Spatial Patterns Of Soil Erosion Research Articles

Soil erosion prediction and spatiotemporal heterogeneity in driving effects of precipitation and vegetation on the northern slope of Tianshan Mountain

Soil erosion changes become more unclear in the future due to climate change and increased human activity. However, the spatiotemporal heterogeneity in the driving effects of key factors on soil erosion remains inadequately understood on the northern slope of the Tianshan Mountains (NSTM). In this paper, the spatiotemporal evolution of soil erosion on the NSTM was evaluated in the historical period (2000-2015) by the Revised Universal Soil Loss Equation (RUSLE) model. Subsequently, the FLUS-CMIP6-RUSLE model was employed to predict soil erosion in the future period (2030-2050). Then, the GTWR model was used to dissect the spatiotemporal heterogeneity in the driving effects of precipitation and vegetation. The results indicated that soil erosion on the NSTM fluctuated from 2000 to 2015, increasing by 1.77 t ha −1 yr −1. Soil erosion areas mainly occurred in Changji City, Fukang City, Hutubi County, Mulei County, Qitai County, and Turpan City. The soil erosion modulus in 2030, 2040, and 2050 under different climate scenarios will be 26.56,28.83, and 37.49 t ha −1 yr −1, respectively. The spatial pattern of soil erosion is similar to the historical period. Precipitation was the main factor intensifying soil erosion in the eastern and western counties from 2000 to 2020, while the soil erosion change in central counties was affected by vegetation. In the future, soil erosion change in the counties (i.e., Karamay, Yili, Tacheng, Shihezi, Manas, Qitai, and Mulei ) will be driven by precipitation. The vegetation will be the main driver of soil erosion change in central and eastern counties (i.e., Urumqi, Turpan, Fukang City, Jimsar County, and Changji City). This study firstly attempts to explore the spatiotemporal heterogeneity in the driving effects of precipitation and vegetation using Spatial-Temporal Geographically Weighted Regression (GTWR) in urban agglomerations of NSTM. It is meaningful to explore erosion-prone regions and to implement rational soil conservation measures in the study region and similar regions in the world.

Mapping and Quantifying Integrated Land Degradation Status of Goa Using Geostatistical Approach and Remote Sensing Data

Globally, land degradation is becoming a grave concern. Over the years, conditions such as drought, extreme weather events, pollution, changes in land use land cover, and desertification have intensified and led to land degradation, affecting both ecological and economic processes. Equally, during the last two centuries, population and urbanization have amplified manifold and increased the demand for additional food and shelter, resulting in alteration in land use land cover, over-grazing, and over-cultivation, loss of nutrient-rich surface soil, greater runoff from the more impermeable subsoil, and reduced water availability. Geographically, Goa is a highly diversified state. It is sandwiched between the West Coast and the Western Ghats. The state is blessed with beaches, mangroves, backwaters, wetlands, wildlife sanctuaries, evergreen forests, barren lands, and other vital ecosystems. The State of Goa, on average, receives more than 3000 millimeters of rainfall annually with high surface runoff. Using both primary and secondary data, this study sought to investigate and quantify the state’s land degradation. Secondary data came from satellites and other sources, while primary data came from field observation and ground truthing. Land degradation factors related to soil loss and the spatial pattern of soil erosion are predicted and evaluated using the Revised Universal Soil Loss Equation (RUSLE) method. Landsat-8 OLI-TIRS images were utilized to decide land use and cover (C factor), while DEM information was utilized to assess (LS factor). A soil map and rainfall data were collected to acquire a better understanding of soil erodibility (K factor) and rainfall erosivity (R factor). The kriging interpolation technique was used to gain a deeper comprehension of land degradation.The purpose of this paper is to comprehend the concept of integrated land degradation and how it affects the environment of Goa. Using remote sensing data and geostatistical methods, the study creates a comprehensive map of land degradation in the region by identifying and analyzing the various forms of land degradation in Goa. The paper also looks at how rainfall and the amount of land cover affect the rate of soil erosion in Goa. According to the findings, intense rainfall makes the eastern part of Goa particularly susceptible to soil erosion, and bare soil has a greater potential for erosion than vegetated land. The paper concludes that comprehensive land degradation mapping can be a useful tool for developing efficient land management strategies to preserve soil and encourage sustainable development in the region.

Exploring driving factors of soil erosion using a Multiscale GWR model: a case study at Central Citarum Watershed, West Java, Indonesia

To address challenges in soil erosion management and ecological rehabilitation, understanding the determinants of soil erosion is crucial. This research aims to achieve two primary objectives: (1) delineating the spatial patterns of soil erosion within the designated region and (2) identifying the influential factors using the Multiscale Geographical Weighted Regression (MGWR) methodology. The methodological framework involved the creation of grid datasets, with soil erosion as the response variable and a combination of physical and socioeconomic attributes as predictors. We extracted 550 data points from raster datasets, specifically centered on village locations, using the ‘extract multi-value to point’ tool in ArcGIS. The R Studio environment was utilized to select the relevant factors influencing soil erosion. The geographical detector technique was applied to determine the explanatory power of each determinant concerning the spatial patterns of soil erosion. Subsequently, data from the Ordinary Least Squares (OLS) model underwent MGWR analysis. The findings reveal that the Central Citarum Watershed experiences an estimated annual soil erosion of 23.16 million tons, averaging 102.01 tons per hectare. The analysis identified LS (slope length and gradient) and CP (vegetative cover and supportive practices) as the primary variables influencing the spatial variability of soil erosion. Notably, the MGWR model demonstrated enhanced explanatory capacity and effectiveness compared to both the OLS and Geographically Weighted Regression (GWR) methodologies.

Assessment of soil loss using RUSLE around Mongolian mining sites: a case study on soil erosion at the Baganuur lignite and Erdenet copper–molybdenum mines

Mining constitutes an integral part of Mongolia’s national economy and dominates the country’s export revenue. At the same time, a wide range of mining impacts on soil, water resources, the atmosphere and the biosphere have been documented across the country. This case study addresses the long-term soil degradation around two mining sites located in the semi-arid steppe zone of Mongolia: the open-cast lignite mine of Baganuur about 140 km east of Ulaanbaatar, and the open-pit copper–molybdenum mine of Erdenet about 240 km northwest of Ulaanbaatar, both of which started commercial extraction in the late 1970s. For the assessment of soil erosion, the RUSLE model was applied in different seasons for the period from 1989 to 2018 at 3-year intervals, considering both climatic variation and the expansion of the mines based on maps and satellite imagery. Rainfall erosivity was identified as the most dominant factor driving soil erosion in the study regions, with mining leading to local increases in soil erodibility. The highest soil erosion rates were found in both areas in July 2018, reaching 7.88 t ha–1 month–1 in the Erdenet area and 9.46 t ha–1 month–1 in the Baganuur area. The spatial patterns of soil erosion showed higher soil loss rates were in the vicinity of the mines and adjoining industrial sites. Particularly high soil losses were identified in July 1998, July and August in 2013 and July 2018 in both mining areas. The combination of the RUSLE model, remote sensing and ground truth data as and their processing by GIS was found to be a time-saving and cost-effective technique for continuous monitoring of soil erosion and planning of preventive measures in and around mining areas.

Exploring Soil Particle Size Fraction and Spatial Redistribution of 137Cs in Sloping Landscapes with Different Lynchet Heights of Terracing Hedgerows in the Remote Mountain Region of Southwestern China

Soil erosion is a global environmental problem related to anthropogenic activities which are influenced by natural factors. The sloping cultivated lands, with serious soil erosion, constitute a significant proportion of the landscape in the remote mountain regions of southwestern China. The traditional soil conservation strategy, involving a certain height of lynchets on the edge of the terracing hedgerows of the sloping lands, plays an effective part in soil and water conservation. A typical sloping landscape with a lynchet of terracing hedgerows was chosen in this study. The objective of this study was to explore soil particle size fraction and spatial redistribution of 137Cs in sloping landscapes with different lynchet heights of terracing hedgerows. The results showed that fine-grained sediments were deposited in front of the lynchet of terracing hedgerows, especially particle sizes grouped at <0.002 mm clay and 0.002–0.02 mm silt. The 137Cs concentration profiles of the lynchet from the upper to the lower sloping landscape showed first increasing and then decreasing trends when the soil depth increased. 137Cs inventory generally increased along with the whole sloping landscapes. Moreover, the results suggested that the mean 137Cs inventory and erosion rate could be represented by the average value of the middle slope position. The highest value of annual erosion modulus reached 4917.06 t km−2 a−1 on the upper site of the sloping lands. Moreover, the annual erosion modulus was synchronously reduced from the upper to the lower sloping landscape and the erosion rate had a similar trend. Meanwhile, the K values of soil erodibility changed from 0.0338 t hm2 h (hm−2 MJ−1 mm−1) to 0.0375 t hm2 h (hm−2 MJ−1 mm−1) along the slope length. There was a logarithmic relationship between the K value and the 137Cs inventory. Therefore, it is useful to study spatial patterns of soil erosion in different slope positions with different heights of lynchet of terracing hedgerows of the whole sloping landscape. Moreover, it is important to implement a soil conservation strategy in the remote mountain regions of China.

Prediction of soil erosion and sediment transport in a mountainous basin of Taiwan

Soil erosion substantially implicates global nutrient and carbon cycling of the land surface. Its monitoring is crucial for assessing and managing global land productivity and socio-economy. The Zhuoshui River Basin, the largest catchment, in Taiwan is highly susceptible to soil erosion by water due to extremely high rainfall, rugged terrain, easily eroded soil, and intensively agricultural cultivation over the steep land. Hence, this study examines the annual soil erosion rate for 2005, 2011, and 2019 and the average long-term soil erosion and sediment yield (SY) during 2005–2019. Coupling of the Revised Universal Soil Loss Equation (RUSLE) and sediment delivery ratio (SDR) models is implemented using remote sensing and GIS techniques. The soil erosion rate is classified into five classes, namely mild (0–10 t ha−1 year−1), moderate (10–50 t ha−1 year−1), moderately severe (50–100 t ha−1 year−1), severe (100–150 t ha−1 year−1), and very severe (> 150 t ha−1 year−1). Over one half of the total area is categorized as moderate and moderately severe classes, and one-third of the whole basin as severe and very severe classes. Recently, mild and moderate classes increase, while moderately severe, severe, and very severe decrease. During 2005–2019, the annual soil loss rate ranges from 0.00 to 6,881.88 t ha−1 year−1 with an average rate of 122.94 t ha−1 year−1. Among the SDR models, the RUSLE combined with the SDR model with the length and slope gradient of mainstream shows satisfactory sediment yield estimation. Predictably, the downstream receives a massive sediment delivery from all upper streams (246.06 × 106 t year−1), and the percent bias values for all sub-basins are below ± 39.0%. The study provides a rapid approach to investigate soil erosion and sediment yield, and it can be applied to the other basins in Taiwan. More importantly, information about spatial patterns of soil erosion and SY is critical to establish suitable measures to achieve effective watershed planning and optimize the regional productivity and socio-economy. The proposed approach is potentially to identify risk areas, conduct scenario estimation for management, and perform spatiotemporal comparison of soil erosion, while adjustment in the empirical formulas of the proposed approach may be needed when it is applied to the other regions, especially outside Taiwan.

Spatial pattern of soil erosion using RUSLE model and GIS software at the Saf Saf watershed, Algeria

Soil erosion is one of the problems threatening the Algerian environment. In agriculture, soil erosion leads to the thinning of the topsoil under the effect of the natural erosive forces of water, or under the effect of agricultural activities. The present study aims to estimate average soil loss rate and to identify vulnerable zones. Through the integration of RUSLE model at the Saf Saf watershed, various parameters are utilized such as the rainfall erosivity factor (R), soil erodibility factor (K), slope length - slope factor (LS), crop management factor (C) and practice management factor (P). All these parameters are prepared and processed through a geographic information system (GIS) and remote sensing using various database sources. The results reveal that the river basin has an average annual soil loss of 3.9 t ha−1 yr−1, and annual soil loss of 4.53 million tonnes for the period 1975-2017. Meanwhile, eighty five percent of the study area is experiencing acceptable rate of soil erosion loss, which is ranging between 0 to 5 t ha−1 yr−1. The present study of risk assessment can contribute to understand the spatial pattern of soil erosion in order to use appropriate conservation practices for sustainable soil management.

Spatial Pattern of Soil Erosion in Relation to Land Use Change in a Rolling Hilly Region of Northeast China

The black soil region in Northeast China is one of the major grain-producing areas of China. Soil erosion in the black soil region caused by natural factors and anthropic activities has attracted much attention, especially in a rolling hilly region. Compared with natural factors, the land use factor of cropland encompasses the most easily optimized measures. Jiutai County of Changchun City, located in the hilly areas of Northeast China, was taken as an example to calculate the soil erosion modulus using the Revised Universal Soil Loss Equation model. The overall soil erosion status of cultivated land in the study area was mainly slight and light, the proportion of cultivated land affected by extremely intensive and severe erosion was relatively small, and the average soil erosion modulus was 7.09 t·hm−2·a−1 in 2019. In view of spatial distribution characteristics of soil erosion revealed by the spatial aggregation and hot spot analysis, the most serious soil erosion intensity was concentrated in the southeast and northeast sloping farmland over 8°. With the increase in elevation and topographic slope, the proportion of slight and light soil erosion gradually decreased, which was closely related to the increase in soil erodibility caused by the space–time migration of soil organic carbon caused by the interaction of hydraulic and tillage erosion in complex topographic areas. The Geographically Weighted Regression model was introduced to explore the driving factors and superposition mechanism of farmland soil erosion in the hilly region of Northeast China. Based on the relationship between soil erosion and landscape fragmentation, landscape fragmentation was an important driving force promoting soil erosion, sediment yield, and sediment transport. This paper is committed to providing a basis for accurately deploying regional soil and water conservation measures and formulating macro land management policies.

Sediment yields variation and response to the controlling factors in the Wei River Basin, China

Assessing regional sediment yield variation and their responses to the potential controlling factors are critical to develop specific strategies of soil conservation measures to adapt to future climate change. This study attempted to investigate the spatial–temporal variation of sediment load in the Wei River basin in the midstream of the Yellow River during 1961–2015 at 15 hydrological stations. The results indicated that annual sediment load in the past six decades decreased significantly (P < 0.01) with the changing trends of −6.43 × 104, −3.86 × 104, −4.6 × 104 t/a at Xianyang, Zhangjiashan, and Zhuangtou stations, respectively. Annual sediment load exhibited abrupt changes in the mid-1990s, which were largely attributed to the strong effects of soil conservation measures in the study area. The spatial pattern of soil erosion were characterized by high sediment yield in the north with sparse vegetation cover and well-developed gullies, and low sediment yield in the south with flat plain and good vegetation cover in the Wei River basin. The results of the partial least squares-structural equation model (PLS-SEM) showed that vegetation changes and rainfall variability explained 62.3%, 47.3%, and 40.1% of the variation in runoff at Xianyang, Zhuangtou, and Zhangjiashan stations, respectively, whereas 59.4%, 17.6% and 48% of the variation in sediment load were explained by the combining effects of rainfall variability, changes of vegetation and runoff. This study provides a deep insight for understanding the effects of driving forces on sediment yield changes, and can be useful to regional soil conservation planning in the region.

Testing the impacts of wildfire on hydrological and sediment response using the OpenLISEM model. Part 1: Calibration and evaluation for a burned Mediterranean forest catchment

Models are typically applied to estimate the potential adverse effects of fire on land degradation and water resources and the potential benefits of post-wildfire rehabilitation treatments. However, few modeling studies have been conducted for meso-scale catchments, and only a fraction of these studies include transport and deposition of eroded material within the catchment or represent spatial erosion patterns. This study presents an application and evaluation of the OpenLISEM physically-based and spatially-distributed hydrological and soil erosion model for a burned Mediterranean meso-scale catchment (18.5 km2) in a data-scarce environment, using a robust parameterization and calibration procedure: (1) integrating satellite imagery and the topographic wetness index to support model parameterization; (2) event-based automated calibration using the Model-Independent Parameter Estimation and Uncertainty Analysis and parameters ensemble for before and after the fire; (3) a jack-knife cross-validation for model evaluation. The study shows that this procedure used in OpenLISEM provides reasonable results for pre- and post-wildfire catchment discharge and sediment transport (r2 and NSE > 0.5; absolute PBIAS < 25% for discharge and 55% for sediment transport). This may serve the needs of model applications in data-scarce burned areas. The results also provide recommended model parameters for burned areas with high severity such as random roughness (rr) = 2.41 cm, and slope manning’s n (n) = 0.038; or post-wildfire to pre-wildfire ratios of model parameters such as saturated hydraulic conductivity (Ks) = 0.98×, channel manning’s n (chn) = 0.44×, grain size (d50) = 0.61 × . The simulation results indicate that wildfire did not lead to significantly enhanced hydrological responses and soil erosion at the catchment outlet, partly explained by the spatial patterns of soil erosion. For both pre- and post-wildfire conditions, higher soil erosion was simulated in areas located far from the catchment outlet. The wildfire led to enhanced hillslope erosion, mostly in the upper part of the catchment, providing ample opportunities for transported sediment to deposit before reaching the outlet.

Landscape scaling of different land-use types, geomorphological styles, vegetation regionalizations, and geographical zonings differs spatial erosion patterns in a large-scale ecological restoration watershed.

Water erosion is one of the main types of soil degradation, but few quantitative estimates have been done in the soil erosion intensity grades of different landscape scaling characteristics in a large-scale ecological restoration watershed. This study comparatively illustrates the utility of high-resolution data and geospatial technique, particularly the GIS-based RUSLE model, for an improved understanding of the spatial patterns of soil erosion under different land use types, geomorphological styles, vegetational regionalizations, and geographical zonings, aiming to promote ecological sustainable watershed management in practice. Results indicate the following:(i) The soil erosion intensity grade showed an overall decreasing trend from northwest to southeast in the Jinghe River Watershed. The percentages of intense erosion and extremely intense erosion in different land use types in 2015 were significantly lower than that in 2000 except middle- and low-coverage grasslands because of the effective implementation of "Grain for Green" project. e.g., the percentage of extremely intense erosion in dry land decreased from 41.72% in 2000 to 8.99% in 2015.(ii) The mid-elevation loessial beams and hills and mid-elevation loessial tableland were both the major sources of intense erosion and extremely intense erosion, with the contribution ratio of 41.89 and 53.94% and 35.79 and 20.83% in 2000 and 44.45 and 48.99% and 36.88 and 18.72% in 2015, respectively.(iii) The intense, extremely intense and severe erosion in the temperate typical grassland subzone accounted for 32.62, 44.47, and 9.6% in 2000 and 36.74, 40.7, and 8.1% in 2015, respectively. There were no severe and extremely intense erosion in the deciduous oak forest subzone of northern and southern warm temperate. (iv) The intense, extremely intense, and severe erosion of the loessial wide valley hills accounted for 32.19, 45.27, and 8.92% in 2000 and 36.32, 41.42, and 7.48% in 2015. The intense and extremely intense erosion of the loessial tableland accounted for 40.58 and 16.02% in 2000 and 40.57 and 14.27% in 2015. More importantly, land use types can be applied to the design and implementation of a development scheme more reliably than other landscapes because the land use system is a more scientific and fine classification based on topography, soils, and vegetation correlated with geology, geomorphology, and climate. The knowledge on the fine landscape scale will be useful for comprehensive watershed management by soil scientists, agronomists, environmental scientists, land managers, and policymakers.

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

BackgroundSoil 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.ResultsThe 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.ConclusionsUse 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.

Assessing spatial patterns of soil erosion in a high‐latitude rangeland

AbstractHigh‐latitude areas are experiencing rapid change: we therefore need a better understanding of the processes controlling soil erosion in these environments. We used a spatiotemporal approach to investigate soil erosion in Svalbarðstunga, Iceland (66°N, 15°W), a degraded rangeland. We used three complementary datasets: (a) high‐resolution unmanned‐aerial vehicle imagery collected from 12 sites (total area ~0.75 km2); (b) historical imagery of the same sites; and (c) a simple, spatially‐explicit cellular automata model. Sites were located along a gradient of increasing altitude and distance from the sea, and varied in erosion severity (5–47% eroded). We found that there was no simple relationship between location along the environmental gradient and the spatial characteristics of erosion. Patch‐size frequency distributions lacked a characteristic scale of variation, but followed a power‐law distribution on five of the 12 sites. Present total eroded area is poorly related to current, site‐scale levels of environmental stress, but the number of small erosion patches did reflect site‐level stress. Small (&lt;25 m2) erosion patches clustered near large patches. The model results suggested that the large‐scale patterns observed likely arise from strong, local interactions, which mean that erosion spreads from degraded areas. Our findings suggest that contemporary erosion patterns reflect historical stresses, as well as current environmental conditions. The importance of abiotic processes to the growth of large erosion patches and their relative insensitivity to current environmental conditions makes it likely that the total eroded area will continue to increase, despite a warming climate and reducing levels of grazing pressure.

Soil erosion risk assessment in the Umzintlava catchment (T32E), Eastern Cape, South Africa, using RUSLE and random forest algorithm

ABSTRACT The Revised Universal Soil Loss Equation (RUSLE), based on remotely sensed data, is an important tool for assessing erosion prone areas and serves as a guide towards soil conservation efforts. Besides being a crucial data source from which RUSLE parameters can be derived, remotely sensed data can also be used independently to delineate erosion features. This study aims to assess soil erosion risk in the Umzintlava catchment using two independent methods, i.e. RUSLE and Random Forest (RF), and explore the relationship between soil loss and erosion factors as represented by different RUSLE parameters. To achieve this, rainfall, soil, digital elevation, and satellite data were used. The results indicate that a considerable portion (>90%) of the catchment area is of ‘very low’ to ‘low’ erosion risk, while the remainder suffers ‘moderate’ to ‘extremely high’ erosion risk. Among erosion factors, the LS-factor (slope length and steepness) showed strong correlation with soil erosion (p < 0.001; R2 = 0.954). This suggests that areas with steep slopes are the most vulnerable to hillslope erosion, whereas gully erosion is prominent in areas with gentle to nearly flat slopes. The integration of RUSLE-derived soil loss and RF-derived erosion features successfully delineated the spatial patterns of soil erosion across the Umzintlava catchment, providing useful information on erosion risk at least costs. This information is instrumental in targeted management interventions to combat soil erosion within the catchment.

Spatial gradient and quantitative attribution of karst soil erosion in Southwest China.

Soil erosion estimation has attracted considerable attention from the scientific community and governments because of its importance to sustainable regional development. In karst areas, the heterogeneous environment and rocky desertification create difficulties in determining the influencing factors and spatial patterns of soil erosion. A quantitative analysis of karst soil erosion distribution was conducted by applying the revised soil loss equation model and the geographical detector method of attribution identification, which was based on spatial variance analysis. The results show that soil erosion was most severe in areas with an elevation of 1200-1800m and intense anthropogenic activity. When the vegetation coverage was below 0.5-0.6, soil erosion showed characteristics of a source-limited regime and increased with the increasing vegetation coverage. When the vegetation coverage was higher than 0.5-0.6, soil erosion followed a transport-limited regime and decreased with the increasing vegetation coverage. The factor detector showed land use to be the dominant factor, explaining 51% of soil erosion distribution. Among various land use types, dry land had the greatest vulnerability to soil erosion. Slope served as a controlling factor at large scales, especially when combined with annual precipitation exceeding 1500mm, and in dry and grassland areas. From the attribution analysis of multiple factors, the combination of land use and slope was the controlling interaction factor explaining 68% of soil erosion distribution. The methods and results of this research could serve as scientific references for decision makers and researchers exploring the characteristics of soil erosion to develop effective measures for its control.

Model-based assessment soil loss by wind and water erosion in China's Loess Plateau: Dynamic change, conservation effectiveness, and strategies for sustainable restoration

Abstract Soil erosion is a widespread environmental problem that threatens environmental sustainability. The Loess Plateau (LP) in China is one of the most severely eroded areas in the world. In this study, the soil erosion dynamics were assessed by applying the Revised Universal Soil Loss Equation and the Revised Wind Erosion Equation; further, the underlying drivers for soil erosion processes were investigated. In addition, implications of constraint effects in soil erosion control were discussed. The results showed that there was a substantial reduction in both wind and water erosion between 2000 and 2015; it is considered that vegetation restoration (mainly in relation to government-aided ecological restoration programs), increased precipitation, and a decrease in wind speed may have contributed to these trends. Land cover and soil properties contribute to spatial patterns of soil erosion. Thus, ecological restoration programs have promoted improvements in soil properties, leading to an eventual reduction. Constraint line analysis indicates that vegetation cover has a nonlinear and threshold effect on soil erosion through constraining the water condition (i.e., rainfall). With respect to water erosion, when rainfall is below the threshold (approximately 450–500 mm), it is not sufficient to maintain a good vegetation cover (about 30–40%), and vegetation cannot efficiently prevent soil erosion; however, once rainfall exceeds the threshold, the soil retention function of the vegetation is enhanced and soil loss is substantially reduced. In addition, there is a lower (10%) and an upper (40%) threshold at which vegetation can control wind erosion, which implies that plant cover lower than 10% has a minimal effect on reducing the wind velocity at the soil surface, and the effect of vegetation on reducing wind erosion is at its maximum when plant cover is 40% or above. However, if human intervention is removed, the limited amount of rainfall on the LP would be insufficient to support large areas of trees in the long-term; therefore, the constraint effects of the water condition on vegetation cover need to be considered to improve the efficiency of afforestation and reforestation efforts aimed at mitigating and preventing soil loss.

Temporal and Spatial Uncertainty of Erosion Soil Loss from an Argisol Under Sugarcane Management Scenarios

ABSTRACT The identification of erosion-susceptible areas is fundamental for the adoption of soil conservation practices. Thus, the best way to estimate the spatial pattern of soil erosion must be identified, in which the process uncertainties are also taken into consideration. The purpose of this study was to evaluate the spatial and temporal uncertainty of soil loss under two scenarios of sugarcane harvest management: green cane (GC) and burnt cane (BC). The study was carried out on a 200-ha area, in [...]

Time series estimation of soil erosion under mining subsidence in sandy area based on GIS/RS, inner Mongolia, China

The land subsidence caused by underground coal mining can lead to topographic change and cause a series of environmental problems such as soil erosion and vegetation degradation, etc. Research on the effects of soil erosion is of great significance to the ecological restoration and environmental protection of mining areas. This paper selected the Bulianta coal mine in Inner Mongolia as a research area. Based on GIS/RS technology, the change in the spatial patterns of soil erosion in the study area were quantitatively evaluated using the rule of mining subsidence and the Universal Soil Loss Equation (USLE). The results show that underground coal mining has a significant effect on terrain by changing the slope length and slope steepness (LS), vegetation growth was also affected by a trend of overall reduction. In general, soil erosion in the Bulianta mining area generally showed an increasing trend from pre-mining topography to surface subsidence after mining, both from the perspective of soil erosion area and the perspective of soil erosion quantity.

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO3) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO3 and changes in elevation consistent with the conceptual model; CaCO3 generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO3 to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO3.

Assessing the impact of land use change on soil erosion on the Loess Plateau of China from the end of the 1980s to 2010

Using combined information from a land use and land cover change database and a soil erosion dynamic database from the end of the 1980s to 2010, we analyzed the temporal and spatial patterns of soil erosion on the Loess Plateau in China. We also analyzed the influence of changes of land use and land cover on soil erosion. The results show that (1) grassland change had the greatest effect on soil erosion in the Loess Plateau, followed by farmland change and unused land change; (2) the soil erosion situation in the Loess Plateau deteriorated from the end of the 1980s to 2000 and then improved from 2000 to 2010 due to the implementation of the Chinese Grain to Green Project; and (3) soil erosion in all regions of the Loess Plateau increased prior to 2000 and decreased after that, except in the desert and agricultural regions.