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 -