sayyad asghari saraskanrood

Flood is one of the natural disasters that causes the highest number of casualties and economic damage every year in different parts of the world and country. Khalkhal county is not an exception to this rule. So the occurrence of this destructive phenomenon during the past few years has destroyed some communication ways, and bridges, and damaged some residential areas, agricultural lands, and gardens located in this county. Therefore, considering the importance of the subject; The main goal of this research is to produce a flood risk map in Khalkhal county. In this research, after preparing the digital layers of the effective parameters, standardization of maps was done using the fuzzy membership function, and according to the CRITIC method, the weighting of each layer was investigated based on its importance in the occurrence of floods. Finally, using the MARCOS multi-criteria analysis method, a five-story zoning map was prepared. The results of the study showed; slope, lithology, land use, and precipitation respectively; With weight coefficients of 0.163, 0.155, 0.144, and 0.135, they are the most important causes of floods in Khalkhal county. In addition, 25.12% and 14.58% of the range area, respectively, are in the very high-risk class, which indicates the potential of this county for floods. Also, according to the use of the ROC curve method and the sub-curve level (0.79), the accuracy of the MARCOS method is good in identifying and zoning areas prone to flood risk in Khalkhal county .

Text Landslides are one of the types of large-scale processes that cause many human and financial losses in many parts of Iran and the world every year. The increase in population and the expansion of human settlements in mountainous areas, the difficulty of predicting the occurrence of landslides and the numerous factors influencing the occurrence of this phenomenon, reveal the necessity of landslide risk zoning. Identifying the effective factors in the occurrence of this phenomenon and its risk zoning is one of the basic and practical methods to achieve its forecasting, control and monitoring solutions. By using field studies, geological and topographical maps, and by reviewing the researches and studies done in this field, as well as examining the existing conditions in the studied area, 9 factors of elevation, slope, slope direction, lithology, distance from the fault. , the distance from the river, the distance from the communication roads, land use and rainfall were investigated as factors affecting the occurrence of landslides. Therefore, the purpose of this research is to investigate and analyze the most important factors involved in creating the risk of landslides in Garami city and to identify the prone areas that will probably be involved in landslides in the near future. In this research, the zoning of prone areas was done with the Aras multi-criteria algorithm in the Edrisi software environment, and according to the results of landslide risk zoning; The criteria of land use, slope, and lithology are the most important factors involved in creating the risk of landslides in the study area with weight coefficients of 0.187, 0.152, 0.152, and 0.142, respectively, and are 361.99 and 450.32, respectively. A square kilometer of the area has a very high probability of danger. Finally, it can be said that the most important factor involved in increasing the amount and potential of landslides in Germi city is the change of land use and the increase of agricultural land and livestock pastures.

Monitoring of land use changes and destruction of vegetation as one of the dominant parameters in soil erosion is one of the important issues for assessment and control in natural resource management. The Hyrcanian forests of Gilan province, over the past years, have deteriorated due to neglect and have taken on a different face. So; The purpose of this research is to reveal the changes in land use and the destruction of forest cover and its effects on soil erosion in the watershed of Ghaleroodkhan Fuman. For this purpose, the changes in land use that took place between 1371 and 1402 were extracted using Landsat images and object-oriented classification techniques and were classified (agriculture, forest, pasture, water, and residential). In the next step, by identifying the effective factors in the erosion of the area and preparing the information layers of each criterion in GIS, the standardization of the layers was done using the fuzzy membership function, the weighting of the criteria using the CRITIC method and the final modeling was done using the MARCOS multi-criteria analysis method. The study of the changes in watershed use shows that the forest cover in 1992, with an area of 222.17 square kilometers, had the largest area among the land uses, and in 2023, its area decreased to 205.03 square kilometers. Also considering the results; Residential use with an increase of 27.17 square kilometers has changed the most during the 30 years of study. According to the erosion zoning map, respectively; The area of the floor with very high and high erosion potential has increased from 18.04 and 31.05 percent in 1992 to 22.52 and 32.34 percent in 2023. According to the obtained results, it is possible to reduce the forest cover and convert it into residential areas, agricultural lands, and pastures, as well; He considered the conversion of agricultural lands to residential areas and the increase of residential and agricultural use in the boundaries and riverbeds as the most important factors involved in increasing the soil erosion potential of the basin.

The expansion of the urban population to more than two-thirds of the world's population by 2050 on the one hand and the prediction of the growth of the risk of natural hazards in the future, on the other hand, necessitate the attention of managers, planners, and urban policy makers to the issue of greater resilience of societies in the face of natural hazard. The aim of this research was to analyze the spatial resilience of urban areas against earthquakes in Kermanshah. This study used a descriptive-analytical method. The statistical population consisted of the citizens of Kermanshah city, 385 people were selected as a statistical sample using Cochran's formula. For analyzing the data, one-sample t-test, Friedman, Kruskal Wallis, and CV method were used. The findings showed that the resilience of Kermanshah city in all indicators (awareness, knowledge, skills, attitude and social capital) is average. The attitude index is the most important regarding resilience and awareness, social capital, knowledge, and skill are respectively in the next ranks. The results of the Kruskal-Wallis test showed that there is a significant difference between the eight regions in Kermanshah metropolis in terms of all resilience indicators and that the status of the eight regions are not at the same level. Among the indicators, the highest inequality is related to the awareness index and the lowest is related to the social capital index. In general, the spatial analysis of resilience in Kermanshah city is average and there are differences between regions.

Today, due to population increase, industrial development, excessive exploitation, droughts, exploitation of underground water has multiplied. Therefore, identifying areas with underground water as one of the important sources for providing drinking water, agriculture, and various industries is considered to be one of the important and necessary issues in water resources management. The purpose of this research is to investigate and zonate the areas with underground water in Khorram Abad plain located in Lorestan province using convolutional neural network method. For this purpose, maps of nine factors affecting underground water were first prepared in the ArcGist environment. In the convolution method, the number of samples was determined as the ratio between the training set and the test set was 70:30, and the convolution neural network framework was used as 2 convolution layers and 2 integration layers, 2 complete connections. layers and finally the sigmoid layer was used for classification from the 3-3 convolution kernel, the Relu function as the activation function and the cross entropy function as the loss function. The obtained maps were classified into 5 classes: very good, good, average, low and very low. Confusion matrix was also used to validate the results of the model. 30% of the real data was used for evaluation, which resulted in an overall accuracy of 92%, that is, the model was able to correctly identify 92% of the data as underground water and 93% as the absence of underground water. The analysis of the groundwater potential map of the convolutional neural network model shows that about 57% of the area is in low groundwater conditions and 43% of the area is in good groundwater conditions.

Given the significance of the Hyrcanian Forests, inscribed by the UNESCO as a world heritage site, it is essential to monitor the changes in and the devastation of the forest cover in this ecoregion for the planning and management of national lands. It is the objective of the present study to monitor changes in both land use and forest cover in Astara region using Landsat TM, OLI 1 & 2 sensors for the years 1995 and 2022. For this purpose, images captured on days with cloud covers of less than 10% were selected over three time intervals and the relevant enhanced Vegetation Index (EVI) values were determined based on Landsat inter-band relations. In the next stage, the index values were combined to derive the land use map using the support vector machine (SVM) algorithm. The results of accuracy evaluation showed that the overall accuracy and Kappa coefficients of the land use map for the year 1995 were equal to 89 and 92% and those for 2022 were 0.86 and 0.75%, respectively, indicating acceptable results. The results of land use changes in Astara city during the period from 1995 to 2022 showed that residential land use had increased by 7% equal to 2954 ha while rangeland and agricultural uses had decreased by 1 to 2% equal to 258 and 997 ha, respectively. However, an important land use along the Caspian coast line – that is, forest cover - stretched over an area of 34283 ha equal to 80% of the study area, which declined in 2022 to 32522 ha equal to 76%, showing a decrease of 4% equal to 1761 ha. It is clear that, owing to its rich archive of satellite images, the GEE system can be used as a strong and useful tool for monitoring and managing forest lands.

Soil erosion is a natural process that causes soil loss due to various environmental factors such as weather, soil, topography and vegetation (Obayat et al., 1400). However, human interventions through land use change and agricultural and construction activities can accelerate this flow (Wenker et al., 2019). For this reason, soil erosion caused by land use change has become the most important problem of land degradation all over the world, and the transformation of the land form and the disruption of the main functions of the ecosystem are the consequences of these geomorphic reactions. Therefore, the purpose of this research is to investigate the impact of land use changes in a period of twenty years on the amount of erosion and sedimentation in Shafa Roud basin.

Shafarood watershed is between 48 degrees and 41 minutes to 49 degrees and 6 minutes and 30 seconds from the Greenwich meridian in the eastern hemisphere and between 37 degrees and 25 minutes to 37 degrees and 41 minutes and 30 seconds from the equator and in the northern hemisphere and in It is located in the west of Gilan province and Talash region. The highest peak of this basin called Sharaf in the northwest of the studied area is 2892 meters high, and its lowest point is about 71 meters above the surface of open water.In this research, the data of rain gauge stations nearby and inside the basin were used to analyze the rainfall data. It should be noted that the quality of the data used was checked before entering the statistical analysis stages, and after removing the possible statistical deficiencies and also checking the outliers of the data, it entered the statistical analysis stage. In the processing of satellite images, it is very important to choose the right time of the images taken to prepare the land use map, so in the present study, the multi-temporal images of the Landsat series in cloudless conditions (Bascula et al., 2017) in the middle of June 2011 and 1401 Shamsi (2004 and 2022 AD) was obtained through the United States Geological Survey (USGS) website. Considering the growing season of pasture plants and the timing of planting and harvesting crops, it seems that the images from mid-June are suitable for preparing land use maps. In this research, the information of all the spectral bands of the mentioned images was used for land use classification. Also, in this research, Excel, 5.4 Arc GIS, Archydo and ENVI 5.3 software were used to analyze data and prepare maps. In this research, the RUSLE model was used to estimate the annual average soil erosion. RUSLE model is a function of 6 input factors including rain erosion (R), soil erodibility (K), slope length and degree (LS), vegetation management (C) and conservation operations (P).

In this research, the supervised classification method and the support vector machine classification method, which has high accuracy, were used to monitor the changes that occurred during the 20-year period. The classification accuracy of both images was evaluated with the Kappa index and overall accuracy, and according to the research of Mather (2005), the results of the current research are statistically acceptable. Based on the results of the classification accuracy in the two images, the classification accuracy in the order of the date of receiving the image had an increasing trend, which is due to the up-to-date information of the region for the 2022 image and the possibility of direct access to the current land use in the region for selecting educational samples. It was natural. In addition, the accuracy obtained in both images is acceptable and shows the high ability of Landsat images to prepare land use maps with acceptable accuracy, which is in line with the research results (Sengari and Broumand, 2013; El Kawi et al., 2011). Correspond. Of course, it should be noted that the high accuracy of classification is not the only result of the implementation of the classification process, in addition to these cases, the collection of educational samples with appropriate distribution and number at the basin level has played a significant role in improving the classification process. In general, the accuracy of the results obtained from the two images had similar results for all applications, which can be attributed to the collection of suitable educational samples and the simultaneous date of the collection of the two images (June). The results of the survey of land use changes during 20 years in the region showed (Table 3), agricultural lands and pastures have decreased in the region. The reason for this can be related to the droughts of recent decades in the region and also the decrease in the level of underground water tables, which has reduced the amount of water available to farmers. This result is in line with the research results (Sultanian et al., 2013), but it is against the research results (Nazari Samani et al., 2006 and Rajesh and Yoji, 2006) that the agricultural land has decreased in a period, of course, It seems that the reduction of agricultural lands in his research was due to the increase of residential constructions in the agricultural lands of the region. On the other hand, the level of residential land in the region has increased, which seems natural due to the increase in population in the region and new construction in the villages. The obtained results show that the amount of erosion in 2011 varies from -0.84 to 5.63 tons per hectare per year and in 1401 from -1.33 to 8.37 tons per hectare per year. These results show that the amount of erosion in This basin has increased slightly in 20 years.The obtained results show that the amount of erosion in 2011 varies from -0.84 to 5.63 tons per hectare per year and in 1401 from -1.33 to 8.37 tons per hectare per year. These results show that the amount of erosion in This basin has increased slightly in 20 years.

The sun is known as the source of energy, the beginning of life and the source of all other energies. The global radiation of the sun is considered one of the fundamental structures of every climate. Therefore, knowing the characteristics and predicting these basic structures has a great impact on energy-related planning. The use of satellite images and remote sensing models as a suitable and low-cost tool for estimating solar radiation has been in recent years. In order to carry out this research, the images of 2020 Landsat 8 satellite, OLI sensor, TIRS sensor and Sabal algorithm were used. ENVI software was used for geometrical, atmospheric and radiometric corrections of satellite images, as well as the execution of calculations related to the Sabal model, and ArcGIS software was used for creating a database, spatial analysis, cartographic operations and finally implementing the model. The results show that the average of the highest incoming shortwave radiation was 904 watts per square meter on 09/08/2020 and the lowest value was 500 watts per square meter on 10/9/2020. Meanwhile, the highest amount of net radiation on 09/08/2020 was calculated as 320 km and the lowest amount on 10/09/2020 as 39 km. The difference in the amount of net radiation reaching the ground in the studied area, It is caused by the difference in the angle of the sun and the number of sunny hours in different months of the year. Finally, it can be concluded that the solar radiation in the region has the necessary potential for the implementation of solar photovoltaic projects in the year under review.

Rivers are very important in terms of human uses and ecological functions. In the present research, the environmental flow components of the Khiavchai River have been determined. Therefore, changes in the EFCs were determined using IHA software. According to the results, the values of low flows were high in the first period (1988 to 2017), but decreased in recent periods. Also, the decrease in the discharge values of the maximum flow indicators and the increase in the number of days with zero discharge to the number of 174 days were also caused by the change of the river regime. The amount of peak rate and decline rate components has increased in recent periods. The values of low flow duration indicators and low flow frequency have increased. Based on the change of flow dispersion index, the occurrence of strong or very low flows in the river has been intensified. According to the FDCs, in the early periods of the river flow regime, river flow was higher than 0.01cms during the year, while in recent periods it has decreased to less than 0.001cms. Based on the changes in the duration of the flow in recent periods, the duration of the river has changed to 50-60% of the days of the year with a very low discharge. Overuse of river flow changes in the flow regime, and successive droughts have increased the severity of the change in the flow regime and the deviation of the river conditions from the normal state.

Today, geotourism and geosites are an important solution for economic development and one of the important ways of generating income for different regions and countries, which has been less addressed and therefore remains abandoned and unknown. Accordingly, considering the importance of the issue, In the present study, Babolrud basin was investigated due to the presence of many geosites and geotourist potentials. In order to know and study its tourist areas, in this research, 8 geosites are included (Babolsar Suspension Bridge, Babolsar Cable Bridge, Babolsar Rubber Dam (Arabkhel), Haft Tirkan Waterfalls). , Sajjadroud, Guru spa, Terez waterfall, Sanbalroud earthen dam) have been investigated and evaluated. According to what was said, the current research is practical and of a descriptive-survey type, in which the method of data collection was document and survey using a questionnaire of experts and tourists; Also, to analyze the data, M-GAM model and statistical tests using SPSS.26 have been used, and the findings show that according to the overall average and the resulting ranking, the geosite of Sanbalroud earthen dam is ranked first in terms of geotourism potential. After that, there are Terez waterfall, Grove waterfall and suspension bridge, and the cable bridge with a score of 74.87 has been assigned the last rank in terms of geosite characteristics for the development of geotourism in Babalrood basin, and the final results have shown that there is a lot of room for improving geotourism. In these places, especially in terms of tourism value

Earthquake, as one of the natural hazards, causes irreparable damage to humans and the environment, and assessing the vulnerability of an earthquake is an important step in planning, preventing, and reducing the damage caused by it. Khalkhal City has a high potential for earthquake occurrence due to the existence of many faults and seismic points. However, despite the importance of the subject, the evaluation and zoning of Khalkhal city's vulnerability to earthquakes has not been investigated yet. Based on this, the current research seeks to evaluate the vulnerability of this city against the risk of earthquakes. For this purpose, first, the effective parameters were identified by examining the resources and paying attention to the environmental conditions of the city. Then, the standardization of the layers was done using the fuzzy membership function and the weighting of the criteria using the CRITIC method. Finally, using the MARCOS multi-criteria analysis method, a five-level vulnerability map was prepared. The results of the study showed that the factors of distance from the fault, land use, and distance from the epicenter, respectively; With the weighting coefficient of 0.133, 0.120, and 0.113, have the greatest impact on the occurrence of earthquakes in the region. In addition, according to the results of the research, 24.53 and 15.83 percent of the city's area has a high and very high vulnerability potential. Also, based on the findings of the research, the cities of Kolur and Hashtjin, along with 28 villages of the city, have a high vulnerability potential, and Khalkhal city and 42 villages of the city are also in the zone of high vulnerability. Therefore, to reduce potential risks, appropriate planning should be done in vulnerable areas. In addition, according to the results, the accuracy of the MARCOS method is excellent in evaluating the vulnerability of Khalkhal City against earthquakes, with the level under the curve (0.86).

 One of the dangers that has occurred in many areas in recent years is the dangers of subsidence. Identifying areas prone to subsidence and estimating its rate plays an important role in managing and controlling this phenomenon. High-precision radar interferometry technique is one of the most suitable methods for detecting and measuring subsidence. This technique compares the phase taken from two radar datasets at two different times and, by creating an interrogram, is able to measure changes in the earth's surface over time. In this study, in order to identify and measure subsidence in Pakdasht plain, radar images of 2015 and 2020 have been used. SARSCAPE software has been used for information processing. In order to verify, the data of piezometric wells and land use maps in the area were used. According to the results, the maximum subsidence rate in 5 years in the region is estimated at 10 cm. The results also showed that the highest rate of subsidence in the period 2020 is in the next categories of agricultural uses with a value of 10 cm, rangeland area with a value of 9 cm and urban and soil uses with a value of 8 cm, respectively. have. Also, the relationship between subsidence and changes in groundwater level showed that in a period of 5 years, groundwater level has decreased by 12 meters, which has led to subsidence of land in the study area.

Most of critical issues such as increase in pollution levels, sudden climatic changes and the rise of temperature in the urban area, leading to the formation of Urban Heat Islands (UHI), have been resulted from urbanization. As population density increases, most terrestrial areas become cities, and cities grow very fast. The reason to do the current study is to compare Single-Channel, SEBAL and Split-Window methods and then choose the best method for estimating land surface temperature. The objectives are as follows: Three independent studies were conducted using a series of Landsat data: (i) to land-use/land-cover (LU/LC) classification by object-oriented method and change detection; (ii) to understand the connection between particular LU/LC class and Land Surface Temperature(LST); and (iii) LST recovery using Single-Channel, SEBAL and Split-Window, as well as comparing these methods together. The results of land-use classification and change detection indicated that urban areas have increased, while agriculture has declined. The results of validation of the three temperature recovery methods demonstrated that due to using two thermal bands simultaneously, the Split-Window method functions better and in these three algorithms, water bodies and wet soils exhibit minimum surface temperatures. Due to less vegetation, areas such as deserts, saline soils and residential area display a higher surface temperature. Vegetation has always been an obstacle for heat input and inversely related to surface heat. In addition, due to fuel pollution of machinery and factory, urban areas experience high temperatures. The only gap of this study was the utilizing 5-cm surface temperature data, which was only available at airports and was not available.

Cities are complex and interdependent systems that are vulnerable to threats caused by natural and human disasters (Zarghami et al., 2016: 78). At the beginning of the 21st century, the world witnessed great natural and unnatural disasters (Sheikh Darani, 2017: 10). Geographically, Iran is one of the ten most prone and vulnerable countries to natural disasters in the world (Mohammadifar et al., 2020: 178) and it is the sixth earthquake-prone country in the world. It is the cause of the most human casualties and the seismic belt covers 90% of our country's soil (Negaresh, 2014: 93). Natural disasters in the world have always been considered as a big challenge in the way of sustainable development, and as a result, ways to achieve this development through models of vulnerability reduction have become necessary. Therefore, reducing the risk of accidents is of particular importance (Rezaei, 2009: 4). With the passage of time, in many countries of the world, the approach of resilience in the planning hierarchy of crisis management, including the regional scale, has received more attention (Brenner, 2004: 173). However, the reduction of vulnerability and risk is often neglected until after an accident -)Ainuddin & Routray, 2012: 27). Creating a resilient city against natural disasters is done in three stages, before the disaster, response to the disaster during the disaster and after the disaster (Karlinsky& Sarah, 2010: 6(, therefore, analysis and reduction of exposure. Being exposed to natural hazards today has become an important and widespread goal in crisis planning and management (Bazdar et al., 2019: 198) The city of Kermanshah is located between the high and folded Zagros and is surrounded by the northern and southern faults (Negaresh, 2014: 99). If the growth and development of cities takes place without any planning and there is no plan to reduce vulnerability and increase people's abilities to face the risks caused by natural disasters, many cities will be exposed to serious damage from These disasters will be placed. Therefore, it is necessary to plan in order to increase urban resilience (Kalantari et al., 2021: 230). The main goal of this research is to evaluate the social resilience components of the city against earthquakes in Kermanshah metropolis and provide effective solutions in this field. The ranking status of the regions of Kermanshah according to four indicators; What is awareness and knowledge, skill, attitude and social capital?

The present research method is of applied type and in terms of descriptive-survey method. The statistical population is the citizens of Kermanshah city, 385 people were selected as a statistical sample using Cochran's formula. To collect information from a questionnaire containing questions about social resilience indicators; It includes four indicators, awareness and knowledge, skill, attitude and social capital (questionnaire used by Dr. Rezaei 2010) and Cronbach's alpha coefficient was calculated as 0.79, which indicates the good reliability of the questionnaire. In this research, Mabak model was used to analyze the data and ArcGIS software was used to display the output results as a map.

With the passage of time, in many countries of the world, the approach of resilience in the hierarchy of crisis management planning, including the regional scale, has received more attention. The city of Kermanshah due to being located on the path of faults, needs attention in the discussion about resilience. The main goal of this research is to evaluate the components of social resilience of the city against earthquakes in Kermanshah metropolis, and according to the goal, to answer the question of ranking the regions of Kermanshah city according to four indicators; It is awareness and knowledge, skill, attitude and social capital. The current research is of applied type and in terms of descriptive-survey method. The Mabak model was used to analyze the data. In response to the question; The ranking status of the regions of Kermanshah according to four indicators; What is awareness and knowledge, skill, attitude and social capital? The results of the Mabak model in the awareness and knowledge index show that region seven ranks first, region five ranks second, region one ranks third, region eight ranks fourth, region six ranks fifth, region four ranks sixth, region Three are in the seventh place and region two is in the eighth place. In the skill index, it shows that region eight ranks first, region seven ranks second, region one ranks third, regions two and six rank fourth, region three ranks fifth, region five ranks sixth, region four ranks They are ranked seventh. In the attitude index, it shows that the fourth region ranks first, the eighth region ranks second, the seventh region ranks third, region six ranks fourth, region three ranks fifth, region one ranks sixth, region five ranks seventh and Region two is ranked eighth. In the social capital index, it shows that region three ranks first, region six ranks second, region seven ranks third, region two ranks fourth, regions four and eight rank fifth, region five ranks sixth, and region one ranks They are ranked seventh. generally; Natural disasters in the world have always been considered as a big challenge in the way of sustainable development, and as a result, ways to achieve this development through models of vulnerability reduction have become necessary. Therefore, reducing the risk of accidents is of particular importance. If this growth and development of cities takes place without any planning and there is no plan to reduce vulnerability and increase people's abilities to face the risks caused by natural disasters, according to the results of the research, the following solutions It is suggested:- Encouraging residents to participate in earthquake awareness training courses
- Implementation of earthquake crisis management plans at the neighborhood level
- Creation of neighborhood aid groups
- Teaching reactions and how to behave properly during an earthquake
- Teaching safety methods against earthquakes
- Encouraging residents to participate in neighborhood affairs
- Increasing people's knowledge about earthquake risk
- Creating a sense of trust among citizens
- Creating a suitable environment for more interaction of citizens
- Holding crisis management course classes.

Snow-covered (SC) surfaces influence the land surface energy balance through albedo feedback, and also have a major impact on climate processes, human activities, and the hydrological cycle. Land surface temperature is one of the main elements in knowing the climate of a region, whose changes and fluctuations in different altitude classes are very useful for hydrological studies. The purpose of this study is to evaluate and investigate the relationship between ground surface temperature and snow cover level with the topographical component of height in Urmia lake basin. In this research, due to the ease of access to remote sensing data and the appropriate temporal and spatial separation of Terra satellite images, monthly, seasonal and annual MODIS sensor images have been used in the period of 1379-1399. The obtained results show that there is an inverse relationship between LST and SC, also the examination of SC maps and elevation classes shows that there is a direct relationship between these two variables, in fact, with the increase in altitude, the stability of snow in the region increases so that at altitudes higher than 3000 m, the amount of snow cover is more than 98% compared to the region. The changes in the temperature of the earth's surface at different altitudes are the reverse of the changes in the snow cover, so at altitudes less than 2000 meters, the annual average temperature is 21-35 Celsius, but at altitudes higher than 3500 meters, the average temperature is about 7-13 Degree.

The increase in urban population due to their unsustainable development as well as the irregular growth of cities and their unsupervised expansion will increase the exposure to natural hazards (Atrachali, 2016: 2). Natural disasters, which are a part of human life and their number and variety are increasing day by day, pose a great challenge to the sustainable development of human societies (Adger and Hodbod, 2014: 91). Earthquake is a natural phenomenon and cannot be predicted (Kahandawa et al, 2018: 2) and is one of the most catastrophic and destructive types of natural hazards. It has been discussed for a long time, especially in developing countries (‌Dong &Shan, 2013: 85). Earthquakes have affected human life for a long time due to the existence of complex connections in cities (Falahi and Jalali, 2013: 6), the crisis risk index of the United Nations Development Program in 2004 shows that From Armenia, Iran has the highest level of earthquake vulnerability among countries in the world, and 31 out of every 40 types of natural disasters have occurred in Iran (Rezaei et al., 2015: 610). In most parts of Iran, seismic activity is directly related to the activity of faults, and most seismicity has occurred along active faults, and most of the known seismic faults in Iran are along their It is more than 40 km and all of them are accompanied by devastating earthquakes with a magnitude of more than 7 on the Richter scale (Azadikhah, 2009: 193). At the global level, there have been significant changes in the attitude towards risk (Ziyari, 2018: 98). Much emphasis has been placed on resilience instead of vulnerability (Mayunga, 2007: 4). Resilience has been studied around the world in various societies prone to traumatic events, including earthquakes ( Kara ırmak, 2010: 19). There is a possibility of an earthquake, especially when the conditions for its occurrence exist, including the existence of various faults, the presence of several faults in Kermanshah and its surroundings indicates the risk of an earthquake, the inadequate access to some areas of the city, the post-earthquake relief operation faces a problem that if no action is taken to manage the earthquake crisis, this can lead to a human disaster (Kamrizadeh, 2016: 4). Considering that it is not possible to prevent the occurrence of an earthquake or the time of its occurrence cannot be predicted, but it is possible to provide solutions to deal with or reduce its vulnerability, these solutions increase initiatives and ideas. will be done against the city's resilience and resilience against earthquakes, so achieving the resilience and resilience of cities and citizens against the risk of earthquakes is one of the main goals. which should be taken into account in urban management, urban development plans, etc. This research deals with an analysis of the vulnerability of Kermanshah city to the risk of earthquakes with emphasis on the resilience approach.

Based on the purpose, the current research is part of applied research and based on the working method and nature, it is placed in the framework of descriptive-analytical research. The statistical population is the residents of Kermanshah city over 15 years old, and Cochran's formula was used to determine the sample size, and 385 people were selected as a statistical sample. The classification method has also been used to select the sample in the regions. Documentary methods and library studies were used to formulate the theoretical foundations of the research and to collect information from the questionnaire, which was used by Dr. Rezaei and Dr. Eshghi Chahar Borj). Cronbach's alpha coefficient was calculated as 0.80, which indicates the good reliability of the questionnaire. One-sample T-tests, Friedman and Kruskal-Wallis tests were used to analyze the data.

Earthquake is a natural phenomenon and cannot be predicted and is one of the most catastrophic and destructive types of natural hazards. The existence of several faults in Kermanshah and its surroundings indicates the risk of earthquakes. Achieving the resilience and resilience of cities and citizens against the risk of earthquakes is one of the main goals. which should be considered in urban management, urban development plans, etc. This research deals with an analysis of the vulnerability of Kermanshah city against the risk of earthquakes with an emphasis on the resilience approach. 385 people were selected as a statistical sample. In order to collect information, a questionnaire (including three indicators of institutional background, institutional relations and institutional performance) was used. For data analysis, the collected data (Kruskal-Wallis, Friedman and TT sample tests) were used. To investigate the status of institutional indicators and ranking of the eight regions of Kermanshah city by components and whether there is a significant difference in the availability of components or not; Kruskal-Wallis test was used. The results show that there is a significant difference in thirteen components and there is no significant difference in five components. Friedman's test was used to rank the components of the institutional resilience index from the point of view of citizens. According to the results obtained from it; How much is the component index of your adherence to legal guidelines in order to prevent accidents caused by earthquakes during construction? How satisfied are you with the performance of the government and local institutions of the council and municipality in relation to the provision of educational services (brochures, brochures, etc.) regarding the safe use of household and workshop equipment? Also, in order to compare the average state of institutional resilience in Kermanshah city, a sample t-tech test was used, the results show. The two indicators of institutional base and institutional performance have an average higher than average and their significance level is less than 0.05. Also, the index of institutional relations is lower than the average level and its significance level is less than 0.05.

In recent decades, land use change under the influence of environmental and human factors has caused serious effects on the environment, economy and society. Hydrological models are a simplified representation of the actual hydrological system that studies the operation of the basin, so model selection requires recognizing the capabilities and limitations of hydrological models in the basin. In the present study, the analysis of land use changes and its effect on runoff in Qara-Chay basin was performed using HEC-HMS models. In order to investigate land use changes on the impact on basin runoff changes, the 2020 annual rainfall of the basin with 2001 land use characteristics was introduced to the model. The results related to land use changes show that the most changes in land use include changes related to land without vegetation in a decreasing manner and a mixture of irrigated and rainfed agriculture in an increasing manner. In the simulation of Gharachai catchment basin in HEC-HMS model, calibration of the basin in six sub-basins based on runoff peak, runoff height and runoff volume was examined. 94% and in the runoff peak element with 95.12% and in the runoff volume element with 97.5% show the correct implementation of the model on the basin. Therefore, it can be said that land use change in Qarachai catchment has increased the peak and volume of runoff and ultimately has led to an increase in runoff in this basin.

Among the risks facing the plains in Iran is subsidence, which causes many problems in agricultural lands, roads, power, and energy transmission lines. In recent years, Sarab plain has faced a sharp drop in the level of underground water, which has caused this area to be exposed to the risk of subsidence. Therefore, the purpose of this research is to investigate and analyze the most important factors involved in creating the risk of subsidence in Sarab plain and to identify the susceptible surfaces that are likely to be involved in subsidence in the future, using the multi-criteria MARCOS and CODAS algorithms. According to the results of subsidence risk zoning, water depth, land use, and slope, respectively, with weight coefficients of 0.194, 0.171, and 0.159, are the most important factors involved in creating the risk of subsidence in the studied area. The output of the MARCOS method showed that, respectively, 167.50 and 276.09 square kilometers of the area of Sarab Plain, and results of applying the CODAS method, showed that 187.13 and 279.03 square kilometers of the area are in the high-risk and critical category. In addition, the map extracted from the MARCOS and CODAS algorithms with the depth of the water level of the wells, respectively, have correlation coefficient values of 0.77 and 0.81. A correlation can be seen between the output of both methods with the water level map. It seems that the results of this study can be of great help to organizational managers and land and soil resource planners for protecting and managing water resources and natural hazards and preventing land degradation.

One of the dangers facing Iran’s plains is subsidence. Land subsidence due to various reasons, such as over-harvesting of underground water sources and climate changes, has caused many problems in agricultural lands, roads, and power and energy transmission lines. Therefore, it is important to deal with the causes and influencing factors to control the risks. Ardabil plain is also one of the areas prone to subsidence due to climate changes and the decrease of underground water in recent years. The aim of the present research is the zoning subsidence risk in this plain. First, the factors influencing subsidence in the Ardabil plain (slope, land use, lithology, distance from the fault, distance from the waterway, drop in water level, distance from the city and the village) were identified. Then the layers of data were applied in the geographic information system. In the next stage, the weighting of the investigated factors was done using ANP method and in the Super Decision software, and the final analysis and modeling were done using the MABAC method as one of the multi-criteria decision making methods. Finally, the resulting map was classified into five categories from very low risk to very high risk. The results showed that such factors as water level drop, distance from the river and lithology have the highest weighting factor. Moreover, 244.29 and 370.59 square kilometers, respectively, of the area of this plain, are in very dangerous and dangerous classes. Finally, due to the high potential of Ardabil plain in terms of subsidence, protective and management measures should be taken into consideration by relevant authorities and institutions.

Background and Objective:

 Land surface temperature is a vital indicator for studying environmental changes, hydrological conditions and the energy balance of the earth, which can also be used to monitor the temperature changes of cities. The lack of meteorological stations in most parts of the country, including the study area, has created information limitations in the field of surface temperature data. There are also a large number of non-remote sensing users who need LST maps, and most of them are not familiar enough with LST computing software and inevitably have to spend a lot of time mapping to prepare their maps. This process can be time-consuming even for remote sensing professionals if the number of images is high. The use of valid data for validation that has the least time difference with the satellite passes time is very important in estimating the accuracy of the results. By reviewing internal research similar to the one under study, most internal studies used only meteorological station data to validate the results, the data recording time at these stations is different from the satellite passes time. In this study, due to the large area of the study area and the insufficient number of meteorological stations, in addition to the surface temperature data measured in synoptic stations, the land surface temperature in two ground stations was recorded simultaneously with the satellite. Creating a graphical user interface (GUI) to automatically calculate the surface temperature of Ardabil city with two single-channel and RTE algorithms and use the results to evaluate the temperature changes of land uses

Materials and Methods:

 In this study, in order to automatically calculate the land surface temperature of Ardabil city from three types of data: Landsat 5 and 8 satellite images, land surface temperature data recorded at two meteorological stations in the study area and also due to an insufficient number of stations Meteorological data land surface temperature data measured with digital thermometers are also used as the satellite passes. After preparing thermal and multispectral images, first MODTRAN web computing software was used to model the atmospheric transferability and atmospheric coefficients were extracted. Then, to create graphical user interfaces and automatic calculation of LST, land surface temperature with two algorithms single-channel and RTE method with Landsat 5 and Landsat 8 satellite images for two dates: 31/07/2000 and 21/08/2019 in MATLAB software were coded and using these codes, graphical user interfaces were created for each algorithm and finally, an automatic land surface temperature calculator application was produced. Also, the land use map of Ardabil city for both mentioned dates was classified and extracted using a random forest algorithm in the Google Earth engine system environment with 7 classes. This algorithm has a much better performance compared to traditional methods such as maximum likelihood due to its hierarchical structure in selecting each pixel to the appropriate class. To validate surface temperature maps from two types of surface temperature data recorded in two meteorological stations and surface temperature recorded by a digital thermometer that simultaneously passes the satellite in two points of the homogeneous non-urban environment with agricultural use (alfalfa) and Bayer that product It was harvested, used. To evaluate the accuracy of land use maps, using Google Earth, which has a better spatial resolution than the image used, 248 ground control points were obtained from pure pixels of different land uses and used in the validation process. Also, statistical parameters such as error matrix, overall accuracy and kappa coefficient were applied to the output of both land use maps.

Results and Discussion :

Using the codes written in MATLAB software, graphical user interfaces (GUI) were created and then the automatic LST calculator application was produced. The output of the application was surface temperature maps with single channel algorithms and radiation transfer equation (RTE) for 31/07/2000 using thermal image (band 6) of Landsat 5 satellite TM and 21/08 / 2019 was created by the 10 TIRS sensor band of Landsat 8 satellite. After comparing the output maps with the meteorological station and ground station data, the results showed that the single-channel method had the lowest temperature deviations compared to the stations in both years. After preparing LST maps and selecting the optimal algorithm (single channel), land use maps of Ardabil city were prepared using a random forest algorithm in the GEE platform. Statistical evaluations of the classification results showed that for 2000, the highest pixel interference was related to the middle and poor rangeland class, which has a 16-pixel displacement with residential and rainfed agricultural classes. Due to the improved spatial resolution of the Landsat 8 satellite compared to the Landsat 5, followed by better class separation, this pixel displacement in the 2019 user map shows a smaller value. The most common error was related to the aquaculture class, which had a displacement of 10 pixels with rich rangeland and rainfed agriculture classes. Finally, using the LST map and land use map, the temperature changes of the land uses over a period of 19 years were evaluated. By entering the input images and atmospheric parameters in the application, the land surface temperature was calculated with two one-channel algorithms and the RTE method. Evaluation of output maps with meteorological and terrestrial data showed that the single-channel algorithm with a difference of +2.5 and -2 with stations 1 and 2 for the year 2000 and with a temperature difference of +1.3, +0.9, -1 and -0.9 with stations 1, 2, 3 and 4 in 2019, respectively, had higher accuracy than the RTE method. Also, the results of validation of land use maps showed an overall accuracy of 0.95 and a kappa coefficient of 0.94 for 2000 and overall accuracy of 0.96 and a kappa coefficient of 0.95 for 2019.

Conclusion :

Assessing the relationship between land surface temperature and land use maps showed that despite the significant physical growth of the urban sector over a period of 19 years, except for residential areas, all land uses in 2019 compared to 2000 with an increase in average surface temperature. It seems that factors such as the expansion of agricultural lands with irrigated cultivation around the urban area up to a radius of 10 km and the entanglement of these farms with the urban sector have a great impact on the temperature adjustment of the urban sector. In 2000, these lands were mainly under cultivation of rain-fed crops, and by solving the water problem (digging deep wells and water transfer projects), they became orchards and irrigated farms such as potatoes. Due to the high water requirement, these products also have high greenery, and this factor has increased the rate of evapotranspiration, followed by cooling of the cultivation area and the urban sector. Among other classes, in both years of water use, the lowest and the use of barren lands had the highest average surface temperature. The generated application can be run on any operating system that supports the exe format, and the user by specifying atmospheric parameters can automatically estimate the LST. This application can also be used in various sectors such as agricultural systems, and climate and water resources management.

One of the dangers that has occurred in many areas in recent years is the dangers of subsidence. Identifying areas prone to subsidence and estimating its rate plays an important role in managing and controlling this phenomenon. In this study, in order to identify and measure subsidence in Azarshahr plain, Sentinel radar images of 2015 and 2020 were used and in order to process information, SARSCAPE software was used. According to the results, the maximum subsidence rate in 5 years in the region is estimated at 6 cm. The relationship between subsidence and changes in the amount of groundwater level showed that in a period of 5 years, the groundwater level has decreased by 4 meters, which has led to land subsidence in the study area. The highest subsidence rates in the period 2015 to 2020 are in the following categories: rangeland uses with a value of 6 cm, coastal area with a value of 5 cm and garden and residential uses with a value of 4 cm. Also, the use of mountains with the same amount of approximately 3 cm has the lowest amount of subsidence. 4 cm subsidence for residential use can be caused by demolition and construction of large buildings in the long run.

the object-oriented method in preparing the land use map of Darre Rood catchment area using Landsat 5 and Landsat 8 images in a period of 30 years, from 1990 to 2019 and its effects on changes in Darre rood river discharge it placed. The images were classified into fourteen classes and the changes in the area of ​​the classes revealed that the classes of irrigated agriculture, rainfed agriculture, rocky areas, residential areas, gardens and lakes with increased area and barren lands, pastures, forest lands and riverbeds decreased They were. To find out the changes in the river flow trend, SCS method was used which was implemented in SWAT model and according to land use in 1990 and 2019 in SWAT model was determined according to the digital elevation layer of the basin and all the necessary parameters to the model. Which included soil layers and land use changes and climate data were called into the model and two separate scenarios for 1990 and 2019 were used. The results showed that with the change of land use, the amount of CN in the second scenario compared to the first scenario increased by 5% and increased from 02.70 to 5.73, which due to the change in land use in favor of the basin becomes more impermeable to rain. Compared to 1990. Also, due to the increase in the type of vegetation, the amount of deep penetration has decreased from the first scenario to the second scenario from 257.09 to 97.9.

Identification of landforms and the way of their distribution is one of the basic needs in applied geomorphology and other environmental sciences and landform maps show the shapes of the earth's surface. This project aims to identify the landforms of the Qaranqu catchment area using object-oriented classification methods including nearest neighbor algorithm and thresholding. Landsat satellite imagery for 1990 (TM) and 2020 (OLI) was used for this purpose. First, to apply the classification, atmospheric and radiometric corrections were applied to the images, then to better identify and extract the phenomena, principal component analysis (PCA), and MNF algorithms were used to classify satellite images using classification methods. Object-oriented, which included the nearest neighbor method and thresholding was used. For the accuracy of the maps produced using the two methods of Kappa index and the overall accuracy of the use, the results revealed that the nearest neighbor method is more accurate than the thresholding method. The classification results showed that the highest rate of decreasing changes during 1990-2020 is related to dense rangeland because it has decreased by 12.49 percent and the highest rate of incremental changes is related to irrigate agriculture which is 10.83 percent. The most important reason for this increase is the construction of Sahand Dam over time. In the absence of well-organized planning and the adoption of appropriate policies, the destruction of rangeland will continue and turning it into arable land, which leads to irreparable environmental and economic losses in the region.

Dams are one of the most important human structures along rivers that are constructed with the aim of generating electricity, flood control, and providing water for agriculture and urban centers. Today, very few large and small rivers remain uncontrolled; what is important geomorphologically is the changes that occur in the performance of erosion processes downstream of the river after dam construction. These changes are not limited to after dam construction, but these morphological changes are the result of changes in the performance of erosion and deposition processes in drainage basins, completely transforming the face of the basin and canals in the area close to the constructed dam. Analyzing the geomorphological changes of rivers due to the creation of human facilities such as dams is one of the most important tasks of geomorphologists. This study is done with different models and methods. Among the common methods of the last decade are the GCD model and machine learning. The GCD method is the result of subtracting two digital elevation models at different times, which are produced by different methods of these dams. To analyze the geomorphological changes caused by the construction of the dams in downstream of the river, in addition to using historical dams, machine learning methods can be used for more accurate modeling by involving a variety of effective maps in detecting changes. The main purpose of this study is to apply the machine learning method using the data obtained from the GCD model to generate regression maps due to the impact of dam operation downstream of the river.

This study was carried out in the Sojasrood River and downstream of Golaber dam. Software and tools used in this research included the following: Arcgis, envi, sagagis software, R software, GCD software and extension, Google Earth Engine system, AutoCAD software, Excel, topographic maps of 1/50000, Elevation digital model and Garmin GPS. Machine learning methods were used to evaluate the effects of Golaber Dam in the period before and after the construction of the dam. In order to access the data required for this research, digital elevation models of stereo pair images of L1A series and L1B satellite ester were used as time series. First, through the GCD model, the volume changes of erosion and sediment downstream of the dam were calculated. Then, the data obtained from this model were used as a target variable along with nine layers of geomorphometry and precipitation and runoff as predictive data to implement machine learning algorithms in three methods of multiple linear regression, decision tree, and random forest. 70% of the data were used for modeling and 30% of the data were used for evaluation in R programming software.

Given that continuous data on erosion and sedimentation rates from the GCD model have been used for the machine learning method, naturally, a regression method (prediction) should be used for the output of the machine learning models. Three steps were taken to achieve the result of machine learning. First, the models were run one by one in R software and evaluated with 30% of the experimental data, and finally, the model maps and their correlation coefficient and RMSE error were calculated. Comparison of the output results of multiple linear regression models and decision tree and random forest showed differences in statistical data and time series maps before and after dam construction. Therefore, the output maps of the models before and after the construction of the dam were also different from each other. The main reason for this is a significant reduction in the runoff, land-use changes, increased vegetation of the bed and riverside, which has led to changes in the independent variable research data in the period after the construction of the dam. Although statistically in multiple linear regression, the p-value was less than 0.05, the output of maps of this model were associated with a large error. And the model did not predict the rate of erosion and sediment well. In the multiple linear regression model, the correlation coefficient of the map before the construction of the dam was higher than the period after the construction of the dam. CART method was used for decision tree modeling. The map produced by this method with a correlation coefficient above 0.6 showed better performance compared to the multiple linear regression model. The best method for modeling erosion and sedimentation rates in both periods was the random forest method. This model with a correlation coefficient above 0.7 provided the most accurate prediction in this study.

Various methods and models have been proposed to estimate the rate of erosion and sediment in rivers. In recent years, new and more accurate models have been formed, especially in the analysis of the time series of river developments. New methods include the GCD model and machine learning (ML). In this study, in order to observe the changes in erosion and sedimentation rate due to the construction of Golaber Dam in the period before and after its construction, first, the GCD model of volumetric erosion and sedimentation changes was estimated through the model of published errors and multiple time series maps were produced. Then, from the data obtained from this model, along with maps and geomorphometric layers and maximum rainfall and runoff data, in order to more accurately predict the impact of the dam on the riverbed in terms of erosion and sedimentation rates and geomorphological changes of the river, machine learning method was used. The results of modeling showed that dam utilization was strongly effective in erosion and sedimentation of river bed and Random Forest algorithm with a correlation coefficient above 70% and RMSE less than the other two models showed the best prediction for both periods before and after dam construction. The maps produced by the decision tree method also modeled the erosion and sedimentation process in the riverbed in both time series analyses well, but the output of the linear regression model was not accurate enough. For an overview of machine learning algorithms, in addition to evaluating the experimental data of the models themselves, the overall average results of some morphometric indices of the river such as number of meanders, center angle, channel length, and Sinuosity index were also evaluated. This comparison showed the accuracy of modeling decision tree and random forest algorithms applied in the present study.

Background and Objective Land use/cover changes have been considered to be one of the most important parts of global environmental changes. These changes are complex and dynamic in relation to other environmental changes (global warming, drought, erosion, and ecosystem degradation). In this context, the impacts of land use/cover changes on hydrologic processes are one of the most important environmental issues and challenges, so the extent of dependency on agriculture and other water-related activities on streams has become a major concern in watershed management. So, Assessing long-term hydrological impacts of land use/land cover (LULC) change is of critical importance for land use planning and water resource management. For example, Increased runoff due to the conversion of forests to other land covers, especially agriculture, as well as increased runoff and flood discharge resulting from the expansion of urban and residential use has been repeatedly reported by various researchers. The present study was aimed at identifying and determining the quantity and quality of land use changes and their relationship with flow discharge changes in catchments of Binalud county in order to guide water resources management and conservation of natural resources at the catchment scale, considering the evidence of land use changes as well as the hydrological regime variations in the catchments. Materials and Methods The data used in this study were as follows: the average monthly discharge of hydrometric stations, including 3 stations of ZirbandGolestan, Hesar, and SarasyabShandiz that were collected during 1990, 2000, 2010, and 2018, and the Landsat satellite images, including 4 satellite images for the years 1990, 2000, 2010 and 2020, acquired in the spring (May). The monthly discharge values of two seasons, winter and spring, were selected to study hydrological regime changes, considering the low and close to zero values of the average monthly discharge during summer and autumn and very small variance in the relevant values. The data were tested for normality at the significance level of 0.05 before entering the correlation test based on the Smirnov-Kolmograph method. In regard to satellite images, the processing steps were as follows: firstly, the atmospheric correction of the images was performed based on the conventional FLAASH method in the ENVI software environment. Then, the combination of visible green, red, and near-infrared bands in false color (4-3-2 in Landsat 5 and 7; 5-4-3 in Landsat 8) was used for classification based on the maximum likelihood algorithm. The land use classes were as follows: 1-garden, 2-residential, 3-water area, 4-rock outcrop, 5-moderate range, 6-poor range, and 7-barren land. The selection of training samples for classification was based on Google Earth images, visual interpretation of satellite images, and of course familiarity with the study area. After classification, the maps were validated based on general accuracy statistics and the Kappa coefficient. However, in order to know the relationship between land use changes and the hydrological regime of the catchments, Pearson two-way correlation test was used in the SPSS software environment. This test was performed at a significance level of 0.05 and between the percentages of the area of each land use and the monthly discharge values (6 months) of hydrometric stations during 4 year. Results and Discussion Preliminary results showed good accuracy of the classification method of the images so that kappa coefficients ranged from 0.78 to 0.95. According to the maps, it is characterized that most area of the catchments belongs to rangelands and barren lands so the changes and conversions of land use occurred mainly between these two land uses. The minimum area percentage of the catchments belonged to the water areas, which at its highest proportion occupied 0.16% and 0.1% of the area of ZirbandGolestan and SarasyabShandiz catchments, respectively. Reagards to land use changes, a decrease in rangelands and the increase of barren lands during the first (1990-2000) and the third (2010-2020) decades have been very considerable, so that 38% and 13% of the moderate rangelands of the Zirband catchment have decreased during the two decades, respectively. In contrast, barren lands have grown by 31% and 15 % over the two decades. Along with these changes, the 8% increase in the area of settlements has been proposed as the most prominent land use change during the second decade (2000-2010) in the catchments. In addition to land use changes, a review of the monthly discharge variations in the catchments showed that the winter months have been experiencing a decreasing trend and, in contrast, the spring months have been experiencing an increasing trend of discharge over the last two decades. The results of the correlation test showed that there are significant relationships between changes in areas of rock outcrop, moderate range, poor range, and discharge variations in the Zirband catchment. In contrast, no significant relationships were found between land uses and monthly discharges in the Sarasyab catchment. In regard to the quality of relationships, positive correlation between the areas of 3 land uses, including residential, rock outcrop, and barren land, and discharges in April and May, and in contrast, a negative correlation between rangeland areas and discharge of the mentioned months was another important result of the study. In general, the increase in human encroachment and occupation in the form of residential and barren land uses has increased the risks of the occurrence of flooding runoff. On the contrary, the rangeland expansion with its protective and moderating effect has reduced the occurrence of spring floods in the studied catchments. Conclusion The results indicate that an important focus of land use change in the catchments has been on rangeland and barren land, so in the last decades, the area of rangelands, which play an effective role in protecting water and soil resources, has been much larger than today. However, due to the lack of protection of pastures and human encroachment on the environment, as well as overgrazing of livestock, the rangelands have gradually retreated to the upstream areas and were replaced by barren lands and residential areas. The existence of a positive correlation between the areas of the residential, rock outcrop, and barren land and discharges in April and May is indicative of acceleration and intensification of the rainfall-runoff process due to the increase in the areas of the land uses. Therefore, the irregular and sprawling growth and expansion of residential areas, as well as barren and abandoned lands, must be prevented. On the other hand, the negative correlation between the percentage area of rangeland and monthly discharge refers to the positive effect of rangelands on the environmental conditions of the catchments in the context of accelerated runoff and erosion processes, which ultimately requires the protection and preservation of natural areas. In general, more attention and focus on the effects of land use change on discharge variations in wet seasons due to the semi-arid climate of the region is necessary.