Slope Steepness Factor Research Articles

Modification of the RUSLE slope length and steepness factor (LS-factor) based on rainfall experiments at steep alpine grasslands

The slope length and slope steepness factor (LS-factor) is one of five factors of the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) describing the influence of topography on soil erosion risk. The LS-factor was originally developed for slopes less than 50% inclination and has not been tested for steeper slopes. To overcome this limitation, we adapted both factors slope length L and slope steepness S for conditions experimentally observed at Swiss alpine grasslands. For the new L-factor (Lalpine), a maximal flow path threshold, corresponding to 100 m, was implemented to take into account short runoff flow paths and rapid infiltration that has been observed in our experiments. For the S-factor, a fitted quadratic polynomial function (Salpine) has been established, compiling the most extensive empirical studies. As a model evaluation, uncertainty intervals are presented for this modified S-factor. We observed that uncertainty increases with slope gradient. In summary, the proposed modification of the LS-factor to alpine environments enables an improved prediction of soil erosion risk including steep slopes.•Empirical experiments (rainfall simulation, sediment measurements) were conducted on Swiss alpine grasslands to assess the maximal flow length and slope steepness factor (S-factor).•Flow accumulation is limited to a maximal flow threshold (100 m) at which overland runoff is realistic in alpine grassland.•Slope steepness factor is modified by a fitted S-factor equation from existing empirical S-factor functions.

Effects of DEM resolutions on LS and hillslope erosion estimation in a burnt landscape

The combined slope length and slope steepness factor (LS) is crucial in soil erosion models such as the revised universal soil loss equation (RUSLE), and is often calculated from digital elevation models (DEMs). With high-resolution DEMs becoming increasingly available in recent years, we face considerable challenges in selecting the optimal DEM for erosion modelling. In this paper, we present a case study on LS factor computation using various DEMs at resolutions ranging from 1 to 90 m over a burnt national park in New South Wales, Australia, aiming to assess the effects of DEM resolution on LS and hillslope erosion estimation. The LS was calculated based on RUSLE specifications and incorporated a variable cutoff slope angle that improves the detection of the beginning and the end of each slope length. Results show the trend of an increase in the estimated LS value as the DEM resolution became coarser. We consider 5–10-m DEMs to have optimal resolution because the LS values calculated at this range were closer to the LS values measured at the 12 soil plots over the study area. We also assessed different sampling methods for LS value extraction and statistical analysis. The sampling method based on contributing area was more representative compared with point-based and buffer sampling methods. Findings from this study will be useful for choosing the optimal DEM resolution and sampling method in hillslope erosion modelling.

ESTIMATION OF SLOPE LENGTH FACTOR (L) AND SLOPE STEEPNESS FACTOR (S) OF RUSLE EQUATION IN THE EUPHRATES RIVER WATERSHED BY GIS MODELING

Specific effects of topography over any area watershed erosion are estimated through the LS factor as the product of slope length L and slope steepness factor S using RUSLE equation. The LS factor map of Euphrates watershed was derived from the raw Digital Elevation Model (DEM) by using of ArcMap 10.2 software, the DEM images were provided from United State Geological Survey (USGS) website. The DEM were consisting of sixteen images with cell resolution of 30m*30m, the DEM of Euphrates watershed is ranged from -100 m to 466 m, 98.83% of Euphrates basin area of DEM within the range 0 to 457 m. The slope in percentage rise map of Euphrates watershed is required to estimate the LS factor, the most of slope of this study was ranged from zero to five which represent about 93.12% from total area of basin. The LS factor of Euphrates basin is ranged from (0) to (8010.61), 94.25% of Euphrates watershed area have the LS factor of the range from (0) to (1). Furthermore, the LS factor values more than 500 of watershed have a small area, so the factor value can be considered up to 500 and neglect the other large values of LS factor. The LS values more than 3 were identified the lines of Euphrates river, distributary channel, rills, interrill and gully.

Regional soil erosion assessment based on a sample survey and geostatistics

Abstract. Soil erosion is one of the most significant environmental problems in China. From 2010 to 2012, the fourth national census for soil erosion sampled 32 364 PSUs (Primary Sampling Units, small watersheds) with the areas of 0.2–3 km2. Land use and soil erosion controlling factors including rainfall erosivity, soil erodibility, slope length, slope steepness, biological practice, engineering practice, and tillage practice for the PSUs were surveyed, and the soil loss rate for each land use in the PSUs was estimated using an empirical model, the Chinese Soil Loss Equation (CSLE). Though the information collected from the sample units can be aggregated to estimate soil erosion conditions on a large scale; the problem of estimating soil erosion condition on a regional scale has not been addressed well. The aim of this study is to introduce a new model-based regional soil erosion assessment method combining a sample survey and geostatistics. We compared seven spatial interpolation models based on the bivariate penalized spline over triangulation (BPST) method to generate a regional soil erosion assessment from the PSUs. Shaanxi Province (3116 PSUs) in China was selected for the comparison and assessment as it is one of the areas with the most serious erosion problem. Ten-fold cross-validation based on the PSU data showed the model assisted by the land use, rainfall erosivity factor (R), soil erodibility factor (K), slope steepness factor (S), and slope length factor (L) derived from a 1 : 10 000 topography map is the best one, with the model efficiency coefficient (ME) being 0.75 and the MSE being 55.8 % of that for the model assisted by the land use alone. Among four erosion factors as the covariates, the S factor contributed the most information, followed by K and L factors, and R factor made almost no contribution to the spatial estimation of soil loss. The LS factor derived from 30 or 90 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data worsened the estimation when used as the covariates for the interpolation of soil loss. Due to the unavailability of a 1 : 10 000 topography map for the entire area in this study, the model assisted by the land use, R, and K factors, with a resolution of 250 m, was used to generate the regional assessment of the soil erosion for Shaanxi Province. It demonstrated that 54.3 % of total land in Shaanxi Province had annual soil loss equal to or greater than 5 t ha−1 yr−1. High (20–40 t ha−1 yr−1), severe (40–80 t ha−1 yr−1), and extreme (> 80 t ha−1 yr−1) erosion occupied 14.0 % of the total land. The dry land and irrigated land, forest, shrubland, and grassland in Shaanxi Province had mean soil loss rates of 21.77, 3.51, 10.00, and 7.27 t ha−1 yr−1, respectively. Annual soil loss was about 207.3 Mt in Shaanxi Province, with 68.9 % of soil loss originating from the farmlands and grasslands in Yan'an and Yulin districts in the northern Loess Plateau region and Ankang and Hanzhong districts in the southern Qingba mountainous region. This methodology provides a more accurate regional soil erosion assessment and can help policymakers to take effective measures to mediate soil erosion risks.

Comparing watershed soil erosion of Taiwan and Thailand

Tropical watersheds in Taiwan and Thailand face the same severe soil erosion problem that is increasing at an alarming rate. In order to evaluate the severity of soil erosion, we quantitatively investigate the issue using a common soil erosion model (Universal Soil Loss Equation, USLE) on the Shihmen reservoir watershed of Taiwan and the Lam Phra Ploeng basin of Thailand, and compare their respective erosion factors. The results show an interesting contrast between the two watersheds. Some of the factors (rainfall factor, slope-steepness factor) are higher in the Shihmen reservoir watershed, while others (soil erodibility factor, cover and management factor) are higher in the Lam Phra Ploeng basin. The net result is that these factors cancel each other out, and the amount of soil erosion of the two watersheds are very similar at 68.03 t/ha/yr and 67.57 t/ha/yr, respectively.

An improved method for calculating slope length (λ) and the LS parameters of the Revised Universal Soil Loss Equation for large watersheds

The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are often used to estimate soil erosion at regional landscape scales. USLE/RUSLE contain parameters for slope length factor (L) and slope steepness factor (S), usually combined as LS. However a major limitation is the difficulty in extracting the LS factor. Methods to estimate LS based on geographic information systems have been developed in the last two decades. L can be calculated for large watersheds using the unit contributing area (UCA) or the slope length (λ) as input parameters. Due to the absence of an estimation of slope length, the UCA method is insufficiently accurate. Improvement of the spatial accuracy of slope length and LS factor is still necessary for estimating soil erosion. The purpose of this study was to develop an improved method to estimate the slope length and LS factor. We combined the algorithm for multiple-flow direction (MFD) used in the UCA method with the LS-TOOL (LS-TOOLSFD) algorithms, taking into account the calculation errors and cutoff conditions for distance, to obtain slope length (λ) and the LS factor. The new method, LS-TOOLMFD, was applied and validated in a catchment with complexly variable slopes. The slope length and LS calculated by LS-TOOLMFD both agreed better with field data than with the calculations using the LS-TOOLSFD and UCA methods, respectively. We then integrated the LS-TOOLMFD algorithm into LS-TOOL developed in Microsoft's .NET environment using C# with a user-friendly interface. The method can automatically calculate slope length, slope steepness, L, S, and LS factor, providing the results as ASCII files that can be easily used in GIS software and erosion models. This study is an important step forward in conducting accurate large-scale erosion evaluation.

Assessment of soil particle erodibility and sediment trapping using check dams in small semi-arid catchments

Check dams can be used as a source of information for studies on sediment characteristics and soil particle erodibility. In this study, sediment yield and grain size distribution (GSD) were measured in twenty small catchments draining into a rock check dam in NW Iran for different runoffs during 2010–2011. Significant correlations were found between sediment yield and slope steepness, vegetation cover and soil erodibility factor (K) of the catchments. The erodibility of soil particles was determined using the comparison of GSD between sediment and original soil. Clay was the most erodible soil particle which showed 2.05 times more percentage in sediment than the original soil. The erodibility of soil particles were strongly affected by the rainfall erosivity (EI30). Check dams showed more effectiveness in trapping coarse particles (sand and gravel). The effectiveness of check dams in trapping coarse particles enhanced with increase in the remaining capacity of check dams.

Comparison of different approaches to LS factor calculations based on a measured soil loss under simulated rainfall.

Geographic Information Systems (GIS) in combination with soil loss models can enhance evaluation of soil erosion estimation. SAGA and ARC/INFO geographic information systems were used to estimate the topographic (LS) factor of the Universal Soil Loss Equation (USLE) that in turn was used to calculate the soil erosion on a long-term experimental plot near Prague in the Czech Republic. To determine the influence of a chosen algorithm on the soil erosion estimates a digital elevation model with high accuracy (1 × 1 m) and a measured soil loss under simulated rainfall were used. These then provided input for five GIS-based and two manual procedures of computing the combined slope length and steepness factor in the (R)USLE. The results of GIS-based (R)USLE erosion estimates from the seven procedures were compared to the measured soil loss from the 11 m long experimental plot and from 38 rainfall simulations performed here during 15 years. The results indicate that the GIS-based (R)USLE soil loss estimates from five different approaches to calculation of LS factor are lower than the measured average annual soil loss. The two remaining approaches over-predicted the measured soil loss. The best method for LS factor estimation on field scale is the original manual method of the USLE, which predicted the average soil loss with 6% difference from the measured soil loss. The second method is the GIS-based method that concluded a difference of 8%. The results of this study show the need for further work in the area of soil erosion estimation (with particular focus on the rill/interrill ratio) using the GIS and USLE. The study also revealed the need for an application of the same approach to catchment area as it might bring different outcomes.

Producing Erosion Risk Map Using Remote Sensing Techniques and RUSLE Method in Halfalı Stream Watershed of Kahramanmaras

In this study, it is aimed to determine the potential erosion risk condition of the area using remote sensing techniques and RUSLE method in Halfalı Stream Watershed, which is 25 km away from Kahramanmaras province. With the purpose of applying this method, rainfall erosivitiy index (R) has been generated by means of modified fournier index (MFI).Soil erodibility factor (K) has been produced using Inverse Distance Weighted technique and soil sample obtained from study area. Slope length and slope steepness factor (LS) has been created by the help of digital terrain elevation model. Vegetation management factor (C) has been generated using remote sensing techniques. Whole factors obtained have been overlapped in Geographic Information System (GIS) and soil loss belonging to study area has been determined using raster calculator command within map algebra module. It has been divided into 5 classes and erosion risk map has been produced. According to potential erosion risk map obtained, 60.35 % of area has been subject to very low erosion risk, while 2.60% , 6.08 %, 10.37 % and 20.60 % has been subject to low, medium, high and very high erosion risk, respectively.

Preparation of Erosion Susceptibility Map of Dhaman Khadi Sub-Watershed in Eastern Gujarat Using ArcGIS Interface

An attempt has been made to model land degradation in term of water erosion of selected Dhaman Khadi sub-watershed (7710.64 ha.) in Eastern Gujarat, India through Revised Universal Soil Loss Equation using ArcGIS interface. The average erosivity of 30 years (1986-2015) annual rainfall using standard formula was estimated to be 480.63 MJ mm ha?1 hr?1 per year. The erodibility factor K was computed as 0.236 and 0.177 mt?hr MJ?1 mm?1 per unit R respectively for clay loam and clay soils using modified formula.. 20 m Digital Elevation Model was prepared from Toposheet No. F43N10 by using ‘Topo to Raster’ interpolation method. The slope length factor L was derived from DEM using Unit Stream Power Erosion and Deposition (USPED) Model. The raster layers of slope steepness factor for slope having < 9 % and ? 9 % was prepared separately to form final slope steepness factor map. Cover management factor map was derived based on cropping pattern for the various land cover categories of the study area. The standard conservation practice factor values for cross-sloped agricultural lands were assigned to the attribute table of the intersected map of LU/LC and slope maps to prepare the P factor map. Average gross soil erosion was minimum for evergreen forest while maximum for wasteland without scrub. Highest area covered by agricultural land (i. e. 41.54) of Dhaman Khadi sub-watershed having 33.28 tons/ha/yr gross soil erosion needs immediatetreatment to prevent land degradation. Soil loss tolerance limit of study area was used to derive erosion susceptibility map in order to identify the priority of conservation programs. As all the factors of RUSLE was estimated precisely at sub-watershed level, the study could help for rapid and reliable planning of watershed development programs in combination with the use of RS and GIS technology.

Effect of different topographic data sources on soil loss estimation for a mountainous watershed in Northern China

The effect of topography on soil loss can be characterized by slope length and slope steepness factors. These factors are commonly evaluated using digital elevation models (DEM) which can be generated from a large variety of topographic data. The purpose of this study was to evaluate the effect of topographic data source on estimated soil loss for a mountainous watershed. The Dongtaigou watershed (90 ha) is located in Huairou District, Beijing, China. Data sources included topographic maps at 1:2000, 1:10,000, and 1:50,000 scales, and 30-m DEM from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) dataset. The grid sizes considered were 2, 5, 10, 25, and 30 m. The results showed that the calculated average slope steepness and soil loss were reduced and the average slope length increased when the map scale was decreased. With results using 1:2000 map and 2-m grid as the reference, the land area with minimal soil loss (<2 t ha−1 year−1) was 5.7 ha, and the area with moderate soil loss (2–25 t ha−1 year−1) was 77.9 ha. Compared to these reference values, the 1:10,000 map would bring out a 42 % increase in the area with minimal soil loss, and a concurrent decrease of −8 % in the area with a moderate soil loss. For the 30-m ASTER GDEM V1, the calculated slope steepness is some 64 % lower than the reference value. The slope length, on the other hand, was increased by 265 % and soil loss decreased by 47 % in comparison with the reference values. For all map scales and grid sizes, the relative errors in the estimated soil loss were about 50 % of the relative errors in slope steepness.

Estimation of soil erosion using RUSLE and GIS techniques: a case study of Barakar River basin, Jharkhand, India

An integrated method has been adopted to estimate soil loss in a plateau and plateau fringe river basin where soil erosion is significant. The integration of Revised Universal Soil Loss Equation model and geographical Information technology has been used for soil loss estimation. In GIS platform, the overlay of rainfall-runoff erosivity factor, soil erodibility factor, slope length factor, slope steepness factor, cover and management factor, support and conservation practices factor results that the high amount of soil loss (more than 100 t ha−1 year−1) is significantly low and occupies 0.08% of the entire study area. High soil loss in upstream of the basin has a close relation to LS and K factor and drainage density. As a result of soil loss in the upper catchment areas, reservoir capacity has been depleted both in dead and live storage space. It is concluded that soil erosion has a significant impact on plateau fringe areas and the estimation of soil loss is an essential input for the adoption of proper land use planning and development strategies.

A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water

The Universal Soil Loss Equation (USLE) model is the most frequently used model for soil erosion risk estimation. Among the six input layers, the combined slope length and slope angle (LS-factor) has the greatest influence on soil loss at the European scale. The S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length. The combined LS-factor describes the effect of topography on soil erosion. The European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil erosion assessment to achieve a better understanding of the spatial and temporal patterns of soil erosion in Europe. The LS-calculation was performed using the original equation proposed by Desmet and Govers (1996) and implemented using the System for Automated Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and contributes to a precise estimation of flow accumulation. The LS-factor dataset was calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole European Union, resulting in an improved delineation of areas at risk of soil erosion as compared to lower-resolution datasets. This combined approach of using GIS software tools with high-resolution DEMs has been successfully applied in regional assessments in the past, and is now being applied for first time at the European scale.

Digital mapping of RUSLE slope length and steepness factor across New South Wales, Australia

The Universal Soil Loss Equation (USLE) and its main derivate, the Revised Universal Soil Loss Equation (RUSLE), are widely used in estimating hillslope erosion. The effects of topography on hillslope erosion are estimated through the product of slope length (L) and slope steepness (S) subfactors, or LS factor, which often contains the highest detail and plays the most influential role in RUSLE. However, current LS maps in New South Wales (NSW) are either incomplete (e.g. point-based) or too coarse (e.g. 250 m), limiting RUSLE-based applications. The aim of this study was to develop automated procedures in a geographic information system (GIS) to estimate and map the LS factor across NSW. The method was based on RUSLE specifications and it incorporated a variable cutoff slope angle, which improves the detection of the beginning and end of each slope length. An overland-flow length algorithm for L subfactor calculation was applied through iterative slope-length cumulation and maximum downhill slope angle. Automated GIS scripts have been developed for LS factor calculation so that the only required input data are digital elevation models (DEMs). Hydrologically corrected DEMs were used for LS factor calculation on a catchment basis, then merged to form a seamless LS-factor digital map for NSW with a spatial resolution ~30 m (or 1 s). The modelled LS values were compared with the reference LS values, and the coefficient of efficiency reached 0.97. The high-resolution digital LS map produced is now being used along with other RUSLE factors in hillslope erosion modelling and land-use planning at local and regional scales across NSW.

Effects of DEM grid size on predicting soil loss from small watersheds in China

The topography (slope gradient and slope length) has an important effect on soil erosion by water. Slope gradient and slope length factor are the parameters of the universal soil loss equation that is commonly used to predict soil erosion. These factors are usually extracted from the digital elevation model (DEM). Thus the DEM grid size will influence topographic factors and therefore soil loss calculation. The purpose of this study was to investigate the effect of DEM grid size on the topographic factors and therefore soil loss prediction on a typical watershed in northeast of China. The site for the case study was Dontaigou watershed which is located at Huairou district, Beijing, China. Contour lines of 2 m interval digitized from 1:2,000 scale of topographic maps were used to generate DEM with grid size of 2 m. The DEMs with grid sizes of 3–30 m, at 1 m intervals, were created using the nearest neighbor resampling method. In total, 29 DEMs with different grid sizes were obtained. Four different scenarios of the channel initiation threshold were used to extract the drainage channel and let the slope length cutoff. Chinese soil loss equation was used to predict the soil loss. The results show that DEM grid size had different effects on the topographic factor. The average calculated slope steepness factor decreased with the increase of the DEM grid size in a linear decay function. The average slope length factor increased with DEM grid size when the drainage channel was not considered. It slightly decreased with DEM grid size and then increased when the drainage channel suitable for DEM grid size was used. However, comparing different grid sizes, the LS factor and the soil loss prediction of DEM grid size less than or equal to 10 m were close to those of 2 m DEM. The channel initiation threshold had effects on the extraction of slope length and then slope length factor. We found that the effect of DEM grid size on predicting soil loss depends on selection of suitable channel initiation threshold.

Climate change and land degradation in Africa: a case study in the Mount Elgon region, Uganda

The aim of this study is to estimate and compare soil erosion, in the Mount Elgon region, eastern Uganda, during the last decade. Possible trends and changes in erosion are linked to precipitation/climate change as well as changes in land cover. Two different versions of the Revised Universal Soil loss Equation (RUSLE) are implemented and compared, one using slope length and the other using flow accumulation to estimate the slope length and steepness factor (LS). Comparisons of the modeled soil erosion vs. field data indicate that RUSLE based on flow accumulation is preferable. The modeling is carried out for the years 2000, 2006, and 2012, and is based on ASTER remotely sensed data, digital elevation models, precipitation data from the study area, as well as existing soil maps. No significant trends in estimated soil erosion are found to be present during the last decade. Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation. Even if there are reports of more intense and increasing amounts of rainfall in the area, this could not be verified, neither through the analysis of climate data, nor by trends in the estimated soil loss.

Sediment delivery estimates in water quality models altered by resolution and source of topographic data.

Moderate-resolution (30-m) digital elevation models (DEMs) are normally used to estimate slope for the parameterization of non-point source, process-based water quality models. These models, such as the Soil and Water Assessment Tool (SWAT), use the Universal Soil Loss Equation (USLE) and Modified USLE to estimate sediment loss. The slope length and steepness factor, a critical parameter in USLE, significantly affects sediment loss estimates. Depending on slope range, a twofold difference in slope estimation potentially results in as little as 50% change or as much as 250% change in the LS factor and subsequent sediment estimation. Recently, the availability of much finer-resolution (∼3 m) DEMs derived from Light Detection and Ranging (LiDAR) data has increased. However, the use of these data may not always be appropriate because slope values derived from fine spatial resolution DEMs are usually significantly higher than slopes derived from coarser DEMs. This increased slope results in considerable variability in modeled sediment output. This paper addresses the implications of parameterizing models using slope values calculated from DEMs with different spatial resolutions (90, 30, 10, and 3 m) and sources. Overall, we observed over a 2.5-fold increase in slope when using a 3-m instead of a 90-m DEM, which increased modeled soil loss using the USLE calculation by 130%. Care should be taken when using LiDAR-derived DEMs to parameterize water quality models because doing so can result in significantly higher slopes, which considerably alter modeled sediment loss.

Development of Erosion Hotspots for a Watershed

AbstractThe loss of topsoil in the United States has resulted in low crop yield, reduction of reservoir capacity, cost increase of water treatment, and detrimental effects on aquatic life and wildlife habitats. An initial step for taking conservation measures in any watershed is to identify locations where erosion protection measures are needed. Applying the Revised Universal Soil Loss Equation (RUSLE) and a geographic information system (GIS), this study attempts to locate the most erodible locations, namely erosion hotspots, for a watershed. Using GIS, the watershed is divided into 25×25-m grids and the RUSLE, including rainfall-runoff erosivity factor, soil erodibility factor, combined slope length and slope steepness factor, cover management factor, and support practice factor, is applied for the estimation of soil erosion potential for each grid cell. By ranking these grid values of erosion potential in a descending order, the top 1% and corresponding locations are defined as the erosion hot spots, w...

Assessing the Effects of DEM Uncertainty on Erosion Rate Estimation in an Agricultural Field

The slope length and steepness (LS) factor is one of the factors in the Revised Universal Soil Loss Equation (RUSLE) needed to estimate average annual erosion rate. The LS factor is often derived from digital elevation models (DEM). DEM errors and uncertainty could affect LS factor estimation and consequently erosion rate estimation. However, DEM uncertainties are not always accounted for, and the effects are not always evaluated in erosion rate estimation. This study compared the erosion rate estimation of a 62.81 ha agricultural crop area using a 7.5 min USGS DEM and DEMs developed using real-time kinematic differential GPS (RTK-DGPS) and dual-frequency DGPS (DF-DGPS) field surveys. Spatial estimation and uncertainty analysis was carried out using sequential Gaussian simulation (SGS). A total of 50 equiprobable DEM realizations were produced using SGS to assess DEM uncertainty and quantify its effect on erosion rate estimation. DEM uncertainty substantially affected the resulting erosion rate estimation. The uncertainty of the average annual erosion rate estimates across the study field was represented using 95% confidence intervals (CI). For the DF-DGPS and USGS DEMs, the percentages of the field area that have erosion rate CIs greater than 11.21 Mg ha-1 year-1 (5 tons acre-1 year-1) were 81% and 85%, respectively, which were substantially larger than that of the RTK DEM (0.41%). The average annual erosion rate map produced using a USGS DEM contained artifacts and underestimated the erosion rate estimation in many areas of the field. The results suggested that higher-accuracy DEMs generated using RTK-DGPS measurements are more appropriate for erosion rate estimation in an agricultural field. Knowledge of DEM uncertainty and its effect on the erosion rate estimation was useful to better judge the reliability of erosion rate estimates.

Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques

Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), vegetation cover (C), and support practice factor (P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient (r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha(-1) ya(-1) in 2005, and the area of 46.61 km(2) (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified.