International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Rapid-response tools and datasets for post-fire remediation: linking remote sensing and process-based hydrological models

M. E. Miller A C , W. J. Elliot B , M. Billmire A , P. R. Robichaud B and K. A. Endsley A
+ Author Affiliations
- Author Affiliations

A Michigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.

B US Department of Agriculture, Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843, USA.

C Corresponding author. Email: mmaryellen@gmail.com

International Journal of Wildland Fire 25(10) 1061-1073 https://doi.org/10.1071/WF15162
Submitted: 4 September 2015  Accepted: 19 July 2016   Published: 3 October 2016

Abstract

Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil properties. Slope, soils, land cover and climate are also important factors that require consideration. Many modelling tools and datasets have been developed to assist remediation teams, but process-based and spatially explicit models are currently underutilised compared with simpler, lumped models because they are difficult to set up and require properly formatted spatial inputs. To facilitate the use of models in conjunction with remote sensing observations, we developed an online spatial database that rapidly generates properly formatted modelling datasets modified by user-supplied soil burn severity maps. Although assembling spatial model inputs can be both challenging and time-consuming, the methods we developed to rapidly update these inputs in response to a natural disaster are both simple and repeatable. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.

Additional keywords: database, forest fire, forestry, hazards, hydrology.


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