculate potential evapotranspiration and multiplies it by the evaporative stress to obtain actual evaporation. The data is available on a daily, monthly, and yearly basis,and the grids are divided into 0.25-degree geographical resolution.GRACE satellite data is obtained from the GRACE spacecraft,measuring changes in Earth's gravity field due to water variations. These data, along with ground-based information like precipitation and runoff, enable the calculation of actual evapotranspiration. By assuming a water balance for a specific watershed and utilizing variables such as precipitation, runoff, and ΔS from GRACE, actual evapotranspiration can be determined.<br /><br />Results and Discussion <br /><br />Based on the comparisons, the best performance GLEAM model was obtained in Rezvan station, with an elevation of 1447 meters, dominant agricultural land use pattern, with statistical metrics of RMSE=0.32, MAE=0.30, R2 of 0.78, and MAPE =13.67.The lowest agreement was related to the "Kalaleh" station, with an elevation of 127 meters, non-agricultural land use pattern, and statistical indices of RMSE =0.77, MAE =0.60, R2=0.49, and MAPE =18.05. Overall, the results indicate that estimating evapotranspiration using the Penman-Monteith FAO equation performs better in high-elevation areas with agricultural land use patterns, while it yields less reliable results in low-elevation areas with non-agricultural land use patterns. The study by Gharekhani et al. (2020) also showed that the GLEAM model exhibits less uncertainty at elevations between 1400 and 1800 meters above sea level and in areas with agricultural land use patterns. For more precise explanations, further examination of the environment and comparison with field data is required. Based on the conducted comparisons, the best GRACE performance is associated with the "Rezvan" station, which has a drier climate compared to other stations, and statistical indices of 0.41 RMSE,0.38 MAE, 0.66 R2, and 17.46 MAPE. The worst performance is related to the "Kalaleh" station, with an elevation of 127 meters, non-agricultural land use pattern, and statistical indices of 0.91 RMSE, 0.77 MAE,0.45 R2, and 23.17 MAPE.<br /><br /><br /><br />Conclusions<br /><br />The use of satellite imagery can provide broader insights into various topics. In this study, the estimation of actual evapotranspiration was conducted using GRACE satellite data and the GLEAM model in the Gharehsu-Gorganrud region of Golestan province. The FAO Penman-Monteith equation was employed for evapotranspiration calculation. The results indicated that the best estimations belongs to Rezvan station, while the worste case performance was observed in Kalaleh station in estimating evapotranspiration based on the FAO Penman-Monteith equation measure and GLEAM data.More precise informatin on land cover maps in the region for ET estimation using vegetation cover dependent coefficents is necessary.https://mmws.uma.ac.ir/article_3221_6e050e18b0e9b2aae8d0bf8feb2196cc.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Evaluation of Ground Water Level using Hybrid Models(Case Study:Khorramabad Plain)Evaluation of Ground Water Level using Hybrid Models(Case Study:Khorramabad Plain)124137299110.22098/mmws.2024.15077.1459FARezaChamanpiraAssistant Professor, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran0000-0003-4508-2807RezaDehghaniPhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, IranIrajVeyskaramiAssistant Professor, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, IranJournal Article20240515Abstract<br /><br />Introduction<br /><br />Groundwater resources are susceptible to both climate changes through (A) direct interaction with surface water sources such as rivers and lakes and (B) indirect interaction via the feeding process. Climate change indirectly affects the discharge and storage of groundwater by changing the nutritional conditions induced by rainfall and runoff; therefore, identifying and analyzing effective parameters such as climatic parameters can greatly help predict serious hazards threatening groundwater resources such as subsidence and drought . Moreover, considering that the relationship between climatic parameters and groundwater resources is complex and non-linear, the application of artificial intelligence models including modern hybrid models is a good solution to solving these problems.<br /><br />Given the non-parametric nature of these models, they are independent of the concept of prediction and the relationship between input variables and output data . As one classic feature of artificial intelligence models, they are capable of performing stochastic analysis of dynamics, patterns, and features associated with input variables used to simulate groundwater surface variables. Therefore, they are more feasible than other conceptual and statistical methods (such as experimental approaches and physics-based models). In general, AI-based models have many local applications. Therefore, these models have a great potential for various applications including hydrological and hydrogeological phenomena.<br /><br />In general, according to research findings, it is necessary to provide a solution and make proper forecasting of groundwater resources in order to prevent subsidence and drought phenomena around the world and in Iran. Therefore, in Iran, Khorramabad plain located in Lorestan province, which is very important in terms of drinking and agriculture whose products in this plain feed on groundwater for growth and development, has been subjected to illegal harvesting and digging of illegal wells. The level of groundwater resources has declined sharply in recent years. Therefore, groundwater level changes are more than necessary for forecasting and management measures to improve it. Therefore, the objective of this study is to analyze and predict climatic parameters using groundwater level forecasting in Khorramabad plain using integrated vector regression model with the help of Wavelet Transform (WT) and modern optimization algorithms such as bat algorithm and Grey Wolf Optimizer algorithm. The basis of climatic parameters is the groundwater level and abstraction from the aquifer.<br /><br /><br /><br />Materials and Methods<br /><br />In this study, . In addition, the Artificial neural network model approach was used to predict the surface water level, assuming that the amount of groundwater abstraction from the Khorramabad plain was consistent with that in the previous statistical period. Since the ANN model is subject to errors according to recent findings, the strategy of optimizing the adjustment parameters using meta-heuristic algorithms was adopted to reduce the model error. In recent years, several studies have investigated the ANN hybrid model using meta-heuristic algorithms; however, this study used new algorithms that have not been studied in hydrologic or hydrogeologic processes to reduce the problems and challenges associated with this model and introduced a new algorithm to facilitate the simulation process. In addition, future groundwater level can be predicted using dependent parameters and its decline be prevented, thus causing irreparable damage to Iran's groundwater resources. Given that this is one of the most fundamental and important problems in Iran's water issues, this study employs a new creative algorithm and black widow spider algorithms. To facilitate the groundwater level simulation, new meta-heuristic algorithms with WANN support vector wave regression model, having acceptable performance according to several studies, were used. This approach can ensure taking an effective step in simulating and predicting groundwater levels.<br /><br />Based on the structure of the SVR, the most basic step is to determine the tuning parameters. The coefficients of these parameters are usually determined through trial and error in ANN. Many factors affect the viability of trial and error and the accuracy of model prediction. Given the nature of trial and error, the predictive power may be generally reduced. Numerous solutions have been proposed by various researchers to address this fundamental weakness. One of these solutions adopted by researchers is to calculate the coefficients of parameter adjustment and optimize these coefficients by using meta-heuristic algorithms. Many decision-making problems can be expressed as finite optimization problems that have several decision variables which are constrained by a few constraints. Hybrid optimization problems are usually easily articulated, but are difficult to solve. Two sets of algorithms for solving hybrid problems include exact and approximate algorithms. Accurate algorithms guarantee finding the best solution, but the problem is that these algorithms are not applicable to difficult problems and the time to find solutions to difficult problems will increase exponentially. For most difficult problems, the exact algorithm is unsatisfactory. If the optimal answer is not achievable by using the exact algorithm in practice, we turn to the approximate algorithm. The approximate algorithm, commonly known as heuristic methods, seeks an appropriate and near-optimal solution. This method shortens the computation time compared to the previous method, but does not guarantee finding the best solution. A meta-initiative is the general framework of an algorithm that can provide solutions to the same problem with minor variations of various problems. There are many meta-heuristic algorithms such as Genetic Algorithm, Forbidden Search Simulation, Bat , gray wolves.<br /><br />Results and Discussion<br /><br />In this study, upon using climate change modeling, meteorological parameters (temperature and precipitation) for the years 2002-2022 were predicted and, then, by using ultra-exploratory hybrid models such as WSVR, Bat-ANN, and GWO-ANN, the groundwater level decline in Khorramabad plain located in Iran was predicted with the help of rainfall, temperature and harvest parameters associated with the four piezometric aquifers (Sarab Pardeh, Sali, Pol baba, and Naservand).<br /><br />Conclusion<br /><br />According to the statistical time periods of 2002-2022, WSVR, Bat-ANN, and GWO-ANN hybrid models in the combined structure including all input parameters had better performance due to higher memory, and WSVR model was more accurate with less error due to the separation of signals into two categories of high-pass and low-pass in WT wavelet transform.Abstract<br /><br />Introduction<br /><br />Groundwater resources are susceptible to both climate changes through (A) direct interaction with surface water sources such as rivers and lakes and (B) indirect interaction via the feeding process. Climate change indirectly affects the discharge and storage of groundwater by changing the nutritional conditions induced by rainfall and runoff; therefore, identifying and analyzing effective parameters such as climatic parameters can greatly help predict serious hazards threatening groundwater resources such as subsidence and drought . Moreover, considering that the relationship between climatic parameters and groundwater resources is complex and non-linear, the application of artificial intelligence models including modern hybrid models is a good solution to solving these problems.<br /><br />Given the non-parametric nature of these models, they are independent of the concept of prediction and the relationship between input variables and output data . As one classic feature of artificial intelligence models, they are capable of performing stochastic analysis of dynamics, patterns, and features associated with input variables used to simulate groundwater surface variables. Therefore, they are more feasible than other conceptual and statistical methods (such as experimental approaches and physics-based models). In general, AI-based models have many local applications. Therefore, these models have a great potential for various applications including hydrological and hydrogeological phenomena.<br /><br />In general, according to research findings, it is necessary to provide a solution and make proper forecasting of groundwater resources in order to prevent subsidence and drought phenomena around the world and in Iran. Therefore, in Iran, Khorramabad plain located in Lorestan province, which is very important in terms of drinking and agriculture whose products in this plain feed on groundwater for growth and development, has been subjected to illegal harvesting and digging of illegal wells. The level of groundwater resources has declined sharply in recent years. Therefore, groundwater level changes are more than necessary for forecasting and management measures to improve it. Therefore, the objective of this study is to analyze and predict climatic parameters using groundwater level forecasting in Khorramabad plain using integrated vector regression model with the help of Wavelet Transform (WT) and modern optimization algorithms such as bat algorithm and Grey Wolf Optimizer algorithm. The basis of climatic parameters is the groundwater level and abstraction from the aquifer.<br /><br /><br /><br />Materials and Methods<br /><br />In this study, . In addition, the Artificial neural network model approach was used to predict the surface water level, assuming that the amount of groundwater abstraction from the Khorramabad plain was consistent with that in the previous statistical period. Since the ANN model is subject to errors according to recent findings, the strategy of optimizing the adjustment parameters using meta-heuristic algorithms was adopted to reduce the model error. In recent years, several studies have investigated the ANN hybrid model using meta-heuristic algorithms; however, this study used new algorithms that have not been studied in hydrologic or hydrogeologic processes to reduce the problems and challenges associated with this model and introduced a new algorithm to facilitate the simulation process. In addition, future groundwater level can be predicted using dependent parameters and its decline be prevented, thus causing irreparable damage to Iran's groundwater resources. Given that this is one of the most fundamental and important problems in Iran's water issues, this study employs a new creative algorithm and black widow spider algorithms. To facilitate the groundwater level simulation, new meta-heuristic algorithms with WANN support vector wave regression model, having acceptable performance according to several studies, were used. This approach can ensure taking an effective step in simulating and predicting groundwater levels.<br /><br />Based on the structure of the SVR, the most basic step is to determine the tuning parameters. The coefficients of these parameters are usually determined through trial and error in ANN. Many factors affect the viability of trial and error and the accuracy of model prediction. Given the nature of trial and error, the predictive power may be generally reduced. Numerous solutions have been proposed by various researchers to address this fundamental weakness. One of these solutions adopted by researchers is to calculate the coefficients of parameter adjustment and optimize these coefficients by using meta-heuristic algorithms. Many decision-making problems can be expressed as finite optimization problems that have several decision variables which are constrained by a few constraints. Hybrid optimization problems are usually easily articulated, but are difficult to solve. Two sets of algorithms for solving hybrid problems include exact and approximate algorithms. Accurate algorithms guarantee finding the best solution, but the problem is that these algorithms are not applicable to difficult problems and the time to find solutions to difficult problems will increase exponentially. For most difficult problems, the exact algorithm is unsatisfactory. If the optimal answer is not achievable by using the exact algorithm in practice, we turn to the approximate algorithm. The approximate algorithm, commonly known as heuristic methods, seeks an appropriate and near-optimal solution. This method shortens the computation time compared to the previous method, but does not guarantee finding the best solution. A meta-initiative is the general framework of an algorithm that can provide solutions to the same problem with minor variations of various problems. There are many meta-heuristic algorithms such as Genetic Algorithm, Forbidden Search Simulation, Bat , gray wolves.<br /><br />Results and Discussion<br /><br />In this study, upon using climate change modeling, meteorological parameters (temperature and precipitation) for the years 2002-2022 were predicted and, then, by using ultra-exploratory hybrid models such as WSVR, Bat-ANN, and GWO-ANN, the groundwater level decline in Khorramabad plain located in Iran was predicted with the help of rainfall, temperature and harvest parameters associated with the four piezometric aquifers (Sarab Pardeh, Sali, Pol baba, and Naservand).<br /><br />Conclusion<br /><br />According to the statistical time periods of 2002-2022, WSVR, Bat-ANN, and GWO-ANN hybrid models in the combined structure including all input parameters had better performance due to higher memory, and WSVR model was more accurate with less error due to the separation of signals into two categories of high-pass and low-pass in WT wavelet transform.https://mmws.uma.ac.ir/article_2991_77b5a874a8e842fa47b7a5824cb6dd16.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Effect of Magnetized Water and Sugarcane Bagasse-derived Biochar on the Growth and Chemical Composition of Spinach Grown under Drought Stress (Limited Irrigation) in Greenhouse ConditionsEffect of Magnetized Water and Sugarcane Bagasse-derived Biochar on the Growth and Chemical Composition of Spinach Grown under Drought Stress (Limited Irrigation) in Greenhouse Conditions138159369810.22098/mmws.2025.16405.1530FAZeinabSaidaviDepartment of Soil Science and Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.Ali AkbarMoosaviDepartment of Soil Science and Engineering, College of Agriculture, Shiraz University,, Iran.RezaGhasemi FasaeiDepartment of Soil Science and Engineering, College of Agriculture, Shiraz University, Shiraz, IranFatemehRazzaghiDepartment of Water Engineering, College of Agriculture, Shiraz University, Shiraz, IranJournal Article20241219Extended Abstract<br /><br />Introduction<br /><br />Water scarcity and drought are becoming global problems, particularly in arid and semiarid regions. Drought stress is one of the factors that negatively affect the quantitative or qualitative growth of plants. changes outside the desired range of environmental factors. Due to the severe limitations of water resources in most regions of the country, moisture stress has been defined as one of the most important stresses adversely affecting plant growth and yield. Drought stress generally occurs when water levels of soil and atmosphere decrease through evaporation and transpiration. Almost all plants are somewhat drought tolerant, but the degree of tolerance varies from species to species. Magnetic water can be one of the promising methods to overcome the problem of lack of water resources, improve the production of agricultural products, and deal with drought stress on the plant in different stages of growth, at the same time, it is environmentally friendly. Adding biochar to the soil is another method of dealing with drought stress, increasing organic matter and, as a result, increasing water retention in the soil. Therefore, this study was conducted to investigate the combined effect of moisture stress, biochar, and magnetic water on spinach growth and chemical composition under greenhouse conditions.<br /><br />Materials and Methods <br /><br />A factorial greenhouse experiment in the form of a completely randomized design with three replications was conducted with drought stress at three levels of field capacity (FC), 75% of field capacity moisture (0.75FC), and 50% of field capacity (0.5FC); and four levels (0, 1, 2, and 3% by weight) of sugarcane bagasse-derived biochar prepared at 400 ºC and two types of water including magnetized water and non-magnetized water. The required soil was taken from a depth of 0 to 30 cm of a calcareous soil, air-dried, passed through a 2-mm sieve, and analyzed for physical and chemical properties. Sugarcane bagasse was collected from Imam Khomeini Sugar Factory located in Khuzestan Province and converted to biochar at 400 °C under limited oxygen conditions for 4 h. Magnetic water of 0.21 Tesla was prepared by repeatedly passing drinking water through a water magnetizing device. According to the results of the soil test, nutrient elements were added to the soil. 15 spinach seeds (Spinacia oleracea L., var. Virofly) were planted in each pot and they were maintained in greenhouse conditions. After one month, the number of plants was reduced to 9 in each pot. All pots were treated by the mentioned moisture levels through daily weighing. Drought treatments were started two weeks after planting and continued throughout the growing season for two months. After harvesting, plant samples were prepared and chemically analyzed. Statistical analysis was performed using Excel and SAS statistical software and means were compared using Duncan's test at a probability level of 5%.<br /><br />Results and Discussion <br /><br />The results showed that in plants irrigated with magnetized water, the application of 1, 2, and 3% of biochar caused an increase of 3.9%, 7.8%, and 8.3%, respectively in the shoot dry weight of spinach, although the changes were not statistically significant, Furthermore, moisture levels of 0.75FC and 0.5FC in the magnetized water caused a decrease of 3.7% and 15.7%, respectively, and in normal water, it caused 8.3% and 24% decrease in shoot dry weight, respectively. In plants irrigated with magnetized water, application of 1, 2, and 3% biochar compared to the control caused a decrease of 19.2%, 32.5%, and 30.6% respectively in the shoot Cu concentration. Whereas, the use of 2% and 3% biochar caused an increase of 6.2% and 11.9% in the shoot Mn concentration. Applying 0.75FC and 0.5FC moisture stress levels compared to the normal conditions caused a significant decrease of 19.5% and 29.7% in shoot Cu concentration, 21.2% and 21.1% decrease in shoot Fe concentration, and 18% and 20% decrease in shoot K concentration, respectively. Whereas, the mentioned moisture levels caused an increase of 24.7% and 47% in the shoot Mn concentration.<br /><br />Conclusion <br /><br />In general, the application of magnetized water compared to normal water significantly increased the shoot Mn and Zn concentration by 3 times and 40.4%, respectively, compared to that of the control. Using magnetized water increased shoot dry weight by 10.8% compared to normal water. The results showed that the application of magnetized water can be used as a suitable solution to increase the concentration of some nutrients and some growth characteristics of spinach. In general, the results showed that the application of magnetized water and biochar, which have been introduced as two strategies to reduce the adverse effects of drought on plants, can be effective on the chemical composition of plants and nutrient concentration of the plant. Further studies are recommended to evaluate the impacts of other biochar derived from livestock manure and plant residues, as well as different levels of biochar, on spinach and other crops, under drought or other stress conditions.Extended Abstract<br /><br />Introduction<br /><br />Water scarcity and drought are becoming global problems, particularly in arid and semiarid regions. Drought stress is one of the factors that negatively affect the quantitative or qualitative growth of plants. changes outside the desired range of environmental factors. Due to the severe limitations of water resources in most regions of the country, moisture stress has been defined as one of the most important stresses adversely affecting plant growth and yield. Drought stress generally occurs when water levels of soil and atmosphere decrease through evaporation and transpiration. Almost all plants are somewhat drought tolerant, but the degree of tolerance varies from species to species. Magnetic water can be one of the promising methods to overcome the problem of lack of water resources, improve the production of agricultural products, and deal with drought stress on the plant in different stages of growth, at the same time, it is environmentally friendly. Adding biochar to the soil is another method of dealing with drought stress, increasing organic matter and, as a result, increasing water retention in the soil. Therefore, this study was conducted to investigate the combined effect of moisture stress, biochar, and magnetic water on spinach growth and chemical composition under greenhouse conditions.<br /><br />Materials and Methods <br /><br />A factorial greenhouse experiment in the form of a completely randomized design with three replications was conducted with drought stress at three levels of field capacity (FC), 75% of field capacity moisture (0.75FC), and 50% of field capacity (0.5FC); and four levels (0, 1, 2, and 3% by weight) of sugarcane bagasse-derived biochar prepared at 400 ºC and two types of water including magnetized water and non-magnetized water. The required soil was taken from a depth of 0 to 30 cm of a calcareous soil, air-dried, passed through a 2-mm sieve, and analyzed for physical and chemical properties. Sugarcane bagasse was collected from Imam Khomeini Sugar Factory located in Khuzestan Province and converted to biochar at 400 °C under limited oxygen conditions for 4 h. Magnetic water of 0.21 Tesla was prepared by repeatedly passing drinking water through a water magnetizing device. According to the results of the soil test, nutrient elements were added to the soil. 15 spinach seeds (Spinacia oleracea L., var. Virofly) were planted in each pot and they were maintained in greenhouse conditions. After one month, the number of plants was reduced to 9 in each pot. All pots were treated by the mentioned moisture levels through daily weighing. Drought treatments were started two weeks after planting and continued throughout the growing season for two months. After harvesting, plant samples were prepared and chemically analyzed. Statistical analysis was performed using Excel and SAS statistical software and means were compared using Duncan's test at a probability level of 5%.<br /><br />Results and Discussion <br /><br />The results showed that in plants irrigated with magnetized water, the application of 1, 2, and 3% of biochar caused an increase of 3.9%, 7.8%, and 8.3%, respectively in the shoot dry weight of spinach, although the changes were not statistically significant, Furthermore, moisture levels of 0.75FC and 0.5FC in the magnetized water caused a decrease of 3.7% and 15.7%, respectively, and in normal water, it caused 8.3% and 24% decrease in shoot dry weight, respectively. In plants irrigated with magnetized water, application of 1, 2, and 3% biochar compared to the control caused a decrease of 19.2%, 32.5%, and 30.6% respectively in the shoot Cu concentration. Whereas, the use of 2% and 3% biochar caused an increase of 6.2% and 11.9% in the shoot Mn concentration. Applying 0.75FC and 0.5FC moisture stress levels compared to the normal conditions caused a significant decrease of 19.5% and 29.7% in shoot Cu concentration, 21.2% and 21.1% decrease in shoot Fe concentration, and 18% and 20% decrease in shoot K concentration, respectively. Whereas, the mentioned moisture levels caused an increase of 24.7% and 47% in the shoot Mn concentration.<br /><br />Conclusion <br /><br />In general, the application of magnetized water compared to normal water significantly increased the shoot Mn and Zn concentration by 3 times and 40.4%, respectively, compared to that of the control. Using magnetized water increased shoot dry weight by 10.8% compared to normal water. The results showed that the application of magnetized water can be used as a suitable solution to increase the concentration of some nutrients and some growth characteristics of spinach. In general, the results showed that the application of magnetized water and biochar, which have been introduced as two strategies to reduce the adverse effects of drought on plants, can be effective on the chemical composition of plants and nutrient concentration of the plant. Further studies are recommended to evaluate the impacts of other biochar derived from livestock manure and plant residues, as well as different levels of biochar, on spinach and other crops, under drought or other stress conditions.https://mmws.uma.ac.ir/article_3698_5d17629d1f17f661796529d39c529003.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Drainage Density Modeling using Precipitation in Different Climates of Kurdistan ProvinceDrainage Density Modeling using Precipitation in Different Climates of Kurdistan Province160181382910.22098/mmws.2025.16773.1559FAKamranChapiDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, IranShamimFaiziM.Sc. in Watershed Management, Student Affairs Deputy, University of Kurdistan, Sanandaj, Iran.ZaniarFatehiM.Sc. in Water Resources Engineering, Department of Water Resources Engineering and Management, Tose Danesh Institute of Higher Education, Sanandaj, IranJournal Article20250210Introduction<br /><br />Drainage density is one of the most important geomorphologic indices of watersheds, which has often been used to express the degree of fluvial dissection, rainfall and infiltration capacity, flooding potential, landslide potential, topography evolution, and basin erosion. This index is greatly influenced by climate, vegetation, bedrock geology, time, and morphometric factors among which, the relationship between drainage density and climate is important, firstly in assessing the sensitivity of water resources and watershed hydrology to climate change; secondly, it determines how close to the truth the selection of this index is for climatic and hydrological studies; and thirdly, it reveales whether there is a significant difference in the amount of drainage density in different climates, so that it is necessary to consider its variability in the study of flooding, landslide and erosion potentials. The main objective of this study is to investigate the effects of climate (mean annual precipitation) on the watershed drainage density of Kurdistan Province, Iran through modeling this relationship, as well as drainage density variation with precipitation in different climates. This information can be utilized as key tools for watershed planning specifically in mitigating environmental crisis.<br /><br /><br /><br />Materials and Methods<br /><br />Study area<br /><br />Kurdistan province is one of the provinces of western Iran, located between longitudes of 45º 31´ and 48º 16´ E and latitudes of 34º 44´ and 36º 30´ N. Its area is about 29137 Km2. The mean annual precipitation is 485 mm and the mean annual temperature is 9 ºC. This province is an important region since it is a huge source of fresh water for 5 large watersheds in Iran. The west of the province is covered by sparse oak forests while the east is mainly agriculture and rangelands. The majority of the province belongs to the Sanandaj-Sirjan geological zone and in terms of geomorphology, the entire Kurdistan province is covered by high mountainous areas and hilly regions. <br /><br /><br /><br />Data Acquisition and Analysis<br /><br />Based on long-term precipitation and temperature data of 8 synoptic weather stations of Kurdistan province, the climate was classified into four types including humid, semi-humid, Mediterranean, and semi-arid climates according to the De Martonne climatic classifications method by Surfer software. A number of 40, 87, 62 and 57 small watersheds (area less than 50 Km2) have been respectively selected from humid, semi-humid, Mediterranean, and semi-arid climates such that their total areas cover at least 10% - 15% of the entire area of each climate; at least, 40 to 100 watersheds exist in each climate for studying; and they have been distributed unformly in each climate. In order to determine the mean annual precipitation of each watershed, the iso-hyetal map of the Kurdistan province was prepared using the above-mentioned 8 weather stations in the ArcGIS environment. Drainage densities (Dd) of the 246 watersheds were calculated using Dd = ΣL/A equation. SPSS and Excel were used to analyze the data and to model the relationship between drainage density and mean annual precipitation in different climates of Kurdistan province.<br /><br /><br /><br />Results and Discussion<br /><br />Results showed that climate has a significant effect (significant level of 0.0001) on drainage density such that the mean of drainage density is significantly different in different climates of Kurdistan province. The maximum mean drainage density was 1.71 Km/Km2 occurred in Mediterranean climate while this climate has showed the minimum coefficient of variation for drainage density (19.9%) and maximum coefficient of variation for precipitation (21.9%). The minimum amount of drainage density was 1.12 Km/Km2 happened in humid climate while this climate showed a high coefficient of variation for drainage density (but not maximum value) and the minimum coefficient of variation for precipitation (10.2%). The modeling of drainage density with mean annual precipitation revealed a non-linear behavior of drainage density in different climates. The relationship of drainage density with mean annual precipitation in humid, semi-humid, Mediterranean, and semi-arid were modelled as exponential, linear, and sigmoid functions, respectively. We also came upon this result that the maximum drainage density has occurred in mean annual precipitation of 400 mm and in precipitations less or more than 400 mm, drainage density decreases. In precipitations less than 400 mm, drainage density increases with increase in precipitation, and in precipitations more than 400 mm, when precipitation increases, drainage density decreases. The 400 mm precipitation, therefore, is introduced as an Index Precipitation for studying morphometric factors specifically drainage density in Kurdistan watersheds and similar regions in future works. The findings of this study are in line with findings of Carlston (1963), Gregory and Gardiner (1975), Gregory (1976), Daniel (1981), Abrahams (1972), Abrahams and Ponczynski (1984), and Moglen et al. (1998) who had previously introduced 1250, 500, 500, 3000, 2000, 280, and 450 mm as Index Precipitation for studying drainage density in different climates at other parts of the world.<br /><br /><br /><br />Conclusions<br /><br />The drainage density of 246 small watersheds of Kurdistan province was investigated and its relationship with average annual precipitation was modeled and also the role of climate in the process of changes in drainage density was determined. The findings of this research showed that the climate has a significant effect on the drainage density of watersheds, therefore, in each climate, the relationship between the drainage density and the amount of precipitation has unique behavior and is different. In Mediterranean and semi-arid climates, changes in drainage density are under the control of precipitation, and in semi-humid and humid climates, changes in drainage density are under the control of vegetation cover, which should be investigated in future studies. A close relationship between drainage density and precipitation proved that this index can be used in the studies of climate change impacts on water resources and other natural hazards, however, it is necessary to consider its variability. Such studies can provide important information for official authorities to prepare better watershed management plans.Introduction<br /><br />Drainage density is one of the most important geomorphologic indices of watersheds, which has often been used to express the degree of fluvial dissection, rainfall and infiltration capacity, flooding potential, landslide potential, topography evolution, and basin erosion. This index is greatly influenced by climate, vegetation, bedrock geology, time, and morphometric factors among which, the relationship between drainage density and climate is important, firstly in assessing the sensitivity of water resources and watershed hydrology to climate change; secondly, it determines how close to the truth the selection of this index is for climatic and hydrological studies; and thirdly, it reveales whether there is a significant difference in the amount of drainage density in different climates, so that it is necessary to consider its variability in the study of flooding, landslide and erosion potentials. The main objective of this study is to investigate the effects of climate (mean annual precipitation) on the watershed drainage density of Kurdistan Province, Iran through modeling this relationship, as well as drainage density variation with precipitation in different climates. This information can be utilized as key tools for watershed planning specifically in mitigating environmental crisis.<br /><br /><br /><br />Materials and Methods<br /><br />Study area<br /><br />Kurdistan province is one of the provinces of western Iran, located between longitudes of 45º 31´ and 48º 16´ E and latitudes of 34º 44´ and 36º 30´ N. Its area is about 29137 Km2. The mean annual precipitation is 485 mm and the mean annual temperature is 9 ºC. This province is an important region since it is a huge source of fresh water for 5 large watersheds in Iran. The west of the province is covered by sparse oak forests while the east is mainly agriculture and rangelands. The majority of the province belongs to the Sanandaj-Sirjan geological zone and in terms of geomorphology, the entire Kurdistan province is covered by high mountainous areas and hilly regions. <br /><br /><br /><br />Data Acquisition and Analysis<br /><br />Based on long-term precipitation and temperature data of 8 synoptic weather stations of Kurdistan province, the climate was classified into four types including humid, semi-humid, Mediterranean, and semi-arid climates according to the De Martonne climatic classifications method by Surfer software. A number of 40, 87, 62 and 57 small watersheds (area less than 50 Km2) have been respectively selected from humid, semi-humid, Mediterranean, and semi-arid climates such that their total areas cover at least 10% - 15% of the entire area of each climate; at least, 40 to 100 watersheds exist in each climate for studying; and they have been distributed unformly in each climate. In order to determine the mean annual precipitation of each watershed, the iso-hyetal map of the Kurdistan province was prepared using the above-mentioned 8 weather stations in the ArcGIS environment. Drainage densities (Dd) of the 246 watersheds were calculated using Dd = ΣL/A equation. SPSS and Excel were used to analyze the data and to model the relationship between drainage density and mean annual precipitation in different climates of Kurdistan province.<br /><br /><br /><br />Results and Discussion<br /><br />Results showed that climate has a significant effect (significant level of 0.0001) on drainage density such that the mean of drainage density is significantly different in different climates of Kurdistan province. The maximum mean drainage density was 1.71 Km/Km2 occurred in Mediterranean climate while this climate has showed the minimum coefficient of variation for drainage density (19.9%) and maximum coefficient of variation for precipitation (21.9%). The minimum amount of drainage density was 1.12 Km/Km2 happened in humid climate while this climate showed a high coefficient of variation for drainage density (but not maximum value) and the minimum coefficient of variation for precipitation (10.2%). The modeling of drainage density with mean annual precipitation revealed a non-linear behavior of drainage density in different climates. The relationship of drainage density with mean annual precipitation in humid, semi-humid, Mediterranean, and semi-arid were modelled as exponential, linear, and sigmoid functions, respectively. We also came upon this result that the maximum drainage density has occurred in mean annual precipitation of 400 mm and in precipitations less or more than 400 mm, drainage density decreases. In precipitations less than 400 mm, drainage density increases with increase in precipitation, and in precipitations more than 400 mm, when precipitation increases, drainage density decreases. The 400 mm precipitation, therefore, is introduced as an Index Precipitation for studying morphometric factors specifically drainage density in Kurdistan watersheds and similar regions in future works. The findings of this study are in line with findings of Carlston (1963), Gregory and Gardiner (1975), Gregory (1976), Daniel (1981), Abrahams (1972), Abrahams and Ponczynski (1984), and Moglen et al. (1998) who had previously introduced 1250, 500, 500, 3000, 2000, 280, and 450 mm as Index Precipitation for studying drainage density in different climates at other parts of the world.<br /><br /><br /><br />Conclusions<br /><br />The drainage density of 246 small watersheds of Kurdistan province was investigated and its relationship with average annual precipitation was modeled and also the role of climate in the process of changes in drainage density was determined. The findings of this research showed that the climate has a significant effect on the drainage density of watersheds, therefore, in each climate, the relationship between the drainage density and the amount of precipitation has unique behavior and is different. In Mediterranean and semi-arid climates, changes in drainage density are under the control of precipitation, and in semi-humid and humid climates, changes in drainage density are under the control of vegetation cover, which should be investigated in future studies. A close relationship between drainage density and precipitation proved that this index can be used in the studies of climate change impacts on water resources and other natural hazards, however, it is necessary to consider its variability. Such studies can provide important information for official authorities to prepare better watershed management plans.https://mmws.uma.ac.ir/article_3829_d085a54112606801bded95b1f6e7e0c6.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Estimating soil moisture using vegetation cover indices and soil surface temperature in agricultural and saline fields of Qazvin plainEstimating soil moisture using vegetation cover indices and soil surface temperature in agricultural and saline fields of Qazvin plain182198286110.22098/mmws.2024.14569.1416FAMohadese SadatFakharPh.D. Student, Department of Water Engineering, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin IranBizhanNazariAssociate Professor of the Department of Irrigation and Development Engineering, Faculty of Agriculture and Natural Resources, University of Tehran & Faculty member of Imam Khomeini International University, Qazvin, Iran,Journal Article20240201Abstract<br /><br />Introduction <br /><br />Soil moisture is a key indicator for defining and identifying agricultural drought. Estimating soil moisture is useful for identifying water scarcity conditions in the early stages and evolving drought events, which have implications for crop yield uncertainty, food security, agricultural insurance, policy-making, and crop planning. Soil moisture can be estimated through various field measurement methods, which offer a wide range of techniques. Point-scale measurements are the most accurate methods for measuring Soil Moisture Content (SMC) and can also be fully automated. However, installing and maintaining these instruments can be time-consuming and costly. Soil moisture is a key indicator for defining and identifying agricultural drought. Estimating soil moisture is useful for identifying water scarcity conditions in the early stages and evolving drought events, which have implications for crop yield uncertainty, food security, agricultural insurance, policy-making, and crop planning This is particularly relevant for dry and semi-arid regions worldwide. Agricultural drought acts as a catalyst, leading to social and political conflicts in developing countries.<br /><br />The advantage of using remote sensing data is the ability to create a large archive of high-resolution data.High soil salinity has negative effects on soil structure, nutrient content, and plant growth, leading to reduced crop yields and increased desertification. Various factors such as inadequate irrigation, excessive use of fertilizers, and land-use changes can contribute to increased soil salinity levels. Climate variations also play a significant role in increasing salt content in the soil, particularly in areas with low water levels and decreased groundwater quality. Therefore, monitoring soil salinity levels is crucial for sustainable soil and agricultural management.<br /><br /><br /><br />Remote sensing has proven to be a suitable method for monitoring salinity in large-scale and heterogeneous landscapes. Through the analysis of remote sensing data, maps and spatial models of salinity and moisture distribution can be generated, aiding land management and developing risk reduction strategies. Modern remote sensing technologies include satellite imagery and aerial photographs, which provide valuable information on vegetation cover, soil composition, and moisture.<br /><br /><br /><br />Over the past few decades, research in remote sensing has made considerable progress, and various tools for data collection and analysis have been developed. Among these tools, multispectral imaging sensors receive information across different wavelengths, providing higher accuracy and resolution in images.<br /><br /><br /><br />Soil moisture plays a crucial role in regulating runoff, vegetation production, and evapotranspiration, making it essential for identifying agricultural drought. Estimating soil moisture can be used in the early detection of water scarcity and drought events. One common approach for estimating soil moisture is the use of remote sensing data. Satellite imagery and aerial photographs can provide useful information about soil color, texture, and vegetation cover, aiding in the estimation of soil moisture.<br /><br /><br /><br /><br /><br /><br /><br />Materials and Methods <br /><br />In this research, Landsat 8 and Sentinel-2 satellite imagery from February 2023 were used to estimate soil moisture. The Google Earth Engine platform was utilized for processing and calculations. Various vegetation indices and the Land Surface Temperature (LST) index were analyzed to assess their relationship with soil moisture. Ground data was collected using the HH2 Moisture Meter device for 23 soil moisture samples.<br /><br />Results and Discussion <br /><br />The results showed that there was a higher correlation between vegetation indices derived from the SENTINEL-2 sensor compared to the LANDSAT-8 sensor. Indices such as NDVI, SAVI, and NDTI had a high correlation with soil moisture content, with NDTI showing the highest correlation of 0.84. Based on the indices with the highest correlation, a regression model was developed to estimate soil moisture content. The results indicated that the regression model using the Land Surface Temperature (LST) and NDTI indices from the LANDSAT-8 sensor had the highest accuracy, with a coefficient of determination (R-squared) of 0.81 and a bias of 0.27.<br /><br />Conclusion <br /><br />The results of this study demonstrate that the use of remote sensing data, particularly LANDSAT-8 and SENTINEL-2 satellite imagery, can be an effective tool for estimating and monitoring soil moisture and soil salinity in agricultural and saline areas of Qazvin plain. Furthermore, continuous utilization of remote sensing data at short time intervals can serve as a useful tool for accurate and continuous soil moisture monitoring.<br /><br /><br /><br />In this study, the correlation matrix between the calculated indices from satellite images and soil moisture was examined. The results showed that all indices, including SAVI, NDVI, NDTI, NDMI, and SMSWIR, have high correlations with soil moisture. In particular, SAVI and NDVI indices in LANDSAT-8 images had the highest correlation with negative values of 0.84 and 0.71, respectively. Moreover, the correlation analysis of land surface temperature with vegetation indices demonstrated high correlations for all indices.<br /><br /><br /><br />In addition to correlation analysis, regression models were developed to estimate soil moisture using two indices, NDTI and LST. These models utilized influential factors on soil moisture and were capable of more accurate estimation of soil moisture using LANDSAT-8 images. Therefore, these models were introduced as regression models with high accuracy within the study area.<br /><br /><br /><br />In general, the results of this section of the study indicate that the use of remote sensing data, particularly LANDSAT-8 and SENTINEL-2 satellite images, is beneficial for soil moisture estimation and salinity monitoring in agricultural and saline areas of Qazvin plain. Furthermore, land surface temperature serves as a useful indicator for predicting soil moisture and analyzing its temporal changes. The use of regression models based on calculated indices from LANDSAT-8 and SENTINEL-2 satellite images can lead to high accuracy in estimating soil moisture and soil salinity in the study areas.<br /><br /><br /><br />By utilizing remote sensing data, soil moisture can be continuously monitored, and its changes over time can be observed. This information can assist farmers and water resource managers in determining the optimal timing for irrigation and efficient water resource management.<br /><br /><br /><br />Overall, the use of remote sensing data in estimating and monitoring soil moisture and soil salinity in agricultural and saline areas of Qazvin plain can significantly improve water resource management and agriculture for this region.Abstract<br /><br />Introduction <br /><br />Soil moisture is a key indicator for defining and identifying agricultural drought. Estimating soil moisture is useful for identifying water scarcity conditions in the early stages and evolving drought events, which have implications for crop yield uncertainty, food security, agricultural insurance, policy-making, and crop planning. Soil moisture can be estimated through various field measurement methods, which offer a wide range of techniques. Point-scale measurements are the most accurate methods for measuring Soil Moisture Content (SMC) and can also be fully automated. However, installing and maintaining these instruments can be time-consuming and costly. Soil moisture is a key indicator for defining and identifying agricultural drought. Estimating soil moisture is useful for identifying water scarcity conditions in the early stages and evolving drought events, which have implications for crop yield uncertainty, food security, agricultural insurance, policy-making, and crop planning This is particularly relevant for dry and semi-arid regions worldwide. Agricultural drought acts as a catalyst, leading to social and political conflicts in developing countries.<br /><br />The advantage of using remote sensing data is the ability to create a large archive of high-resolution data.High soil salinity has negative effects on soil structure, nutrient content, and plant growth, leading to reduced crop yields and increased desertification. Various factors such as inadequate irrigation, excessive use of fertilizers, and land-use changes can contribute to increased soil salinity levels. Climate variations also play a significant role in increasing salt content in the soil, particularly in areas with low water levels and decreased groundwater quality. Therefore, monitoring soil salinity levels is crucial for sustainable soil and agricultural management.<br /><br /><br /><br />Remote sensing has proven to be a suitable method for monitoring salinity in large-scale and heterogeneous landscapes. Through the analysis of remote sensing data, maps and spatial models of salinity and moisture distribution can be generated, aiding land management and developing risk reduction strategies. Modern remote sensing technologies include satellite imagery and aerial photographs, which provide valuable information on vegetation cover, soil composition, and moisture.<br /><br /><br /><br />Over the past few decades, research in remote sensing has made considerable progress, and various tools for data collection and analysis have been developed. Among these tools, multispectral imaging sensors receive information across different wavelengths, providing higher accuracy and resolution in images.<br /><br /><br /><br />Soil moisture plays a crucial role in regulating runoff, vegetation production, and evapotranspiration, making it essential for identifying agricultural drought. Estimating soil moisture can be used in the early detection of water scarcity and drought events. One common approach for estimating soil moisture is the use of remote sensing data. Satellite imagery and aerial photographs can provide useful information about soil color, texture, and vegetation cover, aiding in the estimation of soil moisture.<br /><br /><br /><br /><br /><br /><br /><br />Materials and Methods <br /><br />In this research, Landsat 8 and Sentinel-2 satellite imagery from February 2023 were used to estimate soil moisture. The Google Earth Engine platform was utilized for processing and calculations. Various vegetation indices and the Land Surface Temperature (LST) index were analyzed to assess their relationship with soil moisture. Ground data was collected using the HH2 Moisture Meter device for 23 soil moisture samples.<br /><br />Results and Discussion <br /><br />The results showed that there was a higher correlation between vegetation indices derived from the SENTINEL-2 sensor compared to the LANDSAT-8 sensor. Indices such as NDVI, SAVI, and NDTI had a high correlation with soil moisture content, with NDTI showing the highest correlation of 0.84. Based on the indices with the highest correlation, a regression model was developed to estimate soil moisture content. The results indicated that the regression model using the Land Surface Temperature (LST) and NDTI indices from the LANDSAT-8 sensor had the highest accuracy, with a coefficient of determination (R-squared) of 0.81 and a bias of 0.27.<br /><br />Conclusion <br /><br />The results of this study demonstrate that the use of remote sensing data, particularly LANDSAT-8 and SENTINEL-2 satellite imagery, can be an effective tool for estimating and monitoring soil moisture and soil salinity in agricultural and saline areas of Qazvin plain. Furthermore, continuous utilization of remote sensing data at short time intervals can serve as a useful tool for accurate and continuous soil moisture monitoring.<br /><br /><br /><br />In this study, the correlation matrix between the calculated indices from satellite images and soil moisture was examined. The results showed that all indices, including SAVI, NDVI, NDTI, NDMI, and SMSWIR, have high correlations with soil moisture. In particular, SAVI and NDVI indices in LANDSAT-8 images had the highest correlation with negative values of 0.84 and 0.71, respectively. Moreover, the correlation analysis of land surface temperature with vegetation indices demonstrated high correlations for all indices.<br /><br /><br /><br />In addition to correlation analysis, regression models were developed to estimate soil moisture using two indices, NDTI and LST. These models utilized influential factors on soil moisture and were capable of more accurate estimation of soil moisture using LANDSAT-8 images. Therefore, these models were introduced as regression models with high accuracy within the study area.<br /><br /><br /><br />In general, the results of this section of the study indicate that the use of remote sensing data, particularly LANDSAT-8 and SENTINEL-2 satellite images, is beneficial for soil moisture estimation and salinity monitoring in agricultural and saline areas of Qazvin plain. Furthermore, land surface temperature serves as a useful indicator for predicting soil moisture and analyzing its temporal changes. The use of regression models based on calculated indices from LANDSAT-8 and SENTINEL-2 satellite images can lead to high accuracy in estimating soil moisture and soil salinity in the study areas.<br /><br /><br /><br />By utilizing remote sensing data, soil moisture can be continuously monitored, and its changes over time can be observed. This information can assist farmers and water resource managers in determining the optimal timing for irrigation and efficient water resource management.<br /><br /><br /><br />Overall, the use of remote sensing data in estimating and monitoring soil moisture and soil salinity in agricultural and saline areas of Qazvin plain can significantly improve water resource management and agriculture for this region.https://mmws.uma.ac.ir/article_2861_2c66c75891c6a07e71e1206b43e7df83.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622The effects of climate change on precipitation and temperature using SSP scenarios (case study: Fars province)The effects of climate change on precipitation and temperature using SSP scenarios (case study: Fars province)199218288510.22098/mmws.2024.14691.1425FAParsaHaghighiMaster's, Department of Soil Conservation and Watershed Management Research, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, IranSeyed MasoudSoleimanpourAssociate Professor, Department of Soil Conservation and Watershed Management Research, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, IranAbolfathMoradiAssistant Professor, Department of Soil and Water Research, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, IranJournal Article20240216Introduction<br /><br />Climate is one of the most important ecological factors, and its changes are currently the most important threat to sustainable development. The phenomenon of climate change causes different processes in the atmosphere and the earth. Phenomena such as rising sea levels, changes in meteorological variables such as temperature and precipitation, impact on surface currents, occurrence of floods and droughts, and changes in air currents and storms are only part of the effects of climate change. Therefore, it is necessary to model the future conditions of the climate to know the future conditions. There are various methods for simulating and predicting climate variables in future periods under the influence of climate change, the most reliable of which is the use of General Circulation Model (GCM) data. GCM models are only able to simulate the data of the atmospheric general circulation model at large levels. Even if global climate models are set up with high technical power to predict the future, the need to downscale the results of these models at station scales is felt. Therefore, in this research, the effects of climate change on the threshold values of precipitation and temperature have been evaluated using SSP scenarios.<br /><br />Materials and Methods<br /><br />General circulation models (GCMs) can provide the best information about the response of the atmosphere to increasing greenhouse gas concentrations. In this research, the climatic data of three synoptic stations of Abadeh, Shiraz and Lar, related to Fars province, were used. The data from three models ACCESS-ESM1-5, CNRM-CM6-1, and MRI-ESM2-0 are used from the general rotation models of the sixth report. Daily precipitation and maximum temperature data from 1990 to 2017 were used. Using the statistical model LARS-WG and three scenarios SSP126, SSP245 and SSP585, precipitation and maximum temperature have been downscaling. In this model, the process of generating artificial weather data is done in three parts: model calibration, model validation, and weather data generation. To evaluate the LARS-WG model, coefficient of determination (R2), root mean square error (RMSE) test statistics have been used. To investigate the relationship between precipitation and maximum temperature with different return periods, Gumbel distribution was used. The appropriate distribution for maximum precipitation, temperature, and flood data is Gumble's method; In this study, the distribution of precipitation and maximum temperature for different return periods is presented. In this method, the mean value and standard deviation of the data and the length of the data return period are considered to be the most important effective factors in estimating the maximum values.<br /><br />Results and Discussion<br /><br />Validation of the LARS-WG model was done by comparison between observation data and generated data. To evaluate the efficiency of the model, error test criteria have been used. The results show that the LARS-WG model was able to estimate the maximum temperature and precipitation. The accuracy of the modeling in the maximum temperature parameter has been more appropriate than the precipitation ratio. The monthly precipitation changes of the near future period (2021-2040) compared to the base period (1990-2017) of the three ACCESS-ESM1-5, CNRM-CM6-1 and MRI-ESM2-0 models of Abadeh synoptic station showed the amount of precipitation in April, May, June, August, and September has had a decreasing trend compared to the base period. The amount of precipitation in January, February, and December has also increased compared to the base period. At Abadeh station, it shows an increase in temperature under all three models and scenarios in the near future. At the Shiraz synoptic station, precipitation in April, July, and September has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. At the Lar synoptic station, the precipitation in April, September, and October has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Examining the Gumbel distribution of precipitation values also shows an increase in precipitation in the specified return period in the ACCESS-ESM1-5 model.<br /><br />Conclusion<br /><br />The changes in the maximum temperature of the near future period (2021-2040) compared to the base period (1990-2017) were incremental in three stations and three models. In Abadeh synoptic station, the maximum temperature changes show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.57, 1.59, and 1.63 °C, and the amount of precipitation in the spring and summer seasons is decreasing and Winter precipitation is estimated to be increasing compared to the base period. In the Shiraz synoptic station, the maximum temperature shows an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, 1.37, 1.50, and 1.48 °C, respectively, and in the ACCESS-ESM1-5 model, in all three scenarios, the amount It is estimated that the winter precipitation is decreasing and the amount of spring precipitation is increasing. The changes in the maximum temperature of Lar synoptic station show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.23, 1.37, and 1.28 °C. In the CNRM-CM6-1 model, the winter precipitation of this station is estimated to be a decreasing trend. Fall precipitation is also estimated in the MRI-ESM2-0 model in two scenarios, SSP126 and SSP585, but the ACCESS-ESM1-5 model has estimated an increase in the amount of precipitation in the Lar synoptic station in all seasons and scenarios. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Therefore, extreme and heavy precipitation and the increase in the frequency of extreme events related to it, such as floods and droughts, are among the results of global warming.Introduction<br /><br />Climate is one of the most important ecological factors, and its changes are currently the most important threat to sustainable development. The phenomenon of climate change causes different processes in the atmosphere and the earth. Phenomena such as rising sea levels, changes in meteorological variables such as temperature and precipitation, impact on surface currents, occurrence of floods and droughts, and changes in air currents and storms are only part of the effects of climate change. Therefore, it is necessary to model the future conditions of the climate to know the future conditions. There are various methods for simulating and predicting climate variables in future periods under the influence of climate change, the most reliable of which is the use of General Circulation Model (GCM) data. GCM models are only able to simulate the data of the atmospheric general circulation model at large levels. Even if global climate models are set up with high technical power to predict the future, the need to downscale the results of these models at station scales is felt. Therefore, in this research, the effects of climate change on the threshold values of precipitation and temperature have been evaluated using SSP scenarios.<br /><br />Materials and Methods<br /><br />General circulation models (GCMs) can provide the best information about the response of the atmosphere to increasing greenhouse gas concentrations. In this research, the climatic data of three synoptic stations of Abadeh, Shiraz and Lar, related to Fars province, were used. The data from three models ACCESS-ESM1-5, CNRM-CM6-1, and MRI-ESM2-0 are used from the general rotation models of the sixth report. Daily precipitation and maximum temperature data from 1990 to 2017 were used. Using the statistical model LARS-WG and three scenarios SSP126, SSP245 and SSP585, precipitation and maximum temperature have been downscaling. In this model, the process of generating artificial weather data is done in three parts: model calibration, model validation, and weather data generation. To evaluate the LARS-WG model, coefficient of determination (R2), root mean square error (RMSE) test statistics have been used. To investigate the relationship between precipitation and maximum temperature with different return periods, Gumbel distribution was used. The appropriate distribution for maximum precipitation, temperature, and flood data is Gumble's method; In this study, the distribution of precipitation and maximum temperature for different return periods is presented. In this method, the mean value and standard deviation of the data and the length of the data return period are considered to be the most important effective factors in estimating the maximum values.<br /><br />Results and Discussion<br /><br />Validation of the LARS-WG model was done by comparison between observation data and generated data. To evaluate the efficiency of the model, error test criteria have been used. The results show that the LARS-WG model was able to estimate the maximum temperature and precipitation. The accuracy of the modeling in the maximum temperature parameter has been more appropriate than the precipitation ratio. The monthly precipitation changes of the near future period (2021-2040) compared to the base period (1990-2017) of the three ACCESS-ESM1-5, CNRM-CM6-1 and MRI-ESM2-0 models of Abadeh synoptic station showed the amount of precipitation in April, May, June, August, and September has had a decreasing trend compared to the base period. The amount of precipitation in January, February, and December has also increased compared to the base period. At Abadeh station, it shows an increase in temperature under all three models and scenarios in the near future. At the Shiraz synoptic station, precipitation in April, July, and September has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. At the Lar synoptic station, the precipitation in April, September, and October has decreased compared to the base period. The amount of precipitation in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Examining the Gumbel distribution of precipitation values also shows an increase in precipitation in the specified return period in the ACCESS-ESM1-5 model.<br /><br />Conclusion<br /><br />The changes in the maximum temperature of the near future period (2021-2040) compared to the base period (1990-2017) were incremental in three stations and three models. In Abadeh synoptic station, the maximum temperature changes show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.57, 1.59, and 1.63 °C, and the amount of precipitation in the spring and summer seasons is decreasing and Winter precipitation is estimated to be increasing compared to the base period. In the Shiraz synoptic station, the maximum temperature shows an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, 1.37, 1.50, and 1.48 °C, respectively, and in the ACCESS-ESM1-5 model, in all three scenarios, the amount It is estimated that the winter precipitation is decreasing and the amount of spring precipitation is increasing. The changes in the maximum temperature of Lar synoptic station show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively 1.23, 1.37, and 1.28 °C. In the CNRM-CM6-1 model, the winter precipitation of this station is estimated to be a decreasing trend. Fall precipitation is also estimated in the MRI-ESM2-0 model in two scenarios, SSP126 and SSP585, but the ACCESS-ESM1-5 model has estimated an increase in the amount of precipitation in the Lar synoptic station in all seasons and scenarios. The Gumbel distribution output also showed that in all three stations, in a specific return period, precipitation and maximum temperature will increase compared to the base period. Therefore, extreme and heavy precipitation and the increase in the frequency of extreme events related to it, such as floods and droughts, are among the results of global warming.https://mmws.uma.ac.ir/article_2885_5511dc3b4e9d56e41f27ee57115fced8.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Projection the Wind Field in the Future Based on the CMIP5 and CMIP6 Climate Models in Sistan and Baluchestan Province
IntroductionProjection the Wind Field in the Future Based on the CMIP5 and CMIP6 Climate Models in Sistan and Baluchestan Province
Introduction219233337410.22098/mmws.2024.15772.1493FAFatemehRajaeiAssistant Professor, Department of Environmental Sciences, Faculty of Science, University of Zanjan, Zanjan, IranEbrahimAhmadisharafPh.D, Department of Civil and Environmental Engineering, Florida A&M University, Florida, United StatesJournal Article20240902Introduction<br /><br />Renewable energy plays a crucial role in reducing greenhouse gas emissions and combating global climate change. Among the various renewable energy sources, wind energy stands out due to its production capacity and rapid technological advancement. This study aims to evaluate the capabilities and uncertainties of CMIP5 and CMIP6 models under two scenarios—RCP4.5 and RCP8.5 for CMIP5, and SSP2-4.5 and SSP5-8.5 for CMIP6—in simulating wind speed. Additionally, it will forecast future changes in wind speed (2014-2100) at six synoptic stations in Sistan and Baluchestan, focusing on the differences between CMIP5 and CMIP6 reports regarding wind energy. This research is the first to examine future wind characteristics in Iran using CMIP6 model outputs while comparing the performance of both CMIP5 and CMIP6 in simulating wind speed in the study area. Wind energy is highly sensitive to climate change, as future alterations in wind flow characteristics will significantly impact electricity generation potential. Therefore, understanding future climate scenarios, especially under varying global warming conditions, is vital for estimating changes in wind energy resources over the coming decades.<br /><br />Method<br /><br />Wind speed data were obtained from six synoptic stations operated by I.R. of Iran Meteorological Organization (IRIMO), covering the period from 1990 to 2014, with all stations maintaining continuous data records throughout this timeframe. The data from CMIP5 and CMIP6 climate models were downloaded from the respective websites. This study selected outputs from six general circulation models for the historical period (1990-2014) and the future period (2014-2100) under the emission scenarios SSP2-4.5 and SSP5-8.5 for CMIP6 and RCP4.5 and RCP8.5 for CMIP5. The ability of the CMIP6 and CMIP5 climate models to simulate historical wind speed was evaluated against observational data from Sistan and Baluchestan using statistical criteria, including bias, correlation, and standard deviation. This evaluation determined the capability and accuracy of the models and assessed the uncertainty in their wind speed simulations before applying them to future climate forecasts. A multi-model averaging approach was employed to reduce uncertainties associated with individual models, utilizing the CDFT package in RStudio for downscaling and output bias correction based on cumulative distribution function transformation.<br /><br />Results and Discussion<br /><br />Most CMIP models simulated wind speed effectively. The CanESM5 model in CMIP6 demonstrated improved performance compared to CMIP5, yielding results closer to observational data. In contrast, the CMCC-ESM2 and CNRM-CM6-1 models in CMIP6 were less efficient than their CMIP5 counterparts. CMIP5 indicated a decrease in wind speed, while CMIP6 suggested an increase in annual projection, although these changes were not statistically significant. The projected average wind speeds by 2100 are 3.58 m/s and 3.57 m/s for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, while the RCP4.5 and RCP8.5 scenarios predict averages of 3.1 m/s and 3.2 m/s, respectively. The baseline average wind speed from observational data is 3.49 m/s. CMIP5 indicated a decrease in wind speed across all months under both scenarios, with the most significant reductions occurring in spring (-0.47 m/s) and the least in autumn (-0.29 m/s). Conversely, CMIP6 projected increases in wind speed across all seasons, with the highest increase in spring (0.49 m/s) and the lowest in summer (0.32 m/s) under the SSP2 scenario. Under the SSP5 scenario, the highest increase was observed in winter (0.43 m/s) and the lowest in summer (0.37 m/s). The annual average for CMIP5 models showed a decrease in wind speed at all stations compared to the baseline period, particularly at Khash and Zabul stations. In CMIP6, all stations except Chabahar exhibited increased wind speeds, with Chabahar recording the highest average wind speed and other stations showing minimal differences. The results indicate varying model performance in simulating climate variables, with the historical wind speed uncertainty in CMIP5 models potentially attributed to differences in grid resolution, atmospheric components, and convection scheme parameterization.<br /><br />Conclusion<br /><br />Given the observed biases in the models, future research should involve a comprehensive study utilizing additional models from the CMIP6 and CMIP5 families. These findings are significant for assessing the potential of the wind energy sector in Sistan and Baluchestan, a region known for its wind resources, which may inform future development strategies. It is also recommended that similar assessments be conducted in other regions of Iran to determine whether identified sites with suitable wind power may experience future resource fluctuations. Also, it is suggested that different methods of bias correction and downscaling should be investigated and the best method should be suggested. On the other hand, using satellite data instead of observational data and comparing their results can be considered as another research proposal.<br /><br /><br /><br />Key Words:Downscaling, Wind speed, Sistan and Balochestan, CMIP models <br /><br /><br /><br />Article Type: Research Article<br /><br />Acknowledgement<br /><br />We express our sincere gratitude to the University of Zanjan for their financial and logistical support throughout this research project, identified by code 1174-2-1402.<br /><br />Conflicts of Interest<br /><br />The authors declare no conflicts of interest regarding the authorship or publication of this article.<br /><br />Data Availability Statement<br /><br />Datasets are available upon reasonable request to the corresponding author.<br /><br />Authors’ Contribution<br /><br />Author 1: Formal analysis and investigation, Software<br /><br />Author 2: Writing and manuscript editingIntroduction<br /><br />Renewable energy plays a crucial role in reducing greenhouse gas emissions and combating global climate change. Among the various renewable energy sources, wind energy stands out due to its production capacity and rapid technological advancement. This study aims to evaluate the capabilities and uncertainties of CMIP5 and CMIP6 models under two scenarios—RCP4.5 and RCP8.5 for CMIP5, and SSP2-4.5 and SSP5-8.5 for CMIP6—in simulating wind speed. Additionally, it will forecast future changes in wind speed (2014-2100) at six synoptic stations in Sistan and Baluchestan, focusing on the differences between CMIP5 and CMIP6 reports regarding wind energy. This research is the first to examine future wind characteristics in Iran using CMIP6 model outputs while comparing the performance of both CMIP5 and CMIP6 in simulating wind speed in the study area. Wind energy is highly sensitive to climate change, as future alterations in wind flow characteristics will significantly impact electricity generation potential. Therefore, understanding future climate scenarios, especially under varying global warming conditions, is vital for estimating changes in wind energy resources over the coming decades.<br /><br />Method<br /><br />Wind speed data were obtained from six synoptic stations operated by I.R. of Iran Meteorological Organization (IRIMO), covering the period from 1990 to 2014, with all stations maintaining continuous data records throughout this timeframe. The data from CMIP5 and CMIP6 climate models were downloaded from the respective websites. This study selected outputs from six general circulation models for the historical period (1990-2014) and the future period (2014-2100) under the emission scenarios SSP2-4.5 and SSP5-8.5 for CMIP6 and RCP4.5 and RCP8.5 for CMIP5. The ability of the CMIP6 and CMIP5 climate models to simulate historical wind speed was evaluated against observational data from Sistan and Baluchestan using statistical criteria, including bias, correlation, and standard deviation. This evaluation determined the capability and accuracy of the models and assessed the uncertainty in their wind speed simulations before applying them to future climate forecasts. A multi-model averaging approach was employed to reduce uncertainties associated with individual models, utilizing the CDFT package in RStudio for downscaling and output bias correction based on cumulative distribution function transformation.<br /><br />Results and Discussion<br /><br />Most CMIP models simulated wind speed effectively. The CanESM5 model in CMIP6 demonstrated improved performance compared to CMIP5, yielding results closer to observational data. In contrast, the CMCC-ESM2 and CNRM-CM6-1 models in CMIP6 were less efficient than their CMIP5 counterparts. CMIP5 indicated a decrease in wind speed, while CMIP6 suggested an increase in annual projection, although these changes were not statistically significant. The projected average wind speeds by 2100 are 3.58 m/s and 3.57 m/s for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, while the RCP4.5 and RCP8.5 scenarios predict averages of 3.1 m/s and 3.2 m/s, respectively. The baseline average wind speed from observational data is 3.49 m/s. CMIP5 indicated a decrease in wind speed across all months under both scenarios, with the most significant reductions occurring in spring (-0.47 m/s) and the least in autumn (-0.29 m/s). Conversely, CMIP6 projected increases in wind speed across all seasons, with the highest increase in spring (0.49 m/s) and the lowest in summer (0.32 m/s) under the SSP2 scenario. Under the SSP5 scenario, the highest increase was observed in winter (0.43 m/s) and the lowest in summer (0.37 m/s). The annual average for CMIP5 models showed a decrease in wind speed at all stations compared to the baseline period, particularly at Khash and Zabul stations. In CMIP6, all stations except Chabahar exhibited increased wind speeds, with Chabahar recording the highest average wind speed and other stations showing minimal differences. The results indicate varying model performance in simulating climate variables, with the historical wind speed uncertainty in CMIP5 models potentially attributed to differences in grid resolution, atmospheric components, and convection scheme parameterization.<br /><br />Conclusion<br /><br />Given the observed biases in the models, future research should involve a comprehensive study utilizing additional models from the CMIP6 and CMIP5 families. These findings are significant for assessing the potential of the wind energy sector in Sistan and Baluchestan, a region known for its wind resources, which may inform future development strategies. It is also recommended that similar assessments be conducted in other regions of Iran to determine whether identified sites with suitable wind power may experience future resource fluctuations. Also, it is suggested that different methods of bias correction and downscaling should be investigated and the best method should be suggested. On the other hand, using satellite data instead of observational data and comparing their results can be considered as another research proposal.<br /><br /><br /><br />Key Words:Downscaling, Wind speed, Sistan and Balochestan, CMIP models <br /><br /><br /><br />Article Type: Research Article<br /><br />Acknowledgement<br /><br />We express our sincere gratitude to the University of Zanjan for their financial and logistical support throughout this research project, identified by code 1174-2-1402.<br /><br />Conflicts of Interest<br /><br />The authors declare no conflicts of interest regarding the authorship or publication of this article.<br /><br />Data Availability Statement<br /><br />Datasets are available upon reasonable request to the corresponding author.<br /><br />Authors’ Contribution<br /><br />Author 1: Formal analysis and investigation, Software<br /><br />Author 2: Writing and manuscript editinghttps://mmws.uma.ac.ir/article_3374_63a60584a786e9f13df4e793958e092c.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Flood vulnerability zoning of the basin based on the remote sensing indicators and decision making techniques (Case study: Rakat basin of Khuzestan)Flood vulnerability zoning of the basin based on the remote sensing indicators and decision making techniques (Case study: Rakat basin of Khuzestan)234250347410.22098/mmws.2024.15911.1501FAParvizPavandmehrFormer M.Sc. Student, Department of Nature Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, IranAminZoratipourAssociate Professor, Department of Natuer Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, IranMohammadMoazamiAssistant Professor, Department of Nature Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, IranMitraGheraghiAssociate Professor, Department of Nature Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, IranJournal Article20240929Introduction <br /><br />The expansion of cities in the margins of rivers, alluvial cones, low-altitude coasts, deltas and downstream areas of storage dams has led to an increase in the vulnerability of watersheds to the risk of flooding. This study was carried out with the aim of flood risk zoning and prioritization of flood-prone areas using multi-criteria decision making techniques and remote sensing indicators using Fuzzy AHP and VIKOR model in Rakat Khuzestan watershed. One of the solutions used to identify flood risk and prepare maps of its sensitivity is the use of bivariate and multivariate statistical models, data mining and machine learning. But since many of these models require a lot of data and their calibration is complex, therefore, in recent years, many models have been tested to prepare a flood susceptibility map, among which, the combination of statistical models And decision-making with remote sensing techniques and geographic information system has been of great interest to researchers due to increasing the ability of the model in forecasting. The difference between this study and the studies carried out so far is that in this study, for the first time, multi-criteria decision making techniques and remote sensing indicators are used in the zoning of flood risk in the watershed simultaneously in the watershed. Mountainous and flowing rakat will be used in Khuzestan province and its efficiency will be measured.<br /><br />Materials and Methods <br /><br />After making the necessary corrections on the Sentinel 2 satellite images of the region, vegetation indices (EVI, NDVI and SAVI), vegetation density and land use of the region were extracted. Then, by using two multi-criteria decision making techniques (FAHP and VIKOR), weighting of indicators and prioritization of sub-basin flooding were carried out. Finally, after extracting the topography, elevation, soil and geological maps and producing 15 morphometric indicators effective in the flooding situation of the basin, using two multi-criteria decision making techniques FAHP and VIKOR, the weighting of the indicators and the prioritization of the flood proneness of the basin were carried out. became In order to validate and evaluate the multi-criteria decision making models, in the future, with field survey, the use of remote sensing indicators such as NDVI and MNDVI, twenty-five points, flood-prone areas of the basin were randomly selected and placed, and the output of the multi-criteria decision-making models FAHP and VIKOR were validated with these points.<br /><br /><br /><br />Results and Discussion <br /><br />It was concluded that among all the indicators, the runoff curve number index, vegetation cover and land use and distance from the waterway account for about 50% of the total flood share of the basin and have The greatest impact on the flood phenomenon is in mountain basins, including the Barkat basin in Dehdez County. Also, the direction of the slope range and the rainfall index (due to the uniformity of the index at the basin level) were found to have the least effect (total less than 5%) among the investigated parameters. It can be said that due to the combination of land use and soil maps, vegetation and rainfall of the basin, as well as the simultaneous effect of land use and soil hydrological group on the flood potential of the basin, it can be a more effective indicator in determining the flood benefit of the basin. The results obtained in this study are consistent with the results of Nouri et al., (2019). Another influential parameter in the flooding of the Rakat basin area (19 percent) is vegetation and land use. The vegetation cover of the area includes agricultural lands, medium pastures, oak forest, and high quality pastures, which respectively had the highest and lowest values in the occurrence of floods, belonging to agricultural lands and high quality pastures. The distance from the waterway is the next influential parameter with a weighted value (about 15 percent), the smaller the distance from the waterway, the higher the value in the occurrence of floods, and the greatest flood potential of the region is in this area. The results of EVI, NDVI and SAVI spectral indices in the two methods of Fuzzy AHP and VIKOR showed that the EVI index has an overestimate and vice versa the SAVI index has an underestimation, but the NDVI index has shown more accurate results of locating the flood prone areas of Rakat Basin.<br /><br />Conclusion <br /><br />The results of the study showed that out of the 15 indicators used in the flood zoning of Rakat basin, the vegetation cover indicators are 19%, the runoff curve number is 15% and the distance from the waterway is 15%. The effect was among the investigated parameters. The maps extracted from the two fuzzy AHP and VIKOR methods were determined by using the EVI, NDVI and SAVI spectral indices. On the contrary, the SAVI index has shown the percentage of flooding in high-risk areas with a low estimate, but the NDVI index has shown more accurate results of locating the flood-prone areas of the basin. By summarizing the obtained results, it can be stated that the evaluation of the flood risk maps of the Rakat watershed based on the Vikor model and fuzzy AHP shows the highest agreement with an accuracy of about 68% compared to the Vikor model map with an accuracy of about 40%. with the basic information of the region compared to other models and it is suggested as the optimal model in this region. Finally, the final flood risk map of the basin was located using the fuzzy AHP method, the high risk flood prone areas exactly according to the hydrographic network of the basin, it can be considered the reason for the superiority of this method over the VIKOR method.Introduction <br /><br />The expansion of cities in the margins of rivers, alluvial cones, low-altitude coasts, deltas and downstream areas of storage dams has led to an increase in the vulnerability of watersheds to the risk of flooding. This study was carried out with the aim of flood risk zoning and prioritization of flood-prone areas using multi-criteria decision making techniques and remote sensing indicators using Fuzzy AHP and VIKOR model in Rakat Khuzestan watershed. One of the solutions used to identify flood risk and prepare maps of its sensitivity is the use of bivariate and multivariate statistical models, data mining and machine learning. But since many of these models require a lot of data and their calibration is complex, therefore, in recent years, many models have been tested to prepare a flood susceptibility map, among which, the combination of statistical models And decision-making with remote sensing techniques and geographic information system has been of great interest to researchers due to increasing the ability of the model in forecasting. The difference between this study and the studies carried out so far is that in this study, for the first time, multi-criteria decision making techniques and remote sensing indicators are used in the zoning of flood risk in the watershed simultaneously in the watershed. Mountainous and flowing rakat will be used in Khuzestan province and its efficiency will be measured.<br /><br />Materials and Methods <br /><br />After making the necessary corrections on the Sentinel 2 satellite images of the region, vegetation indices (EVI, NDVI and SAVI), vegetation density and land use of the region were extracted. Then, by using two multi-criteria decision making techniques (FAHP and VIKOR), weighting of indicators and prioritization of sub-basin flooding were carried out. Finally, after extracting the topography, elevation, soil and geological maps and producing 15 morphometric indicators effective in the flooding situation of the basin, using two multi-criteria decision making techniques FAHP and VIKOR, the weighting of the indicators and the prioritization of the flood proneness of the basin were carried out. became In order to validate and evaluate the multi-criteria decision making models, in the future, with field survey, the use of remote sensing indicators such as NDVI and MNDVI, twenty-five points, flood-prone areas of the basin were randomly selected and placed, and the output of the multi-criteria decision-making models FAHP and VIKOR were validated with these points.<br /><br /><br /><br />Results and Discussion <br /><br />It was concluded that among all the indicators, the runoff curve number index, vegetation cover and land use and distance from the waterway account for about 50% of the total flood share of the basin and have The greatest impact on the flood phenomenon is in mountain basins, including the Barkat basin in Dehdez County. Also, the direction of the slope range and the rainfall index (due to the uniformity of the index at the basin level) were found to have the least effect (total less than 5%) among the investigated parameters. It can be said that due to the combination of land use and soil maps, vegetation and rainfall of the basin, as well as the simultaneous effect of land use and soil hydrological group on the flood potential of the basin, it can be a more effective indicator in determining the flood benefit of the basin. The results obtained in this study are consistent with the results of Nouri et al., (2019). Another influential parameter in the flooding of the Rakat basin area (19 percent) is vegetation and land use. The vegetation cover of the area includes agricultural lands, medium pastures, oak forest, and high quality pastures, which respectively had the highest and lowest values in the occurrence of floods, belonging to agricultural lands and high quality pastures. The distance from the waterway is the next influential parameter with a weighted value (about 15 percent), the smaller the distance from the waterway, the higher the value in the occurrence of floods, and the greatest flood potential of the region is in this area. The results of EVI, NDVI and SAVI spectral indices in the two methods of Fuzzy AHP and VIKOR showed that the EVI index has an overestimate and vice versa the SAVI index has an underestimation, but the NDVI index has shown more accurate results of locating the flood prone areas of Rakat Basin.<br /><br />Conclusion <br /><br />The results of the study showed that out of the 15 indicators used in the flood zoning of Rakat basin, the vegetation cover indicators are 19%, the runoff curve number is 15% and the distance from the waterway is 15%. The effect was among the investigated parameters. The maps extracted from the two fuzzy AHP and VIKOR methods were determined by using the EVI, NDVI and SAVI spectral indices. On the contrary, the SAVI index has shown the percentage of flooding in high-risk areas with a low estimate, but the NDVI index has shown more accurate results of locating the flood-prone areas of the basin. By summarizing the obtained results, it can be stated that the evaluation of the flood risk maps of the Rakat watershed based on the Vikor model and fuzzy AHP shows the highest agreement with an accuracy of about 68% compared to the Vikor model map with an accuracy of about 40%. with the basic information of the region compared to other models and it is suggested as the optimal model in this region. Finally, the final flood risk map of the basin was located using the fuzzy AHP method, the high risk flood prone areas exactly according to the hydrographic network of the basin, it can be considered the reason for the superiority of this method over the VIKOR method.https://mmws.uma.ac.ir/article_3474_d06f30e3d11d87156d3ab9a543ccfc33.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Assessment of the Water Resources Carrying Capacity of Anzali Wetland Using a Combined AHP-Entropy Weighting Method and Forward cloud modelAssessment of the Water Resources Carrying Capacity of Anzali Wetland Using a Combined AHP-Entropy Weighting Method and Forward cloud model251271346310.22098/mmws.2024.15960.1505FAMaedehKeyvanfarFormer M.Sc. Student, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran0009-0001-4356-8574SomayeJanatrostamiAssistant Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, IranAfshinAshrafzadehAssociate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, IranJournal Article20241007Introduction<br /><br />For the sustainable development of the socio-economic economy, water resources are not only an important limiting factor but also play an irreplaceable "carrier" role. "Carrying capacity" is a term derived from ecology that refers to the limitation of the maximum number of individuals in specific environmental conditions. Water Resources Carrying Capacity (WRCC) is the support capacity of water resources for the livelihood of the region's population and socio-economic development. Due to the differences in water resource systems, socio-economic conditions, and ecological environmental settings in various regions and natural conditions, the assessment of WRCC does not follow a consistent pattern across different geographical areas, making the evaluation of WRCC a complex issue. The aim of assessing and analyzing WRCC is to determine the relationship between limited water resources and population, the environment, and economic development.<br /><br />Wetlands play a pivotal role in sustainable development as a vital resource. With increasing population and economic growth, the pressure on water resources is rising. The Water Resource Carrying Capacity is defined as the ability of a water system to support human activities. In this study, considering the significance of Anzali wetland, which is being destroyed alongside the advancement of urbanization processes as one of the natural ecosystems, the water resources carrying capacity of Anzali wetland is being evaluated.<br /><br />Materials and Methods <br /><br />To evaluate the water resources carrying capacity, initially, the water resource system of the region was first modeled by using the system dynamics method and VENSIM software. Based on the modeling results, eight evaluation indicators were defined to assess the WRCC of Anzali Wetland, considering three subsystems: population, water resources, economy, and environment. Subsequently, based on the modeling results, indices were defined for each subsystem with the aim of assessing the water resources carrying capacity of Anzali wetland, with a total of 8 indices considered for evaluation. The weighting of the indices was done using the combined AHP-Entropy method .In order to accurately assess the carrying capacity of water resources, aligning the results with the actual situation of the region, and considering the water resources of Anzali Wetland, the current water resources and consumption in the Fumanat study area (located upstream of Anzali Wetland), the economic and environmental conditions of the region, and the use of water resources in the area, the evaluation indices were divided into four levels based on the standards for grading water resource carrying capacity indices in previous research. These levels are: I (loadable), II (weak), III (critical), and IV (extremely critical). Finally, using a fuzzy model and determining the membership degrees of the indices at each evaluation level and for each year, the water resources carrying capacity index of Anzali wetland was calculated for different evaluation levels over a 10-year period.<br /><br />Results and Discussion <br /><br />To determine the initial weights of the evaluation indices, expert opinions were utilized, and acceptable results were obtained using the Analytic Hierarchy Process (AHP) method and Consistency Ratio (CR). Subsequently, composite weights were calculated by combining the AHP weights with entropy, with the highest weight attributed to the indicator of water supply and demand ratio of the wetland (C7), followed by indicators such as the quality of input water resources and the impact of agricultural gross production. The results of this section demonstrate that the final composite weights are more realistic as the entropy method enhances the shortcomings of the AHP method, thereby improving the accuracy of the evaluation.<br /><br />The results of the Water Resources Carrying Capacity (WRCC) assessment indicate a decreasing trend in the water resources carrying capacity of Anzali wetland between the years 1391 to 1400, shifting its capacity from level II (Weak) to level IV (Super Critical). The highest capacity is associated with the year 1391, while the lowest capacities are allocated to the years 1393, 1394, and 1400. Examination of the obstacle factors revealed that surface and groundwater resources significantly impact the water resources carrying capacity of the wetland, with several indicators from the water resources subsystem identified as the main obstacle factors in this regard. These constraints pose challenges to improving the wetland's capacity, and the increase or decrease of these resources, especially under environmental and economic conditions, significantly influences the wetland's status<br /><br />Conclusion <br /><br />The results showed that the WRCC of the wetland had a decreasing trend during the study period and had reached a supercritical state. The most important factors affecting the reduction in carrying capacity were identified as the shortage of surface and groundwater resources and the poor quality of water entering Anzali Wetland.The overall findings of the investigations indicate that Anzali wetland has been in critical and super-critical conditions in recent years, highlighting the necessity for planning to improve its situation. Considering the obstacle factors identified in this study, efforts can be directed towards planning and managing the wetland to move towards a more desirable state.Introduction<br /><br />For the sustainable development of the socio-economic economy, water resources are not only an important limiting factor but also play an irreplaceable "carrier" role. "Carrying capacity" is a term derived from ecology that refers to the limitation of the maximum number of individuals in specific environmental conditions. Water Resources Carrying Capacity (WRCC) is the support capacity of water resources for the livelihood of the region's population and socio-economic development. Due to the differences in water resource systems, socio-economic conditions, and ecological environmental settings in various regions and natural conditions, the assessment of WRCC does not follow a consistent pattern across different geographical areas, making the evaluation of WRCC a complex issue. The aim of assessing and analyzing WRCC is to determine the relationship between limited water resources and population, the environment, and economic development.<br /><br />Wetlands play a pivotal role in sustainable development as a vital resource. With increasing population and economic growth, the pressure on water resources is rising. The Water Resource Carrying Capacity is defined as the ability of a water system to support human activities. In this study, considering the significance of Anzali wetland, which is being destroyed alongside the advancement of urbanization processes as one of the natural ecosystems, the water resources carrying capacity of Anzali wetland is being evaluated.<br /><br />Materials and Methods <br /><br />To evaluate the water resources carrying capacity, initially, the water resource system of the region was first modeled by using the system dynamics method and VENSIM software. Based on the modeling results, eight evaluation indicators were defined to assess the WRCC of Anzali Wetland, considering three subsystems: population, water resources, economy, and environment. Subsequently, based on the modeling results, indices were defined for each subsystem with the aim of assessing the water resources carrying capacity of Anzali wetland, with a total of 8 indices considered for evaluation. The weighting of the indices was done using the combined AHP-Entropy method .In order to accurately assess the carrying capacity of water resources, aligning the results with the actual situation of the region, and considering the water resources of Anzali Wetland, the current water resources and consumption in the Fumanat study area (located upstream of Anzali Wetland), the economic and environmental conditions of the region, and the use of water resources in the area, the evaluation indices were divided into four levels based on the standards for grading water resource carrying capacity indices in previous research. These levels are: I (loadable), II (weak), III (critical), and IV (extremely critical). Finally, using a fuzzy model and determining the membership degrees of the indices at each evaluation level and for each year, the water resources carrying capacity index of Anzali wetland was calculated for different evaluation levels over a 10-year period.<br /><br />Results and Discussion <br /><br />To determine the initial weights of the evaluation indices, expert opinions were utilized, and acceptable results were obtained using the Analytic Hierarchy Process (AHP) method and Consistency Ratio (CR). Subsequently, composite weights were calculated by combining the AHP weights with entropy, with the highest weight attributed to the indicator of water supply and demand ratio of the wetland (C7), followed by indicators such as the quality of input water resources and the impact of agricultural gross production. The results of this section demonstrate that the final composite weights are more realistic as the entropy method enhances the shortcomings of the AHP method, thereby improving the accuracy of the evaluation.<br /><br />The results of the Water Resources Carrying Capacity (WRCC) assessment indicate a decreasing trend in the water resources carrying capacity of Anzali wetland between the years 1391 to 1400, shifting its capacity from level II (Weak) to level IV (Super Critical). The highest capacity is associated with the year 1391, while the lowest capacities are allocated to the years 1393, 1394, and 1400. Examination of the obstacle factors revealed that surface and groundwater resources significantly impact the water resources carrying capacity of the wetland, with several indicators from the water resources subsystem identified as the main obstacle factors in this regard. These constraints pose challenges to improving the wetland's capacity, and the increase or decrease of these resources, especially under environmental and economic conditions, significantly influences the wetland's status<br /><br />Conclusion <br /><br />The results showed that the WRCC of the wetland had a decreasing trend during the study period and had reached a supercritical state. The most important factors affecting the reduction in carrying capacity were identified as the shortage of surface and groundwater resources and the poor quality of water entering Anzali Wetland.The overall findings of the investigations indicate that Anzali wetland has been in critical and super-critical conditions in recent years, highlighting the necessity for planning to improve its situation. Considering the obstacle factors identified in this study, efforts can be directed towards planning and managing the wetland to move towards a more desirable state.https://mmws.uma.ac.ir/article_3463_de5faae9b89a5fd04dde957c9e92ffdc.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Groundwater drought hazard zoning and relationship between meteorological and hydrological drought indices in the Urmia aquiferGroundwater drought hazard zoning and relationship between meteorological and hydrological drought indices in the Urmia aquifer272289367510.22098/mmws.2025.16234.1519FASamanehDadafaridPh.D Student, Department of Rangeland and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, IranMahdiErfanianAssociate Professor, Department of Rangeland and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, IranBehzadHessariAssociate Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran,KhadijehJavanssociate Professor, Department of Geography, Faculty of Literature and Humanities, Urmia University, Urmia, IranJournal Article20241121Extended Abstract<br /><br />Introduction <br /><br />In recent years, the study of climatic changes has gained significant importance due to the exposure of many regions to climate change conditions. Iran, with its predominantly arid and semi-arid climate, is no exception. Groundwater is a vital resource for Iran’s drinking water, agriculture, and industry, playing a crucial role in its economic development. The horticulture and agriculture sectors in the Urmia aquifer plain heavily depend on groundwater resources. Over the past three decades, reductions in precipitation and excessive extraction of water resources for agricultural and other purposes have led to severe drought, causing irreparable damage to agriculture, industry, and human life. Groundwater drought is characterized by reduced groundwater levels or storage and is influenced by natural and human-induced factors, such as climate change and excessive groundwater extraction. The SPI and SPEI are commonly used to monitor meteorological drought, while the SGI measures hydrological drought. This study investigates sustainable management and protection strategies for the Urmia aquifer, emphasizing the effects of climate change and drought on its groundwater resources. The findings highlight the need for climate change adaptation measures.<br /><br /><br /><br />Materials and Methods <br /><br />To analyze the status of the Urmia aquifer, monthly groundwater depth data from piezometers within the aquifer were used to calculate the SGI index. Monthly precipitation data from the Urmia meteorological station in the aquifer plain were utilized to derive the SPI and SPEI indices. The analysis used monthly data from the Urmia aquifer, each providing consistent 20-year datasets. While 30 years is generally recommended as the standard for drought indices, statistical tools such as the correlation coefficient (CC), coefficient of determination (R²), root mean square error (RMSE), Hanna and Heinold index (HH), kappa coefficient (k), Cramer coefficient (V), and class correlation percentage were employed to compare and validate the SPI and SPEI indices for both 20- and 30-year periods. The Pearson correlation coefficient was applied to compare SPI and SPEI at 3, 6, 9, and 12-month scales with the monthly SGI index. ArcMap generated a groundwater drought hazard zoning map, ranking each piezometer based on drought severity, duration, and hazard. The drought severity and duration values were classified into five classes using the Jenks natural interval classification method. The final drought risk values, graded from 1 to 9, were assigned to areas covered by each piezometer using the Thiessen polygon method. Additionally, the correlation for different time lags was estimated to analyze the impact on the correlation between meteorological and hydrological drought indices.<br /><br /><br /><br />Results and Discussion <br /><br />The SPI and SPEI indices from 2001 to 2021 exhibited a strong positive correlation with those calculated from 1991 to 2021. Based on the Pearson coefficient, evaluating the correlation of SPI and SPEI indices with the SGI index at 3- to 12-month time scales for each piezometer revealed that correlations were insignificant at 3- and 6-month scales in most piezometers. However, the highest correlations between SGI and SPEI were observed at 9- and 12-month scales. Applying a time lag initially improved correlations across all scales, but correlations diminished beyond a certain point. On average, a time lag of 3 to 5 months increased the correlation. Analysis of the duration and intensity of hydrological drought events indicated that areas with prolonged but low-intensity droughts remained in a drought state for extended periods, struggling to return to equilibrium. Conversely, areas with shorter but more intense droughts experienced intense droughts over short periods but, despite the severity, managed to return to equilibrium. These findings provide practical implications for understanding and predicting drought conditions, offering valuable information for effective groundwater management strategies.<br /><br /><br /><br />Conclusion <br /><br />This study investigated the relationship between the hydrological drought SGI derived from groundwater level data and meteorological drought indices over a 20-year statistical period (2001-2021). The shorter analysis period was due to the lack of statistical data and the adequate correspondence between the 20-year monthly data for meteorological drought indices (SPI and SPEI) and the drought index time series obtained from 30-year monthly data. The hydrological drought index (SGI) in the piezometers, which correlated significantly with the drought indices (SPI, SPEI), is primarily influenced by climatic conditions. The low correlation between SPI, SPEI, and the SGI index can be primarily attributed to human factors. Groundwater extraction and social and economic issues are the primary causes of drought in aquifer regions with low SPI-SGI correlation. By assessing drought duration and severity across Thiessen polygons affected by the piezometer, a hydrological drought risk zoning map was developed for the Urmia aquifer. The results indicated that hazard levels 9 and 8 dominated the southern areas of the aquifer, covering 14% and 13% of the surface area, respectively. This map can be a critical tool for selecting appropriate methods to maintain the groundwater level balance. Management plans in high-risk areas should prioritize monitoring human activities such as drilling and water withdrawal, changing crop patterns, and implementing artificial recharge projects. Climatic factors exerted heterogeneous effects on the occurrence of hydrological drought across the entire aquifer. These measures can substantially help the planners and managers in the Urmia Lake Restoration Headquarters and the Regional Water Organization.Extended Abstract<br /><br />Introduction <br /><br />In recent years, the study of climatic changes has gained significant importance due to the exposure of many regions to climate change conditions. Iran, with its predominantly arid and semi-arid climate, is no exception. Groundwater is a vital resource for Iran’s drinking water, agriculture, and industry, playing a crucial role in its economic development. The horticulture and agriculture sectors in the Urmia aquifer plain heavily depend on groundwater resources. Over the past three decades, reductions in precipitation and excessive extraction of water resources for agricultural and other purposes have led to severe drought, causing irreparable damage to agriculture, industry, and human life. Groundwater drought is characterized by reduced groundwater levels or storage and is influenced by natural and human-induced factors, such as climate change and excessive groundwater extraction. The SPI and SPEI are commonly used to monitor meteorological drought, while the SGI measures hydrological drought. This study investigates sustainable management and protection strategies for the Urmia aquifer, emphasizing the effects of climate change and drought on its groundwater resources. The findings highlight the need for climate change adaptation measures.<br /><br /><br /><br />Materials and Methods <br /><br />To analyze the status of the Urmia aquifer, monthly groundwater depth data from piezometers within the aquifer were used to calculate the SGI index. Monthly precipitation data from the Urmia meteorological station in the aquifer plain were utilized to derive the SPI and SPEI indices. The analysis used monthly data from the Urmia aquifer, each providing consistent 20-year datasets. While 30 years is generally recommended as the standard for drought indices, statistical tools such as the correlation coefficient (CC), coefficient of determination (R²), root mean square error (RMSE), Hanna and Heinold index (HH), kappa coefficient (k), Cramer coefficient (V), and class correlation percentage were employed to compare and validate the SPI and SPEI indices for both 20- and 30-year periods. The Pearson correlation coefficient was applied to compare SPI and SPEI at 3, 6, 9, and 12-month scales with the monthly SGI index. ArcMap generated a groundwater drought hazard zoning map, ranking each piezometer based on drought severity, duration, and hazard. The drought severity and duration values were classified into five classes using the Jenks natural interval classification method. The final drought risk values, graded from 1 to 9, were assigned to areas covered by each piezometer using the Thiessen polygon method. Additionally, the correlation for different time lags was estimated to analyze the impact on the correlation between meteorological and hydrological drought indices.<br /><br /><br /><br />Results and Discussion <br /><br />The SPI and SPEI indices from 2001 to 2021 exhibited a strong positive correlation with those calculated from 1991 to 2021. Based on the Pearson coefficient, evaluating the correlation of SPI and SPEI indices with the SGI index at 3- to 12-month time scales for each piezometer revealed that correlations were insignificant at 3- and 6-month scales in most piezometers. However, the highest correlations between SGI and SPEI were observed at 9- and 12-month scales. Applying a time lag initially improved correlations across all scales, but correlations diminished beyond a certain point. On average, a time lag of 3 to 5 months increased the correlation. Analysis of the duration and intensity of hydrological drought events indicated that areas with prolonged but low-intensity droughts remained in a drought state for extended periods, struggling to return to equilibrium. Conversely, areas with shorter but more intense droughts experienced intense droughts over short periods but, despite the severity, managed to return to equilibrium. These findings provide practical implications for understanding and predicting drought conditions, offering valuable information for effective groundwater management strategies.<br /><br /><br /><br />Conclusion <br /><br />This study investigated the relationship between the hydrological drought SGI derived from groundwater level data and meteorological drought indices over a 20-year statistical period (2001-2021). The shorter analysis period was due to the lack of statistical data and the adequate correspondence between the 20-year monthly data for meteorological drought indices (SPI and SPEI) and the drought index time series obtained from 30-year monthly data. The hydrological drought index (SGI) in the piezometers, which correlated significantly with the drought indices (SPI, SPEI), is primarily influenced by climatic conditions. The low correlation between SPI, SPEI, and the SGI index can be primarily attributed to human factors. Groundwater extraction and social and economic issues are the primary causes of drought in aquifer regions with low SPI-SGI correlation. By assessing drought duration and severity across Thiessen polygons affected by the piezometer, a hydrological drought risk zoning map was developed for the Urmia aquifer. The results indicated that hazard levels 9 and 8 dominated the southern areas of the aquifer, covering 14% and 13% of the surface area, respectively. This map can be a critical tool for selecting appropriate methods to maintain the groundwater level balance. Management plans in high-risk areas should prioritize monitoring human activities such as drilling and water withdrawal, changing crop patterns, and implementing artificial recharge projects. Climatic factors exerted heterogeneous effects on the occurrence of hydrological drought across the entire aquifer. These measures can substantially help the planners and managers in the Urmia Lake Restoration Headquarters and the Regional Water Organization.https://mmws.uma.ac.ir/article_3675_f798921f67c9120a8c4741e5e2573e39.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Performance assessment of the artificial intelligence models for prediction of the infiltration rate in the Surface Soil of Geological Formations (Case Study: Aleshtar Watershed, Lorestan Province)Performance assessment of the artificial intelligence models for prediction of the infiltration rate in the Surface Soil of Geological Formations (Case Study: Aleshtar Watershed, Lorestan Province)290308377510.22098/mmws.2025.16276.1520FAShokofehHasanvandPhD Student, Department of Range and Watershed Management, Faculty of Natural Resources, Lorestan University, Khorramabad, IranAlirezaSepahvandAssociate Professor, Department of Watershed Science and Engineering, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, IranNasrinBeiranvandPhD Student, Department of Range and Watershed Management, Faculty of Natural Resources, Lorestan University, Khorramabad, IranNegarArjmandMSc Student, Department of Range and Watershed Management, Faculty of Natural Resources, Lorestan University, Khorramabad, IranJournal Article20241129Introduction <br /><br />Water repellency is a property that commonly affects the soil surface layer. It results from hydrophobic coatings on soil particles that originate from organic matter. The most significant effect of soil water repellency is a reduction in infiltration rates. The infiltration rate is one of the primary processes of the hydrological cycle. Hydrogeological and subsurface phenomena as infiltration, percolation mainly affect natural or man-made geotechnical soil. Understanding these phenomena are essential for estimation of runoff process, groundwater seepage, erosion, transport substances, evapotranspiration in surface and into groundwater are mainly influenced by precipitation. It is the property of water by which it moves through the soil particles. Infiltration process plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. Also, Soil infiltration is one of the key processes in design of irrigation systems, water resources management and soil protection and soil erosion control in watershed management and good knowledge of the infiltration rate is useful in calculating the natural and artificial groundwater recharge and surface runoff. Therefore, the prpose of this study was Performance assessment of the artificial intelligence models for prediction of the infiltration rate in the surface soil of geological formations in Alashtar watershed, Lorestan province, Iran.<br /><br /><br /><br />Materials and Methods <br /><br />The study area is a part of Kashkan watershed, Lorestan province, Iran. So, it was selected as a suitable watershed to Modeling of infiltration rate in different vegetation types by the various soft computing techniques. The study area located between 48°10′28″ - 48°23′29″ N latitudes and 33°45′ 17″ - 33°51′ 23″ E longitudes, and covers an area of 112.54 Km2 approximately. Elevation of watershed varies from 3613 to 1481 m a.s.l. The studied area has a cold and semiarid climate with a mean annual rainfall Less than 570 mm. Most parts of Alashtar watershed are rangeland, while forest, dry farming, and irrigation lands are in considerable quantities and The surface lithology in the Alashtar watersheds are covered by the Eocene, Quaternary, Cretaceous, Miocene, Oligocene, Paleocene, and Pliocene geologic formations. In this study, The double-ring infiltrometer was used to measure the infiltration in the surface soil of some geological formations in the study area. After determining the infiltration rate, Gaussian Process (GP), Classification And Regression Tree (CART), and Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) were used to Modeling of infiltration rate in different Surface Soil of Geological Formations. Total data set consists of some physical characteristic of soil out of which 70% data used to train the model and 30% data were used to test the models. Finally, the models’ accuracy was assessed using three statistical parameters, Root Mean Square Error (RMSE), Nash-Sutcliffe model efficiency (NSE), and Coefficient of Correlation (CC), were selected to compare the efficiency of all models. Also for rapid and reliable comparisons, we also used Taylor diagrams. The Taylor diagram displays Root Mean Square Error (RMSE), Coefficient of Correlation (CC) and standard deviation (SD) values with closer positions on the diagram indicating better model performance.<br /><br /><br /><br />Results and Discussion <br /><br />The results indicated that the surface soil of OML geological formations had a higher cumulative infiltration and average infiltration rate. In this study, Gaussian Process (GP), Classification And Regression Tree (CART), Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) were used for infiltration rate in Alashtar watershed, Lorestan province, Iran. Comparison of these models showed that the M5P model tree (M5P) and Reduced Error Pruning Tree (REP Tree) models, with the combination of time, sand, clay, silt, soil density and soil moisture, could estimate infiltration rate with much less error than the other models. The obtained results suggest that the bagging M5P model tree regression technique in training and testing phase (with CC = 0.99, RMSE = 0.009, NSH = 0.006 and CC = 0.99, RMSE = 0.009, NSH = 0.006 respectivly) is more accurate to estimate the infiltration rate as compare to the GP, CART, RF, MARS and REPTree thegiven study area. Finaly The results showed that M5P model is effective in predicting Infiltration Rate (IR) content in the surface soil of geological formations. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models. The performance of the developed models was also compared using a Taylor diagram, in which an accurate model is indicated by a reference point, with a correlation coefficient of 1 having the same amplitude of variation as the observations. Thus, M5P was shown to be the most accurate model for cumulative infiltration prediction.<br /><br />Conclusion <br /><br />Prediction of the infiltration rate is an essential element of hydrologic design, watershed management, irrigation, and agriculture studies. This investigation identifies the optimal model for predicting Infiltration Rate (IR) using several computing approaches, such as Gaussian Process (GP), Classification And Regression Tree (CART), Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) models. In this study, 8 input variables, including time, sand, clay, silt, moisture content, soil bulk density, porosity and infiltration rate, were evaluated using three key performance metrics to assess the efficacy of various predictive models. These metrics comprised the CC, MAE, RMSE. Based on the evaluation results, the soft computing techniques model has a suitable capability to predict the infiltration rate of the soil. Finaly, the results shown that Learning algorithms can be used to quantify the amount of infiltration and also to estimate the amount of runoff in different geological formations. Also, the results shown that these models can be used to quantify the amount of infiltration and estimate the amount of runoff in the Surface Soil of Geological Formations. As well as, the results of this research can be used by the local authority to manage properly, systematically and plan development within their areas.Introduction <br /><br />Water repellency is a property that commonly affects the soil surface layer. It results from hydrophobic coatings on soil particles that originate from organic matter. The most significant effect of soil water repellency is a reduction in infiltration rates. The infiltration rate is one of the primary processes of the hydrological cycle. Hydrogeological and subsurface phenomena as infiltration, percolation mainly affect natural or man-made geotechnical soil. Understanding these phenomena are essential for estimation of runoff process, groundwater seepage, erosion, transport substances, evapotranspiration in surface and into groundwater are mainly influenced by precipitation. It is the property of water by which it moves through the soil particles. Infiltration process plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. Also, Soil infiltration is one of the key processes in design of irrigation systems, water resources management and soil protection and soil erosion control in watershed management and good knowledge of the infiltration rate is useful in calculating the natural and artificial groundwater recharge and surface runoff. Therefore, the prpose of this study was Performance assessment of the artificial intelligence models for prediction of the infiltration rate in the surface soil of geological formations in Alashtar watershed, Lorestan province, Iran.<br /><br /><br /><br />Materials and Methods <br /><br />The study area is a part of Kashkan watershed, Lorestan province, Iran. So, it was selected as a suitable watershed to Modeling of infiltration rate in different vegetation types by the various soft computing techniques. The study area located between 48°10′28″ - 48°23′29″ N latitudes and 33°45′ 17″ - 33°51′ 23″ E longitudes, and covers an area of 112.54 Km2 approximately. Elevation of watershed varies from 3613 to 1481 m a.s.l. The studied area has a cold and semiarid climate with a mean annual rainfall Less than 570 mm. Most parts of Alashtar watershed are rangeland, while forest, dry farming, and irrigation lands are in considerable quantities and The surface lithology in the Alashtar watersheds are covered by the Eocene, Quaternary, Cretaceous, Miocene, Oligocene, Paleocene, and Pliocene geologic formations. In this study, The double-ring infiltrometer was used to measure the infiltration in the surface soil of some geological formations in the study area. After determining the infiltration rate, Gaussian Process (GP), Classification And Regression Tree (CART), and Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) were used to Modeling of infiltration rate in different Surface Soil of Geological Formations. Total data set consists of some physical characteristic of soil out of which 70% data used to train the model and 30% data were used to test the models. Finally, the models’ accuracy was assessed using three statistical parameters, Root Mean Square Error (RMSE), Nash-Sutcliffe model efficiency (NSE), and Coefficient of Correlation (CC), were selected to compare the efficiency of all models. Also for rapid and reliable comparisons, we also used Taylor diagrams. The Taylor diagram displays Root Mean Square Error (RMSE), Coefficient of Correlation (CC) and standard deviation (SD) values with closer positions on the diagram indicating better model performance.<br /><br /><br /><br />Results and Discussion <br /><br />The results indicated that the surface soil of OML geological formations had a higher cumulative infiltration and average infiltration rate. In this study, Gaussian Process (GP), Classification And Regression Tree (CART), Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) were used for infiltration rate in Alashtar watershed, Lorestan province, Iran. Comparison of these models showed that the M5P model tree (M5P) and Reduced Error Pruning Tree (REP Tree) models, with the combination of time, sand, clay, silt, soil density and soil moisture, could estimate infiltration rate with much less error than the other models. The obtained results suggest that the bagging M5P model tree regression technique in training and testing phase (with CC = 0.99, RMSE = 0.009, NSH = 0.006 and CC = 0.99, RMSE = 0.009, NSH = 0.006 respectivly) is more accurate to estimate the infiltration rate as compare to the GP, CART, RF, MARS and REPTree thegiven study area. Finaly The results showed that M5P model is effective in predicting Infiltration Rate (IR) content in the surface soil of geological formations. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models. The performance of the developed models was also compared using a Taylor diagram, in which an accurate model is indicated by a reference point, with a correlation coefficient of 1 having the same amplitude of variation as the observations. Thus, M5P was shown to be the most accurate model for cumulative infiltration prediction.<br /><br />Conclusion <br /><br />Prediction of the infiltration rate is an essential element of hydrologic design, watershed management, irrigation, and agriculture studies. This investigation identifies the optimal model for predicting Infiltration Rate (IR) using several computing approaches, such as Gaussian Process (GP), Classification And Regression Tree (CART), Random Forest (RF), Multivariate adaptive regression splines (MARS), M5P model tree (M5P) and Reduced Error Pruning Tree(REP Tree) models. In this study, 8 input variables, including time, sand, clay, silt, moisture content, soil bulk density, porosity and infiltration rate, were evaluated using three key performance metrics to assess the efficacy of various predictive models. These metrics comprised the CC, MAE, RMSE. Based on the evaluation results, the soft computing techniques model has a suitable capability to predict the infiltration rate of the soil. Finaly, the results shown that Learning algorithms can be used to quantify the amount of infiltration and also to estimate the amount of runoff in different geological formations. Also, the results shown that these models can be used to quantify the amount of infiltration and estimate the amount of runoff in the Surface Soil of Geological Formations. As well as, the results of this research can be used by the local authority to manage properly, systematically and plan development within their areas.https://mmws.uma.ac.ir/article_3775_79c9871200d4875aed6eebb69dd827f3.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250712Soil resistance improvement against windy erosion by bacterium inoculation and addition of some modifiersSoil resistance improvement against windy erosion by bacterium inoculation and addition of some modifiers309322383310.22098/mmws.2025.16806.1561FAMaryamDehghanpourPh.D. Student, Dept. of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.NaeimehEnayatizamirProfessor, Dept. of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.AhmadLandiProfessor, Dept. of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.HeidarGhafariAssistant Prof., Dept. of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Journal Article20250712Introduction <br /><br />To enhance soil mechanical properties, eco-friendly materials are needed. Khuzestan Province in the southwest of Iran faces active dust hotspots, highlighting the importance of wind erosion control. In the present study, plant growth-promoting bacteria and biopolymers were used to protect soil against wind erosion. Biopolymers are environmentally friendly materials that are widely used in different geoenvironmental applications. In this research the feasibility of using chitosan and chitosan and lignosulfonate biopolymers for sandy and silt-loam soil stabilization have been studied. Chitosan, a positively charged biopolymer, interacts with soil through various processes like adsorption, forming polymer films, and connecting soil particles. However, despite its advantages, chitosan has not been widely adopted for soil stabilization, erosion control, and dust suppression compared to other biopolymers. Lignosulfonate is an environmentally friendly byproduct from the wood and paper industries, known for its effectiveness in enhancing cohesive expansive soils without major chemical alterations. It is a crosslinked lignin-based polymer with a negative charge that forms metal ion coordination bonds, which compact the soil. <br /><br /><br /><br />Materials and Methods <br /><br />Soil with sandy texture was prepared from the surface layer of critical wind erosion areas in southeast of Ahvaz with geographical coordinates of 48°59'N and 31°12'E, and silty-loam texture was prepared from geographical coordinates of 48°51'N and 31°4'E. The experiment was carried out in a completely randomized design in each type of soil. The treatments included control (no treatment), bacterial inoculation (Enterobacter cloacae), calcium lignosulfonate (2% w/v), chitosan (2% w/v), calcium lignosulfonate + bacteria and chitosan + bacteria. A certain amount of soil (7-8 kg) was poured into metal containers with dimensions of 50 × 30 × 3 cm. The bacterial suspension was sprayed separately on soil (1.5 × 106 CFU/ml). Chitosan dissolved at a concentration of 3% by weight in citric acid and lignosulfonate in water were sprayed onto the soil surface. The samples were stored for 60 days. To investigate the effect of treatments on soil wind erosion, a wind tunnel were used. At the end of the experiment, the trays containing the soil samples were weighed using a digital scale and the amount of weight loss of the trays compared to the initial weight was considered as the total amount of soil loss. The penetration and impact resistance of soil were measured. The stability of the aggregates was determined manually by the dry sieving test. The erosion-susceptible particles (EF) and the weighted average diameter of the aggregates (MWD) were calculated.<br /><br />Results and Discussion<br /><br />The results of the analysis of variance of the data in silty-loam and sandy soil showed a significant effect of the treatments on the measured characteristics. Comparison of the means with Tukey's test showed that there was a significant difference between the treatments in terms of penetration and impact resistance, MWD and EF in both types of soil at the 5% level. The penetration resistance in loamy-silty soil, in the treatment of chitosan with bacteria, was 3.8 fold, followed by lignosulfonate with bacteria, which was 3.47 fold compared to the control. This increase in penetration resistance in both types of soil was 2.85 fold compared to the control. Impact resistance in the treatments of chitosan with bacteria and lignosulfonate with bacteria in loamy-silty soil decreased by 92% and 72%, respectively, and in sandy soil by 61% and 67% compared to the control. The weighted mean diameter of soil aggregates in the chitosan-bacteria and lignosulfonate-bacteria treatments in loamy-silty soil increased by 2.7 and 2.6 times, respectively, and in sandy soil by 3.6 and 3.4 times compared to the control. The EF decreased with the treatments. In silty-loam soil, chitosan plus bacteria and lignosulfonate plus bacteria treatments had the lowest values with values of 53.1 and 54.8 percent, and in sandy soil with values of 62.9 and 63.05 percent. The comparison test of means showed that the amount of soil loss in silty-loam soil reached zero in chitosan plus bacteria and lignosulfonate plus bacteria treatments. In chitosan, lignosulfonate and bacteria treatments, it decreased by 82, 80, and 69 percent, respectively, compared to the control. In sandy soil, the soil loss in the treatments of chitosan with bacteria, lignosulfonate with bacteria, chitosan, lignosulfonate and bacteria was 87, 86, 74, 73, 53 percent lower than the control treatment. There was a positive correlation between MWD and the penetration resistance and a negative correlation with the impact resistance, EF, and soil loss in both types of soil. Microbial biomass carbon increased with the application of treatments, with the highest amounts measured in the treatment of chitosan plus bacteria, being 2.7 and 2.9 times higher than the control in loam-silty and sandy soils, respectively.<br /><br />Conclusion <br /><br />The findings implied that the inoculation of E. cloacae and the application of chitosan and calcium lignosulfonate, compared to the control, increased penetration resistance and MWD. Meanwhile, these treatments reduced EF and soil loss. Among the treatments, the combination of chitosan or calcium lignosulfonate with bacteria was more effective than the individual application of each. Using soil microorganisms, lignosulfonate, or chitosan to improve soil resistance against wind erosion is an environmentally friendly method. Microorganisms and lignosulfonate, in particular, can be easily applied using spraying equipment. However, more field studies are recommended to enable their use on a larger scale. Additionally, the effects of different concentrations of these compounds, as well as their influence on plant-soil interactions and rhizosphere microorganisms, should be investigated.Introduction <br /><br />To enhance soil mechanical properties, eco-friendly materials are needed. Khuzestan Province in the southwest of Iran faces active dust hotspots, highlighting the importance of wind erosion control. In the present study, plant growth-promoting bacteria and biopolymers were used to protect soil against wind erosion. Biopolymers are environmentally friendly materials that are widely used in different geoenvironmental applications. In this research the feasibility of using chitosan and chitosan and lignosulfonate biopolymers for sandy and silt-loam soil stabilization have been studied. Chitosan, a positively charged biopolymer, interacts with soil through various processes like adsorption, forming polymer films, and connecting soil particles. However, despite its advantages, chitosan has not been widely adopted for soil stabilization, erosion control, and dust suppression compared to other biopolymers. Lignosulfonate is an environmentally friendly byproduct from the wood and paper industries, known for its effectiveness in enhancing cohesive expansive soils without major chemical alterations. It is a crosslinked lignin-based polymer with a negative charge that forms metal ion coordination bonds, which compact the soil. <br /><br /><br /><br />Materials and Methods <br /><br />Soil with sandy texture was prepared from the surface layer of critical wind erosion areas in southeast of Ahvaz with geographical coordinates of 48°59'N and 31°12'E, and silty-loam texture was prepared from geographical coordinates of 48°51'N and 31°4'E. The experiment was carried out in a completely randomized design in each type of soil. The treatments included control (no treatment), bacterial inoculation (Enterobacter cloacae), calcium lignosulfonate (2% w/v), chitosan (2% w/v), calcium lignosulfonate + bacteria and chitosan + bacteria. A certain amount of soil (7-8 kg) was poured into metal containers with dimensions of 50 × 30 × 3 cm. The bacterial suspension was sprayed separately on soil (1.5 × 106 CFU/ml). Chitosan dissolved at a concentration of 3% by weight in citric acid and lignosulfonate in water were sprayed onto the soil surface. The samples were stored for 60 days. To investigate the effect of treatments on soil wind erosion, a wind tunnel were used. At the end of the experiment, the trays containing the soil samples were weighed using a digital scale and the amount of weight loss of the trays compared to the initial weight was considered as the total amount of soil loss. The penetration and impact resistance of soil were measured. The stability of the aggregates was determined manually by the dry sieving test. The erosion-susceptible particles (EF) and the weighted average diameter of the aggregates (MWD) were calculated.<br /><br />Results and Discussion<br /><br />The results of the analysis of variance of the data in silty-loam and sandy soil showed a significant effect of the treatments on the measured characteristics. Comparison of the means with Tukey's test showed that there was a significant difference between the treatments in terms of penetration and impact resistance, MWD and EF in both types of soil at the 5% level. The penetration resistance in loamy-silty soil, in the treatment of chitosan with bacteria, was 3.8 fold, followed by lignosulfonate with bacteria, which was 3.47 fold compared to the control. This increase in penetration resistance in both types of soil was 2.85 fold compared to the control. Impact resistance in the treatments of chitosan with bacteria and lignosulfonate with bacteria in loamy-silty soil decreased by 92% and 72%, respectively, and in sandy soil by 61% and 67% compared to the control. The weighted mean diameter of soil aggregates in the chitosan-bacteria and lignosulfonate-bacteria treatments in loamy-silty soil increased by 2.7 and 2.6 times, respectively, and in sandy soil by 3.6 and 3.4 times compared to the control. The EF decreased with the treatments. In silty-loam soil, chitosan plus bacteria and lignosulfonate plus bacteria treatments had the lowest values with values of 53.1 and 54.8 percent, and in sandy soil with values of 62.9 and 63.05 percent. The comparison test of means showed that the amount of soil loss in silty-loam soil reached zero in chitosan plus bacteria and lignosulfonate plus bacteria treatments. In chitosan, lignosulfonate and bacteria treatments, it decreased by 82, 80, and 69 percent, respectively, compared to the control. In sandy soil, the soil loss in the treatments of chitosan with bacteria, lignosulfonate with bacteria, chitosan, lignosulfonate and bacteria was 87, 86, 74, 73, 53 percent lower than the control treatment. There was a positive correlation between MWD and the penetration resistance and a negative correlation with the impact resistance, EF, and soil loss in both types of soil. Microbial biomass carbon increased with the application of treatments, with the highest amounts measured in the treatment of chitosan plus bacteria, being 2.7 and 2.9 times higher than the control in loam-silty and sandy soils, respectively.<br /><br />Conclusion <br /><br />The findings implied that the inoculation of E. cloacae and the application of chitosan and calcium lignosulfonate, compared to the control, increased penetration resistance and MWD. Meanwhile, these treatments reduced EF and soil loss. Among the treatments, the combination of chitosan or calcium lignosulfonate with bacteria was more effective than the individual application of each. Using soil microorganisms, lignosulfonate, or chitosan to improve soil resistance against wind erosion is an environmentally friendly method. Microorganisms and lignosulfonate, in particular, can be easily applied using spraying equipment. However, more field studies are recommended to enable their use on a larger scale. Additionally, the effects of different concentrations of these compounds, as well as their influence on plant-soil interactions and rhizosphere microorganisms, should be investigated.https://mmws.uma.ac.ir/article_3833_2a5bb867dae16bddd6c756dc2a2b7685.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Investigating the combined effect of different types of irrigation methods and planting rows on the yield and water productivity of pinto beans under different levels of irrigationInvestigating the combined effect of different types of irrigation methods and planting rows on the yield and water productivity of pinto beans under different levels of irrigation323337332510.22098/mmws.2024.15439.1479FAFatemehKeykhaeiAssistant Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, IranAbolfazlHedayatipoorAssistant Professor, Agricultural Engineering Research Department, Markazi Agricultural and Natural Resources Research and Education Center (AREEO), Arak, IranGoodarziMustafaAssistant Professor, Agricultural Engineering Research Department, Markazi Agricultural and Natural Resources Research and Education Center (AREEO), Arak, IranJournal Article20240710Abstract<br /><br />Introduction <br /><br />Beans are the largest source of human food after cereals, and beans are considered one of the most important legumes in the world and Iran. Beans are one of the most important types of legumes from the economic and nutritional point of view. The amount of beans required is 250 thousand tons and its production in the country is between 270 and 280 thousand tons. The cultivated area of beans in Iran is more than 100 thousand hectares and has a production of more than 200 thousand tons. Markazi province with 24,713 hectares is the second most fertile province in the country. On average, beans contain 20-23% protein and have a high nutritional value in the diet, which can be considered a good substitute for meat, especially in low-income areas. Agricultural mechanization is considered as a basic approach in the production of agricultural products. With the development of mechanized row planting of beans, one of the most important issues among farmers is determining the right row on the ridge. In many countries that produce beans, including Latin American countries, the planting method of this product is in rows or stacks in the fields. However, the dominant cultivation method in Iran is flat or linear cultivation, and with the development of drip-tape irrigation in recent years, a change in the cultivation method is necessary. The present research was carried out with the aim of investigating the effect of deficit irrigation on the yield of beans in two methods of drip-tape and furrow irrigation under two and three-row cultivation methods on the ridge.<br /><br />Materials and Methods<br /><br />In order to investigate the effect of full irrigation and deficit irrigation on the yield of beans and its components in two methods of drip-tape irrigation and furrow irrigation in two-row and three-row planting conditions, this research was carried out. This experiment was conducted in the years 2015 and 2016 at the National Research Station of Lubia Khomein. This station is located at an altitude of 1930 meters above sea level with a longitude of 49 degrees and 57 minutes and a latitude of 33 degrees and 39 minutes. The experiment was carried out in the form of split split plots in the form of a randomized complete block design in three replications. Irrigation methods treatment (furrow and drip-tape) was selected as the main factor and different amounts of irrigation water as a secondary factor. Planting method treatments were implemented as a sub-factor. The treatments and irrigation methods included drip-tape and furrow irrigation. Deficit irrigation treatments including irrigation at 100% of the water requirement at 75% of water requirement and irrigation at 55% of the water requirement were considered. <br /><br />Results and Discussion<br /><br />The results showed that the effect of irrigation method, the effect of water consumption management, the effect of planting, the interaction effect of planting method and irrigation method, and the interaction effect of planting method and irrigation amount at the level of 5% on seed yield were significant. The seed yield in the drip-tape irrigation method was associated with an increase of 8.8% compared to the furrow irrigation method. The effect of water consumption management showed that the seed yield in the full irrigation treatment with an average of 4245 kg/ha was associated with an increase of 7.7 and 69.8 percent, respectively, compared to the 75 and 55 percent water requirement treatments. Considering that seed yield is a part of the total dry matter produced by the plant, the reduction of plant dry matter under stress conditions can justify a part of the decrease in seed yield. The probable reason for this issue is that at the end of the growth period, due to the lack of available water, the power of transferring nutrients to the seed is reduced which leads to a drop in seed yield. The seed yield in the conditions of two rows of cultivation on each stack had an increase of 20.9% compared to three rows of cultivation on each stack. The mutual effect of planting method and irrigation method on grain yield showed that the drip-strip irrigation method was associated with an average of 2144 kg/ha under the condition of two rows of crops on each stack. The interaction effect of planting method and amount of irrigation on seed yield indicated the superiority of the treatments of two rows of planting on the ridge and full irrigation with an average of 2383 kg/ha. The effect of the year on none of the investigated parameters was significant, which shows that the effect of the uncontrollable factor did not cause a significant difference on the test results.<br /><br />Conclusion <br /><br />The combined review of the results regarding productivity shows that two-row crops were less productive than two-row crops, both in furrow irrigation and in drip irrigation, so it is necessary to consider this type of cultivation in every irrigation method. be placed On the other hand, the highest efficiency was observed in 75% of water requirement. Therefore, 75% of the water requirement can be considered to achieve higher physical productivity. Of course, in terms of the amount of yield, the drip method is definitely very profitable and suitable for the farmer, and the farmer can achieve the highest physical water productivity by using the scenario of 75% water requirement and two-row cultivation.Abstract<br /><br />Introduction <br /><br />Beans are the largest source of human food after cereals, and beans are considered one of the most important legumes in the world and Iran. Beans are one of the most important types of legumes from the economic and nutritional point of view. The amount of beans required is 250 thousand tons and its production in the country is between 270 and 280 thousand tons. The cultivated area of beans in Iran is more than 100 thousand hectares and has a production of more than 200 thousand tons. Markazi province with 24,713 hectares is the second most fertile province in the country. On average, beans contain 20-23% protein and have a high nutritional value in the diet, which can be considered a good substitute for meat, especially in low-income areas. Agricultural mechanization is considered as a basic approach in the production of agricultural products. With the development of mechanized row planting of beans, one of the most important issues among farmers is determining the right row on the ridge. In many countries that produce beans, including Latin American countries, the planting method of this product is in rows or stacks in the fields. However, the dominant cultivation method in Iran is flat or linear cultivation, and with the development of drip-tape irrigation in recent years, a change in the cultivation method is necessary. The present research was carried out with the aim of investigating the effect of deficit irrigation on the yield of beans in two methods of drip-tape and furrow irrigation under two and three-row cultivation methods on the ridge.<br /><br />Materials and Methods<br /><br />In order to investigate the effect of full irrigation and deficit irrigation on the yield of beans and its components in two methods of drip-tape irrigation and furrow irrigation in two-row and three-row planting conditions, this research was carried out. This experiment was conducted in the years 2015 and 2016 at the National Research Station of Lubia Khomein. This station is located at an altitude of 1930 meters above sea level with a longitude of 49 degrees and 57 minutes and a latitude of 33 degrees and 39 minutes. The experiment was carried out in the form of split split plots in the form of a randomized complete block design in three replications. Irrigation methods treatment (furrow and drip-tape) was selected as the main factor and different amounts of irrigation water as a secondary factor. Planting method treatments were implemented as a sub-factor. The treatments and irrigation methods included drip-tape and furrow irrigation. Deficit irrigation treatments including irrigation at 100% of the water requirement at 75% of water requirement and irrigation at 55% of the water requirement were considered. <br /><br />Results and Discussion<br /><br />The results showed that the effect of irrigation method, the effect of water consumption management, the effect of planting, the interaction effect of planting method and irrigation method, and the interaction effect of planting method and irrigation amount at the level of 5% on seed yield were significant. The seed yield in the drip-tape irrigation method was associated with an increase of 8.8% compared to the furrow irrigation method. The effect of water consumption management showed that the seed yield in the full irrigation treatment with an average of 4245 kg/ha was associated with an increase of 7.7 and 69.8 percent, respectively, compared to the 75 and 55 percent water requirement treatments. Considering that seed yield is a part of the total dry matter produced by the plant, the reduction of plant dry matter under stress conditions can justify a part of the decrease in seed yield. The probable reason for this issue is that at the end of the growth period, due to the lack of available water, the power of transferring nutrients to the seed is reduced which leads to a drop in seed yield. The seed yield in the conditions of two rows of cultivation on each stack had an increase of 20.9% compared to three rows of cultivation on each stack. The mutual effect of planting method and irrigation method on grain yield showed that the drip-strip irrigation method was associated with an average of 2144 kg/ha under the condition of two rows of crops on each stack. The interaction effect of planting method and amount of irrigation on seed yield indicated the superiority of the treatments of two rows of planting on the ridge and full irrigation with an average of 2383 kg/ha. The effect of the year on none of the investigated parameters was significant, which shows that the effect of the uncontrollable factor did not cause a significant difference on the test results.<br /><br />Conclusion <br /><br />The combined review of the results regarding productivity shows that two-row crops were less productive than two-row crops, both in furrow irrigation and in drip irrigation, so it is necessary to consider this type of cultivation in every irrigation method. be placed On the other hand, the highest efficiency was observed in 75% of water requirement. Therefore, 75% of the water requirement can be considered to achieve higher physical productivity. Of course, in terms of the amount of yield, the drip method is definitely very profitable and suitable for the farmer, and the farmer can achieve the highest physical water productivity by using the scenario of 75% water requirement and two-row cultivation.https://mmws.uma.ac.ir/article_3325_63e2b1905f1ac676a45a6c85f47b5819.pdfUniversity of Mohaghegh ArdabiliWater and Soil Management and Modelling2783-25465220250622Investigation of Spatial Distribution and Changes in Temporal Characteristics of Winter Season in the Lake Urmia BasinInvestigation of Spatial Distribution and Changes in Temporal Characteristics of Winter Season in the Lake Urmia Basin338350389410.22098/mmws.2025.17238.1587FANahalehBahramiMSc in Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, IranMajidMontaseriProfessor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, IranSomayehHejabiAssociate Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran.Journal Article20250421Introduction <br /><br />In climate studies based on astronomical methods, static temporal and spatial definitions of seasons are used. However, seasons change with time and space. Changes in the length and timing of seasons have major impacts on nature and human society. For example, shorter winters and reduced snow cover combined with an earlier onset of spring lead to earlier and weaker spring floods and increased river discharge in winter. Changes in climatic parameters, especially snowfall and snow storage, significantly affect the seasonal water balance and the precipitation-runoff process. Furthermore, these changes are predicted to continue in the future due to further climate change. The large range of elevation changes in the Lake Urmia basin has caused the distribution of snow cover in the basin to be heterogeneous. Global warming caused by greenhouse gas emissions can affect the temporal and spatial distribution of snow cover in the Lake Urmia basin, both through changes in the timing and length of the winter season and through increased evapotranspiration, which has the largest contribution to controlling snow cover area. In this regard, this study aims to evaluate and compare the characteristics of the start, end, and length of the winter season in two recent 30-year periods (1964-1993 and 1994-2023) using a winter season definition method based on daily air temperature data. It is worth noting that the method used is based on the inclusion of spatial heterogeneity of winter season characteristics and allows for the investigation of the relationship between winter season characteristics and altitude.<br /><br /><br /><br />Materials and Methods<br /><br />In this study, data from the ERA5-Land reanalysis database were used. Daily ERA5-land data were extracted by coding in Google Earth Engine for the Lake Urmia basin and for the two recent 30-year periods, including 1964-1993 and 1994-2023. To determine the winter season, instead of calendar winter, a cell-based definition was used. In this method, winter was defined based on the 25th percentile of daily temperature (T25) based on each of the two periods. With this method, a set of days with T25 less than zero degrees Celsius was defined as winter. Therefore, the beginning and end of the winter season were defined as the first and last day of the year with T25 less than zero degrees Celsius. The start and end of the winter season were reported in terms of the Julian day-number (starting from January 1), and the length of the winter season (in days) was calculated as the interval from the start to the end of the winter season. Next, the spatial distribution and changes in the day-number of the start, the day-number of the end, and the length of the winter season were compared for the two periods. Also, the correlation of the start, end, and length of the winter season with altitude was examined. For this purpose, global SRTM digital elevation model data with a spatial resolution of 30 meters was used. The elevation corresponding to the coordinates of each ERA5-land grid cell within the basin was extracted using ArcGIS software.<br /><br /><br /><br />Results and Discussion <br /><br />The percentage of cells with an increase in the day-number of the start of the winter (46.11%) is very close to the percentage of cells with a decrease (47.96%), and the average changes in cells with increasing or decreasing changes are, respectively, 7 and -7.5 days. The spatial distribution of the difference in the day-number of start of the winter is uniform at the basin and has a median of zero (meaning no change in the start of the winter). <br /><br />Regarding the end of the winter, the differences in most cells (82.45%) are negative, and the average changes in the day-number of the end of the winter in cells with increasing or decreasing changes are, respectively, 7.5 and -15 days. In general, the day-number of the end of the winter has a median of -11 days, which indicates an earlier end of the winter in the second period (1994-2023). <br /><br />The length of the second period winter in most cells (73.92%) is shorter than the first period. The average change in winter length in cells with increasing or decreasing changes is -17.1 and -17.4 days, respectively. The median change in winter length is also similar to the end of winter, -11 days, indicating a decrease in winter length in the second period.<br /><br />In both periods, the start, the end and the length of winter show significant negative, positive and positive correlations with altitude, respectively, and the slope of change is slightly steeper in the second period.<br /><br /><br /><br />Conclusion <br /><br />The spatial distribution of the start, end, and length of the winter season is non-uniform and follows the topography of the Lake Urmia basin; so that with increasing altitude, the start of the winter season occurs earlier and the end of the winter season occurs later. Therefore, the length of the winter season is longer at higher altitudes. The median change in the length of the winter season is -11 days, which indicates a decrease in the length of the winter season in the second period (1994-2023) compared to the first period (1964-1993). The sensitivity of the characteristics of the winter season to global warming is non-uniform at the basin and the rate of change is greater at lower altitudes. As the trend of increasing air temperature continues, it is predicted that the decrease in the length of the winter season will be even more severe in the coming decades, because according to previous studies under climate change scenarios, the length of the winter season will decrease significantly and reach less than 2 months in the middle latitudes of the Northern Hemisphere. Since global warming can affect the temporal and spatial distribution of snow cover in the Lake Urmia basin, it is recommended to predict changes in winter characteristics in the Lake Urmia basin under climate change scenarios for future studies.Introduction <br /><br />In climate studies based on astronomical methods, static temporal and spatial definitions of seasons are used. However, seasons change with time and space. Changes in the length and timing of seasons have major impacts on nature and human society. For example, shorter winters and reduced snow cover combined with an earlier onset of spring lead to earlier and weaker spring floods and increased river discharge in winter. Changes in climatic parameters, especially snowfall and snow storage, significantly affect the seasonal water balance and the precipitation-runoff process. Furthermore, these changes are predicted to continue in the future due to further climate change. The large range of elevation changes in the Lake Urmia basin has caused the distribution of snow cover in the basin to be heterogeneous. Global warming caused by greenhouse gas emissions can affect the temporal and spatial distribution of snow cover in the Lake Urmia basin, both through changes in the timing and length of the winter season and through increased evapotranspiration, which has the largest contribution to controlling snow cover area. In this regard, this study aims to evaluate and compare the characteristics of the start, end, and length of the winter season in two recent 30-year periods (1964-1993 and 1994-2023) using a winter season definition method based on daily air temperature data. It is worth noting that the method used is based on the inclusion of spatial heterogeneity of winter season characteristics and allows for the investigation of the relationship between winter season characteristics and altitude.<br /><br /><br /><br />Materials and Methods<br /><br />In this study, data from the ERA5-Land reanalysis database were used. Daily ERA5-land data were extracted by coding in Google Earth Engine for the Lake Urmia basin and for the two recent 30-year periods, including 1964-1993 and 1994-2023. To determine the winter season, instead of calendar winter, a cell-based definition was used. In this method, winter was defined based on the 25th percentile of daily temperature (T25) based on each of the two periods. With this method, a set of days with T25 less than zero degrees Celsius was defined as winter. Therefore, the beginning and end of the winter season were defined as the first and last day of the year with T25 less than zero degrees Celsius. The start and end of the winter season were reported in terms of the Julian day-number (starting from January 1), and the length of the winter season (in days) was calculated as the interval from the start to the end of the winter season. Next, the spatial distribution and changes in the day-number of the start, the day-number of the end, and the length of the winter season were compared for the two periods. Also, the correlation of the start, end, and length of the winter season with altitude was examined. For this purpose, global SRTM digital elevation model data with a spatial resolution of 30 meters was used. The elevation corresponding to the coordinates of each ERA5-land grid cell within the basin was extracted using ArcGIS software.<br /><br /><br /><br />Results and Discussion <br /><br />The percentage of cells with an increase in the day-number of the start of the winter (46.11%) is very close to the percentage of cells with a decrease (47.96%), and the average changes in cells with increasing or decreasing changes are, respectively, 7 and -7.5 days. The spatial distribution of the difference in the day-number of start of the winter is uniform at the basin and has a median of zero (meaning no change in the start of the winter). <br /><br />Regarding the end of the winter, the differences in most cells (82.45%) are negative, and the average changes in the day-number of the end of the winter in cells with increasing or decreasing changes are, respectively, 7.5 and -15 days. In general, the day-number of the end of the winter has a median of -11 days, which indicates an earlier end of the winter in the second period (1994-2023). <br /><br />The length of the second period winter in most cells (73.92%) is shorter than the first period. The average change in winter length in cells with increasing or decreasing changes is -17.1 and -17.4 days, respectively. The median change in winter length is also similar to the end of winter, -11 days, indicating a decrease in winter length in the second period.<br /><br />In both periods, the start, the end and the length of winter show significant negative, positive and positive correlations with altitude, respectively, and the slope of change is slightly steeper in the second period.<br /><br /><br /><br />Conclusion <br /><br />The spatial distribution of the start, end, and length of the winter season is non-uniform and follows the topography of the Lake Urmia basin; so that with increasing altitude, the start of the winter season occurs earlier and the end of the winter season occurs later. Therefore, the length of the winter season is longer at higher altitudes. The median change in the length of the winter season is -11 days, which indicates a decrease in the length of the winter season in the second period (1994-2023) compared to the first period (1964-1993). The sensitivity of the characteristics of the winter season to global warming is non-uniform at the basin and the rate of change is greater at lower altitudes. As the trend of increasing air temperature continues, it is predicted that the decrease in the length of the winter season will be even more severe in the coming decades, because according to previous studies under climate change scenarios, the length of the winter season will decrease significantly and reach less than 2 months in the middle latitudes of the Northern Hemisphere. Since global warming can affect the temporal and spatial distribution of snow cover in the Lake Urmia basin, it is recommended to predict changes in winter characteristics in the Lake Urmia basin under climate change scenarios for future studies.https://mmws.uma.ac.ir/article_3894_f1220735513de205f55fa7860d12fcf3.pdf