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 440946
Title Including climate change projections in probabilistic flood risk assessment
Author(s) Ward, P.J.; Pelt, S.C. van; Keizer, O. de; Aerts, J.C.J.H.; Beersma, J.J.; Hurk, B.J.J.M. van den
Source Journal of Flood Risk Management 7 (2014)2. - ISSN 1753-318X - p. 141 - 151.
DOI https://doi.org/10.1111/jfr3.12029
Department(s) Earth System Science
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) klimaatverandering - overstromingen - risicoschatting - modellen - climatic change - floods - risk assessment - models - rhine basin - model - precipitation - uncertainty - simulations - decisions
Categories Climatic Change
Abstract This paper demonstrates a framework for producing probabilistic flood risk estimates, focusing on two sections of the Rhine River. We used an ensemble of six (bias-corrected) regional climate model (RCM) future simulations to create a 3000-year time-series through resampling. This was complemented with 12 global climate model (GCM)-based future time-series, constructed by resampling observed time-series of daily precipitation and temperature and modifying these to represent future climate conditions using an advanced delta change approach. We used the resampled time-series as input in the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV)-96 to simulate daily discharge and extreme discharge quantiles for return periods up to 3000 years. To convert extreme discharges to estimates of flood damage and risk, we coupled a simple inundation model with a damage model. We then fitted probability density functions (PDFs) for the RCM, GCM, and combined ensembles. The framework allows for the assessment of the probability distribution of flood risk under future climate scenario conditions. Because this paper represents a demonstration of a methodological framework, the absolute figures should not be used in decision making at this time.
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