Potential Soil Erosion Risk Research Articles

Modeling of soil erosion risk in a typical tropical savannah landscape

Tropical savannah landscapes are faced with high soil degradation due to climate change and variability coupled with anthropogenic factors. However, the spatiotemporal dynamics of this is not sufficiently understood particularly, in the tropical savannah contexts. Using the Wa municipality of Ghana as a case, we applied the Revised Universal Soil Loss Equation (RUSLE) model to predict the potential and actual soil erosion risk for 1990 and 2020. Rainfall, soil, topography and land cover data were used as the input parameters. The rate of predicted potential erosion was in the range of 0–111 t ha−1yr−1 and 0–83 t ha−1yr−1 for the years 1990 and 2020, respectively. The prediction for the rate of potential soil erosion risk was generally higher than the actual estimated soil erosion risk which ranges from 0 to 59 t ha−1yr−1 in 1990 and 0 to 58 t ha−1yr−1 in 2020. The open savannah areas accounted for 75.8 % and 73.2 % of the total soil loss in 1990 and 2020, respectively. The validity of the result was tested using in situ data from a 2 km2 each of closed savannah, open savannah and settlement area. By statistical correlation, the predicted soil erosion risk by the model corresponds to the spatial extent of erosion damages measured in the selected area for the validation. Primarily, areas with steep slopes, particularly within settlement, were identified to have the highest erosion risk. These findings underscore the importance of vegetation cover and effective management practices in preventing soil erosion. The results are useful for inferences towards the development and implementation of sustainable soil conservation practice in landscapes with similar attributes.

Soil Erosion Risk Assessment in The Niğde Using Corine Model

Soil erosion risk was calculated using the coordination of information on the environment (CORINE) model in this study. The aim of the study is to determine the soil erosion risk of Niğde province, taking into account soil properties, slope and land use. Potential (PSER) and actual soil erosion risks (ASER) were determined using factors which were soil properties, slope, climatic factors, and land cover data. Data were produced using Arc-GIS 10.3 software, and results were obtained on these maps. 34.72% of the soils were classified as moderately actual soil erosion risk, which located in the eastern and southeast part of the study area.51.66% and 13.62% of the soils were classified as low and high actual soil erosion risk, respectively. Areas which have low actual soil erosion risk are located in the middle part, and areas which have high actual soil erosion risk are located in the northwest part of the area. The areas which were categorized as low potential soil erosion risk were increased from 23.52% to 51.66% in the actual soil erosion risk, after combining the land cover map. On the other hand, the total areas classified as high and moderate actual soil erosion risk decreased from 76.48% to 48.34% in the actual soil erosion risk due to land cover types. Soil texture, land cover, and slope are the most important factors that affect erosion risk. This study indicated that the CORINE model integrated with GIS (Geographic Information Systems) and RS (Revised Universal Soil Loss Equation) has a very effective and accurate potential for soil erosion risk assessment.

Soil Erosion Risk Assessment Using Corine Model: A Case Study in Shwan Sub basin, Kirkuk, Iraq

Water erosion is one of the most important problems and challenges facing the optimal management of natural resources, especially soil and water resources, because of its impact on soil degradation. This study aimed to classify the risk of soil water erosion and clarify its distribution in the Shwan Basin in Kirkuk Governorate - Iraq based on the Corine model.In the first stage of the work, the soil erodibilty, erosivity and slope factors were determined, classified and mapped according to the degree of their impact on soil erosion.The potential soil erosion risk map was prepared using Arc GIS 10.4.1.In the second stage, the land cover factor was calculated and classified according to the degree of protection. In the last stage, the actual soil erosion risk map was prepared by multiplying the class of land cover with the map of potential soil erosion risk over the entire study area.The study showed that 28.18% of the studied area is classified under a high risk, 39.57% represents as a medium risk and 32.25% is a low risk .The areas of severe soil erosion risk were concentrated in the southwestern part and some northern and central parts, and the areas of medium risk were concentrated in the northern, central and southern parts, while regarding the low actual risk, they were concentrated in the western parts and in random areas in the center and east of the study area.The results also showed that the Corine model for soil water erosion risk mapping is a highly effective and cost-effective approach.

Potential risk of soil erosion on the Tibetan Plateau during 1990–2020: Impact of climate change and human activities

With global warming and increasing anthropogenic activities, the ecosystems on the Tibetan Plateau are becoming increasingly fragile, exacerbating the risk of soil erosion in the area. However, the interacting effects induced by numerous erosion processes occurring at different times and spaces on the Tibetan Plateau are challenging to quantify using the conventional single-process assessment models (e.g., water erosion, wind erosion, and freeze–thaw). Consequently, our understanding of the complicated state of the plateau with respect to the potential for soil erosion is limited. Therefore, we created a methodological framework using multi-criteria decision-making (MCDM) to evaluate the potential risk of various soil erosion processes driven by climate change and human activity under current soil and topographical circumstances in this region. The results showed that the assessment model was reliable, and the estimated accuracy using the receiver operating characteristic curve for the training and validation datasets was 0.721. The majority of the areas of the plateau (60.69%) were at very-low or low-risk levels, while 17.55% of the plateau (mainly in the southeast and along the surrounding high mountains) was at high risk. In general, the average potential risk of soil erosion significantly increased during 1990–2020, and climate change exerted more pressure on the land surface than human activities did. The potential risk increased dramatically in 28.15% of the total plateau area, and was found to be concentrated in the plateau’s southern, eastern, central, and northern regions. The findings of this study provide a basis for local ecological environmental protection and land resource management on the Tibetan Plateau and propose a new protocol to forecast and prevent soil erosion worldwide.

Estimation of rainfall erosivity on the Chinese Loess Plateau: A new combination of the ERA5 dataset and machine learning

Determining the characteristics of rainfall erosivity at different times and locations is significant for global and regional soil erosion prevention and control. However, accurate quantification of rainfall erosivity remains a challenge due to the limitations of high-precision and long-time series precipitation data. Here, compared with the rainfall erosivity of 291 meteorological stations on the Chinese Loess Plateau, we evaluated the distribution characteristics and accuracy of rainfall erosivity on different temporal and spatial scales calculated by the ERA5 precipitation dataset. A random forest model was constructed to generate a bias dataset to correct the annual rainfall erosivity. Besides, the relationship between erosivity density (ED) and rainfall patterns was quantified to clarify the characteristics of rainfall erosivity. Conducted analysis suggested that the rainfall erosivity calculated from the ERA5 precipitation dataset has high accuracy on average annual, seasonal and monthly scales, and the R2 were 0.62, 0.86, and 0.85, respectively. The addition of the random forest model further optimized the result of average annual rainfall erosivity of ERA5, with root mean square error (RMSE) and absolute value of relative bias (BIAS) decreasing to 185.7 MJ mm ha−1h−1 a−1 and 2.17%, respectively. Meanwhile, ERA5 can well capture the average annual rainfall erosivity in different spaces of the study area. The high correlation between erosivity density and rainstorms (R2 = 0.87) indicated that a larger proportion of rainstorms in erosive rainfall will lead to a greater potential risk of soil erosion. These findings provide a reliable approach to accurately obtain the variable rainfall erosivity in areas with complex terrain and sparse stations, and have important implications for soil sustainability.

A GIS-based approach for estimating fallow-season cropland soil erosion based on rainfall erosivity

ABSTRACT Rainfall erosivity describes the capability of rainfall to cause soil erosion from a surface during a storm. Currently, the average long-term annual and monthly rainfall erosivity for a location may be obtained from isoerodent maps using a sample of climate stations across the United States (US). However, at the state-scale these measures are sporadic, using rainfall data from an increasingly outdated period, which may not reflect possible changing rainfall erosivity. Focusing on the state of Kentucky, US, we developed rainfall erosivity grids from the state Mesonet system to determine if erosivity has changed spatially and temporally between the original erosivity datasets from the latter twentieth-century and 2011–2020. We further used the Mesonet-derived dataset to develop a GIS-based model to estimate potential soil erosion for croplands during the fallow-season when soil is most prone to erosive storms. Results indicated that monthly erosivity increased across the state between the two periods. Larger increases in erosivity during the latter part of the fallow-season resulted in higher estimated soil erosion from croplands, particularly to the west. These findings may provide guidance in identifying and targeting croplands at greatest potential risk of soil erosion during the fallow-season for additional monitoring, mitigation and management practices.

Geoinformatic approach to potential soil erosion risk assessment in Tulasi watershed

The disintegration of soil grains caused by raindrops initiates the erosion process and is the primary source of erosion on small and steep slopes. Soil loss occurs all across the world, but poor countries are more vulnerable to it due to failure to replenish lost soil and minerals. Asian countries have been highlighted as one of the locations on the globe where soil erosion has proven to be a difficult challenge in terms of development and land resource conservation. Because India is the world's second most populous country and agriculture is the backbone of the Indian economy, sustainable land management approaches are urgently needed to protect production potential and provide an adequate supply of healthy food for millions. The Tulasi watershed in Maharashtra, India, is distinguished by heavy rain during the monsoon season, undulating and rocky topography and bare hilltops, all of which increase runoff capacity and make top soil more prone to detachment and loosening. In light of this, the current study employs geo-information technologies and the RUSLE model to derive erosion factors such as rainfall erosivity, soil erodibility, slope length and steepness, land cover and management and to estimate the amount and spatial distribution of soil loss in the study area.

The influence of land cover variation on soil erosion vulnerability around coal mining concession areas in South Borneo

The availability of information about soil erosion vulnerability is necessary as a primary consideration to determine the effort of soil conservation, particularly in the coal mining area. This study aimed to estimate the potential risk of soil erosion from land cover variation in a coal mining concession site in South Borneo. Data were taken from 18 stations of soil erosion monitoring which were evenly distributed in each land cover. Soil erosion vulnerability was quantified using the Universal Soil Loss Equation (USLE) method. The comparison mean of soil erosion among land cover types was evaluated using the Kruskal-Wallis test and followed by the Nemenyi test with a significant level of 5%. Results found that the potential risk of soil erosion was significantly different among land covers (p<0.05). The highest soil erosion vulnerability was noted in the reclamation area of 1,012.3 t ha<sup>-1</sup> year<sup>-1,</sup> while the lowest risk of soil erosion was observed in plantation forests of 47.9 t ha<sup>-1</sup> year<sup>-1</sup>. Surprisingly, the potential risk of soil erosion in natural forests was four times higher than in oil palm plantations. Besides being located in hilly areas with high slope levels, the vegetation density in natural forests was relatively low. However, our study recorded there were two critical factors that highly correlated to soil erosion vulnerability, i.e., soil erodibility (R = 0.89; p<0.05) and slope length and steepness (R = 0.85; p<0.05).

Modelling Potential Soil Erosion and Sediment Delivery Risk in Plantations of Sri Lanka

The current trend in agricultural practices is expected to have a detrimental impact in terms of accelerating soil erosion. Assessment of the cumulative impact of various management strategies in a major plantation is a measure of the sustainably of soil resources. Thus, the current study aimed to develop the potential soil erosion map for a selected plantation (8734 ha in size) in tropical Sri Lanka using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. The estimated mean annual soil loss rate of the selected plantation was 124.2 t ha−1 ranging from 0.1 to 6903.3 t ha−1. Out of the total extent, ~49.5% of the area belongs to the low soil erosion hazard category (0–5 t ha−1 year−1) while ~7.8% falls into very high (25–60 t ha−1 year−1) and ~1.3% into extremely high (60 < t ha−1 year−1) soil erosion hazard classes. The rainfall erosivity factor (R) for the entire study area is 364.5 ± 98.3 MJ mm ha−1 hr−1. Moreover, a relatively higher correlation was recorded between total soil loss and R factor (0.3) followed by C factor (0.2), P factor (0.2), LS factor (0.1), and K factor (<0.1). It is evident that rainfall plays a significant role in soil erosion in the study area. The findings of this study would help in formulating soil conservation measures in the plantation sector in Sri Lanka, which will contribute to the country’s meeting of the UN Sustainable Development Goals (SDGs).

Soil erosion risk under climate change scenarios: A case study in rural area with varying land uses

Soil erosion is one of the major issues in the tropics. The erosion is highly affected by the changes in climate and land cover. Future changes in tropical climate, particularly precipitation are expected to influence the potential risks of soil erosion. In the face of rapid changes in rural land cover for agricultural purposes, the combined forcings of land cover and climate changes have been to be a major threat to the soil conservation due to soil erosion. In this study, climate change scenarios at the northern part of Borneo were developed based on the RCP 4.5 and RCP 8.5 climate scenarios using Weather Research Forecast Model (WRF). The future climate projection scenarios of the total precipitation were used to simulate the potential erosion risks in varying land covers in a rural area of Sabah, Malaysia. The RUSLE model was used for soil erosion modelling, which was integrated with IDRISI Selva that allow the analysis and assessment of erosion risk. The variability of future total precipitations in the area of varying land cover types have resulted in varying degree of potential soil erosion risk The average soil loss at the studied area has increased by 262 t/ha/yr with 35.94 % increment in annual precipitation under RCP 8.5 emission scenario. However, under RCP 4.5, 26.65 % decrement in precipitation has reduced the soil loss by 315.1 t/ha/yr. In this rural area, exceptionally high soil erosion was found at steep slopes and thin vegetation covers. Therefore, an appropriate land use planning, soil conservation practices, and strategic adaptation options plan should be created and developed to ensure the sustainability of the soil conservation and enhance rural agricultural productivity.

Assessment of soil erosion risk using an integrated approach of GIS and Analytic Hierarchy Process (AHP) in Erzurum, Turkiye

Assessing soil erosion hazards and mapping the spatial distribution of soil erosion have an essential role in sustainable forest management. In this study, the potential soil erosion risk was evaluated through the Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) in the Oltu forest planning unit, Erzurum. Seven erosion-related criteria, including slope, bedrock type, relative relief, drainage density and frequency, rainfall, and land use/land cover (LULC) were used for the present assessment. According to the AHP analysis, the slope was the most influential factor (21%) followed by bedrock type (19%), land cover (17%), and relative relief (14%) in the soil erosion process. The soil erosion risk in the study area was strongly influenced by the LULC where 59.46% is bare land with high erosion risk and 12.07%, with the lowest risk, is in an area with any forest cover. The estimated soil erosion risk was classified into five different classes namely very low, low, moderate, high, and very high. The results showed that this study area is highly prone to soil erosion. The larger proportion of the area (39.16%) is exposed to high to very high erosion, mainly determined by forest cover and geomorphology. To analyze the accuracy of the soil erosion risk map, 40 points were selected randomly in this study area. In these points, predicted values were compared to the real values obtained by Google Earth-colored images. The area under the ROC curve (AUC) method was applied to validate the efficiency of the AHP which showed a satisfactory accuracy of 81.00%. Findings presented that including the more influencing factors with a slope instead of including only the slope contributes to a more accurate erosion risk map. This study highlighted that GIS-based multi-criteria decision-making is a valuable and practical tool for decision-makers and land managers in creating soil erosion susceptibility maps and determining high-priority areas that require conservation measures for sustainable land use management by reducing the economic and ecological impacts of soil loss. Also, this approach can be practically applied in other planning units.

Soil loss estimation and prioritization using geographic information systems and the RUSLE model: a case study of the Anger River sub-basin, Western Ethiopia

Abstract Soil erosion is a major environmental problem that affects people's livelihoods and environmental health, particularly in developing countries. The present study aimed to identify soil erosion hotspot areas and prioritization in the Anger River sub-basin for conservation planning. The Revised Universal Soil Loss Equation (RUSLE) model and geospatial technologies were adopted to identify soil erosion hotspot areas and prioritization of the sub-watershed for conservation planning. Key parameters such as rainfall data, soil data, slope length and steepness factor, cover management, and conservation practices were used to estimate potential soil erosion risk in the sub-watershed. The results showed that the annual soil loss rate in the Anger River Sub-basin ranged between 0 and 932.6 t/ha/year with a mean annual soil loss of 83.7 t/ha/year. About 1,140.7 km2 (43.6%) and 220.6 km2 (8.4%) were categorized under very severe and severe soil loss types, respectively. The mid- and upstream areas, as well as the steeper parts of the sub-watershed, were highly exposed to soil erosion. This research provides tangible evidence in the decision-making process for soil and water conservation practices at a sub-watershed scale. Moreover, further research should be conducted at a micro-watershed scale to minimize the effects of soil erosion on the health and sustainability of the watershed.

Soil erosion risk analysis and mapping for conservation planning at Mihtsab-Azmati micro-dam watershed, Northern Ethiopia

This study was conducted to quantify, analyze and map soil erosion risk areas using remotely sensed data and GIS-based Revised Universal Soil Loss Equation (RUSLE) model at Mihtsab-Azmati Irrigation Dam Watershed, Northern Ethiopia. Based on the result, the watershed has potential soil erosion risk which ranges from 0 ton hectare−1 year−1 to 2980.12 ton hectare−1 year−1 with an average annual soil erosion rate of 110.06 ton hectare−1 year−1. From the entire watershed area, only 27% of it has a soil loss a value less than 15 ton hectare−1 year−1 which is relatively categorized under low soil erosion risk, while the remaining area (73%) experiences moderate to very high soil loss rates. A large area (50.7%) is under high to very high soil loss (>50 ton hectare−1 year−1). The majority of the study watershed area experiences a very high soil erosion rate and this could be a major threat to the new irrigation dam constructed at the outlet of the watershed due to the sedimentation problem, which could decrease the economic lifespan of the dam. Hence, from the conservation perspective, a large proportion of the upper watershed area requires immediate and appropriate soil conservation measures.

Synergy of remotely sensed data in spatiotemporal dynamic modeling of the crop and cover management factor

Soil erosion is a threat to the water quality constituents of sediments and nutrients and can cause long-term environmental damages. One important parameter to quantify the risk of soil loss from erosion is the crop and cover management factor (C-factor), which represents how cropping and management practices affect the rates and potential risk of soil erosion. We developed remotely sensed data-driven models for dynamic predictions of C-factor by implementing dynamic land cover modeling using the SWAT (Soil and Water Assessment Tool) model on a watershed scale. The remotely sensed processed variables included the enhanced vegetation index (EVI), the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR), leaf area index (LAI), soil available water content (AWC), slope gradient (SG), and ratio of area (AR) of every hydrologic response unit (HRU) to that of the total watershed, comprising unique land cover, soil type, and slope gradient characteristics within the Fish River catchment in Alabama, USA between 2001 and 2014. Linear regressions, spatial trend analysis, correlation matrices, forward stepwise multivariable regression (FSMR), and 2-fold cross-validation were conducted to evaluate whether there were possible associations between the C-factor and EVI with the successive addition of remotely sensed environmental factors. Based on the data analysis and modeling, we found a significant association between the C-factor and EVI with the synergy of the environmental factors FPAR, LAI, AWC, AR, and SG (predicted R 2 ( R 2 pred ) = 0.51; R 2 = 0.68, n = 3 220, P < 0.15). The results showed that the developed FSMR model constituting the non-conventional factors AWC ( R 2 pred = 0.32; R 2 = 0.48, n = 3 220, P < 0.05) and FPAR ( R 2 pred = 0.13; R 2 = 0.28, n = 3 220, P = 0.31) was an improved fit for the watershed C-factor. In conclusion, the union of dynamic variables related to vegetation (EVI, FPAR, and LAI), soil (AWC), and topography (AR and SG) can be utilized for spatiotemporal C-factor estimation and to monitor watershed erosion.

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Spatial-Temporal Trend Analysis of Rainfall Erosivity and Erosivity Density of Tropical Area in Air Bengkulu Watershed, Indonesia

Abstract There have been many studies on rainfall erosivity and erosivity density (ED). However, it was not widely developed in Indonesia as a tropical country and has unique precipitation patterns. They are indicators for assessing the potential risk of soil erosion. The Air Bengkulu Watershed is undergoing severe land degradation due to soil erosion. This study aimed to analyze spatial-temporal in rainfall erosivity and ED based on monthly rainfall data (mm). The data used consisted of 19 weather stations during the period 2006–2020 and which are sparsely distributed over the watershed. The analysis was done by using Arnold's equation. Then, the trend was tested using parametric and non-parametric statistics, and analysed with linear regression equation, and Spearman's Rho and Mann Kendall's tests. The spatial distribution of both algorithms was analysed using the inverse distance weighted (IDW) method based on the geographic information system (GIS). Unlike previous research findings, The long-term average monthly rainfall erosivity and ED revealed a general increase and decreasing trend, whereas it was found to be non-significant when both indices were observed. However, these results indicate a range from 840.94 MJ · mm−1 · ha−1 · h−1 · a−1, 552.42 MJ · mm−1 · ha−1 · h−1 · a−1 to 472.09 MJ · mm−1 · ha−1 · h−1 · a−1 in that November month followed by December and April are the most susceptible months for soil erosion. Therefore, The upstream area of the region shows that various anthropogenic activities must be managed properly by taking into account the rainfall erosivity on the environment and that more stringent measures should be followed in soil and water conservation activities.

Mapping tillage direction and contour farming by object-based analysis of UAV images

Tillage is a primary agricultural task that causes progressive soil movement and, consequently, severe erosion in sloping farmland, with a high impact on crop productivity, soil quality and landscape features. Governments and public administrations acknowledge the need to control tillage operations for a better soil conservation in vulnerable areas, recommending the practice of contour farming. Despite this importance, there is currently no mechanism for the effective and large-scale monitoring of tillage patterns at the field or regional scale. Accordingly, this research combined aerial images taken with unmanned aerial vehicles and object-based image analysis (OBIA) to develop an innovative OBIA4tillage procedure with three main objectives: (i) analysing plowed agricultural fields, identifying and mapping the tillage marks, and automatically computing the main direction of the tillage furrows, (ii) validating the procedure quality in different scenarios by evaluating the accuracy of the results as affected by the sensor used (visible-light vs. multispectral), background soil hue, and ground vegetation density; and (iii) mapping contour farming and non-contour farming areas as indicators of potential low and high soil erosion risk, respectively. Twenty olive parcels from two different locations with a wide range of tree sizes, soil hue, parcel shapes and land slopes were selected as model systems to develop and validate the procedure. The OBIA4tillage procedure produced tillage maps with very high accuracy for both RGB and multispectral images (R2 of 0.99 and 0.93, respectively), as obtained from the linear equation between estimated and ground-truth values. The results were similar in clear and dark soils (R2 of 0.96 in both cases), although there were notable differences between areas of dense ground vegetation or bare soil (R2 of 0.99 in both cases) and areas of medium vegetation cover (R2 of 0.81). The application of contour farming in the study region was moderate at location 1 (42.35% of the study area) but more widespread at location 2 (72.60% of the study area), which revealed the uneven involvement of the local farmers in the challenge of controlling soil erosion risks. The valuable information in the OBIA4tillage outputs can also be used to study tillage events at various spatial and temporal scales, feed soil erosion models and promote good agricultural and environmental conditions measures implemented at the parcel scale.

Tools for USLE-CP-factor calculation and actual erosion risk on field block level for Switzerland

The calculation of the cover management factor (C-factor) and support practices factor (P-factor) is an important element in the Universal Soil Loss Equation (USLE). In Switzerland, a potential soil erosion risk map of arable land and a field block map that represents the basis of the agriculturally used areas in the country are available. A CP-factor tool was developed adapted to Swiss agronomic and environmental conditions, which allows to calculate CP-factors easily for various crop rotations and management practices. The calculated CP-factor values can be linked to any field block in the potential soil erosion risk map to determine the actual soil erosion risk for the field block. A plausibility check with other C-factor tools showed a sound match. This user-friendly calculation makes the CP-Tool and the actual erosion risk more accessible for authorities and GIS users. With Python and QGIS as open source resources, it is also possible to easily improve the tools. Linking the two tools provides substantial added value for education and training, advising farmers and policy, as well as scientific research, and can serve as a reference for other countries.•USLE-CP-factor and actual erosion risk calculation on small scale field block level.•Developed and programmed based on open source resources for further improvements.•Both tools increase the knowledge of management practices for GIS- and non GIS users.

Prioritization of potential soil erosion susceptibility region using fuzzy logic and analytical hierarchy process, upper Blue Nile Basin, Ethiopia

Soil erosion poses a global threat to soil loss, agricultural land deterioration, and crop yield decreases. This paper accounts for the establishment of sustainable land planning, and methods for evaluating soil erosion rates. The study was conducted based on the system complex analysis to prioritize soil erosion in Ribb watershed to improve soil and water conservation planning. Several geographical-environmental factors affecting soil erosion (land use, soil, slope, SPI, TWI, river Proximity, curvature, aspect) considered to assess potential soil erosion risk. The information levels were focused on expert experience, and the dynamic decision-making process built in a network framework. The weights for each layer were computed by R Studio fuzzy AHP package. The map of soil erosion susceptibility was developed by combining layers in a platform based on their weights and means of erosion-related incidents in regional surveys. Hence, the soil erosion map's overall results showed very high, high, moderate, low, and very low vulnerability to erosion with areal coverage of 13.4 %, 27.93 %, 32.74 %, 17.42 %, and 8.5 %, respectively. The susceptibility map demonstrates that high soil erosion sensitive areas with Fuzzy and AHP respectively cover 11.05 % and 15.75 %. The study indicates that priority should be given to high and very high erosion-prone areas in Rib watershed to adapt to climate change and control sedimentation problems in the Rib reservoir and Lake Tana.

Reassessment of the potential risk of soil erosion by water on agricultural land in Germany: Setting the stage for site-appropriate decision-making in soil and water resources management

Accelerated soil erosion by water on agricultural land is considered one of the most critical forms of soil degradation, as it threatens both agronomic productivity and environmental quality. Given that both the costs and the benefits of soil erosion control measures are subject to spatial variation, the criterion of cost-effectiveness calls for spatially heterogeneous soil conservation policies. Under the German Direct Payments Regulation, federal state authorities are obliged to assess the potential risk of soil erosion by water on arable land in accordance with ABAG – the German version of the well-established Universal Soil Loss Equation (USLE) – and to impose appropriate management restrictions on farmers. However, the responsible federal state authorities have considerable degrees of freedom with regard to the methodology of erosion modelling - and thus in the designation of soil erosion protection areas: First, classification is carried out by multiplication of soil erodibility (K) and slope steepness (S) factors; the inclusion of rainfall erosivity (R) and slope length (L) factors is only optional. Second, classification of RKS- and RKLS-factors corresponds to the assumption of cross-regional standard estimates for R and L, which do not adequately reflect the heterogeneity of local conditions. This inevitably leads to over- and underestimation of local soil erosion hazards. As a consequence, the official risk assessment of soil erosion by water in Germany consists of a patchwork of cadastral risk maps at federal state level, which are neither publicly available nor fully comparable with each other. Hence, under current legislation there is a great danger that management requirements will be imposed on German farmers, which are economically inefficient, distort competition and do not achieve their environmental objectives. The aim of this study is to identify methodological and conceptual potential for improvement and harmonization in the risk assessment of soil erosion by water in Germany. For this purpose, high-resolution nationwide maps of rainfall erosivity (R-factor), soil erodibility (K-factor), slope length (L-factor), and slope steepness (S-factor) are produced in accordance with the revised ABAG using High Performance Computing (HPC) resources. The study area covers arable land, permanent grassland, vineyards, and orchard plantations. The modelling shows that the potential risk of soil erosion by water on agricultural land in Germany is much higher than previously assumed.