A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data
Abstract Wildfire 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...
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| Format: | Article |
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Wiley
2022-09-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2022GL099850 |
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| author | Elijah Orland Dalia Kirschbaum Thomas Stanley |
| author_facet | Elijah Orland Dalia Kirschbaum Thomas Stanley |
| author_sort | Elijah Orland |
| collection | DOAJ |
| description | Abstract Wildfire 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. |
| format | Article |
| id | doaj-art-fb42533394dc4e99b5c2e1b4694852e2 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-fb42533394dc4e99b5c2e1b4694852e22025-08-20T02:10:43ZengWileyGeophysical Research Letters0094-82761944-80072022-09-014918n/an/a10.1029/2022GL099850A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation DataElijah Orland0Dalia Kirschbaum1Thomas Stanley2University of Maryland Baltimore County GESTAR II Baltimore MD USAEarth Sciences Division NASA Goddard Space Flight Center Greenbelt MD USAUniversity of Maryland Baltimore County GESTAR II Baltimore MD USAAbstract Wildfire 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.https://doi.org/10.1029/2022GL099850wildfiredebris flowsmass wastingremote sensingmachine learningIMERG |
| spellingShingle | Elijah Orland Dalia Kirschbaum Thomas Stanley A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data Geophysical Research Letters wildfire debris flows mass wasting remote sensing machine learning IMERG |
| title | A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data |
| title_full | A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data |
| title_fullStr | A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data |
| title_full_unstemmed | A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data |
| title_short | A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data |
| title_sort | scalable framework for post fire debris flow hazard assessment using satellite precipitation data |
| topic | wildfire debris flows mass wasting remote sensing machine learning IMERG |
| url | https://doi.org/10.1029/2022GL099850 |
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