Exploring the potential of SRTM topographic data for flood inundation modelling under uncertainty
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作者:
Yan, Kun
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UNESCO IHE, Inst Water Educ, Dept Integrated Water Syst & Governance, NL-2601 DA Delft, NetherlandsUNESCO IHE, Inst Water Educ, Dept Integrated Water Syst & Governance, NL-2601 DA Delft, Netherlands
Yan, Kun
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Di Baldassarre, Giuliano
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UNESCO IHE, Inst Water Educ, Dept Integrated Water Syst & Governance, NL-2601 DA Delft, NetherlandsUNESCO IHE, Inst Water Educ, Dept Integrated Water Syst & Governance, NL-2601 DA Delft, Netherlands
Di Baldassarre, Giuliano
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Solomatine, Dimitri P.
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机构:
[1] UNESCO IHE, Inst Water Educ, Dept Integrated Water Syst & Governance, NL-2601 DA Delft, Netherlands
[2] Delft Univ Technol, Water Resources Sect, NL-2600 AA Delft, Netherlands
The desirable data for model building and calibration to support the decision-making process in flood risk management are often not sufficient or unavailable. A potential opportunity is now offered by global remote sensing data, which can be freely (or at low cost) obtained from the internet, for example, Shuttle Radar Topography Mission (SRTM) topography. There is a general sense that inundation modelling performance will be degraded by using SRTM topography data. However, the actual effectiveness and usefulness of SRTM topography is still largely unexplored. To overcome this lack of knowledge, we have explored the value of SRTM topography to support flood inundation modelling under uncertainty. The study was performed on a 98 km reach of the River Po in northern Italy. The comparison between a hydraulic model based on high-quality topography and one based on SRTM topography was carried out by explicitly considering other sources of uncertainty (besides topography inaccuracy) that unavoidably affect hydraulic modelling, such as parameter and inflow uncertainties. The results of this study showed that the differences between the high-resolution topography-based model and the SRTM-based model are significant, but within the accuracy that is typically associated with large-scale flood studies.