TY  - JOUR
A1  - Farrag, Mostafa
A1  - Brill, Fabio Alexander
A1  - Nguyen, Viet Dung
A1  - Sairam, Nivedita
A1  - Schröter, Kai
A1  - Kreibich, Heidi
A1  - Merz, Bruno
A1  - de Bruijn, Karin M.
A1  - Vorogushyn, Sergiy
T1  - On the role of floodplain storage and hydrodynamic interactions in flood risk estimation
JF  - Hydrological sciences journal = Journal des sciences hydrologiques
N2  - Hydrodynamic interactions, i.e. the floodplain storage effects caused by inundations upstream on flood wave propagation, inundation areas, and flood damage downstream, are important but often ignored in large-scale flood risk assessments. Although new methods considering these effects sometimes emerge, they are often limited to a small or meso scale. In this study, we investigate the role of hydrodynamic interactions and floodplain storage on flood hazard and risk in the German part of the Rhine basin. To do so, we compare a new continuous 1D routing scheme within a flood risk model chain to the piece-wise routing scheme, which largely neglects floodplain storage. The results show that floodplain storage is significant, lowers water levels and discharges, and reduces risks by over 50%. Therefore, for accurate risk assessments, a system approach must be adopted, and floodplain storage and hydrodynamic interactions must carefully be considered.
KW  - hydrodynamic interactions
KW  - derived flood risk analysis
KW  - flood modelling;
KW  - Rhine basin
Y1  - 2022
U6  - https://doi.org/10.1080/02626667.2022.2030058
SN  - 0262-6667
SN  - 2150-3435
VL  - 67
IS  - 4
SP  - 508
EP  - 534
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  - 
TY  - JOUR
A1  - Brill, Fabio Alexander
A1  - Passuni Pineda, Silvia
A1  - Espichan Cuya, Bruno
A1  - Kreibich, Heidi
T1  - A data-mining approach towards damage modelling for El Nino events in Peru
JF  - Geomatics, natural hazards and risk
N2  - Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.
KW  - Natural hazard
KW  - damage model
KW  - residential buildings
KW  - data-mining
KW  - remote
KW  - sensing
KW  - open data
Y1  - 2020
U6  - https://doi.org/10.1080/19475705.2020.1818636
SN  - 1947-5705
SN  - 1947-5713
VL  - 11
IS  - 1
SP  - 1966
EP  - 1990
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  -