A great number of glacial lakes have appeared in many mountain regions across the world during the last half-century due to receding of glaciers and global warming. In the present study, glacial lake outburst flood (GLOF) risk assessment has been carried out in the Teesta river basin located in the Sikkim state of India. First, the study focuses on accurate mapping of the glaciers and glacial lakes using multispectral satellite images of Landsat and Indian Remote Sensing satellites. For glacier mapping, normalized difference snow index (NDSI) image and slope map of the area have been utilized. NDSI approach can identify glaciers covered with clean snow but debris-covered glaciers cannot be mapped using NDSI method alone. For the present study, slope map has been utilized along with the NDSI approach to delineate glaciers manually. Glacial lakes have been mapped by supervised maximum likelihood classification and normalized difference water index followed by manual editing afterwards using Google Earth images. Second, the first proper inventory of glacial lakes for Teesta basin has been compiled containing information of 143 glacial lakes. Third, analysis of these lakes has been carried out for identification of potentially dangerous lakes. Vulnerable lakes have been identified on the basis of parameters like surface area, position with respect to parent glacier, growth since 2009, slope, distance from the outlet of the basin, presence of supraglacial lakes, presence of other lakes in downstream, condition of moraine, condition of the terrain around them, etc. From these criterions, in total, 18 lakes have been identified as potentially dangerous glacial lakes. Out of these 18 lakes, further analysis has been carried out for the identification of the most vulnerable lake. Lake 140 comes out to be the most vulnerable for a GLOF event. Lastly, for this potentially dangerous lake, different dam break parameters have been generated using satellite data and digital elevation model. The volume and depth have been computed using empirical formulae, and other parameters such as cross-sections from the lake to outlet etc. have been prepared in ArcGIS 9.3. The GLOF which can be triggered by Lake 140 was modelled and simulated using MIKE-11 software's hydrodynamic module. As a result, flood values and hydrograph have been obtained. The flood at lake site comes out to be 2611.136 cumec which get mitigated to 1417.844 cumec at the outlet.
After a flood event there is a need to delineate the hazard footprint as quickly as possible in order to assess the magnitude of losses and to plan for the relief operations. Delineation of such hazard footprint is generally hindered by the lack of geospatial data, technology and related software packages. This paper demonstrates the use of open source data and software packages which can be used to implement most recent technology available for flood hazard footprint delineation. This study utilizes open source software packages and web applications like Geographic Resource Analysis Support System, Quantum geographic information system and Google Earth to implement a complete process of hazard mapping using remotely sensed data which include preprocessing, mapping (both hazard and exposure) and accuracy assessment. In this study, Brisbane flood event of 2011 has been taken as a case study. For built-up extraction, the Landsat 7-band image has been transformed to a stack of 3-band image using vegetation, water and built-up indices. It has been observed by scattergram analysis that these transformations make vegetation, water and builtup classes more separable. Built-up area has been delineated using supervised maximum likelihood classification on the new 3-band image. For flood hazard mapping, thresholding of near-infrared band has been utilized along with the assistance of mid-infrared band to discriminate water from built-up classes. After delineating both exposure and hazard map, final risk map due to flood event has been generated to assess the urban exposure under the flood hazard impact.
The present study demonstrates the use of a new approach for delineating the accurate flood hazard footprint in the urban regions. The methodology involves transformation of Landsat Thematic Mapper (TM) imagery to a three-dimensional feature space, i.e. brightness, wetness and greenness, then a change detection technique is used to identify the areas affected by the flood. Efficient thresholding of the normalized difference image generated during change detection has shown promising results in identifying the flood extents which include standing water due to flood, sediment-laden water and wetness caused by the flood. Prior to wetness transformations, dark object subtraction has been used in lower wavelengths to avoid errors due to scattering in urban areas. The study shows promising results in eliminating most of the problems associated with urban flooding, such as misclassification due to presence of asphalt, scattering in lower wavelengths and delineating mud surges. The present methodology was tested on the 2010 Memphis flood event and validated on Queensland floods in 2011. The comparative analysis was carried out with the widely-used technique of delineating flood extents using thresholding of near infrared imagery. The comparison demonstrated that the present approach is more robust towards the error of omission in flood mapping. Moreover, the present approach involves less manual effort and is simpler to use.
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