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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3,112
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Cumulative views and downloads
(calculated since 09 Apr 2025)
Total article views: 1,262 (including HTML, PDF, and XML)
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909
212
141
1,262
70
149
135
HTML: 909
PDF: 212
XML: 141
Total: 1,262
Supplement: 70
BibTeX: 149
EndNote: 135
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Cumulative views and downloads
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Total article views: 2,222 (including HTML, PDF, and XML)
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2,203
0
19
2,222
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0
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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: 3,484 (including HTML, PDF, and XML)
Thereof 3,423 with geography defined
and 61 with unknown origin.
Total article views: 1,262 (including HTML, PDF, and XML)
Thereof 1,205 with geography defined
and 57 with unknown origin.
Total article views: 2,222 (including HTML, PDF, and XML)
Thereof 2,218 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...