Coastal floods are one of the most common natural disasters, which affect lives and the environment. Coastal areas are prone to extreme weather events as it is highly dynamic and has geo-morphologically complex systems (Balica et al., 2012; Khan and Chatterjee, 2018). It has been estimated that about 23 percent of the world’s population lives both within 100km distance from and 100m above sea level (Small and Nicholls, 2003). Coastal cities are much vulnerable to floods and the impact is high because the land use of the area is altered i.e. agricultural fields to buildings, parking lots, and others (Suriya et al., 2012). Coastal zones with dense populations, low altitudes, high rates of land subsidence, and little adaptive capability are the places most in danger. According to OFDA/CRED (International Disaster Data, Office of Foreign Disaster Assistance/ Centre for Research on the Epidemiology of Disasters) data, flood is the most common disaster as the annual global number of deaths is very high by the flood. Statistically stated, India ranks the third position, next to China and the United States of America, for the loss due to flood disaster for the timeline 1990 to 2021 and estimated about 93,727,634,000 USD economic loss (EM-DAT).
India is one of such countries that have been seriously affected by floods on multiple occasions. Due to the topographic situation, India's east coastal areas are often prone to tropical cyclones/depressions and associated floods, where the bulk of India's population is also concentrated (Dhar and Nandargi, 2003). With no exception, coastal districts of eastern Tamil Nadu and Puducherry usually bear heavy rains during the northeast monsoon which becomes prone to flooding with numbers of swelling rivers and wetlands ( Singh et al., 2019). Identifying flood-prone areas at micro-level unit in the coastal Tamil Nadu is important for effective mitigation and management of flood risks. Although various studies attempted for flood risk assessment, but all of them either covered conducted for smaller region with micro-units or covered entire plains with district as unit. In addition, there are global flood risk assessment system based on global hydrological models and global impact assessment models for river floods (Winsemius et al., 2013) with no scope of downscaling to micro-levels. The availability of microwave satellite data sets and GIS based modeling approaches now offers better environment for quantifying the severity of flooding, the damage to the built environment, mapping flood-vulnerable areas, and eventually mitigation and management of flood-prone areas (Faiz Ahmed and Kranthi, 2018). The public availability of
SENTINEL 1 images for extracting flood inundation has made possible to harness the satellite technology for detailed flood assessments (Uddin et al., 2019). In addition to optical satellite datasets, microwave data based approaches are now most commonly exists to identify the flood inundation at various levels/scales (Joshi, 2012; Patel & Dholakia, 2010; A. K. Singh & Sharma, 2009; Zope et al., 2015). Due to its thick cloud penetration capability during the rainy seasons, microwave data offers unique opportunity for the identification the flood inundation region. It can also be used to understand the probability and magnitude of flooding (depth, velocity, and intensity) by combining multi-temporal datasets with DEMs and other datasets (Demirkesen et al., 2007; Van de Sande et al., 2012; Ehrlich et al., 2018).
Surface runoff is the key factor for any flood event and understanding runoff coefficient using empirical equations would greatly assist flood inundation mapping (Jariwala & Samtani, 2012). The National Resources Conservation Service - Curve Number (NRCS-CN) was widely used to evaluate floodwater retarding projects in the early years (1950–1980) due to its simplicity, predictability, stability, applicability for ungauged watersheds (Williams et al., 2012). The comparison of surface runoff potential and average seasonal rainfall would forms sound basis for characterizing the flood prone regions.
Flood risk assessment methods mainly consist of four steps, including hazard assessment, exposure assessment, vulnerability assessment, and risk assessment (Kvočka et al., 2016). Although the satellite data based assessment is necessary for accurate delineation of flood prone regions, vulnerability of exposed population can be assessed mainly through census based or sample based indicators. Vulnerability has multiple dimensions (physical, social, economic, and environmental) and increase the susceptibility of the exposed elements to the impact of flood hazards (UNISDR, 2009; De Brito et al., 2018). Vulnerability is an essential component of flood risk analysis and, as such, it has to be deeply investigated (Papathoma-Köhle et al., 2019). Due to its complex nature, vulnerability is always assessed in terms of indirect indicators. The census and other published household survey reports can be utilized to generate vulnerability indicators. However, structural field survey and direct interview of exposed population is precautionary before concluding anything on the basis of vulnerability indicators. The direct impacts and economic loss caused by floods can be better estimated by comparing vulnerability indicators and field surveys (Bahinipati et al., 2015). It is also imperative that survey of individuals is not wise option although the sample designing is sound as individuals often biased with their own judgments on flood risks. Therefore, group survey of deprived communities is advisable to understand the perceptions of the public (Xia et al., 2011), as the risk of personal danger to people caused by a flood varies both in time and place across a flood-prone area. By cognizing all above facts, we attempted a comprehensive analysis of flood risk in the coastal plains of Tamil Nadu to generate firsthand baseline datasets and to demonstrate the integrative methods for flood risk assessment which would help in all phases of disaster management ranging from preparedness to long-term planning strategies.