جستجوی مقالات مرتبط با کلیدواژه « soil erodibility » در نشریات گروه « محیط زیست »

Currently, soil erosion is considered one of the important environmental problems in Iran's watersheds. Accurate estimation of sedimentation and soil erodibility is very important for the protection and management of natural resources. Soil erodibility indicates the potential ability of soil to erode, and its increase is considered a serious threat to the stability and production capacity of agricultural lands. Measuring soil erodibility by direct method is expensive and time-consuming, and nowadays indirect methods are used to determine it. One of the most common indirect methods for estimating this parameter is the use of the Wischmeier and Smith nomograph, which was developed for non-calcareous soils. However, the majority of the soils in Iran is calcareous and the use of the nomograph method does not provide a correct estimate of the amount of erodibility. Loess is one of the most important Quaternary sedimentary formations in northeastern Iran, which consists of a relatively high percentage of lime. Therefore, the main goal of current research is to determine the most suitable indirect model to determine soil erodibility in Loess lands with the origin of calcareous sediments.

In the current study, Arab-Qara-Haji watershed with an area of 2596 hectares located in the east of Golestan province was selected as a typical loess area. The average annual precipitation of this watershed is 489 mm and it has a moderate semi-arid climate. This area is one of the sedimentary basins of Kopeh-Dagh zone related to the Jurassic period and consists of limestone and loess sediments. Based on the field studies, three main types of soil including hills (67.1%), loess plateaus (20.6%) and river terraces (12.3%) were identified in this watershed and the land unit was selected as working units. For this purpose, 48 surface samples (0-10 cm depth) were collected from each part of the land unit. In this research, two series of samples were taken from each section. The first series of samples was used to determine the soil physicochemical properties and the second series was sampled by a special cylinder to determine the soil saturated hydraulic conductivity using the falling head method. In the laboratory, soil texture was determined by hydrometric method (D<0.075), soil granularity by sieve analysis (D>0.075), soil organic matter by wet burning method and neutral lime percentage by Nelson method. The experience of the soil expert of this research was used to determine the soil structure in field. Then, soil erodibility was estimated using four indirect methods, including Weishmeier and Smith nomograph, Torri, Shirazi, and Vaezi. In this study, the soil actual erodibility was measured using rain simulator in the field (in plots of 1 m2). Then the validation of the models was performed using three standard indices MAD, RMSE and MAPE and the most appropriate model was selected.

Soil texture assessment in this watershed revealed that around 90 percent of soil samples have silt loam texture and the rest of samples has loam one. Also, the average lime in study area was estimated about 29%. The high percentage of lime (29%) and silt (60%) in this watershed has greatly increased the tunnel erosion probability. The validation results of the used models revealed that the use of the Vaezi method to estimate the soil erodibility factor in the loess lands of the study area has the highest accuracy (RMSE=0.00225). However, it is less estimated than the actual values. These results were confirmed in all 3 soil types of Arab-Qara-Haji watershed. After Vaezi model, Shirazi model (RMSE=0.03600) is more suitable than other models to estimate the soil erodibility factor in the loess lands of the area. This model has overestimated in all three types of soil including hill, loess plateau, and alluvial terrace, which is 11.2 times higher than the actual data from the rain simulator in the Arab-Qara-Haji watershed. After the Vaezi and Shirazi models, Wischmeier nomograph and Torri models, respectively, have a weaker performance in determining the soil erodibility factor in the limestone lands of the study area. Like the Shirazi method, Wischmeier nomograph and Tori methods have overestimated the soil erodibility factor. In the study area, this increase has been18.6 and 21.9 times higher than the actual data for Wischmeier nomograph and Torri models, respectively.

The present research results revealed that the use of Vaezi model to estimate soil erodibility in loamy lands with a high percentage of neutral lime is more appropriate compared to other used models. Based on the Vaezi and colleagues model, if the amount of neutral lime in the soil is less than 13%, it has a controlling role on the soil erodibility factor. While, this model is not able to estimate the soil erodibility in areas with neutral lime more than 30%. Therefore, for a number of soil samples in which the neutral lime was above 30%, the Vaezi model was not able to calculate the soil erodibility factor. It seems that currently, for the areas where the percentage of neutral lime in the surface soil is less than 30%, the use of the Vaezi model is more suitable than other models. It is suggested to recalibrate and update the Vaezi model for areas where the percentage of lime is more than 30%. The present research results revealed that the use of Vaezi model to estimate soil erodibility in loamy lands with a high percentage of neutral lime is more appropriate compared to other used models. Based on the Vaezi and colleagues model, if the amount of neutral lime in the soil is less than 13%, it has a controlling role on the soil erodibility factor. While, this model is not able to estimate the soil erodibility in areas with neutral lime more than 30%. Therefore, for a number of soil samples in which the neutral lime was above 30%, the Vaezi model was not able to calculate the soil erodibility factor. It seems that currently, for the areas where the percentage of neutral lime in the surface soil is less than 30%, the use of the Vaezi model is more suitable than other models. It is suggested to recalibrate and update the Vaezi model for areas where the percentage of lime is more than 30%.

Soil erosion is a natural process (Lee et al., 2021). which causes the level of soil loss by various environmental factors such as weather, soil, topography and vegetation (Chen et al., 2019). However, human interventions through land use change and agricultural and construction activities can accelerate this flow (Wenker et al., 2019; Barley et al., 2017). For this reason, nowadays, soil erosion caused by land use change has become the most important issue of land degradation all over the world, and the transformation of the land form and the disruption of the main functions of the natural environment are the consequences of these geomorphic reactions (Paul et al., 2019). T (2017) aimed to study and estimate the spatial and temporal soil erosion in the periods of 1994-1999-2008-2015 in the Manderjan sub-basin located in the west of Isfahan province. Using remote sensing and GIS technologies, they concluded that the amount of soil erosion in 1994-1999-2008-2015 was 0.001 to 233, 0.001 to 297, 0.001 to 231, 0.001 respectively. It is up to 215 tons per hectare per year. Also, the height and height factor in the region with a correlation coefficient of 80% has the greatest effect in B The annual soil erosion rate was estimated by the RUSLE model. Nejad Afzali et al. (2018) used the Revised Global Model of Soil Erosion (RUSLE) to estimate soil erosion in Dehkhan watershed south of Kerman. Their results showed that the annual soil erosion in the study area is estimated at 50 tons per hectare per year. Khosravi-Aghadam et al. (2018), in order to estimate the soil erodibility factor and its relationship with some land characteristics, using the USLE model in a part of the Nazlu Chai watershed of Urmia. Their results showed that the value of K factor varies in the range of 0.079 to 0.029 tons per hour per megajoule mm. Also, in terms of erodibility, the soils of the region are in low and very low erodibility classes.

Ateshgah watershed is located in the southwest of Ardabil city at the position of 47°50' to 48°2' east longitude and from 38°12' to 38°16' north latitude. The main branches of this basin originate from Sablan heights in the west of the basin. The area of this basin is 40.5 square kilometers and the maximum height of this basin is about 3596 meters at the extreme end of the western part of the basin and its minimum height is 1798 meters at the outlet of the basin in the eastern part. The location of Atashgah watershed is shown in Figure 1. Research data and tools The current research is of an applied type and its research method is an analysis based on the integration of data analysis, geographic information system, remote sensing and the use of the revised global model of soil erosion (RUSLE). The data and tools used in the research include 1:25000 digital layers of the National Mapping Organization, digital elevation model (DEM), with a spatial resolution of 30 meters, rainfall data from the National Meteorological Organization, Landsat OLI 8 satellite image for 2020 with a spatial resolution of 30 meters, the studied area from the website www.usgs.gov, the collection of educational samples was also done through field visits and the creation of false color combinations, and the soil laboratory data of the studied basin is from the watershed deputy of the country's organization of forests, pastures and watershed. In this research, ArcGIS 10.3 software was used to draw maps and analyzes related to it, as well as ENVI 5.3 software to prepare vegetation and land uselayers of the study area, and statistical software such as Excel 2016 and SPSS 17 for statistical calculations and The regression relations of the equations have been used.Landsat satellite images include the longest archive of global images with moderate resolution, multispectral data from unique sources for functional planning at various scales, including land use and land cover, change detection and monitoring of natural environment dynamics (Taherparour et al. , 2015). Therefore, Landsat OLI 8 satellite images were used in this research. The specifications of the satellite image used in this research are presented in Table After classifying the satellite image using the Support Vector Machine (SVM) method, the obtained land use map was separated into seven land use classes, including good, poor pastures, irrigated agriculture, rainfed, residential and irrigated areas (Figure 2). The accuracy of the obtained map for 2020 was checked using the Google Earth image and ground control points, as well as the false color image of the same year. In this research, the overall accuracy for the land use map of Atashgah basin was 0.90 and the Kappa coefficient was 0.87. 3-2- Soil erosion (RUSLE) According to (Figure A), the results of the rain erosive factor (R) vary from 74 to 34.98 MJ/mm/hectare/hour per year, the highest value of which is related to the north and southeast parts and the lowest value is related to the central parts. and southwest. The average amount of soil erodibility factor (K), according to figure (b), varies between 0.12 and 0.37 tons/hectare per year in the study area. According to figure (c), the value of LS factor in the studied area varies between 0 and 3.98, which is higher in steep slopes, especially around waterways. Using the Normalized Vegetation Index (NDVI), the vegetation factor (C) of the Atashgah basin was prepared based on equation 4 and 5, which is presented in (Figure d and e). Based on this figure, the value of the C factor varies between -0.16 and 0.74. In general, it can be said that the eastern and central half of the basin has lower amounts of C due to the presence of dry and unused lands, and the southern and western parts of the basin have the highest amounts due to the presence of pasture lands. The soil protection operation factor (P) was also considered to be 1 due to the lack of available information from the region for the entire region.Annual soil erosion (RUSLE), to prepare the average annual soil erosion map of Atashgah watershed through the product of rain erosion factors (R), soil erodibility (K), topography (LS), vegetation cover (C) and soil protection operations (P) It was calculated in the Raster Calculator plugin in the ArcGIS 10.3 environment using (Relation 1). The annual soil erosion values in the studied basin vary between 0.09 and 11.02 tons per hectare per year. Also, the average amount of soil erosion in the studied area is 0.16 tons per hectare per year and its standard deviation is 0.55 tons per hectare per year. In (Figure 3), the average annual soil erosion (RUSLE) map of Atashgah basin is presented.

Land use is one of the important factors in causing soil erosion, and in recent years, the mutual impact of land use change and soil erosion has become a major environmental concern. Considering the importance of the topic, in the current research, the amount of soil erosion on land use in the Atashgah watershed has been investigated using the RUSLE model. For this purpose, first, the land use map was classified using the Landsat OLI 8 satellite image and using the support vector machine algorithm into seven land use classes, including barren land, good pastures, poor pastures, rainfed agriculture, water, residential areas, and water. The overall accuracy and Kappa coefficient for the prepared land use map were obtained as 0.90 and 0.87%, respectively. Then the maps of R, K, LS, C and P factors of the RUSLE model were prepared in the GIS environment and after combining these layers through the Raster Calculator in the Arcmap environment, the average annual soil erosion map for the entire Atashgah watershed between 0.09 and 11.02 tons It was calculated per hectare per year. The results of the evaluation of the soil loss map on the land uses of the studied area showed that dry land use with an average soil erosion of 0.48 tons per hectare per year has the highest soil loss and good pastures with an average erosion of 0.21 tons per hectare per year has the lowest. They have soil waste among other uses in the region. In this research, it was tried to use GIS capabilities to create the required data of RUSLE model. Finally, it is suggested to control the process of land use changes in the Atashgah watershed by determining grazing capacity, vegetation management, and take steps to restore, improve and develop pastures. Therefore, it is expected that this study and the results of this research will pave the way for the implementation of better and more scientific management by competent managers and planners in this field.