شهرام خلیقی سیگارودی

subsidence in urban areas poses significant risks to infrastructure, including buildings, roads, railways, pipelines, sewage systems, and wells. Therefore, assessing its potential is crucial. This study models the subsidence risk in Karaj city using Geographic Information Systems (GIS) and the Weight of Evidence (WoE) model. To achieve this, we created maps of factors influencing subsidence, such as slope, alluvial thickness, groundwater fluctuations, aquifer layering, particle size, and permeability. These maps were then compared with recorded subsidence data to determine the weight of each factor's influence. By integrating the effects of these factors, a Subsidence Index (SI) map was generated and categorized using the Success Rate Curve (SRC), identifying five sensitivity zones from very sensitive to very low sensitivity. The effectiveness of the WoE model was evaluated, revealing that the subsidence sensitivity prediction map covers 93.64% of actual occurrences. Results indicated that aquifer layering positively influences subsidence development, with the highest impact arising from alluvial deposits with good permeability and fine particles. This factor, with a weight of 3.72, demonstrates significant influence among all evaluated parameters. In terms of thickness, the most significant subsidence occurred in alluvial deposits exceeding 200 meters. Areas experiencing groundwater level declines of over half a meter annually markedly contributed to subsidence. Additionally, slopes of less than two degrees were identified as the most susceptible to subsidence. Thus, while many areas in Karaj are relatively safe, the threat is notably higher in the southern and southwestern parts, requiring special attention in urban management.

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.

In the present study, we detected the characteristics of precipitation (the amount and the number of events) in the Saharo-Sindian vegetation region located in the south and southeast of Iran over the last three decades. Daily precipitation data during 1987-2019, recorded by eighteen meteorological stations were analyzed. The Mann-Kendall (MK) test was used for trend analysis. Over the past thirty-three years, the mean annual precipitation was 214 mm (SD: 61 mm). Mean maximum and minimum yearly precipitations were recorded in Dogonbadan (439 mm) and Iranshahr (109 mm), respectively. Out of the eighteen stations, only two stations, Ramhormoz and Kish Island, showed significant annual precipitation trends (11% of the total trends). During 33 years, a total of 17275 events (mean event: 7.5 mm; SD: 6.9 mm) were recorded in the stations. The correlation coefficient (r) between the amount of precipitation and the number of events varied from 0.41 to 0.86 (mean: 0.67; SD: 0.13). We found that 83% of the mean annual precipitation occurred during the autumn and winter seasons. The highest and lowest yearly precipitations fell within the 20-40 mm (21%, 45 mm) and over 40 mm (30%, 64 mm) classes, respectively. Over the past three decades, the mean number of dry days per year was 336. Any changes in precipitation characteristics severely impact the vulnerable forest ecosystems in the Saharo-Sindian vegetation region.

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.

Flood is one of the most devastating natural disasters, causing financial and human losses each year. At the same time, many rivers in Iran's watersheds lack complete and accurate statistics and information. On the other hand, estimating the flow of floods is one of the most important factors for the design and implementation of water structures. In such cases, one of the appropriate solutions to estimate the maximum flow rate with different return periods is flood analysis. In order to conduct the present study, 55 hydrometric stations with a common statistical period of 20 years were considered to perform the work after the statistical deficiencies were eliminated. Then, based on the distribution of the third type of Pearson logo with the lowest error rate and the highest number of first rank as the most suitable fit function, the amount of discharge in different return periods was estimated. The following information was collected on the types of physiography, land use, climate and geology variables. After collecting information about all independent variables using Gamma test, the most important variables affecting the maximum instantaneous flow, including area, drainage density, maximum 24-hour rainfall and watershed environment, were selected and modeled using methods. Random forest modeling and support vector modeling were performed and their efficiency was determined based on statistical indicators With an efficiency coefficient of 74 to 83%, the error of 3.05 to 32.11 m3 and the coefficient of explanation of 76 to 91 are more accurate than the random forest model.

Vegetation provides valuable ecosystem services to various terrestrial ecosystems, such as carbon sequestration, fodder supply, and water resource regulation. It is predicted that the fluctuation and changes of climate in the future will have a great impact on terrestrial ecosystems, including the destruction of rangelands and desertification, increased drying of trees and reduction of carbon sequestration. Drought is a complex and climatic phenomenon that occurs frequently or intermittently in any type of climate and its progress is slow, so that its effects may appear gradually. Due to the high sensitivity of vegetation to climate fluctuations especially in arid and semi-arid regions, droughts can affect vegetation faster and disrupt its natural growth. Therefore, knowing the severity, duration and place covered by the drought phenomenon could reduce the damages. As the global air temperature is continuously increasing under the influence of climate change, the spatio-temporal pattern of rainfall and land use is expected to change significantly in the coming decades. Therefore, it is necessary to develop more accurate and effective drought monitoring tools. In this case, remote sensing plays an important role in drought studies since it ptovides unique data from the earth's surface. Satellite data provide the possibility to identify the damaged vegetation in different types of uses and assess the severity of the damage. Using satellite data to monitor plant activities in a large area has many advantages compared to studies based on traditional methods and is currently a hot topic of pioneering research in the field of drought diagnosis. The purpose of this research is to evaluate the dynamics of vegetation cover and the effect of climate change on it in Eshtehard Karaj township. It is hoped that its results will be used by managers and planners of natural resources for sustainable management of vegetation.

This research was done in two parts. In the first part, the effect of drought on vegetation was investigated using Standardised Precipitation Evapotranspiration Index (SPEI) and Enhanced vegetation index (EVI) , and in the next part, the vulnerability of vegetation was calculated using EVI index. In the second part, SPEI of 3, 6, 9 and 12 months were calculated in MATLAB software for 8 meteorological stations. It should be noted that SPEI has positive and negative values, in which the more negative values indicate the severity of drought. After calculating the SPEI index in the scale of 3, 6, 9 and 12 months for all 8 weather stations, drought intensity maps were obtained using the Inverse Distance Weighted (IDW) method. Then, the ready-made EVI, the MOD13Q1 product of the MODIS sensor was used from the beginning of 2001 to the end of 2022. First, the average EVI index for the entire county of Eshtehard was extracted on a monthly basis and the month with the highest average EVI was selected as the target month. Pearson correlation and coefficient of determination (R2) were calculated between SPEI maps of 3, 6, 9 and 12 months and EVI of the selected month in Earth Trend Modeler (ETM) TerrSet software. Then the time scale of SPEI with the highest R2 was extracted. In the following, the intensity of EVI changes of the selected month was calculated in terms of SPEI changes (slope of changes) using linear analysis in ETM tool of TerrSet software. In the second part, the probability of vegetation vulnerability was calculated using the probability of vegetation vulnerability index (PVVI). For this reason, vegetation condition index (VCI) was first calculated using EVI index. After calculating the VCI index for the selected month of each year in the period from 2001 to 2022, this index was classified into five classes. Then the probability percentage of each class was calculated. Finally, the PVVI index was calculated by multiplying the weight of each class by the probability of its occurrence.

The monthly average EVI index showed that the months of April and October have the highest and lowest average value of EVI in the studied area, respectively. The month of April was chosen as the target month. According to the R2, the relationship between SPEI in different time scales and EVI of the chosen month, in 68.9% of the studied area, three-month SPEI has the strongest relationship with vegetation compared to 6, 9, 12-month SPEI. Most of the mountainous areas in the south of Eshtehard city are in this class. Also, parts of the northwest are included in this class. SPEI of six months in 17.3% of the study area has the strongest relationship with vegetation and SPEI ratio in other time scales. Examining the results of this section showed that the general spatial pattern of vegetation vulnerability probability obtained from the probability of vegetation vulnerability index (PVVI) is to an acceptable extent similar to the pattern of the intensity map of vegetation changes compared to drought. Therefore, the middle belt from west to east is in the probability of vegetation vulnerability map in the high and very high vulnerability class (26.4% and 14.4% of the study area, respectively). In general, 13% and 20.6% of the studied area are placed in the classes of low and very low probability of vulnerability, respectively. These areas are mostly concentrated in the northern belt from east to west, as well as south and southwest of the studied area.

The results of evaluating the probability of vegetation vulnerability in Eshtehard city showed that the general spatial pattern of the probability of vegetation vulnerability obtained from the PVVI index is to an acceptable extent similar to the pattern of the map of intensity of changes in vegetation compared to drought. Therefore, it can be said that the PVVI index can determine areas with high vulnerability of vegetation for better drought management.

Extended AbstractIntroductionHydrological modeling is an essential tool in water resources management, and its accuracy and reliability are critical to successful design, planning, and decision-making processes. Calibration and validation are two essential processes used to evaluate the performance of hydrological models. The warm-up period is a crucial component of hydrological modeling that allows the model to reach an equilibrium state by representing the initial system conditions accurately.This study aimed to investigate the impact of the length of the warm-up period on the performance of four different hydrological models, namely AWBM, Sacramento, SimHyd, and TANK, in the Kashkan watershed. The study used different optimization methods in the RRL software package during the calibration and validation periods. The proposed warm-up periods of 5%, 7%, and 10% of the initial data length were used without considering drought and wet conditions.The findings of this study provide valuable insights into the impact of the warm-up period on hydrological modeling performance. The study showed that the length of the warm-up period does have a significant impact on model performance, with the best results obtained when the warm-up period was set to 5% or 7% of the initial data length. These findings have important implications for the design and implementation of hydrological models, as they highlight the importance of carefully selecting the warm-up period length to ensure accurate and reliable modeling results. Overall, this study adds to the body of knowledge on hydrological modeling and provides useful guidance for future research and practical applications.Materials and methodsThe Kashkan River watershed, with an area of over 9,000 hectares, was selected as the study area for this research. The Kashkan River is an important sub-watershed of the Karkheh River watershed, and daily rainfall, potential evapotranspiration, and potential evapotranspiration for the Kashkan watershed were used in this study, with a statistical period of 29 years (1988-2018). Since the rainfall-runoff process was investigated for the entire watershed, the Thiessen polygon method was used to obtain the weighted average of rainfall and evapotranspiration for the entire study area. Additionally, the Hargreaves-Samani (H-S) method was used to obtain potential evapotranspiration data.The data used in this study were divided into two parts, training and testing, based on trial and error and a review of sources. The training data accounted for 70% of the total data, while the remaining 30% was used for testing. The AWBM, Sacramento, SimHyd, and TANK models in the RRL software package were investigated, along with seven optimization methods using the Nash-Sutcliffe objective function.The findings of this study provide insights into the application of different hydrological models and optimization methods in the Kashkan River watershed. The study highlights the importance of accurately representing initial system conditions during modeling and the impact of the length of the warm-up period on model performance. These findings have important implications for water resource management, particularly in the design and implementation of hydrological models for the Kashkan River watershed and other similar regions.Results and DiscussionThis study examined the influence of different durations for the warm-up period on the calibration and validation of RRL software package models. Seven optimization methods and the Nash-Sutcliffe criterion were utilized in the analysis. Specifically, the warm-up phase of the software, which constitutes the initial segment of the statistical period, was investigated during the calibration and validation processes. Durations of 5%, 7%, and 10% were selected from the onset of the statistical period. The study involved conducting over 4000 iterations for all the examined models and optimizers.Given the characteristics of the optimizers, up to 5 iterations were performed for each optimizer in each model. The resulting average NSE value (Nash-Sutcliffe Efficiency) was analyzed and examined.The findings indicate that, on average, configuring the warm-up period to account for 5% and 7% of the complete dataset in the calibration and validation processes enhances the efficiency of the model compared to the recommended period suggested by the software. However, it is important to note that the outcomes may vary depending on the specific problem and prevailing conditions. Therefore, these results should be interpreted cautiously and in conjunction with other factors. Overall, this study offers a practical guideline for selecting an appropriate warm-up duration in the calibration and validation of RRL software package models.

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.

A significant part of the precipitation falls in the form of snow in mountainous areas, the flow resulting from melting snow constitute significant part of the runoff, which is effective in providi- ng water sources and feeding underground water tables in some cases, it causes destructive floods. Therefore, knowing the characteristics of snow is of great importance for the effectively managing of water resources. Due to the harsh conditions and lack of access to mountainous areas, it isn’t easy to measure on the ground. Therefore, it is essential to use remote sensing facilities to estim_ate information about snow. The purpose of this study is to investigate the factors affecting the durability of snow cover and the correlation between them using satellite images. In this study, we used the pictures of April and January 2016 and 2019 of the studied area, and we calculated the NDVI and NDSI indices for the correlation between them, and the area and correlation between the two were evaluated. The results showed that in land classification, the relationship between snow and vegetation is the most important, and the correlation and area between the two indicators indicate a strong relationship between these two indicators. Also, the kappa coefficient obtained for 2016 was 0.96 and 0.99 and for 2019 was 0.98 and 0.98 respectively.

One of the functions of rangeland ecosystems is to prevent soil erosion and increase nutrient retention capacity. The present study was conducted to highlight the effect of Rangelands degradation on the preservation of Mg, Ca, N, P, and K elements in the soil and emphasize the role of rangeland plants in preventing the wastage of these elements in the rangelands of the Salman watershed in Qom. For this purpose, a sample was taken from the soil of the habitat in each work unit, in the classes of poor and good rangeland conditions, representing degraded and non-degraded rangeland, respectively. Then, the amount of eroded soil was determined from each unit using the modified PSIAC (MPSIAC) model. In the following, the amount of loss of nutrients using the results of soil analysis and related relationships, was calculated and based on the replacement cost method; by estimating the alternative fertilizer, the monetary value of maintaining nutrients by the ecosystem was calculated. The results showed that the destruction of the rangeland ecosystems of the region causes erosion equivalent to 35.68 tons per hectare per year, which damages the functional services of the ecosystem at an annual cost of 11.6 billion rials (11606427781 rials) by reducing the nutrients of the soil. Also, each hectare of good rangelands in the region can prevent the loss of nutrients with an approximate value of 335.6 thousand rials. The results indicate the effective role of the rangeland ecosystem in controlling erosion and preventing the loss of soil nutrients. What is certain to emphasize more the role of rangeland ecosystems in soil erosion control; it is recommended that are also estimated the amount of soil erosion by other common methods, such as the global model of soil erosion (USLE), EPM, etc., and the results are compared with each other in future research.

Reliable estimation of precipitation is one of the most essential needs in water resources management. However, in many parts of the world, especially in Iran, the lack of time and place of rainfall data is very noticeable. Therefore, the use of satellite information is one of the ways to compensate for the lack of information. The purpose of this research is to compare the accuracy of rainfall information of TRMM-3B42 and PERSIANN-CDR products on a daily scale. The products of these two satellites are available daily for free in the pixel size of 0.25 degrees. The daily rainfall of 12 stations in the southern slopes of Alborz in a statistical period of 2000-2014 was used. The results show that these two satellite products are not the same in different statistical parameters, so that CDR and 3B42 have estimated 100% and 25% more rainfall events than the stations, respectively. Also, PERSIANN satellite is significantly superior to 3B42 in terms of RMSE, POD and CSI parameters, but on the other hand, it is weaker in terms of Bias and FAR parameters. Therefore, the selection of the desired satellite product should be based on the proper parameters.

This study was conducted to investigate the effect of degradation due to traditional forest exploitation on runoff and sediment following the change of some soil characteristics in the Zagros vegetation area in Manesht and Qalarang protected areas in the north of Ilam. Based on the objectives of the study, the treatments of degraded forest, intact forest (control) and agricultural lands in the region were considered in three slopes (15, 25 and 35%) and each in three replications. In this study, runoff and sediment due to rainfall were considered. Analysis of variance test was used to investigate the effect of vegetation cover on runoff and sediment, Duncan's test was used to compare means, and Pearson's correlation method was used to determine the significance and extent of each variable measured in the soil on runoff and sediment. The results showed that with increasing the slope, the amount of water infiltration in the soil decreases and as a result, more runoff and sediment is produced. The results showed that the amount of runoff, sediment and runoff coefficient in degraded forest lands is higher than the treatment of intact forest (control) and agricultural lands. In degraded forest lands, the amount of runoff is 5.43 mm and sediment is 0.3671 tons per hectare and in untouched forest lands (control), the amount of runoff is 0.33 mm and sediment is 0.0019 tons per hectare, respectively, and in agricultural lands, the amount of runoff, and sediment were calculated as 22. 0.00 mm and 0.004 t / ha, respectively. The results of correlation coefficient showed that the parameters of acidity, clay percentage, silt percentage had a positive correlation and sand percentage had a negative correlation with runoff and acidity parameters, silt percentage had a positive correlation and sand percentage had a negative correlation with sediment content. According to the results of this study, it can be concluded that eliminating or changing the use of vegetation, especially forest cover reduces the amount of soil permeability and thus increases the amount of soil runoff and sediment. Therefore, by preserving, rehabilitating and developing forest trees and shrubs, as well as preventing the change of use of these lands to agriculture, especially in sloping forest lands, runoff and sediment production can be prevented.

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.

Issues related to implanting national policies for reducing natural hazards in developing countries are seen as an important and worrying challenge. In this research, the factors affecting the vulnerability, hazard, and risk of groundwater salinity in the southern plains of the Bakhtegan watershed with an area of 6137 km2 were assessed using statistical index models, Classification and Regression Tree (CART), and fuzzy analytic hierarchy process (FAHP). At first, by assessing the data of groundwater quality from 124 wells in the watershed, the points of groundwater salinity occurrence (based on the electrical conductivity parameter) were recognized. After calculating and preparing basic maps related to each of the indicators, we looked for a way for selecting indicators to identify key indicators. To this end, the recursive feature removal (FRE) method was utilized for finding and selecting the main combination of indicators to prepare a map of groundwater salinity hazards. Accordingly, after determining the significance of each indicator by calculating the jackknife test, the salinity hazard map of groundwater was predicted using two models (SI) and (CART). Also, the degree of vulnerability of the region to the salinity of the groundwater was determined using the (FAHP) process. The sensitivity, specificity, accuracy and kappa coefficient statistics were used to evaluate the performance of two-variable models. Also, in order to evaluate the performance of multivariate models, fore criteria of coefficient of determination (R2), conformity coefficient (CC), Kling-Gupta efficiency (KGE), and correlation coefficient (COR) were used. The results of groundwater salinity class risk study showed that 4.13 square kilometers of irrigated areas are in a very high-risk class, which among the various land uses with the highest vulnerability is related to this type of land use. Based on the results of this study, it can be stated that the development of comprehensive management plans, monitoring of executive activities, including change of uses, or taking into account the prevailing conditions in the implementation of rehabilitation operations can help control and manage risk in these areas.

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.

Climate change affects many plants and animals by changing the growing season or temperature patterns that stimulate life cycle changes. Hordeum bulbosum is widely distributed in the Zagros region, especially under the storey of western oak forests in the vegetation zone of the Irano-Turanian region, and is considered as an important species for rangeland restoration and improvement. In this study, the effect of climate change on the geographical distribution of this species was assessed in Chaharmahal and Bakhtiari Province, located in central Zagros region. For this purpose, 73 points of presence were recorded using the global positioning system (GPS). In addition,11 environmental variables including bioclimatic, physiographic and landuse variables were used in the modeling process. Ensemble modeling include artificial neural network model, generalized boosting method, generalized linear model, flexible discriminant analysis, random forest and multivariate adaptive regression. Finally, the most reliable model was determined as the random forest model. The final forecast for 2070 is based on three scenarios of increasing greenhouse gases (SSP126, SSP370, and SSP585) as well as two general circulation models 9GFDL-ESM4 and MRI-ESM2-0). Findings showed that 25% of the study area is currently identified as suitable habitat for Hordeum bulbosum, and the most effective variables in species distribution are the Annual precipitation, mean temperature of the wettest quarter, and Total rainfall is the hottest season of the year. It is also predicted that based on GFDL-ESM4 and MRI-ESM2-0 models, 27.32% (SSP370) - 31.02% (SSP585) and 26.06% (SSP370)-31.68% (SSP585) of the suitable habitats will be reduced, respectively, by 2070 due to climate change. The present and future habitat maps prepared in this study can be used for the development and implementation of the given species’ management and conservation plans.

 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.

Using machinery wood extraction in steep slope in the Hyrcanian forests can increase the soil bulk density and soil compaction, decrease the infiltration water into soil of skid trail, which results in an increase runoff and soil loss and has detrimental effects on soil nutrients loss and sedimentation into the downstream infrastructure. The aim of this study was to investigate the optimal distance of water diversion in skid trails to mitigate sthe ediment and runoff in compartments 315 and 316 of Gorazbon district in Kheyrud forest. In this study, 9 plots with length of 20, 30 and 40 m and slope gradient of 15, 25 and 35% were established along the longitudinal slope of the skid trails. A tank was installed to collect runoff at the outlet of each plot. Also, runoff in each rain event as well as soil bulk density, moisture content, organic matter, and soil texture of the plots were measured. The results revealed that the optimal distance among water diversions in the skid trails to suppress the runoff was 30 m and the most appropriate slope for mitigating runoff on the skid trails was up to 25% slope gradient. Runoff had a significant relationship with plot length. The results showed that the amount of rainfall was significantly correlated with runoff at different lengths and slopes. Therefore, it should be avoided to plan the skid trails on steep slopes for mitigating runoff, and some engineering measures such as the installing water diversion structures (by length of 30 m and slope gradient of 20-25%) to decrease the runoff should be done.

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.

Water deficit caused by climate change due to the extent and magnitude of the social and economic damages it causes, is one of the most dangerous natural disasters that causes irreparable damage to the agricultural and water resources of the country. In other words, it has devastating effects on the productive, economic, social and environmental sectors. The purpose of this study was to analyze the components of resilience and present a model of resilience of the local community in the face of climate change. The statistical population of the study consisted of rural community of Nodushan watershed which was used for sample size of Cochran formula and 100 people were surveyed based on a researcher-made questionnaire. The Resilience Component Analysis Questionnaire was used. Data were analyzed using SPSS25 and LISREL8.8 software. To investigate the fit of the resilience component measurement model, the data was analyzed used LISREL software. The results showed that among the resiliency indices, the indicators of "local networks" and "financial and infrastructure" with 91 percent and 84 percent path coefficients were better than other indices, respectively. "Compatible management status" and "risk-taking" with 10 percent and 8 percent path coefficients, respectively, are not appropriate. Also, results showed that the goodness-of-fit indices had values and confirmed the resilience dimension measurement model with the data. Therefore, it can be acknowledged that the results of this study can in actions and can be effective in promoting and adopting climate change crisis mitigation mechanisms to create resilience in the local community.

One of the requirements for implementing natural resource governance and empowerment of rangeland users is the analysis of social relations in the social network of users. In this research, the analysis of the social network of rangeland users with an emphasis on the links of trust, participation, and consultation in rangeland management measures was compared with the calculation of macro-level indicators of the network in the three customary systems of Fasanqar, Parvand, and Malvand. Also, the complete network method (census) has been used, and in three customary systems, all rangeland users, including 100 people, have been considered a statistical population. The results show that among the users of the customary system, the amount of social capital, stability of relations, and network balance with a density of 50.8% on average and consultation between them with a rate of 43.8 and 40.5 for the two villages Malvand and Fasanqar are poorly institutionalized. Low social capital and low turnover of trust, participation and consultation between the users of these two villages are important challenges in creating a participatory management structure, and consequently, improving rangeland governance in these areas and the need to further strengthen social capital by increasing building trust in the network and creating the desire of users of all three customary systems to participate in rangeland activities to create success in rangeland governance. However, in the two villages of Malvand and Fasanqar, there is a need to further strengthen social relations and increase social capital compared to the Parvand village.

The application of hydrological models in watersheds has always been considered by water resources researchers. This subject is of special importance in arid and semi-arid regions due to the greater complexity of hydrological processes in these region. In this research, the efficiency of WetSpa distributed hydrological model in Menderjan semi-arid basin has been evaluated and the sensitivity analysis and uncertainty analysis of the model parameters have been performed using PEST software.  Moreover, the efficiency of the Extended Kalman filter in improving the results has been investigated. The Nash-Sutcliffe coefficient for the calibration and validation periods was 0.63 and 0.58, respectively, confirming that the WetSpa model has a good performance in runoff simulation in Menderjan basin. The results of sensitivity analysis also show the high sensitivity of Kg and K_ss parameters and the low sensitivity of G_max and P_max parameters in the study area. Moreover, the results of uncertainty analysis are generally consistent with the results of sensitivity analysis and indicate the high certainty of sensitive parameters and the low certainty of non-sensitive parameters. The results of applying Kalman filter also show the improvement of the results. So that the Nash-Sutcliffe coefficient increased from 0.63 to 0.71 in the calibration period and from 0.58 to 0.69 in the validation period.

Land use change are the result of complex interactions between many processes. Many models have been developed in recent decades to understand these dynamics. The present study, using Landsat satellite images and applying the maximum probability classification algorithm, examined the land use pattern of Simine River basin in 2000, 2005, 2010 and 2017 and identifies the historical transformations of them. Finally, the various scenarios of land use change in 2040 will be examined using the latest version of the Dyna-CLUE land use model. The results of modeling indicate Dyna-CLUE model performance in analyzing and predicting future land use patterns at the basin level and its calibration and validation coefficients in 2005 and 2010 are 0.84 and 0.69, respectively. Analysis of land use changes between 2000 and 2017 has shown that the most change was the conversion of rangeland lands to irrigated lands; So that about 343 square kilometers of rangeland lands (equivalent to 13%) decreased and irrigated agricultural lands increased by about 127%. Also, the spatial arrangement of land use classes in 2040 in the form of three optimistic, pessimistic and the continuation of the previous trend scenarios and its outputs have shown that the future pattern of most land use classes in the region has not changed much compared to historical spatial patterns and shows a marginal spatial expansion. The results of this study can be used as an effective tool for environmental planners to manage the future infrastructure of the Simine River basin.

Surface water resulted from rainfall-runoff responses in a basin is a potential water source that if it is managed properly can be useful to demand secure. In this study, hub coating algorithm (PR) and geographic information system (GIS) were used to identify areas with potential in Birjand plain for rainwater harvesting. The weight of 18 decision-making indices was calculated and prioritized using TOPSIS method.  After determining the weight of each of these indices for locating rainwater harvesting potential, indices maps were prepared in Arc GIS 10.3 software and then for final mapping of rainwater harvesting potential of Birjand plain, points having common characteristics of 6 highest weight indices were determined. In total, 220 points were extracted as the best points from the TOPSIS method. Using the hub overlay method with algorithm (pr) and coding in Matlab environment, out of 220 points, 20 points were identified as suitable locations (hubs). Finally, the final map was drawn by inserting these points into ArcGIS software. The results showed that in the north and northwest of Birjand, the soil has a high capacity for rainwater harvesting, these areas mainly have sandy-pebbly soil texture, high permeability, moderate rainfall and residential land use.