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|a Global flood modelling |h [electronic resource] |b statistical estimation of peak-flow magnitude |y English. |
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|a Geneva, Switzerland : |b UNEP/GRID-Europe ; |a [S.l.] : |b World Bank Development Research Group, |c 2006-02. |
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|a UNEP - World Bank 2006 |
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|a Herold, C., Mouton, F. (2006). Global flood modelling: statistical estimation of peak-flow magnitude. World Bank Development Research Group, United Nations Environment Programme (UNEP)/Global Resource Information Database (GRID)-Europe Early Warning Unit. |
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|a This document outlines the application of GIS technology and earthquake engineering to high resolution satellite imagery for earthquake damage assessment. Automatic and visual techniques were applied to pre- and post-earthquake images of 2-m resolution KVR-1000 and 1-m resolution IKONOS satellite sensors respectively. This study compares the quality of the resulting analyses and examines key issues associated with their implementation. The images were subjected to histogram adjustment and high frequency filtering prior to comparing the reflectances of the images. Reflectance comparisons via radiometric profiling, automatic classification, and false color composition on the contour images were found to be unreliable. Photo interpretation analysis was conducted using both mono-temporal and multi-temporal techniques. The mono-temporal technique was applied to the post-earthquake image, wherein damaged structures were visually identified. The multi-temporal technique focuses on change detection. This method can be hampered by variations in resolution and optical angle, as inconsistencies in these characteristics can distort the analysis. While the researchers had access to imagery of sufficient resolution, the pre-earthquake image was obtained 3 years prior to the event. The temporal gap limited the accuracy with which results could be obtained, as structures built since that date could have been decimated by the earthquake. These buildings would be omitted from the analysis. Images not taken immediately following an event present similar obstacles as new construction may already be underway. Human loss estimates were conducted using HAZUS-based techniques and simplified statistical engineering hypotheses. Different degrees of structural damage were matched with corresponding estimated percentages of dead and injured occupants. Physical damage assessments conducted by visual interpretation were found to be superior to automatic, software-driven methods. Conversely, HAZUS-based methods were efficient when applied to human loss estimates, as these could be obtained through statistical methods. |
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|a Abstract p. 1; Acknowledgements p. 1; 1 Introduction p. 1; 1.1 Project definition p. 1; 1.2 Choice of a global method p. 1; 1.3 Definition of Peak Flow p. 2; 2 GIS-Processing p. 2;
2.1 Discharge stations dataset p. 2; 2.2 Variables used for peak flow estimation p. 3; 2.2.1 Hydromorphometric and Land cover variables p. 3; 2.2.2 Climatic variables p. 4; 2.2.3 Climatic zones p. 5; 3 Statistical Analysis p. 5; 3.1 Peak-flow values for gauging stations p. 6; 3.2 Transformation of variables p. 6; 3.3 Descriptive analysis for North American gauging stations p. 7; 3.4 Groups, regressions and predictions p. 7; 4 Flooded area estimation p. 8; 4.1 Manning’s equation p. 8; 4.2 GIS-processing p. 8; 4.3 Calibration p. 9; 5 First test zone: North America p. 9; 5.1 GIS-processing p. 10; 5.2 Composition of groups p. 11; 5.3 Regression formulae p. 12; 5.4 Peak-flow values for ungauged sites p. 13; 5.5 Flooded area estimations p. 14; 6
Second test zone: South America p. 14; 6.1 GIS-processing p. 15; 6.2 Composition of groups p. 15; 6.3 Cross-validation between South and North America p. 16; 6.4 Test of the PLS regression p. 19; 6.5 Final regressions p. 20; 6.6 Flooded area estimations p. 20; 7 Remarks and recommendations for further studies p. 21; 7.1 Data p. 21; 7.1.1 SRTM and HYDRO1k p. 21; 7.1.2 Climatic variables p. 21; 7.1.3 Soil characteristics p. 21; 7.2 GIS-processing p. 21; 7.2.1 GRDC stations spatial selection p. 21; 7.2.2 Main channel p. 21; 7.2.3 Manning’s equation and discharge vs. stage rating curves p. 21; 7.3 Statistical analysis p. 22; 7.3.1 Composition of groups p. 22; 7.3.2 Regressions p. 22; 7.3.3 Validations p. 22; 8 Conclusion p. 22; 9 Appendixes p. 23 |
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|a Electronic reproduction. |c Florida International University, |d 2013. |f (dpSobek) |n Mode of access: World Wide Web. |n System requirements: Internet connectivity; Web browser software. |
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|a Natural hazards and disasters |x floods. |
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|a Geographic information systems. |
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|a Herold, Christian |g GIS-analyst |u UNEP/GRID-Europe. |
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|a Mouton, Dr Frédéric |g Statistician |u University of Geneva (Switzerland), Math. and University of Grenoble (France), Institut. |
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|a Disaster Risk Reduction Program, Florida International University (DRR/FIU), |e summary contributor. |
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|u http://dpanther.fiu.edu/dpService/dpPurlService/purl/FI13042535/00001 |y Click here for full text |
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|a http://dpanther.fiu.edu/sobek/content/FI/13/04/25/35/00001/FI13042535_coverthm.jpg |
