پژوهشنامه مدیریت حوزه آبخیز
پیاپی 30 (پاییز و زمستان 1403)

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.

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.

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.

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.

Precipitation is one of the most critical inputs in hydrological models. Due to its significant spatiotemporal variability, high-resolution temporal and spatial data are required for accurate hydrological modeling. Precise precipitation measurements are generally obtained from conventional meteorological stations. However, sparse rain gauge stations can lead to poor spatial representation of precipitation. This spatial inaccuracy can have a significant impact on modeling results, especially in areas with significant geographic variations, such as mountainous regions. The lack of high-resolution precipitation data can result in low-quality hydrological simulations and inadequate solutions for water resource problems. In developing countries such as Iran, financial and technical limitations result in sparsely and unevenly distributed rain gauge networks, creating major challenges in water resource prediction and management. To address this gap, precipitation, temperature, and evapotranspiration data required for hydrological modeling are provided by various organizations using satellite-based remote sensing products. Gridded precipitation products on a global or quasi-global scale with various temporal and spatial resolutions have been produced in recent decades. These products are compiled from various sources and processed using complex models to generate high-accuracy precipitation maps. Therefore, evaluating the quality, suitability, and accuracy of these products in different regions is necessary before their use in hydrological modeling and water resource decision-making.

This study evaluates the quality of four widely used high-resolution satellite-based precipitation estimation products: CMORPH, 3B42RT, 3B42, and PERSIANN, for simulating water flow using the Soil and Water Assessment Tool (SWAT) hydrological model in the 312 km2 mountainous Tigh Siah catchment in southeastern Iran. The unique geographic and climatic features of the Tigh Siah watershed provide an appropriate environment for assessing the accuracy and efficiency of satellite data in hydrological simulations. Two different calibration approaches for the SWAT model were examined to assess the quality of satellite-based precipitation estimates. The first approach involved calibration using measured precipitation data from rain gauge stations as model inputs. These measured data served as a reference for evaluating the accuracy and precision of the satellite data. The second approach involved calibration using each of the satellite precipitation products as model inputs. In this method, satellite data were directly input into the SWAT model, and the simulated water flow results were compared with those obtained using measured data. This comparison aimed to determine the accuracy and efficiency of each satellite product in simulating water flow in the Tigh Siah watershed. The results of this study can help identify the strengths and weaknesses of each satellite-based precipitation product and provide strategies for improving the accuracy of hydrological simulations.

The results indicate significant errors in the estimates from satellite-based precipitation data, despite variation in the performance of each satellite product. Specifically, 3B42RT and CMORPH demonstrated better quality in precipitation estimation than 3B42 and PERSIANN. These differences in the accuracy of satellite data can significantly affect hydrological simulation outcomes. The model calibrated with satellite-based precipitation estimates performed better in simulating flow than that calibrated with rain gauge station data. However, the model calibrated with satellite data led to the overestimation of the Curve Number (CN), suggesting caution when using parameter values calibrated with satellite-based inputs. To improve simulation accuracy, the error correction of satellite precipitation estimates was conducted in two stages. First, the error in each satellite data group was estimated in each pixel by dividing the monthly precipitation estimate by the corresponding rain gauge precipitation. Then, the monthly precipitation of each satellite data was multiplied by the estimated monthly error to eliminate errors in all satellite precipitation estimates. The results showed that error correction significantly improved flow simulation using the SWAT model, indicating that satellite data with appropriate corrections can be a valuable tool for hydrological simulations, especially in areas with limited or scattered ground data. This study also emphasizes that using corrected data can enhance the accuracy and reliability of modeling results, aiding better decision-making in water resource management.

This study demonstrates that precipitation estimates from satellites, when directly converted to simulated flow by the hydrological model, result in substantial errors, which may be attributed to the small and mountainous nature of the Tigh Siah watershed. In this case, correcting the errors in satellite-based precipitation estimates significantly improved the model simulation. The findings suggest that the best model simulations are obtained using satellite precipitation inputs after error correction and recalibrating the model with corrected satellite data. These results highlight the importance of correcting satellite data before using them in hydrological models. They show that higher accuracy in predictions and water resource management decisions can be achieved with appropriate corrections.

Climate changes can significantly affect socioeconomic activities and quality of life, especially in countries that are currently facing water tensions. Climate models play a key role in assessing the impact of climate change and developing adaptation and resilience strategies. Considering the importance of food security and then water security in resilience against climate change, as well as the significant contribution of Mazandaran province in the production of agricultural products and food supply of the country, it is very important to examine the drought situation of this province and its climate change process. In this study, therefore, the wet and dry durations in the 20-year base period of Mazandaran province were evaluated using the standardized standard precipitation index. Then, projections of temperature and precipitation at the local scale were made in future periods using the five global circulation models (GCM) available in phase 6 of the climate output project (CMIP6) under three scenarios SSP2.6, SSP4.5, and SSP8.5.

In this research, six meteorological stations, viz. Ramsar, Noshahr, Siyabisheh, Babolsar, and Qarakhil, were selected due to the coverage of the most statistical years and suitable spatial distribution in the region. Time series of precipitation and daily maximum/minimum temperatures were collected for six selected stations in the region with a base statistical period of 20 years (January 1999 to December 2018). After ensuring the quality of the data, the trend of their changes was analyzed using Mann-Kendall and age slope tests. Standard precipitation index values were calculated and evaluated in different intervals. Finally, large-scale data from five general circulation models (ACCESS-CM2, CanESM2, CNRM-CM-6-1, MRI-ESM2-0, and NESM3) were downscaled by the LARS-WG6 climate generator. Thus, predictions of seasonal and annual changes for Tmax, Tmin, and precipitation in two future periods (2040-2060 and 2080-2100) were made using the average of selected GCMs.

As a result of the statistical analysis of the above data, minimum and maximum temperatures increased and precipitation decreased during the standard period, but no significant trend was found at the 0.05 level. The analysis also shows that the state's worst droughts occurred in 2007, 2009, late 2011, early 2012, and 2018, with SPI values below-1.0 at stations. The wettest years in the region are 2004-2006 and 2017. The frequency of wet periods is higher than dry periods for all seasons in the region. In the microscale aspect, the results confirmed the ability of the LARS-WG6 model to simulate temperature more accurately than local precipitation, with more precipitation errors in wet seasons. Among these results, the lowest value of the correlation coefficient (0.941) was obtained for maximum and minimum monthly temperatures, which means that the squared error value is between 1.05 and 3.82°C. The largest differences between modeled precipitation and observations occurred during the rainy season when GCMs underestimated precipitation. The analysis of future climate changes revealed that all five GCMs indicated a continued increase in temperature in the study area. However, differences in the magnitude of signal changes were observed in different GCMs and SSPs. These predicted temperature changes are significant and reliable because all models agree on the direction of temperature change across the province. Overall, the increase in average Tmax and Tmin is significant in SSP8.5 compared to SSP4.5 because of no reduction in greenhouse gas emissions. Thus, the largest mean changes in the SSP8.5 scenario for 2050 and 2090 at the provincial level for maximum temperature are increases of 2.64 and 4.72 °C during spring, and the largest mean changes in minimum temperature were calculated to rise to 2.97 and 4.83 °C during autumn. Future changes in precipitation proved to be more complex and unpredictable than temperature. The largest incremental changes in local precipitation in 2090 under the SSP4.5 (40.5%) and SSP8.5 (51.9%)scenarios were shown by the Ramsar station. In the study area, it is projected 38.86% and 43.95% on average in the feature period (2040-2060) and 45.11% and 65.94% in the feature period (2080-2100) under SSP4.5 and SSP8.5. Thus, the results of this study show that the shift toward wetter seasons at the provincial level in the future will cause more precipitation in the western part of the province.

Examining the drought situation in the base period shows the occurrence of periods with near-normal rainfall in longer intervals, and Sari and Qarakhail stations report more drought than stations in the west of the province. All GCM forecasting models presented the same results in significant warming trends in this province. For precipitation, however, it is suggested to investigate other models in this regard due to the sensitivity of the issue and the uncertainties involved in the issue. Considering the effect of changes in temperature parameters and precipitation on water resources and floods in the region, it is necessary to adopt suitable management strategies for the future to be resilient against climate change.

Long-term hydrometeorological variables can be used for planning and managing water resources at the basin level using different physical models, such as hydrological and hydraulic models. However, such variables are often accompanied by missing data, which makes analysis difficult or sometimes impossible. Data gaps cause problems in interpretation, model calibration, and biased statistics. In this study, the validity of a non-parametric random learning machine algorithm, called MissForest, has been evaluated to fill the gap of daily streamflow series in a region with scarce data and strong climate variability.

The daily streamflow data in the gauge stations of the Southern Baluchestan catchment were analyzed in a long-term hydrological period (09/23/1972 to 09/22/2018). First, the missingness percentage was selected based on a conventional criterion (less than 50%) as an acceptable ratio of the missing rate in the streamflow data, followed by investigating the mechanisms and patterns of the missing data. Accordingly, the number of gauge stations was reduced to seven samples. Then, the temporal distribution of the missing daily streamflows during the months of the year and the relative frequency of gap length were investigated during the period. Next, the performance of the missing data reconstruction algorithm was challenged with two different artificial missing data scenarios. Two types of artificial gaps were generated, namely a) Removed contiguous segments: at each gauge only a segment (having lengths of 7, 14, 21, 30, 60, 180, and 365 days) was randomly removed from the entire record (1972–2018); b) Removed single data points: observed values (30, 60, 90, 120, 180, and 365 days) were randomly removed from the entire record (1972–2018) at each of the gauges. MissForest was applied to fill the gaps contained in the records together with the artificial gaps. Our analysis includes reconstructions of the 1972–2018 period at each of the streamflow gauges. Finally, the performance of MissForest in infilling daily streamflow data was tested by comparing the filled series with the observed data using goodness-of-fit (GoF) indicators, coefficient of determination (R2 ), the percent bias (PBIAS), and the Kling-Gupta efficiency (KGE).

The MissForest algorithm generally performed satisfactorily, allowing for accurately and reliably simulating lost data quickly and automatically. The performance of the MissForest algorithm is highly dependent on the number of predictor records, record length, and streamflow type. Finally, the reconstruction of real gaps in streamflow data was possible by applying this intelligent algorithm. The river flow time series were simulated with the natural flow regime with good performance; however, this performance dropped slightly for flow rate changes as a result of water storage and diversion for irrigation, especially downstream of dams. The performance of this algorithm in filling the daily time series of flow with severe changes in the flow regime, such as peak discharge, was not evaluated optimally. This drop in performance is more related to the hydroclimatic conditions of the studied watershed than the structure of the algorithm. The reconstructed hydrographs allow for analyzing flow variability and their interaction with key climate variables.

The MissForest algorithm is introduced as one of the imputation methods based on machine learning with high credibility and performance in reconstructing the missing data of the daily streamflow. It can also be used automatically and intelligently in the reconstruction of the statistical defects of the river flow in the scale used daily. Future studies are suggested to analyze the effects of different watersheds with specific hydro-physical-climatic characteristics on the performance of the MissForest algorithm. The other issues that need to be addressed in future studies include the investigation of the proposed method of this study in other climatic and geographical regions, the sensitivity measurement to the rainfall and flow regime, and finally, the investigation of its performance compared to other common methods.

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.

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.

 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.

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.

Land use/land cover (LULC) change in a specific area results from environmental and socioeconomic issues, as well as human activities over time and space. Increases and decreases in population growth in the system, climate, financial development, and physical elements, such as landscapes, soil grade, and type all influence LULC changes. The examination of conducted and documented research regarding the impact of land use change on the hydrological response of a watershed indicates that numerous studies have focused on this field both in Iran and globally. However, the effect of the pattern of landscape and the trend of its characteristics has not been observed to date. In this study, the processes of changing the landscape of the land,  management scenarios, and the runoff of the Tajan watershed were investigated taking into account a 20-year period in the basin using the SWAT model under the scenarios of the continuation of land use changes. The relationship between flow and topography parameters was examined to take into consideration the potential possibilities of using topography parameters in watershed management.

The study area is the Tajan River basin (about 4000 km2) surrounded by the Alborz Mountains in the south and the Caspian Sea in the north, located in Mazandaran Province. The environmental hydrological flow index was investigated using the SWAT hydrological model, which is a semi-distributed, continuous hydrological model developed for water resource managers to select the most appropriate strategy or solution by considering the impact of various management practices on river flow and non-point source pollution. Then, the flow intensity of the area was investigated using the land use maps of the region under the periods 1991, 1995, 2000, 2010, 2016, and 2020 in terms of the changes in the landscape. Calibration and validation periods were selected based on the availability of hydro-metric station statistics located on the Tajan River for the years 1997-2012 and 2013-2018, respectively.

The simulation results showed good simulation capability of the SWAT model in estimating the flow of Kordakhail, Parvij, Rigcheshmeh, Varand, Vastan, Garmrood, Karchai, and Aliabad hydrometric stations. EFC indicators, which are categorized into low monthly flows, very low flows, high flow pulses, small floods, and large floods, were also considered in this study because they are crucial for maintaining the ecological integrity of the river. The first EFC group, low monthly flow, represents surface flow, indicating that changes in these parameters could be associated with the availability of surface flow. Essentially, a minor decrease in low monthly flow can be observed in April, May, and October to December, ranging from 0.2% to 1.1% based on the average forecast of the ensemble. Conversely, low monthly flows from July to September are expected to increase significantly by 4.3% to 9.4% at a 95% confidence level. Overall, the flow process was estimated using the SWAT OUTPUT VIEWER. After examining the capability of the model for the effect of its effect on the amount of runoff, the results showed that the amount of runoff will increase in the land LULC, it is necessary to develop strategies for the river runoff management. The reason for the low results at some stations was the lack of a sufficient number of the rain gauge and synoptic stations in the areas to record heavy rainfall on a daily scale with better spatial coverage.

In the present study, the SWAT modeling and land change analysis were investigated as new planning tools to investigate land dynamics in the Tajen watershed from 1990 to 2020. The results of the analysis of changes during the years 1990-2020 showed the reduction of forests by 1211 hectares. The most deforestation was observed in the margins of former agricultural lands due to better access. In addition to the increase in population growth, the use of forest wood as fuel and the construction and repair of villagers' houses are other reasons for deforestation in the study area. The destruction of forest lands can also be attributed to livestock grazing. Similar to other modeling studies, the results presented in this research include various inherent limitations, and the modeling uncertainty was multiplied due to the application of land cover flow simulation in the hydrology model. This study clearly predicted the effects of vegetation change conditions in the Tajen watershed in several intervals. However, the decrease in water availability in late spring and summer can jeopardize the amount of water for irrigation downstream the basin, unless proper water management and storage is implemented along with better use and management of runoff. The results of this study especially confirm the importance of land cover, which can have a small effect, decrease, or increase the amount of water. Moreover, this study provides valuable and important information for decision-makers to design compatible scenarios in accordance with the principles of sustainable development, which aims to manage the flow. Finally, the results of this study especially confirm the importance of land cover, which can have a small, decreasing, or increasing effect on water quantity.

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.

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.

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.

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.

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.

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.

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. 

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.

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.

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 11parameters, 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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.