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A storm surge inundation model of the northern Bay of Bengal using publicly available data
Author(s) -
Lewis Matt,
Bates Paul,
Horsburgh Kevin,
Neal Jeff,
Schumann Guy
Publication year - 2013
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2040
Subject(s) - shuttle radar topography mission , storm surge , environmental science , coastal flood , flood myth , digital elevation model , hindcast , storm , forcing (mathematics) , terrain , hydrology (agriculture) , meteorology , geology , climatology , climate change , remote sensing , geography , oceanography , sea level rise , cartography , geotechnical engineering , archaeology
A computationally inexpensive inundation model has been developed from freely available data sources for the northern Bay of Bengal region to estimate flood risk from storm surges. This is the first time Shuttle Radar Topography Mission (SRTM) terrain data have been used in a dynamic coastal inundation model. To reduce SRTM noise, and the impact of vegetation artefacts on the ground elevation, the SRTM data were up‐scaled from their native 90 m resolution to 900 m. A sub‐grid routine allowed estuary channels with widths less than this resolution to be simulated efficiently, and allowed six major river flows to be represented. The inundation model was forced with an IIT‐D model hindcast of the 2007 cyclone Sidr flood event, using parameters from two cyclone databases (IBTrACs and UNISYS). Validation showed inundation prediction accuracy with a root mean squared error (RMSE) on predicted water level of ∼ 2 m, which was of the same order of magnitude as the forcing water‐level uncertainties. Therefore, SRTM and other publicly available data can be useful for coastal flood risk management in data‐poor regions, although the associated uncertainty needs to be expressed to end users. Better SRTM processing techniques may improve inundation model performance, and future work should also seek to improve storm tide uncertainties in this region. Copyright © 2012 Royal Meteorological Society