Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 510903
Title Uncertainties in Predicting Debris Flow Hazards Following Wildfire
Author(s) Hyde, K.D.; Riley, Karin; Stoof, C.R.
Source In: Natural Hazard Uncertainty Assessment: Modeling and Decision Support / Riley, Karin, Webley , Peter, Thompson, Matthew, American Geophysical Union (Geophysical Monograph Series ) - ISBN 9781119027867 - p. 287 - 299.
DOI https://doi.org/10.1002/9781119028116.ch19
Department(s) Soil Geography and Landscape
PE&RC
Publication type Peer reviewed book chapter
Publication year 2016
Abstract Wildfire increases the probability of debris flows posing hazardous conditions where values-at-risk exist downstream of burned areas. Conditions and processes leading to postfire debris flows usually follow a general sequence defined here as the postfire debris flow hazard cascade: biophysical setting, fire processes, fire effects, rainfall, debris flow, and values-at-risk. Prediction of postfire debris flow hazards is a problem of identifying and understanding the spatial and temporal interactions within this cascade. This chapter summarizes present knowledge of the processes involved in this postfire debris flow hazard cascade and identify uncertainties in terms of knowledge gaps, contradictions in current process understanding, stochastic system variables, and limits to data to support hazard prediction. Understanding these uncertainties can improve delineation of areas threatened by postfire debris flows, can guide future research, and, when addressed, contribute to development of comprehensive and robust modeling and prediction systems that may ultimately reduce threats to values-at-risk.
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