A method for estimating the socioeconomic impact of Earth observations in wildland fire suppression decisions
Vincent Herr A E , Adam K. Kochanski B , Van V. Miller C , Rich McCrea D , Dan O’Brien D and Jan Mandel A
+ Author Affiliations
- Author Affiliations
A Department of Mathematical and Statistical Sciences, University of Colorado Denver, 1201 Larimer Street, Denver, CO 80204, USA.
B Department of Atmospheric Sciences, University of Utah, 135 S 1460 E, Salt Lake City, UT 84112, USA.
C Michigan Tech Research Institute, 3600 Green Court Ste. 100, Ann Arbor, MI 48105, USA.
D Wildland Fire Associates, 2006 Saint Clair Avenue, Brentwood, MO 63144, USA.
E Corresponding author. Email: vincent.herr@ucdenver.edu
International Journal of Wildland Fire 29(3) 282-293 https://doi.org/10.1071/WF18237
Submitted: 29 December 2018 Accepted: 20 December 2019 Published: 28 January 2020
Abstract
A method for estimating the socioeconomic impact of Earth observations is proposed and deployed. The core of the method is the analysis of outcomes of hypothetical fire suppression scenarios generated using a coupled atmosphere–fire behaviour model, based on decisions made by an experienced wildfire incident management team with and without the benefits of MODIS (Moderate Resolution Imaging Spectroradiometer) satellite observations and the WRF-SFIRE wildfire behaviour simulation system. The scenarios were based on New Mexico’s 2011 Las Conchas fire. For each scenario, fire break line location decisions served as inputs to the model, generating fire progression outcomes. Fire model output was integrated with a property database containing thousands of coordinates and property values and other asset values to estimate the total losses associated with each scenario. An attempt to estimate the socioeconomic impact of satellite and modelling data used during the decision-making process was made. We analysed the impact of Earth observations and include considerations for estimating other socioeconomic impacts.
Additional keywords: fire economics, fire management modelling, fire simulation modelling, remote sensing, socioeconomic analysis.
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