Modelling and mapping rainfall erosivity in New South Wales, Australia

Yang, Xihua; Yu, Bofu

doi:10.1071/sr14188

Cited by 45 publications

(30 citation statements)

References 21 publications

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“…Lu et al 2003;Yang 2014;Yang and Yu 2015). Accuracy was also assessed by using a measure of the bias (B) as:…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…Other factors are not discussed in this paper, but briefly the rainfall erosivity (R-factor) was estimated from daily rainfall data using a daily rainfall erosivity model (Yang and Yu 2015); cover (C-factor) was estimated from the time-series fractional cover products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Yang 2014); and soil erodibility (K-factor) was estimated from a Great Soil Group map and soil database (OEH 2014b). Time-series hillslope erosion maps produced at such high spatial resolution (same resolution as LS factor) have great potential in many local and regional applications such as land-use planning, erosion control, bushfire risk and water quality assessment, and climate impact assessment.…”

Section: Methods and Proceduresmentioning

confidence: 99%

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Digital mapping of RUSLE slope length and steepness factor across New South Wales, Australia

Yang¹

2015

Soil Res.

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The Universal Soil Loss Equation (USLE) and its main derivate, the Revised Universal Soil Loss Equation (RUSLE), are widely used in estimating hillslope erosion. The effects of topography on hillslope erosion are estimated through the product of slope length (L) and slope steepness (S) subfactors, or LS factor, which often contains the highest detail and plays the most influential role in RUSLE. However, current LS maps in New South Wales (NSW) are either incomplete (e.g. point-based) or too coarse (e.g. 250 m), limiting RUSLE-based applications. The aim of this study was to develop automated procedures in a geographic information system (GIS) to estimate and map the LS factor across NSW. The method was based on RUSLE specifications and it incorporated a variable cutoff slope angle, which improves the detection of the beginning and end of each slope length. An overland-flow length algorithm for L subfactor calculation was applied through iterative slope-length cumulation and maximum downhill slope angle. Automated GIS scripts have been developed for LS factor calculation so that the only required input data are digital elevation models (DEMs). Hydrologically corrected DEMs were used for LS factor calculation on a catchment basis, then merged to form a seamless LS-factor digital map for NSW with a spatial resolution~30 m (or 1 s). The modelled LS values were compared with the reference LS values, and the coefficient of efficiency reached 0.97. The high-resolution digital LS map produced is now being used along with other RUSLE factors in hillslope erosion modelling and land-use planning at local and regional scales across NSW.

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“…Lu et al 2003;Yang 2014;Yang and Yu 2015). Accuracy was also assessed by using a measure of the bias (B) as:…”

Section: Methods and Proceduresmentioning

confidence: 99%

Section: Methods and Proceduresmentioning

confidence: 99%

Digital mapping of RUSLE slope length and steepness factor across New South Wales, Australia

Yang¹

2015

Soil Res.

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“…Two parameters of the model were set to be constant for the study area and another parameter was related to the ratio of the mean summer rainfall to the mean annual rainfall. Yang and Yu (2015) indicated that the parameters of Lu and Yu (2002) may change with the period of reference and improved the model by using the geographic location and elevation to predict the parameters instead of rainfall. The second one is to estimate the at‐site rainfall erosivity with observations first and then interpolate erosivity values for the sites without observations by geostatistical techniques such as inverse distance weighting (Sadeghi et al, 2017), ordinary kriging (Oliveira et al, 2012a), co‐kriging (Qin et al, 2016), regression kriging (Meusburger et al, 2012; Borrelli et al, 2016), or Gaussian process regression (Panagos et al, 2015).…”

Section: Mapping Outside the United Statesmentioning

confidence: 99%

Rainfall Erosivity: An Overview of Methodologies and Applications

Yin

Nearing

Borrelli

et al. 2017

Vadose Zone Journal

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The rainfall erosivity factor (R factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), which together reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI 30 (the product of kinetic energy and maximum 30-min intensity) over a year and calculated based on rainfall hyetograph data. The R factor was developed in the various versions of the USLE, including the definition of the individual event and the criterion for selecting events used in the calculation, the equation used to estimate the unit kinetic energy from the rainfall intensity, the estimation of erosivity from the snowmelt and thaw, and erosivity mapping. Most research on rainfall erosivity deals with any of three aspects: developing estimation methods for deriving erosivity from courser resolution rainfall data (such as daily, monthly, and annual) but with greater spatial and temporal coverages than those from hyetograph data; preparing erosivity maps including those for annual average, monthly, and 10-yr recurrence erosivity; and documenting temporal trends in erosivity. Rainfall erosivity research on these three aspects is summarized to provide a greater understanding of the R factor.

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“…Soil erosion models play an important role in soil erosion predictions and among them the USLE (Wischmeier and Smith, 1978) and RUSLE (Renard et al, 1997) are the most widely used. Rainfall is the main driver for soil erosion by water and the relationship between rainfall and sediment yield is given by rainfall erosivity (Yang and Yu, 2015). Rainfall erosivity is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minute rainfall intensity (Wischmeier and Smith, 1978).…”

Section: Introductionmentioning

confidence: 99%

Mapping monthly rainfall erosivity in Europe

Ballabio

Borrelli

Spinoni

et al. 2017

Science of The Total Environment

150

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Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha− 1 h− 1) compared to winter (87 MJ mm ha− 1 h− 1).The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

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Modelling and mapping rainfall erosivity in New South Wales, Australia

Cited by 45 publications

References 21 publications

Digital mapping of RUSLE slope length and steepness factor across New South Wales, Australia

Digital mapping of RUSLE slope length and steepness factor across New South Wales, Australia

Rainfall Erosivity: An Overview of Methodologies and Applications

Mapping monthly rainfall erosivity in Europe

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