year 12, Issue 1 (Spring 2022)                   E.E.R. 2022, 12(1): 58-75 | Back to browse issues page

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Jokar Sarhangi E, Safarrad T, Shotatzadeh M. Evaluation and Prioritization of Gully Erosion Susceptibility Using Density Area and TOPSIS Models (Case Study: Chenarli Watershed, Golestan Province). E.E.R.. 2022; 12 (1) :58-75
URL: http://magazine.hormozgan.ac.ir/article-1-657-en.html
Geography Department, Faculty of human, University of Mazandaran, Babolsar , e.jokar@umz.ac.ir
Abstract:   (279 Views)
1- Introduction
Gully erosion is an advanced form of water erosion that needs more research, given the vast mass of soil degradation and its subsequent impacts. An accurate evaluation of gully erosion susceptibility based on the density area model can help identify and predict the areas with advanced gully erosion in the units and classes of each factor. Also, the TOPSIS model and proper prioritization of erosion susceptibility at sub-watershed can be critical for conservation activities. Researchers thus far have used the TOPSIS model to estimate soil erosion in a watershed or evaluate the factors affecting soil erosion intensity, disregarding its potential to prepare efficient gully erosion susceptibility maps. However, the study area, located in the northeast of Golestan, a province in northern Iran, is susceptible to gully erosion due to the horrific deforestation for agricultural and animal husbandry purposes and the expansion of loess soil. Watershed operations are imperative to prevent the spread of this type of erosion, although since the credits are limited, further preventative measures should be prioritized.
2- Methodology
This study aimed to prepare a gully erosion susceptibility map of the region using the density area model as well as prioritizing sub-watershed based on the TOPSIS model. It has also been attempted to evaluate their efficiency to prevent time and capital resources waste by identifying erosion-susceptive watersheds and implementing conservation programs as required. The Chenarli watershed is located northeast of Kalaleh in Golestan, Iran. The average elevation of the area is about 628 meters. The elevation, slope, aspect, lithology, vegetation, and land use have been used in this study to prepare gully erosion susceptibility maps. The layer of the area's lithology units was obtained from the 1:100,000 map of the geological survey of Iran, and the raster layers of elevation, slope, aspect of the digital elevation model (DEM) were obtained from an area of 30 m2 of the region. Landsat images were used to prepare a vegetation map and land use area. The Gully erosion distribution map of the region was created using Google Earth images, including 93 gullies, out of which 64 gullies were used for susceptibility map preparation and 29 gullies were used for map validation. The density area model was used to determine the weight of the effective factors. In this study, the layers of independent variables overlayed with gully erosion distribution in ArcGIS. The gully density was calculated in each class of factors. Technically the TOPSIS model, one of the most famous multi-attribute decision models, prioritizes gully erosion susceptibility at the sub-watershed level. In this research, sub-watersheds have been regarded as the criteria affecting gully erosion. The T output of the measured density area model was used as a reference map to evaluate the validity of the model results. Also, the coefficient of determination (R2) was calculated.
3- Results
In this study, the frequency of gully erosion in the region was identified by overlaying the factor maps, including elevation, slope, aspect, lithology, vegetation, and land use, by gully distribution map in ArcGIS, and the weight values of the classes of each factor were calculated using the density area model. The relationship between topography factors and gully erosion in the region indicated the higher sensitivity of lower elevation classes, lower slopes, and western and southern slopes. Calculation of gully surface density in different rocks showed that shale and loess were more susceptible to gully erosion, respectively. Furthermore, and in terms of land use, the highest amount of gully erosion was observed in farmlands and rangelands, but in areas with natural forests, gully erosion susceptibility was negligible. After calculating the weight of each class of factors affecting the occurrence of the gully in the region, the weight maps were overlayed, and the raster map of gully erosion susceptibility was prepared using the study model. The results of the final map of the density area model indicate that 11.61% of the area has a significantly low susceptibility, and 23.88% are in the low class, 23.03% in the middle class, 25.4% in the high class, and 16.1% of the area is in the very high susceptibility class. The TOPSIS model was used to prioritize gully erosion susceptibility at the sub-watersheds of the region. For this purpose, the study watershed was divided into ten sub-watersheds. Then, the score of the effective variables, the same variables in the density area model, was determined separately. The calculation of the proximity coefficient based on the TOPSIS model showed that sub-watershed no. 2, with a coefficient of 0.791, had the highest gully erosion susceptibility, and sub-watershed no. 6, with a coefficient of 0.144, had the lowest gully erosion susceptibility.
4- Discussion & Conclusions
Gully erosion susceptibility map of Chenarli watershed was prepared by overlaying weighted maps of effective factors using the density area model.  The empirical probability was calculated for the model (KS=0.913), indicating the high accuracy of this model in preparing the gully erosion susceptibility map of the region. The density area validation model was used as a field reality map, and the coefficient of signification (R2) was calculated to compare and assess the TOPSIS model's accuracy. The coefficient of determination in this study shows the level of coordination and relationship between the results of the TOPSIS model and real data (the output of the gully erosion density area model). The results displayed a significant and appropriate relationship between the results of the TOPSIS model and density area model in the region since this model with the coefficient of determination equal to (R2= 0.597) could predict the susceptibility to gully erosion at the sub-basin level, indicating its ability to prioritize the susceptibility of gully erosion of sub- watersheds. It can be concluded that recognizing gully erosion susceptibility in classes of the effective factors using the density area model and introducing erosion priorities of sub-watersheds with the TOPSIS model can help improve the selection of gully control and soil conservation methods and contribute to the required operational focus on the field.
 
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Received: 2021/07/25 | Published: 2022/03/12

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