جستجوی مقالات مرتبط با کلیدواژه « rusle » در نشریات گروه « جغرافیا »

Soil erosion is very obvious and noticeable in Iran, where a large part of it is a desert and the soil does not have a suitable cover. In order to reduce soil erosion and control it, it is necessary to identify factors affecting soil erosion. Land exploitation methods, forest and pasture exploitation, creation of residential and urban areas, geological conditions, precipitation, weather factors, etc. are some of the factors that affect the intensity of erosion in the region. Therefore, knowing the quantitative values of soil erosion is effective in accurately estimating the adverse, hidden, and intangible effects of erosion

In this research, the Revised Universal Soil Loss Equation (RUSLE) Model has been used. The amount of soil erosion in the basin was calculated in two 20-year periods (1980-2000 and 2000-2020). This soil erosion model is calculated through six factorsA=R×K×L×S×C×P      (R) the Rainfall erosivity factor, (k) soil erodibility factor, (LS) are slope length and slope degree factors respectively, (c) vegetation management factor, and (P) soil protection factor. The data in this research for the implementation of this model including the rainfall erosivity factor (R), topography including slope length and slope degree (LS), and vegetation management (C) were prepared from the Google Earth Engine system. The soil erodibility factor (K) was obtained from the natural resources report. The BLM model was also used to estimate the evaluation of the RUSLE model.  The Google Earth Engine system was used to prepare the land use maps of each of the 20-year periods. The land use map of the first period was prepared using the supervised classification method and calling Landsat 5 images, and The land use map of the second period was done using the CGLS-LC100 product, which is produced and updated at the Copernicus Global Earth Center and using Sentinel satellite images

By analyzing the obtained maps, the rain erosion factor for the first period (1980-2000) has an average of 2.02, the minimum value is 1.80 and the maximum is 2.29, and the second period (2000-2020) is from 1.55 to 2.07 is variable, its average is 1.783. The erodibility factor of the region's soils varies from 0 to 0.349 and its average value is 0.0264. The topography factor of the studied basin varies from 0 to 250 and its average value is 8.77. The vegetation management factor varies between 0.181 and 0.505 and its average is 0.353. This factor varies between 0.315 and 0.494 in the second period, and its average is 0.429 and has an increasing trend compared to the first period, which indicates the decrease of vegetation in the study area compared to the first period. The ground protection factor is also considered to be 1. To calculate the annual average soil erosion rate of the Award Basin in the first period (1980-2000), the various factors of the RUSLE model were converted to the same raster format and cell size, and coordinate system. To determine the risk of soil erosion, the produced layers including the rainfall erosivity layers, soil erodibility, topography, vegetation and soil protection factor were multiplied with the help of the Spatial Analyst extension of the ArcGIS program. The amount of annual loss of soil in terms of (tons per hectare per year) was obtained on a cell-by-cell basis. According to the results, the amount of erosion in the first period varies from 0 to 59.62 tons per hectare and its average is 1.64. The highest amount of soil erosion is in the middle and eastern regions with high slopes. In the second period, the layers of the soil erodibility factor, topography and soil protection factor are the same as in the first period because they are considered constant. The amount of erosion in the area in the second 20-year period varies from 0 to 63.38 tons per hectare, and its average is 1.75 and shows an increasing trend compared to the first period. The RMSE, MAE and MSE statistical indices were used to evaluate the accuracy of the RUSLE model and the degree of agreement of its erosion classes with the output of the BLM based on the map of sampling points. Examination of the values of the mentioned indicators showed that the root mean square error, mean absolute error and mean square error statistics in the RUSLE model have low values of 1.23, 0.96 and 1.52, respectively, as a result this model has It is a small error. After preparing the land use map for the first period, it was determined that in this period, the studied basin includes four land uses: forest, agriculture, pastures and woodlands, and residential. In the second period, there have been forest uses, destroyed forests, agriculture, pastures and woodlands, and residential areas. Then the land use maps prepared in the Google Earth Engine in two periods were transferred to the GIS environment system and the area of each of the above uses was obtained. The area of forests in the studied basin has decreased by 1590.956 hectares and 2014.827 hectares have been added to the area of degraded forests. Also, 2128/368 hectares have been added to the area of agriculture in this basin, and 2557/865 hectares have also decreased from the area of pastures in this area. It should be noted that 5.6266 hectares have been added to the area of residential areas of this basin.Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text. Click here and insert your abstract text.

The findings of this research in two 20-year periods (1980 2000) and (2000 2020) in the studied area show that the amount of precipitation in the second period has decreased compared to the first period. The amount of Rainfall erosivity in the first period had a minimum value of 1.80 and a maximum value of 2.29. In the second period, its value varied between 1.55 and 2.07. Thus, the amount of precipitation in the second period did not greatly affect the process of increasing soil erosion. However, the examination of the pictures and maps prepared on the amount of land use changes shows that the amount of forest and pasture area in this region has decreased and the area of destroyed forests and agricultural and residential lands has been added. Therefore, the role of land use changes in increasing soil erosion is significant. The research results of Teimouri (2018), Mayahi (2021) and Arkhi (2022) also confirm that increasing rainfall, changing land use and reducing vegetation are effective in increasing soil erosion.

Water soil erosion is one of the most common types of soil degradation, which has very destructive effects on the environment and human life. This phenomenon reduces the quality of the soil in the site and may cause sedimentation problems in the downstream or reservoirs. The severity of soil erosion is usually estimated using different models. The Revised Universal Soil Loss Equation (RUSLE) is the most common soil erosion model due to its simplicity, less need for data, and adaptability to different environmental conditions. Studies around the world show that this experimental model is very efficient for estimating annual soil losses at the watershed scale. Sediment Delivery Ratio (SDR) is used to estimate the rate of sediment yield based on the soil erosion rate obtained from RUSLE. Dizgaran watershed, located on the border of Kurdistan and Kermanshah provinces, western Iran, is one of the mountainous areas of the country and has rough topography, vegetation cover, and weather conditions that make it prone to severe soil erosion. This research aims to estimate the annual average rate of soil erosion and sediment yield in the Dizgaran watershed and also to investigate the correlation between different RUSLE factors and the erosion rate to better understand the effect of each factor on the erosion rate. In this research, the rainfall-runoff erosivity (R) factor was created using the WTLS linear regression method based on the Digital Elevation Model (DEM). According to the results of the RUSLE/SDR model, the average rate of soil erosion was 45.09 tons per hectare per year and the average rate of sediment production was 19.42 tons per hectare per year in the watershed. 1.3% and 4.66% of the area of the area were placed in very low and low erosion classes, respectively. 16.18% of the area was in the moderate erosion class, and 36.18% and 41.68% of the area were in the severe and very severe erosion classes, respectively. Also, the results indicate that the two factors of topography (with a correlation coefficient of 79%) and cover and management (with a correlation coefficient of 47%) have the highest correlation with the soil erosion rate, respectively, and therefore play the most important role in changing the erosion rate in the watershed. The results of this research show that this watershed is involved in severe erosion, as can be seen from the presence of different forms of soil erosion in the field investigation. The results of this research can be used to carry out management measures to reduce the rate of soil erosion in the points with a high rate of erosion and to protect and manage the Dizgaran watershed.

The increase in greenhouse gases in the last few decades has caused a disturbance in the global climate balance and climate changes (Aalst, 2006). Climate change and the increase in extreme conditions have caused disasters (Helmer & Hilhorst, 2006), increasing floods, and tropical storms (Haqtalab et al., 2012). Other consequences of climate change are changes in erosion and soil loss (Akbarian & Khoorani, 2022). Soil loss and sediment transport have always been one of the most critical problems of land management. Climate change and, consequently, changes in precipitation affect soil erosion and loss from various aspects such as the amount, intensity-duration, and distribution of rainfall (Gabris et al., 2003). As a result of climate change, the erosive power of rain is expected to increase (Sun et al., 2002). In order to predict the effects of climate change on the erosivity of rainfall, it is necessary to predict rainfall with climate models and then estimate the erosive power of rainfall with suitable erosion estimation models. Rainfall is the most crucial active driver of soil loss that displaces soil particles (Talchabhadel, 2020). It is difficult to accurately evaluate raindrops' characteristics with soil displacement and detachment. Therefore, the empirical methods are used based on rainfall. Some of these empirical models can be used with future climate data. The Revised Universal Soil Loss Equation (RUSLE) is one of the models used to predict soil erosion. Since precipitation is one of the main factors in this model, many researchers have used it to reflect upon the role of climate change on erosion changes. According to Goldies et al. (2022), using the RUSLE-GIS approach can estimate the current and future annual soil erosion rates in watersheds by reflecting climate change to the R factor based on the latest CMIP6 phase 6 climate forecasts.
Soil loss and sediment transport have always been one of the most critical problems of land management. According to the mentioned materials, the use of different models to estimate erosion is essential in the basins that are faced with a lack of data and statistics. The area studied in this research is the Minab watershed, which is vital from various economic and social aspects, especially the drinking water supply of Bandar Abbas in Hormozgan province. Accordingly, this research tried to determine the changes of erosion in these periods by examining the climate condition in the past, present, and future periods so that this information becomes available to decision-makers and managers for better planning. The Minab watershed is located in the range of 48°56° to 57°59° east and 27° to 28°32° north latitude. This basin is one of the sub-basins of Bandar Abbas-Sedich, located in the northeast of Hormozgan province and the south of Kerman province. The area of the basin is 10613 square kilometers. The Minab River, which is the result of the connection of the Jaghin and Rodan rivers, is considered the most important river in this basin. With the construction of the Minab Dam, its water resources are used to provide drinking water to Bandar Abbas.

This research used monthly data from meteorological stations, precipitation data extracted from different climate models and scenarios, soil data, and satellite images. The climate data are in different periods (base period 2020-2002 and future period 2020-2050). The RUSLE model was used to study erosion changes due to climate change. First, by preparing the RUSLE model invoices, the model was implemented for the 2010 and 2020 time periods. Next, two climate models, BCC-CSM2-MR and CanESM5, based on the sixth report and under scenarios 2.6, 4.5, and 8.5, were used to predict precipitation. After evaluating the models, the precipitation erosion layer in 2040 was prepared by the two models.

The results showed that the average rain erosivity layer has increased from 41.57 in 2010 to 52.01 in 2020. There is not much difference between the prediction results of the BCC-CSM2-MR model and the observed value, so it can be said that the model performed well in different scenarios. Among the used scenarios, the 8.5 scenario has the slightest error, and the 4.5 scenario is placed at the end. In the case of the CanESM5 model, there is no significant difference between the observed and the predicted values. Although the difference increases for more distant years, such as 2040, the results of different scenarios are similar. The output of the two BCC-CSM2-MR and CanESM5 models also indicate an increase in the rate of precipitation erosion in 2040. The average erosion in 2010 was 13.8 t/ha/y, which increased to 17 t/ha/y in 2020 and was predicted to increase to 17.3 t/ha/y in 2040.

The average rain erosion layer in 2010 was 41.57; in 2019, this value reached 52.01. According to the results, the analysis of precipitation data obtained from climate scenarios and models shows an increase in the erosivity factor of rain in the near future (2040). Considering the importance of the rainfall erosivity layer in water erosion, if other factors affecting erosion remain constant, in the near future, we will see an increase in erosion and soil loss in the Minab basin. The results of the RUSLE model in the three periods also indicate an increase in erosion over time. The spatial changes of basin erosion are a function of LS (topographic factor), and the rainfall erosivity factor (P) causing temporal changes in erosion.

Soil erosion has been considered as the primary cause of soil degradation because soil erosion leads to the loss of topsoil and soil organic matter, which are essential for the growing of plants. Quantification of soil loss is a significant issue for soil and water conservation practitioners and policy makers. At the watershed level, the two most important hydrological phenomena that can occur from rainfall procedures are surface runoff and soil erosion.There are many models to study the soil erosion. One of the most widely used model for estimating this phenomenon is the Revised Universal Soil Loss Equation (RUSLE). Six factors are included in the RUSLE model: Rainfall erosivity, soil erodibility, slope length and steepness, vegetation cover and management and supporting practices. In this study, multi-sources data are used to generate the necessary parameters of the RUSLE model. Since Surface runoff and soil erosion are the two significant hydrologic reactions, the Soil Conservation Service Curve Number (SCS-CN) model is used to estimate runoff. This study has shown that a strong relationship between the surface runoff estimation and the maximum erosion potential in the study area. The advantage offered by this model is its simplicity and the diversity of the parameters used reflecting the function of runoff under the hydrological system.

Soil erosion is one of the environmental problems that pose a threat to natural resources, agriculture and the environment. Quantitative assessment of erosion and sediment using erosion and sediment estimation models is one of the solutions through which erosion and sediment can be controlled to some extent and its amount minimized. In the present study, the aim is to predict the annual soil loss potential and sediment load. To predict these cases, the Revised Universal Soil Loss Equation (RUSLE) has been used in the context of the GIS. The amount of annual rainfall erosivity factor was calculated using 22-year monthly rainfall data at 18 stations around the basin. Its spatial variations were then estimated using conventional kriging. Soil erodibility index was obtained from soil map, which was prepared using field survey and remote sensing data. The topographic factor was extracted from the digital elevation model with a spatial resolution of 30 m. The annual vegetation factor was also estimated from remote sensing data. Since soil protection operations are insignificant in the study basin, the amount of soil protection factor was considered 1 throughout Basin. In this study, the mean values ​​of R, K, LS, C and P factors were equal to 264 MJ mm ha-1h-1y-1, 0.35 Mg ha h ha-1MJ-1mm-1, 2.00, 0.51 and 1. The average annual sediment yield in the study basin was estimated to be 16.137 th-1y-1, which was close to the value obtained from the sediment measuring station of the basin outlet (16.58 tons per hectare per year). The results of the erosion map in this model show that most erosion is located in the western and middle part of the basin. Because this region is composed of unstable formations and prone to erosion. The steep slope of the region, in addition to rainfall and land use change in this area, has faced many areas with soil erosion crisis. According to the results, the largest area of ​​the basin is related to the very low, low and medium erosion class, which is generally distributed throughout the basin, and the lowest basin area is in the high to very high erosion class (20%). Due to the fact that 20% of the study basin is in the high to very high erosion class, the need for protection measures in these areas is mandatory. The results of this study also showed that LS factor with a correlation coefficient of 0.81 had the greatest effect on estimating annual soil erosion by RUSLE model. This study proved the effectiveness of RS and GIS for quantitative estimation of soil erosion amounts, sediment yield and also erosion management.

Evaluation and zoning of soil erosion using models that are more accurate, helps to implement conservation activities and control soil erosion within the watershed and reduce the amount of sediment outside it. The aim of this study was to zoning the soil erosion intensity of Baladeh watershed using RUSLE and ICONA models and toa investigate their accuracy with observations and terrestrial reality values. RUSLE model factors including rainfall erosion (R), soil erodibility (K), topography (LS), vegetation (C) and conservation operations (P) were calculated from rainfall data, soil map, digital elevation model and remote sensing techniques, respectively. In ICONA model, soil erodibility map was obtained by combining two layers of slope and rock surfaces. Then, in order to prepare soil conservation layer, vegetation index and land use layers of the area were overlapped and by combining erosion and soil conservation layers, erosion zoning map was prepared with this model. The accuracy of these models was evaluated using statistical indices and BLM method, all of which were obtained during field observations. For this purpose, a map with 2500 points was prepared as regular networking for sampling the maps obtained from the models and accordingly, RMSE statistics (root mean squares error), MAE (mean absolute error), MSE (mean squares error) and NSEC (Nash-Sutcliffe Model Efficiency Coefficient) were calculated. The results of the mentioned statistical indices and the errors of the models shows that the mentioned models are not sufficient in baladeh watershed, but the efficiency and degree of adaptation of the erosion classes of ICONA model with BLM output as reference map is higher, because the amount of RMSE, MAE and MSE is lower and NSEC is closer to number one.

The history of life on Earth suggests that humans have always been exposed to a variety of natural disasters (Mayahi, 2021). Some of these disasters are related to climatic factors and fluctuations such as droughts and some are related to human factors (Obiahu, 2020). Land use change and land cover are among the most important environmental issues that have caused global concern (Ota, 2018). Such changes are usually caused by human activities such as deforestation, urbanization, agricultural intensification, overgrazing, and subsequent land degradation. In addition  to, natural factors can also lead to these changes. Factors such as intensive agriculture and overgrazing are major causes of land degradation in arid areas. Changes due to human intervention can lead to the destruction of natural resources. Currently, land use changes from natural lands such as forests and savannas to other uses such as agricultural lands, pastures and settlements have intensified (Chen, 2014.(

2.1 Data used The satellite data used in the present study include two satellite images of Landsat 5TM sensors dated 06/17/2010 and Landsat 8, OLI sensors dated 10/06/2019 with row and transit numbers 159 and 41-42. The digital elevation model (DEM) of ASTER sensor with a resolution of 30 square meters was also used. Satellite imagery and digital elevation models were obtained from the archives of the USGS Web site. 1: 25000 topographic maps from Iran Mapping Organization were used as well. Furthermore, layer construction and composition were performed using ERDAS IMAGINE 2014, ENVI and ArcGIS software.
2-2 Preprocessing of satellite images
In order to control the satellite image in terms of accurate ground recording, geometric correction was performed on the image. To achieve this, images from TM sensors and the OLI sensors were referenced. Because changes in lighting conditions affect the actual radiation reaching to a pixel, atmospheric correction must be made on the images. In the present study, in order to perform atmospheric correction, ATCORE plugin in ERDAS IMAGINE 2014 software and metadata file along with satellite images were used (Iranmehr, 2014).
2-3 Processing satellite images and preparing land cover maps
In order to select educational samples in order to perform supervised classification, aerial photographs, Google Earth images and GPS captured points were used in field operations. The distribution of educational samples in the study area was homogeneous and with proper distribution to the extent possible. The number of pixels selected in each training sample should be at least ten times the number of spectral bands used in the image (Sharma, 2011), which was observed in the present study. The appropriate band composition for classification was determined from the Evaluate command in the Signature Editor based on the Best Average Separability. Based on this, a band composition was used to classify the TM sensor image and a band composition of 2357 for the OLI sensor image.
2-4 Assessing the accuracy of land cover maps
In the present study, after classifying the satellite images, the accuracy of the classified image was evaluated using educational samples that were not involved in the classification process. For this purpose, the classification accuracy was evaluated by using the error matrix and calculating the overall accuracy coefficients and Kappa coefficient.
In order to determine the effect of land use change on soil erosion, the amount of erosion per year in each land use was obtained and compared with each other.

3.1 Classification accuracy assessment
In the present study, the accuracy of image classification was performed by using educational samples and the error matrix and calculating the statistical indicators of overall accuracy and kappa coefficient (Table 4). Using the obtained results, the overall accuracy and kappa coefficient in 2010 are 0.92 and 0.90, respectively, and for 2019 are 0.93 and 0.91, respectively.
3-2 Land use assessment
Examination of the results of land use changes in Figures 3 and 4 as well as Table 5 shows that the land use of the region has changed significantly to the extent that in this 9-year period agricultural land has increased by 54.5 percent from 4.24 percent. The percentage in 2010 has reached 9.78 percent in 2019. Bare and saline lands and residential areas also increased by 3.27 and 0.23 percent, respectively, while forests, pastures and aquifers decreased by 0.01, 7.34 and 1.61 percent, respectively.

 

Soil erosion is one of the environmental problems that is a threat to natural resources, agriculture and the environment and it is considered one of the main land degradation processes in different parts of the world, including Iran (Patil, 2013 & Brath, 2002). According to the results of  Miahi et al. (2021), basins that have heavy rainfall and showers with a direct impact on rain erosion index will increase the potential for soil erosion (Mohammadi, 2018 & Mayahi, 2021). The results showed that, the erosion class showed a very high increase of nearly 5%. Also, these changes in erosion in the use of pastures and bare and saline lands have been more evident because, in this period, rangelands have decreased and bare and saline lands have increased. According to the findings, the decrease in vegetation due to vegetation degradation, especially in arid and semi-arid areas, will
increase soil loss because vegetation as a protective factor of soil against direct rainfall reduces the erosive force of rain and loss (Obiahu, 2020). Therefore, with the loss of vegetation and land use changes, the possibility of direct rainfall colliding with the soil surface provides the soil to be harvested by water and transported in the direction of the slope. Consequently, soil losses become significant (Descheemaeker, 2008). A similar study by Ota et al. (2018) showed that changes in slope cause differences in chemical and physical properties of soil that lead to changes in soil nutrients and, thus, increase soil loss (Ota, 2018).

In this study, quantitative assessment of soil erosion and sediment was done using the famous RUSLE model in the framework of GIS and remote sensing technology. The results showed the erosion values ​​of the basin vary from 0.001 to 257 (t ha-1y-1) at the pixel level and the average erosion in the study basin is 17.62 (t ha-1y-1). Medium rangelands have high average erosion values for two reasons; One is due to the steep slope and consequently the runoff speed in the destruction and transport of particles, which intensifies the role of LS factor and weakens the vegetation factor and the other is the high values of precipitation, which shows the role of rain erosion factor (R). Classification of erosion values showed that about 42% of the study basin with value higher than 16 (t ha-1y-1) is in a severe situation in terms of soil loss. Therefore, due to the erosion situation in the study basin, the role of watershed management, soil protection, and watershed management operations with a focus on the upstream basin and in areas with high slopes is more necessary.

Soil erosion is an important factor that’s not dependent to only geo-ecological factors, rather dependent to land use changes and landscaping too. In this study, assessments the land use changes and its effect on soil erosion in Hoor al-Azim wetland located at southwestern of Iran and southeastern of Iraq countries. To do this study, using Landsat satellite imagery in May 1986 and 2016. For classification imagery, using the maximum likelihood classification method. The land cover map of Hoor al-Azim wetland was prepared. The accuracy values of classification assessments with calculating Kapa coefficient and general accuracy assessments with using error matrix. For TM sensor image, Kapa coefficient and general accuracy calculated are respectively 85.0 and 92.0 and for OLI sensor image respectively 89.0 and 94.0. Next, the soil erosion of study area, assessments with using the Universal Equation Assessment of Soil Erosion (RUSLE) in a period from 1986 to 2016. The general results indicate that in the period between 1986 and 2016, the area of land cover floors with low erosion class has been reduced and the area of high erosion classes has been increased and mainly the erosion class of more than 5 tons per hectare per year. According to the information obtained for 1986, about 26% of the area and for 2016, about 41% of the area, the amount of erosion is more than acceptable and in some parts is critical. Therefore, the results of this study clarify the need to address this issue and provide protection and management solutions.

Introduction:

 There are many equations to prepare the R factor using synoptic stations data in the basin areas (Kamaludin et al. 2013 and Nikolova, 2016). The amount of this factor is estimated using different interpolation methods for the study areas. In addition, there are various methods to estimate the R factor at unknown points such as algebraic methods (linear regression and IDW ) and geostatistics methods (ordinary kriging, simple kriging and ....). Some researchers used ordinarily kriging (Men and Zhenrong, 2009, Moradi Motlagh, 2017 and Shabani, 2011), regression-kriging (De Mello et al. 2015), Co-kriging (Khorsandi et al. 2012), local polynomial (Hoyos et al. 2005), distinctive kriging (Zhang et al. 2009) and Linear Regression interpolation methods (Moradi Motlagh, 2017 and Sazab Pardazan cons. Eng. Co, 1998) to estimate the R factor. Instead of using synoptic data, some researchers used TRMM satellite images to produce R factor (Kumar Das and Guchait, 2016 and Zhu et al. 2011). Some rough topographic lands in the small basins such as the Balarood basin have valleys and elevations; the R factor estimate lower in valleys than in elevations by using the linear regression interpolation method (algebra); while the rainfall is seen uniformly over the elevations and valleys. This problem fix in different kriging interpolation methods and its predictions are close to the reality; therefore, the purpose of this study is to investigate the role of linear regression and geostatistics interpolation methods to produce the R factor and their effect on estimating the erosion of basins by the RUSLE model.Study area The geo-location of the study area (Fig. 1) is between 3601770.582860 mN and 3654377.5862609 mN and 328342.235576 mE and of 534721.260746 mE. 

 Materials and Methods:

 In this study, satellite image and their processing methods, GIS techniques and the RUSLE erosion model are used. Fig. 3 illustrates the materials and methods in the research, which describe as the following. 3.1. RUSLE soil erosion model Universal Soil Loss Equation presented for the first time by Whishmeier and Smith (1977). This model estimates the soil erosion by Eq. (1): (1) A=R×K×L×S×C×P Where A is the estimated soil loss per area and time unit and in this study, is in tons per hectare unit (metric system). R, K, L, S, C and P are the rainfall-runoff erosivity factor (MJ.mm.ha-1.hr-1.yr-1), soil erodibility factor (ton.ha.h. (MJ.mm.ha)-1), the slope length factor, the slope steepness, the cover-management factor and the support practice factor alternatively. 3.1.1. Calculating the R factor for each synoptic station The R factor indicates the power of rainfall erosivity and Renar and Freimund (1994) equation have been used to calculate it (Eq. (3, 4 and 5)). 3.1.2. Topographic Factor (LS) LS is the topographic factor, where L is the slope length factor and calculated by the ratio of lost soil from the sloped area to the lost soil from the experimental plots when the soil type and the degree of the slope are similar. This factor is calculated using Eq. (6). Interpolation methods use the R factor calculated at each station to prepare the R-layer in the basin area. In this study in order to provide the R layer, algebraic (linear regression) and ordinary kriging interpolation methods are used. 3.2. Semivariogram and its application in choosing the best R-factor interpolated. A large number of studies have proved the efficiency of the semivariogram in spatial analysis and environmental studies. Semivariogram equations (Eq. (11)) with different models (Spherical, Gaussian, Linear, Exponential, and Circular) use to estimate spatial auto-correlation. 3.3. Pearson Correlation Coefficient (r) Generally, the most common equation to express the correlation between two variables over time and place is the Pearson correlation coefficient. This coefficient shows the direction and degree of correlation and can be calculated using standard deviation and standard methods (Eq. (12 and 13) respectively). 3.4. The coefficient of determination (R2) Correlation coefficient shows the correlation between two variables but does not give us more information about the nature of this correlation. It determines the high, low, or relative correlation (Balyani and Hakimdost, 2014). The accuracy of the model is higher and we can determine the optimal model for fitting if the coefficients of determination of data go towards 1. 3.5. Data resources In this study, to produce LS, P, C, K, and the R factors, 1:25000 topographic maps, ASTER satellite image, area soil map and precipitation data of 12 synoptic stations (Table 5) have been used respectively (Table 4)

Conclusions:

The results indicated the R factor interpolated by linear regression method has more auto-correlation (R2=0.985) than once interpolated by ordinary kriging method (R2=0.964). Though the coefficients of determination are close, this difference could justify the use of linear regression interpolation method in the preparation of the R factor. The R factor interpolated by linear regression is higher than the R factor interpolated by ordinary kriging method on average.The maps of soil erosion risk indicated that by using the linear regression and ordinary kriging interpolation methods to prepare the R factor, the risk of soil erosion at the basin (ton per hectare) estimate 0 to 77824.5 and 0 to 55277.2 respectively. The mean annual erosions from linear regression and ordinary kriging interpolations have been estimated 19315/135 t.ha-1.yr-1 and 14223/726 t.ha-1.yr-1 alternatively. It demonstrated using linear regression interpolation method in preparing the R factor layer leads to the higher estimation of this factor and ultimately to the higher estimation of the erosion by the RUSLE experimental model. The average of the erosion estimated using the R factor interpolated by linear regression method has less difference (1651/865 t.ha-1.yr-1) then another one in the previous study (Sazab Pardazan Cons. Eng. Co, 1998). Comparison of estimated erosions with each of their factors indicated both of the estimated erosions have the lowest and highest correlation with their R and LS factors, respectively. This study also demonstrates that remote sensing and GIS are valuable tools in assessing soil erosion and its factors.

The soil is one of the most important factors of production that has a great influence on human economic and social life. The surface of the earth is generally covered by soil and other surface deposits. Soil erosion is one of the most important problems and problems we face today. Increasing exploitation and lack of proper human management of the natural environment have a great effect on the intensification of soil degradation and erosion processes. In this research, the effective parameters analysis of erosion and sediment production in Dakehah basin with a total area of 9923.2 ha in southern Kerman province was studied using the Revised Universal Soil Erosion Model (RUSLE). Data and tools used in the research include data from meteorological stations, digital elevation model (DEM), ETM 2015 satellite imagery, GIS and remote sensing (RS) It should be. By studying the effective factors in this model, which includes rainfall erosivity factor, soil erodibility factor, topographic factor, and vegetation, the purpose of this study is to estimate the annual soil erosion in the study area. The erosion rate of the basin is estimated. Accordingly, annual soil erosion in the whole study area is estimated at 67 tons per hectare per year. The results of this study show that the highest effect on the estimation of erosion, a topographic factor with the highest coefficient of explanation was 92.6 In this research, the effectiveness of RUSLE technologies of annual soil erosion is confirmed by the new model of GIS and remote sensing for quantitative estimation of soil erosion values.

Soil erosion is a natural process that has become a major global environmental threat as a result of human activities such as irrational land conversion and vegetation degradation. The purpose of this research is to model spatial distribution of likely soil erosion by water in Gharesoo watershed, assess the impacts of land use and slope on erosion rate, and identify soil erosion hotspots and prioritize them. For prioritization, the affected land patches with the highest potential and the effects of landuse and slope were studied. The Revised Universal Soil Loss Equation (RUSLE) was used in conjunction with Geographic Information system (GIS) to model soil erosion. The average soil erosion in the watershed was 18.65 t ha-1 yr-1. The slight, moderate and severe erosion classes encompass 62%, 26% and 12% of the watershed, respectively. Assessing the impacts of land use and slope confirms importance of interaction between these factors in increasing soil erosion, especially in agricultural areas with steep slopes. Zonal land suitability was used to delineate 15 patches as hotspots of soil erosion based on the RUSLE outcome. The results of this research reveals location and priority of land patches for implementation of mitigation measures such as terracing and vegetation rehabilitation, along with land use planning to reduce erosion and prevent it's negative impacts on environment and economy.

Soil erosion is one of the most important environmental problems we face today. Increasing human exploitation and lack of proper management of the natural environment has a great impact on intensifying the process of soil degradation and erosion. In this study, the erosion and sediment yield in a Kohpayeh - segzi plain with 136 thousand hectares were studied using the Revised Universal Soil Erosion Model (RUSLE). Data and tools used in the study included Weather stations data, Information 21soil sample, digital elevation model (DEM), Satellite image OLI sensors, geographic information system (GIS) and remote sensing (RS). by investigation efficient parameters in RUSLE model, including rain erosion factor, K-factor, the factor is the topography, vegetation factor, and protective operations factor, The amount of the erosion in the region has been estimated. Accordingly, on the result the annual erosion in the entire study area has been estimated zero to 95 tons of soil per hectare per year. The results showed that topographical factor with the highest coefficient of determination (R2=0.9) has the greatest impact on estimates of annual soil erosion using RUSLE model. This research approves the effectiveness of new technologies, GIS and remote sensing for estimating soil erosion.

IntroductionNowadays you can rarely find areas on the earth surface which are not exposed to degradation and erosion and this is mainly due to population growth and excessive use of the land. The severity and extensity of erosion was not the same in different times and places, and it is related to the natural conditions, topography, soil characteristics, and status of land use. The phenomenon of soil erosion and sediment yield has created many problems in human society; accordingly, soil erosion is one of the most serious problems in developing countries and many developed countries now. Although it is not possible to stop the erosion geology, however, prevention and control of soil erosion is a basic need in the basins within the framework of utilization projects of water, soil, and watershed. One of the problems of erosion and sediment studies is the shortage of necessary data and information. This problem was more acute in developing countries and our country, Iran, is one of those countries that faces with this problem.
Theoretical FrameworkGhale chay basin is located at the altitude of 37 ° 27' 44 to 37° 42' 25 N and longitude of 45° 54' 36 to 46° 20' 40 E. Ghale chay basin have caused replacement of various human centers due to its coverage with Ghale chay river alluvium, suitable soil, relatively enough water and appropriate environmental conditions that makes it inevitable to investigate the status of erosion and to prepare erosion intensity map in this region. Due to the complexity of the processes, lack of appropriate statistics and measurement stations, and also to facilitate the work, most of the time erosion and sedimentation studies are done using experimental methods in Iran. Therefore the purpose of this study is soil erosion and sediment yield modeling using RUSLE model and Geographic Information System (GIS) and also identification of erosion susceptible areas to perform soil and water conservation operations in Ghale chay basin.
MethodologyIn this study, the amount of soil erosion has been studied in Ghale chay basin. To this purpose, the Revised Universal Soil Loss Equation (RUSLE) model has been adopted in Geographic Information System (GIS) technique framework that includes the R, K, LS, C, and P factors. To achieve the target, various documents, such as topographic maps with the scale of 1:50,000 including Hargalan sheet number 5265 III, Shiramin sheet number 5165II, Maragheh sheet number 5264IV, and Ajab Shir sheet number 5164I to perform analysis of topography, check slope and hydrographic network, elevation and quantitative analysis; geological maps of Azar Shahr, Osku, Ajab Shir and Maragheh with a scale of 1:100,000 for the stratigraphy, lithology, the nature of materials, geological structure, development stages etc., and also the rainfall data from weather stations around the basin and Digital Elevation Model (DEM) have been used. Average annual soil loss was calculated by multiplying the rainfall-runoff erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover management factor (C) and support practice factor (P) using intended model equation in Raster calculator extension in Arc GIS10.2.
Results and DiscussionGhale chay basin has highly variable topography. This is determined with the range of zero to 10.32 of LS factor. Examining Rain Erosion (R) factor map in the basin indicated that this factor is variable from 3 to 3.71 MJ mm ha. The results also indicated that erosion in the northeast and southwest region under study has a decreasing and increasing trend, respectively. To prepare the C factor map land use and vegetation maps were used. This factor is variable from 0.04 to 0.45 that represents fairly well vegetation in the basin. To calculate the K factor the necessary information of geological map with the scale of 1: 100000 and soil layer were used. The average value of K factor is variable in the basin from 0.1 to 0.5.
Conclusion and suggestionsEvaluation of soil erosion risk map shows that amount of risk soil erosion is variable in the plain from 0 to 2.225 tons per hectare per year. According to this map, the studied area was considered as a very low to low sedimentation class. The calculation results of sediment delivery ratio methods show that in the studied area the ratio of sediment delivery is variable from 0.08 to 0.29 and maximum sediment yield is variable from 0.19 to 0.64 tons per hectare per year. Finally, comparing the estimated total deposition in USDA method with the amount of the obtained in EPM model from the research study of Roostaei, Rasooli, and Ahmad Zadeh, shows the ability to integrate RUSLE and GIS models in estimating soil erosion and sediment load.