پژوهشنامه مدیریت حوزه آبخیز

Extended Abstract Background: Forest roads serve as essential communication links to forested areas, playing a pivotal role in a wide range of forestry activities. These roads not only facilitate access to forest resources for exploitation, conservation, and restoration but also contribute significantly to non-productive sectors, such as tourism and recreation. In the absence of these roads, many forestry operations and forest management practices would be unfeasible. However, numerous forestry-related functions have been suspended due to the cessation of logging activities and inadequate funding. This disruption has resulted in a halt to the ongoing maintenance and repair of forest roads, which are critical for the continued functionality of these transportation networks, leading to considerable deterioration. In this context, understanding the extent, nature, and trends of damage to these roads is of paramount importance for effective management and maintenance. Especially in light of the implementation of the "Forest Breathing Law" and reduced monitoring of forest roads, this study aims to identify and analyze the damage sustained by forest roads during this period. The primary objective of this research is to pinpoint damaged areas along forest roads and precisely map their locations, providing vital information for the optimal management and rehabilitation of these infrastructure assets. Methods: Due to the absence of prior data and concerns about the uneven distribution of road damage, this study initially employed a comprehensive 100% inventory and field survey approach to collect accurate and reliable information on all existing forest roads in the counties of Neka and Behshahr, covering a total length of 333.6 kilometers. During this phase, all road damages were recorded as point-specific observations, with each damage event fully documented in terms of its characteristics. To compare the results from the 100% inventory with a sampling-based method, the roads in both counties were divided into segments according to various factors, including topography, slope, accumulation points, and other relevant criteria. These segments were then subjected to random and systematic sampling. The length of each sampled section was determined to range between 100 and 200 m, depending on local conditions. A random number between zero and twenty was first generated to establish the starting point for the survey, and the survey began at a point on the road corresponding to this number. A linear transect perpendicular to the median axis of the road was then placed, with additional transects set at 20-meter intervals along the road's length. This methodology enabled a comprehensive assessment of the current road conditions, providing forest managers with reliable, statistically sound data to facilitate informed and evidence-based decision-making. Results: The results of this study revealed a total of 1,030 instances of various forms of damage across the surveyed forest roads. These damages encompassed a diverse array of structural failures and infrastructural issues, each of which significantly compromised the functionality of the roads. Specifically, 106 instances of soil embankment failure were documented, typically resulting from the instability of soil on slopes and the inability to adequately stabilize embankment walls against natural forces, such as rainfall and ground movement. Additionally, 48 cases of embankment sliding were observed, where the displacement of soil materials caused structural damage to the road. Furthermore, 74 instances of road slippage were identified, particularly on sloped sections of the roads, posing considerable risks to vehicular traffic. In terms of surface-related issues, 167 instances of speed-restricting potholes were recorded, primarily attributed to heavy traffic and inadequate maintenance of the road surface. Moreover, 280 cases of blocked roadside ditches were documented, resulting from the accumulation of sediments and organic matter in surface water drainage channels, thereby obstructing proper stormwater flow. Additionally, 116 instances of blocked cross-drainage culverts and 98 instances of damaged cross-drainage culverts were identified, typically caused by insufficient maintenance and repair of the water passage systems across the roads. Finally, 141 cases of road blockages due to fallen trees were observed, usually caused by the fall of trees from adjacent forested areas, which obstructed vehicular passage. In Behshahr County, the distribution of damages was as follows: 83 instances on primary roads, 61 on secondary roads, and 36 on tertiary roads. This distribution suggests that primary roads, which are the main transportation routes with higher traffic volumes, are more susceptible to damage. In contrast, the damage distribution differed in Neka County: 108 instances on primary roads, 469 on secondary roads, and 273 on tertiary roads. Notably, secondary roads in Neka exhibited the highest frequency of damage, likely due to their weaker substructures and lower traffic volumes than primary roads, which, although receiving less traffic, tend to suffer from structural weaknesses, rendering them more prone to damage. Conclusion: In Behshahr, the majority of damage is concentrated on primary roads. These roads are more vulnerable to damage due to their greater length and higher traffic volumes. As key transportation routes, they bear significant pressure from daily traffic, resulting in various forms of deterioration, such as slippage and structural wear. Conversely, the most severe damage in Neka is observed on secondary roads. This is primarily due to the relatively consistent traffic flow on these roads, coupled with their weaker substructures than primary roads. Although secondary roads generally experience less traffic pressure, their less robust construction and substructure make them more prone to damage and failure. The results of this study indicate that the cessation of forest utilization and the prolonged lack of maintenance have led to significant deterioration in the condition of forest roads. Specifically, in the counties of Neka and Behshahr, the damage is more prevalent on secondary and tertiary roads, underscoring the need for targeted intervention and budget allocation for their repair and upkeep. These findings suggest that immediate action is necessary to restore and maintain forest roads in these areas to prevent further degradation and the loss of natural resource productivity. Furthermore, the implementation of rehabilitation programs and improvements to road infrastructure should be prioritized in forest management policies to not only improve road conditions but also facilitate the sustainable management of forest resources.

Extended Abstract Background: Soil erosion is the land that separates soil particles from their original location, then changes to other places through various processes, and then deposits. Soil erosion and sediment production are among the most important problems in most regions of the world, causing a decrease in soil productivity, an increase in flood flows, a decrease in the health of water systems, and an increase in sediments in dam reservoirs. In addition to identifying the sources of sediment production, a model of potential sediment delivery to the waterway network is made to manage soil erosion and sediment production. As a result, the sedimentation of watersheds and sediment production areas, its delivery patterns from production sources to the network, and finally, depositing it in reservoirs, are needed for soil erosion management. One of the most important methods and characteristics of watershed sediment production is the sediment index of connectivity (IC). The connection process is an emerging concept to better understand the processes occurring at the surface of the watershed that affect water flow and sediment movement in different ways. This index explains the degree of connection of the sediment flow throughout the watershed, especially between the sediment source and the downstream area. Sediment management includes all measures that affect erosion, transportation, redistribution, and deposition of sediments in the direction of sediment diversion or trapping in waterway systems. Accordingly, sediment connectivity as an emerging concept in sediment management has been considered in recent years to investigate sediment transport in different parts of watersheds. The concept of connectivity is related to the state of a system, such as a watershed, which determines how and the degree of ease of material and energy transfer throughout the watershed system. In other words, connectivity indicates the continuity or discontinuity of runoff and sediment paths at a specific time and place. In this regard, the assessment of the sediment connectivity index in the Shazand Watershed was mentioned in the present study. ods: This study was conducted in the Shazand Watershed located in Markazi Province. The spatial pattern of sediment yield in the watershed and the sediment connectivity map of the watershed were investigated using the Sediment Delivery Ratio (SDR) model, the method presented by Borselli et al., and the definition of the connectivity index. IC consists of two parts, including upstream and downstream. This index was calculated by incorporating the distance from the sediment sink (i.e., the end of the slope or the connection to the main waterway), the characteristics of the sediment movement path, factors affecting the cumulative flow of runoff, and local conditions in each part considering as a weighting factor (W). The more positive IC and tending to the positive infinity, the higher the probability of structural connection for sediment transport. The probability of sediment connectivity will decrease with more tendency to the negative infinity. Therefore, the sediment IC was estimated using the corrected digital elevation model and the cover management factor with a spatial accuracy of 30 m. Next, spatial changes and values of the sediment IC were calculated for 24 sub-watersheds of the study. Results: The changes in the sediment IC in different sub-watersheds ranged from -7 to 1.4. The slope, land use, and land cover maps were used for this purpose. Comparing the land use map with the sediment IC shows the role of human disturbance in the natural functioning of ecosystems and natural systems. According to these maps, mountainous and high areas and rainfed lands transfer sediments and hydrological and sediment connectivity to the downstream area due to the naturalness of the areas and human interventions. Moreover, the lowland and steep highland areas mainly have the minimum and the maximum values of this index, respectively. One of the main factors affecting connectivity is the type of vegetation that affects the movement of particles so that connectivity increases and leads to more runoff production in areas without vegetation or with little coverage, but less runoff is produced, penetration increases, and connectivity decreases in areas with vegetation. Based on the resulting map and the concept of the sediment IC, the smaller its value is, the lower the probability of sediment transfer. In general, considering this index and its distribution can be important for prioritizing areas in terms of soil erosion and sediment management. Conclusion: Considering the importance of preserving Iran's water and soil resources and providing suitable solutions for managing sediments in watersheds, the integrated management of watersheds in the country requires a specific framework in planning related to sediment monitoring and control. Recently, the concept of sediment connectivity has been more frequently used in the studies of soil erosion and sediment yield of watersheds. In this connection, the spatial variability and zoning of the sediment IC in the Shazand Watershed were evaluated in the present study for the management of sediment resources. The results of the present study can, therefore, be employed to plan management measures to control sediments in the Shazand Watershed, Markazi Province, Iran. Nevertheless, conducting more detailed and extensive studies, especially in different geomorphological conditions, will provide the necessary platforms for comprehensive conclusions.

Extended Abstract Background: A broad understanding of the dimensions and elements of integrated management is necessary to achieve the effective management of water resources. According to the report of the Technical Committee of the Global Water Partnership, integrated management of water resources is a process that contributes to the protection, development, and coordinated exploitation of land water resources and other related resources to maximize economic and social well-being in an equitable manner without jeopardizing the stability of vital ecosystems. Achieving this goal requires providing the necessary tools to create the necessary infrastructure for the correct implementation of integrated management of water resources and achieving sustainable development goals. In this regard, the current research was conducted to identify the tools required for the integrated management of water resources to influence sustainable development. Methods: In the current descriptive-analytical research, information was collected through library studies and distribution of questionnaires. At first, the comprehensive concept of integrated management of water resources was chosen by reviewing different and varied international views and the results of world water meetings and conferences. Then, the principles, structure, challenges, and goals of integrated water resources management and the relationship with sustainable development were also examined by referring to international sources, such as World Bank reports, FAO documents and meetings, United Nations Development Program, documents related to the perspectives of the Technical Committee of the Global Water Partnership Program, documents of the United Nations Global Water Assessment Program, the United Nations 2030 document and also approved upstream documents, the water management of the country (including the macro water policies, the eighteen water policies of the country, the twenty-year vision document in the water sector, and the fourth development plan, documents and reports related to national and international conferences focusing on the integrated management of water resources and sustainable development, as well as the studies of researchers. To identify the tools needed for the integrated resource management approach to facilitate the sustainable development process, the effectiveness of four criteria, including 1) water resource protection and exploitation criteria, 2) policy making, 3) social, and 4) economic, on the implementation of the integrated water resources management approach in the Tajen catchment basin was evaluated by distributing 40 questionnaires among professors and students of the water engineering department at Sari University of Agricultural Sciences and Natural Resources. The questionnaire of the water engineering department of Sari University of Agricultural Sciences was validated after making corrections, and the final version was completed for distribution. The reliability of the questionnaire was evaluated using Cronbach's alpha coefficient. The questionnaire was compiled in such a way that there were 13 items and 5 subcategories for each component based on the Likert scale with numerical scores including very low (1), low (2), medium (3), high (4), and very high (5). The Cronbach's alpha obtained for the prepared questionnaires was equal to 0.891, which indicates the very good reliability of the prepared questionnaires. Finally, the effectiveness of each of these components in the implementation of the integrated water resources management approach was determined based on percentage by using the ratio of the total scores of each component to the number of distributed questionnaires. Results: Based on the evaluations, the component of protection and exploitation of water resources had the most effectiveness (60.64%) on the implementation of integrated management of water resources in the Tajen catchment basin. On the other hand, policy levers had the second priority (40.52%) of effectiveness, and socioeconomic sectors had the third (52.40%) and fourth (43.30%) priorities, respectively. According to the above-mentioned results, it can be realized that in the current situation and the water crisis, the protection and exploitation of water resources is one of the basic pillars of achieving integrated management of water resources and ease in achieving sustainable development in the region. On the other hand, the impact of other components cannot be ignored because the successful implementation of an integrated approach to water resources and achieving sustainable development depends on comprehensive attention to all managerial, economic, social, and environmental sectors. This is because these tools are complementary to each other and the disruption in the availability of each of them leads to limitations in achieving the goals and perspectives of sustainable development in a region. This also requires the creation of necessary infrastructure in different sectors. Conclusion: Factors such as population growth, economic development, and climate change have adversely affected the water resources of the Tajen catchment basin. Since goals such as sustainable water supply, ensuring public health, wastewater treatment, irrigation and drainage plans, and watershed protection cannot be properly implemented by taking temporary measures, the integrated management of water resources will ensure the continuous implementation of these goals. In general and according to the studies conducted in the current research, integrated management of water resources and sustainable development in the Tajen watershed and other areas are two inseparable components for the continued survival of a region. Therefore, providing the necessary infrastructure for this matter should be considered the main pillar of watershed management planning. This will not be possible except with the participation and coordination of all bodies, organizations, stakeholders, and users of water resources. The lack of a cooperative perspective is one of the biggest challenges in managing water resources and, consequently, achieving sustainable development. Therefore, the expansion of the participatory management approach in all dimensions related to water resources and achieving the goals of sustainable development can improve the current conditions and guarantee favorable future conditions to some extent.

Extended Abstract Background: Monitoring and evaluating water resources are crucial steps in increasing the knowledge of a country's water resource conditions. The results of these investigations should inform water resource management. Iran, with its arid and semi-arid climate, faces challenges due to low rainfall rates and uneven distribution. Climate change has further complicated these issues, affecting meteorological variables and the hydrological cycle. Factors such as population growth, urbanization, industrialization, and agricultural development have increased water demand. The increased use of groundwater in arid regions has led to a decrease in groundwater storage. To address the negative balance of Dehgolan groundwater resources and prevent depletion, the Regional Water Company of Kurdistan announced the development of Dehgolan groundwater resources in 2003. This research aims to investigate the quantitative effects of climate change on the Dehgolan aquifer, the largest aquifer in Kurdistan Province. Utilizing climate models to predict climate change and integrating them with hydrological models can help predict future hydrological changes. Methods: The Dehgolan plain, the largest plain in Kurdistan Province, covers an area of 982.8 km2. This plain is located between the longitudes 47˚10' to 47˚45' and the latitudes 35˚05' to 35˚35'. The Dehgolan aquifer and Talvar catchment areas are 779.8 km2 and 2491 km2, respectively. The geophysical studies determined the Dehgolan aquifer as an unconfined aquifer. The elevation in the Dehgolan aquifer ranges from 1740 to 2045 m. Various land uses cover the watershed, with dryland farming, range, irrigated agriculture, residential areas, and water bodies accounting for different percentages. Climate data, including rainfall and temperature, were collected. The average annual rainfall in the Dehgolan Plain is 319.32 mm. The average annual temperature ranges between 9.39 and 9.12 ˚C, respectively. This study utilized the IPCC Fifth Assessment Report to predict meteorological variables (minimum and maximum temperature, precipitation, and relative humidity) under different scenarios including RCP2.6, RCP4.5, and RCP8.5 using the SDSM model. The MODFLOW model was employed for 3D groundwater flow modeling. The conceptual model of the Dehgolan aquifer was developed in GMS software, incorporating boundary conditions, aquifer network, and other parameters. Monthly elevation of the water table was estimated under RCP2.6, RCP4.5, and RCP8.5 scenarios. Simulation and prediction were conducted for specific periods. Groundwater information from September 2008 to September 2017 was used for model calibration, and then groundwater was predicated for 10 hydrological years from September 2017 to September 2027. Model efficiency was evaluated using various coefficients such as R2, ENS, PBIAS, and MAE. Results: The SDSM and MODFLOW models showed accuracy in simulating climate variables and groundwater flow. Maximum similarity between simulated and observed data belonged to minimum and maximum temperatures. Sensitivity analysis revealed hydraulic conductivity as the most sensitive parameter whereas the specific yield had the least effect on groundwater changes. Results from the MODFLOW model projected a negative balance in the Dehgolan aquifer under different scenarios. Under the RCP2.6, RCP4.5, and RCP8.5 scenarios, the average annual water table in the predicted period will be -1.60, -1.61, and -1.36 m, and the average annual groundwater storage will be -23.69, -23.85, and -20.21 mcm, respectively. The RCP2.6 and RCP4.5 scenarios indicated a more critical situation than RCP8.5. Conclusion: Climate models and RCP scenarios suggest changes in climatic conditions and hydrological processes in the Talvar River basin. Increased atmospheric temperatures and decreased precipitation will impact groundwater resources. The high atmospheric temperature will increase the rate of evaporation. High evaporation should have negative impacts on both quantitative and qualitative parameters of groundwater resources. The future may see fluctuations in groundwater levels following natural patterns.

Extended Abstract Background: Clustering time series of precipitation and other hydrological elements by direct use of classic methods, such as K-means, can be misleading because there is a time-lagged correlation in time series observations that is ignored in classic methods. A periodically correlated time series is a type of non-stationary time series with a periodic covariance function. Since there is periodic behavior in time series observed in hydrology, meteorology, climatology, etc., the use of periodically correlated time series has attracted the attention of experts in recent years. Time series data can be studied with two different approaches, time domain and frequency domain. Frequency domain is usually used to identify the structure of a time series. In this approach, time series are studied using Fourier transforms, which are functions of frequency. The clustering of meteorological stations in terms of precipitation time series provides important information about a geographical area and plays an important role in water resources management in that area. The main purpose of this research is to calculate the distance between the monthly precipitation of meteorological stations based on the observations of periodically correlated time series in the frequency domain and then use a clustering method to group the meteorological stations. For clustering, the fuzzy clustering method is compared with the usual k-means clustering method. Methods: In this research, the monthly precipitation time series of 34 meteorological stations in Golestan Province were collected over a common period of 15 years from the Regional Water Company of Golestan. Considering that monthly precipitation values are time-dependent, these data were first arranged as a time series. Because of the dispersion of precipitation values and the presence of zero values in a number of meteorological stations, the one-to-one transformation was used to stabilize the variance. Since monthly precipitation data has a period of 12, they can be studied as periodically correlated time series. In this research, the periodically correlated time series of monthly precipitation was clustered in the spectral domain. First, the distance between the monthly precipitation times series of meteorological stations in the frequency domain was measured based on the periodic multiple correlation coefficient index. Then, clustering was performed by a fuzzy method based on the calculated distance matrix. After calculating the distance matrix, clustering was also done using the k-means method. The mean square error index of monthly precipitation was used and the results were compared to compare the accuracy of the clustering methods. All calculations of this research were done in MINITAB 17 and R 4.3.1 software. Results: Investigating the seasonal trend for the monthly precipitation time series revealed a period of 12 in all the selected meteorological stations of Golestan Province; therefore, these data were analyzed based on periodically correlated time series. The distance between meteorological stations was determined based on the periodic multiple correlation index. Then, using two methods of fuzzy and k-means clustering, eight groups were identified for 34 selected meteorological stations in Golestan Province. The largest and smallest groups in fuzzy clustering included eight and one stations, respectively, and seven and one meteorological stations in the k-means method. Comparing the accuracy of the methods based on the mean square error index showed that the value of this index for the fuzzy clustering method was 13.67, while this index was calculated at 165.11 in the k-means method. In this research, it was also found that the accuracy of the spectral domain method along with fuzzy clustering was almost 12 times higher than the spectral domain method based on the k-means method. Moreover, the investigation of the groups in the fuzzy clustering method shows the similarity of precipitation changes in the meteorological stations assigned to each group, which indicates the accuracy of the spectral domain method along with the fuzzy clustering. Conclusion: In this research, the distance between the monthly percipitation time series of 34 meteorological stations in Golestan Province was measured in the frequency domain based on the alternating multiple correlation index. Then, the selected meteorological stations were clustered in the spectral domain based on fuzzy and k-means clustering methods, and eight groups were identified in each method. The mean square error index for fuzzy and k-means clustering methods was calculated at 13.67 and 165.11, respectively. These values showed that the accuracy of the fuzzy method for clustering the monthly percipitation time series of the selected meteorological stations was almost 12 times higher than the usual k-means method. The similar trend of changes in the monthly precipitation of meteorological stations assigned to each of the clusters shows the considerable efficiency of using the frequency domain method with fuzzy clustering for grouping periodically correlated time series such as monthly precipitation. In addition to the simplicity and accuracy of the clustering method in the frequency domain, the other advantage is to consider the periodic structure of time series in clustering. Furthermore, spectral clustering can be used for time series with unequal lengths recorded in hydrological studies.

Extended Abstract Background: Floods are one of the most important natural hazards that annually inflict irreparable devastation across the country. Spatial analysis of flood hazard sensitivity and the preparation of hazard maps are important approaches in flood management. The Aji Chai Basin's large area and special geographical conditions make it one of the basins with high flooding hazards. As a result, the current study mainly aims to use a statistical model and multi-criteria decision-making techniques to create a flood hazard map for this basin. For this reason, flood hazard maps were created using 18 parameters affecting the occurrence of floods. The investigated parameters were Elevation, Slope, Aspect, Topographic wetness index, Sediment transport index, Stream power index, Earth curvature, Drainage texture, Rainfall, Distance to the river, River density, Lithology, Hydrological soil groups, Geomorphology, Distance to bridge, Distance to dam, Normalized Difference Vegetation Index, and Land use. Methods: The study area of this research is the Aji Chai Basin, which is located in East Azerbaijan Province in terms of political divisions. The Aji Chai Basin is located in an almost rectangular shape between the Sabalan Mountain and the Ghoshe Dagh mountain range in the north, the Boz Gosh mountain range in the south, and the Sahand Mountain in the southwest. The area of this basin is about 10985.9 km2. The elevation changes of the basin are from 1255 m at the outlet of the basin to 3816 m on the slopes of Sabalan Mountain. In general, the Sabalan and Sahand mountains, with a height above 3600 m, are considered the most important topographic features in the region's roughness. The average annual rainfall of the Aji Chai Basin is about 315 mm based on the information from synoptic stations (Four stations, viz. Tabriz, Bostan Abad, Sarab, and Heris) and rain gauges (24 stations) available in the region. To achieve the research aim, the Analytical Network Process (ANP) model was employed as a multi-criteria decision analysis method, and the Statistical Index (SI) model was utilized as a two-variable statistical method. The Analytical Network Process is one of the multi-criteria decision-making methods developed by Saaty in 1996. The statistical index (SI) method was introduced by Van Westen in 1997. The research models were implemented using the location of 274 flood points that happened in the past. The map of the location of flood points in the area was prepared through the information of the regional water company of East Azerbaijan Province, field survey, and the Landsat 8 satellite image of the OLI-TIRS sensor. The accuracy of the results was evaluated using three statistical indices, namely Sensitivity, Specificity, and Accuracy, along with the ROC curve and the area under the curve (AUC). Results: The results of parameter weighting using the ANP model showed that the rainfall, geomorphology, and slope are the three parameters with the highest weight with coefficients of 0.137, 0.104, and 0.101, respectively, indicating the great influence of these factors on the occurrence of floods in the region. On the other hand, the sediment transport index and stream power index were the two parameters of the lowest weight. The evaluation of the importance of the parameters using the SI model also showed that areas near rivers and bridges and low-altitude and low-slope areas were susceptible to flood hazards. The final maps were prepared from the product of the weights of each of the parameters in their information layers and in five classes from very low to very high potential. Examining the final maps showed that the distribution pattern of hazard zones was similar in both models, and flat and plain surfaces were identified as areas with a high flooding hazard. The important cities of the basin, such as Tabriz, Sarab, and Bostanabad, are also in high and very high hazard classes, which shows the vulnerability of these cities when destructive floods occur. Since these cities are formed along the rivers, it shows the need for authorities to pay serious attention to urban flood management. On the other hand, the heights and steep slopes have the lowest potential for flooding. Conclusion: Examining the area of each flood hazard class in the research models showed that about 34% and 46% of the areas of the region in the ANP and SI models, respectively, were in high and very high areas in terms of flooding. Examining the maps shows that the metropolis of Tabriz, which is considered the most important population center in the basin, is located in high and very high classes in terms of flooding due to its development along the Mehranroud and Aji Chai rivers. This shows the need for the serious attention of the regional authorities to manage the flood hazard in the basin as best as possible. The results of evaluating the accuracy of the models showed that the performance of both models was good in preparing the maps of the flood hazard potential in the region. Nevertheless, the SI model with a coefficient of 0.945 has the highest value of the AUC, which indicates the greater accuracy of this model compared to the ANP model.

Extended Abstract Background: The climate change phenomenon and its effects and consequences have become a challenging issue for managers and planners, especially for water resources. Currently, climate change has attracted the attention of scientists due to its effects on human societies. Snow plays an important role in the protection of biodiversity, and changes in the amount of snow cover affect animal and plant life as well as the structure of ecosystems. Snow cover is very important in mountainous areas. Since snow is considered solid water, it is an important source for providing drinking water. Because snow cover contains a lot of air, it is a weak conductor of heat, thus the snow cover can protect agricultural products and trees from extreme cold. The current research aims to investigate changes in snow cover concerning land surface temperature, evapotranspiration, and vegetation cover components in the Aras Basin using MODIS sensor data products in annual, seasonal, and monthly periods. Methods: The studied area is the Aras Basin, which is considered a part of the western Caspian Lake sub-basin and forms the political border between Azerbaijan, Iran, Turkey, and Armenia countries. In this research, Terra satellite images were used to calculate snow cover, land surface temperature, vegetation cover, and evapotranspiration. In this way, the annual average, monthly average, and seasonal average were calculated for each of the mentioned variables based on the solar date. Daily products of snow, vegetation, surface temperature, and 8-day evapotranspiration product of the Terra satellite were used here. Finally, the images were transferred to the ArcMap 10.8 environment for calculation. To calculate the averages of the studied variables in the period of 2011-2018, 8644, 8642, 8325, and 1058 images were processed for snow cover, land surface temperature, vegetation cover, and evapotranspiration, respectively, using coding in Google Earth Engine. Results: The results showed that the hottest and highest temperatures were in 2000, 2001, and 2014, respectively, during 2000-2022, with the average maximum temperatures of 42, 40, and 40 °C. The coldest year of the studied statistical period was 2017, with average maximum and minimum temperatures of 35 and 1 °C, respectively. The highest average amount of greenness belonged to the years 2019 and 2021 with a value of 0.44, and the lowest average amount of greenness was recorded for the years 2007 and 2003 with a value of 0.34. In the studied years, 2019 and 2022 had the lowest and the highest annual averages of evapotranspiration, respectively. The evapotranspiration in 2018 was at the highest (20.96) and the lowest (3.57 kg/m3), levels, respectively. In 2022, evapotranspiration was 37.42 in the highest state and 2.60 kg/m3 in the lowest state. In all the studied years, the southeastern and northern parts of the studied basin had the highest average evapotranspiration. In 2018, the maximum and the minimum average land surface temperatures were equal to 37.12 and 0.14 °C, respectively. In 2022, the maximum temperature of the land surface temperature was 39.80 and its minimum was 5.66 °C. As can be seen, there is a direct relationship between temperature and evapotranspiration in these years. During the years 2000-2022, the lowest and the highest averages of NDSI were observed in 2000 (17.31) and 2017 (26.23). In all the studied years, the most snow-bearing areas were the high-altitude areas located in the southern, southeastern, and southwestern parts of the Aras Basin. Conclusion: The results of the survey of the surface temperature maps showed that the years 2000 and 2001 started with average maximum temperatures of 42.37 and 40.20 °C, respectively, and continued with a decrease in the average maximum temperature. In 2020 and 2021, the maximum temperature reached 39 °C, after which evapotranspiration also changed according to the land surface temperature. The trend of changes in the vegetation cover of the Aras Basin generally shows an increase in vegetation cover during the 22 years, but the trend of changes in the snow cover has been a slight decrease. Higher temperatures are seen in the low and flat parts of the northeast and northwest of the Aras Basin, and lower temperatures occur in the high areas of the southeast and west of the basin. Evapotranspiration is often observed in the northern and southeastern parts of the Aras Basin, which has more snow cover. Although these parts have higher altitudes and lower temperatures than the other regions, they have more evapotranspiration. Winter and autumn are the snowy seasons of the study area. The highest amount of snow cover was in February with an area of 37234.32 km2 and the lowest was in August with 4.71 m2. The high areas with snow cover (southeast, west, and north of the basin) had snow cover even in the years when the surface average snow cover was at its lowest.

Extended Abstract Background: Climate change as an influential phenomenon causes changes in climate systems, increasing both the temperature and the moisture capacity of the atmosphere so that the increase in global temperature has increased the evaporation from the surface and the atmosphere water content. Therefore, an increase in temperature and the moisture capacity of the air, when there is a moisture source, increases the specific humidity of the air and decreases relative humidity. This causes the total water vapor in the atmosphere to rise, and water vapor, as a greenhouse gas, causes global warming. In this regard, one of the most important consequences of climate change is its influence on factors affecting the hydrological status of basins, such as temperature, precipitation, and reference evapotranspiration (ET). Therefore, knowing the rate of evapotranspiration in each point, especially in arid and semi-arid regions such as Iran, and more specifically in the Lake Urmia basin, where its activities are mainly related to agriculture, is very important for determining the water needs of plants and managing water resources, hence the study of ET is more necessary under the influence of climate change. Therefore, climate change is a phenomenon affecting reference ET as the most important part of the hydrological cycle because climate changes can have significant effects on the biosphere. Methods: In this research, the effect of climate change on minimum and maximum temperatures and average reference evapotranspiration in the Lake Urmia basin was evaluated on a monthly scale with CMIP6 climate models in mm per month using the observation data of Urmia, Tabriz, and Saghez synoptic stations. The minimum and maximum temperatures on a monthly scale and for the statistical period of 1975 to 2014 were received from the IRAN Meteorological Organization (IRIMO). The second category is the data of CMIP6 climate models in the historical period of the model, which have a common period with the observation period (1975-2014), the periods of the near future (2020-2059), and the far future (2060-2099). Sixth report models were obtained from historical and future periods on a monthly scale. Two climate models (CESM2 and IPSL-CM6A-LR) under the optimistic SSP1-2.6 and pessimistic SSP5-8.5 scenarios in the near future (2020-2059) and far future (2060-2099) were used to investigate the effects of climate change and predict minimum and maximum temperature changes as well as changes in reference ET on a monthly scale. Downscaling was done using the LS method, and climate models were validated using R2 and MAE statistics. Finally, the crop reference ET was calculated for basic, near, and far future by Makkink and Turc methods. Results: In the present study, the results of CESM2 and IPSL-CM6A-LR models using R2 and MAE for the future period compared to the base period showed that the optimistic scenario would perform better in the near future in both models. In the mentioned models and in Urmia, Tabriz, and Saghez stations, the optimistic scenario showed the proper performance of the models with a high R2 (0.99, 0.99, and 0.99 for Tmin and 0.99, 0.99, and 1.00 for Tmax) and a low MAE (1.48, 1.27, and 1.37 for Tmin and 1.54, 1.49, and 1.55 for Tmax), respectively. The results of examining the changes in minimum and maximum temperatures and reference ET were presented in different tables and graphs. Accordingly, the minimum and maximum temperature ranges and, accordingly, the reference ET obtained from the Makkink and Turc methods will increase in optimistic and pessimistic scenarios and in the near and far future periods in Urmia, Tabriz, and Saghez stations. According to the results, the average minimum and maximum temperatures will increase in future periods and under both scenarios. Moreover, the average minimum and maximum temperatures will increase in the investigated stations according to optimistic and pessimistic scenarios. An effect of climate change is the increase in temperature, and one of its consequences is the increase in ET and the water requirement of plants. Furthermore, the main part of activities in the Lake Urmia basin is focused on agriculture and plays an important role in the employment and economy of this region. Therefore, climate change will cause many environmental problems by affecting the temperature and reference ET in the coming years. Conclusion: To know the average minimum and maximum temperature changes and reference ET in the study area, the effect of climate change on minimum and maximum temperatures and reference ET in the Lake Urmia basin was evaluated using CMIP6 models. The mentioned variables were predicted using the observation data of Urmia, Tabriz, and Saghez stations as well as CESM2 and IPSL-CM6A-LR models of the sixth report. Downscaling was done by the LS method during the base period (1975-2014) and two future periods (2020-2059 and 2060-2099) under optimistic and pessimistic scenarios. R2 and MAE statistics were used to validate climate models. Finally, the Makkink and Turc methods were used to calculate reference ET. The results indicate that the minimum temperature will increase from 0.05 to 3.02 and 0.60 to 4.31 ℃, and the maximum temperature will increase from 0.25 to 3.84 and 0.55 to 5.41 ℃ in the near and far future compared to the base period, respectively. Moreover, the average reference ET will increase from 0.72 to 4.68 and 0.08 to 4.80 mm/month by the Makkink method and from 0.24 to 5.23 and 0.71 to 5.59 mm/month by the Turc method in the near and far future, respectively.

Extended Abstract Background: Improving the hydromorphological conditions of a river is a very important issue in the sustainable management of rivers. In December 2000, the European Union Framework Directive (WFD) was presented and published in the official journal (OJL327), and the most appropriate way to achieve this goal has been left to the discretion of the EU member states. A guideline published in Germany in 2000 by the Water Management Group for small to medium-sized rivers was developed as the River Structural Quality Classification Method (LAWA-OS) in the framework of the WFD. A review of previous studies indicates that the LAWA-OS method in domestic studies has been done in the form of evaluating only one river, but in this research, two Pehnehkola and Hardau rivers were compared with each other. The mentioned method has also been investigated on large rivers in Iran with a high width and flow. This research aims to investigate the "Structural Quality Classification of Rivers in Germany (LAWA-OS)" method and to compare the hydromorphological status of the Hardau River in Germany and the Pahnehkola River in Iran. Methods: The River Structure Classification Method in Germany (LAWA-OS) examines the damage and negative changes caused by human factors over time. The evaluation of the mentioned method consists of four stages. The first stage is data collection and preparation of basic maps, such as a topography map, a soil science map, and a land use map. The mentioned information for the Pahnehkola River was obtained from the Sari City Regional Water Company and the General Directorate of Natural Resources and Watershed Management of Mazandaran Province. The topographic map of the Hardau River was prepared from the Topografic-Map.Com website. The second step is to determine the range and its type, which according to the location of the Pahnehkola River in the mountains and the Hardau River in the plains, the range type was determined and 1 km from both rivers was selected and divided into 100 m intervals. The third stage is a field visit. The forms related to the LAWA-OS method are filled by visiting the left and right banks and river elements. A set of 25 individual parameters is available in six groups from the main parameter in the mentioned form. The fourth step is to evaluate the results. Determining the river class is a process to evaluate it, and it is classified into five classes, which are calculated according to the points assigned to each parameter, and the structural quality class of the river is determined in the end. The final evaluation includes two evaluation parts based on functional units (the performance of individual parameters with a specialized evaluation of the water zone structure by an expert) and index-based evaluation (the performance of individual parameters based on index values). If the difference in the two evaluations is greater than one structural class, re-examination to determine the existing errors is on the agenda. Results: The evaluations showed that the highest negative degradation score in the Hardau River belonged to individual parameters of flow curvature, special bank cover, special flow structures, depth changes, the type of river profile, and variety of bed materials. The parameters that are almost natural in the Hardau River are profile depth, width erosion, and land use, respectively. Except for the mentioned parameters, there are several parameters in the Hardau River in a completely natural state. In the studied area of the Hardau River, there was no transverse structure either in the bed or in its body. In this regard, the individual parameter of water return was not observed in any of the studied sections of the Hardau River. Individual parameters related to land use also have a low destruction score because the boundaries of the river are completely free and there are no factories, garbage, paved surfaces, or even flood protection structures in the river floodplain. In the Pahnehkola River, the highest negative degradation score belonged to the individual parameters of the special bank structure, profile depth, and width erosion, respectively. The single parameters that received the minimum destruction score in the Pahnehkola River are the vegetation cover of the river bank, land use, and the border around the river. Moreover, the individual parameters of the transverse structure, water return, piping, changes in the flow path, bed materials, bed cover, and specific cases of land use in the Pahnehkola River have not been affected by human interference. Conclusion: The results of the present research indicate that the Hardau River with a score of 2.53 has slightly changed in the structural classification, and the Pahnehkola River with a score of 1.71 is classified structurally without changes. According to the results evaluated in Hardau and Pahnehkola Rivers, the Pahnehkola River has a more natural hydromorphological condition than the Hardau River. The reason for the placement of the Hardau River in a slightly changed structural layer can be the ease of access to the river and the use of nearby agricultural lands, which have caused human interference in disrupting the natural structure of the river.

Extended Abstract Background: Floods are caused by several reasons, including rainfall intensity, vegetation destruction, and encroachment of rivers. The high power of floods damages buildings, bridges, and existing structures, and also reduces the capacity of the river bed. Moreover, the excessive volume of water leads to human and financial losses and the destruction of animal habitats. Structural measures (such as dam construction) and non-structural measures (such as increased vegetation coverage, forecasting, and flood warning systems) are carried out to deal with a flood and its damage. Flood forecasting is the process of estimating the time and place of flood occurrence and the volume of water and, as an efficient and low-cost tool for flood management and damage reduction, has received a lot of attention in recent years. Rainfall-runoff modeling is one of the measures of flood management. Simulation is done using hydrological models to understand the relationship between rainfall and runoff parameters, as well as to determine the peak discharge value and the time to reach the peak discharge. One of the hydrological software packages in this field is the HEC-HMS software. By considering three components of the basin, meteorological, and control specification models, the value of losses, runoff, base flow, and routing are calculated using existing methods, and finally, optimization is performed to reduce the difference between observed and simulated hydrographs. Precipitation is one of the most important input parameters in simulating floods. Therefore, the correct estimation of its amount is considered necessary and important. Considering the number of rain gauge stations and the lack of sufficient stations in Iran, especially in mountainous areas, the use of numerical weather prediction model information and satellite rainfall data plays an important role in flood forecasting. Numerical weather prediction models predict weather conditions using mathematical models. Forecasts are divided into three short-range, medium-range, and long-range categories, and also, into regional and global models. One of these models is the numerical weather prediction model, called GFS, which predicts and provides data such as temperature, wind, and precipitation. Heavy rainfall, destruction of forests, sand and gravel harvesting, and construction in floodplains are among the causes of floods in Mazandaran Province, especially the Tajan River, in recent years. The main goal of this research was to estimate the value of peak discharge by simulating flood events and evaluating the results using the precipitation information of the GFS model in the Tajan watershed located in Sari City, Mazandaran Province. Methods: In this research, data were collected from the hydrometric stations of the Tajan watershed, including the hourly measurements of recorded floods, as well as the information required by the evaporation and rain gauge stations in this area, including precipitation obtained by the Mazandaran Regional Water Company for the 10-year period of 2011-2021. Furthermore, precipitation data were received online (from the following webpage: https://openweathermap.org) through the output of the GFS numerical weather prediction model in the mentioned period. The curve number of each subbasin was determined using land use and soil hydrological group layers in ArcGIS software, and the physiographic characteristics of the Tajan watershed were extracted using the HEC-GeoHMS extension. Then, four events 04 October 2011, 01 December 2011, 14 November 2016, and 01 December 2017 were simulated using the physiographic characteristics of the sub-basins, the precipitation data of the Tajan watershed, and the flood discharge obtained by the Mazandaran Regional Water Company in HEC-HMS software. The Soil Conservation Service curve number method was used to calculate losses, the SCS unit hydrograph method was used to calculate the runoff method, and the lag method was used for routing. Subsequently, sensitivity analysis was performed to determine the sensitivity of the curve number, lag time, and initial abstraction parameters. The optimal values of the parameters in the optimization process were determined using nine objective functions available in the HEC-HMS software, including Mean of Absolute Residuals, Mean of Squared Residuals, Peak-Weighted Root Mean Square Error, Peak-Weighted Variable Power, Percent Error in Peak Discharge, Root Mean Square Error, Sum of Absolute Residuals, Sum of Squared Residuals, and Time-Weighted RMSE. In the next step, validation was performed by event 01 December 2017 using the optimal values of the parameters. Finally, after HEC-HMS software optimization and verification, the aforementioned flood events were simulated using the data of the GFS numerical weather prediction model. Results: The results showed a strong correlation between observed and calibrated hydrographs. Besides, the best objective function was peak-weighted variable power. The results of the sensitivity analysis showed that the peak discharge was more sensitive to the changes in the initial abstraction and curve number parameters. Validation was performed to verify the validity of the results obtained in the calibration process, and the results indicated no significant differences between the averages of the two groups, viz. observed and calibrated flow rates. Moreover, the simulation results using the GFS numerical weather prediction model showed no significant differences (at a 95% confidence level) between the observed and simulated hydrographs. Conclusion: According to the results, using the precipitation data of the GFS numerical weather prediction model and the HEC-HMS rainfall-runoff software makes it possible to simulate the flood with acceptable confidence in predicting the peak discharge of floods.

Extended Abstract Background: Studying and monitoring the water level of rivers and canals by the Ministry of Energy and related organizations is an important part of water resources management in the catchment area. On the other hand, water level measurement is a vital task in hydrological monitoring, but it often faces limitations such as a lack of resources, high costs, and high time requirements. These limitations often lead to delays in measurements and potential inaccuracies, especially in remote or harsh environments. In general, most of the traditional methods have significant errors and costs and make continuous monitoring and control almost impossible. Recent advances in technology have led to a paradigm shift toward image-processing systems for water level monitoring. These non-contact methods have attracted attention due to their high potential in terms of accuracy, reliability, cost-effectiveness, and reduced time required. This research presents a new approach of combining image processing and machine learning in an attempt to reduce costs and increase performance to extract water level indicators and measure the water level instantly and automatically. This approach is based on creating a set of gauge images recorded by a smartphone (which is a common device with easy access) for clear and turbid water states to train machine learning-based models. Unlike the traditional methods with instantaneous and continuous measurement of the water level, this method makes water supply systems work better and manage critical situations such as floods, river overflows, and erosion. Methods: This paper contributes to this growing research by evaluating an image-based water level detection system using a standard smartphone camera. In this research, the RGB image-processing algorithms include filtering, noise reduction, color detection, resizing, grayscale conversion, Hough detection and transformation, and projection to obtain digital characters and watermarks that only include the area of scale lines. Moreover, all the mentioned steps and modeling steps have been done in Mathematica software. The experimental data include 244 observation data, which were randomly considered to be 201 training data images, and 43 test data images, which were captured by a mobile phone camera with a fixed position. Considering the capabilities of various machine learning models, including artificial neural networks (ANNs) and deep learning (DL) in image processing and analysis, this study focused on these models for accurate water level estimation. Our study involves taking water level images, identifying the water edge in a gauge, and using these models to estimate the water level. Machine learning, a branch of artificial intelligence, aims to develop computer systems with the capacity to learn from data. This process involves computer learning through hands-on experience, starting with organizing data, choosing a machine learning algorithm, entering data, and enabling the model to independently learn patterns or generate predictions, and gain self-programming capability. In general, the comparative analysis of the performance of these models aims to show the potential of combining image processing and machine learning in overcoming the traditional obstacles of water level measurement in hydrological studies. Results: The results of the model were evaluated using three evaluation criteria: coefficients of determination (R), root mean square error (RMSE), and Nash-Sutcliffe agreement coefficient (NSE), as well as visual charts. In this research, two ANNs and DL models, which are the subsets of machine learning models, were used to estimate the water level in muddy and clear conditions by image processing. The results showed that the DL model was acceptable in both conditions of performance. According to the evaluation indicators of the DL model with the lowest error of 28.39 mm and the highest R-value (0.973), it was chosen as the best model for water level estimation. Therefore, according to the performance of the DL model, it is possible to automatically and continuously examine the process of examining and monitoring the water level, in addition to laboratories, in hard-to-reach places and without high costs, and prevent possible accidents by making the right decisions. Conclusion: Due to the problems of manual measurement and field monitoring, automatic and continuous monitoring of the water level by humans becomes difficult and even impossible. Despite these obstacles, researchers' interest in image processing systems has currently increased with the advancement of technology. According to the work done to detect and estimate the water level, most of the current methods go toward achieving maximum efficiency with the minimum facilities. Finally, two methods for extracting information from digital images for water level monitoring systems are compared here. Combined techniques for water level detection in clear and muddy images were compared based on visual evaluation and statistical accuracy. Based on the experimental results, these techniques and models were all able to extract water level information from the image. The image processing technique and DL model for detecting and estimating water surface features from images, which include two turbid and clear states at three levels of low, medium, and high altitudes, had acceptable results and high efficiency. Due to the increasing progress in the field of image processing and machine learning, future research can add different states of water and create models based on new algorithms of machine learning and artificial intelligence.

Extended Abstract Background: According to the opinion of most specialists, the resources and health of the country's ecosystems are being destroyed. In this regard; While preventing destruction and reducing the pressure on ecosystems, it is necessary to be aware of the health of watersheds to plan and manage properly. A healthy watershed is one where natural vegetation has provided the necessary substrate for hydrological and terrestrial processes. In addition, healthy ecosystems are resistant to stress and pressure and provide high quality and quantity of services for the welfare of communities. Thus, assessing the health of ecosystems is important not only in terms of preserving the environment but is also important for the social and economic activities of communities. In this regard, the current research is planned to investigate the temporal changes in ecosystem health indicators using the VOR model in the Chehelkhaneh sub-watershed located in the Zayandeh Roud Watershed. The selection of this watershed for the present study is justified since the Zayandeh Roud Watershed is one of the most sensitive watersheds in Iran and irreparable environmental damage has been caused to this watershed in the last two decades. Methods: To investigate the changes in the health indicators of the Chehelkhaneh sub-watershed, the VOR (power-structure-resilience) model was used during a 10-year statistical period (2011-2021). In this method, V, O, and R represent the production power (function), structure, and resilience of ecosystems, respectively. This model is closely related to the issue of ecosystem stability, which indicates the watershed's ability to maintain its structure and function over time against external factors. To carry out the present study, therefore, land use maps for 2010 and 2010 were first selected from Landsat 5 and 8 satellite images, followed by required pre-processing. Indeed, a false color combination was created using the correlation method between the bands, and then each of the land use classes was separated from each other in separate stages using the supervised classification method. Next, land cover maps (NDVI) related to the power indicator were estimated after preparing the land use map of the study area. This indicator shows the density of land cover in the region. In the next step, Fragstats software was used to extract structure (SPLIT and PD) and resilience (LPI and AI) indicatorsThe fragmentation (SPLIT), patch density (PD), largest patch (LPI), and aggregation (AI) indices were used for this purpose. It should be noted that the Fragstats software is based on land use. Afterward, standardization was done and the health of the Chehelkhaneh sub-watershed was determined after calculating all the mentioned indicators. Results: Based on the results in 2011, rangeland (42.43%), irrigation and dry farming (30.84%), dry farming (250.30%), irrigation farming (1.14%), and rock (0.30%) had the largest area assigned, respectively. In 2021, irrigation and dry farming (50.95%), rangeland (39.80%), and rocky (9.26%) had the largest area in the region, respectively. In other words, the land use of irrigation and dry farming is dominant in the region, along with the decreased area of natural lands, which indicates the destruction in the study area. In fact, large user units become small units and lead to the disintegration of the region. Moreover, the values of power, structure, and resilience indicators were 0.69, 0.9, and 0.79, respectively, in 2011 and 0.47, 0.71, and 0.64, respectively, in 2021. In this regard, the values of time changes in power indicators in 2011 and 2021 were respectively 0.69 and 0.47, structure values were 0.9 and 0.71, and resilience values were 0.79 and 0.64. It should be mentioned that one of the main reasons for the decrease in the values of health indicators in the Chehelkhaneh sub-watershed is the lack of forest cover and the change in land use to low-yield lands. This important issue has led to a decrease in the quantity and quality of the region's ecosystems. Finally, based on the obtained values of the indicators, the health of the Chehelkhaneh sub-watershed in 2011 and 2021 was 0.49 (Moderate) and 0.21 (partly not healthy), respectively. In this regard, the structure indicator has the most impact on the health of the Chehelkhaneh sub-watershed, and resilience and power indicators have been assigned the next priorities. In other words, the results showed that the effect of the ecosystem’s health indicators was different and the health level of the studied region has decreased over time, which is progressing toward being unhealthy. Conclusion: The present study is new in terms of the comparative investigation of the temporal changes in ecosystem health indicators using the new VOR model and in terms of the attention of managers and experts to the necessity to evaluate the health of ecosystems based on the obtained results. Therefore, the land use change in this region due to the lack of water and the low production potential of the land, which is abandoned after several times of cultivation and is not cost-effective. Hence, it is suggested to carry out protective operations to reduce the destruction of the region with the participation of local communities and hold educational-promotional courses to promote and raise the awareness of the communities. Institutions should be created compatible with the watershed situation while increasing intra- and extra-organizational cooperation in the direction of the policy, laws, and environmental risk management of the projects. In general, it can be acknowledged that conducting the present research contributes significantly to the preparation of the health atlas of the country's ecosystems.

Extended Abstract Background: The use of groundwater for agricultural, industrial, and drinking purposes is significantly increasing worldwide. These resources are considered an important part of the renewable water ecosystem and have various advantages over surface waters, such as higher quality and less contamination. However, recent intermittent droughts and a noticeable decrease in surface water resources have led to the excessive use of groundwater sources and a decline in their quality. Therefore, understanding the quality of groundwater is crucial for proper planning and management of these resources and requires serious attention and detailed analysis. Additionally, one of the health problems in developing areas is the lack of access to safe drinking water, and human health is at the core of sustainable development in the region. Therefore, ensuring the welfare and health of the community at an acceptable level is not possible without access to clean and standardized drinking water. Water is important from both health and economic perspectives as it serves as a catalyst for industrial growth and the prosperity of the agricultural sector. In this regard, this research aims to evaluate and analyze the quality and spatial changes in groundwater quality based on the GQI assessment index. Methods: This research focuses on evaluating and analyzing spatial changes in groundwater quality within the study areas of Khanmirza, Lordegan, Boroujen, Ardal, and Kiar, located in Chaharmahal and Bakhtiari province, for drinking purposes. Practical information is provided regarding the status of available water resources in the region for drinking purposes. To this aim, 28 groundwater samples were collected from legally operating wells in various locations within the mentioned counties during the 2020-2021 period and subjected to chemical analysis in the laboratory. The GQI (Groundwater Quality Index) was used to assess the quality of these samples for drinking purposes. In this study, the GQI was calculated based on the concentration values of 11 parameters, including electrical conductivity, acidity, total dissolved salts, calcium ion, sodium, magnesium, potassium, carbonate, bicarbonate, chloride, and sulfate. Subsequently, spatial zoning of the overall parameter values was performed using the chemical characteristics of the collected samples and employing the inverse distance weighting (IDW) interpolation method in the GIS software environment, and the desired information layers were obtained in raster format. Furthermore, overlays were performed by applying computational functions to the available information layers,, followed by estimating the GQI values and preparing raster maps of the index. The output maps can be utilized not only to determine the qualitative characteristics within the study area but also to analyze the trends of their variations, prepare zoning maps for each parameter, and compare them with standard values. Results: The calculated GQI using measured samples categorizes a significant portion of the study area within the excellent and good quality categories suitable for drinking. Additionally, the color spectrum of the zoning map indicates better water quality in the western and southern parts of the study area than in the other sections. Generally, water quality decreases from the south toward the north and northeast. The sensitivity analysis of the model focuses on examining the impact of changing one input variable on a model's output variable. The sensitivity analysis revealed that parameters such as acidity, calcium, magnesium, total dissolved salts, and electrical conductivity had a negative effect (meaning an increase in index values and water quality improvement after removing these parameters and deterioration with their addition in the index calculation). Conversely, the concentrations of bicarbonate ions, sulfate, sodium, potassium, and chloride had a positive impact on water quality (meaning a decrease in index values and deterioration after removing these parameters and improvement with their addition in the index calculation). These parameters have allocated the highest to lowest changes in estimating the GQI index. Therefore, the GQI quality index shows greater sensitivity to the presence or absence of acidity and calcium bicarbonate than the other parameters used in determining the index, influencing decision-making regarding the classification of drinking water quality more than the other parameters. Despite having higher weights in calculating the index, some parameters show insignificant percentage changes in the index due to their presence or absence. For example, despite a lower weight of bicarbonate than magnesium ions, it shows a higher percentage change in the index after its removal than the percentage change caused by removing magnesium. Thus, higher weights for these components do not necessarily imply greater sensitivity of the model to them. Conclusion: The results of this study indicate that the groundwater quality in the studied areas is suitable for drinking purposes, and the groundwater in the study area has not been affected by changes resulting from the tested parameters in the drinking sector. Additionally, this study contributes to comprehensive and useful planning for the management and conservation of groundwater resources, enabling more informed decision-making based on groundwater quality maps in this regard.

Extended Abstract Background: As a public belief, religion has always played a fundamental role in the development and strengthening of social institutions. Since the creation of mankind, religion has not only acted as a personal guide but also as a measure for regulating social behaviors and has played an important role in various areas, including natural resource management. One of the interesting patterns in the relationship between religion and nature is the connection formed based on the significance and sanctity of water in religious teachings. In this context, water is introduced as a symbol of purity, innocence, and spiritual health. According to this pattern, spiritual health, which refers to a deep connection to religious and divine values, plays a key role in regulating religious, civic, and individual relationships. Despite the great importance of this topic, few studies have investigated the role of religious beliefs in the consumption of natural resources, especially in Islamic societies. This shortage of research is largely due to the cultural and social challenges associated with studying such topics, avoided by many researchers. In reality, people’s religious beliefs and attitudes are often naturally inspired by nature and its resources, among which water is one of the most vital elements of nature, playing a crucial role in the survival and life of humanity. Islam, particularly in its Qur'anic teachings, repeatedly emphasizes the importance of water conservation and careful use. The Holy Qur'an refers to water as the source of life and stresses the need to care for this divine blessing. Alongside these teachings, religious beliefs have always played a central role in shaping people's attitudes and behaviors. One such belief, which is recognized as part of religious practice, is the concept of spiritual health, which refers to an individual's deep connection with religious teachings that can guide them toward more responsible use of natural resources, including water. This connection is not only evident in religious and ritualistic behaviors but also influences everyday life aspects, such as social interactions and the use of natural resources. However, fewer studies have specifically examined the relationship between spiritual health and optimal water use, particularly in Islamic societies. Cultural and social complexities associated with studying such topics have always posed challenges, leading to limited research in this field. Considering the role of water and its connection to religious beliefs and spiritual health, the present study seeks to investigate the relationship between religious attitudes, income levels, and water consumption in the community of Gorgan. This study aims to answer the question: Is there a relationship between individuals' religious attitudes and their water consumption? Additionally, the study explores the influence of other variables, such as gender, income level, family size, and employment status, on water consumption. Methods: This descriptive-correlational study focuses on the urban population of Gorgan in 2021. A total of 100 individuals were selected from this population using simple random sampling. The samples were selected to ensure diversity in demographic, economic, and cultural characteristics. Standard and validated questionnaires were used as data collection tools, including a Religious Attitude Scale, a Spiritual Health Questionnaire, and a Financial Questionnaire. The Spiritual Health Questionnaire, by Polotzin and Ellison, consists of 20 questions designed to measure two dimensions: religious health and existential health. This questionnaire determines an individual's overall spiritual health score from 0 to 100. The second questionnaire assessed individuals' attitudes toward water consumption and their inclination toward water conservation. This questionnaire included 21 questions to evaluate cognitive, emotional, behavioral, and responsibility dimensions related to water consumption. The validity of these tools was confirmed through content validity, and their reliability was verified using Cronbach's alpha, with values of 0.82 for spiritual health and 0.94 for the water conservation attitude questionnaire. Data were analyzed using SPSS statistical software. Initially, descriptive statistics were employed to describe demographic variables and study indices (mean, standard deviation, and percentages). Then, inferential statistics, including Spearman's correlation coefficient and multivariate regression analysis, were used to examine the relationship between independent variables (spiritual health, gender, income, family size, and employment status) and the dependent variable (water consumption) at a significance level of 0.05. All ethical principles, including informed consent from participants and confidentiality of information, were observed in this study. Results: A significant positive relationship was observed between spiritual health and water consumption, meaning that individuals with higher spiritual health were more inclined to conserve water. Additionally, a significant relationship was observed between water consumption and age. Older individuals were more likely to conserve water than younger ones. Multivariate regression analysis showed that spiritual health and age were the two main predictors of water-saving behaviors. In other words, an increase in spiritual health and age was significantly associated with a greater tendency toward optimal water use. Other demographic variables, such as gender, income level, employment status, and family size, did not significantly affect water consumption. The results suggest that improving spiritual health can lead to better water consumption behaviors and a reduction in water waste. This impact is particularly noticeable among older individuals. Conclusion: The findings of this study indicate that spiritual health, as an important and influential factor, plays a key role in improving water consumption behaviors. Strengthening spiritual health and religious beliefs in society, especially in arid and water-scarce regions, can serve as an effective strategy for addressing the water crisis and promoting the optimal use of natural resources. The findings also suggest that age is an important factor in reducing water consumption and encouraging conservation. Therefore, promoting and educating religious concepts related to natural resource conservation and protection, particularly through religious and educational institutions, can play a crucial role in changing water consumption patterns in society. Overall, the results of this study indicate that enhancing spiritual health through education and raising awareness can lead to reduced excessive water consumption and better protection of natural resources. This is especially important in communities facing water scarcity, requiring greater attention from policymakers and cultural and educational planners.

Extended Abstract Background: Decreasing or increasing diversity affects a community’s sustainability because biodiversity in any living community determines its specific function, and on the other hand, sustainability is considered the ability of an ecosystem to preserve and maintain its function. At the same time as the global threats of climate change and the increase in harvesting intensity in the last few decades, ecosystem functions have been the focus of many researchers. Due to the impact of biodiversity on the protection of different ecosystem functions, many studies have investigated biodiversity indicators and ecosystem functions in different environmental conditions. In the present study, species diversity (richness, Shannon-Wiener, Simpson, and evenness) and functional (functional richness, functional evenness, functional divergence, and CWM indices) indices were used to investigate their relationships with different grazing managements (light and heavy) and two different climates in the basins of Mazandaran province. This research aims to determine the role of variables influencing plant diversity and the pasture ecosystem functioning to help better manage summer pastures that play a protective role for downstream areas in watersheds. Methods: The effects of livestock grazing were investigated on the diversity of two selected watersheds, Zaremrood and Cheshmehkileh, in the east and west of Mazandaran province. After determining plant types in each of the areas, light and heavy grazing sites were determined based on the accessibility and distance to sources, such as livestock husbandry, watering places, and roads. For sampling in the sites, the northern slopes with the same slope (0-20%) were selected to ensure homogeneity. Sampling was done using five main plots (10 × 10 m) so that three plots (1 × 1 m2) were within each plot. Thus, a total of 15 plots were used in each grazing site (light and heavy), and a total of 30 plots were used in the random-systematic design. To measure functional diversity indicators, the easily measurable functional characteristics of plants related to the level of ecosystem functioning were selected and measured in addition to species abundance data. Based on experts, opinions and a review of available sources, five traits (leaf area, leaf dry matter content, leaf specific area index, plant height, and leaf dry weight) were used to determine functional diversity. Species diversity indices were calculated in PAST software by calculating three indices of species diversity, including species richness, Shannon-Wiener diversity, and Simpson diversity. Data were statistically compared using the analysis of variance (ANOVA) in the form of the general linear model (GLM) and principal component analysis (PCA) to separate the grazing regions that can be separated using functional diversity indices. Results: The dependent variables (weighted average of leaf area and dry matter content) were significantly different in various climates and different livestock grazing patterns. Similarly, the independent variables (area and livestock grazing) were significantly different from the variables of species richness, Shannon-Wiener, and weighted average of leaf area. In the light grazing site of Chashtkhoran rangelands in the Cheshmeh-Kileh basin, the indices, including Simpson (P-value = 0.000), Shannon-Wiener (P-value = 0.000), functional richness (P-value = 0.001), and functional divergence (P-value = 0.08), were more than the heavy grazing site in the Mianband rangeland of the Zaremrood basin. Moreover, the weighted average of the leaf area (P-value = 0.001) was greater in the Chashtkhoran area under light grazing than in the Mianband rangeland. It was higher in the heavy grazing of the Mianband than in the Chashtkhoran Rangeland, but the weighted average of the dry matter content (P-value = 0.001) was vice versa. According to the PCA results, the first component belonged to Simpson, Shannon-Wiener, functional richness, and functional divergence, and the second component is related to evenness, functional evenness, the weighted average of leaf area, the weighted average of dry matter content, and height mean. Due to their high impact, therefore, these factors were identified as the most effective factors in separating areas under light and heavy grazing. The first and second components explain 36.3% and 19.8% of the changes, respectively. Conclusion: The significant interaction between climate and grazing shows that the difference between the light and heavy grazing areas of the two climate zones is characterized by two variables, viz. the weighted averages of leaf area and dry matter content. The leaf area showed higher values for heavy grazing plots in the dry region, and this factor was greater in the light grazing of the humid region. This study highlights the importance of grazing control as an effective management tool for vegetation conservation. It seems that the plant composition in the mountainous rangelands of Iran is more influenced by grazing intensity than climate differences.

Extended Abstract Background: Soil organic carbon is one of the important parameters to determine soil fertility, and production ability and a mind index for showing soil quality of dry and semi-dry lands. On the other hand, rangelands are one of the main dry ecosystems of carbon reservoirs. Knowing about carbon reservoir distribution and changes for detecting the controller mechanisms of the carbon world cycle and carbon stability is vital for managing ranges. Since field operations include soil sampling from different places and depths to measure the amount of soil carbon sequestration, it is very time-consuming and costly., On the other hand, different soil characteristics may be measured and available in many rangeland areas for other purposes using modeling and prediction under various inputs, including soil properties such as texture, acidity, electrical conductivity, etc. Researchers in the rangeland field can estimate and evaluate soil carbon. Novel prediction methods, including artificial intelligence, are of high interest in this field. The present research aims to study the ability of the adaptive neuro-fuzzy inference system (ANFIS) to predict the carbon sequestration (CS) of rangeland soil. Methods: The studied area in this research includes the rangelands of the southwestern slopes of Mount Damavand in the Lar watershed with an area of about 2000 hectares and an altitude between 2500 and 3460. With a statistical climate period of 36 years, it is a semi-humid to ultra-cold region where the average rainfall is 550 mm. Dominant plant species in the region are Onobrychis cornuta, Astragalus ochrodeucus, Astragalus microcephalus, Thymus pubescens, etc. Considering the Lar watershed region geographic conditions, four height groups relative to sea level, including height group 1 (2500-2700 m), group 2 (2700-2900 m), group 3 (2900-3100 m), and group 4 (3100-up m) were selected for sampling soil at different depths in this research. Thirteen random points were determined at all height groups, and three samples from each point were dug at depths 0-15 cm and 15-30 cm. In total, 312 soil samples were collected in the entire region and transferred to a soil science laboratory, where soil characteristics (texture, organic carbon, and bulk gravity) were measured as an average of three repetitions. These characteristics were used to calculate the amount of soil carbon deposition. After soil sampling and measuring the amount of carbon sequestration under the effect of soil depth and height in the sampling location at the Mount Damavand rangelands at the Lar watershed region, the regression and ANFIS prediction equations were developed and their accuracy was compared together for CS prediction plus introducing the more accurate approach. The root mean square error (RMSE) and correlation coefficient (R2) were applied to evaluate the regression and ANFIS models. The regression analysis was performed using the SPSS20 software. Excel software was used to draw descriptive charts. ANFIS modeling was created in MATLAB software and is based on the input/output dataset of a fuzzy inference system (FIS). This system is based on the combination of three components: membership functions of input and output variables (fuzzification), fuzzy rules (rule base), mechanism inference (a combination of rules with fuzzy input), output characteristics, and results of the system (de-fuzzification). Results: The results of the analysis of variance (ANOVA) revealed that only soil sampling depth significantly affected the soil CS, but the effect of sampling location and the interaction effect of depth and height were not significant. More amount of CS was obtained at a depth of 15-30 cm than at a depth of 0-15 cm, and the utmost amount of CS was measured in gang 4 of height (3119-3545 m) at both depths. The highest amount of CS (604656) belonged to gang 4 of height and a depth of 15-30 cm. In fact, the amount of soil CS increased at higher and lower altitudes while its amount decreased at medium altitudes. After gang 4 of height, the second most CS was recorded for the gang 1 of height. In the modeling part, the ANFIS model with higher accuracy (R2 = 0.4736) and lower error (RMSE = 0.0274) predicted the soil CS related to a regression model with lower accuracy (R2 = 0.4308) and higher error (RMSE = 0.069). This result indicates the higher ability of the ANFIS model than the regression model in creating a relationship between input and output and its proximity to the measured values. Conclusion: The increase in the correlation coefficient and the reduction of the mean error deviation in the ANFIS method compared to the linear multivariate regression show that the ANFIS method is more successful in estimating the amount of soil CS under the effects of various factors in the studied land use. The better performance of the ANFIS model than statistical regression methods can be found in its estimation and prediction capability for the nonlinear estimation with a small amount of data. This is despite the fact that the performance and accuracy of regression methods strongly depend on the sample size, and a small sample size can be a limiting factor in such statistical models. The adaptive neuro-fuzzy inference system (ANFIS) satisfied operation in predicting rangeland soil CS under the different sampling depths and heights. Furthermore, it will be used as an intelligent tool for predicting different parameters in studied ranges and rangeland science, such as above and underground biomass volume, distribution of rangeland plant species, and so on.

Extended Abstract Background: Vegetation is one of the main components of biosphere preservation that acts as a link between soil, water, and atmosphere. It is crucial in providing organic matter, regulating the carbon cycle, and exchanging energy on the surface of the earth. In recent years, climate change and global warming have caused frequent events, such as floods, heat waves, and droughts, which can damage terrestrial ecosystems. Climate change directly affects the growth of vegetation; on the other hand, changes in vegetation cover give feedback to climate change by regulating water, energy exchange, and carbon dioxide concentration. Methods: The research was carried out in Mazandaran province to analyze vegetation trend in the study area during the 2001-2020 period. The 16-day composite MODIS-NDVI time series data, named MOD13Q1, with a spatial resolution of 250 meters (920 NDVI images) were used for this purpose. The non-parametric Mann-Kendall method was employed to investigate changes in vegetation activity and trend significance. The overlying vegetation trend map and the location of big cities and main roads of the province were also investigated in this research. Results: A decreasing trend of vegetation cover was observed in 16% of the total studied area, and the rest showed an increasing trend, although the significant decrease and increase of vegetation cover occurred in 5% and 65% of the area, respectively, with a 95% confidence level. The vegetation trend map showed that the most significant reduction of vegetation in the last 20 years occurred in coastal areas and low-altitude regions, especially around big cities and main roads entering the province. Decreased vegetation around the metropolises is expected due to the increase in population and the need for urban development. However, the results showed that the most significant decrease in vegetation occurred in the cities of Mahmudabad (19%), Babolsar (17%), Ghaemshahr (10%), and Jouybar (9%). Unlike the big cities of Sari and Ghaemshahr, the cities of Sorkhrood, Mahmudabad, and Babolsar are at the top of the cities with reductions in vegetation cover in the last 20 years. Unfortunately, this is not due to urban development and increasing population, but drastic changes in the use of agricultural land and citrus orchards and turning them into private villas are the main factor in the reduction of vegetation. Comparing the vegetation trend map with the main roads of the province reveals that a significant reduction of vegetation has occurred around the main roads entering Mazandaran province, especially on the Haraz, Firouzkouh, and Farim roads. In contrast, smaller areas of vegetation cover reduction were observed around the Chalus road. The investigation of the areas with positive vegetation cover trends showed that the highlands of the province, especially the eastern highlands, experienced a significant increase in vegetation cover. However, a less significant positive trend of vegetation was observed in the western highlands. Rather, most of these areas have experienced no trend conditions in the past 20 years, which could be due to the recent global warming and the higher temperature in the east of the province than in the western regions, which generally has caused suitable temperature conditions for the growth of vegetation in the eastern highlands of the province. It seems that the western highlands still do not have suitable temperature conditions for the growth of vegetation. Conclusion: The results of this research show that the changes in vegetation in Mazandaran province are under the control of two natural and human factors, and the former (climate) has caused an increase in vegetation in 65% of the area of the province, especially in the highlands, probably caused by the increase in temperature. The recent global warming has made it possible to provide living conditions for plants in the highlands of the province, especially the eastern highlands. Nevertheless, the human factor has been destroying vegetation throughout the province, especially in tourist areas and those with easy access. As such, a significant trend of vegetation reduction was observed both on the coasts, around metropolises, in the heart of the Hyrkan forests, and in the heights near the main roads. A significant decrease in vegetation cover has occurred in 5% of the area of the province, and these decreases were observed mostly in the plains, coastal strip, low altitudes with low slopes, outskirts of cities, and roads of the studied area. Vegetation is also being destroyed in the marginal areas of the roads from Ramsar to the neighboring western province, Gorgan Sari, Tehran, Chalus, Haraz, and Firuzkouh, which can be the main reason for the increase in traffic load, changes in land use, and the construction of recreational facilities and villas. Based on the results of this research, extreme changes in land use in the last 20 years are very evident, and if the human process of land use change continues along with the loss of water and soil resources, we may see irreparable blows to the ecosystem of the Caspian systems in the near future.

Extended Abstract Background: One of the major goals of watershed management operations is to reduce soil erosion and waste, and in the next step, to prevent the exit of eroded particles from the watershed. For this purpose, sediment control structures are usually built throughout watersheds. Check dams also play a special and important role in reducing the sediment load of rivers. Sediment dams are constructed to prevent the entry of sediment particles caused by the erosion of upstream lands into the main river. Particle size distribution is one of the key physical characteristics of sediment materials that is used as an important factor in sediment management in watersheds. Many quantitative and qualitative characteristics of sediments, such as porosity, permeability, transferability, chemical reactivity, and erodibility, are influenced by the size of particles and their mode of distribution. To achieve better results in watersheds and especially sediment trapping behind check dams, knowing the type of sediment and its characteristics helps in the better management of watersheds and water and soil resources. Comparing check dams with other water and soil conservation measures to reduce sedimentation, it seems that check dams are the most effective way to quickly reduce the entry of coarse sediments into rivers. Considering the role of sediment traps in trapping sediment particles, it is very important to study the sediments behind these dams in terms of particle size distribution characteristics. This research aims to investigate the size distribution of sediment particles behind check dams and to investigate the efficiency of sediment trapping dams in trapping sediments in the Lashkaran Salmas watershed. Methods: The Lashkaran watershed is located in the northwest of Salmas city in West Azerbaijan province. According to the country division, it is located in the Urmia Lake River basin, and its geographical coordinates are in the range of 44°38'‌ to 44°40'‌ east longitude and 38°17'‌ to 38°18 '‌north latitude, with an area of of 363 hectares. First, the location and dimensions of the structures and the height of the accumulated sediment behind the structures were examined and measured through field monitoring. To determine the size distribution of sediment particles, the sediments behind each check dam were sampled at two depths of 0-25 and 25-50 cm. In total, 32 sediment samples were collected from eight dams. The samples were transferred to the laboratory, air dried, and then passed through a 2 mm sieve. The particle size distribution of sediment samples was determined by the hydrometric method. To quantitatively investigate sediment particle size distribution, an optimal model was selected from several particle size distribution models, and particle size distribution indices, such as D50, were calculated with the optimal model. Results: In the studied watershed, check dams have been built on the streams with three orders, including order one, three, and four. Seven dams have been built on the third-order streams, one dam on the fourth-order stream, and one dam on the first-order stream. The results show that the particle size distribution does not follow a specific and regular pattern in order three streams, and the average percentages of sand, silt, and clay are 65.7%, 28.2%, and 6.1%, respectively. In order four streams, the average percentages of sand,silt, and clay are 80.4%, 16.5%, and 3.2%, respectively. The respective percentages are 82.7%, 9.8%, and 7.6% in the first order. In general, the average percentage of sand particles, followed by silt, is the largest volume of particles making up sediments. The sediment size distribution of the third-order steams shows that the amount of changes in sediment particles does not have a regular trend, but in general, the percentages of clay and silt increase with increasing distance from the outlet of the watershed, with a decrease in the amount of sand particles. The fourth-order stream has high flow intensity and assumedly carries larger particles. Along with approaching the outlet, the amount of coarse particles decreases due to the presence of check dams and the accumulation of sediments behind the dams. As the distance from the outlet increases, the amount of clay and silt tends to decrease while sand particles tend to increase. The first-order stream has the lowest flow intensity. The amount of clay is constant with increasing distance, with increased silt and decreased sand percentages. Three efficiency coefficients were used to assess the efficiency of the models. The results of the sediment granulation distribution showed that check dams were most efficient in trapping sediments such as sand with a share of 69.5%. The results of examining the efficiency coefficients of particle size distribution models show that the Fredlund model with the highest coefficient of determination (0.98), the lowest RMSE index (0.03), and Akaike's statistic (59.54) is the most efficient model in measuring the distribution of sediment particle size compared to the other seven models. Conclusion: The average particles of sand, silt, and clay (69.5%, 24.7%, and 5.8%, respectively) indicate the performance of check dams in controlling coarse-grained sediments such as sand. The results show that the dams upstream the watershed have restrained larger particles, but this trend is not seen in other check dams. It seems that the type and sequence of the dam are more effective in sediment changes. Moreover, the runoff carries fine to coarse particles, and regular diameter differentiation cannot be obtained due to the short distance between the dams.

Extended Abstract Background: The primary issue facing the Earth in this century is the increase in global temperatures and changes in climate variables due to industrialization and rising greenhouse gas emissions. Therefore, it is crucial to investigate temperature trends and climatic changes on both global and regional scales. While several general circulation models have been developed to predict future climate states, different and new methods have been invented to use the output of these models on regional and local scales due to the lack of optimal use of the output of these models caused by the limitation in spatial resolution on the local scale. The Gedarchay watershed is significant for its Gedarchay river basin and groundwater resources—especially in agriculture—, hence it has been the focus of various studies. However, no research has yet studied the impacts of climate change under SSP scenarios of the 6th report, which incorporate socio-economic factors. Thus, this study aims to analyze future changes in climate variables for the Gedarchay Naghadeh watershed under the RCP emission scenarios of the fifth report (CMIP5) and the SSP scenarios of the sixth report (CMIP6), integrating greenhouse gas emissions and socioeconomic activities. The findings could significantly inform future water resource policymaking and planning. Methods: This research utilized the SDSM microscale exponential model to analyze climatic variable changes in the Gedarchay Naghadeh watershed in northwestern Iran. The model's effectiveness was first assessed for climate variables, followed by predictions extending to 2100. Calibration and recalibration were performed using observational data from the Mahabad Synoptic Station and NCEP data. The model's performance was evaluated using correlation coefficients, mean absolute error, and mean square error. After confirming the model's reliability, outputs from the CanESM2 and CanESM5 models were studied for the periods 2031-2050 and 2081-2100 under the RCP 2.6, 4.5, 8.5 and SSP1-2.6, 2-4.5, 5-8.5 scenarios by the microscale SDSM statistical model. Results: The model's evaluation and recalibration were done using NCEP, CanESM2, and CanESM5 data to forecast and compare precipitation, maximum, and minimum temperatures for the Mahabad station across two periods 2031-2050 and 2081-2100, against a baseline. The accuracy of the SDSM model was assessed using average absolute error statistics, and the errors for precipitation, maximum, and minimum temperatures were 1.645, 0.029, and 0.031, respectively, with CanESM2; their values were 0.73, 1.10, and 1.89. Correlation coefficients were also calculated, yielding 0.998, 0.999, and 0.999 for the CanESM2 model and 0.999, 0.993, and 0.971 for the CanESM5 model. The mean squared errors were 2.240, 0.043, and 0.045 for CanESM2, and 0.89, 1.49, and 2.07 for CanESM5. Results indicate that the average maximum temperature is projected to rise by 0.93 °C from 2031 to 2050 under the RCP scenario but it remains stable from 2081 to 2100. Increases of 1.24 °C in 2031-2050 and 0.35 °C in 2081-2100 were anticipated under the SSP scenario. The average minimum temperature increases for the RCP scenario were 0.27 and 0.28 °C for the respective periods, and 0.46 and 0.43 °C for the SSP scenario. Rainfall is projected to rise by 0.59 and 0.38 mm in the RCP scenario during the two periods, compared to increases of 2.15 and 1.64 mm, respectively, under the SSP scenario. Conclusion: The evaluation of the SDSM model's accuracy in predicting precipitation, maximum temperature, and minimum temperature using R, MAE, and RMSE statistics indicates a strong alignment between predicted values and the base period. Results show an increase in precipitation and minimum temperature in both the near and distant futures, with a rise in maximum temperature in the near future and stability in the distant future. Given the significance of climate change and its impacts on agriculture, the environment, and water resources, it is essential for managers and planners to implement effective solutions. These include altering cultivation patterns, using drought-resistant crops, establishing early warning systems, training farmers in climate adaptation methods, and promoting renewable energy to mitigate climate change effects.

Extended Abstract Background: In recent years, changes in climate and land use have led to fluctuations in water resources. These changes have affected the river flow, environment, and drinking and agricultural water. Land use change has four important effects on watersheds, namely changes in peak flow characteristics, changes in total runoff volume, changes in water quality, and changes in hydrological balance. To prevent natural disasters, it is important to identify the current conditions and predict the future situation. Overcoming these crises and reducing their adverse effects are only possible in the shadow of management, planning, and relying on practical knowledge. The present study aimed to determine the impact of climate change and land use on the river flow in the Talar basin between 2020 and 2050. Methods: The effects of future land use, climate changes, and their combined effect in the Talar basin (Mazandaran province) have not been seriously investigated using the sixth climate change report. Therefore, this study analyzed data based on CMIP6 climate change scenarios and land use projections for 2035 and 2050. First, the SWAT model was used to evaluate the effects of climate and land use on the river flow in the Talar River basin. After calibration and validation of the model using the best parameters from 2001 to 2020, CMIP6 data were downscaled based on six models and projected under two scenarios SSP2-4.5 and SSP5-8.5. The scale of atmospheric general circulation models was reduced using two methods: the delta method and quantile mapping (Qm). These methods were chosen due to the large scale of the models. In this research, the Markov prediction model (CA-Markov) was used to simulate and predict land use changes for the years 2035 and 2050. Precipitation and temperature data obtained from climate change and land use scenarios were entered into the SWAT model to predict the average monthly flow during the years 2020-2035 and 2020-2050. Results: Calibration and validation at the Kiakola station as the output of the Talar watershed showed that the Nash-Sutcliffe index (NSE) had efficiencies of 0.8 and 0.76, respectively. The best values of the validation indices were obtained by the INM model. The Delta method for downscaled precipitation data and the Qm method for downscaled minimum and maximum temperatures showed better evaluation values. For example, the presented tables show that the values of RMSE, NRMSE, and MAE for the rainfall of the Kiakola station are 2.185, 0.0402, and 1.716, respectively, using the Delta method. All these values show the good accuracy of these downscaling methods for SWAT model inputs to predict the streamflow in the Talar River basin. These methods were implemented for all the studied stations, and the downscaled values of the aforementioned parameters were used to predict the streamflow of the Talar River basin at the Kiakola station. Conclusion: The predicted results for 2035 and 2050 show a decrease in the runoff volume, wetlands, and urban land. Therefore, land use activities in the future should be based on appropriate land use development and land use regulation to reduce the long-term adverse effects of land use changes. In the Talar River basin, land use changes are mainly controlled by internal factors, such as agricultural land expansion and urbanization, while climate change is regarded as an external factor. Both have an important role in changing the hydrological processes of the basin. This study evaluates the combined effects of land use and future climate changes on the water balance in the Talar River basin. The combination of land use change and climate change has a more obvious effect on surface runoff. On a monthly scale, runoff from surface runoff decreases significantly across seasons, indicating that more extreme events (i.e., droughts) could potentially occur in the future. With land use changes, these effects can only be reduced by less than 20%. Therefore, more measures (for example, soil conservation) are needed in addition to land use planning to increase infiltration and aquifer nutrition and, subsequently, reduce risks from land use and climate change impacts. This research presents the effects of changes in land use and climate on the available water in the Talar River basin in the future. Furthermore, this paper presents a study on the use of the SWAT model in hydrology to help the scientific field. The findings of this study can also be useful for officials in reducing water stress through proper management of land use in the future. The results indicate that the average monthly streamflow of the Talar River basin has decreased due to land use changes, such as the expansion of urban areas and the reduction of agricultural land. In the future, changes in land use and land cover (LULC) may affect streamflow. The main drivers of LULC changes include agricultural development, deforestation, urban planning, land tenure policy, and organization development.