Managing risks from extreme events will be a crucial component of climate change adaptation. In this study, we demonstrate an approach to assess future risks and quantify the benefits of adaptation options at a city-scale, with application to flood risk in Mumbai. In 2005, Mumbai experienced unprecedented flooding, causing direct economic damages estimated at almost two billion USD and 500 fatalities. scenario could see the likelihood of a 2005-like event more than double. We estimate that total losses (direct plus indirect) associated with a 1-in-100 year event could triple compared with current situation (to $690-$1,890 million USD), due to climate change alone. Continued rapid urbanisation could further increase the risk level. The analysis also demonstrates that adaptation could significantly reduce future losses; for example, estimates suggest that by improving the drainage system in Mumbai, losses associated with a 1-in-100 year flood event today could be reduced by as much as 70%. We show that assessing the indirect costs of extreme events is an important component of an adaptation assessment, both in ensuring the analysis captures the full economic benefits of adaptation and also identifying options that can help to manage indirect risks of disasters. For example, we show that by extending insurance to 100% penetration, the indirect effects of flooding could be almost halved. We conclude that, while this study explores only the upper-bound climate scenario, the risk-assessment core demonstrated in this study could form an important quantitative tool in developing city-scale adaptation strategies. We provide a discussion of sources of uncertainty and risk-based tools could be linked with decision-making approaches to inform adaptation plans that are robust to climate change.

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.