Soil Loss Prediction Research Articles

Relationship between remote sensing data and field-observed interril erosion

Abstract: The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.

Assessment of soil factors controlling ephemeral gully erosion on agricultural fields

AbstractThe soil factor is crucial in controlling and properly modeling the initiation and development of ephemeral gullies (EGs). Usually, EG initiation has been related to various soil properties (i.e. sealing, critical shear stress, moisture, texture, etc.); meanwhile, the total growth of each EG (erosion rate) has been linked with proper soil erodibility. But, despite the studies to determine the influence of soil erodibility on (ephemeral) gully erosion, a universal approach is still lacking. This is due to the complex relationship and interactions between soil properties and the erosive process. A feasible soil characterization of EG erosion prediction on a large scale should be based on simple, quick and inexpensive tests to perform. The objective of this study was to identify and assess the soil properties – easily and quickly to determine – which best reflect soil erodibility on EG erosion. Forty‐nine different physical–chemical soil properties that may participate in establishing soil erodibility were determined on agricultural soils affected by the formation of EGs in Spain and Italy. Experiments were conducted in the laboratory and in the field (in the vicinity of the erosion paths). Because of its importance in controlling EG erosion, five variables related to antecedent moisture prior to the event that generated the gullies and two properties related to landscape topography were obtained for each situation. The most relevant variables were detected using multivariate analysis. The results defined 13 key variables: water content before the initiation of EGs, organic matter content, cation exchange capacity, relative sealing index, two granulometric and organic matter indices, seal permeability, aggregates stability (three index), crust penetration resistance, shear strength and an erodibility index obtained from the Jet Test erosion apparatus. The latter is proposed as a useful technique to evaluate and predict soil loss caused by EG erosion. Copyright © 2018 John Wiley & Sons, Ltd.

Predicting soil loss in central and south Italy with a single USLE-MM model

The USLE-MM estimates event normalized plot soil loss, Ae,N, by an erosivity term given by the runoff coefficient, QR, times the single-storm erosion index, EI30, raised to an exponent b1 > 1. This modeling scheme is based on an expected power relationship, with an exponent greater than one, between event sediment concentration, Ce, and the EI30/Pe (Pe = rainfall depth) term. In this investigation, carried out at the three experimental sites of Bagnara, Masse, and Sparacia, in Italy; the soundness of the USLE-MM scheme was tested. A total of 1192 (Ae,N, QREI30) data pairs were used to parameterize the model both locally and considering all sites simultaneously. The performances of the fitted models were established by considering all erosive events and also by distinguishing between events of different severity. The b1 exponent varied widely among the three sites (1.05–1.44) but using a common exponent (1.18) for these sites was possible. The Ae,N prediction accuracy increased in the passage from the smallest erosion events (Ae,N ≤ 1 Mg ha−1, median error = 3.35) to the largest ones (Ae,N > 10 Mg ha−1, median error = 1.72). The QREI30 term was found to be usable to predict both Ae,N and the expected maximum uncertainty of this prediction. Soil erodibility was found to be mainly controlled by the largest erosion events. Development of a single USLE-MM model appears possible. Sampling other sites is advisable to develop a single USLE-MM model for a general use.

Process-Based Modeling of Infiltration, Soil Loss, and Dissolved Solids on Saline and Sodic Soils

Abstract. The Colorado River is a central socio-economic resource of the western U.S. but is vulnerable to excessive salt load. To improve knowledge of the surface processes controlling salt loading, a series of rainfall simulation experiments were conducted in saline rangelands of the upper Colorado River basin (UCRB). In this study, data from these rainfall simulation experiments were used to develop predictive equations for the process-based Rangeland Hydrology and Erosion Model (RHEM). Runoff and soil loss prediction performances were assessed with the Nash-Sutcliffe efficiency (NSE), the coefficient of determination (R2), and the percent bias (PBIAS). Calibration on 36 individual plots, randomly selected to cover each treatment, yielded improved runoff prediction (NSE = 0.73, R2 = 0.74, and PBIAS = 6.93%) compared to the non-calibrated RHEM parameter estimation equation (NSE = 0.65, R2 = 0.68, and PBIAS = 32.03%) when a refined ground cover coefficient was used to estimate the effective hydraulic conductivity (Ke). Soil loss prediction with the calibration data was also improved compared to the non-calibrated parameter estimation equation (NSE = 0.94, R2 = 0.94, and PBIAS = 4.25% vs. NSE = 0.81, R2 = 0.85, and PBIAS = 6.47%) when the soil sodium adsorption ratio (SAR) was included in estimation of the sheet and splash erosion parameter (Kss). Improvements in runoff and soil loss predictions with the calibration data were maintained with an independent set of 36 plots from the original rainfall simulation dataset not used for calibration. Overall, soil sodicity was an important consideration in the performance of the newly developed Kss parameterization equation in this study. Performance on sodic soils (SAR = 15) gained the most from the inclusion of SAR in the Kss estimation. Salt load was linearly related to soil loss (R2 = 0.94), and this linear model performed well in estimating runoff salt load from RHEM-predicted soil loss. These new developments will provide a physically based modeling scheme for land managers for predicting rainfall-driven soil and salt load to surface waters of the UCRB. Keywords: Erosion modeling, Hydrology, Rangeland, Salinity prediction, Soil erosion, Water quality.

USE OF USLE/ GIS TECHNOLOGY FOR IDENTIFYING CRITERIA FOR MONITORING SOIL EROSION LOSSES IN AGRICULTURAL AREAS

ABSTRACT The assessment of land use and occupation is essential for predicting soil loss due to water erosion. The objective of this study was to determine criteria for monitoring soil loss in agricultural areas using the Universal Soil Loss Equation (USLE). Soil loss data were intersected with USLE factors and slope using geoprocessing techniques for the Samambaia River watershed, located in Cristalina, Goias state, Brazil. A slope of 3% to 8% was found in approximately 50% of the study area. However, mean soil losses <10 t ha-1 year-1 were observed in slopes ≤ 3%, where the mean soil length (L) and slope steepness (S) were ≤2 and 0.2, respectively. The intersection of soil loss data with USLE factors was useful for identifying criteria for monitoring soil loss. The mean topographic factor (LS) in non-irrigated crops, pasture, and silviculture was 0.4 ± 0.4, 0.4 ± 0.7, and 2.2 ± 2.6, respectively. Therefore, LS values adequately indicated the type of land use and occupation considering that only silviculture could maintain soil losses below the tolerable limit of 10 t ha-1 year-1 for an LS mean of 2.2.

Near real-time monitoring of post-fire erosion after storm events: a case study in Warrumbungle National Park, Australia

Wildfires in national parks can lead to severe damage to property and infrastructure, and adverse impacts on the environment. This is especially pronounced if wildfires are followed by intense storms, such as the fire in Warrumbungle National Park in New South Wales, Australia, in early 2013. The aims of this study were to develop and validate a methodology to predict erosion risk at near real-time after storm events, and to provide timely information for monitoring of the extent, magnitude and impact of hillslope erosion to assist park management. We integrated weather radar-based estimates of rainfall erosivity with the revised universal soil loss equation (RUSLE) and remote sensing to predict soil loss from individual storm events after the fire. Other RUSLE factors were estimated from high resolution digital elevation models (LS factor), satellite data (C factor) and recent digital soil maps (K factor). The accuracy was assessed against field measurements at twelve soil plots across the Park and regular field survey during the 5-year period after the fire (2013–17). Automated scripts in a geographical information system have been developed to process large quantity spatial data and produce time-series erosion risk maps which show spatial and temporal changes in hillslope erosion and groundcover across the Park at near real time.

Comparing Two Applicative Criteria of the Soil Erosion Physical Model Concept

Core Ideas The physical model concept allows testing of the reliability of soil loss predictions. This concept was applied by using the original and a new criterion in southern Italy. The new criterion was generally more restrictive than the original one. The physical model represented by a replicated plot has been suggested to be the best possible, unbiased, real world model to predict plot soil erosion. The aim of this investigation was to compare the original applicative criterion of the physical model concept proposed by Nearing with that later suggested by Bagarello et al. The comparison was performed by using three empirical soil erosion models (the Universal Soil Loss Equation [USLE], a modified USLE [USLE‐MM], and the Central and Southern Italy [CSI] model) and plot soil loss data collected at the experimental station of Sparacia, in Sicily (southern Italy). The investigation showed that (i) the new criterion was generally more restrictive, i.e., less prone to accept the similarity hypothesis between predictions and measurements, than the original one; (ii) the new criterion gave a similar number of acceptable predictions as the original one when absolute differences between measured and predicted soil losses by the replicated plot were associated with a frequency occurrence factor of 0.87; (iii) for both tested criteria, the percentage of acceptable predictions could be considered time independent, and consequently, the checked performances of the three soil loss models could be considered generally representative of their capability in predicting soil losses at the sampled site; (iv) with the exception of the calibrated USLE, the hypothesis that, according to the original criterion, an effectiveness coefficient greater and lower than 0.6 could be expected for a calibrated and uncalibrated model, respectively, was confirmed; and (v) the new criterion allowed establishment of an effectiveness coefficient of 0.12, which discriminates between uncalibrated and calibrated models.

A step towards a holistic assessment of soil degradation in Europe: Coupling on-site erosion with sediment transfer and carbon fluxes

Soil degradation due to erosion is connected to two serious environmental impacts: (i) on-site soil loss and (ii) off-site effects of sediment transfer through the landscape. The potential impact of soil erosion processes on biogeochemical cycles has received increasing attention in the last two decades. Properly designed modelling assumptions on effective soil loss are a key pre-requisite to improve our understanding of the magnitude of nutrients that are mobilized through soil erosion and the resultant effects. The aim of this study is to quantify the potential spatial displacement and transport of soil sediments due to water erosion at European scale. We computed long-term averages of annual soil loss and deposition rates by means of the extensively tested spatially distributed WaTEM/SEDEM model. Our findings indicate that soil loss from Europe in the riverine systems is about 15% of the estimated gross on-site erosion. The estimated sediment yield totals 0.164 ± 0.013Pgyr−1 (which corresponds to 4.62 ± 0.37Mgha−1yr−1 in the erosion area). The greatest amount of gross on-site erosion as well as soil loss to rivers occurs in the agricultural land (93.5%). By contrast, forestland and other semi-natural vegetation areas experience an overall surplus of sediments which is driven by a re-deposition of sediments eroded from agricultural land. Combining the predicted soil loss rates with the European soil organic carbon (SOC) stock, we estimate a SOC displacement by water erosion of 14.5Tg yr−1. The SOC potentially transferred to the riverine system equals to 2.2Tgyr−1 (~15%). Integrated sediment delivery-biogeochemical models need to answer the question on how carbon mineralization during detachment and transport might be balanced or even off-set by carbon sequestration due to dynamic replacement and sediment burial.

Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed

The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing, wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, etc. which causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover (LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation (USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor, topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential (reversible soil loss) or 0–1 t ha−1 yr−1 soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC condition. Results from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions (1984–2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significance of LULC in the control of erosion. Maps generated from the study may be useful to planners and land use managers to take appropriate decisions for soil conservation.

Evaluation and Modification of the MUSLE in Predicting Soil Loss in Micro Plots using Simulated Rainfalls

Evaluation and Modification of the MUSLE in Predicting Soil Loss in Micro Plots using Simulated Rainfalls

The Rangeland Hydrology and Erosion Model: A Dynamic Approach for Predicting Soil Loss on Rangelands

AbstractIn this study, we present the improved Rangeland Hydrology and Erosion Model (RHEM V2.3), a process‐based erosion prediction tool specific for rangeland application. The article provides the mathematical formulation of the model and parameter estimation equations. Model performance is assessed against data collected from 23 runoff and sediment events in a shrub‐dominated semiarid watershed in Arizona, USA. To evaluate the model, two sets of primary model parameters were determined using the RHEM V2.3 and RHEM V1.0 parameter estimation equations. Testing of the parameters indicated that RHEM V2.3 parameter estimation equations provided a 76% improvement over RHEM V1.0 parameter estimation equations. Second, the RHEM V2.3 model was calibrated to measurements from the watershed. The parameters estimated by the new equations were within the lowest and highest values of the calibrated parameter set. These results suggest that the new parameter estimation equations can be applied for this environment to predict sediment yield at the hillslope scale. Furthermore, we also applied the RHEM V2.3 to demonstrate the response of the model as a function of foliar cover and ground cover for 124 data points across Arizona and New Mexico. The dependence of average sediment yield on surface ground cover was moderately stronger than that on foliar cover. These results demonstrate that RHEM V2.3 predicts runoff volume, peak runoff, and sediment yield with sufficient accuracy for broad application to assess and manage rangeland systems.

Behavior of farmers in regard to erosion by water as reflected by their farming practices

The interplay between natural site conditions and farming raises erosion by water above geological background levels. We examined the hypothesis that farmers take erosion into account in their farming decisions and switch to farming practices with lower erosion risk the higher the site-specific hazard becomes. Erosion since the last tillage was observed from aerial orthorectified photographs for 8100 fields belonging to 1879 farmers distributed across Bavaria (South Germany) and it was modeled by the Universal Soil Loss Equation using highly detailed input data (e.g., digital terrain model with 5×5m2 resolution, rain data with 1×1km2 and 5min resolution, crop and cropping method from annual field-specific data from incentive schemes). Observed and predicted soil loss correlated closely, demonstrating the accuracy of this method. The close correlation also indicted that the farmers could easily observe the degree of recent erosion on their fields, even without modelling. Farmers clearly did not consider erosion in their decisions. When natural risk increased, e.g. due to steeper slopes, they neither grew crops with lower erosion potential, nor reduced field size, nor used contouring. In addition, they did not compensate for the cultivation of crops with higher erosion potential by using conservation techniques like mulch tillage or contouring, or by reducing field size. Only subsidized measures, like mulch tillage or organic farming, were applied but only at the absolute minimum that was necessary to obtain subsidies. However, this did not achieve the reduction in erosion that would be possible if these measures had been fully applied. We conclude that subsidies may be an appropriate method of reducing erosion but the present weak supervision, which assumes that farmers themselves will take erosion into account and that subsidies are only needed to compensate for any disadvantages caused by erosion-reducing measures, is clearly not justified.

Effects of slope gradient on hydro-erosional processes on an aeolian sand-covered loess slope under simulated rainfall

The aeolian sand-covered loess slope of the Wind-Water Erosion Crisscross Region of the Loess Plateau in China may play a key role in contributing excessive sediment to the Yellow River. Understanding its hydro-erosional processes is crucial to assessing, controlling and predicting soil and water losses in this region and maintaining the ecological sustainability of the Yellow River. Simulated rainfall (intensity 90mmh−1) was used to investigate the runoff and soil loss from loess slopes with different slope gradients (18%, 27%, 36%, 47%, and 58%) and overlying sand layer thicknesses (0, 5 and 10cm). As compared with uncovered loess slopes, an overlying sand layer delayed runoff production, reduced cumulative runoff and increased cumulative soil loss, as well as enhancing variations among slope gradients. Cumulative runoff and soil loss from the sand-covered loess slopes increased with increasing slope gradients and then slightly decreased, with a peak at about 47% gradient; they both were greater from the 10-cm sand-covered loess slope than from the 5-cm except for with 18% slope gradient. In general, differences in cumulative runoff between sand layer thicknesses became smaller, while those in cumulative soil loss became larger, with increasing slope gradient. Runoff and soil loss rates on the sand-covered loess slopes exhibited unimodal distributions during the rainstorms. Maximum values tended to occur at the same rain duration, and increased considerably with increasing slope gradient and sand layer thickness on slopes that were less than 47%. Liquefaction process might occur on the lower loess slopes covered with thinner sand layers but failures similar to shallow landslides might occur when the sand layer was thicker on steeper slopes. The presence of an overlying sand layer changed the relationship between runoff and soil loss rates during intense rainstorms and this change varied with different slope gradients. Our results demonstrated that the effects of slope gradient on hydro-erosional processes on the sand-covered loess slopes were important and vary with changing sand thickness. These effects should be considered when assessing and predicting soil losses from such slopes in the study region and from similar slopes elsewhere.

Uncertainty and Sensitivity Analyses of the Modified Wind Erosion Equation for Application in Canada

AbstractThe wind erosion equation (WEQ) was used for several decades for predicting soil loss by wind erosion, but few systematic studies on the uncertainty of predicting wind erosion have been conducted. Several researchers found that WEQ is not accurately representing soil erosion and under‐represents soil erodibility that consequently results in WEQ underestimations. WEQ was modified under Canadian conditions for the National Agri‐Environmental Health Analysis and Reporting Program, referred as (WEQ‐NAHARP). The model, input, and parameter uncertainties that control model efficiency were used to analyze the local and universal uncertainties for WEQ‐NAHARP. One and ninety‐nine percentiles were used as lower and upper boundaries of uncertainty bound when using general likelihood uncertainty estimation for estimating the uncertainty of WEQ‐NAHARP's prediction. The soil erodibility (I), climate factor (C), and soil surface roughness factor (K) were found as the three most sensitive factors in predicting wind erosion in WEQ‐NAHARP. The vegetation cover factor (V) was discovered not sensitive to the prediction model as it is less than 1,000 kg ha−1 and became very sensitive as V‐value is greater than 5,000 kg ha−1. Field length along the prevailing wind erosion direction (L) and V have lower local sensitivity indexes than the other three factors. WEQ‐NAHARP underestimated wind erosion rate of Pampas, Argentina, and overestimated at Washington State, USA. This probably reflected the nature of WEQ‐NAHARP's behavior, which had a great uncertainty of its prediction. The model appears to underestimate total annual soil loss for coarse soil and overestimate annual soil loss for finer soil. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Subdividing Large Mountainous Watersheds into Smaller Hydrological Units to Predict Soil Loss and Sediment Yield Using the GeoWEPP Model

Subdividing Large Mountainous Watersheds into Smaller Hydrological Units to Predict Soil Loss and Sediment Yield Using the GeoWEPP Model

Soil loss estimation using RUSLE model, GIS and remote sensing techniques: A case study from the Dembecha Watershed, Northwestern Ethiopia

Soil loss is a major cause of land degradation worldwide, especially in large areas of arid and semi-arid regions. With advent of new software and technologies such as remote sensing (RS) and GIS, there is a necessity to integrate them to achieve important information in a faster manner. The aims of present study were to evaluate soil erodibility (K-factor) using standard plots under natural rainfall and prediction of soil loss by integrating RUSLE, GIS and RS in Fars Iran. The RUSLE factors were evaluated as following: the R-factor was calculated using modified Fournier index; K-factor was measured in the field using erosion plots and estimated by the USLE equation; the C-factor map was created using the NDVI; the LS-factor map was generated from digital elevation model with 10m resolution, and the P-factor map was assumed as 1. Spatial distribution of annual soil loss in the Simakan watershed was obtained by multiplying these factors in GIS. The average of the measured K was 0.014thMJ−1mm−1 and 2.08 times less than the average of the estimated K (0.030thMJ−1mm−1). The performance of RUSLE was highly influenced by the K, because the annual soil loss predicted using estimated K (11.0th−1ya−1) was about twice as much as the measured K (5.7th−1ya−1). The spatial distribution of soil loss classes predicted was: 73.64% very low, 14.79% low, 10.19% moderate and 1.25% severe. Areas of severe soil loss are situated in the northern portion of the study area, which needs suitable conservation practices.

Effects of gravel on concentrated flow hydraulics and erosion in simulated landslide deposits

Gravel has an important influence on concentrated flow hydraulics and soil and water loss. We conducted a field investigation and a subsequent simulated scouring experiment with four gravel contents (0, 25, 33.3, 50%) and three inflow rates (4, 8, 12L/min) under two steep slopes (67.5, 72.7%). We assessed the characteristics of concentrated flow hydraulics, including flow velocity, flow depth, Reynolds number (Re), Froude number (Fr), Darcy-Weisbach friction coefficient (f), and the rate of soil and water loss (SW). We observed significant effects of gravel and inflow rate on hydraulics and erosion, as well as interactions between gravel and inflow rate. The mean flow velocity reduced as the gravel content increased. The changes in flow depth fluctuated with gravel content in the order 25>0>50>33.3%. Re was larger in 0% gravel than in 25, 33.3 and 50% gravel. Fr was >1 in 0% gravel but was approximately equal to 1 in gravel-containing soils. Values of f were lowest in 0% gravel. The f values for gravel-containing soils not only decreased as the gravel content increased, but also had a significant relationship with the corresponding Re values. SW was maximal in 25% gravel content and gradually declined for gravel content beyond 25%. SW had a significant logarithmic relationship with Re. The results have significant implications for predictions of soil and water loss steeply sloping, gravel-rich, soils.

A comparison of the abilities of the USLE-M, RUSLE2 and WEPP to model event erosion from bare fallow areas

Traditionally, the Universal Soil Loss Equation (USLE) and the revised version of it (RUSLE) have been applied to predicting the long term average soil loss produced by rainfall erosion in many parts of the world. Overtime, it has been recognized that there is a need to predict soil losses over shorter time scales and this has led to the development of WEPP and RUSLE2 which can be used to predict soil losses generated by individual rainfall events. Data currently exists that enables the RUSLE2, WEPP and the USLE-M to estimate historic soil losses from bare fallow runoff and soil loss plots recorded in the USLE database. Comparisons of the abilities of the USLE-M and RUSLE2 to estimate event soil losses from bare fallow were undertaken under circumstances where both models produced the same total soil loss as observed for sets of erosion events on 4 different plots at 4 different locations. Likewise, comparisons of the abilities of the USLE-M and WEPP to model event soil loss from bare fallow were undertaken for sets of erosion events on 4 plots at 4 different locations. Despite being calibrated specifically for each plot, WEPP produced the worst estimates of event soil loss for all the 4 plots. Generally, the USLE-M using measured runoff to calculate the product of the runoff ratio, storm kinetic energy and the maximum 30-minute rainfall intensity produced the best estimates. As to be expected, ability of the USLE-M to estimate event soil loss was reduced when runoff predicted by either RUSLE2 or WEPP was used. Despite this, the USLE-M using runoff predicted by WEPP estimated event soil loss better than WEPP. RUSLE2 also outperformed WEPP.

Estimation of Soil loss by USLE Model using GIS and Remote Sensing techniques: A case study of Muhuri River Basin, Tripura, India

Soil erosion is a most severe environmental problem in humid sub-tropical hilly state Tripura. The present study is carried out on Muhuri river basin of Tripura state, North east India having an area of 614.54 Sq.km. In this paper, Universal Soil Loss Equation (USLE) model, with Geographic Information System (GIS) and Remote Sensing (RS) have been used to quantify the soil loss in the Muhuri river basin. 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 soil loss amount in the study area. All of these layers have been prepared in GIS and RS platform (Mainly Arc GIS 10.1) using various data sources and data preparation methods. In these study DEM and LISS satellite data have been used. The daily rainfall data (2001-2010) of 6 rain gauge stations have been used to predict the R factor. Soil erodibility (K) factor in Basin area ranged from 0.15 to 0.36. The spatial distribution map of soil loss of Muhuri river basin has been generated and classified into six categories according to intensity level of soil loss. The average annual predicted soil loss ranges between 0 to and 650 t/ha/y. Low soil loss areas (<25 t/ha/y) have been recorded under very densely forested areas and intensely plantation (mainly Rubber plantation) area. The high rate (>70 t/ha/y) of soil erosion was found along the main course of Muhuri River.

Runoff and soil loss characteristics on loess slopes covered with aeolian sand layers of different thicknesses under simulated rainfall

In the Wind-Water Erosion Crisscross Region of the northern Loess Plateau, parts of loess slopes have been covered by layers of aeolian sand of different thicknesses. Knowledge of soil erosion processes and magnitudes on these slopes is essential to understanding the coupled water-wind erosion processes and to address the resulting downstream coarse sediment problems in the Yellow River. Simulated rainfall (intensity 90mmh−1) was performed to explore the effects of sand layer thickness on runoff and soil loss from loess slopes covered with different sand layer thicknesses (0, 0.5, 2, 5, 10, 15, 20, and 25cm). Initial runoff time increased with increasing sand layer thickness, with greater changes occurring for the increases in the thinner (0–5cm) than for the thicker layers (5–25cm). Total runoff yield from the sand-covered loess slopes was 18%–55% lower than from the uncovered loess slope and decreased with increasing sand layer thickness. In contrast, total sediment yield was up to 14times greater from the sand-covered loess slopes than from the uncovered loess slope and rapidly increased with increasing sand layer thickness. During the rainstorm, runoff and soil loss rates exhibited unimodal distributions, and they were related by a positive linear function, both before and after the maximum soil loss rate, that had a high determination coefficient (R2>0.8, p<0.05) on loess slopes with sand layer thicknesses greater than 5cm. The maximum runoff and soil loss rates tended to occur simultaneously and increased abruptly with increasing sand layer thickness. During the rainstorms, some runoff rates on the loess slopes with thicker sand layers were higher than the rainfall intensity due to rainwater combining with water emerging from the saturated sand, which could never occur on the uncovered loess slope. The critical sand layer thickness, which produced a qualitative change in runoff and sediment production modes, appeared to be in the range of 5–10cm. These results indicated that the thickness of the sand layer on the loess slope significantly influenced runoff and sediment production processes and mechanisms. These effects should be considered when assessing and predicting soil losses in this region and from similar slopes elsewhere.