Predicting the Floods that Follow the Flames

Gourley, Jonathan J.; Vergara, Humberto; Arthur, Ami; Clark, Robert A.; Staley, Dennis M.; Fulton, John W.; Hempel, L. A.; Goodrich, David C.; Rowden, Katherine; Robichaud, Peter R.

doi:10.1175/bams-d-20-0040.1

Cited by 4 publications

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References 15 publications

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“…These processes lead to increasing risks of flash floods, severe erosion, and debris flows for local communities downstream from the affected areas [5]. Given that wildfires are increasing in size, duration, and intensity, and so has the wildland-urban interface [31], risk for wildfire effects on society can be expected to rise as well [12].…”

Section: Introductionmentioning

confidence: 99%

Wildfire burn scar encapsulation

2021

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Wildfires burn annually across the United States (US), which threaten those in close proximity to them. Due to drastic alterations of soil properties and to the land surfaces by these fires, risks of flash floods, debris flows, and severe erosion increases for these areas, which can have catastrophic consequences for biota, people and property. Computational tools, such as the WildfireRain algorithm, have been designed and implemented to assess the potential occurrence of debris flows over burn scars. However, in order to efficiently operate these tools, they require independent, non-overlapping buffers around burned areas to be defined, which is not a trivial task. In this paper we consider the problem of efficiently subsetting the conterminous US (CONUS) domain into optimal subdomains around burn scars, aiming to enable domain-wide WildfireRain product outputs to be used for operations by the National Weather Service (NWS). To achieve this, we define the Object Encapsulation Problem, where burn scars are represented by single-cell objects in a gridded domain, and circular buffers must be constructed around them. We propose a Linear Programming (LP) model that solves this problem efficiently. Optimal results produced using this model are presented for both a simplified synthetic data set, as well as for a subset of burn scars produced by severe wildfires in 2012 over the CONUS.

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Section: Introductionmentioning

confidence: 99%