Spatial and temporal distribution of rainfall erosivity in New Zealand

Klik, Andreas; Haas, Kathrin; Dvorackova, Anna; Fuller, Ian C.

doi:10.1071/sr14363

Cited by 41 publications

(55 citation statements)

References 26 publications

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Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

“…Klik et al (2015) emphasised the need to understand the drivers of soil erosion, including whether rainfall intensity had a stronger effect compared to mean annual rainfall. In an assessment of spatial and temporal variations in rainfall erosivity over New Zealand, December and January were associated with higher erosivities while August was associated with lowest erosivity (Klik et al, 2015). Similar work by Diodato (2004) has cited the use of monthly erosivity data to be more useful with respect to managing crop growing cycles and tillage practices, especially during seasons where high rainfall erosivity is 5 expected.…”

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

“…Work by Bonilla & Vidal (2011) produced an R-factor equation for Chile and published erosivity values similar to those produced by work in areas of similar geography and geology. For New Zealand, Klik et al (2015) proposed equations for calculating 20 the annual R-factor and seasonal R-factor with coefficients that change depending on the study area's location within the country.…”

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

“…As an example of how R-factor equations can give differents estimates of rainfall erosivity, the equations by Klik et al (2015) developed in New Zealand, Loureiro and Coutinho (2001) developed in Portugal, and Ferreira and Panagopolous (2014) also developed in Portugal were used to estimate annual and seasonal erosivity in the Mangatarere watershed ( Figure 10 1, Table 1, and Table 2). The Mangatarere watershed (~157km 2 ) in New Zealand has forested areas in the hill country, and agricultural activity such as dairy and drystock farming in the plains area.…”

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

“…In Southeast Asia, Shamsad et al (2008) developed an R-factor equation in Malaysia that was used in the Philippines by Delgado & Canters 30 (2012). In New Zealand, the monthly precipitation can be aggregated to seasonal precipitation and used in the equation for seasonal R-factor derived by Klik et al (2015).…”

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

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A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

Benavidez¹,

Jackson²,

Maxwell³

et al. 2018

Preprint

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Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape and use land management and other strategies to effectively manage the problem. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is 10 the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviewed the different components of USLE and RUSLE etc., and analysed how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with R/USLE and related approaches. We investigate some of the limitations of R/USLE, such as issues 15 in data-sparse regions, its inability to account for soil loss from gully erosion or mass wasting events, and that it does not predict sediment pathways from hillslopes to water bodies. These limitations point to several future directions for R/USLE studies: incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and using consistent units for future literature. These recommendations help to improve the applicability of the R/USLE in a range of geoclimatic regions with varying data availability, and at finer spatial and temporal scales for scenario analysis. 20

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Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

See 3 more Smart Citations

A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

Benavidez¹,

Jackson²,

Maxwell³

et al. 2018

Preprint

View full text Add to dashboard Cite

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Estimation of event‐based rainfall erosivity from radar after wildfire

Zhu

Yang

et al. 2018

Land Degrad Dev

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Rainfall erosivity impacts all stages of hillslope erosion processes and is an important factor (the ‘R factor’) in the Revised Universal Soil Loss Equation. It is estimated as the average annual value of the sum of all erosive events (EI30) over a period of many years. For each storm event, the EI30 value is the product of storm energy, E in MJ ha−1, and peak 30‐min rainfall intensity (I30, mm hr−1). Previous studies often focused on estimation of the R factor for prediction of mean annual or long‐term soil losses. However, many applications require EI30 values at much higher temporal resolution, such as postfire soil erosion monitoring, which requires a time step at storm events or on a daily basis. In this study, we explored the use of radar rainfall data to estimate the storm event‐based EI30 after a severe wildfire in Warrumbungle National Park in eastern Australia. The radar‐derived rainfall data were calibrated against 12 tipping bucket rain gauges across an area of 239 km2 and subsequently used to produce a time series of rainfall erosivity maps at daily intervals since the wildfire in January 2013. The radar‐derived daily rainfall showed good agreement with the gauge measurements (R2 > 0.70, Ec = 0.66). This study reveals great variation in EI30 values ranging from near zero to 826.76 MJ·mm·ha−1·hr−1 for a single storm event. We conclude that weather radar rainfall data can be used to derive timely EI30 and erosion information for fire incident management and erosion control. The methodology developed in this study is generic and thus readily applicable to other areas where weather radar data are available.

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