Soil Loss Prediction Research Articles

Comparison and Applicability of Selected Soil Erosion Estimation Models

Soil erosion is a globally challenging issue that hinders agricultural productivity by enhancing land degradation and loss to the top fertile soil. Although it is a global issue, its effect is adverse on farmers dwell in developing countries. Hence, providing information on soil loss is crucial to plan and implement appropriate soil and water conservation measures. Accordingly, erosion estimation models were developed and grouped as empirical, conceptual, and physical-based broad umbrella. This review paper primarily is intended to compare the opportunities and limitations of widely implemented soil erosion estimation models and review their applicability by selecting widely used models such as: USLE, RUSLE, SLEMSA, and WEPP. The result of this review revealed that the so reviewed erosion models have been designed to predict soil loss from sheet and rill erosion. Evidence from studies indicated that R/USLE models can be universally used by calibrating to the local environmental conditions. They are simple, requires less data and computational time, however; they are not event responsive and measure soil loss from gully and stream-bank erosion. But, RUSLE model has different parameter calculation procedure than the USLE. This study also depicts the SLEMSA model treats soil erosion factors as a separate entities and is highly influenced by LS factors. The WEPP model has capability to estimate soil loss in a short time scale and out-of-place erosion rates, but; it only works for individual hillslope. Thus, based on the result of this review the following recommendations are forwarded for further study to fill the gaps; upgrading of R/USLE parameters, modification of topographic sub-model of SLEMSA, and revision of essential parameters in WEPP model to estimate erosion from large catchments.

Applicability of the Wind Erosion Prediction System for prediction of soil loss by wind in arable land

Applicability of the Wind Erosion Prediction System for prediction of soil loss by wind in arable land

MODELING OF WATER EROSION IN A SUB-BASIN IN RIO GRANDE DO SUL USING THE RUSLE MODEL

The intense use of farming land has caused many consequences to the environment, among them, water erosion. The scale study of river basins through modeling allows the identification and estimation of soil losses, aiming at the conservationist planning of the site. The objective of this work was to predict soil loss in the Micaela sub-basin, with an area of ??37 km2 located in the municipality of Pelotas, Rio Grande do Sul. For the prediction of soil loss, the Revised Universal Soil Loss Equation (RUSLE) was used. Erosivity was obtained from data from the literature and erodibility was estimated by means of the inherent soil attributes and the topographic factor calculated according to the accumulated flow and declivity in each pixel. For the cover factor, data from the literature were used, according to use recommendation and existing soil cover. The study area shows a strong erosivity, which ranged from 8,045 to 8,833 MJ mm ha-1 h-1 year-1. The Argissolos occupy 81.31% of the sub-basin and present high erodibility, varying from 0.0369 to 0.0422 Mg ha h ha-1 MJ-1 mm -1. The sites with the largest vegetation cover were those with the lowest soil losses. However, in more than 36% of the area, the soil losses are above the tolerable therehsold, indicating that they are more prone to degradation and, therefore, the systems of land use and adopted management should be reviewed.

Evaluation of soil erosion risk and identification of soil cover and management factor (C) for RUSLE in European vineyards with different soil management

Vineyards show some of the largest erosion rates reported in agricultural areas in Europe. Reported rates vary considerably under the same land use, since erosion processes are highly affected by climate, soil, topography and by the adopted soil management practices. Literature also shows differences in the effect of same conservation practices on reducing soil erosion from conventional, bare soil based, management. The Revised Universal Soil Loss Equation (RUSLE) is commonly adopted to estimate rates of water erosion on cropland under different forms of land use and management, but it requires proper value of soil cover and management (C) factors in order to obtain a reliable evaluation of local soil erosion rates. In this study the ORUSCAL (Orchard RUSle CALibration) is used to identify the best calibration strategy against long-term experimental data. Afterwards, ORUSCAL is used in order to apply the RUSLE technology from farm based information across different European wine-growing regions. The results suggest that the best strategy for calibration should incorporate the soil moisture sub-factor (Sm) to provide better soil loss predictions. The C factor, whose average values ranged from 0.012 to 0.597, presented a large spatial variability due to coupling with local climate and specific local management. The comparison across the five wine-growing regions indicates that for the soil protection management, permanent cover crop is the best measure for accomplishing sustainable erosion rates across the studied areas. Alternate and temporary cover crops, that are used in areas of limited water resources to prevent competition with vines, failed to achieve sustainable erosion rates, that still need to be addressed. This raises the need for a careful use of C values developed under different environmental conditions.

Erosion Process and Temporal Variations in the Soil Surface Roughness of Spoil Heaps under Multi-Day Rainfall Simulation

The extensive artificially accelerated erosion of spoil heaps on newly engineered landforms is a key ecological management point requiring better understanding. Soil surface roughness is a crucial factor influencing erosion processes; however, study on spoil heap erosion with a view of surface roughness is lacking. This study investigated the erosion processes and the spatiotemporal variation of surface roughness on spoil heaps, and then, analyzed how the roughness affected the hydrological and sediment yield characteristics. Sequences of four artificial rainstorms with constant rainfall intensity (90 mm/h) were applied to cone-shaped spoil heaps (ground radius 3.5 m, height 2.3 m) of a loess soil containing 30 mass percent rock fragments. The surface elevation was sampled by a laser scanner. For the surface roughness indicators, the root mean square height (rmsh) and the correlation length (cl) increased sharply during the first rainfall event, and in the last three rainfall events, rmsh increased slightly and cl showed a relative decrease. The initial rmsh/cl of the whole slope surface ranged from 0.063 to 0.135, and increased with the rainfall sequence, thus, indicating that the spoil heap surface became rougher. Increasing soil roughness in the rainfall sequence delayed the initial runoff time and increased the runoff yield. The average runoff coefficient of the spoil heaps was 0.658. The average erosion rate of each rainfall event can be simulated by a regression equation of the corresponding average runoff rate and median cl (R-square of 0.816). Soil slumping with an average volume of 0.014 m3 occurred in the first two rainfall events, thus, significantly changing the roughness and peak instant erosion rate. Together, the results revealed the effects of surface roughness on the erosion of spoil heaps and would provide a useful reference for soil loss prediction and control.

Critical slope length for soil loss mitigation in maize-bean cropping systems in SW Kenya

Soil erosion and land fragmentation threaten agricultural production of sub-Saharan African highlands. At our study site in Western Kenya, farm size is mostly < 2 ha, laid out in narrow strips in slope direction and ploughed downhill. Soil conservation measures like hedgerows and green manures can reduce effective slope length for erosion, but compete with crops for space and labour. Knowledge of critical slope length can minimise interventions and trade–offs. Hence, a maize–bean intercrop (MzBn) slope length trial on 20, 60 and 84 m long plots, replicated twice on three farms was carried out in Rongo, Migori County, during one rainy season. Soil loss from 84 m slope length (SL) plots was 250 % higher than from 60 m and 710% higher than from 20 m plots, while soil loss from 20 and 60 m plots did not differ (p < 0.05). Conversely, runoff was lower on the 84 m than on the 60 m (p < 0.05) or the 20 m SL (p < 0.05). Across all three farms slope gradient and length had highest explanatory power to predict soil loss. At individual farm level, under similar slope and soil texture, slope length and profile curvature were most influential. Regarding results of the slope length experiments, food crop plot lengths < 50 m appear essential considering soil loss, sediment load, and soil loss to yield ratio under the given rainfall, soil and slope (10–14%) conditions. Our results call for designing integrating slope length options and cropping systems for effective soil conservation. We recommend planting Mucuna and Calliandra–hedgerows as buffer strips below the critical slope length, and legume cash crops and maize uphill. Such approaches are critical against the backdrop of land fragmentation and labour limitation to sustainably maximise food production from the available land area in the region.

Prediction of soil loss due to erosion using support vector machine model

Soil erosion refers to the detachment and removal of soil particles from land (topsoil), by the natural physical forces (water, glacier and wind). Soil erosion causes soil loss in the catchment or any land areas severely impacting agriculture activity, sedimentation in the dam reservoirs, and hampering developmental activities. Therefore, it is desirable to accurately measure soil loss due to erosion for the development and management of an area. With this objective, a well-known machine learning algorithm Support Vector Machine (SVM) has been used in the development of the soil loss prediction model. Eight erosion affecting variable inputs: ambient temperature Tair, rainfall, Antecedent Moisture Conditions (AMC), rainfall intensity, slope, vegetation cover, soil temperature Tsoil and moisture of the soil. Data on published literature was used in the model study. The accuracy of the proposed SVM was assessed by using three statistical performance evaluation indicators namely Person correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Squared Error (MAE). Partial Dependence Plots (PDP) was used to investigate the dependence of prediction results of eight input variables used in the model study. Model validation results showed that SVM model performed well for the prediction of soil loss for testing (R = 0.8993) and also for training (R=0.9123). Rainfall intensity and vegetation cover were found to be the two most important affecting input parameters for the soil loss prediction.

CLIGEN as a weather generator for predicting rainfall erosion using USLE based modelling systems

CLIGEN is a stochastic weather generator that has been used as input to WEPP. Normally, USLE based models predict erosion using parameter values that are long-term averages but RUSLE2 has a facility to predict erosion for single storms through user entered data. This enables CLIGEN to be used as a weather generator for RUSLE2 when EI30 values for CLIGEN generated rainfall are determined separately. This can be achieved using daily erosivity density (EI30 per unit quantity of rain) data generated by RUSLE2 for each location or by other methods that have the capacity to determine daily EI30 values independently of RUSLE2. One such method developed by Yu was compared with the RUSLE2 based method in terms of its ability to predict temporal variations in soil loss during the calendar year from bare fallow and cropped areas. The process of determining EI30 values by the Yu method involves generating EI30 values so as to match R-factor values used by RUSLE2. This enables CLIGEN to predict soil loss values that are as useful those generated using RUSLE2 erosivity densities in terms of predicting long-term variations in soil loss during the year. However, CLIGEN does not necessarily produce stochastic rainfall data evenly over decades. Consequently, the process of matching R-factors associated with RUSLE2 with those generated by using CLIGEN should be undertaken using the same time frame as used for obtaining the long-term mean soil loss amounts.

An assessment of rainfall-induced land degradation condition using Erosivity Density (ED) and heatmap method for Urmodi River watershed of Maharashtra, India

Soil erosion caused by rainfall is a serious issue in hilly terrain areas. Rainfall creates erosivity impact on soil; this force can be calculated using R-factor of Revised Universal Soil Loss Equation (RUSLE). For assessment of rainfall impact at Urmodi River watershed (Maharashtra, India), data of six weather stations were used of 22 years, from 1991–2013. Using the same data rainfall pattern map, R-factor map was plotted. The ratio of R-factor of RUSLE to annual rainfall gives us the erosivity density (ED). All maps are prepared using Inverse Distance Weighted (IDW) method of interpolation. Along with the maps, the precipitation pattern at each weather station was plotted using heatmap. The average rainfall value in the watershed was found to be 1247.13 mm/year. The mean R-factor at Urmodi River watershed was 531.69 MJ mm ha−1 h−1 year−1. Whereas, mean ED value at whole catchment level shows 0.42 MJ ha−1 h−1. At the upper catchment of the study area, there located ‘Kaas Plateau’ which is a world heritage site. As the region is an eco-sensitive area, this study makes possible to assess the impact of rainfall and predict soil loss. This information is important because it allows to adapt the soil protection measures.

Dynamic depth distribution of cesium-133 near soil surfaces in packed soils under multiple simulated rains

Various 137Cs conversion models have been used to predict soil erosion. A knowledge of the initial distribution of 137Cs is vital to improve soil loss prediction. The objectives were to (1) characterize the dynamic distributions of 133Cs near the soil surfaces in multiple rains spiked with 133Cs using a rainfall simulator, and (2) further estimate the relaxation mass depth (H) for use in an improved 137Cs mass balance model. Three silt loam soils were packed to soil boxes (1 × 0.5 × 0.1 m) and rained on at 30 mm h−1 and 10% slope in five consecutive rains. Soil cores (62.5 mm i.d.) were taken following each rain and were sectioned in the 1–10-mm intervals. Event runoff and sediment were collected. Cs+ was extracted with an NH4OAc method using a centrifuge procedure and analyzed with Inductively Coupled Plasma-Mass Spectrometer. The results showed initial Cs distributions near the soil surfaces were approximately exponential, and the mean H value could adequately characterize the initial Cs distributions for each soil for any sequence of rains. The averaged H values were 0.338, 0.304, and 0.354 g cm−2 for the three soils, equivalent to the soil depths of 2.76, 2.20, and 2.58 mm, indicating that majority of Cs was distributed within the top 3-mm depth. Cs+ was strongly adsorbed by the soils as indicated by the large Kd values of >32.6 L kg−1, showing the limited mobility of the freshly deposited Cs+ in the soils. Better H estimation should lead to more accurate soil loss prediction using the improved Cs mass balance model.

Modelling Hydrological Processes in Agricultural Areas with Complex Topography

Agricultural intensification and soil mismanagement have been recognized among the main causes of soil erosion in Mediterranean climate areas such as the Arbia stream basin (Tuscany, Italy). This study aims at predicting soil loss from agricultural fields as it is essential for providing reliable information for prioritizing soil conservation measures. Thus, measured soil loss from 243 agricultural fields within the Arbia stream basin during the period 2007–2010 were used to calibrate and validate the ArcSWAT 2012 model at hydrological response units (HRU) scale. Analysis of variance with post-hoc Tukey honest significant test was used to assess significant measured soil loss differences between slope steepness classes and land covers. Soil loss estimation was always “very good” for irrigated field crops, olive groves, and vineyards, “good” for unirrigated field crops, and “unsatisfactory” for broad-leaved forest. The model succeeded in the quantitative assessment of erosive processes at HRU scales. Its application to the whole Arbia stream basin estimated that 31% of the total surface is subjected to higher erosion levels. This approach might help facilitate the identification of priority areas that need the implementation of conservation measures.

Determining C- and P-factors of RUSLE for different land uses and management practices across agro-ecologies: case studies from the Upper Blue Nile basin, Ethiopia

ABSTRACT Cover and management (C) and support practice (P) are the most dynamic factors of the Revised Universal Soil Loss Equation- a widely used model to estimate mean annual soil loss. The aim of this study was, therefore, to determine C- and P-factors for four land management practices and three land use types in three agro-ecologies: Guder (highland), Aba Gerima (midland), and Dibatie (lowland) through selecting a rainfall erosivity (R)-factor model. We collected two seasons’ daily soil loss data from 42 runoff plots. Compared to the control, the P-factor in cropland ranged from 0.15 to 0.53 for soil bund, 0.18 to 0.5 for fanya juu, and 0.06 to 0.44 for soil bund with grass; and in non-cropland plots from 0.03 to 0.42 for trench with exclosure. The C-factor ranged from 0.004 for teff to 0.64 for chili pepper in crop land; and from 0.001 in degraded bushland to 0.49 in grassland for non-cropland. Overall, the average P-values decreased in the order midland > highland > lowland for cropland plots and highland > lowland > midland for non-cropland plots. Accurate determination of C- and P- factor values for the local conditions helps greatly improve soil loss prediction in this basin and other similar regions.

RainfallErosivityFactor: An R package for rainfall erosivity (R-factor) determination

RainfallErosivityFactor is an R package developed as a tool for the analysis of rainfall data and the calculation of the R-factor, a relevant parameter for soil loss prediction due to water erosion. The package consists of a user-friendly routine for loading large rainfall datasets into the R software environment and classifying data into erosive and non-erosive events. Erosive rainfall events are then used to compute rainfall erosivity values. This paper explains the development of the package with the purpose of showing how rainfall data can be analyzed accurately, fast and efficiently. An example is provided for Pirassununga, SP, Brazil, using a 7-year rainfall dataset with 10-minute interval between measurements. This package proved to be an efficient and fast tool for the determination of rainfall erosivity, which can be subsequently used to predict soil erosion. Considerations for future developments may come in the form of feedback, such as the need for more options of time interval.

Equations for predicting interrill erosion on steep slopes in the Three Gorges Reservoir, China

Abstract The Three Gorges Reservoir region suffers from severe soil erosion that leads to serious soil degradation and eutrophication. Interrill erosion models are commonly used in developing soil erosion control measures. Laboratory simulation experiments were conducted to investigate the relationship between interrill erosion rate and three commonly hydraulic parameters (flow velocity V, shear stress τ and stream power W). The slope gradients ranged from 17.6% to 36.4%, and the rainfall intensities varied from 0.6 to 2.54 mm·min−1. The results showed that surface runoff volume and soil loss rates varied greatly with the change of slope and rainfall intensity. Surface runoff accounted for 67.2–85.4% of the precipitation on average. Soil loss rates increased with increases of rainfall intensity and slope gradient, Regression analysis showed that interrill erosion rate could be calculated by a linear function of V and W. Predictions based on V (R 2 = 0.843, ME = 0.843) and W (R 2 = 0.862, ME = 0.862) were powerful. τ (R 2 = 0.721, ME = 0.721) did not seem to be a good predictor for interrill erosion rates. Five ordinarily interrill erosion models were analyzed, the accuracy of the models in predicting soil loss rate was: Model 3 (ME = 0.977) &gt; Model 4 (ME = 0.966) &gt; Model 5 (ME = 0.963) &gt; Model 2 (ME = 0.923) &gt; Model 1 (ME = 0.852). The interrill erodibility used in the model 3 (WEPP) was calculated as 0.332×106 kg·s·m−4. The results can improve the precision of interrill erosion estimation on purple soil slopes in the Three Gorges Reservoir area.

WATERSHED TOPOGRAPHIC CHARACTERISTICS AND CAUSES ANALYSIS OF THE UPPER REACHES OF THE WEIHE RIVER

Abstract. The morphology of watershed is the most intuitive information carrier to reflect regional tectonic activity, surface erosion and morphologic evolution. Active tectonic and fluvial system play a significant role in patterns and characters of regional morphology. Taking twenty-nine tributaries of the upper reaches of the Weihe River as the main study objects, four parameters, such as gully density (GD), basin topography ratio (BTR), roundness ratio (RR), river longitudinal profile fitting exponent (RLPFE), etc., were used to quantitatively analyse the topographic characteristics in this area. To reveal the main cause of the characteristics, the hypsometric integral (HI) were also applied in this area. The results showed that: (1) There is a positive linear function between basin topography ratio (BTR) and mean slope, and the mean values of four indexes in northern channels are smaller than southern channels; (2) The mean HI value is 0.44, indicating that the main topographic characteristics of this area is in maturity, which is in the transitional period of adjustment of the deep erosion and uplift movement; (3) The main cause of this topographic changes is tectonic. These results are consistent with other geological background, and will enrich regional basin morphology research and tectonic activity evaluation, provide important basic data for regional disaster prediction and analysis of soil and water loss.

Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems

The negative hydrological effects of wildfire are very difficult to predict in Mediterranean forest ecosystems, due the intrinsic climate and soil characteristics of these areas. Among the hydrological models simulating surface runoff and soil erosion in these environmental contexts, the semi-empirical Morgan-Morgan-Finney (MMF) model can ensure the representation of the main physical processes, while offering ease of use and limiting the number of input parameters. However, literature reports very few modelling studies using MMF in burned areas of the Mediterranean environment with or without post-fire rehabilitation measures. To fill this gap, the capacity of the MMF model to predict the seasonal surface runoff and soil loss in a Mediterranean forest was verified and improved for unburned plots and areas affected by a wildfire, with and without post-fire straw mulch treatment. The application of MMF with default input parameters (set up according to the original guidelines of the model’s developers) led to poor performance. Conversely, after introducing some changes in input data for both the hydrological and erosive components (seasonal values of evapotranspiration, reduction of the soil hydrological depth, including soil water repellency effects in burned soils, and modelling erosive precipitation only), MMF was able to predict seasonal runoff volumes and soil loss with good reliability in all the experimented conditions.This modelling experiment has shown the capacity of the MMF model to simulate the seasonal hydrological and erosion response of the experimental unburned and burned soils of Mediterranean semi-arid forests. Although more research is needed to validate the model's prediction capacity in these conditions, the use of MMF as a management tool may be suggested to predict the hydrogeological risk in these delicate ecosystems threatened by wildfire, as well as to evaluate the potential efficiency of soil treatments after fire.

A comprehensive analysis of Universal Soil Loss Equation‐based models at the Sparacia experimental area

AbstractImproving Universal Soil Loss Equation (USLE)‐based models has large interest because simple and reliable analytical tools are necessary in the perspective of a sustainable land management. At first, in this paper, a general definition of the event rainfall‐ runoff erosivity factor for the USLE‐based models, REFe = (QR)b1(EI30)b2, in which QR is the event runoff coefficient, EI30 is the single‐storm erosion index, and b1 and b2 are coefficients, was introduced. The rainfall‐runoff erosivity factors of the USLE (b1 = 0 and b2 = 1), USLE‐M (b1 = b2 = 1), USLE‐MB (b1 ≠ 1 and b2 = 1), USLE‐MR (b1 = 1 and b2 ≠ 1), USLE‐MM (b1 = b2 ≠ 1), and USLE‐M2 (b1 ≠ b2 ≠ 1) can be defined using REFe. Then the different expressions of REFe were simultaneously tested against a data set of normalized bare plot soil losses, AeN, collected at the Sparacia (south Italy) site. As expected, the poorest AeN predictions were obtained with the USLE. The observed tendency of this model to overestimate small AeN values and underestimate high AeN values was reduced by introducing in the soil loss prediction model both QR and an exponent for the erosivity term. The fitting to the data was poor with the USLE‐MR as compared with the USLE‐MB and the USLE‐MM. Estimating two distinct exponents (USLE‐M2) instead of a single exponent (USLE‐MB, USLE‐MR, and USLE‐MM) did not appreciably improve soil loss prediction. The USLE‐MB and the USLE‐MM were recognized to be the best performing models among the possible alternatives, and they performed similarly with reference to both the complete data set and different sub‐data sets, only including small, intermediate, and severe erosion events. In conclusion, including the runoff coefficient in the soil loss prediction model is important to improve the quality of the predictions, but a great importance has to be paid to the mathematical structure of the model.

Soil Erosion Modelling for Sustainable Environmental Management in Sebeya Catchment, Rwanda

Soil erosion models can be understood as a virtual laboratory that brings together data, observations and knowledge from different fields for sustainable environmental management. The present study was carried out on Sebeya catchment which is located in the Western Province of Rwanda. The main objective of this study was to develop a Universal Soil Loss Equation type of erosion model to be used in predicting soil loss and associated crop yields for sustainable agriculture management in Sebeya catchment at the level of parcels. USLE parameters were determined on each parcel in Sebeya catchment using map overlapping techniques as applied in Geographical Information System (GIS). Applying a combination of 0, 1, 2 and 3 soil erosion control measures on each of 259,673 parcels, the simulated annual soil loss for Sebeya catchment was 849.94; 143.27; 88.64 and 28.59 t/ha/yr respectively. Soil Loss and Crop Yield (SOLCY) model has been developed to predict soil loss and crop yields for each main cultivated crop in Sebeya catchment. A combination of 3 soil erosion control measures such as (bench terrace + mulching + drainage channels) has been found to be the most effective in reducing soil erosion on each parcel with slope range of (16 - 60)%. Farmers and agriculture technicians can use SOLCY model. Finally, researchers should develop similar models on other catchments based on SOLCY model design concept.

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.

Process for The Parameterization of a Soil Erosion Model Based on a Small Rainfall Simulator

Soil erosion due to water is an extensive problem in many countries, especially accelerated erosion, where the natural rate has significantly increased in the most recent period. Its extent impacts on land management practices and is the main factor causing soil degradation. Rainfall simulators are very popular as a standard instrument to conduct research on topics such as soil erosion, infiltration or surface runoff. They are used in laboratory or field experiments in erosion studies to assess the impacts of changes in the tillage management with regard to the erodibility of soil. Results of the experiments can be used in the modelling of surface runoff and sediment transport by hydrological models. This study presents the results of experimental measurements of surface runoff made by an artificial rainfall on an experimental plot. The artificial rainfall was generated using a small rainfall simulator, which allows changes in rainfall intensity and duration. The object of this study was analysed after the surface runoff occurred and the weight of the soil particles was transported from the experimental plot. These results have been used to determine the soil input parameters for the physically-based EROSION-2D model and for the verification of the model parameters. The EROSION-2D model represents a physically and event-based approach for the prediction of soil loss and runoff. The model only requires eight soil input parameters. Some of those parameters can be easily obtained by classic and widely-used methods (soil analyses, terrain measurements, soil map evaluations), and other parameters can be determined from rainfall experiments which represent the main aim of the study. The rainfall intensity has been set at 5 mm/min with 20 minutes of simulated rain, and it corresponded to an intensity with a return period of 100 years. During the experiment a continuous course of the surface runoff and soil moisture was recorded. The results from the simulations were applied to simulate the soil erosion processes in the physically-based EROSION-2D model. For the parametrization of the model, artificial rainfall events were used with an intensity of 2.7 mm/min and rain durations of 12 and 13 minutes. The parametrization of such models represents a necessary and significant part of any scientific work. The experimental results show that outputs from the rainfall simulations can be reproduced successfully, and, based on the results, the process of determining the model´s parameters can be successfully performed.