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Integration of RADARSAT and GIS modelling for estimating future Red River flood risk
Michael Stephen Chubey and Salah Hathout
Vol. 59, No. 3, Systems Modelling Across Geography's Interface (2004), pp. 237-246
Published by: Springer
Stable URL: http://www.jstor.org/stable/41147846
Page Count: 10
You can always find the topics here!Topics: Floods, Flood predictions, Pixels, Logistic regression, Markov models, Modeling, Remote sensing, Rivers, Hydrological modeling, Spatial models
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A new geomatics-based approach for flood prediction was developed and used to model the magnitude and spatial extent of a future Red River flood in southern Manitoba. This approach combines the statistical modelling capabilities of Markov (non-spatial) analysis and logistic regression (spatial) within a geographic information system (GIS) environment, utilizing modelling inputs derived from remotely sensed RADARS AT imagery and other digital geographic data. The 1997 Red River flood was the second largest in recorded history, and the largest for which accurate data are available. The results indicate: (i) a flood "one time interval-in terms of 3 days time unit measurement-larger in area" than the 1997 flood is expected to affect 17.6% more land (an additional 47.6 km 2 ) within the study area compared to 1997 levels based on Markovian probability derived from observations from the 1997 event; and (ii) the majority of this excess flooding will take place on agricultural land; no additional communities are expected to be at risk. Quantitative assessment verified the capability of this modelling approach for producing statistically significant results. The methodology used in this research would be easily transferable to other areas, and may provide the basis for a viable alternative to conventional hydrologic-based flood prediction approaches
GeoJournal © 2004 Springer