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Informationen zum Autor Kun Yan was born on 31st December 1985, in Bengbu, Anhui Province, China. Kun received his Bachelor degree from the College of Hydrology and Water Resourses, Hohai University in May 2008. He then enrolled in the Master program of Ecohydrology at Hohai University. A year later, he moved to the Netherlands and joined the Master program of Hydroinformatics of UNESCO-IHE. He started his PhD at UNESCO-IHE in July 2011, and after two months obtained his MSc degree. His PhD topic is the integration of low-cost space-borne data into hydraulic modelling of floods. Kun was also involved in the EC FP7 KULTURisk project, which aims at developing a culture of risk prevention for natural disasters including floods. Kun`s research interests including remote sensing, flood inundation modelling and uncertainty. He is now an advisor/researcher at Deltares. Klappentext This thesis explores the potential and limitations of low-cost, space-borne data in flood inundation modelling under unavoidable, intrinsic uncertainty. In particular, the potential in supporting hydraulic modelling of floods of: NASA's SRTM topographic data, SAR satellite imagery of flood extents and radar altimetry of water levels are analyzed in view of inflow and parametric uncertainty. Three river reaches with various scales (from medium to large) and topographic characteristics (e.g. valley-filling, two-level embankments, large and flat floodplain) are used as test sites. In conclusion, the usefulness of this data is discussed. Zusammenfassung This thesis aims to explore the potential and limitations of low-cost, space-borne data in flood inundation modelling under unavoidable, intrinsic uncertainty. In particular, the potential in supporting hydraulic modelling of floods of: NASA’s SRTM (Shuttle Radar Topographic Mission) topographic data, SAR (Synthetic Aperture Radar) satellite imagery of flood extents and radar altimetry of water levels are analyzed in view of inflow and parametric uncertainty. To this end, research work has been carried out by either following a model calibration-evaluation approach or by explicitly considering major sources of uncertainty within a Monte Carlo framework. To generalize our findings, three river reaches with various scales (from medium to large) and topographic characteristics (e.g. valley-filling, two-level embankments, large and flat floodplain) are used as test sites. Lastly, an application of SRTM-based flood modelling of a large river is conducted to highlight the challenges of predictions in ungauged basins. This research indicates the potential and limitations of low-cost, space-borne data in supporting flood inundation modelling under uncertainty, including findings related to the usefulness of these data according to modelling purpose (e.g. re-insurance, planning, design), characteristics of the river and considerations of uncertainty. The upcoming satellite missions, which could potentially impact the way we model flood inundation patters, are also discussed. Inhaltsverzeichnis 1 Introduction; 2 Inundation modelling of a medium river: SRTM topography and ERS-2 flood extent; 3 Inundation modelling of a medium-to-large river: SRTM topography and ENVISAT flood extent; 4 Inundation modelling of a large river: SRTM topography and ENVISAT altimetry; 5 SRTM-based inundation modelling of a large river in data-scarce areas: regional versus physically-based methods; 6 Synthesis, conclusions and future research...
