TY  - JOUR
A1  - Ganguli, Poulomi
A1  - Paprotny, Dominik
A1  - Hasan, Mehedi
A1  - Güntner, Andreas
A1  - Merz, Bruno
T1  - Projected changes in compound flood hazard from riverine and coastal floods in northwestern Europe
JF  - Earth's future
N2  - Compound flooding in coastal regions, that is, the simultaneous or successive occurrence of high sea levels and high river flows, is expected to increase in a warmer world. To date, however, there is no robust evidence on projected changes in compound flooding for northwestern Europe. We combine projected storm surges and river floods with probabilistic, localized relative sea-level rise (SLR) scenarios to assess the future compound flood hazard over northwestern coastal Europe in the high (RCP8.5) emission scenario. We use high-resolution, dynamically downscaled regional climate models (RCM) to drive a storm surge model and a hydrological model, and analyze the joint occurrence of high coastal water levels and associated river peaks in a multivariate copula-based approach. The RCM-forced multimodel mean reasonably represents the observed spatial pattern of the dependence strength between annual maxima surge and peak river discharge, although substantial discrepancies exist between observed and simulated dependence strength. All models overestimate the dependence strength, possibly due to limitations in model parameterizations. This bias affects compound flood hazard estimates and requires further investigation. While our results suggest decreasing compound flood hazard over the majority of sites by 2050s (2040-2069) compared to the reference period (1985-2005), an increase in projected compound flood hazard is limited to around 34% of the sites. Further, we show the substantial role of SLR, a driver of compound floods, which has frequently been neglected. Our findings highlight the need to be aware of the limitations of the current generation of Earth system models in simulating coastal compound floods.
KW  - compound flood
KW  - storm surge
KW  - river floods
KW  - sea level rise
KW  - climate
KW  - change
KW  - Europe
Y1  - 2020
U6  - https://doi.org/10.1029/2020EF001752
SN  - 2328-4277
VL  - 8
IS  - 11
PB  - Wiley-Blackwell
CY  - Hoboken, NJ
ER  - 
TY  - JOUR
A1  - Paprotny, Dominik
A1  - Kreibich, Heidi
A1  - Morales-Napoles, Oswaldo
A1  - Wagenaar, Dennis
A1  - Castellarin, Attilio
A1  - Carisi, Francesca
A1  - Bertin, Xavier
A1  - Merz, Bruno
A1  - Schröter, Kai
T1  - A probabilistic approach to estimating residential losses from different flood types
JF  - Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards
N2  - 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.
KW  - fluvial floods
KW  - coastal floods
KW  - pluvial floods
KW  - Bayesian networks
KW  - flood
KW  - damage surveys
Y1  - 2020
U6  - https://doi.org/10.1007/s11069-020-04413-x
SN  - 0921-030X
SN  - 1573-0840
VL  - 105
IS  - 3
SP  - 2569
EP  - 2601
PB  - Springer
CY  - New York
ER  -