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Title: Flood risk curve development with probabilistic rainfall modelling and large ensemble climate simulation data: A case study for the Yodo river basin
Authors: Tanaka, Tomohiro  kyouindb  KAKEN_id
Tachikawa, Yasuto  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-1647-8899 (unconfirmed)
Ichikawa, Yutaka  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-1269-2975 (unconfirmed)
Yorozu, Kazuaki  kyouindb  KAKEN_id
Author's alias: 立川, 康人
市川, 温
萬, 和明
Keywords: flood risk curve
ensemble projection
d4PDF
climate change
Issue Date: 2018
Publisher: 水文・水資源学会/日本地下水学会/日本水文科学会/陸水物理研究会
Journal title: Hydrological Research Letters
Volume: 12
Issue: 4
Start page: 28
End page: 33
Abstract: A flood risk curve is the relation between annual maximum economic damage due to floods and its exceedance probability, which provides useful information for quantitative flood risk assessment. This study proposed to examine the applicability of d4PDF, a large ensemble climate projection dataset, to develop a probabilistic flood risk curve for the Yodo River basin (8, 240 km²), Japan. The d4PDF is a climate dataset under historical and 4 K increase conditions with tens of ensembles and provide a physically-based and reliable estimation of ensemble flood risk curves and their future changes. We identified that d4PDF rainfall data has bias for the spatial variability of rainfall probably due to coarse spatial resolution, while not for basin-averaged rainfall. This typical type of bias was removed by incorporating basin-averaged rainfall of d4PDF and observed spatial pattern of rainfall into analytically-based probabilistic rainfall modelling. Derived ensemble flood risk curves provided a histogram of T-year flood damage. The histogram had a long tail and showed that T-year flood damage may be larger than its deterministic estimate located at the median. Estimated ensemble flood risk curves at present/future climates showed a clear increase of flood risk and its uncertainty at 4 K increase scenario.
Rights: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
URI: http://hdl.handle.net/2433/241090
DOI(Published Version): 10.3178/hrl.12.28
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