Ranking sources of uncertainty in flood damage modelling: a case study on the cost‐benefit analysis of a flood mitigation project in the Orb Delta, France

Abstract

International audienceCost-benefit analyses (CBA) of flood management plans usually require estimating expected annual flood damages on a study area, and rely on a complex modelling chain including hydrological, hydraulic and economic modelling as well as GIS-based spatial analysis. As most model-based assessments, these CBA are fraught with uncertainty. In this paper, we consider as a case-study the CBA of a set of flood control structural measures on the Orb Delta, France. We demonstrate the use of variance-based global sensitivity analysis (VB-GSA) to i) propagate uncertainty sources through the modelling chain and assess their overall impact on the outcomes of the CBA, and ii) rank uncertainty sources according to their contribution to the variance of the CBA outcomes. All uncertainty sources prove to explain a significant share of the overall output variance. Results show that the ranking of uncertainty sources depends not only on the economic sector considered (private housing, agricultural land, other economic activities), but also on a number of averaging-out effects controlled by the number and surface area of the assets considered, the number of land use types or the number of damage functions

Archival photogrammetric analysis of river–floodplain systems using Structure from Motion (SfM) methods

“…Photogrammetry is a well-established technique that has been used to quantify morphologic change for many decades (Chandler and Moore, 1989;Lane et al, 1993Lane et al, , 2000. In river-floodplain systems it has been applied to the investigation and monitoring of morphological change and sediment transport (Heritage et al, 1998;Chandler et al, 2002;Brasington et al, 2003;Lane et al, 2010;Wheaton et al, 2010Wheaton et al, , 2013, river bank erosion (Barker et al, 1997;Pyle et al, 1997;De Rose and Basher, 2011), flood risk assessment (Sanyal and Lu 2004;Saint-Geours et al, 2015), river restoration and ecology (Gilvear et al, 1995;Kondolf and Larson, 1995;Pasquale et al, 2011;Dietrich, 2016), and archaeology (Pérez Álvarez et al, 2013). For the investigation of morphological change, particularly in braided river systems, a thorough theoretical and practical basis has been developed, including the assessment of error and uncertainties (Lane et al, , 2004Westaway et al, 2003;Wheaton et al, 2010).…”

A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates

“…The assumption of spatially uniform uncertainty for the flood depth could be an important reason for this difference. Alternatively, de Moel et al () and Saint‐Geours et al () used 2.5th and 97.5th percentiles and found a factor range of 16–33 and 2–10, respectively. Using these percentiles, a factor of 4.5 is found in our study, which is much smaller than what de Moel et al () obtained.…”

“…Past efforts to explore the impact of the contributing drivers on the uncertainty of loss estimates did not employ an integrated H&H modelling approach. These studies either assigned a plausible range to simulated flood depth (de Moel & Aerts, 2011;de Moel, Asselman, & Aerts, 2012;de Moel, Bouwer, & Aerts, 2014) or applied a sole hydraulic model (Apel, Merz, & Thieken, 2008;Chinh, Dung, Gain, & Kreibich, 2017;Kalyanapu et al, 2012Kalyanapu et al, , 2015Merz & Thieken, 2009;Saint-Geours, Grelot, Bailly, & Lavergne, 2015), fed by a joint flood frequency-shape analysis (often on peak discharge). The latter has been found to be the dominant uncertainty source in loss estimation (Tate, Munoz, & Suchan, 2015) because a synthetic hydrograph needs to be fitted to this estimated peak (derived via a flood frequency analysis).…”

Effects of digital terrain model uncertainties on high‐resolution urban flood damage assessment

“…Direct tangible flood losses are commonly assessed based on stage-damage curves (Huizinga, de Moel, & Szewczyk, 2017;Merz, Kreibich, Schwarze, & Thieken, 2010;Romali, Sulaiman, Yusop, & Zulhilmi, 2015), which represent the vulnerability of exposed objects for given hazard parameters (i.e., flood depth). Amongst the main sources of uncertainties in flood damage assessment are hydrologic data, topographic data, hydraulic models assumptions and parameters, and socio-economic data for cost estimation and vulnerability curves (Bales & Wagner, 2009;Brown, Spencer, & Moeller, 2007;De Moel & Aerts, 2011;Saint-Geours, Grelot, Bailly, & Lavergne, 2015). A digital terrain model (DTM) is a key source of topographic information for the set-up of flood models, which are necessary for obtaining flood depths for assigned hydrological scenarios.…”

“…This often occurs when urban areas are inundated for high return period events and historical data are scarce or lacking. Only a few works in the literature analyse how uncertainties in input parameters of inundation models affect the final damage assessment (Bhuyian & Kalyanapu, 2017;Boettle et al, 2011;Saint-Geours et al, 2015). Even fewer works account for both hazard and losses employing high-resolution DTMs, for example, 1-2 m (Bhuyian & Kalyanapu, 2017).…”