A probabilistic approach to post-wildfire debris-flow volume modeling

A probabilistic approach to post-wildfire debris-flow volume modeling As populations continue to move into more mountainous terrain, a greater understanding of the processes controlling debris flows has become important for the protection of human life and property. The potential volume of an expected debris flow must be known to effectively mitigate any hazard it may pose, yet an accurate estimate of this parameter has to this point been difficult to model. To this end, a probabilistic method for the prediction of debris flow volumes using a database of 1351 yield rate measurements from 33 post-wildfire, runoff-generated debris flows in the Western USA is presented herein. A number of geomorphological, climatic, and geotechnical basin characteristics were considered for inclusion in the model, and correlation analysis was conducted to identify those with the greatest influence on debris flow yield rates. Groupings within the database were then clustered based on their similarity levels; a total of six clusters were identified with similar slope angle and burn intensity characteristics. For each of these six clusters, a probability density function detailing the distribution of yield rates within the cluster was developed. The model uses a Monte Carlo simulation to combine each of these distributions into a single probabilistic model for any basin in which a debris flow is expected to occur. This approach was validated by applying the model to ten basins that experienced debris flows of known volumes throughout the Western USA. The model predicted nine of the ten debris flow volumes to within the 95% confidence interval of the final distribution; a regression analysis for the ten volumes resulted in an R 2 of 0.816. These results compared favorably with those generated by an existing volume model. This approach provides accurate results based on easily obtainable data, encouraging widespread use in land planning and development. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Landslides Springer Journals

A probabilistic approach to post-wildfire debris-flow volume modeling

Loading next page...
 
/lp/springer_journal/a-probabilistic-approach-to-post-wildfire-debris-flow-volume-modeling-4cweIgYojl
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Earth Sciences; Natural Hazards; Geography, general; Agriculture; Civil Engineering
ISSN
1612-510X
eISSN
1612-5118
D.O.I.
10.1007/s10346-016-0786-3
Publisher site
See Article on Publisher Site

Abstract

As populations continue to move into more mountainous terrain, a greater understanding of the processes controlling debris flows has become important for the protection of human life and property. The potential volume of an expected debris flow must be known to effectively mitigate any hazard it may pose, yet an accurate estimate of this parameter has to this point been difficult to model. To this end, a probabilistic method for the prediction of debris flow volumes using a database of 1351 yield rate measurements from 33 post-wildfire, runoff-generated debris flows in the Western USA is presented herein. A number of geomorphological, climatic, and geotechnical basin characteristics were considered for inclusion in the model, and correlation analysis was conducted to identify those with the greatest influence on debris flow yield rates. Groupings within the database were then clustered based on their similarity levels; a total of six clusters were identified with similar slope angle and burn intensity characteristics. For each of these six clusters, a probability density function detailing the distribution of yield rates within the cluster was developed. The model uses a Monte Carlo simulation to combine each of these distributions into a single probabilistic model for any basin in which a debris flow is expected to occur. This approach was validated by applying the model to ten basins that experienced debris flows of known volumes throughout the Western USA. The model predicted nine of the ten debris flow volumes to within the 95% confidence interval of the final distribution; a regression analysis for the ten volumes resulted in an R 2 of 0.816. These results compared favorably with those generated by an existing volume model. This approach provides accurate results based on easily obtainable data, encouraging widespread use in land planning and development.

Journal

LandslidesSpringer Journals

Published: Jan 11, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off