Simulating flood risk under non-stationary climate and urban development conditions – Experimental setup for multiple hazards and a variety of scenarios

Abstract

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A spatial decision support system for enhancing resilience to floods: bridging resilience modelling and geovisualization techniques

“…Consequently, these characteristics and advantages make geovisualization an interesting methodology for studying risk management. Crisis management is, indeed, a concrete example of where it is useful to use visual, map-based tools to integrate, assess and apply multisource geospatial information and data (MacEachren et al, 2004). Indeed, in a context of climate change and related uncertainties, modelling or simulating disasters such as floods is becoming increasingly complex.…”

Urban pluvial flood risk assessment – data resolution and spatial scale when developing screening approaches on the microscale

“…The data were averaged to a resolution of 5 m. Figure 1 shows terrain elevations, footprints of the existing buildings and the network of existing major roads. We refer to Löwe et al (2019) for a detailed evaluation of the characteristics of the urban layout in the case study area. Figure 2 illustrates the overall problem.…”

Using Meta-Models for Tsunami Hazard Analysis: An Example of Application for the French Atlantic Coast

“…A variety of metamodels have been applied in the water resources literature (Santana-Quintero et al, 2010;Razavi et al, 2012b). Moreover, some examples of applications have already been published in the context of flood management (e.g., Yazdi and Salehi Neyshabouri, 2014;Löwe et al, 2018) and in the field of tsunamis (e.g., Sarri et al, 2012;Sraj et al, 2014;Rohmer et al, 2018). The classical steps for meta-models construction and validation are reported in various studies (i.e., Saltelli, 2002;Saltelli et al, 2008;Faivre et al, 2013), and are shortly summarized in Table 1.…”

“…For the study presented here, we rely on conditional Gaussian processes (also known as kriging (Roustant et al, 2012), derived from Danie Krige's pioneering work in mining (Krige, 1951), later formalized within the geostatistical framework by Matheron (1963). Kriging meta-model has already shown good predictive capacities in many practical applications (see Marrel et al, 2008, for example), it became a standard meta-modeling method in operational research (Santner et al, 2003;Kleijnen, 2005) and it has performed robustly in previous water resource applications (Razavi et al, 2012b;Villa-Vialaneix et al, 2012;Löwe et al, 2018). A general kriging model "M(x)" (which later provides an estimation of the maximum tsunami height in a given location) can be defined for x = (x 1 , ..., x d ) ∈ D ∈ R d as the following Gaussian process "N(.…”