TY - CONF

T1 - Linking the relative importance of input uncertainties of a flood risk model with basin characteristics.

AU - Sarailidis, Georgios

AU - Pianosi, Francesca

AU - Wagener, Thorsten

AU - Lamb, Rob

AU - Styles, Kirsty

AU - Hutchings, Stephen

PY - 2022/3/27

Y1 - 2022/3/27

N2 - Floods are extreme natural hazards often with disastrous impacts on the economy and society. Flood risk assessments are required to better manage risk associated with floods. Nowadays, numerous flood risk models are available at various scales, from catchment to regional or even global scale. They involve a complex modelling chain that estimates risk as the product of probability of occurrence of an event (hazard) with its footprint (exposure) and the consequences over society and economy (vulnerability). Each component of this chain contains uncertainties, that propagate and contribute to the uncertainty in the model outputs. Much effort has been made to quantify such output uncertainty and attribute it to the various uncertainty sources in the modelling chain. However, the key drivers of uncertainty in flood risk estimates are still unclear because previous studies have reached conflicting conclusions. Two things could possibly explain these ambiguous outcomes. First, these studies were implemented with different models and with different data, as well as different assumptions for the uncertainty and sensitivity analysis. Second, the studies were conducted at catchment and/or city scale with limited variability of physical and socio-economic characteristics within a study region, but with potentially large differences across study regions. In this project, we study the question of uncertainty quantification and attribution at much larger scale, namely the heterogeneous region of the Rhine River basin. In this way, we can identify spatial patterns of dominant input uncertainties and link them to characteristics, e.g. physical, socio-economic, in the different sub-basins. To this end, we use an industry flood risk model (catastrophe model) provided by JBA Risk Management which is capable of simulating flood risk across such a large region. Our ultimate goal is to provide evidence of how the importance of uncertainties varies across places with different climatic, hydrologic and socio-economic characteristics.

AB - Floods are extreme natural hazards often with disastrous impacts on the economy and society. Flood risk assessments are required to better manage risk associated with floods. Nowadays, numerous flood risk models are available at various scales, from catchment to regional or even global scale. They involve a complex modelling chain that estimates risk as the product of probability of occurrence of an event (hazard) with its footprint (exposure) and the consequences over society and economy (vulnerability). Each component of this chain contains uncertainties, that propagate and contribute to the uncertainty in the model outputs. Much effort has been made to quantify such output uncertainty and attribute it to the various uncertainty sources in the modelling chain. However, the key drivers of uncertainty in flood risk estimates are still unclear because previous studies have reached conflicting conclusions. Two things could possibly explain these ambiguous outcomes. First, these studies were implemented with different models and with different data, as well as different assumptions for the uncertainty and sensitivity analysis. Second, the studies were conducted at catchment and/or city scale with limited variability of physical and socio-economic characteristics within a study region, but with potentially large differences across study regions. In this project, we study the question of uncertainty quantification and attribution at much larger scale, namely the heterogeneous region of the Rhine River basin. In this way, we can identify spatial patterns of dominant input uncertainties and link them to characteristics, e.g. physical, socio-economic, in the different sub-basins. To this end, we use an industry flood risk model (catastrophe model) provided by JBA Risk Management which is capable of simulating flood risk across such a large region. Our ultimate goal is to provide evidence of how the importance of uncertainties varies across places with different climatic, hydrologic and socio-economic characteristics.

U2 - 10.5194/egusphere-egu22-3122

DO - 10.5194/egusphere-egu22-3122

M3 - Abstract

T2 - EGU General Assembly 2022

Y2 - 23 May 2022 through 27 May 2022

ER -