Data-driven flood hazard zonation of Italy. (15th September 2021)
- Record Type:
- Journal Article
- Title:
- Data-driven flood hazard zonation of Italy. (15th September 2021)
- Main Title:
- Data-driven flood hazard zonation of Italy
- Authors:
- Marchesini, Ivan
Salvati, Paola
Rossi, Mauro
Donnini, Marco
Sterlacchini, Simone
Guzzetti, Fausto - Abstract:
- Abstract: We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE inAbstract: We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed. Highlights: Areas in Italy subject to river inundations are delineated using a data-driven approach. Data-driven models delineate larger inundation areas than previous hydrological models. Quality of the data-driven models is higher in small and medium-size catchments. New modelled inundation areas are good predictors of flood risk to the population. … (more)
- Is Part Of:
- Journal of environmental management. Volume 294(2021)
- Journal:
- Journal of environmental management
- Issue:
- Volume 294(2021)
- Issue Display:
- Volume 294, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 294
- Issue:
- 2021
- Issue Sort Value:
- 2021-0294-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09-15
- Subjects:
- Inundation -- Statistical modelling -- Hazard zoning -- Hydro-morphometry
Environmental policy -- Periodicals
Environmental management -- Periodicals
Environment -- Periodicals
Ecology -- Periodicals
363.705 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03014797 ↗
http://www.elsevier.com/journals ↗
http://www.idealibrary.com ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1016/j.jenvman.2021.112986 ↗
- Languages:
- English
- ISSNs:
- 0301-4797
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4979.383000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17545.xml