Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  A probabilistic approach to estimating residential losses from different flood types

Paprotny, D., Kreibich, H., Morales-Nápoles, O., Wagenaar, D., Castellarin, A., Carisi, F., Bertin, X., Schröter, K., Merz, B. (2021): A probabilistic approach to estimating residential losses from different flood types. - Natural Hazards, 105, 2569-2601.
https://doi.org/10.1007/s11069-020-04413-x

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
5004063.pdf (Verlagsversion), 5MB
Name:
5004063.pdf
Beschreibung:
-
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Paprotny, Dominik1, Autor              
Kreibich, H.1, Autor              
Morales-Nápoles, Oswaldo2, Autor
Wagenaar, Dennis2, Autor
Castellarin, Attilio2, Autor
Carisi, Francesca2, Autor
Bertin, Xavier2, Autor
Schröter, Kai1, Autor              
Merz, B.1, Autor              
Affiliations:
14.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146048              
2External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: DEAL Springer
 Zusammenfassung: Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 20202021
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1007/s11069-020-04413-x
GFZPOF: p4 T5 Future Landscapes
OATYPE: Hybrid - DEAL Springer Nature
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Natural Hazards
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 105 Artikelnummer: - Start- / Endseite: 2569 - 2601 Identifikator: Anderer: 1573-0840
Anderer: Springer Nature
ISSN: 0921-030X
CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals351
Publisher: Springer