Resource Inventory—Sheet, Rill and Wind Erosion

The Daily Erosion Project (DEP) began about 20 years ago as a daily sheet and rill erosion estimator for the state of Iowa, USA by using weather data from US National Weather Service NEXRAD radar and automated weather stations as well as USDA National Resource Inventory (NRI) survey data accessed at the township level and Soil Survey Geographic (SSURGO) soil properties data. In 2014 we revised our framework to use remote sensing methods to gather all inputs at the field level. These efforts have led to the development of the Agricultural Conservation Planning Framework, https://acpf4watersheds.org, a spatial database and geoprocessing toolbox that allows one to generate candidate locations for site specific conservation practices. The addition of automated Digital Elevation Model (DEM) enforcement techniques allowed development of a new sampling strategy to estimate sheet and rill erosion on hundreds of thousands of agricultural flowpaths across the Midwestern United States; https://dailyerosion.org. This is done by using the US 12-digit Hydrologic Unit Code (HUC) basin numbering system in conjunction with an even finer system of delineation to define approximately 100 ha DEP subcatchments. One agricultural flowpath is randomly selected and modeled in each subcatchment and the mean of the HUC12 is the estimated erosion. However, DEP estimates still do not include ephemeral gully or channel erosion processes and only occur on agricultural lands, thus DEP still lacks simulation of many erosion processes. Our objective in this project is to develop methods to add sheet and rill erosion estimates on forested lands and channel erosion estimates on agricultural lands to DEP. Considered alone, the addition of sheet and rill erosion forest estimates is somewhat straightforward as many inputs remain the same. However, the basic land management unit used by the DEP processing pipeline is the agricultural field, so expanding to include forested areas required developing new algorithms. To achieve this, we started with the LANDFIRE canopy cover database and street map datasets from the US Forest Service (USFS) and Open Streetmaps, respectively. LANDFIRE specifies the amount of canopy cover on forested lands throughout the United States, enabling selection of appropriate forest management for each 30m pixel from USFS files developed for each 10% canopy cover increment. Additionally, forest erosion is highly sensitive to channelization due to road networks, so hillslope erosion estimates are truncated at roads to keep the modeled areas constrained to places where sheet and rill erosion processes dominate. Last, modeled hillslopes are considered either agricultural or forested, but not both (agricultural hillslopes truncate at forest and forested hillslopes truncate at agricultural fields) to maintain estimates for each part separately. Adding channel erosion estimates to DEP is also necessary to adequately estimate overall erosion but requires alteration of current DEP practices to use the watershed version of WEPP (rather than hillslope). In furtherance of this goal, new algorithms are being developed to define and parameterize basins inside DEP subcatchments, develop flow networks for the basins, parameterize flow network channel geometry from QL2 LiDAR and to map land cover and management in channels as most datasets do not resolve vegetated waterways. Basin and flow network delineation can be accomplished by setting critical grid order thresholds similar to the current DEP process for hillslope termination. Model results for forest erosion are consistent with expectations but extensive field validation presents difficulties. Model results can be combined with the grid order threshold process to identify areas of potential channel erosion and the relative likelihood that erosion will occur in a given channel. The addition of these estimates to DEP helps further soil degradation process modeling and will enable future analyses into erosion forms and dynamics across different temporal and geological scales.

Field experiments were conducted on various runoff plots within the Zhangjiachong watershed, located in the Three Gorges Reservoir Area, to study rainfall‐runoff processes and sediment yields under different slope land use patterns. A hillslope run‐on model considering vegetation interception was used to simulate runoff generation and concentration. A slope erosion model including raindrop erosion, sheet erosion, and rill erosion was developed to estimate sediment yield and concentration. The parameters of both models were calibrated and validated using observed runoff and sediment yield data. The results show that the runoff simulations in the validation have relative errors below 20% for all runoff plots, except those planted with crops. The saturated hydraulic conductivity of soil was found to be ca. 10% higher on slope lands with hedgerows than those without. In addition, it was about 15% higher on slope lands with crops than on slope lands with tea plants and citrus. The sediment yield was shown to be influenced by both precipitation characteristics and antecedent sediment conditions. There was a significant relationship between sheet erosion, rill erosion, and vegetation coverage, while the amount of raindrop erosion was similar across different runoff plots. The proportions of sheet and rill erosion on slope land with crops were minimal whether hedgerows existed or not. The proportions of the three main forms of soil erosion on slope lands with tea plants and citrus were more or less the same under concentrated rainfall. For more evenly distributed rainfall, raindrop erosion was the primary form on slope lands with hedgerows‐tea plants and citrus. The proportions of sheet and rill erosion significantly increased on slope lands without hedgerows.

Characterizing erosion processes on a convex slope based on 3D reconstruction method

Ethiopia is currently facing a major environmental problem caused by soil erosion. In order to tackle this problem, it is essential to implement a comprehensive watershed management approach and give priority to conservation efforts depending on the level of severity. Therefore, the objective of this research is to evaluate the mean annual soil erosion and rank the sub-watersheds for conservations in the Ayu watershed, utilizing the Revised Universal Soil Loss Equation (RUSLE) model and the Sub-Watershed Prioritization Tool (SWPT). RUSLE was utilized to predict the annual average soil erosion rate, while SWPT was applied to conduct Weighted Sum Analysis (WSA) for ranking sub-watersheds. Support Vector Machine (SVM) was employed for classifying land use and land cover. The Relative importance of morphometric and topo-hydrologic features in the SWPT was analyzed using a Random Forest model. The Bland-Altman plot and Wilcoxon Signed Rank Test were employed to assess the agreement in prioritizing watersheds between RUSLE results and the SWPT. Furthermore, field observations were conducted to validate the land use classification by collecting ground data. In addition, the study was enhanced with local viewpoints by conducting focus group discussions with agricultural experts and farmers to obtain qualitative insights and validation of resuts. The findings showed that soil loss varied from 0 to 110 t/ha/yr, with an average of 8.95 t/ha/yr, resulting in a total loss of 384365.3 tons annually. The comparison of RUSLE and SWPT showed a moderate positive relationship (r = 0.59). The results of the Bland-Altman plot indicate a consistent agreement between the two methods. However, there is inconsistency among the five sub watersheds. This study enhances the knowledge of soil erosion patterns and offers useful guidance for watershed conservation techniques. It can be also used as a beneficial framework for managing watersheds, with possible uses outside of the Ayu watershed.

The Universal Soil Loss Equation (USLE) is an erosion estimation model to assess the soil losses that would generally result from splash, sheet, and rill erosion. At the present study, spatial distribution of different erosion prone areas were identified by USLE model to determine the average annual soil losses at Mashhad plain, northeast of Iran. Soil losses were estimated on a 100 × 100 m cell basis resolution by overlaying the five digital parameter layers (R, K, LS, C, P). To determine the critical soil loss regions at the plain, cell-based USLE parameters were multiplied by Arc-GIS ver.9.3. The estimated annual soil losses values were subsequently grouped into five classes ranging from 0 to 0.25 t/h/year around the trough line of the plain at Kashaf-rud River to 2–10 t/ha/year at the hills and pediment plains. Our results indicated a good correlation between land units of hills and pediment plains with the values of soil losses at the study area (R2 = 0.72), also the statistical analysis exhibited a high correlation between land use/cover of dry farming and soil losses (R2 = 0.78).

Concentrated runoff increases erosion and moves fine sediment and associated agrichemicals from upland areas to stream channels. Ephemeral gully erosion on croplands in the U.S. may contribute more of the sediment delivered to the edge of the field then from sheet and rill erosion. Typically, conservation practices developed for sheet and rill erosion are also expected to treat ephemeral gully erosion, but science and technology are needed to account for the separate benefits and effects of practices on each of the various sediment sources.Watershed modeling technology has been widely developed to aid in evaluating conservation practices implemented as part of a management plan, but typically lacks the capability to identify how a source, such as sheet and rill erosion, ephemeral gully erosion, or channel erosion, is specifically controlled by a practice or integrated practices. The U.S. Department of Agriculture’s Annualized Agricultural Non-Point Source pollutant loading model, AnnAGNPS, has been developed to determine the effects of conservation management plans on erosion and provide sediment tracking from all sources within the watershed, including sheet and rill, ephemeral gully, and channel erosion. This study describes the ephemeral gully erosion capabilities within the AnnAGNPS model and discusses research needs to further improve these components for integrated conservation management planning.  Conservation management planning by agencies within the U.S. and by international organizations requires a systematic approach when determining the extent of ephemeral gully erosion impacts on a field, watershed, or national basis, and/or to predict recurring or new locations of ephemeral gullies prior to their development.  This technology provides the capability to separate the impact of ephemeral gullies on erosion from other sources and then evaluate the impact of targeted practices to control erosion at the source and subsequent downstream resources.

Soil erosion is one of the most critical environmental hazards of recent times. It broadly affects to agricultural land and reservoir sedimentation and its consequences are very harmful. In agricultural land, soil erosion affects the fertility of soil and its composition, crop production, soil quality and land quality, yield and crop quality, infiltration rate and water holding capacity, organic matter and plant nutrient and groundwater regimes. In reservoir sedimentation process the consequences of soil erosion process are reduction of the reservoir capacity, life of reservoir, water supply, power generation etc. Based on these two aspects, an attempt has been made to the present study utilizing Revised Universal Soil Loss Equation (RUSLE) has been used in integration with remote sensing and GIS techniques to assess the spatial pattern of annual rate of soil erosion, average annual soil erosion rate and erosion prone areas in the MAN catchment. The RUSLE considers several factors such as rainfall, soil erodibility, slope length and steepness, land use and land cover and erosion control practice for soil erosion prediction. In the present study, it is found that average annual soil erosion rate for the MAN catchment is 13.01-tons/ha/year, which is higher than that of adopted and recommended values for the project. It has been found that 53% area of the MAN catchment has negligible soil erosion rate (less than 2-tons/ha/year). Its spatial distribution found on flat land of upper MAN catchment. It has been detected that 26% area of MAN catchment has moderate to extremely severe soil erosion rate (greater than 10-tons/ha/year). Its spatial distribution has been found on undulated topography of the middle MAN catchment. It is proposed to treat this area by catchment area treatment activity.

The universal soil loss equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices by the effective integration of the GIS-based procedures to estimate the factor values on a grid cell basis. This study was performed for five different lands uses of Indagi Mountain Pass, Cankiri to predict the soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Of the USLE factors, rainfall-runoff erosivity factor (USLE-R) and topographic factor (USLE-LS) were greatly involved in GIS. These were surfaced by correcting USLE-R site-specifically using DEM and climatic data and by evaluating USLE-LS by the flow accumulation tool using DEM and watershed delineation tool to consider the topographical and hydrological effects on the soil loss. The study assessed the soil erodibility factor (USLE-K) by randomly sampled field properties by geostatistical analysis. Crop management factor for different land-use/land cover type and land use (USLE-C) was assigned to the numerical values from crop and flora type, canopy and density of five different land uses, which are plantation, recreational land, cropland, forest and grassland, by means of reclassifying digital land use map available for the site. Support practice factor (USLE-P) was taken as a unit assuming no erosion control practices. USLE/GIS technology together with the geostatistics combined these major erosion factors to predict average soil loss per unit area per unit time. Resulting soil loss map revealed that spatial average soil loss in terms of the land uses were 1.99, 1.29, 1.21, 1.20, 0.89 t ha−1 year−1 for the cropland, grassland, recreation, plantation and forest, respectively. Since the rate of soil formation was expected to be so slow in Central Anatolia of Turkey and any soil loss of more than 1 ton ha−1 year−1 over 50–100 years was considered as irreversible for this region, soil erosion in the Indagi Mountain Pass, to the great extent, attained the irreversible state, and these findings should be very useful to take mitigation measures in the site.

Soil landscapes, land cover change and erosion features of the Central Plateau region of Tigrai, Ethiopia: Photo-monitoring with an interval of 30 years

Soil erosion significantly impacts agriculture and the environment, posing threats to food security, economic stability, and social development . Today, modern technologies are effectively used by world scientists to assess soil erosion and take measures against it. This study leverages modern technological advancements for the evaluation and management of soil erosion, specifically within the Ugam Chatkal National Park in Tashkent region. This research employed Geographic Information Systems (GIS) and the Revised Universal Soil Loss Equation (RUSLE) model to assess erosion processes and quantify annual soil loss in the park. Thematic layers (R,P,C,K,LS) for this model were created using GIS. In this way, the impact of each factor on soil erosion can be seen more clearly. According to the obtained results, it was found that the R and LS factors have a greater effect on soil erosion than the other factors. Findings indicate a heightened susceptibility of the Ugam Chatkal National Park to water erosion. The estimated average annual soil erosion rate ranges between 0.00 and 250 tons per hectare per year (t ha-1 yr-1). The results underscore the need for developing and implementing strategic measures to prevent water erosion in the park and to mitigate its impacts. The study emphasizes the importance of current technologies in recognizing and addressing environmental issues, particularly soil erosion, which has far-reaching implications for ecological preservation and sustainable development. This study not only sheds light on the erosion dynamics of the Ugam Chatkal National Park, but it also stresses the critical importance of taking proactive actions to protect the park’s ecological integrity. Failure to treat erosion in time could result in lasting damage to the park’s rich biodiversity and scenic vistas. The integration of modern technologies such as GIS and the RUSLE model not only enhances our understanding of soil erosion processes but also empowers conservation efforts, offering a promising pathway towards ensuring the long-term sustainability of natural ecosystems.

Estimation of annual rate and spatial distribution of soil erosion in the Jamuna basin using RUSLE model: A geospatial approach

Rubber (Hevea brasiliensis) plantations have a lifespan in the range of 25–40 years. We aimed at assessing soil losses in rubber plantations of different ages (4, 12, 18, 25 and 36 years old) and relating erosion risk to surface cover and fine root density by applying the Universal Soil Loss Equation (USLE) model. We measured runoff and sediment yields in rubber plantations in Xishuangbanna, Southwest China during 1 year. Fine root density, surface cover and understory plant cover were measured monthly. Annual soil loss in different plantations varied from 0.33 to 2.80 t ha−1. Combined annual cover, management and support practice factor CP from the USLE model varied with the growth phase of rubber in the range of 0.006–0.03. Monthly CP factor was related exponentially with the surface cover. Inhibited undergrowth and high decomposition both contributed to decreasing surface cover in mid-age rubber resulting in highest erosion risk phase.

The combination of runoff plot studies and soil tracking using a silt size magnetic sediment tracer, allowed a better understanding of the relative contribution of different zones in olive (Olea europaea L.) orchard plots to total soil losses. The average erosion rates were different in the tree rows (tr) (0.8 kg m−2 mo−1) and in the inter-tree rows (itr) (1.4 kg m−2 mo−1), inter-tree rills (r) being the most eroded areas (4 kg m−2 mo−1) from October 2008 to April 2010. Since soil under the olive canopies has a high infiltration capacity, splash is a major erosion process in this area, while, in the itr, sheet and rill erosion are the dominant processes. The proposed magnetic tracer technology was an effective tool for determining the cumulative soil losses at the plots for a 17 mo period (average 141 Mg ha−1) with an accuracy of 7.2 Mg ha−1. To achieve this accuracy, determination of bulk density, selectiveness in the transport process, tracer distribution in the soil profile and field calibration of the magnetic susceptibility probe were required. The evolution of tracer distribution provided insight into soil displacement within the runoff plots due to erosion processes. The tracer distribution maps also indicate a high coefficient of variability of the incorporation of tagged soil into the plot, which should be improved in future research to increase the usefulness of this approach in water erosion studies.

Analysis on trend evolution and driving factors of soil protection services in eastern sandy region of China

Soil loss is a serious environmental threat in humid sub-tropical State Tripura. The present study is carried out in Gumti River Basin of Tripura State, North East India having an area of 2492 km2. Long term daily rainfall intensity is very high in this region and that’s why sheet erosion of soil is common phenomena. Spatial distribution of soil loss in the study area was estimated by integrating five essential parameters of universal soil loss equation model in GIS mapping platform (mainly ArcGIS 10.1). Those five parameter are runoff–rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness (LS), cropping management factor (C), and support practice factor (P). In these study ASTER digital elevation model having 30 m resolution and LISS III image have been used for estimation of LS factor and C factor. The daily rainfall data (2001–2010) of seven rain gauge stations located in the Gumti River Basin and its adjoining area have been used to predict the R factor. Predicted average annual soil loss of the basin has been classified into five categories according to intensity level of soil loss. The high rate (>45 t ha−1 year−1) of soil erosion was found along the main course of Gumti River, interhill valley portion and also the flood plain area of the basin, whereas low amount of soil loss (<10 t ha−1 year−1) was found at densely forested areas and intensely plantation area of the basin.