Estimation of event‐based rainfall erosivity from radar after wildfire

Abstract

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.

Extreme rainfall, rainfall erosivity, and hillslope erosion in Australian Alpine region and their future changes

“…() have revealed that rainfall erosivity is the most important parameter that has the direct effect of climate change on water erosion. More importantly, hillslope erosion occurs mostly during a few severe storm or extreme events (Zhu et al ., ). Large and erosive storms are even more variable than annual rainfall totals.…”

The Warrumbungle Post‐Fire Recovery Project—raising the profile of soils

“…These main components were supported and supplemented with: a burn severity map (Storey, ); rainfall and erosivity calculations of the 1 February 2013 storm (Zhu et al., ); Light Detection and Ranging (LiDAR) data of the fire ground and a 1 m digital elevation model (DEM); and soil mapping for spatial extrapolation of site data.…”

“…• Understanding impacts on soil organic carbon (SOC) and nitrogen (N), • measurement and modelling soil erosion and groundcover recovery, and • understanding fluvial geomorphic and bedload change. These main components were supported and supplemented with: a burn severity map (Storey, 2014 ); rainfall and erosivity calculations of the 1 February 2013 storm (Zhu et al, 2018 ); Light Detection and Ranging (LiDAR) data of the fire ground and a 1 m digital elevation model (DEM); and soil mapping for spatial extrapolation of site data. Impacts on Soil organic carbon (SOC) was selected by NPWS as a proxy for understanding changes to soil structure and its relationship to soil stability and erosion and recovery of vegetation.…”

“…The rainfall intensity of the 1 February 2013 storm was the 19th most intense on record at the Bureau of Meteorology Westmount station (Zhu et al, 2018 ). Modelling of soil loss showed that the impacts of this event varied widely (Figure 7 ), with the heaviest rain and most damaging erosion in the eastern and south-eastern parts of WNP.…”

Pembuatan Penakar Hujan Berbiaya Rendah Menggunakan Sensor Beban Berbasis Arduino Uno

“…Perbandingan antara data radar cuaca yang telah di kalibrasi dengan 12 penakar hujan tipping bucket untuk melakukan estimasi kejadian erosi. Hasil penelitian ini menunjukkan data radar cuaca dapat digunakan untuk menunjukkan waktu kejadian erosi dan sebagai informasi manajemen kejadian kebakaran hutan dan pengaturan tindakan terhadap erosi [22].…”