Can Atmospheric Reanalysis Data Sets Be Used to Reproduce Flooding Over Large Scales?

Kostantinos Andreadis, Guy Schumann, Dimitrios Stampoulis, Paul Bates, G. Robert Brackenridge, Albert Kettner

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)
537 Downloads (Pure)

Abstract

Floods are costly to global economies and can be exceptionally lethal. The ability to produce consistent flood hazard maps over large areas could provide a significant contribution to reducing such losses, as the lack of knowledge concerning flood risk is a major factor in the transformation of river floods into flood disasters. In order to accurately reproduce flooding in river channels and floodplains, high spatial resolution hydrodynamic models are needed. Despite being computationally expensive, recent advances have made their continental to global implementation feasible, although inputs for long-term simulations may require the use of reanalysis meteorological products especially in data-poor regions. We employ a coupled hydrologic/hydrodynamic model cascade forced by the 20CRv2 reanalysis data set and evaluate its ability to reproduce flood inundation area and volume for Australia during the 1973–2012 period. Ensemble simulations using the reanalysis data were performed to account for uncertainty in the meteorology and compared with a validated benchmark simulation. Results show that the reanalysis ensemble capture the inundated areas and volumes relatively well, with correlations for the ensemble mean of 0.82 and 0.85 for area and volume, respectively, although the meteorological ensemble spread propagates in large uncertainty of the simulated flood characteristics.
Original languageEnglish
Number of pages9
JournalGeophysical Research Letters
Early online date25 Oct 2017
DOIs
Publication statusE-pub ahead of print - 25 Oct 2017

Keywords

  • flood
  • reanalysis
  • modeling

Fingerprint Dive into the research topics of 'Can Atmospheric Reanalysis Data Sets Be Used to Reproduce Flooding Over Large Scales?'. Together they form a unique fingerprint.

Cite this