Hypothetically Quantifying Flood Vulnerability in a Reservoir Tributary Employing 3-Dimensional Geomorphological Terrain Related Covariants, a Stochastic Iterative Quantitative Interpolator and a Space-time Global Circulation Model Paradigm
Toni Panaou, Samuel Alao and Benjamin Jacob*
Department of Global Health, College of Public Health, University of South Florida, Tampa, USA
- *Corresponding Author:
- Benjamin Jacob
Department of Global Health, College of Public
Health, University of South Florida, Tampa, USA
E-mail: [email protected]
Received date: November 04, 2016; Accepted date: November 30, 2016; Published date: December 02, 2016
Citation: Panaou T, Alao S, Jacob B (2016) Hypothetically Quantifying Flood Vulnerability in a Reservoir Tributary Employing 3-Dimensional Geomorphological Terrain Related Covariants, a Stochastic Iterative Quantitative Interpolator and a Space-time Global Circulation Model Paradigm. J Remote Sensing & GIS 5:183. doi: 10.4182/2469-4134.1000183
Copyright: © 2016 Panaou T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Deaths from flooding in the United States are preventable with the right planning maps and mitigation. This research is revolutionary as it forecast the most vulnerable flooding areas of higher population regions by incorporating future precipitation projections, soil classifications, a 3-dimensional (3-D) digital elevation model (DEM) and the Geographic Information System (GIS) kriging algorithmic iterative interpolation tool to determine the optimal geolocations where storm water drainage detention or retention and improvements should occur. Firstly, utilizing spatial tools and a global circulation models (GCMs), precipitation was mapped to determine high vulnerability areas for future potential flooding. A robust semi variogram, geospatial explanatory locations of precipitation were then parsimoniously constructed for a sample site in Hillsborough County, Florida. Overlaying this data on 3-D temporal geomorphological terrain related elevation models, high risk flooding areas were geolocated employing geospectrotemporal geospatial techniques. For this region, two-thirds of the precipitation occurs during the summer months; therefore, June, July and August were analyzed. Furthermore, just focusing on one month, e.g., August, would not take into account antecedent ecogeohydrology conditions which impact run off volume and flooding. Soil characteristics such as capillary action, permeability and drainage porosity were considered as some soils have a high water-holding saturation capacity and poor infiltration capability, increasing flooding. Finally, extracting forecasted slope coefficient from 3-D models were examined to determine if they were feasible to help extract geolocations where there is prevalent standing water during wet season.