TY  - JOUR
A1  - Kreibich, Heidi
A1  - Di Baldassarre, Giuliano
A1  - Vorogushyn, Sergiy
A1  - Aerts, Jeroen C. J. H.
A1  - Apel, Heiko
A1  - Aronica, Giuseppe T.
A1  - Arnbjerg-Nielsen, Karsten
A1  - Bouwer, Laurens M.
A1  - Bubeck, Philip
A1  - Caloiero, Tommaso
A1  - Chinh, Do T.
A1  - Cortes, Maria
A1  - Gain, Animesh K.
A1  - Giampa, Vincenzo
A1  - Kuhlicke, Christian
A1  - Kundzewicz, Zbigniew W.
A1  - Llasat, Maria Carmen
A1  - Mard, Johanna
A1  - Matczak, Piotr
A1  - Mazzoleni, Maurizio
A1  - Molinari, Daniela
A1  - Dung, Nguyen V.
A1  - Petrucci, Olga
A1  - Schröter, Kai
A1  - Slager, Kymo
A1  - Thieken, Annegret
A1  - Ward, Philip J.
A1  - Merz, Bruno
T1  - Adaptation to flood risk
BT  - Results of international paired flood event studies
JF  - Earth's Future
N2  - As flood impacts are increasing in large parts of the world, understanding the primary drivers of changes in risk is essential for effective adaptation. To gain more knowledge on the basis of empirical case studies, we analyze eight paired floods, that is, consecutive flood events that occurred in the same region, with the second flood causing significantly lower damage. These success stories of risk reduction were selected across different socioeconomic and hydro-climatic contexts. The potential of societies to adapt is uncovered by describing triggered societal changes, as well as formal measures and spontaneous processes that reduced flood risk. This novel approach has the potential to build the basis for an international data collection and analysis effort to better understand and attribute changes in risk due to hydrological extremes in the framework of the IAHSs Panta Rhei initiative. Across all case studies, we find that lower damage caused by the second event was mainly due to significant reductions in vulnerability, for example, via raised risk awareness, preparedness, and improvements of organizational emergency management. Thus, vulnerability reduction plays an essential role for successful adaptation. Our work shows that there is a high potential to adapt, but there remains the challenge to stimulate measures that reduce vulnerability and risk in periods in which extreme events do not occur.
KW  - flooding
KW  - vulnerability
KW  - global environmental change
KW  - adaptation
Y1  - 2017
U6  - https://doi.org/10.1002/2017EF000606
SN  - 2328-4277
VL  - 5
SP  - 953
EP  - 965
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Vogel, Kristin
A1  - Weise, Laura
A1  - Schröter, Kai
A1  - Thieken, Annegret
T1  - Identifying Driving Factors in Flood-Damaging Processes Using Graphical Models
JF  - Water resources research
N2  - Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in-)dependencies between the considered variables. The models are learned based on postevent surveys with flood-affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property-level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property-level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.
KW  - flood loss
KW  - Bayesian Network
KW  - Markov Blanket
KW  - vulnerability
KW  - Germany
Y1  - 2018
U6  - https://doi.org/10.1029/2018WR022858
SN  - 0043-1397
SN  - 1944-7973
VL  - 54
IS  - 11
SP  - 8864
EP  - 8889
PB  - American Geophysical Union
CY  - Washington
ER  -