scholarly journals Debris Flow Susceptibility Map for Mount Rainier, Washington Based on Debris Flow Initiation Zone Characteristics from the November, 2006 Climate Event in the Cascade Mountains

2000 ◽  
Author(s):  
Kassandra Lindsey
2021 ◽  
Author(s):  
Laurie Jayne Kurilla ◽  
Giandomenico Fubelli

Abstract Debris flows, and landslides in general, are worldwide catastrophic phenomena. As world population and urbanization grow in magnitude and geographic coverage, the need exists to extend focus, research, and modeling to a continental and global scale.Although debris flow behavior and parameters are local phenomena, sound generalizations can be applied to debris flow susceptibility analyses at larger geographic extents based on these criteria. The focus of this research is to develop a global debris flow susceptibility map by modeling at both a continental scale for all continents and by a single global model and determine whether a global model adequately represents each continent. Probability Density, Conditional Probability, Certainty Factor, Frequency Ratio, and Maximum Entropy statistical models were developed and evaluated for best model performance using fourteen environmental factors generally accepted as the most appropriate debris flow predisposing factors. Global models and models for each continent were then developed and evaluated against verification data. The comparative analysis demonstrates that a single global model performs comparably or better than individual continental models for a majority of the continents, resulting in a debris flow susceptibility map of the world useful in international planning, and future debris flow susceptibility modeling for determining societal impacts.


Geomorphology ◽  
2014 ◽  
Vol 226 ◽  
pp. 249-260 ◽  
Author(s):  
Nicholas T. Legg ◽  
Andrew J. Meigs ◽  
Gordon E. Grant ◽  
Paul Kennard

2021 ◽  
Author(s):  
Laurie Jayne Kurilla ◽  
Giandomenico Fubelli

Abstract. In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows, demonstrates the impact of uncertainty in defining appropriate predisposing factors, and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented, and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Five Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points, and localized distribution, and hence susceptibility results likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent and context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility of the continent of Europe. A novel representation of the uncertainty is expressed, and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.


2006 ◽  
Vol 33 (10) ◽  
pp. n/a-n/a ◽  
Author(s):  
Fumitoshi Imaizumi ◽  
Roy C. Sidle ◽  
Satoshi Tsuchiya ◽  
Okihiro Ohsaka

2016 ◽  
Vol 9 (3) ◽  
pp. 91-100 ◽  
Author(s):  
Fumitoshi IMAIZUMI ◽  
Satoshi TSUCHIYA ◽  
Okihiro OHSAKA

2016 ◽  
Author(s):  
Jordan Carey ◽  
◽  
Nicholas Pinter ◽  
Andrew L. Nichols

2021 ◽  
Vol 106 (1) ◽  
pp. 881-912
Author(s):  
Jingbo Sun ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Yang Chen ◽  
Gang Su ◽  
...  

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