Author(s): Mustafa Altinakar; Molly Mary Finster; Kyle Burke Pfeiffer; Preston W. Sr. Wilson

Linked Author(s): mustafa altinakar

Keywords: Flood Damage Assessment; Building Footprint Data; Asset Location Point Data; Consequence Analysis; Numerical Model

Abstract: Traditionally, building and infrastructure damage analysis is performed by interfacing point data showing the locations of buildings and other assets (often in the form of a shapefile) with observed or numerically simulated flood depth and/or flood unit discharge (depth x velocity) maps. The flood depth and/or unit discharge values extracted at asset locations are then used to count the number of buildings in flood zones (by danger levels). Appropriate damage curves help assess the level of damage at each asset. However, the use of point data to represent assets has significant limitations and weaknesses. Representing a building or a critical infrastructure asset, which occupies a finite area, by a single point is overly simplistic and may lead to an underestimation of flood damages. This is especially the case when buildings or assets are located near the edge of an inundation area, such that they are only partially flooded. Recently, geospatial data layers of building footprints have become more widely available from various sources, especially in and around major metropolitan areas. Building footprint data, often obtainable as a shapefile, represents each building by a polygon feature. The present paper demonstrates how a better estimation of flood damages can be achieved by interfacing building polygon data with flood depth and flood unit discharge maps. The proposed methodology is applied using an illustrative case study of flooding that occurred in mid-Michigan in May 2020 due to the cascading failure of two dams. The building damage levels are determined using two methods, the building polygons from IBM Building Footprint data and the centroid point data of each feature. The resulting counts of buildings and the damage levels are compared. The results of the comparison are presented, along with conclusions and recommendations.

DOI: https://doi.org/10.3850/IAHR-39WC2521711920221515

Year: 2022