A method for regional estimation of climate change exposure of coastal infrastructure: Case of USVI and the influence of digital elevation models on assessments.

Climate DEM Development Infrastructure RCP SIDS

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
25 Mar 2020
Historique:
received: 13 06 2019
revised: 11 12 2019
accepted: 14 12 2019
pubmed: 10 1 2020
medline: 10 1 2020
entrez: 10 1 2020
Statut: ppublish

Résumé

This study tests the impacts of Digital Elevation Model (DEM) data on an exposure assessment methodology developed to quantify flooding of coastal infrastructure from storms and sea level rise on a regional scale. The approach is piloted on the United States Virgin Islands (USVI) for a one-hundred-year storm event in 2050 under the IPCC's 8.5 emission scenario (RCP 8,5). Flooding of individual infrastructure was tested against three different digital elevation models using a GIS-based coastal infrastructure database created specifically for the project using aerial images. Inundation for extreme sea levels is based on dynamic simulations using Lisflood-ACC (LFP). The model indicates transport and utility infrastructure in the USVI are considerably exposed to sea level rise and modeled storm impacts from climate change. Prediction of flood extent was improved with a neural network processed SRTM, versus publicly available SRTM (~30 m) seamless C-band DEM but both SRTM based models underestimate flooding compared to LIDAR DEM. The modeled scenario, although conservative, showed significant flood exposure to a large number of access roads to facilities, 113/176 transportation related buildings, and 29/66 electric utility and water treatment buildings including six electric power transformers and six waste water treatment clarifiers. The method bridges a gap between large-scale non-specific flood assessments and single-facility detailed assessments and can be used to efficiently quantify and prioritize parcels and large structures in need of further assessment for regions that lack detailed data to assess climate exposure to sea level rise and flooding caused by waves. The method should prove particularly useful for assessment of Small Island Developing State regions that lack LIDAR data, such as the Caribbean.

Identifiants

pubmed: 31918185
pii: S0048-9697(19)36158-3
doi: 10.1016/j.scitotenv.2019.136162
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

136162

Informations de copyright

Copyright © 2019. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest This work was funded in part by the Cruise Ship Charitable Foundation. The authors declare no conflicts of interest.

Auteurs

Gerald Bove (G)

University of Rhode Island, Coastal Institute, 1 Greenhouse Road, Suite 205, Kingston, RI 02881, USA. Electronic address: gerald_bove@uri.edu.

Austin Becker (A)

University of Rhode Island, Coastal Institute, 1 Greenhouse Road, Suite 205, Kingston, RI 02881, USA.

Benjamin Sweeney (B)

University of Rhode Island, Coastal Institute, 1 Greenhouse Road, Suite 205, Kingston, RI 02881, USA.

Michalis Vousdoukas (M)

European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, I-21027 Ispra, Italy.

Scott Kulp (S)

Climate Central, Princeton, NJ, USA.

Classifications MeSH