Coastal high-rise structure collapse vulnerability determination with integrated geospatial modeling automation approach
Abstract
Aquifer in the southeast USA is covered by limestone/dolomite carbonate rock that dissolves in water and leads to increased risk of sinkholes formation. Soil subsidence in coastal areas are a global phenomenon. With severe increase of climate change impact, hurricanes are hitting coastal areas with too many numbers over the years. Thus, infrastructure damages in coastal United States, especially to the Southern Florida coat is of higher probability. The goal of this study was to develop three automated geospatial models to determine i) spatial locations with high-risk potential for sinkholes, ii) spatial locations highly affected by soil subsidence, and iii) hurricane and sea-level rise affected spatial locations and integrate these models to find the damage risk probability to Florida southern coast high-rise buildings. Geology, soil, land-use, aquifer, groundwater depth, road, fault line, elevation, precipitation, and evapotranspiration data produced nine sinkhole vulnerability layers: subsidence or surface change, average aquifer well depth, groundwater vulnerability (DRASTIC), groundwater travel time, road density, aquifer-media, geology type, slope, and land-use types. Each layer was reclassified and assigned a value from 1-10 using the Delphi Method of Weight Assignment, according to its sinkhole vulnerability. The weighted layers were analyzed interpretively producing a Sinkholes Vulnerability Raster. The 30m DEM recorded/created with the Shuttle Radar Topography Mission (SRTM) data and latest LiDAR based DEM comparison model provided the spatial soil subsidence vulnerability. NOAA based coastal bathymetry record provided changes in sea-level and Climate Change model suggested sea-level rise information was used to model spatial vulnerability of coastal zone from flooding in case of a hurricane. These three models were integrated to determine most vulnerable spatial location in Florida Southern coast. The LiDAR data was used to find the high-rise building locations and matched with the integrated model raster to obtain the climate susceptible infrastructures. This study would support the managers to take pro-active measures to safeguard lives and resources.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.H25D1083P