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Low-cost space-borne data for inundation modelling: topography, flood extent and water level: UNESCO-IHE PhD Thesis [Pehme köide]

(UNESCO-IHE Institute for Water Education, Delft, The Netherlands)
  • Formaat: Paperback / softback, 134 pages, kõrgus x laius: 240x170 mm, kaal: 272 g
  • Sari: IHE Delft PhD Thesis Series
  • Ilmumisaeg: 29-Oct-2015
  • Kirjastus: CRC Press
  • ISBN-10: 1138028754
  • ISBN-13: 9781138028753
Teised raamatud teemal:
Low-cost space-borne data for inundation modelling: topography, flood extent and water level: UNESCO-IHE PhD ThesisSuurem pilt
  • Formaat: Paperback / softback, 134 pages, kõrgus x laius: 240x170 mm, kaal: 272 g
  • Sari: IHE Delft PhD Thesis Series
  • Ilmumisaeg: 29-Oct-2015
  • Kirjastus: CRC Press
  • ISBN-10: 1138028754
  • ISBN-13: 9781138028753
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781138028753.html
Märksõnad:
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.
Summary vi
Samenvatting vii
Contents x
Chapter 1 Introduction
1(32)
1.1 Background
2(2)
1.2 Globally freely available topography as input data for hydraulic modelling
4(10)
1.2.1 Error characteristics of SRTM and its global assessment
5(4)
1.2.2 SRTM application in hydraulic modelling
9(2)
1.2.3 Other freely available or low-cost global DEMs
11(2)
1.2.4 River width and depth database
13(1)
1.3 Satellite imagery and remotely sensed water levels in hydraulic modelling
14(10)
1.3.1 Low-cost satellite imagery for flood extent
14(8)
1.3.2 Freely available radar altimetry for water level
22(2)
1.4 Uncertainties and probabilistic flood mapping
24(2)
1.5 Objectives
26(1)
1.6 Methodology
26(6)
1.6.1 Research approach
26(2)
1.6.2 Modelling tools
28(3)
1.6.3 Model performance measures
31(1)
1.7 Outline of this thesis
32(1)
Chapter 2 Inundation modelling of a medium river: SRTM topography and ERS-2 flood extent
33(14)
2.1 Introduction
34(1)
2.2 Study site and data availability
35(2)
2.3 Hydraulic modelling
37(1)
2.4 The effect of topography resolution
38(1)
2.5 Uncertainty analysis within a Monte Carlo framework
39(1)
2.6 Results and discussion
40(5)
2.7 Conclusions
45(2)
Chapter 3 Inundation modelling of a medium-to-large river: SRTM topography and ENVISAT flood extent
47(38)
3.1 Introduction
48(2)
3.2 Study site and data availability
50(1)
3.3 Hydraulic modelling
50(2)
3.4 Model calibration
52(3)
3.5 Model evaluation
55(1)
3.6 Uncertainty analysis within a Monte Carlo framework
55(4)
3.6.1 Parameter uncertainty
57(1)
3.6.2 Inflow uncertainty
58(1)
3.6.3 Combined uncertainty
58(1)
3.7 Results and discussion
59(3)
3.8 Conclusions
62(23)
Chapter 4 Inundation modelling of a large river: SRTM topography and ENVISAT altimetry
85
4.1 Introduction
66(1)
4.2 Study site and data availability
67(2)
4.3 Hydraulic Modelling
69(1)
4.4 Model calibration
70(2)
4.5 Model evaluation
72(1)
4.6 Results and discussion
72(3)
4.7 Conclusions
75(2)
Chapter 5 SRTM-based inundation modelling of a large river in data-scarce areas: regional versus physically-based methods
77(10)
5.1 Introduction
78(1)
5.1.1 Regional envelope curve
78(1)
5.1.2 Physical model cascade
78(1)
5.2 Study site and data availability
79(1)
5.3 Design flood estimation
80(1)
5.3.1 Design flood derived from REC
80(1)
5.3.2 Design flood derived from PMC
81(1)
5.4 Hydraulic modelling
81(1)
5.5 Results and discussion
82(4)
5.6 Conclusions
86(1)
Chapter 6 Synthesis, conclusions and future research
87(15)
6.1 Synthesis
88(6)
6.1.1 DEM resolution and accuracy
88(1)
6.1.2 Treatment of other sources of uncertainty
89(1)
6.1.3 Equifinality and data-model relation
90(1)
6.1.4 Flood frequency and micro-topography
91(1)
6.1.5 Absence of in-channel data
91(1)
6.1.6 Modelling purpose matters
92(2)
6.2 Conclusions in brief
94(1)
6.2.1 Main conclusions
94(1)
6.2.2 Specific findings
94(1)
6.3 Recommendations
95(1)
6.4 Data from the future satellite missions
96(3)
6.4.1 TanDEM-X
96(1)
6.4.2 Sentinel-1
97(1)
6.4.3 SWOT
98(1)
6.4 Future research
99(3)
References 102(15)
Acknowledgements 117(2)
About the author 119
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.