دکتر علی سلاجقه

The industrialization of communities has led to an increase in greenhouse gases over recent decades. This increase has caused the warming of the earth's atmosphere, and it affected other components of the climate system and led to climate change. The evidence confirms that the global average temperature of the Earth is increasing, especially in recent years. Moreover, precipitation intensity has changed over time. It is expected that changes in temperature and precipitation will cause a series of climatic extreme events. Therefore, it is an undeniable fact that the intensification of global climate change has affected the development and survival of mankind. The purpose of this research is to investigate and predict these changes in the future decades in the Taleqan watershed, Iran.

The study area, Taleqan watershed with a mountainous topography, is located in the northwest of Alborz province, Iran. The annual precipitation and temperature of the region are 485.8mm and 11.4℃, respectively. To estimate and generate data for the future period (2021-2040), we used daily data from regional stations, including precipitation data from the base period (1979-2014) and average temperature data from the base period (2003-2014). We also utilized output data from the General Circulation Model (CanESM5) under climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). CanESM5 is a global model developed for simulating future climate change and developing seasonal and decadal forecasts. CanESM5 is usually used for large-scale projections, therefore, SDSM is chosen to downscale climate data. The changes of average precipitation and temperature parameters for three future periods (2021-2040, 2041-2060, and 2081-2100) were evaluated and RMSE, MAD and R were used to evaluate model accuracy.

The greatest increase in precipitation in Armot station is in March, May and November and the greatest decrease in precipitation in September and October is predicted under the SSP scenarios for the periods 2021-2040, 2041-2060 and 2081-2100. In Sakranchal station, the highest increase in precipitation is in three periods of March, February and May, and the highest decrease is in September and October under the SSP1-2.6, SSP2-4.5 and SSP5-8.5. The highest increase in precipitation in the Zidasht station is in March, May and November, April and December under the SSP scenarios. Also, the biggest decrease is in September under the SSP scenarios for the three forecast periods. In Gateh Deh station, the highest increase in precipitation during the periods of 2021-2040 and 2081-2100 is related to March, May and November, and for the period of 2041-2060, it is related to the months of March, February and May under the SSP5-8.5. During the periods of 2021-2040 and 2041-2060, the greatest decrease will be in September under the SSP scenarios, also in the period of 2081-2100, the greatest decrease will be in July under the SSP scenarios and in October, under the SSP5-8.5. During three periods, the greatest increase in Jovestan station precipitation is predicted in February, March, May and November. Also, the greatest decrease in the period of 2021-2040 will be in September and August under the SSP5-8.5 and in the periods of 2041-2060 and 2081-2100 in October and July under the SSP1-2.6. Based on the results of the temperature forecast in Zidasht station during three periods, the average and average maximum temperature in January and February under the SSP scenarios have a decreasing trend and other months show an increasing trend.

The results show that precipitation has a decreasing trend in some months and some increasing trend. The obtained results indicate that the precipitation and temperature variables in the periods of the 2021-2040, 2041-2060 and 2081-2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 will experience an increasing trend compared to the base period. The highest increase in temperature and precipitation is in the period of 2021-2040 under scenarios SSP2-4.5 and SSP1-2.6, respectively. This study demonstrates that Taleqan watershed will be vulnerable to future climate change. An increase in temperature can cause snowmelt and reduce snow storage. The stability time of water reserves in the watershed will be reduced. Precipitation changes in the region can alter the precipitation pattern from snow to rain. This reduces surface and underground water, and can affect crop yield.

Evaporation is one of the important parameters in hydrology that plays a significant role in the water cycle. This parameter, in addition to various spatial distributions, with having altitude distribution causes the complexity of evaporation modeling The aim of this research is to present a new approach for spatio-temporal modeling of evaporation changes, which can be used in rain-runoff models.

In order to carry out this research, monthly evaporation data over a 20-years period (2002-2021) were used from the Maroun evaporation monitoring station located in the Paskouhak catchment, 27 km west of Shiraz, as well as three stations surrounding Paskouhak catchment including Shiraz, Ghalat, and Dasht Arjan stations. Initially, by using regression modeling and determining the relationship between evaporation and elevation above sea level for each month, monthly evaporation raster maps were drawn for the study area. Then, using the proposed approach of using the ratio equations method, the initial spatio-temporal model of evaporation changes was prepared. Due to the dynamic nature and sensitivity of the evaporation parameter, the impact of various factors on the intensity of evaporation was simulated and the initial raster maps were corrected to a large extent. For this purpose, correction coefficients obtained in the form of raster maps or numerical coefficients were used. These coefficients included the correction coefficient of evaporation intensity due to the ratio of water depth at the target surface to the water depth in the evaporation pan, the effect of different days of the year on the conversion coefficient of the evaporation pan, and the correction coefficient based on changes in elevation from the ground surface. All stages of the research were performed in the SNAP and MATLAB software. Finally, the final result was obtained in the ArcGIS software.

The results showed that using the linear regression model and elevation parameters above sea level, it is possible to obtain the spatial distribution of evaporation with high accuracy (R2=0.81 in December and R2=0.99 in March and October) in the form of a regular pixel grid (in this study 100 m2). In addition, the final spatio-temporal distribution model of evaporation showed that there is a noticeable difference between the results of the initial and the final evaporation models in some areas of the study region (pixels). This highlights the need for more corrective coefficients.

In this study, using the proposed approach, it is possible to model the spatiotemporal distribution of evaporation in the study area at time steps corresponding to the time series of data available at the evaporation monitoring stations. It is recommended to apply this model under various climatic and topographic conditions and to evaluate its results.

Users prepare accurate data of the amount of rainfall by using rain gauge stations. However, an interpolation of rainfall data is difficult due to temporal and spatial variability. Therefore, rain gauge stations are not well distributed in many areas, especially in mountainous areas. In a mountainous area, understanding the interaction between the resolution of the Digital Elevation Model (DEM) and climate variables is necessary for accurate spatial interpolation of average rainfall in many areas, and on the other hand, the need for accurate information in hydrological modeling and many environmental studies and it is climatic. One of the problems that exists in many hydrological studies is that rainfall maps are always prepared using interpolation or available DEM, regardless of rainfall, which have an estimated rainfall error.

In this study, four DEMs with spatial resolutions of 30, 90, 1000, and 10000 m, which are the most common DEMs in studies, were used to introduce the best elevation digital model for extracting the rainfall gradient map from the data of 11 meteorological stations in Kermanshah province. A rainfall map for Kermanshah province was prepared using a linear regression model fitted between the height of each station and the 20-year average rainfall. The best DEM for rainfall estimation was then determined on the basis of error evaluation criteria.

The results of this research showed that in estimating rainfall, DEMs with cell sizes of 1000 and 10000 m (R2 = 0.76, 0.81) were more accurate than DEMs with spatial accuracy of 30 and 90 m (R2 = 0.75). In the examination of the Nash–Sutcliffe coefficient (NS), compared to other digital height models of accuracy, DEM with a spatial resolution of 1000 m (one km) with a Nash–Sutcliffe coefficient of 0.76, a significance level of 0.01, and a correlation coefficient of 0.81 was found to have greater accuracy.

The results of the present study can be used to estimate and generalize rainfall in areas that do not have stations and to prepare rainfall maps in areas where the number of stations is limited. In addition, it should be used in univariate interpolation methods that do not have proper accuracy because spatial distances are not considered. In addition, due to the complex topography of the earth and the non-uniformity of meteorological stations on the earth’s surface, high-resolution models with higher spatial resolution are required for the estimation of rainfall, which increases the accuracy of digital models in the evaluation of rainfall studies by removing topographical levels that cause errors.

The aim of this study is to propose an approach for modeling spatiotemporal changes in rainfall that can be used as input for rainfall-runoff models.

To achieve this, rainfall data from four rain gauge stations in the Paskouhak catchment were used. Five parameters, including elevation, slope, aspect, longitude, and latitude, were identified. The different combinations of these five parameters were prioritized using the gamma test in WinGammaTM software. After the use of different regression models, the best model was selected based on evaluation criteria such as R2, RMSE, and the Taylor diagram. A raster map of a selected rainfall event was drawn in the Arc GIS environment. Finally, using the proposed approach of relative equations, the spatiotemporal changes in rainfall were modeled.

The results showed that using a second-degree nonlinear model and parameters of elevation and latitude, it is possible to accurately obtain the spatial distribution of rainfall in the form of a regular pixel grid (100 square meters) with high precision (R2=0.917 and RMSE=0.2277).

In different rainfall events in small catchment areas, the variation in rainfall in each pixel is almost constant relative to other pixels, including the rain gauge station, the proposed approach in this study can model the spatiotemporal changes of each rainfall event as a three-dimensional matrix in the study area. The approach can be valuable in predicting potential flood events and in water resource management and planning. However, further research is required to validate the results and test the approach in other areas.

Urban runoff management models are considered as a useful tool in planning, design and development. One of the most usable models in this field is the SWMM. One of the main problems in studying urban drainage network with these models is the correct choice of parameters. To overcome this problem, the sensitivity analysis method could be used, which shows the relationships between variables included in the model with each other and the priority of their impacts on the model output. The purpose of this study is to determine the sensitivity of affective variables that influence urban flood peak using the SWMM model in Azimiyeh region of Karaj. In order to that, the estimated initial value of the percentage of impervious area, slope, width, curve number, N-Manning for impervious area, depth of depression storage on impervious area, percent of impervious area with no depression storage reduction were decreased and increased to a constant value in the range of acceptable changes and their effect on peak discharge was investigated as a dependent variable. Based on the results, the amount of impervious areas and the percent of impervious area with no depression storage had highest and lowest effect on peak discharge, respectively. 30% increase in the amount of impervious areas, as the most effective parameter, caused peak discharge to be increase 5.09 percent.

GCM models are widely used to assess climate change on a global scale, but outputs of these models are not sufficient and accurate to assess climate change at local and regional levels. Therefore, in this study, SDSM model was used for micro-scaling of CanESM2 model data and climate conditions of Semirom region based on three scenarios of RCP2.6, RCP4.5 and RCP8.5 in the period 2020 to 2100. The results of model evaluation based on NCEP database showed that the model was more accurate in estimating and predicting temperature data especially mean temperature. Comparison of observation and simulated data of temperature and precipitation of GCMs in the baseline period (1980 to 2005) based on NCEP predictor variables showed the mean correlation of precipitation data of 0.52, mean temperature of 0.88, maximum temperature of 0.80 and minimum temperature of 0.70 for validation and verification periods. The results of the estimation of precipitation variations in different scenarios also predicted a decrease of at least 7.24% and a maximum of 18.55% for the time period of 2020 to 2100 compared to the baseline period (1980-2005). The results of precipitation prediction also show the changes of precipitation pattern. The comparison of the scenarios also shows that the RCP2.6 scenario as the most optimistic scenario has the least rainfall while the RCP8.5 scenario predicts the highest rainfall reduction. Examination of the predicted changes in temperature also shows an increase for the mean, minimum and maximum temperatures,

The impact of dust storms on the ecosystem, human health, and economy is significant in the affected areas. In arid and semi-arid regions, dust is a natural occurrence that covers about one third of the earth's surface. This phenomenon is caused by wind erosion and factors such as the size of soil particles and their adhesion force, surface roughness, weather conditions such as long-term droughts cause it to intensify. To understand the impact of the dust phenomenon on humans and the environment, it is essential to evaluate the spatial and temporal distribution of dust and its changes and effects. Numerous studies have been carried out in the field of evaluating dust changes using different methods. For example, using CMIP5 models, synoptic fog systems are predicted to increase during the 21st century in the Middle East. In addition, using AOD obtained from MODIS and MERRA-2 sensors, researchers showed a significant upward trend in dust changes from 2000 to 2010. A significant upward trend was shown in Iran's winter AOD values during the period 2000 to 2010, and a decreasing trend during the period 2010 to 2018. A point can be spotted by examining various studies in the Middle East and Iran that evaluate the spatio-temporal changes of dust. Statistical tests of time series study, such as Mann-Kendall spatially and pixel by pixel, have been used in limited research to evaluate the trend of dust changes. In Iran, there is a research gap in not using spatial and pixel-by-pixel statistical tests to evaluate the trend of dust changes, as stated. This research aimed to provide a solution and address the problem by analyzing the spatial and temporal changes of dust using the AOD index in the eastern half of the country.

 In this research, in order to evaluate the temporal-spatial changes of dust, the AOD data of the blue band (470 nm) of the MCD19A2 product of the MODIS sensor was used. AOD parameter is known as one of the most key factors in studying the climatic effects of aerosols and atmospheric pollution. In order to extract AOD data, monthly data from 2001 to 2022 were obtained in the Google Earth Engine system by averaging the daily AOD data. Over a 22-year period, the average of each month was calculated. The months that had the highest average AOD values were chosen and their changes were evaluated. In this research, the Mann-Kendall test was used to evaluate the change process. Menkendall's ZM coefficient was calculated for months in the Earth Trend Modeler (ETM) of the TerrSet software to achieve this. In the next step, the intensity of monthly AOD changes per time unit was calculated for 22 years in selected months. To simulate the process of changes, linear regression analysis can be utilized for this purpose. This method is used to determine the linear relationship between all the data of a dependent variable and the corresponding data of the independent index. If the slope is higher than zero, the dependent variable will change in the same direction as the independent variable. The dependent variable changes in the opposite direction of the independent variable if the slope is smaller than zero. The steeper the slope of changes, the greater the impact of the independent variable on the dependent variable. The Earth Trend Modeler (ETM) of TerrSet software also carried out this step.

Based on the evaluation of the monthly average AOD changes in the studied area, the trend and intensity of AOD changes from 2001 to 2022 were assessed in April, May, June, and July. In most areas of the studied area, AOD is increasing with a probability of more than 70%, and the intensity of changes is mostly high and very high in April. It can be concluded that AOD is experiencing a strong increase in April. This is despite the fact that in May, June and July, respectively, a considerable part of the western half, northern half and eastern half is increasing with different intensities with a probability of more than 70%. It can be concluded that the trend and intensity of AOD changes in the above-mentioned months follow a different spatial pattern. The dispersion of dust production centers inside and outside Iran, and the local and regional synoptic conditions governing dust production centers is the cause of changes in the spatio-temporal patterns of dust storms. The unprincipled extraction of water resources by humans, land degradation, soil moisture reduction, and the loss of vegetation due to climate change all affect these factors in turn. The results showed that the monitoring of monthly average AOD changes can help to identify new hotspots and evaluate the results of wind and dust erosion control and management activities. Therefore, it can be suggested that a system based on remote sensing must be designed and presented to monitor dust changes, so that the management of the dust phenomenon in Iran becomes more. We need to pay attention to the factors that influence these changes and evaluate their impact on the dust phenomenon.  On the other hand, by modeling the environmental factors affecting on the trend of dust changes in each region by using methods such as dust evaluation, it is possible to determine the role of each factor and the most important factor affecting the trend of dust changes in each region.

The phenomenon wherein precipitation increases as altitude ascends is referred to as the precipitation gradient, that significant importance in the field of hydrological forecasting in mountain basins and management of water resources. The present study aims to investigate the changes in the topography of the earth on the formation and movement pattern of precipitation, the average rainfall of the IMERG V06 satellite and the dem of the 0.1 degree elevation of the precipitation gradient for eight main directions or coding in the Python environment of the Alborz Mountains were extracted. Then, in order to determine the movement pattern of precipitation, longitudinal and latitude profiles were used. The study findings indicate that in the northeast region of Golestan province (positive gradient), there exists a positive gradient of precipitation, which amounts to 0. 44 mm.m elevation. Furthermore, it is worth noting that in the southeastern region of Semnan, there exists a negative gradient of 1. 91 mm.m reduction in precipitation of elevation gain. Additionally, it was found that the precipitation gradient was significantly influenced by both the north and south directions, accounting for 16. 8% and 25.4% of the effect, respectively, at a significance level of 0. 01. On the northern slopes, a negative correlation has been observed between the amount of precipitation and latitude, wherein higher latitudes are associated with lower precipitation levels. Conversely, a positive correlation has been noted between the amount of precipitation and latitude in other regions, where higher latitudes correspond to increased levels of precipitation. The intricate nature of precipitation in mountainous regions presents a challenge in establishing a comprehensive pattern due to the multifaceted influence of anthropogenic, climactic, and terrestrial variables, nevertheless, comprehending precipitation patterns in the context of decision-making can benefit water resource management.

The maximum canopy storage capacity is an important parameter in hydrological models. The aim of the present study, which was conducted in a region of Fars province, was to model the maximum canopy storage capacity for storing rainfall. Therefore, the parameters of leaf area index and branch area index were used as a pattern of the vertical distribution of canopy. Additionally, the ability of 10 vegetation indices produced from narrow- red edge bands compared to 10 vegetation indices produced from broad bands that resulted from Sentinel-2 satellite imagery in estimating branch and leaf area indices was investigated. Subsequently, using sampling in autumn and spring seasons, laboratory results and regression models, maps of maximum canopy storage capacity were drawn for these seasons. The results showed that the CIRE index, which was produced from narrow- red edge bands, was able to estimate both branch area index )R2=0.74) and leaf area index )R2=0.92) values very well and was the best index. Additionally, the modeling results of maximum canopy storage capacity showed that this capacity varied from zero to 1.32 mm per pixel in autumn and from zero to 1.74 mm per pixel in spring.

Accurate slope map and the governing river network of a river basin are key inputs of hydrologic models. The purpose of this study was to investigate the effectiveness of the PriorityFlow tool in topographic data processing with the aim of improving slope map extraction in the eastern Faryab Basin. PriorityFlow is a tool in the R programming environment, which aims to generate the correct slope map and to form a network of continuous pathways that matches the observed river network. To process the digital elevation model and prepare the slope map, three input files were used, including the digital elevation model, the representative map of the watershed and the representative map of the observed river network of the study area. After producing the slope map using this package, the obtained results were compared with the outputs obtained from other traditional tools available in the Parflow hydrological model. Specifically, the river network map related to the two slope maps were extracted by the Parflow model under the condition of very low permeability of the domain surface and compared with the observed river network of the study basin. The results showed that the slope map produced by the PriorityFlow tool has led to the production of a more accurate river network that matches the real river network of the eastern Faryab basin. Also, this software package is able to produce the slope map with enhanced accuracy and based on non-diagonal connections along the cell levels in the digital elevation model of the study area.

Precipitation is of the most important inputs of runoff modeling. The availability of precipitation data with appropriate temporal and spatial accuracy is very important and necessary for watersheds with small and scattered rainfall stations. Nowadays, climatic satellites are practical and widely-used tools in precipitation estimations. In this study, first the efficiency of TRMM satellite precipitation data in the monthly time series of Chehelchai Watershed was evaluated using R2, RMSE, NSE and Bias statistical indices by comparing the precipitation data of rain gauge stations (observed)  and the values of these statistical indices were 0.54, 22.70, 0.44 and -14.86, respectively. Considering the value of the coefficient of determination (R2), it can be concluded that the TRMM satellite was able to estimate the 0.54 of observed precipitation. In the next step, three base data models including MLP, ANFIS and SVR were used to estimate the monthly runoff. Two different input scenarios were selected :1) observed precipitation data in t and t-1 time steps and runoff in t-1 time step and 2) satellite precipitation data in t and t-1 time steps and runoff in t-1 time step. To compare the accuracy and error of the models, R2 and RMSE of the validation stage were used. The ANFIS model with the values of R2 and RMSE were 0.80 and 0.97 for the first type input combination and 0.78 and 1.02 for the second type input combination, respectively, as the suitable single model for estimating runoff in the study area were selected. Then weighted-mean method was used in the data fusion approach to provide a data driven combination model for each combination of inputs into the model in the studied watershed. This data fusion approach data-driven model improved the values (R2=0.81) and (Bias=-4.85) for the first type input combination and also improved the value (R2=0.79) for the second type input combination.

The water shortage crisis that is sweeping the globe today has posed serious harm and threat to people around the world. Meanwhile, Iran is one of the water-scarce countries due to its location in the arid and semi-arid belt and fluctuations in rainfall. The water status in most of its regions is in a state of tension and sometimes crisis. The pervasive nature of water and the existence of numerous human and environmental factors affecting its reliability have greatly complicated the interactions between the human-environmental dimensions. Therefore, the study of these complex environmental systems that are affected by human actions is a major scientific challenge. This challenge must bridge the gap between the natural sciences and the social sciences and dominate modeling at different scales. Therefore, there is a need to integrate knowledge from the natural and social sciences. In this regard, the purpose of this study is to analyze the water crisis and the concept of water security by presenting a framework for Human-Environment System (HES) interactions in the Neishabur Plain watershed. In this regard, first, the process of changing the water issue in the Neishabur Plain watershed during 2011-2020 was investigated, and then, according to the identification of the prevailing situation in the watershed, considering the influence of various human and natural factors in the current crisis, to identify the main factors affecting the occurrence of the crisis and determining how they interact and feedback for a comprehensive analysis of the water crisis and the consequences of this water shortage in the region. The results of the investigation of the water issue in the area generally show a decrease in precipitation, warming of the air, increase in evaporation and transpiration, dryness, and more dehydration. This is while the largest volume of water harvesting in the basin (ca. 450 million m3 in the 2019-2020 water year) is also dedicated to the agricultural sector. In addition, the results show that understanding the hierarchical levels of environmental awareness and ultimately learning and practice based on key components and interactions identified by the HES framework, facilitates the analysis of system complexity.

Precipitation plays an important role in climatic, water, energy and biogeochemical cycles. Several global and regional data sets currently provide historical estimates of this variable over Iran, including the MWEP and WFDEI forcing datasets and production of some institutions such as MOHC, SMHI and IITM. All these datasets provide data with different resolutions based on gage stations, satellite Images and models output. In this study, we do an inter comparison between these data sets during 1990- 2008. We also validate all ten data sets against independent ground station observations over 30 second-order basins of Iran. MSWEP and WFDEI have an acceptable compatibility with observational data on different spatial and temporal resolutions. RMSE and Bias are 5.68, 6.34 and 0.58, 2.75 for these two datasets during 228 months, respectively. However, it is needed that MSWEP improves in the western and northwestern parts of the country and WFDEI in June and September months. Our findings in this research provide valuable guidance for a variety of stakeholders, including rainfall- runoff and land-surface modelers, watershed management studies and data providers.

As an undeniable environmental phenomenon, climate change can be defined as a reversible change or variability in the average climate and its relevant variables, including the temperature, precipitation, humidity, climate patterns, wind, radiation, etc., which lasts for a long period of time. Located in a special geographical location that suffers from insufficient precipitation, Iran faces inappropriate distribution of rainfall temporally and spatially. On the other hand, the world seems to be facing new challenges in terms of water resources. Moreover, the most important consequence of the change in the hydrological cycle is the tendency toward extreme events ​​such as torrential rains, widespread droughts, and in some cases, regional wetlands. In this regard, it can be said that the frequency and severity of floods are among the terrible or deadly natural disasters brought about by climate change. Therefore, it is necessary to determine the best probability for the distribution of flood discharges, measure the best probability distribution for management and planning in cases where climate change occurs, and finally assess the frequency and severity of floods in Iran.

To analyze the floods' frequency and severity under different climate change scenarios, the minimum and maximum values of precipitation and temperature were measured using the CanESM2 general circulation model under RCP2.6 and RCP8.5 climate change scenarios and the SDSM4.2.9 linear multiple regression downscaling model. Then, the collected precipitation data was processed and analyzed and the flood pattern was simulated for future periods via NetSTORM software (separating the rainfall from the hourly data). Moreover, the HEC-HMS model was used to simulate floods in basic and future periods. Accordingly, the SCS method, the Clark unit hydrograph method, and the Muskingum method were calibrated and evaluated to calculate the infiltration, convert the rainfall to runoff, and rout the river, respectively. Finally, to analyze the floods' frequency and severity, the probabilistic distribution function was fitted for the future periods' baseline data and propagation scenarios using the SMADA software for different statistical distributions (normal, two- and three-parameter log-normal, Pearson type III, Log-Pearson Type III and Gumbel), followed by the selection of the best-fit distribution model based on the RMSE and MSE tests.

The results of the climatic model showed that under the RCP2.6 and RCP8.5 scenarios, the maximum temperature rate would increase in 2011-2055-and 2056-2100 by 3.02˚C, 3.27˚C, and 3.2˚C, and 5.47˚C, respectively. Furthermore, the minimum temperature rate would increase in the same periods by 0.62, 0.87, and 1.1 and 2.82 degrees Celsius, respectively. However, the monthly precipitation data did not reveal any specific trend throughout the future periods.The Emamah watershed's data concerning the flood discharge and maximum daily precipitation rate during the study period (1999-2019) were used to select flood and pervasive events. The predicted data were then analyzed under RCP2.6 and RCP8.5 scenarios at five separate periods. Finally, the selected events' data were imported to the HEC-HMS model and simulated. After selecting the flood events with the highest magnitude compared to other events, they were decomposed into one-hour or fewer rainfall events using the NETSTORM model. Then the flood discharge values were calculated ​​for the base and future periods and their probabilistic distribution function was obtained through the SMADA software. Finally, the Pearson type III distribution, the best distribution among normal, two-and three-parameter lognormal, Pearson type 3, Log-Pearson Type 3, and Gamble distributions were selected for each base and future time series using the goodness-of-fit test.According to the results of the best frequency distribution, flood values ​​were estimated with return periods of 2, 10, 25, 50, 100, and 200 years. Moreover, one third of the floods with a 2-year return period witnessed an increase in discharge rate compared to the base period. However, the discharge rate decreased or remained unchanged in floods with other return periods. On the other hand, floods estimated with 10 and 25-year return periods were increased in two-thirds of the periods, the highest increase of which occurred in the second period under the RCP8.5 scenarios by 12.68 and 25.76 percent, respectively. It should also be noted that the highest chances of increase in flood occurrence with a return period of 200 years belonged to the second period by 56.12% increase rate and 10.07 m2 discharge rate under the RCP8.5 scenarios.

throughout the next hundred years, climate change would experience significant changes in precipitation patterns, leading to the risks of severe floods and droughts. Moreover, the results of the analysis and study of climate change indicated that the temperature increasing trend in the periods under the studied scenarios and that the biggest increase belonged to the RCP8.5 scenarios. It was also found that the temperature rate would increase more in the period 2056-2100 compared to the 2011-2055 period. However, the results of precipitation simulation under the scenarios did not show a definite trend for the future periods, with the precipitation increasing and decreasing in different months of the year. On the other hand, the simulation of basin floods for the future periods and the comparison of peak discharge values within the future and the observation periods indicated a change in the regime of river flood discharges. Accordingly, the maximum discharge rate increased in the constant returns period. Furthermore, the discharge rates significantly increased in the maximum constant flow period with an increase in the return period.

Uncontrolled emissions of greenhouse gases into the atmosphere have been cited as a major reason and threat to climate change. Accordingly, it causes decreasing in precipitation, increasing of temperature, and the occurrence of extreme climatic consequences in the future. Due to the characteristics of communities and limitations, it has harmful consequences. Understanding the trend of these changes in global climate change projects is crucial to understanding future climate change. In this research, based on global atmospheric circulation models has been used a statistical model for regional downscaling. This study has been carried out in the North Kroon basin in order to study the trend of precipitation components and average temperature, to reveal the impact of influence on the region's climate, that the results have been shown in the forecast range of (2100 -2020). Using both optimistic and pessimistic scenarios (RCP2.6 and RCP8.5), the model evaluation performance for the precipitation variable (R = 0.85, MAD = 0.58, MSE = 0.73, RMSE = 0.84) and for the temperature variable (R = 0.93, MAD = 2.2, MSE = 1.5, RMSE = 1.3). All in all, the average precipitation will increase 35.97 mm compared to the observation period under RCP2.6 scenarios and will increase by 73.57 mm under RCP8.5 scenarios. Also, the temperature in both scenarios RCP2.6 and RCP8.5 will respectively increase by 3.01 and 4.79 ° C.

 River water quality is one of the important factors related to human health, living organisms and is strongly influenced by the prevailing conditions in the watershed. These activities are performed naturally or artificially with human intervention. In this regard, the present study assess the hydrological and edaphic indexes of the Ghareh Sou river. The trend of health data obtained from the software in each station was examined separately. According to the results obtained from the stations, three stations had a decreasing trend and two stations had an increasing trend. Nahar Khoran station had a decreasing trend at the level of 0.95 and the two stations, Pol Ordughah and Ghaz Mahale had a decreasing trend at the level of 0.99. The results of Tables show that Yasaghi station had the lowest level of health and Ghaz Mahale station had the highest health index score among the other stations. Ghaz Mahale station with a score of 0.86 has the highest health score among the studied stations and is the healthiest station in terms of hydrological health and Yasaghi station with a score of 0.72 has the lowest score among the stations. Health in Naharkhoran, Pol Ordughah and Ghaz Mahale stations had a decreasing trend at 99% , Yasaghi and Sade Kosar stations had an increasing trend. The Ghaz Mahale station has the highest downward slope.

Forest fires can have a significant impact on water quality, especially in basins that provide drinking water. To investigate the effects of low, medium and high intensities of prescribed fire on runoff quality, 12 runoff plots were established in Kheyrud educational and research forest, north of Iran. In the first rainfall event after fire, six and 12 months after the fire, runoff samples were collected and transferred to the laboratory. Qualitative characteristics of the runoff were pH, electrical conductivity (EC), total suspended solids (TSS), total soluble solids (TDS) and total hardness (TH). The results showed that all the quality characteristics of the runoff after the fire increased compared to the control plots, but the differences among them were not significant. Most of these characteristics were in the high fire intensity, but as time passed after the fire, they had had a declining trend. The time elapsed after the fire had a significant effect only on TSS and TH. TSS values after six months and pH values after one year at low and medium fire intensities reached the level of control plots. TDS values immediately in the first event of precipitation in control, low, medium and high intensities were 22, 46, 53 and 78 mg/l, respectively. After one year, the values were 25, 36, 41 and 44 mg, respectively. Fire causes changes in the quality characteristics of runoff, element loss and reduced soil fertility. In order to reduce soil degradation, forest soil management measures after fire are necessary.

Scouring is a natural phenomenon that occurs as a result of the erosive factor of water flow. Due to the impact of water on the base of the pier, vortices called horseshoe vortices are formed. Horseshoe vortices are more active in front of the base, which is the main cause of scouring. In this study, the effect of using triangular structures on reducing scour around the pier bases was investigated in a laboratory. This research was used in a laboratory flume with a 90-degree arc. Triangular protective structures with angles of the 15, 30, 45 and 60 degrees and a curved triangular structure were considered. Experiments were performed at the 6, 7.5, 8.5 and 10 liters per second. The results showed that the installation of triangular protective structures at a maximum flow rate of the 10 liters per second, can reduce the scour depth flow up to 84 and 78 percent, respectively, upstream and downstream. The best performance is for the 15° upstream protection structure as well as the 60° downstream protection structure. The results show that the scour depth increases with increasing landing number. Also, the curved protective structure had a benign performance current upstream and a lower performance current downstream than other protective structures.

The removal of alluvial deposits from the riverbeds causes changes in the balance of the rivers. These changes are not limited to extraction and harvesting, but the miles appear higher and lower. Due to the rapid growth of industrial activities, urbanization and related developments, demand for sand has increased over the past few decades worldwide, especially in developing countries. In the present study, the HEC-RAS5.0.6 model was used to simulate the sediment flow of Allah Ramhormoz River and the hydrometric statistics of the junction stations as the upstream boundary conditions and the hydrometric statistics Meshrageh of the station as the lower boundary conditions in the 44 years old(1352-1396), transverse sections of the river, which were taken in year 87, longitudinal profile of the river, aggregate of the bed material was introduced into the model. The results of this study showed that the Ichres-White sedimentation transfer function with R2 equaled 0.87 best results have given. Also, the study of erosion and sedimentation in the river showed that from the beginning of the Allah River range to the intersection of Allah River and Zard River with an equivalent of 165 thousand cubic meters per year, the best area for harvesting.

In this study, the flood vulnerability of Bandar Abbas was investigated using the indicators of socio-economic status, urban density, population density and building quality and TOPSIS-hierarchical method. Therefore, at first, the weight and importance of the indicators were determined based on expert opinions and hierarchical analysis method. Then the vulnerability of the city to floods was calculated using the TOPSIS method. The study area was divided into 5 urban blocks, and then the decision matrix was formed according to 5 blocks and 4 indicators. Finally, the final map of Bandar Abbas urban flood vulnerability was prepared in ArcGIS software. The results of vulnerability analysis showed that the population density factor with a weight of 0.306 is the most important. Finally, the comprehensive analysis of Bandar Abbas flood vulnerability proved that the southern and central parts of the city and urban block No. 5 have higher vulnerabilities and these parts are a high priority for urban runoff and flood management. High population density can be one of the reasons for the vulnerability of this region to floods. The development of green space and increasing the capacity of the street runoff collection system are among the important measures to reduce urban flooding. Also, the spatial distribution of population density and urban density in Bandar Abbas was unbalanced and this issue should be considered in the management plans of this city to reduce the concentration of population in areas with high vulnerability.

The purpose of this study was to investigate the trend of hydro-climatic variables, detect the occurrence of climate change and subscale the climatic variables during future periods and evaluate the intensity and magnitude of future floods. The trend of hydroclimatic variables was first investigated using Mann-Kendall and Sen tests. Then, the output data of maximum temperature, minimum temperature and precipitation of CANESM2 general circulation model were sub-scaled under RCP2.6 and RCP8.5 climate change scenarios using SDSM 4.2.9 model. Later, HEC-HMS model was used to evaluate the intensity and magnitude of events in the Emameh watershed in future periods. The results showed that 20.8 and 14.6% of the maximum temperature and minimum temperature series had a significant upward trend. While 5.5% of the rainfall time series had a significant upward trend. Therefore, unlike the temperature variable, the monthly precipitation variable did not have a definite trend during the observation period as in future periods, in some months it showed an increasing trend and in some months it showed a decreasing trend. However, the maximum and minimum temperatures under the diffusion scenarios increased in the following periods. The peak flow and volume ​​of the simulated floods, under the most severe events in each period, was significantly smaller for future periods than for the observation period. Comparison of the most severe events with each other in different periods showed an increase in the volume and magnitude of the flood in the RCP2.6 scenario compared with RCP8.5. Therefore, in the context of climate change, the number of floods and their destructive power has increased and managers and planners are recommended to pay attention to this, especially in urban areas.

With the growth of urban population, due to the increase in air pollution and also reaching the desired per capita green space, the need for urban green space development becomes clearer than before. However, the scarcity of extractable water resources raises concerns in this area. Water resources management is the only appropriate and principled solution to compensate for a large part of the water shortage required by green space. Calculating and estimating the water demand of urban green space can be a great help in planning for the optimal use of water resources. In this study, the evapotranspiration of urban green space plants in area one of Tehran Municipality with a net area of ​​1535.4 hectares was estimated using three methods WUCOLS, IPOS and PF. The study area includes 52 different plant species. To calculate the reference evapotranspiration and the amount of effective rainfall, climatic data of the North Tehran Meteorological Station for a period of 30 years (1365-1396) were used. The results showed that the highest amount of calculated water requirement is related to the PF method at the rate of 786.72 mm per year and the lowest is related to the IPOS method at the rate of 378.08 mm per year. Also, the results of this study indicate that the WUCOLS method with a total amount of calculated water requirement of 771.33 mm per year, due to having more and more complete parameters and more appropriate relationship with all objectives in urban green space, a method It is more suitable for estimating the water demand of urban green space.With the growth of urban population, due to the increase in air pollution and also reaching the desired per capita green space, the need for urban green space development becomes clearer than before. However, the scarcity of extractable water resources raises concerns in this area. Water resources management is the only appropriate and principled solution to compensate for a large part of the water shortage required by green space. Calculating and estimating the water demand of urban green space can be a great help in planning for the optimal use of water resources. In this study, the evapotranspiration of urban green space plants in area one of Tehran Municipality with a net area of ​​1535.4 hectares was estimated using three methods WUCOLS, IPOS and PF. The study area includes 52 different plant species. To calculate the reference evapotranspiration and the amount of effective rainfall, climatic data of the North Tehran Meteorological Station for a period of 30 years (1365-1396) were used. The results showed that the highest amount of calculated water requirement is related to the PF method at the rate of 786.72 mm per year and the lowest is related to the IPOS method at the rate of 378.08 mm per year. Also, the results of this study indicate that the WUCOLS method with a total amount of calculated water requirement of 771.33 mm per year, due to having more and more complete parameters and more appropriate relationship with all objectives in urban green space, a method It is more suitable for estimating the water demand of urban green space.With the growth of urban population, due to the increase in air pollution and also reaching the desired per capita green space, the need for urban green space development becomes clearer than before. However, the scarcity of extractable water resources raises concerns in this area. Water resources management is the only appropriate and principled solution to compensate for a large part of the water shortage required by green space. Calculating and estimating the water demand of urban green space can be a great help in planning for the optimal use of water resources. In this study, the evapotranspiration of urban green space plants in area one of Tehran Municipality with a net area of ​​1535.4 hectares was estimated using three methods WUCOLS, IPOS and PF. The study area includes 52 different plant species. To calculate the reference evapotranspiration and the amount of effective rainfall, climatic data of the North Tehran Meteorological Station for a period of 30 years (1365-1396) were used. The results showed that the highest amount of calculated water requirement is related to the PF method at the rate of 786.72 mm per year and the lowest is related to the IPOS method at the rate of 378.08 mm per year. Also, the results of this study indicate that the WUCOLS method with a total amount of calculated water requirement of 771.33 mm per year, due to having more and more complete parameters and more appropriate relationship with all objectives in urban green space, a method It is more suitable for estimating the water demand of urban green space.

In order to study the effect of fire on some physical, chemical and biological characteristics of soil, 36 sample plots with three low, medium and high fire intensities and control treatments were set up in the Namkhaneh district of University of Tehran Forest. Soil samples were then taken from 0-10 cm layer in three phases (one day, 6 and 12 months after the fire) and some soil properties were studied. Data analysis was done by repeated measures ANOVA in SPSS software. The results showed that fire intensity had no significant effect on soil physical properties including bulk density, moisture content and soil texture. The results of soil chemical properties analysis showed that fire intensity had a significant effect on electrical conductivity, organic carbon, total nitrogen, carbon to nitrogen, phosphorus, calcium, potassium and magnesium ratios but had no significant effect on pH. In terms of biological characteristics, fire intensity had no significant effect on carbon microbial biomass but on microbial respiration and nitrogen microbial biomass. Time after fire had significant effect on moisture content, microbial respiration, microbial carbon and nitrogen, available phosphorus and potassium but had no significant effect on other physical, chemical properties. Since fire causes long-term depletion of soil nutrients, some post-fire preventive and rehabilitative measures in areas with moderate and high fire severities are required to restore soil functions more quickly.