Soil Loss Prediction Research Articles

Response of flow hydraulic parameters to different rock fragment coverages and sizes under simulated rainfall

Rock fragments widely occur on slopes and play a vital role in flow hydraulics, which in turn greatly affects soil erosion. This study aimed to explore the effects of the rock fragment coverage and size on flow hydraulic parameters and determine the optimal flow hydraulic predictor of soil loss rate. A series of simulated rainfall experiments (intensity: 60 mm h-1) were conducted in a 2 × 1 × 0.3 m flume with a gradient of 15°. The rock fragment treatments included a combination of eight sizes (0.7, 1.1, 3.0, 5.5, 12.0, 18.8, 26.4, 36.0 cm) and four coverages (15, 30, 45, 60%). Bare land was used as a control. The results indicated that compared with bare land, rock fragments remaining on the soil surface decreased the flow velocity (V) by 1–51% but increased the flow depth by 8–315%. Laminar flow occurred in all experiments (Reynolds number < 500). Only the overland flow under rock fragment sizes ≤ 3 cm was subcritical flow. Resistance (f) exponentially increased with rock fragment coverage at each size, while grain resistance percentage (f'/f) exponentially decreased with rock fragment coverage. Rock fragment size impacted positively on f’/f, grain shear stress (τ'), stream power (ω), grain stream power (ω'), but negatively on f. τ’ and ω’ decreased with rock fragment coverage in exponential equations, and the parameters in the equations logarithmically increased with rock fragment size. The soil loss rate increased exponentially with V and τ’, and increased linearly with ω’. ω’ was the optimal hydraulic parameter for soil loss prediction (R2 = 0.88; NSE = 0.80). Our findings can help to deepen the understanding of the mechanisms of the soil erosion process and provide insights into establishing soil loss prediction models on stony slopes.

The role of conventional tillage in agricultural soil erosion

Conventional tillage refers to the tillage process, especially the spring and autumn plowing process in which all the soil surface layer is disturbed, generally tilling approximately 20 cm deep. Conventional tillage is still widely used in sloping land, which leads to serious soil erosion and land degradation. The effect of tillage on soil erosion is defined by Tillage factor (T) in the model. The purpose of this study is to analyze the effect of conventional tillage on soil erosion and to calculate the value of the conventional tillage Factor T. The values of the conventional tillage factor (T value) were calculated for different soil and water conservation zones in China by collecting published literature and plot measurements from unpublished monitoring sites. The results showed that (1) the tillage factors of conventional tillage in China ranged from 0.27 to 0.55, with the largest being the North earth-stony region, the smallest being the Qinghai-Tibet plateau region, and the other six regions, namely, the Northeastern mollisol, the Northern wind erosion, the Northwest Loess Plateau, the South red soil, the Southwestern purple soil and the Southwestern karst region, were 0.34, 0.28, 0.53, 0.37, 0.35 and 0.29, respectively. In the regional soil erosion evaluation, the values can be taken by each of the eight main erosion type regions. (2) In the water erosion area except for the northeast, there is an obvious northsouth difference in the distribution of the T value, showing a decreasing trend from north to south. In practical use, the T value of the corresponding latitude area can be determined according to the Equation T = 0.2351φ- 0.02225, where φ is the latitude (°). (3) The T values of contour tillage, no-till, and monopoly tillage measures are significantly lower than the T values of conventional tillage, which have significant soil and water conservation benefits. The study initially determined the conventional tillage T values for each water conservation subzone, which can provide basic parameters for soil loss prediction on sloping land.

GIS-Based Soil Erosion Risk Assessment in the Watersheds of Bukidnon, Philippines Using the RUSLE Model

The sustainability of watersheds for supplying water and for carbon sequestration and other environmental services depends to a large extent on their susceptibility to soil erosion, particularly under changing climate. This study aimed to assess the risk of soil erosion in the watersheds in Bukidnon, Philippines, determine the spatial distribution of soil loss based on recent land cover maps, and predict soil loss under various rainfall scenarios based on recently reported climate change projections. The soil erosion risk assessment and soil loss prediction made use of GIS and the RUSLE model, while the rainfall scenarios were formulated based on PAGASA’s prediction of drier years for Bukidnon in the early-future to late-future. Results showed that a general increase in soil loss was observed in 2015, over the period from 2010 to 2020, although some watershed clusters also showed a declining trend of soil erosion, particularly the Agusan-Cugman and Maridugao watershed clusters. Nearly 60% of Bukidnon has high to very severe soil loss rates. Under extreme rainfall change scenario with 12.61% less annual rainfall, the soil loss changes were only +1.37% and −2.87% in the category of none-to-slight and very severe, respectively. Results showed that a decrease in rainfall would have little effect on resolving the excessive soil erosion problem in Bukidnon. Results of this study suggest that having more vegetative land cover and employing soil conservation measures may prove to be effective in minimizing the risk of soil erosion in the watersheds. This study provides valuable information to enhance the sustainability of the watersheds. The erosion-prone areas identified will help decision-makers identify priority areas for soil conservation and environmental protection.

Soil loss through fissures and its responses to rainfall based on drip water monitoring in karst caves

Owing to the binary geologic structure, soil loss in the karst region is characterized as a combination of surface erosion and underground soil loss. However, we know little about the process and amount of underground soil loss by now due to the difficulty in observation and identification. To predict soil loss accurately and control rocky desertification effectively, it is urgent to collect data of underground soil loss directly by new ways. In this study, 137Cs tracer technique, high frequency online monitoring and UAV survey were used to investigate soil loss through fissures. The soil samples were collected in five karst caves of Guizhou Province for determining 137Cs activity. In two of these caves, the improved monitoring devices were adopted to monitor drip water online, and the sediment content in drip water was measured once a month. Furthermore, depending on 5 exposed karst sections, the distribution density of fissures was surveyed with UAV to calculate the average underground soil loss modulus in the watershed. The results showed that (1) the soil materials deposited in caves were mainly transported by runoff from the cave entrance, rather than through fissures above the caves. (2) The initial response time and duration of drip water in caves to rainfall were controlled by surface land coverage and the process of precipitation. And the variation of sediment content in drip water kept almost constant with rainfall except at the beginning of the rainy season. (3) The directly measured underground soil loss rate monitored through the fissure above a cave with high bedrock exposure rate and a cave with high vegetation coverage was 2.42 g·yr−1 and 0.36 g·yr−1, respectively. (4) The average underground soil loss modulus in the area with high bedrock exposure rate and with high vegetation coverage was 0.35 t·km−2·yr−1 and 0.05 t·km−2·yr−1, respectively. This study is helpful for understanding the process and intensity of underground soil loss.

Characteristics of soil and water loss and its control technology in Power grid construction in the Loess Arid Area

Due to the variety of power transmission and transformation projects, through different types of erosion along the way, affected by topography, topography, climate and other factors, the forms of soil and water loss are also different. Taking A Power Supply Project In An Arid Arae (Part Of Qinghai Province) As An Example The Characteristics And Intensity Of Soil And Water Loss Caused By The Project Are Described From The Perspective Of Soil And Water Loss Prevention Area. Damage area of soil and water conservation facilities, soil and water loss risk analysis, soil and water loss prediction, etc. According to the prediction results, the project construction period (including construction preparation period and construction period) is determined as the main runoff area. This paper analyzes and evaluates the distribution of soil and water conservation measures, ecological restoration and effective management measures, and predicts the benefits of soil and water conservation measures. The Results Show That The Measures Adopted In Different Control Areas are reasonable And effective, and have good effect on Soil erosion control during the construction period. It can provide reference for soil and water loss prevention and control of the same production and construction projects in dryland and desert areas.

Calibration, validation, and evaluation of the Water Erosion Prediction Project (WEPP) model for hillslopes with natural runoff plot data

The Water Erosion Prediction Project (WEPP) model has been widely used for estimating runoff and soil loss. The evaluation of the latest version (version 2021.133) under a range of environmental conditions can provide confidence to its users. The objectives of this study were to evaluate the WEPP model for runoff and soil loss predictions using 1159 plot years of rainfall-runoff events data from field experimental plots with various climates, soils, topographies, and crops. WEPP runoff and soil loss predictions were compared to the observations before and after input parameter calibration. The results showed good predictions of runoff and soil loss were obtained with both the uncalibrated and calibrated WEPP model for all considered scales with Nash-Sutcliffe model efficiencies (NSE) over 0.4. The calibration of input baseline effective hydraulic conductivity (ke), baseline critical shear stress (τc), baseline rill erodibility (kr), and baseline interrill erodibility (ki) improved WEPP model performance with NSE values of 0.98 and 0.91 for average annual runoff and soil loss predictions, respectively. The WEPP model tended to underestimate the runoff and soil loss for large events with runoff over 100 mm and soil loss over 120 t/ha. Good event runoff and soil loss predictions (NSE ≥0.4) were obtained for the most common cropping/management systems considered, including corn, cotton, tilled fallow, and wheat after calibration. This study illustrates the most recent WEPP model's performance for runoff and soil loss predictions, and provides a comprehensive set of results.

A comparative experimental study of rill erosion on loess soil and clay loam soil based on a digital close-range photogrammetry technology

Accurately describing rill development is important for building physical process models of soil erosion. The objectives of this study were to investigate the evolution processes of rill erosion morphology, their impacts on runoff and sediment yield, and the relationship between flow hydraulic characteristics and rill erosion dynamic mechanisms on slopes of two soil types, namely, clay loam soil (CLS) and loess soil (LS), under two successive rainfall events at a rainfall intensity of 120 mm h−1. We used digital close range photogrammetry technology to obtain overhead images of two experimental soil bins (5 m long, 1 m wide and 0.5 m deep with a slope gradient of 20°) at 5-minute time intervals to monitor the dynamic processes of rill erosion. The results showed that rill erosion processes exhibited important impacts on sediment and runoff and its hydrodynamic characteristics. Compared with LS, runoff formed and rills developed earlier in the CLS slope. Soil with high clay content was more prone to form rills, but less susceptible to severe soil erosion than LS. Once rills began to develop in LS, soil loss increased and the morphology of rills changed rapidly, as did runoff rate. The flow velocity for the two soils decreased gradually with time and increased along the flow direction. The trend of Froude number (Fr) decreased with time, whereas the Darcy-Weisbach resistance coefficient (f) exhibited an opposite trend. The Reynolds number (Re) of LS increased first and then decreased, while Re of CLS decreased gradually with time. Sediment yield and runoff were positively correlated with f, but significantly negatively correlated with Fr. Moreover, Re was significantly negatively correlated with sediment yield, but not with runoff. These results are of great importance for soil loss prediction.

Soil Loss Potential Assessment for Natural and Post-Fire Conditions in Evia Island, Greece

A devastating forest fire in August 2021 burned about 517 km2 of the northern part of Evia Island, affecting vegetation, soil properties, sediment delivery and the hydrological response of the catchments. This study focuses on the estimation of the annual soil loss in the study area under natural (pre-fire) and post-fire conditions. The assessment of the soil loss potential was conducted with the application of the Universal Soil Loss Equation (USLE), which is an empirical equation and an efficient way to predict soil loss. The USLE factors include rainfall erosivity (R), soil erodibility (K), the slope and slope length factor (LS), the cover management factor (C) and the erosion control practice factor (P). The USLE quantified the annual soil erosion (in t/ha/year) for both pre- and post-wildfire conditions, and the study area has been classified into various soil loss categories and soil erosion intensity types. The results showed that the annual soil loss before the forest fires ranged from 0 to 1747 t/ha, with a mean value of 253 t/ha, while after the fire the soil loss significantly increased (the highest annual soil loss was estimated at 3255 t/ha and the mean value was 543 t/ha). These values demonstrate a significant post-fire change in mean annual soil loss that corresponds to an increase of 114% compared to the pre-fire natural condition. The area that is undergoing high erosion rates after the extreme wildfire event increased by approximately 7%, while the area of moderate rates increased by 2%. The calculated maximum potential of soil erosion, before and after the 2021 extreme wildfire event, has been visualized on spatial distribution maps of the average annual soil loss for the study area. The present study underlines the significant post-fire increase in soil loss as part of the identification of the more vulnerable to erosion areas that demand higher priority regarding the protective/control measures.

UAV-based vegetation monitoring for assessing the impact of soil loss in olive orchards in Brazil

Vegetation cover is one of the most critical factors in soil erosion processes. Notably, olive orchards have been cultivated in shallow and sloping soils, with low vegetation cover and increasing the soil exposure to raindrop impact. In the tropics, considerable care is required to adequately use cover crops to control water erosion in new frontiers of olive plantations. In this context, we proposed a new technique to correlate the cover-management factor (C-factor) with vegetation indices from images obtained by unmanned aerial vehicle (UAV) and evaluate soil erosion losses under natural rainfall. We studied the relationship between different cover indices (vegetation cover index, non-photosynthetic vegetation cover index, and total cover index) with the C-factor of the USLE/RUSLE. This study was carried out in standard erosion plots with different vegetation cover systems associated with olive cultivation. UAV images were classified by Random Forest algorithm, and soil losses were quantified by sampling after each erosive rainfall event. Results showed a good performance in UAV image classification: average user's accuracy of 94% for vegetation class and 91% for bare soil. The Total cover index presented a better performance in predicting soil loss and determining the C-factor for exponential model (R2 = 0.87). UAV-based imaging demonstrates promising potential in monitoring vegetation cover crops and their impact on soil erosion. Total cover index performs better in estimating C-factor and predicting soil loss. However, the result of response surface analysis suggested that the association between total cover index and rainfall erosivity using second-order model presented the best prediction (R2 = 0.98), positive correlation between rainfall erosivity and C-Factor, and negative correlation between C-factor and total cover index and rainfall erosivity.

Predicted Soil Loss from Shrub Willow Production Systems Across the Production Cycle Using the Revised Universal Soil Loss Equation (RUSLE2)

Predicted Soil Loss from Shrub Willow Production Systems Across the Production Cycle Using the Revised Universal Soil Loss Equation (RUSLE2)

Monitoring seasonal and phenological variability of cover management factor for wheat cropping systems under semi-arid climate conditions.

In modeling studies, the use of spatial data derived from geographic information systems and remote sensing applications to simulate the impact of phenological and seasonal changes on soil loss has a promising effect on the accuracy of predictions. The objective of this work was to estimate the C-factor (cover management) as a dynamic-factor RUSLE (revised universal soil loss equation) model based on an NDVI (Normalized Difference Vegetation Index) approach derived from high-resolution Landsat 8 and Landsat ETM7 satellite images for 140 different rain-fed wheat parcels in terms of seasonal and phenological-based by the integrated use of remote sensing and GIS. Overall, it was found that the highest C values, an average of 0.70, were estimated for the emergence period of the wheat, while the lowest value of 0.06 was found in the booting period. Seasonally, the estimated average C values in these parcels were 0.69, 0.63, 0.13, and 0.44 for the autumn, winter, spring, and summer, respectively. Corresponding soil losses for those seasons were 1.70, 1.55, 0.28, and 1.13 t ha-1year-1 respectively. Comparatively, without considering the phenological growing periods of wheat, the annual predicted soil loss rate was 11.5% higher than the conditions considered. The present study concluded that an assessment of seasonal and phenological changes in the C-factor for fragile ecosystems with weak crop-cover development could significantly improve the accuracy of the RUSLE model predictions and effectively manage limited soil and water resources.

Application of three erosion prediction models in the center of the Iberian Peninsula: incorporation and evaluation of new parameters with influence on soil losses / Aplicación de tres modelos de predicción de la erosión en el centro de la Península Ibérica: incorporación y evaluación de nuevos parámetros con influencia en las pérdidas de suelo

Erosion prediction models are useful tools for assessing the impact of land use practices on soil and water conservation. These models are often used by environmental protection authorities for the establishment of guidelines. This study examines the application of three erosion models of different complexity to predict soil loss in a small basin located in the center of the Iberian Peninsula, under a semi-arid Mediterranean climate. The models applied are: the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE) and an adaptation of the USLE model, which we have called AUSLE, to include soil characteristics not included in the original model. The average value of erosion at the watershed level obtained by the AUSLE model does not show significant differences with respect to the value obtained with RUSLE 1.06c, while the USLE shows an overestimation with respect to the other two models. The AUSLE model allows obtaining values similar to the more complex model (RUSLE) in a simple way, which makes it interesting for its application in conservation plans.

Simulating the potential effects of elevated CO2 concentration and temperature coupled with storm intensification on crop yield, surface runoff, and soil loss based on 25 GCMs ensemble: A site-specific case study in Oklahoma

Proper simulation of storm intensification is critical in projecting crop yield, surface runoff, and soil loss under climate change conditions. We developed a total of 100 climate scenarios coupled with storm intensification, which were based on 25 downscaled GCMs (General Circulation Model) projections under RCP4.5 and RCP8.5 (Representative Concentration Pathways) for two time periods of 2021–2050 and 2051–2080. The climate files were applied to a modified WEPP (Water Erosion Prediction Project) model to estimate crop yield, runoff, and soil loss under 29 combinations of cropping and tillage systems. The results showed that future extreme storm events in the study area would significantly increase during 2021–2080 (P < 0.01). However, average monthly precipitation in summer would decrease by 12.5% in June and 10.8% in July, along with an annual precipitation decline of 2.6%, leading to a decrease in crop yield in this rainfed agricultural region. The amount of runoff (soil loss) from extreme storms would account for 45.9 (64.3%) of the total annual runoff (soil loss) when averaged across two time periods and two RCPs. The average annual runoff depth (soil loss amount) from crop rotation or double cropping systems would be 27.6 (78.1%) less than the corresponding values in a continuous monoculture cropping system. No-till and crop rotations with alfalfa are the best agricultural management alternatives to mitigate soil erosion rates under future climate change. The analysis of variance (ANOVA) indicated that the uncertainty contributions of GCMs and cropping and tillage systems reached 45.5% and 30.4% in annual surface runoff prediction (P < 0.001), while cropping and tillage systems (71.7%, P < 0.001) was the major uncertainty source in annual soil loss simulations. This study improved the prediction of crop yield, runoff, and soil loss from various cropping and tillage systems under climate change conditions by integrating storm intensification from multiple GCM ensembles.

Evaluating the Changes in Soil Erosion for Micro Watersheds of Sabarkantha and Aravalli Districts of Gujarat, India

Soil erosion is the most critical environmental problem in the semi-arid region of the sabarkantha-Aravalli districts of Gujarat state. The present study is carried out on micro-watersheds of Mazum and Vatrak tributaries of the Sabarmati river basin of Gujarat state, west India, having an area of 8638 sq. km. In this paper, the Universal Soil Loss Equation (USLE) model has been used to quantify soil loss in micro watersheds. Five essential parameters such as Runoff-rainfall erosivity factor (R), soil erodibility Factor (K), slope length and steepness (LS), cropping management factor (C), and support practice factor (P) have been used to estimate the soil loss amount in the study area. All of these factors have been calculated using various data sources and data preparation methods. The soil erodibility (K) factor in the study area ranged from 0.46 to 0.93. All essential parameters have been calculated for before-after watershed scenarios. Changes in soil erosion have been estimated after the implementation of micro-watershed development projects under various government schemes. The average annual predicted soil loss ranges between 45 and 230 t/ha/y before implementation and 14.50 and 138 t/ha/y after implementation. Low decreases in soil loss areas (111%) in soil erosion was found to have a good provision of land treatment with watershed interventions along the watersheds on the main course of the Mazum and Watrak rivers.

Rates of Historical Anthropogenic Soil Erosion in the Midwestern United States

AbstractErosion degrades soils, reduces crop yields, and diminishes ecosystem services. The total amount of soil that has been eroded since the initiation of farming is unknown in most agricultural landscapes, which hinders assessment of soil erosion trends. In the Midwestern U.S., erosion has caused native prairie remnants to become perched above surrounding farmland, providing an opportunity to measure historical soil erosion rates. We use high‐resolution topographic surveys conducted across erosional escarpments at the boundary between 20 prairies and adjacent agricultural fields and show the median reduction in soil thickness ranges from 0.04 to 0.69 m, corresponding to erosion rates of 0.2–4.3 mm year−1, with a median value of 1.9 mm year−1. We used an association between the measured reduction in soil thickness and topographic curvature to predict regional soil erosion integrated since the beginning of farming to the present. We estimate a median historical erosion rate of 1.8 ± 1.2 mm year−1, which is nearly double the rate considered tolerable by the U.S. Department of Agriculture (USDA). Current soil loss predictions from the USDA National Resources Inventory (NRI) and the Daily Erosion Project (DEP) are lower than our historically averaged erosion rate by a factor of 3 and 8, respectively. We suggest that the NRI and the DEP underpredict soil loss rates because they do not include tillage erosion, a process shown to be important throughout the Midwestern U.S. Our findings indicate that further implementation of conservation practices is needed to reduce the high centennial‐averaged soil erosion rates that we measure to sustainable levels.

A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes

AbstractSoil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth, Lp, and a hillslope diffusion coefficient, D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.

Determination of Soil Erosion and Sediment Yield in the Bonsa River Basin Using GIS and Revised Universal Soil Loss Equation (RUSLE)

The Bonsa river is an important tributary of the Ankobra river in the Western Region of Ghana. The catchment of the Bonsa river has been undergoing rapid land cover changes due to human activities such as farming, illegal mining, population growth, among others which are likely to promote soil erosion and sediment yield in the river basin. To estimate the amount of soil eroded over a period and subsequent sediment yielded along the Bonsa river basin, the Revised Universal Soil Loss Equation (RUSLE) was integrated with Geographic Information System (GIS) to model the spatial distribution patterns in soil erosion and sediment yield within the catchment. Data used included annual rainfall records, soil map, Digital Elevation Model (DEM) and land-use map of the study area. Parameters of the model were determined and converted into raster layers using the raster calculator tool in ArcMap to produce a soil erosion map. The concept of Sediment Delivery Ratio (SDR) was applied to determine the annual sediment yield by combining a raster SDR layer with soil erosion map. The predicted soil loss and sediment yield values were found to be low. This may be due to high soil protective cover provided by vegetation as well as low topographic relief in the river basin. Though, the elements and processes responsible for soil erosion and sediment yield prevailing in the basin was found to be low, adverse situations could be developed with time if the prevailing conditions are not checked, as soil erosion is a natural gradual slow process. The gains made could be sustained by putting measures in place to control human activities, particularly, illegal mining (galamsey) in the basin, indiscriminate cutting down of trees and farmining activities along the Bansa river basin. This study will support monitoring, planning of water resources and help to improve sustainable water quality.

Determination of Cover and Land Management Factors for Soil Loss Prediction in Cameron Highlands, Malaysia

Many new agricultural activities resulted in severe soil erosion across the Cameron Highlands’ land surface. Therefore, this study determines the cover (C) and land management (P) factors of the USLE for predicting soil loss risk in Cameron Highlands using a Geographic Information System (GIS). For this study, data from the Department of Agriculture Malaysia (DOAM) and the Department of Town and Country Planning Malaysia (PLANMalaysia) were used to generate several C&amp;P factors in the Cameron Highlands. Data from both agencies have resulted in C factors with 0.01 to 1.00 and P factors with 0.30 to 0.49. Due to the cover and land management factor varies depending on the data collected by the various agencies, this study used the two data sets to come up with a C&amp;P factor that accurately reflected both agricultural and urban growth effects. RKLS factors of USLE were obtained from the DOAM with values R (2375–2875), K (0.005), LS (2.5–25), respectively. The Cameron Highlands’ soil loss risk with these new C&amp;P values resulted in a soil loss of 6.72 per cent (4547.22 hectares) from high to critical, with a percentage difference range of −0.77 to +3.37 under both agencies, respectively.

Assessment of soil erosion models for predicting soil loss in cracked vegetated compacted surface layer

Assessment of soil erosion models for predicting soil loss in cracked vegetated compacted surface layer

Soil loss estimated by means of the RUSLE model in a subtropical climate watershed

Erosion process occurs naturally, shaping the Earth’s surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and [...]