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A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation DataWildfire is a global phenomenon that has dramatic effects on erosion and flood potential. On steep slopes, burned areas are more likely to experience significant overland flow during heavy rainfall leading to post fire debris flows (PFDFs). Previous work establishes methods for PFDF hazard assessment, often relying on regional-scale parameterizations with in-situ rainfall measurements to categorize hazard as a function of meteorological and surface properties. We present a globally scalable approach to extend the benefit these models provide to new areas. Our new model relies on publicly available satellite-based inputs with a global extent to provide first order hazard assessments of recently burned areas. Our results show it is possible to identify the conditions relevant for PFDF-initiation processes across a variety of physiographic settings. Improvements to satellite borne rainfall intensity data and increased availability of PFDF occurrence data worldwide are expected to enhance model skill and applicability further.
Document ID
20220015050
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Elijah Orland ORCID
(University of Maryland, Baltimore County Baltimore, Maryland, United States)
Dalia Kirschbaum ORCID
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Thomas Stanley
(University of Maryland, Baltimore County Baltimore, Maryland, United States)
Date Acquired
October 5, 2022
Publication Date
September 16, 2022
Publication Information
Publication: Geophysical Research Letters
Publisher: AGU
Volume: 49
Issue: 18
Issue Publication Date: September 28, 2022
ISSN: 0094-8276
e-ISSN: 1944-8007
Subject Category
Earth Resources and Remote Sensing
Funding Number(s)
WBS: 346751.02.01.01.58
CONTRACT_GRANT: NNH18ZDA001N
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
External Peer Committee
Keywords
Wildfire
Debris Flows
Mass Wasting
Remote Sensing
Machine Learning
IMERG
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