Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu&Kashmir): An application of geoinformatics

Abstract

Massive flooding in the Kashmir valley was induced by heavy rainfall due to combined effects of western disturbances and Indian summer monsoon during September 04-10, 2014. The populous Sonawari sub-district was severely affected by flood waves that caused complete damage to standing crops and disrupted man-made linear structures (embankment, canal, and road network). High value land use classes (cropped land, built-up and plantation), which encroached on swamps and wetlands between 1972 and 2014, were badly damaged. The main aim of this study is to assess flood hazard at village level in the sub-district. Village-wise standardized flood hazard indicators were flood affected areas, mean turbidity, mean flood depth and duration. These indicators were derived from the Landsat 7 ETM+, Landsat 8 OLI-TIRS images and SRTM (30 m) DEM. Linear combination of these standardised indicators in GIS environment formulated Flood Hazard Index (FHI). Furthermore, the computed FHI were divided into four zones. Low, moderate, high and very high flood hazard zones occupied 16, 32, 34 and 19 per cent of the total area, respectively. Further, these zones accounted for 21, 23, 33 and 23 per cent of the total number of villages and towns (43), respectively. FHI was validated with overall changes in NDVI values with the help of linear regression model that showed a fair association between village-wise FHI and changes in mean NDVI values as the coefficient of determination (R 2) was 0.75 with a least standard error of 0.05. Village-wise flood hazard assessment has immense importance to plan future land use and to implement flood management strategies during pre and post-floods in the sub-district.

Urban Growth and Flood Disasters in the Coastal River Basin of South-Central Chile (1943–2011)

“…However, this does not mean the complete elimination of hazard in the area. As indicated by Gomes et al [3]; Tingsanchali [4]; Liao et al [7]; and Kumar [68], the sensation of security generated by these public works tends to attract more people to the river. However, the possibility of another major flood event outside the capacity of the design could destroy defenses, putting more people and property at risk than before.…”

“…In this model, flood hazard, depth and velocity are essential components in which to evaluate potential damage [68]. Vulnerability corresponds to the conditions (economic, social, physical) that can increase the susceptibility of a community to the impact of flooding [4].…”

Climatic, geomorphic and anthropogenic drivers of the 2014 extreme flooding in the Jhelum basin of Kashmir, India

“…The census of India data was used to determine the changes in the total population, population density, households and other relevant socioeconomic factors in the floodplains of Jhelum basin between 1981 and 2011. Siltation (Khan et al 2004) and loss of wetlands and water courses (Kumar 2016) and urbanization of the Jhelum floodplains (Kumar and Acharya 2016) exacerbated the flooding in a basin. The urbanization and loss of wetlands in the floodplain of the Jhelum basin was assessed from 1972 to 2013 using the change detection analysis of the Landsat MSS and Landsat ETM satellite data.…”

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

“…Flood duration products have not been considered in the context of emergency mapping to date, due to a lack of automation of existing methods [20][21][22] and often limited data availability from the onset of a disaster. Existing studies have not specifically considered this aspect and focused only on using data from single sensors [22][23][24][25][26]29,30] or sensors groups of optical [27] or SAR [20,21] missions.…”