Modelling and quantifying the spatial distribution of post-wildfire ash loads
Chris J. Chafer A C , Cristina Santín B and Stefan H. Doerr B
+ Author Affiliations
- Author Affiliations
A WaterNSW, 2–6 Station Street, Penrith, NSW 2751, Australia.
B Department of Geography, College of Science, Wallace Building, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
C Corresponding author. Email: chris.chafer@waternsw.com.au
International Journal of Wildland Fire 25(2) 249-255 https://doi.org/10.1071/WF15074
Submitted: 30 March 2015 Accepted: 21 October 2015 Published: 25 January 2016
Abstract
Ash is generated in every wildfire, but its eco-hydro-geomorphic effects remain poorly understood and quantified, especially at large spatial scales. Here we present a new method that allows modelling the spatial distribution of ash loads in the post-fire landscape, based on a severe wildfire that burnt ~13 600 ha of a forested water supply catchment in October 2013 (2013 Hall Road Fire, 100 km south-west of Sydney, Australia). Employing an existing spectral ratio-based index, we developed a new spectral index using Landsat 8 satellite imagery: the normalised wildfire ash index (NWAI). Before- and after-fire images were normalised and a differenced wildfire ash image (dNWAI) computed. The relationship between dNWAI and ash loads (t ha−1) quantified in situ at nine sampling locations burnt under a range of fire severities was determined using a polynomial regression (R2 = 0.98). A spatially applied model was computed within a geographic information system (GIS) to illustrate the spatial distribution of ash across the area burnt and to estimate ash loads in the five subcatchments affected by the wildfire. Approximately 181 000 tonnes of ash was produced by the wildfire, with specific loads increasing with fire severity. This new tool to model wildfire ash distribution can inform decisions about post-fire land management in future wildfires in the region. It can also be adapted for its application in other fire-prone environments.
Additional keywords: eucalypt forest, fire severity, post-fire erosion, water contamination.
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