7 Ash is generated in every wildfire, but its eco-hydro-geomorphic effects remain poorly 8 understood and quantified, especially at large spatial scales. Here we present a new method 9 that allows modelling the spatial distribution of ash loads in the post-fire landscape. Based on 10 a severe wildfire that burnt ~13,600 ha of a forested water supply catchment in October 2013 11 (2013 Hall Road Fire, 100km south west of Sydney, Australia), Based on an existing spectral 12 ratio-based index, we

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... veloped a new spectral index using Landsat 8 satellite imagery: the 13 normalized wildfire ash index (NWAI). Before-and after-fire images were normalised and a 14 differenced wildfire ash image (dNWAI) computed. The relationship between dNWAI and 15 ash loads (t ha -1 ) quantified in situ at nine sampling locations burnt under a range of fire 16 severities was determined using a polynomial regression (R 2 =0.98). A spatially applied 17 model was computed within a Geographic Information System (GIS) to illustrate the spatial 18 distribution of ash across the area burnt and to estimate ash loads in the five subcatchments 19 affected by the wildfire. Approximately 181,000 tons of ash was produced by the wildfire 20 with specific loads increasing with fire severity. This new tool to model wildfire ash 21 distribution can inform decisions about post-fire land management in future wildfires in the 22 region. It can also be adapted for its application in other fire-prone environments. 23 24 Short summary 25 We present a new methodology that allowed modelling the amount and spatial distribution of 26 wildfire ash (t ha -1 ) in a burnt SE-Australian eucalypt forest. This tool can be applied in the 27 region, and, if adapted, elsewhere, to inform post-fire land management for mitigating 28 impacts from ash, such as debris flows or water contamination.