Slope Length Factor Research Articles

An integrated approach of GIS, RUSLE and AHP to model soil erosion in West Kameng watershed, Arunachal Pradesh

Soil erosion has always been a major environmental problem in many parts of the world including the northeastern region of India. An increase in the rate of soil erosion has tremendous implications on land degradation, biodiversity loss, productivity, etc. Hence, assessment of soil erosion hazard and its spatial distribution is essential to serve as a baseline data for effective control measures. The present study uses revised universal soil loss equation (RUSLE) and analytical hierarchy process (AHP) approach integrated with geospatial technology for modeling soil erosion hazard zone of West Kameng watershed of Arunachal Pradesh, Northeast India. The assessment showed that the erodibility factor of soil ranged between 0 and 0.38 t/ha/MJ/mm and slope length and steepness factor increases with increase in slope angle. Lower normalized difference vegetation index (NDVI) values depict vegetation cover and higher values represent the rocky area or barren land. Spatial distribution of conservation support practice on soil loss indicated the variability (0–1) where lower value represents the higher conservation practice. The predicted average soil erosion rate was 124.21 t/ha/Yr. Normalized eigenvector values ranged between 0.03 and 0.20. The areas with more slope, relative relief, drainage density, lineament density, and frequency have shown comparatively higher eigenvector values, and it has been noticed that the strength of these eigenvectors reduces with a decrease in the values of the parameters. The spatial soil erosion potential map was delineated using eight geo-environmental variables (LULC, geomorphology, slope, relative relief, drainage density, drainage frequency, lineament density, and lineament frequency). The soil hazard map showed that the moderate soil erosion has the maximum (57.71%) area cover followed by high erosion class (26.09%) which depicts that most of the watershed areas are moderate to high vulnerable to soil erosion. The efficiency of the AHP was validated applying area under curve (AUC) method which result 84.90% accuracy in the present study. Based on the findings, it is being recommended that present watershed requires adequate control procedures on a priority basis to conserve soil resources and reduce flood events and siltation of water bodies.

Selection of training samples for updating conventional soil map based on spatial neighborhood analysis of environmental covariates

Selection of training samples plays an important role in updating conventional soil maps with data mining models. In this paper, we developed a method to determine spatial locations of training samples based on spatial neighborhood analysis of environmental covariates for each soil polygon. Training samples were selected based on a single environmental variable or integrated variables generated using multiple variables. Sensitivity analysis was also conducted to test the effect of different spatial neighborhood sizes and selected sample numbers on soil mapping accuracy. Random selection of training samples from soil polygons and soil types respectively were applied to compare with the proposed method in a study area in Raffelson watershed in La Crosse, Wisconsin of USA. Random forest was adopted as the soil prediction model. Results showed that training samples selected using single variables such as Topographic Wetness Index (TWI), slope, plan curvature, profile curvature or slope length factor with the proposed method improved the overall mapping accuracies compared with the conventional soil map, of which using TWI achieved the highest improvement of 27%. The proposed method using TWI, slope or slope length factor performed better than random selection strategies. Random selection from soil polygons generated higher overall mapping accuracies than from soil types. It was concluded that using composite environmental variables which could represent the soil forming environment of a study area well is recommended when applying the proposed method. The proposed method is not sensitive to the selected sample number, but an appropriate neighborhood size is needed for using the proposed method. In our study area with small spatial coverage, neighborhood size 5 × 5 or 3 × 3 is recommended.

Determination of the most dominant cause factors due to erosion in the farmland of Sibolangit sub-district

Solving erosion problems is often inefficient and effective because of lack a deep understanding of the most dominant causal factors. It really depends on how the results of our research of the factors that influence and relate to each other. Erodibility, slope and crop management are three factors that influence the amount of erosion. Each of these factors has a different index value indicating differences in the magnitude of the effect on erosion. The erosion model designed to predict long-term erosion is the USLE Method. The formula for the USLE equation is as follows: A = R.K.L.S.C.P where A=the amount oferoded soil in tonsha−1 year−1; R=rainfall factor; K=soil erodibility factor; L=slope length factor; S=slope factor; C=crop management factors; P=soil conservation factor. The statistical analysis used to answer the most dominant factor is multiple linear regression analysis. The results of this research showed that the highest erosion was 11,302.79 tons ha−1 year−1 in SPT-24, followed by SPT-5 of 3192.88 tons ha−1 year−1 and SPT-25 of 2408.33tonsha−1year−1 while the lowest erosion on SPT-26 is 21.06 tons ha−1year−1. Based on the analysis results obtained the most dominant factor is soil erodibility with a coefficient of determination (R2) of 61.4% so that the focus on erosion handling offarmland in Sibolangit Sub-Districtis minimum and contour tillage, optimize use of green manure and compost, land cover with plant litter. All of the aim is to improve each factor that affects erodibility, namely texture, structure, permeability and soil organic matter.

A data-mining approach towards damage modelling for El Niño events in Peru

Compound natural hazards like El Niño events cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after the El Niño event 2017 – which caused intense rainfall, ponding water, flash floods and landslides – enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.

Evaluation of open Digital Elevation Models: estimation of topographic indices relevant to erosion risk in the Wadi M’Goun watershed, Morocco

Various Global Digital Elevation Models (DEM) are available freely on the Web. The main objective of this work is to evaluate the latest digital elevation models towards the estimation of morphological and topographic erosion parameters in the Wadi M’Goun watershed. We have evaluated multiple DEMs: SRTM (3-arcsec resolution, 90 m), ASTER GDEM (1-arcsec resolution, 30 m), SRTMGL1 V003 (30 m), and ALOS-PALSAR (12.5 m). We have applied for this purpose open source GIS software. To compare and evaluate each DEM, different processing methods have been applied to estimate the Wadi M’Goun watershed characteristics, namely Hypsometry, topographic slope extraction, retrieval of Slope Length and Steepness factor (LS-factor) and topographic wetness index. The accuracy of the ALOS-PALSAR and SRTMGL1 V003 (30 m) DEMs met the requirements applying to the required morphometric parameters. DEMs vertical accuracy has been evaluated by applying the root mean square error (RMSE) metric to DEM elevations vs. actual heights of 353 sample points extracted from an accurate survey-based map (toposheet). The RMSE was 1718 mm for ALOS-PALSAR, 1736 for SRTM 1-arcsec, 1958 for ASTER GDEM 1-arcsec and, 3189 for SRTM 3-arcsec. These results indicate that best accuracy is achieved with the high-resolution of the ALOS PALSAR DEM. This study suggests potential uncertainties in the open-source DEMs, which should be taken into account when estimating topographical and morphometric parameters related to erosion risk in the Wadi M’Goun watershed.

Soil erosion topographic factor (LS): Accuracy calculated from different data sources

The availability and precision of topographic data determines the reliability of the calculated slope length and slope gradient (LS) factor, which limits the use of the universal soil loss equation (USLE) and its adapted versions in region-scale soil erosion risk assessment. One of the common complications is the effectiveness of topographic data with different resolutions or the compatibility of topographic data originated from different sources. In this study, the topographic data from five common sources, 5-m digital elevation model (DEM) based on 1:10,000 topographic maps (5-m topo DEM of 1:10,000), 25-m DEM derived from 1:50,000 topographic maps (25-m topo DEM of 1:50,000), 30-m ASTER GDEM, 30-m and 90-m SRTM DEMs, were individually applied to calculate the LS factors of five catchments with distinct terrain characteristics. Our results show that the computation accuracy of the LS factors in the five study catchments decreased with greater grid sizes derived from the five topographic data sources. Compared to the most precision 5-m topo DEM of 1:10,000, the relative computation error of the mean LS factors was less than 10% when calculated from the 25-m topo DEM of 1:50,000, and that was less than 25% from the 30-m ASTER and 30-m SRTM DEMs. For scenarios in gently rolling areas, the 90-m SRTM DEM with the relative computation error less than 15% could be recommended when there are noises on the open sources DEMs surface. Therefore, depending on the requirements of data accuracy, different data sources can be applied individually or in combined to obtain the optimal predictions of the LS factors. However, such recommendations on data sources proposed in this study appeared to be more applicable for regions with complex terrains. Further studies over a range of terrain features and spatial scales are required to validate the effectiveness of different topographic data sources in calculating the LS factor.

English

Soil erosion is the most challenging and continuous environmental problems resulting in both on-site and off-site effects in the world particularly in Ethiopia. Karesa watershed is one of the most erosion-prone watersheds which received little soil conservation attention. This study was conducted to estimate average annual soil loss rate using Geographic Information System and Universal Soil Loss Equation Model adapted to Ethiopian condition. The following datasets were obtained from different sources for estimating annual soil loss such as 15 years mean annual rainfall data for estimating erosivity factor, digital soil map for estimating soil erodibility factor, 30 m × 30 m resolution Digital Elevation Model for estimating slope length and slope steepness (LS) factor, Landsat6ETM+ images with 30 m × 30 m resolution for detecting vegetation cover and conservation practice factor. The result reveals that 42,413.72 ton per year soil loss from 9939 ha entire watershed or 4.27 tons per hectare per year average annual soil loss rate was observed. The mean annual soil loss rate was classified into four erosion severity classes as very less, less, moderate and high. The result also implies that 94.4% (9383.07 ha) of the watershed areas contributes 81.13% of the total soil loss which were observed from two slope classes (0-15% and 15-30%) and categorized under very less to less soil loss (0-6.25 tons ha-1yr-1). On the other hand, moderate to high soil loss (6.25-25 tons ha-1yr-1) was obtained on slope classes of >30% which covers 555.93 ha (5.6%) of the watershed areas and contributes 18.82% of the total soil loss indicating the maximum share of slope mainly due to cultivation of marginal land,intensive cultivation, poor vegetation cover during critical rainfall period. Moreover, about 2,184.93 ha of the watershed area requires integrated soil and water conservation measures. Key words: Conservation priority, soil erosion, Universal Soil Loss Equation Model (USLE), geographic information system (GIS), Karesa watershed   &nbsp

Modeling of Soil Loss and Identification of Erosion Hot Spot Areas Using RUSLE Integrated with GIS for Appropriate Conservation Practices in Muga Watershed, Highlands of Ethiopia

Over cultivation, deforestation and free grazing are major factors facilitating soil erosion. Nowadays; in lower parts of Muga watershed soil erosion become as a continuous environmental problem. In this study an attempt has been made to modeling soil loss and identify the most erosion sensitive areas by using Revised Universal Soil Loss Equation integrated with GIS and remote sensing techniques for planning appropriate conservation measures in Muga watershed. The annual soil loss amount was estimated by using the Revised Universal Soil Loss Equation (RUSLE). Digital Elevation Model, digital soil map, thirty years rainfall records of six stations, and land cover data (Landsat images) were used to develop RUSLE soil loss variables. The annual soil loss rate from the catchment were estimated by integrating RUSLE parameters using raster calculator tool. The annual soil loss rate varies between 0.02 ton/ha/yr and 41.789 ton/ha/yr. The total annual soil loss in the watershed was 59751.41 tones, of these, 12806.15 tons were lost from 371.19 km 2 , 26562.44 tons from 214.30 km 2 , 15300.94 tons from 50.52 km 2 , 4059.05 tons from 4.61 km 2 , and 1022.83 tons from 0.37 km 2 of land per year. The rate of soil eroion was high in the lower part of the watershed. Slope gradient and length factor was the main factor for soil erosion increment followed by Support Practice (P) factor. As result of soil erosion cross tabulation; steep slopes, Rendzic leptosols and dominantly cultivated areas were detected as very severe erosivity. Therefore, the lower parts of the study needs to undertake effective soil and water conservation practices. Keywords: Soil loss, RUSLE, GIS, Remote sensing, Moga Watershed, Erosion hot spot areas DOI : 10.7176/JEES/9-10-03 Publication date :October 31 st 2019

Modelling terrain erosion susceptibility of logged and regenerated forested region in northern Borneo through the Analytical Hierarchy Process (AHP) and GIS techniques

This research examines the susceptibility of logged and regenerated forest region to erosion through the application of the analytical hierarchy process (AHP) and geographical information systems (GIS). In order to estimate terrain erosion susceptibility, ten geo-environmental variables were taken into account as possible factors relevant to terrain erosion. They are slope, aspect, relative relief, slope length and steepness (LS) factor, curvature, landforms, topographic wetness index (TWI), stream power index (SPI), stream head density, and land use/land cover. Pairwise comparison matrixes were generated to derive the weightages and ratings of each variable and their classes. These were integrated to generate the terrain erosion susceptibility index (TESI) map. Among the variables used in the analysis the land use/land cover, slope, SPI, stream head density, and LS factor were shown to have high contribution towards terrain erosion susceptibility. The areas with a concave slopes > 25° and high relative relief, LS factor, TWI, and stream head densities were found to be more susceptible to erosion such as gullying or landslides. The conversion of TESI into terrain erosion susceptibility zonation (TESZ) map shown that 25% of the total area is highly susceptible to erosion. Among this, 10% of the area possesses a very high vulnerability to landslides and gullying or soil slips and these areas coincide with logging roads and skidder trails. Linear regression analysis between TESI and TESZ with spatial distribution of mean annual rainfall in the region does not show any significant relationships (p > 0.10). However, high rainfall triggers rapid downstream movement of unsupported slopes in the region. The terrain erosion susceptibility zonation map expresses the realistic condition of logged terrain matching with field observations in the area in terms of erosion. The results can serve as basic data for future development programs in the region, in any projects where the terrain susceptibility is critical by planning infrastructure to avoid high risk zones.

Mapping surface water erosion potential in the Soummam watershed in Northeast Algeria with RUSLE model

The present study aims to estimate the annual soil loss in the Soummam watershed in the northeast of Algeria, using the Revised Universal Soil Loss Equation (RUSLE), geographic information system (GIS), and remote sensing (RS). RUSLE model has been used for modelling the main factors involved in erosive phenomena. The Soummam watershed covers a surface area of 9108.45 km2 of irregular shape, northeast —southwest towards southeast. It is characterized by an altitude varying between 2 m in the northeast and 2308 m in the northwest. Results showed that the average erosivity factor (R) is 70.64 (MJ·mm)/(ha·h·year) and the maximum value reaches 140 (MJ·mm)/(ha·h·year), the average soil erodibility factor (K) is 0.016 (t·h·ha)/(MJ·ha·mm) and maximum values reach 0.0204 (t·h·ha)/(MJ·ha·mm) in the southeast regions of the watershed, the average slope length and steepness factor (LS) is 9.79 and the mean C factor is estimated to be 0.62. Thematic maps integration of different factors of RUSLE in GIS with their database, allowed with a rapid and efficient manner to highlight complexity and factors interdependence in the erosion risk analyses. The resulting map for soils losses, with an average erosion rate of 6.81 t/(ha·year) shows a low erosion (<7.41 t/(ha·year)) which covers 73.46% of the total area of the basin, and a medium erosion (7.42 to 19.77 t/(ha·year)), which represents 17.66% of the area. Areas with extreme erosion risk exceeding 32.18 t/(ha·year) cover more than 3.54% of the basin area. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Soummam watershed.

Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model

Climate change due to precipitation is one of the important dominant variables that determine the trend of soil loss in future period. In the present study the MIROC5 model of RCP 2.6, 4.5, 6.0 and 8.5 scenarios have been used to estimate the future period precipitation in storm rainfall event. Statistical downscaling approaches have been applied to estimate the precipitation for the time period of 1900 to 2010 and 2070 to 2100. Then the rainfall and runoff erosivity (R) factor has been estimated from the predicted precipitation scenario with the help of Modified Fourier Index from 2070 to 2100 in different return period such as 5 year, 10 year and 15 year return period. SRTM (Shuttle Radar Topographic Mission) DEM (Digital Elevation Model) and Landsat 8 OLI (Operational Land Imager) have been used to prepare the necessary thematic inputs for RUSLE (Revised Universal Soil Loss Equation) model in GIS environment. In this study the information regarding the soil characteristics have been accounted based on the primary information. In the present study, 2 sq km * 2 sq km grids of the entire basin have been taken into consideration randomly for collecting the soil samples within this region. Then the soil texture has been estimated and identified through the automatic sieve shaker in laboratory. Despite the soil texture, the soil pH and organic matter have also been estimated in the laboratory for estimating the soil erodibility factor (Kfactor) more accurately. Slope length and steepness factor (LS) have been estimated from the SRTM DEM in GIS environment. NDVI (Normalized Difference Vegetation Index) was derived from the Landsat 8 OLI data for estimating the cover and management factor (C), support practice factor (P) related to slope direction has been estimated based on the primary observation during the field visit. Apart from that the SCS curve number values and the weighted curve number values for each and every individual LULC classes have been derived to estimate the R factor. It was revealed that the average annual soil loss in the severe region (very high) in the base year is 12.6% and it would be 25.12% (5 year return period), 26.48% (10 year return period), 27.59% (15 year return period) in RCP 2.6 scenario. The average annual soil loss in this region would be28.53% (5 year return period), 30.00% (10 year return period), 30.97% (15 year return period) in RCP 4.5 scenario. The amount of soil in the severe region would be 32.19% (5 year return period), 33.48% (10 year return period), 34.05% (15 year return period) in RCP 6.0 scenario. In the RCP 8.5 scenario the average annual soil loss would be32.78% (5 year return period), 32.97% (10 year return period), 33.28% (15 year return period). This type of study is more helpful for the decision makers and regional planner for adopting the suitable measures with keeping in the view the local environment.

Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change

Carbon stored in soils contributes to a variety of soil functions, including biomass production, water storage and filtering, biodiversity maintenance, and many other ecosystem services. Understanding soil organic carbon (SOC) spatial distribution and projection of its future condition is essential for future CO2 emission estimates and management options for storing carbon. However, modeling SOC spatiotemporal dynamics is challenging due to the inherent spatial heterogeneity and data limitation. The present study developed a spatially explicit prediction model in which the spatial relationship between SOC observation and seventeen environmental variables was established using the Cubist regression tree algorithm. The model was used to compile a baseline SOC stock map for the top 30 cm soil depth in the State of Wisconsin (WI) at a 90 m × 90 m grid resolution. Temporal SOC trend was assessed by comparing baseline and future SOC stock maps based on the space-for-time substitution model. SOC prediction for future considers land use, precipitation and temperature for the year 2050 at medium (A1B) CO2 emissions scenario of the Intergovernmental Panel on Climate Change. Field soil observations were related to factors that are known to influence SOC distribution using the digital soil mapping framework. The model was validated on 25% test profiles (R2: 0.38; RMSE: 0.64; ME: −0.03) that were not used during model training that used the remaining 75% of the data (R2: 0.76; RMSE: 0.40; ME: −0.006). In addition, maps of the model error, and areal extent of Cubist prediction rules were reported. The model identified soil parent material and land use as key drivers of SOC distribution including temperature and precipitation. Among the terrain attributes, elevation, mass-balance index, mid-slope position, slope-length factor and wind effect were important. Results showed that Wisconsin soils had an average baseline SOC stock of 90 Mg ha−1 and the distribution was highly variable (CV: 64%). It was estimated that WI soils would have an additional 20 Mg ha−1 SOC by the year 2050 under changing land use and climate. Histosols and Spodosols were expected to lose 19 Mg ha−1 and 4 Mg ha−1, respectively, while Mollisols were expected to accumulate the largest SOC stock (62 Mg ha−1). All land-use types would be accumulating SOC by 2050 except for wetlands (−34 Mg C ha−1). This study found that Wisconsin soils will continue to sequester more carbon in the coming decades and most of the Driftless Area will be sequestering the greatest SOC (+63 Mg C ha−1). Most of the SOC would be lost from the Northern Lakes and Forests ecological zone (−12 Mg C ha−1). The study highlighted areas of potential C sequestration and areas under threat of C loss. The maps generated in this study would be highly useful in farm management and environmental policy decisions at different spatial levels in Wisconsin.

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.

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.

Spatial prediction of susceptibility to gully erosion in Jainti River basin, Eastern India: a comparison of information value and logistic regression models

The research on effective methods for assessing areas susceptible to gully erosion has environmental, agricultural and economic benefits to society. This study aimed to identify the areas with high susceptibility to gully erosion using two statistical methods and compare the performances. Remote sensing and field data along with two statistical methods, information value (InfVal) and logistic regression, were applied in the analysis. Gullies, boundary demarcation and regulating erosion processes were surveyed and mapped in a GIS environment. Gully sites were split into training and validation set for modelling and validating susceptibility results. The geo-environmental variables selected, according to study area, were land use/land cover, slope steepness, slope aspect, elevation range, length-slope factor, topographical wetness and position index, stream power index, geology, soil type, rainfall erosivity index and altitude. Weights for each class of each variable were computed through InfVal method and probabilities of occurrence of 1,000,000 random locations were extracted to run the logistic regression operation. A weighted linear combination approach and an inverse distance weighted approach were applied for the InfVal and logistic regression methods, respectively, to generate the gully proneness maps. Both results were classified into five susceptibility classes and they depict 18.44% (Infval) and 12.67% (logistic regression) area of the catchment as high to very highly susceptible to gully erosion. Correlation analysis between the results revealed a coefficient value of 0.8379. For InfVal model, the success and prediction rate is 78% and 75% whereas for the logistic regression model, it is 73% and 72.9% accordingly.

Modeling of water induced surface soil erosion and the potential risk zone prediction in a sub-tropical watershed of Eastern India

Soil is the earth’s fragile skin that anchors all life on earth. Half of the topsoil on the planet has been lost in the last 150 years. Land degradation due to soil loss is one of the major environmental concerns which can be influences by the natural as well as anthropogenic activities. These impacts include compaction, loss of soil structure, nutrient degradation, and soil salinity. The effects of soil erosion go beyond the loss of fertile land. It has led to increased pollution and sedimentation in streams and rivers. And degraded lands are also often less able to hold onto water which can worsen flooding. Revised Universal soil Loss Equation (RUSLE) model and integration with Geographical Information System (GIS) have been taken into consideration for estimating the average annual soil loss in Arkosa watershed. The Overlay Analysis technique have been adopted in RUSLE model for estimating the influences of different factors namely rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support practice factor (P) etc. The average annual soil loss of Arkosa watershed ranged between 0 to 10 tons/ha/year. Here the combined index method has been adopted to show the impact spatially of combine index of these five factors, i.e., R, K, LS, C and P. Apart from this there are total 29 points have been selected randomly for securing that the present soil loss model sounded with ground reality or not. The actual soil loss and predicted soil loss show the positive relationship with them in an r2 value of 0.882. Besides this the present study provides a reliable prediction for future on potential soil erosion risk zones which ranged between 0 and 16 tons/ha/year. To overcome from extreme or severe soil loss situation suitable soil conservation practices or support practices have to be taken care off for minimizing the erosion of the fertile soil or the top soil for making the region less vulnerable from soil erosion in present rate. Sustainable land use can help to reduce the impact of agriculture and livestock, preventing the soil degradation and erosion and the loss of valuable land to deforestation.

Tillage‐induced surface roughness and topographic conditions for rill initiation in Angereb watershed, Ethiopia

AbstractRill erosion makes up one source of sediment detachment and transport on agricultural fields in the Ethiopia highlands. It is so important to understand the conditions for rill initiation and demarcating rill prone areas for management planning. In this paper, we estimated rill incision considering tillage‐induced surface roughness, upslope contributing area, and slope gradient. Field experiments were installed on two fresh‐tilled smallholder agricultural plots of 3.2 m wide and 14.4 m long in the Angereb watershed of the upper Lake Tana subbasin in Ethiopia. The plots mainly represent clay and sandy clay loam soils. Time series digital elevation models generated by digital photogrammetry were used to derive topographic and surface roughness variables corresponding to 46, 116, 174, and 410‐mm cumulative rainfall. A non‐linear regression model was fitted on the slope gradient, upslope contributing area, and surface random roughness of individual rills to predict threshold area for rill initiation. Subsequent rainfall events have resulted in rough surfaces later. The change in random roughness in each rainfall event compared with the first stage amounts to 1.81, 1.62, 1.68, and 1.74 cm on clay soil and −0.05, 0.86, 1.55, and 2.06 cm on sandy clay loam soil. Prediction of the threshold area for rill initiation has improved when considering random roughness and its temporal changes. The average prediction parameter of the unit contributing area was 0.03 ± 0.05 and 0.04 ± 0.04, and of the random roughness was −0.08 ± 0.13 and −0.13 ± 0.05 on clay and sandy clay loam soils, respectively. The prediction resulted in a threshold area between 0.20 and 0.47 (average of 0.36 ± 0.11) on clay soil and 0.28 and 0.39 (average of 0.34 ± 0.05) on sandy clay loam soil. The significant variation of threshold area (p &lt; 0.05) over the subsequent rainfall events ensures the role of incorporating surface roughness in predicting rill initiation. The result demonstrates the need to consider the variable threshold area over rainfall periods for rill incision modelling and indirectly improves the slope–length factor of erosion models.

GIS Based Soil Erosion Estimation Using Rusle Model: A Case Study of Upper Kangsabati Watershed, West Bengal, India

An accelerated rate of soil erosion is a serious and continuous endemic environmental problem in the western part of West Bengal. The present study is carried out in the upper Kangsabati watershed of Puruliya district, West Bengal with an area of 276.19 km2. It is fact that the surface runoff of seasonal rainfall is more in this area due to its sloping terrain characteristics. Average annual soil erosion has been estimated based on the five parameters defined in the Revised Universal Soil Loss Equation (RUSLE) and with the help of Geographical Information technology. Overlay of five parameters, viz., rainfall–runoff erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support and conservations practices factor (P) has been done in GIS platform. Potential average annual soil erosion of the watershed has been classified into four categories.

Effects of Landslide Sampling Strategies on the Prediction Skill of Landslide Susceptibility Modelings

In this study, landslide susceptibility assessments were achieved using logistic regression, in a 523 km2 area around the Eastern Mediterranean region of Southern Turkey. In reliable landslide susceptibility modeling, among others, an appropriate landslide sampling technique is always essential. In susceptibility assessments, two different random selection methods, ranging 78–83% for the train and 17–22% validation set in landslide affected areas, were applied. For the first, the landslides were selected based on their identity numbers considering the whole polygon while in the second, random grid cells of equal size of the former one was selected in any part of the landslides. Three random selections for the landslide free grid cells of equal proportion were also applied for each of the landslide affected data set. Among the landslide preparatory factors; geology, landform classification, land use, elevation, slope, plan curvature, profile curvature, slope length factor, solar radiation, stream power index, slope second derivate, topographic wetness index, heat load index, mean slope, slope position, roughness, dissection, surface relief ratio, linear aspect, slope/aspect ratio have been considered. The results showed that the susceptibility maps produced using the random selections considering the entire landslide polygons have higher performances by means of success and prediction rates.