Abstract
ABSTRACTThe aim of the present study is motivated to build an inundation library for a range of gauge heights, which can be used by decision-makers to anticipate the likely extent of inundation and provide quick response towards warning the habitation at threat. In the present study, two approaches for developing a series of static flood-inundation extent libraries for a range of potential flood levels using historical satellite images, gauge heights and digital elevation model (DEM) are demonstrated. First method is based on the geotagging of gauge height data with corresponding satellite observed inundation extent and the other method supplements the first method in the absence of adequate satellite data-sets by simulating inundation using gauge data and DEM for a range of gauge heights. Simulated inundation extents are validated with the satellite-derived reference inundation extents using spatial statistics, which measure the correspondence between the estimated and observed occurrence of events like probability of detection (POD), false-alarm ratio, and critical success index (CSI). A good correlation between the simulated inundation and satellite-derived inundation extents, with POD varying between 87% and 94%, CSI between 75% and 80% is observed.
Highlights
Floods are the most frequent and catastrophic natural disasters occurring worldwide (Berz et al 2001; ISDR 2004) and affecting societies and economies globally (Schumann & Moller 2015)
A river is said to be in flood when its gauge height touches or exceeds danger level (DL) at that particular site; when the gauge height is 1 m or more above DL, it is known as major flood (Dhar & Nandargi 2000; Nandargi & Dhar 2003)
If warning in the form of gauge height values can be translated to spatial dimension, decision-makers can better anticipate and visualize the possible inundation due to flood disaster and take steps to alert and evacuate the population likely to get affected at different gauge height forecasts
Summary
Floods are the most frequent and catastrophic natural disasters occurring worldwide (Berz et al 2001; ISDR 2004) and affecting societies and economies globally (Schumann & Moller 2015). Global annual average losses occurring from flood disaster is assessed to be about US$104 billion (GAR 2015). In the last decade of the twentieth century, about 100,000 persons are reported to have lost their life and about 1.4 billion people are estimated to have been affected due to flood disasters, (Jonkman 2005). Flood disasters have maximum impact on the poorest people in the developing countries. Due to high population density, poor infrastructure development, poverty and illiteracy, the developing countries are more vulnerable and less equipped to withstand the damaging effects of the hazards (Irasema 2002). Due to the scarcity of hydrological observations from well-distributed network of gauge stations and poor availability of fine resolution digital elevation model (DEM) (Sanyal & Lu 2005; Sanyal & Lu 2006), land use and land cover information, software for inundation modelling and technically trained manpower to run the model and interpret the results
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