Soil erosion evolution and spatial correlation analysis in a typical karst geomorphology using RUSLE with GIS

Abstract. Although some scholars have studied soil erosion in karst landforms, analyses of the spatial and temporal evolution of soil erosion and correlation analyses with spatial elements have been insufficient. The lack of research has led to an inaccurate assessment of environmental effects, especially in the mountainous area of Wuling in China. Soil erosion and rocky desertification in this area influence the survival and sustainability of a population of 0.22 billion people. This paper analyzes the spatiotemporal evolution of soil erosion and explores its relationship with rocky desertification using GIS technology and the revised universal soil loss equation (RUSLE). Furthermore, this paper analyzes the relationship between soil erosion and major natural elements in southern China. The results are as follows: (1) from 2000 to 2013, the proportion of the area experiencing micro-erosion and mild erosion was at increasing risk in contrast to areas where moderate and high erosion are decreasing. The area changes in this time sequence reflect moderate to high levels of erosion tending to convert into micro-erosion and mild erosion. (2) The soil erosion area on the slope, at 15–35°, accounted for 60.59 % of the total erosion area, and the corresponding soil erosion accounted for 40.44 %. (3) The annual erosion rate in the karst region decreased much faster than in the non-karst region. Soil erosion in all of the rock outcrop areas indicates an improving trend, and dynamic changes in soil erosion significantly differ among the various lithological distribution belts. (4) The soil erosion rate decreased in the rocky desertification regions, to below moderate levels, but increased in the severe rocky desertification areas. The temporal and spatial variations in soil erosion gradually decreased in the study area. Differences in the spatial distribution between lithology and rocky desertification induced extensive soil loss. As rocky desertification became worse, the erosion modulus decreased and the decreasing rate of annual erosion slowed.

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Soil erosion evolution and spatial correlation analysis in a typical karst geomorphology, using RUSLE with GIS

10.5194/se-2017-1 ◽

2017 ◽

Author(s):

Cheng Zeng ◽

Shijie Wang ◽

Xiaoyong Bai ◽

Yangbing Li ◽

Yichao Tian ◽

...

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Erosion Rate ◽

Temporal Evolution ◽

Karst Area ◽

Mountainous Area ◽

Gis Technology ◽

Rocky Desertification ◽

Erosion Area ◽

The Relationship

Abstract. In spite of previous studies on soil erosion in Karst landform, limited data are available regarding the spatial and temporal evolution and the correlation of spatial elements of soil erosion in Karst. The lack of this study leads to misassessment of environmental effects on the region especially in the mountainous area of Wuling in China. Soil erosion and rocky desertification in this area influence the survival and development of 0.22 billion people. For this reason, the typical Karst area in South China is the object of this study. This paper aims to analyze the spatial and temporal evolution characteristics of soil erosion and investigate the relationship between soil erosion and rocky desertification by using GIS technology and modified universal soil loss equation (RUSLE) model to reveal the relationship between soil erosion and major natural elements in this area. (1) In 2000–2013, the proportion of the area of micro- and slight erosion increases, whereas the proportion of the area of moderate erosion and above decreases. Erosion of moderate and above levels changes into micro- and slight erosion. (2) The soil erosion area in slope zones at 15°–35° accounts for 60.59 % of the total erosion area and 40.44 % of total erosion. (3) The amplitude reduction in the annual erosion rate is higher in the Karst area than that in the non-Karst area. Soil erosion in different outcrop areas of rock generally shows an improving trend, but the dynamic changes in soil erosion significantly differ among various lithological distribution belts. (4) The soil erosion rate of rocky desertification area with moderate and below levels of erosion decreases, whereas the erosion rate of rocky desertification area with severe erosion level increases. Results show the gradual decrease in the temporal and spatial variation of soil erosion in the study area. Lithology is the geological basis of soil erosion. Changes in the spatial distribution of lithology and rocky desertification induce high soil loss. The area is characterized by high rocky desertification, low erosion module, and decreasing annual erosion rate.

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Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

ENVIRONMENTAL SYSTEMS RESEARCH ◽

10.1186/s40068-020-00203-3 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Veera Narayana Balabathina ◽

R. P. Raju ◽

Wuletaw Mulualem ◽

Gedefaw Tadele

Keyword(s):

Remote Sensing ◽

Land Use ◽

Soil Erosion ◽

Soil Loss ◽

Erosion Rate ◽

Soil Erodibility ◽

Lake Tana ◽

Erosion Rates ◽

Soil Erosion Rate ◽

Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

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Sediment Yield and Soil Loss Estimation Using GIS Based Soil Erosion Model: A Case Study in the MAN Catchment, Madhya Pradesh, India

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10348 ◽

2021 ◽

Vol 8 (1) ◽

pp. 26

Author(s):

Manti Patil ◽

Radheshyam Patel ◽

Arnab Saha

Keyword(s):

Spatial Distribution ◽

Soil Erosion ◽

Soil Loss ◽

Crop Production ◽

Erosion Rate ◽

Agricultural Land ◽

Reservoir Sedimentation ◽

Reservoir Water ◽

Slope Length ◽

Soil Erosion Rate

Soil erosion is one of the most critical environmental hazards of recent times. It broadly affects to agricultural land and reservoir sedimentation and its consequences are very harmful. In agricultural land, soil erosion affects the fertility of soil and its composition, crop production, soil quality and land quality, yield and crop quality, infiltration rate and water holding capacity, organic matter and plant nutrient and groundwater regimes. In reservoir sedimentation process the consequences of soil erosion process are reduction of the reservoir capacity, life of reservoir, water supply, power generation etc. Based on these two aspects, an attempt has been made to the present study utilizing Revised Universal Soil Loss Equation (RUSLE) has been used in integration with remote sensing and GIS techniques to assess the spatial pattern of annual rate of soil erosion, average annual soil erosion rate and erosion prone areas in the MAN catchment. The RUSLE considers several factors such as rainfall, soil erodibility, slope length and steepness, land use and land cover and erosion control practice for soil erosion prediction. In the present study, it is found that average annual soil erosion rate for the MAN catchment is 13.01-tons/ha/year, which is higher than that of adopted and recommended values for the project. It has been found that 53% area of the MAN catchment has negligible soil erosion rate (less than 2-tons/ha/year). Its spatial distribution found on flat land of upper MAN catchment. It has been detected that 26% area of MAN catchment has moderate to extremely severe soil erosion rate (greater than 10-tons/ha/year). Its spatial distribution has been found on undulated topography of the middle MAN catchment. It is proposed to treat this area by catchment area treatment activity.

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Assessment of Soil Erosion by the RUSLE Model Using Remote Sensing and GIS: A Case Study of Jilin Province of China

10.20944/preprints202011.0435.v1 ◽

2020 ◽

Author(s):

Ping ZHOU ◽

Yajin GE ◽

Yue JIANG ◽

Yanan XIE ◽

Zhiwen SI ◽

...

Keyword(s):

Remote Sensing ◽

Soil Erosion ◽

Soil Conservation ◽

Soil Loss ◽

Food Production ◽

Erosion Rate ◽

Slope Angle ◽

Jilin Province ◽

Rusle Model ◽

Soil Erosion Rate

The accurate assessment and monitoring of soil erosion is of great significance for guiding food production and ensuring ecological security, and it is a current research hotspot. In this paper, remote sensing and geographic information systems (GISs) are combined with the Revised Universal Soil Loss Equation (RUSLE model) to carry out research on soil erosion monitoring and make a quantitative evaluation. According to five factors, including rainfall erosivity, soil erodibility, topography, vegetation cover, crop management and water and soil conservation measures, the distribution of the soil erosion rate in Jilin Province in 2019 was mapped, and the soil erosion rate was divided into 5 levels according to the degree of erosion, including very slight, slight, moderate, severe and extremely severe erosion. Based on the segmented S-slope factor model and the unique topographical features of the study area, the relationships among the soil erosion rate, erosion risk level, erosion area, erosion amount and slope angle (θ) were systematically analysed, and a slope angle of 15° was identified as the threshold for soil erosion on sloped farmland in Jilin Province. The total soil erosion in Jilin Province was 402.14×106 t in 2019, the average soil erosion rate was 21.6 t·ha-1·a-1, and the average soil loss thickness was 1.6 mm·a-1; these values were far greater than the soil erosion rate risk threshold of 10 t ·Ha-1·a-1. Thus, the province has a strong level of soil erosion. We conclude that soil degradation is accelerating, and food production and the ecological environment will face severe challenges. It is suggested that soil erosion control should be carried out according to different types and slopes of land, with an emphasis on the management of forestland and farmland because forestland and farmland are currently the first types of land to be managed in Jilin Province. This paper aims to explore a timely, fast, efficient and convenient soil erosion monitoring and evaluation method and provide effective monitoring tools for agricultural water and soil conservation, ecological safety management and stable food production in Jilin Province and similar black soil areas.

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Assessment of Soil Erosion at Multiple Spatial Scales Following Land Use Changes in 1980-2017 in the Black Soil Region, (NE) China

10.20944/preprints202009.0082.v1 ◽

2020 ◽

Author(s):

Haiyan Fang ◽

Zemeng Fan

Keyword(s):

Land Use ◽

Soil Erosion ◽

Soil Loss ◽

Erosion Rate ◽

Spatial Scales ◽

Land Use Changes ◽

Arable Land ◽

Rainfall Erosivity ◽

Erosion Rates ◽

Soil Erosion Rate

Impact of land use and land cover (LULC) change on soil erosion is still imperfectly understood, especially in northeastern China (NEC). Based on the Revised Universal Loss Equation (RUSLE), the variability of soil erosion at different spatial scales following land use changes in1980, 1990, 2000, 2010, and 2017 was analyzed. The regionally spatial patterns of soil loss coincided with the topography, rainfall erosivity, soil erodibility, and use patterns, and around 45% soil loss came from arable land. Regionally, soil erosion rates increased from 1980 to 2010 and decreased from 2010 to 2017, ranging from 3.91 to 4.45 t ha-1 yr-1 with an average of 4.22 t ha-1 yr-1 in 1980-2017. The rates of soil erosion less than 1.41 t ha-1 yr-1 decreased from 1980 to 2010, and increased from 2010 to 2017, and opposite changing patterns occurred in higher erosion classes (i.e., above 5 t ha-1 yr-1). At a provincial scale, Liaoning Province experienced the highest soil erosion rate of 9.43 t ha-1 yr-1, followed by Jilin Province, the east Inner Mongolia, and Heilongjing Province. Arable land continuously increased at the expense of forest in the high-elevation and steep-slope areas from 1980 to 2010, and decreased from 2010 to 2017, resulting in increased areas with erosion rates higher than 7.05 t ha-1 yr-1. At a county scale, around 75% of the countries had soil erosion rate higher than its tolerance level. The county numbers with higher erosion rate increased in 1980-2010 and decreased in 2010- 2017, resulting from the sprawl and withdrawal of arable land. The results indicate that appropriate policies can control soil loss through limiting arable land sprawl in areas of unfavorable regions in the NEC.

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Impact of Land Cover Change Due to Armed Conflicts on Soil Erosion in the Basin of the Northern Al-Kabeer River in Syria Using the RUSLE Model

Water ◽

10.3390/w12123323 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3323

Author(s):

Hussein Almohamad

Keyword(s):

Soil Erosion ◽

Land Cover ◽

Land Cover Change ◽

Forest Fires ◽

Armed Conflict ◽

Soil Loss ◽

Erosion Rate ◽

Armed Conflicts ◽

Rusle Model ◽

Soil Erosion Rate

Due to armed conflicts, the sudden changes in land cover are among the most drastic and recurring shocks on an international scale, and thus, have become a major source of threat to soil and water conservation. Throughout this analysis, the impact of land cover change on spatio-temporal variations of soil erosion from 2009/2010 to 2018/2019 was investigated using the Revised Universal Soil Loss Equation (RUSLE) model. The goal was to identify the characteristics and variations of soil erosion under armed conflicts in the basin of the Northern Al-Kabeer river in Syria. The soil erosion rate is 4 t ha−1 year−1 with a standard deviation of 6.4 t ha−1 year−1. In addition, the spatial distribution of erosion classes was estimated. Only about 10.1% of the basin is subject to a tolerable soil erosion rate and 79.9% of the study area experienced erosion at different levels. The soil erosion area of regions with no changes was 10%. The results revealed an increase in soil erosion until 2013/2014 and a decrease during the period from 20013/2014 to 2018/2019. This increase is a result of forest fires under armed conflict, particularly toward the steeper slopes. Coniferous forest as well as transitional woodland and scrub are the dominant land cover types in the upper part of the basin, for which the average post-fire soil loss rates (caused by factor C) were 200% to 800% higher than in the pre-fire situation. In the period from 2013/2014 to 2019/2020, soil erosion was mitigated due to a ceasefire that was agreed upon after 2016, resulting in decreased human pressures on soils in contested areas. By comparing 2009/2010 (before war) with 2018/2019 (at the end of the war stage), it can be concluded that the change in C factors slowed down the deterioration trend of soil erosion and reduced the average soil erosion rate in more than half of the basin by about 10–75%. The area concerned is located in the western part of the basin and is relatively far from the centers of armed conflicts. In contrast, the areas with increased soil erosion by about 60–400% are situated in the northeast and east, with shorter distances to armed conflict centers. These findings can be explained by forest fires, after which the burned forests were turned into agricultural land or refugee camps and road areas. Understanding the complex biophysical and socio-economic interactions of exposure to land loss is a key to guarantee regional environmental protection and to conserve the ecological quality of soil and forest systems.

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Estimation of Soil Erosion in Nepal Using a RUSLE Modeling and Geospatial Tool

Geosciences ◽

10.3390/geosciences9040147 ◽

2019 ◽

Vol 9 (4) ◽

pp. 147 ◽

Cited By ~ 13

Author(s):

Pooja Koirala ◽

Sudeep Thakuri ◽

Subesh Joshi ◽

Raju Chauhan

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Erosion Rate ◽

Agricultural Land ◽

Entire Area ◽

Barren Land ◽

Erosion Risk ◽

Soil Erosion Rate ◽

Severe Erosion ◽

The Mean

Soil erosion is a major issue, causing the loss of topsoil and fertility in agricultural land in mountainous terrain. Estimation of soil erosion in Nepal is essential because of its agriculture-dependent economy (contributing 36% to national GDP) and for preparing erosion control plans. The present study, for the first time, attempts to estimate the soil loss of Nepal through the application of the Revised Universal Soil Loss Equation (RUSLE) model. In addition, it analyzes the effect of Land Use and Land Cover (LULC) and slope ( β ) exposition on soil erosion. Nation-wide mean annual soil loss of Nepal is estimated at 25 t ha−1 yr−1 with a total of 369 million tonnes (mT) of potential soil loss. Soil erosion based on the physiographic region of the country shows that the Middle Mountains, High Mountains, High Himal, Chure, and Terai have mean erosion rates of 38.0, 32.0, 28.0, 7.0, and 0.1 t ha−1 yr−1. The soil erosion rate by basins showed that the annual erosions of the Karnali, Gandaki, Koshi, and Mahakali River basins are 135, 96, 79, and 15 mT, respectively. The mean soil erosion rate was significantly high (34 t ha−1 yr−1) for steep slopes (β > 26.8%) and the low (3 t ha−1 yr−1) for gentle slopes (β < 5%). Based on LULC, the mean erosion rate for barren land was the highest (40 t ha−1 yr−1), followed by agricultural land (29 t ha−1 yr−1), shrubland (25 t ha−1 yr−1), grassland (23 t ha−1 yr−1), and forests (22 t ha−1 yr−1). The entire area had been categorized into 6 erosion classes based on the erosion severity, and 11% of the area was found to be under a very severe erosion risk (> 80 t ha−1 yr−1) that urgently required reducing the risk of erosion.

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Soil loss estimation and severity mapping using RUSLE model and GIS: a case study in Megech Watershed, Ethiopia

10.21203/rs.3.rs-1256356/v1 ◽

2022 ◽

Author(s):

Legese Abebaw Getu ◽

Attila Nagy ◽

Hailu Kendie Addis

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Erosion Rate ◽

Risk Category ◽

Model Parameters ◽

Main Concern ◽

Slope Length ◽

Rusle Model ◽

Soil Erosion Rate ◽

Very High

Abstract AbstractBackground: Soil erosion is the most serious problem that affects economic development, food security, and ecosystem services which is the main concern in Ethiopia. This study focused on quantifying soil erosion rate and severity mapping of the Megech watershed for effective planning and decision-making processes to implement protection measures. The RUSLE model integrated with ArcGIS software was used to conduct the present study. The six RUSLE model parameters: erosivity, erodibility, slope length and steepness, cover management, and erosion control practices were used as input parameters to predict the average annual soil loss and identify erosion hotspots in the watershed. Results: The RUSLE estimated 1,399,210 tons yr-1 total soil loss from the watershed with a mean annual soil loss of 32.84 tons ha-1yr-1. The soil erosion rate was varied from 0.08 to greater than 500 tons ha-1yr-1. A severity map with seven severity classes was created for 27 sub-watersheds: low (below 10), moderate (10-20), high (20-30), very high (30-35), severe (35-40), very severe (40-45) and extremely severe (above 45) in which the values are in tons ha-1yr-1. The area coverage was 6.5%, 11.1%, 8.7%, 22%, 30.9%, 13.4%, and 7.4% for low, moderate, high, very high, severe, very severe, and extremely severe erosion classes respectively. Conclusion: About 82 % of the watershed was found in more than the high-risk category which reflects the need for immediate land management action. This paper could be important for decision-makers to prioritize critical erosion hotspot areas for comprehensive and sustainable management of the watershed.

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GIS and Remote Sensing Supported Soil Erosion Assessment of Kamala River Watershed, Sindhuli, Nepal

International Journal of Applied Sciences and Biotechnology ◽

10.3126/ijasbt.v7i1.23307 ◽

2019 ◽

Vol 7 (1) ◽

pp. 54-61

Author(s):

Kamal Sah ◽

Sushil Lamichhane

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Erosion Rate ◽

Monsoon Season ◽

Field Measurements ◽

Landsat 8 ◽

Organic Carbon Content ◽

Gis And Remote Sensing ◽

River Watershed ◽

Soil Erosion Rate

This study analysed the situation of water-induced soil erosion in Kamala River watershed of Sindhuli, Nepal covering 23,194.33 hectares of land, extending from 85°58'11.6"E to 86°18'16.8"E longitude and 26°56'45.9"N to 27°5'44.4"N latitude. Revised universal soil loss equation was applied in GIS environment using the satellite-based data, field measurements, surveys and lab analysis. R factor predicted from the average annual precipitation. K factor based on the soil texture and organic carbon content. LS factors derived from the DEM of 20m resolution. C factor derived from the NDVI value extracted from Landsat 8 OLI imagery of the pre-monsoon season. P factor assigned according to the land cover of the study area. The study explored the massive diversity of erosion rates even within the narrow span of a landscape in the Churia range of the Himalayan foothills. As predisposed by the diversity of terrain and vegetation cover, and aggravated further by the dominance of silts in the texture of soils, soil erosion rate has been found to vary and noticeably occur in higher ranges of severity. Overall, total potential of soil loss in the watershed was 1.460 million tons/ year, out of which only 0.297 million tons of soil was estimated to be actually eroded from the watershed in the existing conditions. Conservation measures are advisable in the areas having severe soil loss. The resulting soil erosion rate map can be a guideline for developing sustainable land management strategy in the concerned and similar lower foothills of Himalayan mountain landscapes. Int. J. Appl. Sci. Biotechnol. Vol 7(1): 54-61

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Influence of DEM resolution on the RUSLE model: Case of abandoned quarries in Settat province (Morocco)

E3S Web of Conferences ◽

10.1051/e3sconf/202131404004 ◽

2021 ◽

Vol 314 ◽

pp. 04004

Author(s):

Nabil Aouichaty ◽

Yassine Bouslihim ◽

Said Hilali ◽

Abdeljalil Zouhri ◽

Yahya Koulali

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Erosion Rate ◽

Loss Rate ◽

Model Case ◽

Rusle Model ◽

Topographic Slope ◽

Soil Erosion Rate ◽

The Impact ◽

Soil Loss Rate

Topographic slope information is one of the critical variables, which governs soil erosion. This topographic slope can be derived from the Digital Elevation Model (DEM). Significant discrepancies are found in the estimation of soil erosion using different DEMs of different resolutions. In the present study, the Revised Universal Soil Loss Equation (RUSLE) was used for soils in the Settat province (Morocco) to assess the risk of water erosion caused by abandoned quarries. The soil erosion rate was divided into five classes to illustrate the erosion rate variability using two DEMs (30m and 90m). The impact of topography on erosion was determined by calculating the value of the LS factors. In this case, the values obtained vary between 0 - 120.623 for ASTER DEM (30m) and 0 - 10.225 for DEM SRTM (90m). The results also show that most quarries have a soil loss rate that varies between 0 t/ha/year and 8.1 t/ha/year for ASTER DEM (30 m). However, for DEM SRTM (90 m), the soil loss rate is zero. This suggests that RUSLE model users should use high-resolution input data for a close representation of reality and capture the maximum results with reasonable accuracy.

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