Soil Erosion Assessment Research Articles

Predicting the Spatial Distribution and Severity of Soil Erosion in the Global Tropics using Satellite Remote Sensing.

Simple SummaryThis research paper presents the first consistent spatial assessments of soil erosion prevalence based on Earth observation data and systematic field surveys to train models that predict soil erosion prevalence across the global tropics. The results of the study have major implications for efforts to identify land degradation hotspots and design interventions to restore degraded lands given the importance of soil erosion as a major process of land degradation globally. The paper also presents analysis of changes in soil erosion prevalence over the 15 year period from 2002 to 2017. The results of the study can also be used to better understand ecosystem health and resilience in the global tropics, for assessments of food security, and the implications of land degradation on climate change.Soil erosion has long been recognized as a major process of land degradation globally, affecting millions of hectares of land in the tropics and resulting in losses in productivity and biodiversity, decreased resilience of both marine and terrestrial ecosystems, and increased vulnerability to climate change. This paper presents an assessment of the extent of soil erosion in the global tropics at a moderate spatial resolution (500 m) based on a combination of systematic field surveys using the Land Degradation Surveillance Framework (LDSF) methodology and Earth observation data from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. The highest erosion prevalence was observed in wooded grassland, bushland, and shrubland systems in semi-arid areas, while the lowest occurrence was observed in forests. Observed erosion decreased with increasing fractional vegetation cover, but with high rates of erosion even at 50–60% fractional cover. These findings indicate that methods to assess soil erosion need to be able to detect erosion under relatively dense vegetation cover. Model performance was good for prediction of erosion based on MODIS, with high accuracy (~89% for detection) and high overall precision (AUC = 0.97). The spatial predictions from this study will allow for better targeting of interventions to restore degraded land and are also important for assessing the dynamics of land health indicators such as soil organic carbon. Given the importance of soil erosion for land degradation and that the methodology gives robust results that can be rapidly replicated at scale, we would argue that soil erosion should be included as a key indicator in international conventions such as the United Nations Convention to Combat Desertification.

Assessment of Soil Erosion in Siruvani Watersheds based on USLE and Hypsometric Curve Methods

Soil erosion poses a serious threat over the maintenance activities of a reservoir and its watershed. This study has been taken us to assess the extent of soil erosion in the watersheds of the Siruvani Reservoir located in the district of Kerala. Two methods namely, Universal Soil Loss equation (USLE) and Hypsometric curve methods are adapted in this study. Hypsometry of watersheds (area-elevation analysis) has generally been used to reveal the stages of geomorphic development (stabilized, mature and young). The watersheds of Siruvani Reservoir were delineated from the generated Digital Elevation Model (DEM) using Geographic Information System (GIS). Various set of data like land use map, rainfall and soil map have been used for this analysis to generate five factors namely finally, the soil erosion risk map was created to identify the regions which are susceptible to erosion. Hypsometric analysis deals with measurement of the interrelationships between basin area and altitude of basin which has been used to understand the influence of various factors such as climate, geology and tectonic changes. GIS provides advanced tools to obtain hypsometric information and also helps to estimate the associated parameters of landforms. The entire study area has been sub-divided into 5 watersheds for hypsometric analysis and their area ranges from 1.77 to 6.94 km2. The hypsometric curve of the whole the watersheds reflects the mature geomorphic terrain whereas hypsometric integral indicates that the drainage basin has already eroded per cent of land masses. These findings would emphasize the importance of soil and water conservation measures to be taken up in the Siruvani watersheds for controlling further erosion, reducing the sediment outflows and conserve water

Assessment of Soil Erosion in Siruvani Watersheds Based on USLE and Hypsometric Curve Methods

Soil erosion poses a serious threat over the maintenance activities of a reservoir and its watershed. This study has been taken us to assess the extent of soil erosion in the watersheds of the Siruvani Reservoir located in the district of Kerala. Two methods namely, Universal Soil Loss equation (USLE) and Hypsometric curve methods are adapted in this study. Hypsometry of watersheds (area-elevation analysis) has generally been used to reveal the stages of geomorphic development (stabilized, mature and young). The watersheds of Siruvani Reservoir were delineated from the generated Digital Elevation Model (DEM) using Geographic Information System (GIS). Various set of data like land use map, rainfall and soil map have been used for this analysis to generate five factors namely finally, the soil erosion risk map was created to identify the regions which are susceptible to erosion. Hypsometric analysis deals with measurement of the interrelationships between basin area and altitude of basin which has been used to understand the influence of various factors such as climate, geology and tectonic changes. GIS provides advanced tools to obtain hypsometric information and also helps to estimate the associated parameters of landforms. The entire study area has been sub-divided into 5 watersheds for hypsometric analysis and their area ranges from 1.77 to 6.94 km2. The hypsometric curve of the whole the watersheds reflects the mature geomorphic terrain whereas hypsometric integral indicates that the drainage basin has already eroded per cent of land masses. These findings would emphasize the importance of soil and water conservation measures to be taken up in the Siruvani watersheds for controlling further erosion, reducing the sediment outflows and conserve water.

Assessment of soil erosion rates in a Mediterranean cultivated and uncultivated soils using fallout 137Cs

Fallout radionuclides, such as 137Cs, have been recognized as a valuable means for studying soil erosion processes. In this study, the 137Cs technique was used to assess soil erosion magnitude and to investigate the effectiveness of terrace cultivations in the High Atlas of Morocco, particularly, the Ourika watershed. 137Cs depth distribution profiles were established along slope gradients associated with land use considering cereal crop, arboriculture and native forests.Along the slope gradient, depth distribution profiles highlight dissimilarities reflecting differences in land use, topographic roughness, soil particle distribution and stoniness. 137Cs inventory decreases exponentially with depth and its penetration increases along the slope. It becomes higher at the bottom of the slope, with penetration reaching 30 cm.137Cs mass activity and inventory significantly decrease in cultivated terraces, compared to native forest. Although the pattern of 137Cs gain/loss varied according to topography, soil properties and vegetation cover, showing eroding and aggrading profiles, most samples had 137Cs values lower than the reference value, suggesting net-loss of soil as a consequence of erosion processes.The net erosion rates were estimated about 8.5 and 6.0 t ha−1 yr−1 in cereal crop and arboriculture agrosystems, respectively, whereas in the forest, the net erosion rate was lower at about 4.2 t ha−1 yr−1.Soil loss on agricultural terraces is not significantly above tolerable erosion rates for Morocco (<7 t ha−1 yr−1), particularly for arboriculture terraces. A strong correlation was observed between 137Cs and both soil organic carbon and slope gradient, in uncultivated area, whereas, there was no correlation for cultivated terraces.Terraced arboriculture systems should be encouraged for better soil preservation against water erosion in the Ourika watershed.

Soil Erosion Spatial Prediction using Digital Soil Mapping and RUSLE methods for Big Sioux River Watershed

A recent conversion of the grasslands to cropland degrading the soil quality (SQ), and impacting the soil erosion and crop productivity in the West Corn Belt (WCB) of the USA. The current study was conducted to estimate the spatial distribution of soil erosion at Big Sioux River (BSR) watershed scale using the Geographical Information System (GIS)-enabled Revised Universal Soil Loss Equation (RUSLE). Spatial data such as weather, a digital elevation model (DEM), land use maps and soils were used for assessment of soil erosion was downloaded from the easily available online sources. Data showed that about 7% of grassland acreage reduced from 2008 (24%) to 2015 (17%), whereas, about 7.4% of cropland acreage increased from 2008 (64.6%) to 2015 (72%) in the BSR watershed. This grassland conversion to cropland increased the soil erosion (estimated using the RUSLE model) from 12.2 T ha−1 year−1 in 2008 to 14.8 T ha−1 year−1 in 2015. The present study concludes that grassland conversion to cropland in the BSR watershed increased the soil erosion, therefore, management practices essential to be applied to reduce the erosion risk and various other ecosystem services.

Quantitative Assessment of Soil Erosion Based on CSLE and the 2010 National Soil Erosion Survey at Regional Scale in Yunnan Province of China

Regional soil loss assessment is the critical method of incorporating soil erosion into decision-making associated with land resources management and soil conservation planning. However, data availability has limited its application for mountainous areas. To obtain a clear understanding of soil erosion in Yunnan, a pixel-based estimation was employed to quantify soil erosion rate and the benefits of soil conservation measures based on Chinese Soil Loss Equation (CSLE) and data collected in the national soil erosion survey. Results showed that 38.77% of the land was being eroded at an erosion rate higher than the soil loss tolerance, the average soil erosion rate was found to be 12.46 t∙ha−1∙yr−1, resulting in a total soil loss of 0.47 Gt annually. Higher erosion rates mostly occurred in the downstream areas of the major rivers as compared to upstream areas, especially for the southwest agricultural regions. Rain-fed cropland suffered the most severe soil erosion, with a mean erosion rate of 47.69 t∙ha−1∙yr−1 and an erosion ratio of 64.24%. Lands with a permanent cover (forest, shrub, and grassland) were mostly characterized by erosion rates an order of magnitude lower than those from rain-fed cropland, except for erosion from sparse woods, which was noticeable and should not be underestimated. Soil loss from arable land, woodland and grassland accounted for 52.24%, 35.65% and 11.71% of the total soil loss, respectively. We also found significant regional differences in erosion rates and a close relationship between erosion and soil conservation measures adopted. The CSLE estimates did not compare well with qualitative estimates from the National Soil Erosion Database of China (NSED-C) and only 47.77% of the territory fell within the same erosion intensity for the two approaches. However, the CSLE estimates were consistent with the results from a national survey and local assessments under experimental plots. By advocating of soil conservation measures and converting slope cropland into grass/forest and terraced field, policy interventions during 2006–2010 have reduced soil erosion on rain-fed cropland by 20% in soil erosion rate and 32% in total soil loss compared to the local assessments. The quantitative CSLE method provides a reliable estimation, due to the consideration of erosion control measures and is potentially transferable to other mountainous areas as a robust approach for rapid assessment of sheet and rill erosion.

Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

ABSTRACT Rapid accurate estimation of the fractional cover of non-photosynthetic vegetation (f NPV) is essential for monitoring desertification, managing grassland resources, assessing soil erosion and grassland fire risk, and preserving the grassland ecological environment. However, there have been very few studies using multispectral remote sensing images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) images in this study) to estimate f NPV in typical grassland areas in northern China. In this study, using field spectra obtained from ground measurements in May and October 2017 and corresponding f NPV data, we calculated eight non-photosynthetic vegetation indices (NPVIs) from the simulated MODIS bands. We then determined the NPVIs that were suitable for the estimation of f NPV. Based on the determined NPVIs, we established a remote sensing estimation model for f NPV in typical grassland areas using MODIS image data. The spatial distribution of f NPV in the studied area was also investigated. The results indicated that the determined NPVIs, including the dead fuel index (DFI), shortwave-infrared ratio (SWIR32), normalized difference tillage index (NDTI), modified soil-adjusted crop residue index (MSACRI), and soil tillage index (STI), used bands 6 and 7 in the shortwave-infrared region of the MODIS data; the DFI had the best performance, with a coefficient of determination (R2 ) of 0.68 and root mean square error of leave-one-out cross-validation (RMSECV) of 0.1390. The models based on MODIS image data for the estimation of f NPV using NPVIs had relatively good regression relations, and we determined that the DFI linear regression model was the best remote sensing model for monitoring f NPV in typical grassland areas, with an estimation accuracy exceeding 73.00%. Additionally, our results indicated that the distribution of non-photosynthetic vegetation exhibited substantial spatial heterogeneity and that f NPV gradually decreased from the north-eastern to south-western portions of the study area.

Estimation of annual average soil loss using the Revised Universal Soil Loss Equation (RUSLE) integrated in a Geographical Information System (GIS) of the Esil River basin (ERB), Kazakhstan

The Revised Universal Soil Loss Equation (RUSLE) has enormous potential for integrating remote sensing and Geographical Information System (GIS) technologies for producing accurate and inexpensive assessments of soil erosion. In this study, the RUSLE method was applied to the Esil (Ishim) River basin (ERB), which is situated in Northern and Central Kazakhstan. The northern part of the ERB extends through the Tyumen and Omsk regions of the Russian Federation to the confluence of the Irtysh River. This article may be of interest to experts and specialists in the field of agriculture, as the findings can assist agricultural producers and government entities in making decisions that prevent soil degradation and promote optimal cropping systems for land and crop cultivation. The objective of this research is to detect, estimate and map areas of land plots most vulnerable to potential soil erosion within the ERB, using the RUSLE model under Arc GIS 10.2. The results reveal that average annual soil loss during the study period ranges from 0 to 32 (t y−1) and that 108,007.5 km2 (48%) of the ERB has no erosion. The remainder of the basin is prone to soil erosion ranging from 1 to 32 t ha−1 y−1, which comprises 117,216.9 km2 (52%), and total soil erosion is 565,368.7 (t y−1). Soil erosion in the ERB is relatively moderate due to low hill steepness and low annual precipitation (198–397 mm). Exceptions occur in plots which feature high slope length steepness, which are scattered throughout the region.

Quantitative assessment of rill and interrill soil erosion in Romania

AbstractSoil erosion is an important geomorphological process with potential negative consequences especially on land agricultural potential. Unsuitable agricultural practices may increase soil erosion, leading to rapid loss of soil fertility and decrease of crop production. It is therefore important to correctly quantify soil erosion rates in order to adapt agricultural practices and implement proper conservation measures. This study attempts to assess the rill and interrill erosion in Romania, using the Romanian soil erosion model and GIS techniques. The database includes the digital terrain model, the soil map of Romania, the land use map of Romania and the rainfall erosivity regions. The results show that the high and very high erosion risk classes include 4.1% of the Romanian territory (9,627 km2). Most of this land is present in the hilly and plateau areas (Subcarpathians, Moldavian Plateau, Getic Plateau, Western Hills, Dobrogea Plateau). The model was validated by comparison of its predictions with long‐term erosion measurements from different locations in the country. Comparison with previous non‐GIS assessments of soil erosion at national level shows that the total estimated rill and interrill erosion in our study was very close to previous estimates. Comparison with the RUSLE 2015 model computed for Europe as a whole reveals that the two models assign almost 54% of their shared area to the same erosion class, while for 39% of the territory there is one class difference between the models. The results can be used for evaluations of erosion risk at national and regional scales.

Multi-criteria-based sub-basin prioritization and its risk assessment of erosion susceptibility in Kansai\u2013Kumari catchment area, India

Environmental processes are interrupted by the water action like soil erosion, mass movement as well as siltation on the dam in the undulating catchment area. Soil erosion is one of them and degraded the basin potentiality. This paper demonstrates that erosion susceptibility status in the 13 sub-basins of Kansai–Kumari catchment area has been determined depending upon its morphometric, lithology, geomorphic, land use/land cover (LULC), slope and soil characteristics used by integrated micro-watershed prioritization rank which is based on susceptible capacity under geographical information system platform. Risk assessment of soil erosion was measured by hypsometric characteristic (Hi) and denudation rate (tu) to assess the geological stage of landform and risk status for conservative practices. The result shows that SB13, 2 have a high risk (tu > 90 t/km2/year) due to the presence of low priority rank of morphometric, geomorphic, slope, soil and high priority rank of lithological set-up and LULC but reached under the late mature geological stage (Hi > 0.35). SB3, 4, 10 have a low risk (tu < 83 t/km2/year) due to the presence of high priority rank of morphometric, geology, geomorphic, slope and soil types under the mature stage of geological setting (Hi > 0.5). But SB7, 8, 9 have the medium risk (tu < 85 t/km2/year) due to the presence of erosion-prone LULC patterns like cropland and laterite cover but having low final priority rank and old geological stage (Hi < 0.35). Therefore, erosion susceptibility does not depend on morphometric aspects but also depends on other determinant themes at the sub-basin level.

Spatial–temporal distribution of rainfall erosivity, erosivity density and correlation with El Niño–Southern Oscillation in the Huaihe River Basin, China

Rainfall erosivity (RE) is the ability of rainfall to cause soil or regolith erosion. Understanding the spatial distribution and temporal trends of RE is critical for assessing soil erosion risk and improving upon soil conservation planning. The aim of this paper is to study the temporal and spatial changes in RE in the Huaihe River Basin, China. This will be based on daily precipitation data from 67 meteorological stations in the Huaihe River Basin for the period 1971 to 2016. The assessment of the resulting RE values involved inverse distance-weighted (IDW) interpolation, Sen's slope estimation, and the Mann-Kendall nonparametric test. In addition, the possible influence of the El Niño – Southern Oscillation (ENSO) on RE in the Huaihe River Basin will be examined by the use of Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC), where the relationship between RE and the multivariate ENSO index (MEI) are analyzed. The results showed that the spatial distribution of RE is characterized by it increasing from west to east and north to south. In most areas, the annual RE has increased slightly, while this upward trend in the southeastern part of the study area was more significant. The rainfall erosivity of rainstorms (RE-SM) in the Huaihe River Basin played a leading role in the annual RE, and 97% of the stations displayed the order of importance of the different categories of rainfall as rainfall erosivity of rainstorms (RE-SM) > rainfall erosivity of heavy rain (RE-HR) > rainfall erosivity of moderate rain (RE-MR). Over monthly time scales, the RE was the highest in July, while it was lowest in December, with the monthly differences being apparent. The RE in the Huaihe River Basin were relatively large during non-El Niño/La Niña periods and relatively small during El Niño/La Niña periods. Finally, correlations between RE and the MEI in various parts of the basin showed different characteristics over time and space, with both displaying similarities and differences.

How reliable are our methods for estimating soil erosion by water?

Soil erosion is a problem of global significance for land management. Attempts to assess soil erosion by water encompass plot studies, monitoring, modelling, and the use of tracers. All of these approaches are shown to have shortcomings, calling into question the reliability of current estimates. There is an urgent need to develop approaches to estimating rates of soil erosion that are consistent with current understanding of erosion processes. Such approaches need always to be reviewed in the light of advances in understanding of processes. Only then can reliable estimates of soil erosion be made on which to base reliable land-use and policy decisions.

Assessment of Soil Erosion in Uma Oya Catchment, Sri Lanka

Soil erosion is one of the major threats which affects farm productivity and water quality in river catchments. Quantification of human-induced soil loss is an imperative measure in catchment management. Among the catchments in Sri Lanka, the highest sediment yield has been reported from the Upper Uma Oya catchment (UOC) which is a sub-catchment of the Upper Mahaweli catchment. The Mahaweli watershed is the most important watershed in Sri Lanka since the size and the economic importance of it. Hence, this study was conducted considering the ten selected sub-watersheds of Uma Oya Watershed (UOW) in Sri Lanka to quantify soil erosion. Soil erosion of UOW was calculated using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. It was found that the current soil erosion rates in the watershed is varied between 14.0–648.6 t/ha/yr. Results of the study disclose that the soil erosion rates of UOW are 3 to 130 times faster than the soil loss tolerance in Sri Lanka. Findings of the study suggest that the implementation of sustainable watershed management options are crucial in order to reduce soil erosion and to enhance the quality of the watershed.

Comparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India

A Comparative Study of different models for assessment of soil erosion (Universal Soil Loss Equation and Revised Universal Soil Loss Equation) and sediment yield (Modify Universal Soil Loss Equation and Sediment delivery ratio) carried out in Pairi Watershed, Chhattisgarh, India. The main objective is to estimate the spatial pattern of annual soil erosion rate and sediment yield using Remote Sensing and GIS technique which are promising and cost-effective tools for mapping critical areas. To calculate the soil erosion and sediment yield different spatial data sets were utilized such as rainfall, soil texture, topography, land use land cover etc. Obtained result of soil erosion and sediment yield was validated using field check using Baronda CWC Observatory data. It is found that, RUSLE model for soil erosion and MUSLE model for sediment yield has been found to be most reliable for the study area.

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

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.&#x0D; Int. J. Appl. Sci. Biotechnol. Vol 7(1): 54-61

Soil erosion assessment using RUSLE model and its validation by FR probability model

The objective of the current study is to estimate the annual average soil loss through RUSLE model and furthermore assess the soil erosion risk and its distribution using frequency ratio (FR) probability algorithm. At first, soil erosion risk zones were identified using FR model by the consideration 14 soil erosion conditioning factors such as land use (LU/LC), slope, slope aspect, normalized difference vegetation index (NDVI), altitude, plan curvature, stream power index, distance from river, road, and lineament, soil types, rainfall erosivity, slope length and lineament density. Secondly, the spatial pattern of annual average soil loss rates was estimated using RUSLE model with consideration of five factors such as, rainfall erosivity (R), cover management (C), slope length (LS), soil erodability (K), and conservation practice factors (P). In order to map soil erosion susceptibility by the FR model, dataset divided randomly into parts 70/30 percent for training and validation purposes, respectively. Based on the FR value, the susceptibility map was reclassified into five different critical erosion probability zones. Among this, the severe and high erosion zones occupy 13.69% and 16.26%, respectively, of the total area, where as low and very low susceptibility zones together constitute 32.98% of the River Basin. The assessed high amount of average annual soil erosion (more than 100 t/ha/year) is occupied 9.55% of the total study area. It is conclude that high soil erosion susceptibility and yearly average soil loss were performed in this study area. Therefore, the produced soil erosion susceptibility maps and annual average soil erosion map can be very useful for primary land use planning and soil erosion hazard mitigation purpose for prioritizing areas.

A Geographic Information System Based Soil Erosion Assessment for Conservation Planning at West Hararghe, Eastern Ethiopia

Soil erosion by water is a serious concern worldwide problem and it is difficult to assess its economic and environmental impacts accurately. West Hararghe zone is one of the most erosion-prone areas in the eastern highland of Ethiopia which little attention is given. The objectives of this study were to estimate the magnitude of soil loss rate, assess the change of erosion risk, and elucidate their implication for SWC planning in West Hararghe Zone, Eastern Ethiopia. Applying remote sensing data, the study first derived the Revised Universal Soil Loss Equation (RUSLE) model parameters in an ArcGIS 10.3 environment and estimated the soil loss rates. This model was developed based on the integrated use of precipitation data, Landsat images in 2017, and 2018, terrain parameters (slope gradient/steepness and slope length), soil loss management practice and soil composition in west Harerge Zone, Oromia Regional state, Ethiopia. The estimated total soil loss in the Zone was 47,288,835.17 tons in 2017/18 with a mean erosion rate of 28.62 t ha −1 yr −1 . The study area was divided into six erosion risk classes ranging from low to high. About 27.70% (457,687.19 ha) of zone was classified under high to very high soil loss class while the remaining 72.30% (1,194,613.1314 ha) of the zone categorized under slow to moderate soil loss class. Keywords: - GIS, RUSLE, Soil loss potential, West Harerge Zone, Ethiopia DOI : 10.7176/CER/11-2-02 Publication date :March 31 st 2019

Assessment of Topographical Factor (LS-Factor) Estimation Procedures in a Gently Sloping Terrain

The major uncertainty in soil erosion assessment studies is derived from LS-factor constituting slope length and slope steepness factors. Empirical soil erosion models employing different algorithms for estimation of LS-factor using raster-based digital elevation models (DEMs). Different algorithms have been adopted for LS-factor determination in soil erosion studies without proper justification for their selection according to the terrain characteristics; a few among them addressed suitability of the algorithms on hilly terrains. The present study focused on the performance of LS-factor estimation methods involving specific contributing area (SCA) method and cumulative slope length method for slope length factor and USLE, RUSLE and USPED algorithms for slope steepness factor in a gently sloping terrain. The results showed that SCA method is the best performing method in gently sloping terrain since the effect of contour length exponent get minimized since there are less influence from diagonal flow direction. The pixel-to-pixel-based slope length exponent may result in more appropriate estimation of slope length factor in gently sloping terrains. The SCA-based slope length estimation along with USLE S-factor algorithm was found to perform well under different elevation classes and slope classes in both SRTM DEM and ASTER DEM. The results from the study may be helpful in appropriate prediction of soil erosion in gently sloping terrains.

Hillslope and point based soil erosion – an evaluation of a Landscape Evolution Model

AbstractExcessive soil erosion and deposition is recognised as a significant land degradation issue. Quantifying soil erosion and deposition is a non‐trivial task. One of these methods has been the mathematical modelling of soil erosion and deposition patterns and the processes that drive them. Here we examine the capability of a landscape evolution model to predict both soil erosion rate and pattern of erosion and deposition. This numerical model (SIBERIA) uses a Digital Elevation Model (DEM) to represent the landscape and calculates erosion and deposition at each grid point in the DEM. To assess field soil redistribution rates (SRR) and patterns the distribution of the environmental tracer 137Cs has been analysed. Net hill slope SRR predicted by SIBERIA (a soil loss rate of 1.7 to 4.3 t ha‐1 yr‐1) were found to be in good agreement with 137Cs based estimates (2.1 – 3.4 t ha‐1 yr‐1) providing confidence in the predictive ability of the model at the hillslope scale. However some differences in predicted erosion/deposition patterns were noted due to historical changes in landscape form (i.e. the addition of a contour bank) and possible causes discussed, as is the finding that soil erosion rates are an order of magnitude higher than likely soil production rates. The finding that SIBERIA can approximate independently quantified erosion and deposition patterns and rates is encouraging, providing confidence in the employment of DEM based models to quantify hillslope erosion rates and demonstrating the potential to upscale for the prediction of whole catchment erosion and deposition. The findings of this study suggest that LEMs can be a reliable alternative to complex and time consuming methods such as that using environmental tracers for the determination of erosion rates. The model and approach demonstrates a new approach to assessing soil erosion that can be employed elsewhere. © 2018 John Wiley &amp; Sons, Ltd.

Assessing the integrity of soil erosion in different patch covers in semi-arid environment

Soil erosion accelerates the degradation of semiarid ecosystems. How to explain the erosion processes is increasingly attracting scientists’ and practitioners’ attention. In this study, we hypothesized the intensity and uncertainty of runoff and sediment events as the ecological function of soil erosion by using structural equation modeling (SEM), and integrated observed influencing factors to represent the ecological structure of soil erosion. We further assessed the interactions of ecological structures and functions in four typical patch covers containing bare patch (B type), Andropogon patch (G type), Artemisia coparia patch (H type) and Spiraea pubescens patch (J type) in the Loess Plateau over three rainy seasons. We found that (1) the highest and lowest probabilities of runoff (approaching to 0.37 and 0.22) and sediment (approaching to 0.27 and 0.03) events were in B and J types respectively. B type produced the highest runoff coefficients (0.004) and yielded 2–3 times more sediment than other three vegetation patch covers. (2) Antecedent soil moisture and crown width were two relative more crucial indicators to simultaneously affect the uncertainty and intensity of soil erosion in different patch cover patterns. (3) Soil saturated hydraulic conductivity connected the soil and plant latent variables to indicate the complexity of ecological structure. (4) The measurement error of sediment yield became the main source of system error of the soil erosion assessment by SEM. These results suggest that the uncertainty of runoff and sediment event was an important descriptor to improve the interpretation of ecological function of soil erosion. The logistic regression and SEM form a new analysis framework to explore the interaction between ecological structure and function of soil erosion. This framework could enhance the understanding of erosion processes, and be beneficial to managing the restoration vegetation and utilizing the available water and soil resources in semiarid environment.