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Post-fire debris flow modeling analyses: case study of the post-Thomas Fire event in California

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  • Priscilla Addison

    (Michigan Technological University)

  • Thomas Oommen

    (Michigan Technological University)

Abstract

There is an increased risk in post-fire debris flow (DF) occurrences in the western USA with recent increase in wildfire frequencies. DFs are destructive, causing high loss to lives and infrastructure. A lot of effort is going into possible preventive and/or mitigation measures. Recent research efforts in this niche focus on developing statistical models that assist emergency responders in isolating high-hazard locations after fires. There are two general approaches to this statistical modeling: linear and nonlinear. This study has looked into applying a linear-based logistic regression model and a nonlinear-based C5.0 decision tree model to assess the strength of each in predicting the locations within the Thomas Fire boundary that produced DFs. To do this, DF scars were delineated by running a change detection protocol known as delta normalized difference vegetation index (dNDVI), using high spatial resolution data from Planet Labs. These scars were further validated with data gathered by Santa Barbara County officials on affected areas. Results from the two statistical models were then overlain on the delineated DF scars and compared against each other to determine predictive strengths. The results revealed both models to perform well in predicting high probabilities for locations that were shown to produce DFs. The logistic regression model predicted an overall ~ 44,800 ha (49%) more high-hazard coverage compared to the C5.0 tree and therefore showed greater urgency. However, a closer look at the basin predictions with the delineated DF scars showed that most of these high-hazard basins identified by the logistic regression did not to have any discernible scars. It was projected that most of these locations were likely false positives and further fine-tuning of the model was recommended. Further recommendation was made concerning the development of additional models to predict potential DF inundation paths. Combining origination and inundation models will be most beneficial since the greatest associated danger with DFs is not necessarily where they start, but rather further downstream where most communities and infrastructure are situated.

Suggested Citation

  • Priscilla Addison & Thomas Oommen, 2020. "Post-fire debris flow modeling analyses: case study of the post-Thomas Fire event in California," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(1), pages 329-343, January.
    • Handle: RePEc:spr:nathaz:v:100:y:2020:i:1:d:10.1007_s11069-019-03814-x
    DOI: 10.1007/s11069-019-03814-x
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    1. Karl Widerquist, 2018. "The Bottom Line," Exploring the Basic Income Guarantee, in: A Critical Analysis of Basic Income Experiments for Researchers, Policymakers, and Citizens, chapter 0, pages 93-98, Palgrave Macmillan.
    2. Ben Bond-Lamberty & Scott D. Peckham & Douglas E. Ahl & Stith T. Gower, 2007. "Fire as the dominant driver of central Canadian boreal forest carbon balance," Nature, Nature, vol. 450(7166), pages 89-92, November.
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    Cited by:

    1. Randell, Heather & Jiang, Chengsheng & Liang, Xin-Zhong & Murtugudde, Raghu & Sapkota, Amir, 2021. "Food insecurity and compound environmental shocks in Nepal: Implications for a changing climate," World Development, Elsevier, vol. 145(C).
    2. R. E. S. Moss & N. Lyman, 2022. "Incorporating shear stiffness into post-fire debris flow statistical triggering models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 913-932, September.

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