Journal article
On the decadal predictability of the frequency of flood events across the U.S. Midwest
International journal of climatology, Vol.39(3), pp.1796-1804
03/15/2019
DOI: 10.1002/joc.5915
Abstract
Skilful predictions of the frequency of flood events over long lead times (e.g., from 1 to 10 years ahead) are essential for governments and institutions making near‐term flood risk plans. However, little is known about current flood prediction capabilities over annual to decadal timescales. Here we address this knowledge gap at 286 U.S. Geological Survey gaging stations across the U.S. Midwest using precipitation and temperature decadal predictions from the Coupled Model Intercomparison Project (CMIP) phase 5 models. We use the 1–10‐year predictions of precipitation and temperature as inputs to statistical models that have significant skill in reproducing inter‐annual and decadal changes in the observed frequency of flood events. Our results indicate that the limited skill of basin‐averaged precipitation predictions suppresses the skill of flood event frequency predictions, even at the shortest lead time, but downscaling and bias correction improves predictions across all lead times and especially in spring.
Details
- Title: Subtitle
- On the decadal predictability of the frequency of flood events across the U.S. Midwest
- Creators
- Andrea Neri - Sapienza University of RomeGabriele Villarini - University of IowaKaustubh A Salvi - College of Engineering, PuneLouise J Slater - University of OxfordFrancesco Napolitano - Sapienza University of Rome
- Resource Type
- Journal article
- Publication Details
- International journal of climatology, Vol.39(3), pp.1796-1804
- DOI
- 10.1002/joc.5915
- ISSN
- 0899-8418
- eISSN
- 1097-0088
- Publisher
- John Wiley & Sons, Ltd
- Number of pages
- 9
- Grant note
- Broad Agency Announcement Program (W913E5‐16‐C‐0002) Cold Regions Research and Engineering Laboratory (W913E5‐16‐C‐0002) Engineer Research and Development Center (W913E5‐16‐C‐0002) National Science Foundation (AGS‐1349827)
- Language
- English
- Date published
- 03/15/2019
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984197550702771
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