فصلنامه پژوهش های ژئومورفولوژی کمی
سال سیزدهم شماره 3 (پیاپی 51، زمستان 1403)

Today, soil erosion is considered as one of the important issues of watershed management at the national and global level. Considering that the calculation of erosion and sediment values through hydrometric and sediment measurement stations and direct measurements in different parts of the basins is a costly and time-consuming process.Therefore, finding experimental methods to accurately estimate the amount of erosion and sedimentation of watersheds seems necessary and inevitable.Erosion is a process in which soil particles are separated from their substrate by erosive agents and transported to another place with the help of one of the transfer agents.If the particle separator is wind and refrigerator. It is called wind erosion and glacial erosion respectively.Water erosion also occurs due to improper land management, destruction of vegetation and lack of water flow control, and causes surface runoff and soil transfer.

Zimkan river basin is located in the north of Dalaho city and west of Kermanshah province. This basin is limited from the south and east to the Zamkan Dam basin,from the west to the Piran basin and from the north to the Posht Teng basin and the Lima river basin. The basin is located at the geographical coordinates of 46°4΄ to 46°11 ΄ east longitude and 34°35 ΄ to 34°22΄ north latitude. Among the residential areas in the basin, we can mention the villages of Ghoshchi Bashi, Asiyab Tanureh, Deh Kohene, Seyed Baqer and Reza Ali Farm. The studied basin has an area of 2324 square kilometers and an average height of 2044.42 meters.In general, there are two methods for measuring erosion and sedimentation; Direct and indirect. Direct methods are carried out using various measuring tools and devices. In these methods, erosion and sedimentation are usually measured in stages and in different ways and their amounts are presented quantitatively.In this research, two EPM and RUSLE models are compared in order to estimate erosion and deposition, and in this regard, the factors of the two methods are investigated.The four factors of the EPM model include:1. Basin erosion2. Land use3. Sensitivity of soil and rock to erosion4. The average slope of the basinAnd also the inputs of RUSLE model include:1. Rainfall erosion factor (R)2. Soil erodibility factor (K)3. Slope length factor (L)4. Slope factor (S)5. Plant cover agent (C)6. Soil protection agent (P)These factors are evaluated. And the soil erosion maps obtained from two models are obtained from the results of the models.

In this research, EPM and RUSLE models and RS and GIS techniques were used to determine the erosion intensity and sedimentation potential of the studied basin. And according to the results of these two models, effective factors in erosion were evaluated and scored, and according to the relationship between sedimentation rate and sedimentation rate, the sedimentation potential of the basin was determined. Also, the amount of erosion and its severity were evaluated and its digital map was prepared and drawn with the help of GIS.

According to the expected results of the statistical tests by SPSS and GIS software, finally the RUSLE model was chosen as a more reliable model than the EPM model to estimate the amount of erosion and sedimentation in the researched basin, and the EPM model was chosen as the shadow and secondary model. It will be determined after the RUSLE model. Therefore, the aim of this research is to introduce the RUSLE model in the estimation of erosion and sedimentation and finally to provide an optimal and compatible model for the Zimkan river basin, which is located in Dalaho city, Kermanshah province.

Land subsidence is a global geological hazard resulting from human activities and natural factors, requiring thorough investigation in many countries, including Iran, the United States, the United Kingdom, Australia, China, Egypt, France, Germany, India, Italy, Japan, Mexico, Poland, Saudi Arabia, Sweden, and the Netherlands. Iran is one of the most hazardous regions globally, experiencing numerous hazards each year. One of the risks that occurs due to climate change and population growth in plains is land subsidence. Reports indicate that 50% of Iran's plains (300 plains) are at risk of subsidence, causing significant problems for agricultural, residential, and transportation areas. While the consequences of land subsidence may not be as apparent as earthquakes or floods, its long-term and heightened impacts are more significant.In recent years, attention has been given to land subsidence in Iran, with numerous studies published on this topic. A preliminary review of the conducted research reveals their dispersion and lack of consistency. The importance of land subsidence and the abundance of scattered studies in this field prompted the authors to provide a systematic review of these studies. The systematic review approach aims to organize and integrate research findings from various studies in a specific domain to present new insights. Therefore, the main objective of this study is to comprehensively analyze the status of land subsidence in Iran using a systematic review approach. The systematic review approach is a research method that explores and extensively analyzes available resources in the studied field, providing reliable results.

The aim of the present study is to provide a comprehensive analysis of the status of land subsidence in Iran. To achieve this objective, a systematic review was conducted to assess the state of land subsidence in Iran. Systematic review is an essential tool for presenting evidence in a rigorous and reliable manner, making it suitable for gaining a comprehensive understanding of land subsidence. The literature for this study was selected based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. PRISMA is a validated method for guiding systematic reviews of academic literature.

The rate of land subsidence varies in different provinces and time periods. For example, in Azarshahr, it is 2.1 centimeters per year, in Marand plain, it is 2 centimeters per year, in Salmas plain, it is 11 centimeters in 2019, in Ardabil plain, it is 4.4 centimeters per year, in Meshkin Shahr plain, it is 9.35 centimeters per year, in Khorramdarreh, it is 4 centimeters per year, in Abhar, it is 4.3 centimeters per year, in Qorveh plain, the average is 11 centimeters per year, and in Mahidasht, it is 3 centimeters per year.The systematic review of research findings indicates that the subsidence rate in Tehran ranges from 3 to 43 centimeters. Additionally, subsidence rates of 11 to 27 centimeters have been reported in Shahriar and 20 centimeters in Varamin. In Ghorveh, the subsidence rate is 6.28 centimeters per year, in Dindarlu, it is 30 centimeters per year, in Marvdasht, it is 5.2 centimeters per year, in Noorabad, it is 4 centimeters per year, in Jiroft, it is 12 centimeters per year, in Kerman, it is 6 centimeters, and in Minab, it is 13 centimeters per year. In Mahyar plain, the subsidence rate ranges from a minimum of 4.6 to a maximum of 2.8 centimeters per year, in Najafabad, it is 7.7 centimeters per year, in Abarkuh, the range is from 5.5 to 12 centimeters per year, and in Dezful, it is 9.5 centimeters peryear. In Jooyin plain, the subsidence rate is 4.6 centimeters per year, in Sabzevar, it is 2 centimeters per year, in Mashhad, the range is from 14 to 23 centimeters per year, in Neyshabur, it is 10 centimeters per year, in Gorgan, it is approximately 5 centimeters per year, in Semnan, it ranges from 10 to 13 centimeters per year, and in Eyvanki, it is 11 centimeters per year. In Hashtgerd plain, the subsidence rate is 7.4 centimeters per year, in Qazvin, it is 3 centimeters per year, in Qom, it is 3 centimeters per year, in Aliabad, it is 16 centimeters per year, and in Shazand, it is 6 centimeters per year.

The continuation and stability of the river catchments depends on the passage of water and the transfer of sediment. Just as river flow information is vital for many practical applications such as water allocation, long-term planning, catchment management operations, flood forecasting, design of hydraulic structures, etc., amount of sediment yield in rivers is important from a variety of aspects such as river morphology, engineering designs to river water resource, reservoir dams, river organization to Inhibition of erosion and floods, irrigation networks, etc. are important. Proper and sustainable use of water and soil resources within the river catchments requires awareness of the spatial variations of hydrogeomorphic elements (water discharge, sediment discharge) and the determinants of these variations. Various studies refer to the key role of forests in the water cycle, soil protection and habitat protection, and show that increasing or decreasing forest cover has had an effective role in the hydrogeomorphic regime of the rivers. Due to the importance of interdisciplinary studies and understanding of interactions between different biological and non-biological components of river catchments in regards to sustainability and continuity of natural and human environment, the present study intends to be aware of the quality and quantity of relationships between forest cover and discharge/sediment yield in the Talesh catchments, NW. Iran.

This study was based on correlation and regression analysis of the ecohydrological relationships. The research statistical population included the Talesh region and the research sample population included 12 important catchments in this region. The data used included monthly water discharge and sediment yield data of hydrometric stations on the one hand and the digital satellite images included the Aster digital elevation model (DEM) and Landsat 8 image on the other hand. Data analysis tools include geographical information system (GIS), Google Earth, SPSS and Excel. The steps of conducting the research were such that: firstly, the hydrogeomorphic variables including the mean, maximum and minimum rates of water discharge and sediment yield (S.Y), were calculated for 2020yr. Then, the 12 watersheds were extracted based on the position of the hydrometric stations from the DEM with a spatial resolution of 30 meters. The next step was to extract the forest cover from the Landsat 8 image, which was obtained on June 3, 2022. In this regard, the satellite image classification in the form of two class of forest and non- forest was through the maximum likelihood algorithm. Satellite images were classified using 300 training points. Finally, the correlation test between the forest cover and water discharge and sediment yield was performed in the SPSS software. The significant level of correlation relationships was ≤0.05.

The results of the correlation test showed that there was a significant relationship between the percentage of forest cover and the water discharge in the catchments. The correlation coefficients (R) were -0.58, 0.58, -0.46, respectively for mean, maximum and minimum discharges, indicating that definite and positive role of the forest in preventing rapid and erosive runoff. In contrast, the relationship between the percentage of forest cover and S.Y in the studied catchments was not significant and the correlation coefficients for mean, maximum and minimum S.Y are at -0.07, -0.05, -0.06 respectively, which is indicative of the weak correlational relationship between the two variables. However, the separation of catchments as large catchments and small led to improvement of correlation relationship between forest cover and S.Y, despite being insignificant, so that correlation coefficients in small basins for mean, maximum and minimal S.Y, respectively of -0.656, -0.60, 0.339, respectively. Given the significant relationships, it was possible to provide regression prediction equations for monthly mean and maximum discharge based on the percentage forest cover in the catchments. The insignificant relationships between forest cover and the sediment load can be explained by complex nature of the sediment load transfer, especially storage and re-movement of sediment load, making it difficult to establish significant relationships between the sediment yield (S.Y) and the environmental variables affecting it. On the other hand, the relationship between vegetation and the S.Y in catchments is usually complex and nonlinear, and therefore the use of nonlinear relationships may make the relationship more complete and obvious. Finally, the multiplication of the factors involved in the production and transferring of sediment load makes it difficult to model and predict the spatial variations of sediment load.

The present study attempted to provide a quantitative analysis of the spatial relationship between the forest cover and the rates of water discharge and sediment yield (S.Y) in the Talesh catchments by adopting an interdisciplinary approach. The results of correlation analysis show that the presence of significant and inverse relationships between the percentage of forest cover and water discharge of the catchments reflects the distinct role of the forest in reducing runoff and the incidence of dangerous floods in the Talesh catchments. In contrast, the relationships between forest cover and S.Y in the studied catchments are not significant. This conclusion shows that we are not able to provide a prediction model of S.Y variations based on the percentage of forest cover in the catchments, but the reverse relationship between the percentage of forest cover and the S.Y of the catchments implies the protective role of the forest in adjusting the process of precipitation, runoff- erosion and enhancing of the environmental quality in catchment systems. First of all, it should be noted that the forest cover variable is considered as a modulator variable or interface between the inlets and outputs of the catchments system, so it is likely that the direct role of the forest on runoff and sedimentation couldn’t be easily grasped by statistics analysis. In this context, there are various natural and human factors that may play a more effective role in the S.Y variation of the catchments than the factor of forest cover. Increasing of correlation coefficients (R) between the percentage of forest cover and S.Y in small catchments indicate that these catchments are an ideal scale for identifying controlling factors of sediment yield.

Avalanche is the fast and downward movement of large masses of snow and avalanches can endanger human lives and cause huge financial losses. Therefore, zoning and evaluating areas prone to avalanches is one of the necessities of environmental planning to prevent crises and The reduction of human and financial losses in different regions. The connection road between Khalkhal and Shahrood sector has attracted special attention due to the development of the communication network in the region and its economic, tourism, and transit importance. This route is located in a mountainous area with special geomorphic and geological conditions, and the occurrence of avalanches in this area has caused many human and financial losses every year. For this reason, the evaluation and zoning of avalanche-prone areas on the Khalkhal Road to the Shahrood section is very important.One of the most basic stages of conducting any research is collecting data and information. In this research, various data and information have been used, including a geological map of Ardabil province with a scale of 1.250000, from which the information about faults was extracted, topographic map of Khalkhal. With a scale of 1/20000, the road map is extracted from this map, the field data of the avalanche points has been collected through the field survey and GPS device, and the important remote sensing data and information include vegetation, land use, elevation map, slope map, slope direction map It was calculated from the images of Sentinel 2 and ALOS-PALSAR satellites. This research is based on field, analytical, and statistical works. Arc GIS, SPSS Modeler, and ENVI software were used to prepare the layers and implement the research model.According to field studies and analysis of satellite images to identify factors affecting avalanches on the road from Khalkhal to Shahrood, 8 layers in order: 1- Height 2- Vegetation 3- Slope direction 4- Distance from the fault 5- Distance from the road 6- Snow area 7- Land use 8- Slope was used with the standard raster format and all the layers were collected together after production and a single raster image was created. After finishing the classification process, modeling was done in SPSS Modeler software with 8 input neurons, 8 intermediate neurons and 1 output, and 70% of the data were allocated for model training and 30% of the data for model testing. The type of algorithm of SVM and MLP model is after error propagation. At the same time, this algorithm is very efficient and makes the model learn as well as possible, and the model process is completed when the minimum amount of error is reached.The results show that in the support vector machine model, the highest weight value for the snow layer is 0.26 and for the slope layer and the distance from the road is 0.18 and 0.15, respectively, which indicates that avalanches and hazards It is more dependent on these variables. Also, in the multi-layer perceptron model, the highest weight value was assigned to the snow area factor with a value of 0.20, followed by the layers of the distance from the road, both values of 0.17 and 0.13.Also the area of risk classes shows that in the support vector machine model, the most class in terms of risk is 36.34 square kilometers and in the multi-layer perceptron model it is 45.83 square kilometers, which shows that the multi-layer perceptron model is better than the volume vector machine model. A large part of the area is classified as high-risk class. Also, in the support vector machine model, the value of 61.57 square kilometers for the high-risk class and 49.32 square kilometers for the multi-layer perceptron model is classified, which shows that there is a big difference between the area values of the models that the support vector machine model had a realistic performance and was able to correctly identify the areas where there was an avalanche process and the risks arising from it, while the multilayer perceptron model did not have a good performance in the high-risk section. In the medium risk section, the results of the gear mark area values are that the support vector machine model with an area of 23.93 and the multilayer perceptron model with a value of 25.83 square kilometers are classified and there is not much difference between the values of the areas, but in the low risk category, the multilayer perceptron model with The area of 15.73 square kilometers is slightly different from the area of the support vector machine model with an area of 14.87 square kilometers.According to the results of two models regarding avalanche risk zoning in Khalkhal axis to Shahroud, also according to the mechanism of avalanche and the risks arising from it, it has been found out by field studies that the morphology of the slopes overlooking the road, the slopes of the central part in the area of Eskistan village due to the angle A slope of 20 to 35% and too close to the sacred road with snow and its accumulation during the winter seasons with the least mobility causes avalanches. The occurrence of avalanches in the region is in the form of slides and masses and does not have a powder state, because the length of the range and also the road factor have a great impact on the occurrence of this avalanche model. The cause of this provocation is known to be the avalanche. The results of the support vector machine model with relatively few deviations from the multi-layer perceptron model have high reliability and have been able to identify dangerous areas well. . Also, the overlap of the identified areas with real points is high in both models, but the results related to the identification of avalanche-prone areas in the support vector machine model have better performance than the multilayer perceptron and can identify the areas realistically and carefully. Therefore, both of these models can be used to identify areas prone to avalanches.

In recent years, due to heavy rainfall affected by climate change, global warming, and human activities, the occurrence of landslides and the reactivation of old landslides have increased, and every year in many countries of the world, landslides cause great damage to human structures such as buildings, roads, power lines, etc.There is no systematic information on the age, type, frequency, and distribution of landslides in the world, so the lack of this knowledge will have negative consequences. Therefore, preparing an inventory map of landslides, assessing the risk and hazard of landslides for areas with a high concentration of landslides is important for predicting and preventing future hazards.One of the applications of landslide inventory maps is in landslide hazard and risk studies. Landslide hazard and risk assessment is a complex operation that requires the combination of different geomorphological and geological techniquesThis research, by combining historical geomorphological data, remote sensing, and field studies with the proposed method of Cardinali et al. (2002), studies the hazard and risk of landslides in the Kalpush catchment and predicts the vulnerability of elements in order to plan and reduce future damage.

The Kalpush Catchment is located in the north of Semnan province and adjacent to Golestan province. This Catchment is located in the east of the forested heights of the cities of Galikash and Minoodasht in Golestan province.The research method used in this applied research and based on a systems approach using library, field survey and remote sensing methods.First, an inventory map of landslides was prepared from historical images and field survey. Then, the relative age of the landslide event, geometric and morphological characteristics, type of landslide, etc. were calculated. After collecting the above documents, the landslide hazard and risk assessment was carried out in 5 stages using the geomorphological method proposed by Cardinali et al. (2002).

109 landslides with an approximate area of 11 square kilometers were identified, which constitute 10 percent of the total basin. These landslides are concentrated in the south and southwest of the Kalpush basin. The landslides were divided into 4 classes based on the relative time of occurrence (54-year period), image dates, rainfall events of 2018-2019, and the decrease in the water level of the Kalpush dam lake in 2021: before 1968, 1968-2019, 2019-2021, and 2021-2022. 66 percent of the landslidesin2019-2021 and 55 percent of the landslides in 1968-2019 occurred on landslides older than 1968. All new landslides in 2021-2022 were also formed on the shores of the dam lake.According to the type of landslides in the Kalpush Catchment and based on the classification of Cruden and Varnes (1996), 98 percent of the landslides (107 cases), which include creep, rotational, translational, and lateral spreading landslides, have slow movement, and only 2 debris flows in the group of landslides in 2019-2021 have rapid movement. Therefore, almost all landslides in the Kalpush catchment have occurred with slow speed and movement from the past to the present.Deep landslides older than 1968, which were probably affected by different geomorphological, climatic, and earthquake conditions at the time of occurrence, had high intensity and relatively low frequency of occurrence at the time of formation and formed 8 landslide hazard zones. These zones have been reduced to 5 zones for deep landslides in 1968-2019 and 2019-2021, and the reactivation of landslides has less intensity and more frequency, so that they showed a variable landslide hazard and the landslide of Hossein Abad village recorded the maximum landslide hazard.The elements at risk of Kalpush and the location of landslides, the villages of Hossein Abad, Gushhe Degarman, and Korang, due to their high population and density and the roads leading to them, have a higher potential vulnerability hazard (V).The results of the landslide risk zoning map show that there is no area with very high landslide risk in the Kalpush basin due to the absence of rapid landslides (collapse, etc.). Also, Hossein Abad village is completely located in the high landslide risk zone. High landslide risk (R3) refers to areas with slow landslides with high intensity and frequency, high probability of structural and functional damage to structures and infrastructure, and less risk of death. Part of Karang village is located in the medium landslide risk zone (R2). Medium landslide risk (R2) refers to areas with slow or rapid landslides with mild intensity and superficial vulnerability. The results of the above discussions show that in the Kalpush basin, 9 landslide hazard zones (LHZ) with an area of 17 square kilometers have been identified in a concentrated manner in the south and west of the basin

The proposed geomorphological method is a specialized, accurate, and efficient method that has different responses in different locations. This method is based on the geomorphological observations of experts and the preparation of a multi-temporal landslide inventory map. If the older landslides, their morphological pattern in the region, and aerial photographs are evident, this method can be used. Also, this method is reliable and cost-effective for different scales of watershed, provincial, city, and village. As in this research, it provided the correct answer for the Kalpush basin and its villages.Therefore, it is suggested that given the complete presence of Hossein Abad village in the high landslide risk zone, the village be completely relocated to reduce the probability of vulnerability of elements at risk of landslides in the future. Also, this method is introduced and proposed as a reliable and compatible method for geomorphologists, engineers, and crisis management in the Alborz and Zagros slopes of Iran.

In order to use the successfully hydrological model must model parameters be determined carefully. The lack of physical data specific and as well as the measurement field that are very costly. Measuring all values of model parameters is not possible. The estimation parameters are usually output by model and observational data in a process of trial and error done. On the other hand the successful application of hydrological models depends on the accuracy of the calibration model. So before you apply the results to make different decisions, should be carefully calibrated to increase the reliability model. In this study IHACRES model is used to study rainfall - runoff process in Gharasoo Basin located in Kermanshah. IHACRES model has 3 input variables: Daily time series data of rainfall, stream flow and temperature. Model with daily data flow (2000-1981) was calibrated and then during the period (2010-2001) were about validation Although the model could not simulate the maximum discharges suitably, but in total with regard to low deviations and suitable simulation of the minimum discharges and based on two indices (R2=0.66) in the calibration and (R2=0.61) in evaluation and (CE=0.008) in the calibration and (CE=0.029) in evaluation, it can be said that the model performance in the studied basin was reasonable.The average amount of predicted daily effective precipitation was estimated at 0.36 mm in the calibration stage of the model. In general, the amount of effective daily precipitation in this stage fluctuated between 0 and 12.31 mm, which is in close agreement with the natural conditions of the region. The average amount of effective daily precipitation is 0.23 mm in the validation phase. The results of the statistical evaluation related to the stages of calibration and validation of the IHACRES model are specified .The parameter tq (the time constant of rapid flow response reduction) indicates the time it takes for the rapid flow to decrease, the higher the value of this parameter, the later the basin responds to the flow and the later the rapid flow decreases, this parameter is for the basin in question. The study was estimated to be 12.43 days, since the parameter (the time constant of slow current response reduction) was estimated to be 25.83 days, so it can be concluded that slow current responds to fast current in a shorter time and more time is needed than slow current. The watershed should be reduced It is obvious that the parameters obtained by this model should be determined separately based on the conditions of different regions and based on the recalibration of the model. In general, according to the obtained results, it can be said that the model simulates low watershed flows well, but it has little ability in simulating maximum flows and simulates smaller values. But in general, considering the low deviations of the model and the good simulation of the values, at least it can be said that the performance of the model in the studied basin is satisfactory, and it can be said that this model is easy to use, has more limited inputs, and reduces time spent according to the level. Its accuracy shown in this study was used in various fields such as evaluation and estimation of hydrological effects, runoff forecast for future periods.The actual values of observed flow volume in the time period of 1981-2020 is studied. There are a limited number of evapotranspiration and rain gauge stations in the Qara-Su watershed. In this research, the basic data used includes the observational data of temperature, precipitation and runoff in the period of 1981 to 2020 from selected stations in the region. For the temperature variable, the daily data of Kermanshah synoptic station was chosen as the basis. According to the difference between the figure of this station and the average figure of the Qarasu basin, using the temperature and altitude gradient, the average temperature data of the basin was calculated. For the rainfall variable, after completing the daily data for 7 existing stations, the average daily rainfall of the basin was obtained by considering the amount of each station compared to the average amount of the basinAlso, the amount of effective daily precipitation for all days has been determined based on the parameters estimated by the model. The values of drought severity parameters, temperature adjustment factor, storage moisture, time constant of rapid flow response reduction, soil moisture index threshold and slow flow volume ratio, related to the calibration stage.These parameters were calculated for the studied area as 4.9 days, 2.6 degrees Celsius, 0.13 mm, 12.43 days, 0.11 and 0.9 respectively.

Soil erosion is a spatiotemporal phenomenon influenced by variable processes, necessitating multiple observations over time and space. These measurements inherently contain a level of uncertainty and are costly and time-consuming. With the advancement of spatial technologies, Geographic Information Systems (GIS), interpolation methods, and the increasing range of environmental data and remote sensing, soil erosion models play a crucial role in designing and implementing soil conservation management and strategies. The most significant empirical models for estimating annual soil erosion losses include the USLE and its modified version, RUSLE, widely used globally for soil erosion estimation. These models consider factors such as raindrop erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, with most parameters now readily obtainable through high-quality remote sensing data.

The study area, the Alashtar watershed plain, spans 80305 hectares in the northern part of Lorestan province and Selseleh county. Geomorphologically, it lies within the elevated or folded Zagros unit, characterized by thrust faulting and multiple fractures. The watershed predominantly features a mountainous landscape, with hills and mountains covering 65.39% of the area. The minimum elevation is 1500 meters, the maximum is 3600 meters, and the average elevation is 2100 meters. According to the De Martonne method, the watershed has a Mediterranean climate with an average annual rainfall of 506 millimeters.The study area's aquifer is free-flowing, with all wells situated in the alluvial aquifer. In the mountainous part of the Alashtar watershed, carbonate formations, fracture systems, and weathering in the form of snow have developed karstic aquifers, which are the source of the Kehman River. The Kehman River, after originating from the southern heights of Green and hydrating the Alashtar plain, joins the Simreh and then the Karkheh rivers. The Alashtar plain, at the center of the watershed, is a graben surrounded by the Green, Varkhash, Mahab, Sarakheh, Darikanan, and Nashate heights, with geological formations dating back to the Mesozoic and Cenozoic eras. The predominant Jurassic-Cretaceous limestone rocks cover a significant portion of the Alashtar watershed, serving as the primary recharge units (karstic water sources). The soils of Alashtar belong to the brown soil group with a clay concentration horizon, characterized by very deep, dark brown to reddish-brown soils with a heavy texture, containing 3-15% coarse gravel in the surface layers and relatively high amounts of hard limestone grains in the sublayers.The LAMPT model is basically based on the global soil erosion equation, which calculates the net rate of soil erosion. The environmental parameters of the model, including climatic data, land cover and geomorphology, were obtained from meteorological departments, natural resources and Sentinel-2 sensor satellite images during the target year (2023). To complete the data, field surveys were conducted and training data samples were also selected to prepare the land use map. The basis of LAMPT model is RUSLE model, in this model Sediment delivery ratio index (SDR) is used to show the spatial patterns of distribution and performance of pure soil sediments at the basin level. This model integrates the general characteristics of the basin landscape (land use classes, landform parameters, soil types, land cover and management) and precipitation values to simulate the gross soil erosion rate, SDR and net sediment yield at the basin level.

The findings indicate that the average annual soil loss across the watershed is 9.42 tons per hectare, which is consistent with the sediment yield measured at the Sarab-e-Seyyed Ali station (watershed outlet) with an average annual rate of 10.1 tons. The model demonstrates adequate precision in estimating soil erosion and resulting sedimentation. An assessment of soil erosion losses across different land use classes, derived from Sentinel-2 satellite images, shows that 60% of the Alashtar watershed area comprises pastures. The soil erosion rates in pastures with weak, moderate, and dense canopy cover are 17.7, 11.3, and 9.1 tons per hectare per year, respectively. Given the extent of pastures in the Alashtar watershed and the high erosion rates, particularly in those with weak canopy cover, land use planning and optimal utilization of pastures are essential to reduce soil erosion rates in this area.

Conclussion:

In this research, the LAMPT model based on climate, land cover and geomorphological data was used to estimate the annual net rate of soil erosion in the Alashtar watershed. Estimating the net rate of soil erosion during 1402 using this model showed that the average annual soil loss at the basin level is 9.42 tons per hectare per year. The classification of the soil erosion map shows that 35% of the basin has an annual soil loss of more than 10 tons. In agricultural uses, rainfed fields have the highest rate of soil erosion during the year with the amount of 7.5 tons per hectare during the year. Considering the area of pastures in the Elashtar catchment area, the high rate of soil erosion in them, especially pastures with weak canopy, planning land use and optimal use of pastures is necessary to reduce the rate of soil erosion in this basin. The evaluation of the accuracy of the LAMPT model in comparison with the annual average sedimentation rate at the sediment measuring station of Sarab Said Ali (outlet of the basin) showed that the LAMPT model has a high accuracy in estimating the soil erosion and the sediment caused by it, and its difference with the ground data is small, so the use of This model is recommended to calculate soil erosion and its annual losses. According to the findings of the research, it is suggested that the northern and northeastern sub-basins of the basin should be prioritized for protection and watershed management measures. In addition to this, pastures with weak crown cover should be given special attention in terms of land use for watershed management and pasture management.

The environmental consequences of land subsidence are destructive, costly and irreparable, and include creating cracks on the surface of the earth, damaging human structures such as building foundations, streets, bridges, roads, and power transmission lines. Sewage is destruction of irrigation systems and fertile agricultural soils and damage to ancient sites. Remote sensing methods, unlike mapping data and topographical maps, which are in physical contact with terrestrial phenomena, are without the slightest interference on terrestrial phenomena, and measuring and evaluating changes in phenomena are evaluated from a distance. Short receiving time and high spatial accuracy of radar images have made it used as a general and widely used tool to estimate land subsidence. According to the statistics announced in the country of Iran, the adverse effects caused by land subsidence are not a low number and are rapidly developing and spreading in different regions of the country. Leave irreparable damage. Ardabil plain, with its rich underground water resources and good soil, has always been of interest in the last half century and has been a suitable place for providing drinking water and agriculture. With the boom of agriculture from the 1960s onwards and as a result the excessive harvest from the aforementioned table since 1363, the aforementioned source began to decline and the continuation of this situation in the following years caused this plain to become more critical.

In order to carry out this research, the data of 22 piezometric wells in the Ardabil plain have been used. The time period used in this research is a 7-year period from 1395 to 1402. The method used for the data of this section is the BRF method, as one of the methods of radial functions, which is used due to its low error value and high accuracy. SAR images of the European Space Agency's Sentinel 1 satellite in SLC format and with vv polarization have been used to find out the changes in land subsidence in the Ardabil plain. The images used by the Sentinel 1 satellite (in c-band with a wavelength of 5.6 cm) are in the group of sensors with medium resolution in terms of spatial resolution. The radar interferometry method provides the possibility of producing a digital model of the ground height, whose optimal height accuracy for c-band data with a wavelength of 5.6 cm is about 5 meters. This method is able to reveal surface changes in the ground in different intervals with millimeter accuracy by using at least 2 or more radar images. In this method, an artificial interferogram is produced with the help of digital elevation model of the earth and conversion of height into phase, and in this way, with the help of reverse DEM data, the phase effect caused by topography is calculated and removed from the phase difference values. The remaining phase difference belongs to the effect of surface displacement and atmosphere.

The results of the investigation of piezometric wells in the area of Ardabil Plain show that the maximum drop in the underground water level is 48.77 meters in the southeast of Ardabil Plain and the lowest drop in the underground water level is 1.57 meters in the north. Eastern Ardabil plain was calculated. The amount of fluctuations in the water level of piezometric wells shows that the highest amount of fluctuations was in the area of Pirqavam wells, Arallovi Bozor and Khalil Abad lands. The lowest fluctuation was also observed in the area of Agchechai wells, Nojedeh. In the studied time period, the water level of Khalilabad piezometric well in 2015 was 2.96 meters, while in 1402, the water level in this area reached 46.3 meters. During 7 years, the water level has dropped by 43 meters, which indicates a critical situation in this sector. The lowest fluctuation of the water level is also for the Aghche-chai well. The land subsidence map of Ardabil plain shows that: the south-eastern parts of Ardabil city and also to some extent in the southern part have suffered ground subsidence due to the extraction of underground water. In the next order, the western parts of Ardabil city are prone to land subsidence. Based on this map, the maximum amount of ground subsidence has been calculated at around 598 mm in the area of Khalil Abad well. In the area of Khalil Abad well, the situation of underground water level drop is very critical and it has dropped by 43 meters during 7 years from 1395 to 1402. The overlapping results of the underground water level and co-depth curves with the results of radar interferometry show the accuracy of the findings in this section. Overlapping the location of the historical sites with the land subsidence map shows that, first of all, Tapraqlu hill (first millennium BC) is in the area of land subsidence with a rate of 250 mm.

The results of the application of this method revealed a very high level of land subsidence for the Ardabil plain (598 mm during a 7-year period). The southeastern areas of Ardabil plain have found a critical situation in recent years due to unprincipled exploitation and lack of proper management. Overlapping the location of the historical sites with the land subsidence map showed that Taparaglu hill with a rate of 250 mm and Ozrik Tepe-si with a rate of 69 mm are in the area of land subsidence, respectively. The three historic sites of Qala-Bovini, Niar Tepesi and Tezre Tepesi are also located in the area prone to land subsidence with rates of 27 mm, 46 mm and 49 mm, respectively. The cause of land subsidence in the Ardabil plain, according to the studies conducted on the changes in the underground water level, is the excessive exploitation of the underground water resources for the cultivation of agricultural products and providing the possibility of compaction of the underlying layers. In general, it can be said that the research results indicate the involvement of historical sites in the area of land subsidence.

Earthquake is one of the inevitable natural disasters that, if it occurs, causes countless damages and problems for the economy, environment and human life. Therefore, earthquake crisis management is essential. Experience has shown that preventing the occurrence of a crisis is better than organizing it after it occurs. Therefore, the phases before the crisis in the crisis management cycle are of particular importance.Iran's strategic geographical location positions it susceptible to substantial seismic activity, establishing its standing as one of the most earthquake-prone nations globally. The convergence of the Alborz and Zagros mountain ranges has increased this susceptibility, notably due to the interaction of the Saudi plate with the Zagros range, thereby causing seismic hazards for various urban centers located in close proximity to fault lines and mountainous regions. Identifying areas prone to these environmental hazards is fundamental as a primary measure in implementing effective risk mitigation strategies, urban planning initiatives, and construction endeavors. Therefore, the present investigation was conducted to evaluate and map the seismic vulnerability of Hormozgan province, with the objective of pinpointing and categorizing high-risk zones within the province.

Hormozgan Province is situated in the southern region of Iran, positioned to the north of the Strait of Hormuz. Geospatially, the province spans from approximately 25 degrees and 25 minutes to 27 degrees and 18 minutes north latitude, and 52 degrees and 39 minutes to 59 degrees and 14 minutes east longitude from the Prime Meridian. With a land area of around 71,193 square kilometers, Hormozgan Province encompasses about 3.4% of Iran's total land area and comprises 13 cities, 39 districts, 88 villages, and 50 townships.This study utilized ArcMap software to analyze earthquake data from the past 20 years. They created maps for factors like earthquake magnitude, depth, and proximity to faults. Additionally, density maps for both faults and historical earthquake epicenters were generated. After processing the data and assigning weights using expert opinions through Fuzzy AHP, the software combined these layers to produce a final map that zones earthquake hazard levels across the region.As aforementioned, the research methodology employed in this study involved the integration of fuzzy logic models and hierarchical analysis within a Geographic Information System (GIS) framework to assess the seismic vulnerability of Hormozgan province to earthquake risks.The Fuzzy Hierarchy Analysis Method (FAHP) incorporates fuzzy logic concepts into hierarchical frameworks to accommodate subjectivity in complex decision-making. Through the hierarchical arrangement of decision-making criteria and the use of fuzzy sets with logical operations, this method enables visualization of ambiguous and inaccurate data and provides a versatile and user-friendly strategy for decision analysis and problem solving.To assess seismic risk levels across the province, this study considered key earthquake vulnerability indicators: slope, elevation, proximity and density of fault lines, earthquake depth and magnitude, distance from epicenters, and density of seismic points. By integrating these factors, we were able to create a robust assessment that identified areas with varying degrees of earthquake vulnerability.

Our analysis revealed that a significant portion (around 22%) of Hormozgan province, encompassing 15,807.64 square kilometers, faces very high earthquake risk. Additionally, 8,133.15 square kilometers (11.50%) were identified as areas with very low seismic risk. By combining fuzzy logic models and hierarchical analysis, this study effectively identified specific zones within the province that require targeted earthquake preparedness and mitigation measures. These findings demonstrated the nuanced distribution of seismic risk levels across Hormozgan province, highlighting the need for tailored interventions to improve disaster resilience.This research highlights the importance of proactive earthquake risk assessment and zoning. By identifying areas with varying levels of seismic risk, this approach provides a crucial foundation for strategic decision-making. This allows for targeted risk management strategies and informed urban development planning in earthquake-prone regions like Hormozgan province, ultimately safeguarding communities and infrastructure from the potential impacts of seismic events.

Ultimately, the primary objective of this study is to evaluate and zone earthquake hazard levels in Hormozgan province using a hybrid Fuzzy AHP model. The application of Fuzzy AHP facilitates the analysis of various spatial parameters based on theirimportance weights to generate a vulnerability map. This approach proved instrumental in effectively assessing and zoning the vulnerability of Hormozgan province to seismic hazards.By leveraging integrated methodologies, we were able to comprehensively assess seismic risk and identify areas requiring targeted interventions. These insights provide valuable resources for policymakers, urban planners, and disastermanagementstakeholders, which emphasize the importance of informed decision-making to bolster earthquake resilience in vulnerable regions.Moving forward, this research lays the foundation for informed strategies to enhance disaster preparedness and mitigate the impact of seismic events in Hormozgan province and similar earthquake-prone areas.

Soil erosion is a natural geomorphic process. An increasing number of studies show that changes in the rate of erosion and sediment yield in watershed are often strongly correlated with seismicity. Peak ground acceleration is equal to the maximum acceleration of the ground that occurred during the shaking of the earthquake in a place. Therefore, the purpose of this study is to investigate the relationship between soil erosion and seismic activities and erosion control factors, how high erosion zones are related to the Peak ground acceleration and other factors, and to compare the efficiency of two fuzzy logic and entropy models in erosion zoning.

Talar drainage basin is located on both sides of Qaimshahr-Tehran axis. In terms of geographic coordinates, it is located between ˚52 '35 "22 to˚53 '23 "34 east longitude and ˚35 '44 "23 to ˚36 '19 "1 north latitude. For this purpose, important indices such as erosivity, erodibility, slope-length, cover management have been calculated in the RUSLE equation. Then, the zoning map of the peak ground acceleration has been used as the erosion control factor using the seismic hazard analysis method obtained in the study (Ashtari et al., 2023).

Fuzzy logic and entropy:

For the purpose of zoning, all the layers produced in the previous step were placed as base layers in fuzzy logic and entropy models. Linear function was used to quantify all layers in fuzzy logic. In order to combine the layers, the fuzzy gamma operator was used. In the entropy model, based on the map of erosion levels from specialized studies of the Talar drainage basin, the number of sample sites in each of the erosion zones is as follows: The areas of surface erosion are (16), lateral stream erosion (10), groove erosion (9), gully erosion (8), rock and rock mass erosion (6) and badland erosion (1). The steps of the entropy method are as follows: forming the decision matrix, Ej function and determining the value of entropy, calculating the degree of deviation of criteria dj, determining the weight of each criterion Wij, regional erosion risk model Hi.

Rainfall erosivity factor: This factor expresses the kinetic energy of rain when the drops hit the soil particles, and in other words, it is the intensity of precipitation and erosion resulting from the impact of rain drops on the ground during rainfall. The highest and lowest amount of this factor is respectively in the northern side of the sub-basin 3 with (369-457-8) and in the southern side of the sub-basin 1 with (13.9-102).Erodibility factor: It is in accordance with the average sensitivity factor of formations to erosion of sub-basin 1 (6.09), sub-basin 2 (6.39), sub-basin 3 (7.6). Due to the extent and variety of geological formations, there is a high potential for erosion in all 3sub-basins.length - slope: which is also known as the topography factor, is a function of the height in the basin. The lowest amount of this factor is in the areas of valleys and river streams (>6) and the highest amount corresponds to slopes and heights (>23).cover management: Vegetation factor represents the amount of vegetation waste in different uses. The lower the amount of c, the more vegetation in the area. The highest coefficient c (0.5-0.6) is in sub-basin 1, which indicates the decrease of vegetationand the potential for erosion, and the lowest c (0.2-0.3) in sub-basin 3, which indicates the richness of land uses from forest vegetation.Peak ground acceleration factor: The highest amount of ground acceleration is near the active faults in the region. Due to the structure of the faults, which have an east-west trend, in all 3 sub-basins there are areas of maximum ground acceleration during each event. The highest areas of ground acceleration levels (0.5-0.6) are sub-basin 1 and the lowest are sub-basin 3, which is due to the structure of active faults such as Firuzukuh fault, IRQ112 and IRQ357.

The highest area is in the very low class (839 km2 and 39.9 %) and the lowest area is in the low class (162.9 km2 and 7.7 %). The largest expansion of the mentioned areas is located in sub-basin 1. The distribution of high and very high-risk areas especially in sub-basin 2 and the low coverage of these areas in sub-basin 1 do not match with the relative contribution of high sediment production in sub-basin 1 based on studies.Erosion zonation map by entropy methodThe results of the entropy model showed that the erodibility factor was 20.62%, the peak ground acceleration factor was 20.20%, the cover management was 20%, the erosivity factor was 19.60%, and the slope-length factor was 19.58% in the occurrence of erosion in the region. The area of very high risk with 13.5% of the area of the basin includes erosional facies containing surface, groove, gully and lateral stream. In general, it can be said that the highest erosion risk zones are not related to the highest weighting of the layers, but the placement of a set of factors influencing the occurrence of erosion, which shows the highest amount of erosion.

The peak ground acceleration has a direct impact on the control of sediment yield and erosion processes. The placement of a small part of the IRQ112 fault has caused the extent of ground acceleration levels (0.5-0.6) to be less in sub-basin 3 and more in sub-basin 1 due to the placement of 3 faults, Firuzukuh, IRQ112 and IRQ357. The placement of erodible formations in the ranges of peak ground acceleration has caused the process of sediment production and erosion to accelerate. The coordination of the erosion occurrence zones with the ground acceleration levels is due to the regionality of the entropy model in contrast to the fuzzy model.