Details
| Original language | English |
|---|---|
| Article number | nhess-20-1689-2020 |
| Pages (from-to) | 1689-1703 |
| Number of pages | 15 |
| Journal | Natural Hazards and Earth System Sciences |
| Volume | 20 |
| Issue number | 6 |
| Publication status | Published - 8 Jun 2020 |
Abstract
Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- General Earth and Planetary Sciences
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In: Natural Hazards and Earth System Sciences, Vol. 20, No. 6, nhess-20-1689-2020, 08.06.2020, p. 1689-1703.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Event generation for probabilistic flood risk modelling
T2 - Multi-site peak flow dependence model vs. weather-generator-based approach
AU - Winter, Benjamin
AU - Schneeberger, Klaus
AU - Förster, Kristian
AU - Vorogushyn, Sergiy
N1 - Funding Information: HiFlow-CMA (KR15AC8K12522) funded by the Austrian Climate and Energy Fund (ACRP 8th call). Furthermore, we would like to thank the vice-rectorate for research and the faculty of Geo-and Atmospheric Sciences at the University of Innsbruck for providing open-access funding.
PY - 2020/6/8
Y1 - 2020/6/8
N2 - Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area.
AB - Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area.
UR - http://www.scopus.com/inward/record.url?scp=85086939871&partnerID=8YFLogxK
U2 - 10.5194/nhess-20-1689-2020
DO - 10.5194/nhess-20-1689-2020
M3 - Article
AN - SCOPUS:85086939871
VL - 20
SP - 1689
EP - 1703
JO - Natural Hazards and Earth System Sciences
JF - Natural Hazards and Earth System Sciences
SN - 1561-8633
IS - 6
M1 - nhess-20-1689-2020
ER -