Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, Wisconsin, USA
Luke A. McGuire
Department of Geosciences, University of Arizona, Tucson, Arizona, USA
Palak Patel
Data Intensive Studies Center, Tufts University, Medford, Massachusetts, USA
Abani Patra
Data Intensive Studies Center, Tufts University, Medford, Massachusetts, USA
E. Bruce Pitman
Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York, USA
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3,352
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189
3,900
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PDF: 359
XML: 189
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Cumulative views and downloads
(calculated since 09 Apr 2025)
Total article views: 1,593 (including HTML, PDF, and XML)
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BibTeX
EndNote
1,065
359
169
1,593
87
182
160
HTML: 1,065
PDF: 359
XML: 169
Total: 1,593
Supplement: 87
BibTeX: 182
EndNote: 160
Views and downloads (calculated since 14 Apr 2026)
Cumulative views and downloads
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Total article views: 2,307 (including HTML, PDF, and XML)
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2,287
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20
2,307
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Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 3,900 (including HTML, PDF, and XML)
Thereof 3,839 with geography defined
and 61 with unknown origin.
Total article views: 1,593 (including HTML, PDF, and XML)
Thereof 1,536 with geography defined
and 57 with unknown origin.
Total article views: 2,307 (including HTML, PDF, and XML)
Thereof 2,303 with geography defined
and 4 with unknown origin.
Fire in steep landscapes increases the potential for debris flows that can develop during intense rainstorms. To explore possible debris flow hazards, we utilize a computational model of the physical processes of debris flow initiation and runout. Such process-based models are computationally intensive and of limited use in rapid hazard assessments. Thus we build statistical surrogate of these physical models to examine how inundation footprints vary with rainfall intensity and time since fire.
Fire in steep landscapes increases the potential for debris flows that can develop during...