Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 2020EF001851 |
| Fachzeitschrift | Earth's future |
| Jahrgang | 9 |
| Ausgabenummer | 2 |
| Frühes Online-Datum | 17 Jan. 2021 |
| Publikationsstatus | Veröffentlicht - 23 Feb. 2021 |
Abstract
Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof-of-concept for an impact-based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network-based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio-temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact-based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact-based forecast could be used to disseminate impact-based early warnings.
ASJC Scopus Sachgebiete
- Umweltwissenschaften (insg.)
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
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in: Earth's future, Jahrgang 9, Nr. 2, 2020EF001851, 23.02.2021.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Impact-Based Forecasting for Pluvial Floods
AU - Rözer, V.
AU - Peche, A.
AU - Berkhahn, S.
AU - Feng, Y.
AU - Fuchs, L.
AU - Graf, T.
AU - Haberlandt, U.
AU - Kreibich, H.
AU - Sämann, R.
AU - Sester, M.
AU - Shehu, B.
AU - Wahl, J.
AU - Neuweiler, I.
N1 - Funding Information: The research presented in this study was mainly conducted under the framework of the project “EVUS—Real‐Time Prediction of Pluvial Floods and Induced Water Contamination in Urban Areas” (BMBF, 03G0846B). The authors would like to acknowledge the Stadtentwässerung Hannover who supported our work by providing the pipe network model and precipitation data. Additional financial support for V.R. by the Z Zurich Foundation, the Grantham Foundation for the Protection of the Environment and the ESRC via the Centre for Climate Change Economics and Policy under Grant number: ES/R009708/1 is gratefully acknowledged. Open access funding enabled and organized by Projekt DEAL.
PY - 2021/2/23
Y1 - 2021/2/23
N2 - Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof-of-concept for an impact-based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network-based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio-temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact-based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact-based forecast could be used to disseminate impact-based early warnings.
AB - Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof-of-concept for an impact-based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network-based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio-temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact-based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact-based forecast could be used to disseminate impact-based early warnings.
KW - early warning
KW - impact-based forecasting
KW - pluvial floods
UR - http://www.scopus.com/inward/record.url?scp=85101533927&partnerID=8YFLogxK
U2 - 10.1029/2020EF001851
DO - 10.1029/2020EF001851
M3 - Article
AN - SCOPUS:85101533927
VL - 9
JO - Earth's future
JF - Earth's future
SN - 2328-4277
IS - 2
M1 - 2020EF001851
ER -