Flood-risk assessment in urban environment by geospatial approach: a case study of Ambala City, India

Type Journal Article - Applied Geomatics
Title Flood-risk assessment in urban environment by geospatial approach: a case study of Ambala City, India
Author(s)
Volume 8
Issue 3-4
Publication (Day/Month/Year) 2016
Page numbers 163-190
URL https://link.springer.com/article/10.1007/s12518-016-0174-7
Abstract
Looking at land use and functional structure characteristics, floods in urban environment are costlier and difficult to manage than in rural environment. In India, flooding is an inevitable problem for several cities. In the state of Haryana, Ambala City has a long history of serious flooding problem. Based on primary as well as secondary data, the present study attempts to examine the natural and anthropogenic causes of flooding on catchment and city scale. Geographic Information System (GIS)-based flood-risk modeling and consequences of flooding are presented for the selected most critical zone. Based on the past 21-year maximum discharge data of the Tangri River, flood probability is calculated for a 2-, 5-, 10-, and 20-year return period using Weibull’s plotting position formula, and the likely maximum discharge of 500, 1000, 1200, and 1500 m3, respectively, is used for the prediction of flood extent using Hydraulic Engineering Center-River Analysis System (HEC-RAS) software. Flood depth is calculated by employing spatial interpolation method using observed flood depth samples. Model result revealed that the flood inundation areas are 690, 1135, 1530, and 2300 ha, respectively, and accordingly likely impact on land use and population are assessed. The modeled 5-year return flood extents were validated using the observed data of the latest flood event in July 2010, including remote sensing imagery and field survey. Hence, in order to mitigate adverse impact of flooding in urban environment, such output can be used by urban local bodies, town planners, and policy makers to support decision-making in risk-sensitive land use planning by integrating climate change scenarios.

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