Evaluation of the PESERA model in two contrasting environments

The performance of the Pan‐European Soil Erosion Risk Assessment (PESERA) model was evaluated by comparison with existing soil erosion data collected in plots under different land uses and climate conditions in Europe. In order to identify the most important sources of error, the PESERA model was evaluated by comparing model output with measured values as well as by assessing the effect of the various model components on prediction accuracy through a multistep approach. First, the performance of the hydrological and erosion components of PESERA was evaluated separately by comparing both runoff and soil loss predictions with measured values. In order to assess the performance of the vegetation growth component of PESERA, the predictions of the model based on observed values of vegetation ground cover were also compared with predictions based on the simulated vegetation cover values. Finally, in order to evaluate the sediment transport model, predicted monthly erosion rates were also calculated using observed values of runoff and vegetation cover instead of simulated values. Moreover, in order to investigate the capability of PESERA to reproduce seasonal trends, the observed and simulated monthly runoff and erosion values were aggregated at different temporal scale and we investigated at what extend the model prediction error could be reduced by output aggregation. PESERA showed promise to predict annual average spatial variability quite well. In its present form, short‐term temporal variations are not well captured probably due to various reasons. The multistep approach showed that this is not only due to unrealistic simulation of cover and runoff, being erosion prediction also an important source of error. Although variability between the investigated land uses and climate conditions is well captured, absolute rates are strongly underestimated. A calibration procedure, focused on a soil erodibility factor, is proposed to reduce the significant underestimation of soil erosion rates. Copyright © 2009 John Wiley & Sons, Ltd.     

Implications of model uncertainty for the mapping of hillslope‐scale soil erosion predictions

An integrated modelling approach (MIRSED) which utilizes the process‐based soil erosion model WEPP (Water Erosion Prediction Project) is presented for the assessment of hillslope‐scale soil erosion at five sites throughout England and Wales. The methodology draws upon previous uncertainty analysis of the WEPP hillslope soil erosion model by the authors to qualify model results within an uncertainty framework. A method for incorporating model uncertainty from a range of sources is discussed as a first step towards using and learning from results produced through the GLUE (Generalized Likelihood Uncertainty Estimation) technique. Results are presented and compared to available observed data, which illustrate that levels of uncertainty are significant and must be taken into account if a meaningful understanding of output from models such as WEPP is to be achieved. Furthermore, the collection of quality, observed data is underlined for two reasons: as an essential tool in the development of soil erosion modelling and also to allow further constraint of model uncertainty. Copyright © 2001 John Wiley & Sons, Ltd.     

Impact of model simplifications on soil erosion predictions: application of the GLUE methodology to a distributed event‐based model at the hillslope scale

In this paper, we analyse how the performance and calibration of a distributed event‐based soil erosion model at the hillslope scale is affected by different simplifications on the parameterizations used to compute the production of suspended sediment by rainfall and runoff. Six modelling scenarios of different complexity are used to evaluate the temporal variability of the sedimentograph at the outlet of a 60 m long cultivated hillslope. The six scenarios are calibrated within the generalized likelihood uncertainty estimation framework in order to account for parameter uncertainty, and their performance is evaluated against experimental data registered during five storm events. The Nash–Sutcliffe efficiency, percent bias and coverage performance ratios show that the sedimentary response of the hillslope in terms of mass flux of eroded soil can be efficiently captured by a model structure including only two soil erodibility parameters, which control the rainfall and runoff production of suspended sediment. Increasing the number of parameters makes the calibration process more complex without increasing in a noticeable manner the predictive capability of the model. Copyright © 2015 John Wiley & Sons, Ltd.     

Use of caesium‐137 data to evaluate SHETRAN simulated long‐term erosion patterns in arable lands

The caesium‐137 method of quantifying soil erosion is used to provide field data for validating the capability of the SHETRAN modelling system for predicting long‐term (30‐year) erosion rates and their spatial variability. Simulations were carried out for two arable farm sites (area 3–5 ha) in central England for which average annual erosion rates of 6·5 and 10·4 t ha^?1 year^?1 had already been determined using caesium‐137 measurements. These rates were compared with a range of simulated values representing the uncertainty in model output derived from uncertainty in the evaluation of model parameters. A successful validation was achieved in that the simulation range contained the measured rate at both sites, whereas the spatial variability was reproduced excellently at one site and partially at the other. The results indicate that, as the caesium‐137 technique measures the erosion caused by all the processes acting at a site, it is relevant to hydrologically based models such as SHETRAN only if erosion by wind, agricultural activities and other processes not represented in the model are insignificant. The results also indicate a need to reduce the uncertainty in model parameter evaluation. More generally, the caesium‐137 technique is shown to provide field data that improve the severity of the validation procedure (accounting for internal as well as outlet conditions) and that add spatial variability to magnitude as a condition for identifying unrealistic parameter sets when seeking to reduce simulation uncertainty. Copyright © 2004 John Wiley & Sons, Ltd.     

A one‐dimensional model for simulating armouring and erosion on hillslopes: 1. model development and event‐scale dynamics

This paper presents an erosion model, ARMOUR, which simulates time‐varying runoff, erosion, deposition and surface armour evolution down a hillslope either as a result of a single erosion event or as the cumulative impact of many events over periods up to decades. ARMOUR simulates sediment transport for both cohesive and non‐cohesive soil and dynamically differentiates between ‘transport‐limited’ and ‘source‐limited’ processes. A variety of feasible processes for entrainment of different size classes can be modelled and evaluated against data. The generalized likelihood of uncertainty estimation (GLUE) technique was used to calibrate and validate ARMOUR using data collected during rainfall simulator experiments at two contrasting sites: (1) non‐cohesive stony sediments at Ranger Uranium Mine, Northern Territory, Australia; and (2) cohesive silty sediments at Northparkes Gold Mine, NSW, Australia. The spatial and temporal variations of model predictions within the individual runoff events showed that some entrainment processes could not model the spikes in concentration and subsequent depletion, while the hiding model of Andrews and Parker best simulated the concentration trends for both calibrated and independent runoff events. ARMOUR also successfully captured the coarsening of the surface material, though small, over the duration of the rainfall simulator trials. This was driven by the depletion of the finest size class of the soil. For a constant discharge, ARMOUR simulated higher sediment flux at the start of the storm with the sediment flux and concentration diminishing with time. For natural rainfall a power law relationship between sediment flux and discharge was observed. The calibration exercise showed that sediment concentration and discharge alone are insufficient to calibrate all aspects of the physics, in particular the armour depth. This appears to be because the armouring during the short duration events is driven by depletion of the finest classes of the sediments (diameters less then 62·5 mm), which are not normally measured. Copyright © 2006 John Wiley & Sons, Ltd.     

Evaluation of the LISEM soil erosion model in two catchments in the East African Highlands

Under increasing population pressure, soil erosion has become a threat in the East African Highlands, and erosion modelling can be useful to quantify this threat. To test its applicability for this region, the LISEM soil erosion model was applied to two small catchments, one in the Usumbara Mountains, Tanzania, and the other on the slopes of Mount Kenya. Input data for the model were collected in both catchments, as were data on runoff and erosion that were used for calibration and validation of the model. LISEM was first calibrated on catchment outlet data, and afterwards simulated spatial patterns of erosion were compared to available erosion data. The results showed that LISEM can, after calibration, give good discharge predictions for some events, but not for all. However, LISEM generally overpredicted soil loss from the catchments. Comparison with observed erosion patterns did not show overprediction, but according to the model, erosion was more widespread than was observed. There are several reasons for these discrepancies. First, it is difficult to obtain enough accurate data to run the model, such as accurate maps, rainfall data and soil and plant characteristics. Second, it is also difficult to obtain accurate data to evaluate the performance of the model, either for the catchment outlet or spatially, therefore observed erosion rates are also uncertain. Third, the model could not deal correctly with complex events, i.e. those having double rainfall peaks, and might also have difficulties with catchment characteristics such as soil type and the complexity of land use. Finally, LISEM could not deal with events in which throughflow or baseflow played a role, which was to be expected since those processes are not simulated by LISEM. Nevertheless, LISEM could be calibrated to give good discharge predictions for some events, and also gave reasonable results when compared to data obtained from erosion plots. Furthermore, only complex, distributed, storm‐based models such as LISEM can give spatial predictions for single storms. Therefore, it is concluded that if the aim is spatial prediction on an event basis, there is no alternative to complex erosion models such as LISEM, but if the aim is to predict average annual erosion, the data‐demanding, physically based LISEM erosion model may not be the most appropriate model. Copyright © 2005 John Wiley & Sons, Ltd.     

EVALUATION OF EROSION PRODUCTIVITY IMPACT CALCULATOR （EPIC） MODEL FOR MIDDLE MOUNTAIN REGION OF NEPAL

This study verifies the applicability of EPIC model for an erosion plot (61 .2 m~2) and an uplandterraced watershed (72 ha) using a total of 94 rainfall events over a study period of two years. Inorder to analyze the effect of storm size on runoff and soil loss processes, rainfall events aredivided into three groups: small (<25mm), moderate (25--50mm) and large (>50mm). Resultsindicate that the model could predict reasonably well the runoff and soil loss from the erosion plotand the watershed for the moderate and large rainfall events. However, the runoff and soil lossprediction for the small rainfall events is found to be poor. On annual basis, both surface runoff andsoil loss predictions match well the observations. In ligh of the importance of the moderate andlarge rainfall events in producing most of the annual runoff and soil loss in the study area, the EPICmodel is applied to assess the impacts of erosion on agricultural productivity and to evaluatemanagement practices to protect watersheds in the     

Uncertainty and sensitivity analysis of runoff and sediment yield in a small agricultural watershed with KINEROS2 / Analyse d'incertitude et de sensibilité des simulations d'écoulement et de transport solide de KINEROS2 dans un petit bassin versant agricole

Abstract

Using the Monte Carlo (MC) method, this paper derives arithmetic and geometric means and associated variances of the net capillary drive parameter, G, that appears in the Parlange infiltration model, as a function of soil texture and antecedent soil moisture content. Approximate expressions for the arithmetic and geometric statistics of G are also obtained, which compare favourably with MC generated ones. This paper also applies the MC method to evaluate parameter sensitivity and predictive uncertainty of the distributed runoff and erosion model KINEROS2 in a small experimental watershed. The MC simulations of flow and sediment related variables show that those parameters which impart the greatest uncertainty to KINEROS2 model outputs are not necessarily the most sensitive ones. Soil hydraulic conductivity and wetting front net capillary drive, followed by initial effective relative saturation, dominated uncertainties of flow and sediment discharge model outputs at the watershed outlet. Model predictive uncertainty measured by the coefficient of variation decreased with rainfall intensity, thus implying improved model reliability for larger rainfall events. The antecedent relative saturation was the most sensitive parameter in all but the peak arrival times, followed by the overland plane roughness coefficient. Among the sediment related parameters, the median particle size and hydraulic erosion parameters dominated sediment model output uncertainty and sensitivity. Effect of rain splash erosion coefficient was negligible. Comparison of medians from MC simulations and simulations by direct substitution of average parameters with observed flow rates and sediment discharges indicates that KINEROS2 can be applied to ungauged watersheds and still produce runoff and sediment yield predictions within order of magnitude of accuracy.     

The Daily Erosion Project – daily estimates of water runoff,soil detachment,and erosion

Water runoff and sediment transport from agricultural uplands are substantial threats to water quality and sustained crop production. To improve soil and water resources, farmers, conservationists, and policy‐makers must understand how landforms, soil types, farming practices, and rainfall interact with water runoff and soil erosion processes. To that end, the Iowa Daily Erosion Project (IDEP) was designed and implemented in 2003 to inventory these factors across Iowa in the United States. IDEP utilized the Water Erosion Prediction Project (WEPP) soil erosion model along with radar‐derived precipitation data and government‐provided slope, soil, and management information to produce daily estimates of soil erosion and runoff at the township scale (93 km² [36 mi²]). Improved national databases and evolving remote sensing technology now permit the derivation of slope, soil, and field‐level management inputs for WEPP. These remotely sensed parameters, along with more detailed meteorological data, now drive daily WEPP hillslope soil erosion and water runoff estimates at the small watershed scale, approximately 90 km² (35 mi²), across sections of multiple Midwest states. The revisions constitute a substantial improvement as more realistic field conditions are reflected, more detailed weather data are utilized, hill slope sampling density is an order of magnitude greater, and results are aggregated based on surface hydrology enabling further watershed research and analysis. Considering these improvements and the expansion of the project beyond Iowa it was renamed the Daily Erosion Project (DEP). Statistical and comparative evaluations of soil erosion simulations indicate that the sampling density is adequate and the results are defendable. The modeling framework developed is readily adaptable to other regions given suitable inputs. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Disaggregating soil erosion processes within an evolving experimental landscape

Soil‐mantled landscapes subjected to rainfall, runoff events, and downstream base level adjustments will erode and evolve in time and space. Yet the precise mechanisms for soil erosion also will vary, and such variations may not be adequately captured by soil erosion prediction technology. This study sought to monitor erosion processes within an experimental landscape filled with packed homogenous soil, which was exogenically forced by rainfall and base level adjustments, and to define the temporal and spatial variation of the erosion regimes. Close‐range photogrammetry and terrain analysis were employed as the primary methods to discriminate these erosion regimes. Results show that (1) four distinct erosion regimes can be identified (raindrop impact, sheet flow, rill, and gully), and these regimes conformed to an expected trajectory of landscape evolution; (2) as the landscape evolved, the erosion regimes varied in areal coverage and in relative contribution to total sediment efflux measured at the outlet of the catchment; and (3) the sheet flow and rill erosion regimes dominated the contributions to total soil loss. Disaggregating the soil erosion processes greatly facilitated identifying and mapping each regime in time and space. Such information has important implications for improving soil erosion prediction technology, for assessing landscape degradation by soil erosion, for mapping regions vulnerable to future erosion, and for mitigating soil losses and managing soil resources. Copyright © 2017 John Wiley & Sons, Ltd.     

Evaluating soil erosion models using measured plot data: accounting for variability in the data

One of the important methods used to evaluate the effectiveness of soil erosion models is to compare the predictions given by the model to measured data from soil loss collected on plots taken under natural rainfall conditions. While it is recognized that plot data contain natural variability, this factor is not quantitatively considered during such evaluations because our knowledge of natural variability between plots which have the same treatments is very limited. The goal of this study was to analyse sufficient replicated plot data and present methodology to allow the model evaluator to take natural, within‐treatment variability of erosion plots into account when models are tested. A large amount of data from pairs of replicated erosion plots was evaluated and quantified. The basis for the evaluation method presented is that if the difference between the model prediction and a measured plot data value lies within the population of differences between pairs of measured values, then the prediction is considered ‘acceptable’. A model ‘effectiveness’ coefficient was defined for studies undertaken on large numbers of prediction versus measured data comparisons. This method provides a quantitative criterion for taking into account natural variability and uncertainty in measured erosion plot data when those data are used to evaluate erosion models. Published in 2000 by John Wiley & Sons, Ltd.     

Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.     

Modified MMF (Morgan–Morgan–Finney) model for evaluating effects of crops and vegetation cover on soil erosion

Modifications are made to the revised Morgan–Morgan–Finney erosion prediction model to enable the effects of vegetation cover to be expressed through measurable plant parameters. Given the potential role of vegetation in controlling water pollution by trapping clay particles in the landscape, changes are also made to the way the model deals with sediment deposition and to allow the model to incorporate particle‐size selectivity in the processes of erosion, transport and deposition. Vegetation effects are described in relation to percentage canopy cover, percentage ground cover, plant height, effective hydrological depth, density of plant stems and stem diameter. Deposition is modelled through a particle fall number, which takes account of particle settling velocity, flow velocity, flow depth and slope length. The detachment, transport and deposition of soil particles are simulated separately for clay, silt and sand. Average linear sensitivity analysis shows that the revised model behaves rationally. For bare soil conditions soil loss predictions are most sensitive to changes in rainfall and soil parameters, but with a vegetation cover plant parameters become more important than soil parameters. Tests with the model using field measurements under a range of slope, soil and crop covers from Bedfordshire and Cambridgeshire, UK, give good predictions of mean annual soil loss. Regression analysis of predicted against observed values yields an intercept value close to zero and a line slope close to 1·0, with a coefficient of efficiency of 0·81 over a range of values from zero to 38·6 t ha^?1. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling event soil losses using the QREI30 index within RUSLE2

Recently, a USDA Curve Number‐based method for obtaining estimates of event runoff has been developed for use in enhancing the capacity of Revised Universal Soil Loss Equation (RUSLE2) to deal with runoff‐driven phenomena. However, RUSLE2 still uses the EI₃₀ index as the basis for determining the erosivity of the rainfall for sets of runoff producing storms at a location even though the product of the runoff ratio (Q_R) and EI₃₀ index is better at prediction event erosion when runoff is known or predicted well. This paper reports the results of applying the Q_REI₃₀ index using data available from tables within RUSLE2 to predict storm event soil losses from bare fallow areas and areas with continuous corn at Holly Springs, MS, and Morris, MN. In RUSLE2, all rainfall during a calendar year is considered to detach soil material that is flushed from the area if and when runoff occurs. However, the Q_REI₃₀ index is calculated using the EI₃₀ value for the amount of rain in the storm that produces runoff. Consequently, changes were made to the timing of events during the calendar year in order to meet the criteria for using the Q_REI₃₀ index. As a general rule, the peak event soil loss produced using the Q_REI₃₀ index were higher than produced by RUSLE2, and the peak event soil loss for the bare fallow occurred later than for the continuous corn. The results of the work reported here show that the Q_REI₃₀ index may be used to model event erosion produced by a set of storms within RUSLE2 provided that the appropriate mathematical rules upon which the USLE was developed are adhered to. Copyright © 2013 John Wiley & Sons, Ltd.     

The European Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments

The European Soil Erosion Model (EUROSEM) is a dynamic distributed model, able to simulate sediment transport, erosion and deposition over the land surface by rill and interill processes in single storms for both individual fields and small catchments. Model output includes total runoff, total soil loss, the storm hydrograph and storm sediment graph. Compared with other erosion models, EUROSEM has explicit simulation of interill and rill flow; plant cover effects on interception and rainfall energy; rock fragment (stoniness) effects on infiltration, flow velocity and splash erosion; and changes in the shape and size of rill channels as a result of erosion and deposition. The transport capacity of runoff is modelled using relationships based on over 500 experimental observations of shallow surface flows. EUROSEM can be applied to smooth slope planes without rills, rilled surfaces and surfaces with furrows. Examples are given of model output and of the unique capabilities of dynamic erosion modelling in general. © 1998 John Wiley & Sons, Ltd.     

Distributed dynamic modelling of interrill overland flow

A distributed, dynamic, process-based model for interrill overland flow that has previously been shown to predict accurately both total runoff and runoff hydraulics for a site on semi-arid shrubland is assessed in terms of (i) its portability, (ii) its sensitivity to the quality of data inputs, and (iii) its sensitivity to the size of cell used in the model. It is found that the model can be used at another site, but only after modifications to take account of the local controls of runoff routing. The model is portable, but not readily so. The model is sensitive to both the quality of data input and the size of cell. Data input cannot be reduced by use of stochastic distribution of model parameters without significant loss of accuracy in model predictions, particularly of runoff hydraulics. Larger cells produce poorer predictions of the runoff hydrograph. It is concluded that process-based modelling of interrill runoff may not be a realistic tool for predicting soil erosion, but is one that may be useful for identification of our present poor understanding of erosion processes. Such models help to define the research agenda for soil erosion studies. © 1997 John Wiley & Sons, Ltd.     

Validation of WEPS for soil and PM10 loss from agricultural fields within the Columbia Plateau of the United States

Wind erosion from agricultural fields contributes to poor air quality within the Columbia Plateau of the United States. Erosion from fields managed in a conventional winter wheat–summer fallow rotation was monitored during the fallow period near Washtucna, WA, in 2003 and 2004. Loss of soil and PM10 (particulates ≤10 µm in diameter) was measured during six high wind events (sustained wind speed at 3 m height >6·4 m s^?1). Soil loss associated with suspension, saltation and creep as well as PM10 emission was used to validate the Wind Erosion Prediction System (WEPS) erosion submodel. Input parameters for WEPS simulations were measured before each high wind event. The erosion submodel produced no erosion for half of the observed events and over‐predicted total soil loss by 200–700 kg ha^?1 for the remaining events. The model appears to over‐predict total soil loss as a result of overestimating creep, saltation and suspension. The model both over‐predicted and under‐predicted PM10 loss. High values for the index of agreement (d > 0·5) suggest that the performance of the model is acceptable for the conditions of this study. While the performance of the model is acceptable, improvements can be made in modeling efficiency by better specifying the static threshold friction velocity or coefficients that govern emissions, abrasion and breakage of silt loams on the Columbia Plateau. Copyright © 2006 John Wiley & Sons, Ltd.     

Evaluation of the SWEEP model during high winds on the Columbia Plateau

A standalone version of the Wind Erosion Prediction System (WEPS) erosion submodel, the Single‐event Wind Erosion Evaluation Program (SWEEP), was released in 2007. A limited number of studies exist that have evaluated SWEEP in simulating soil loss subject to different tillage systems under high winds. The objective of this study was to test SWEEP under contrasting tillage systems employed during the summer fallow phase of a winter wheat–summer fallow rotation within eastern Washington. Soil and PM10 (particulate matter ≤10 µm in diameter) loss and soil and crop residue characteristics were measured in adjacent fields managed using conventional and undercutter tillage during summer fallow in 2005 and 2006. While differences in soil surface conditions resulted in measured differences in soil and PM10 loss between the tillage treatments, SWEEP failed to simulate any difference in soil or PM10 loss between conventional and undercutter tillage. In fact, the model simulated zero erosion for all high wind events observed over the two years. The reason for the lack of simulated erosion is complex owing to the number of parameters and interaction of these parameters on erosion processes. A possible reason might be overestimation of the threshold friction velocity in SWEEP since friction velocity must exceed the threshold to initiate erosion. Although many input parameters are involved in the estimation of threshold velocity, internal empirical coefficients and equations may affect the simulation. Calibration methods might be useful in adjusting the internal coefficients and empirical equations. Additionally, the lack of uncertainty analysis is an important gap in providing reliable output from this model. Published in 2009 by John Wiley & Sons, Ltd.