The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data

Abstract

Abstract. Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall-runoff models, generally aimed at estimating flood magnitudes - typically discharges or return periods – at selected river cross-sections. The approach presented here goes one step ahead by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies). The proposed approach includes, in addition to a distributed rainfall-runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach: i.e. number of inundated buildings versus discharge. Theses curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the South of France, where well documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.

Real‐time monitoring and estimation of the discharge of flash floods in a steep mountain catchment

“…The method deriving the flow discharge on the basis of the empirically generated rating curve has been widely used in some steep ungauged catchments (Bihan et al, ; Gaume et al, ; Ran et al, ). The calibration subsets, which consist of 420 observed discharges (Figures and ), were applied for the estimation of the rating curve parameters (Table ).…”

“…It has been demonstrated that understanding and characterizing dynamic processes in fluid density and flow regimes during a large flash flood is important for hydraulic analysis and prediction of flash floods, as well as for risk assessment (Trieste, ; Lumbroso & Gaume, ; Asano & Uchida, ; Ran et al, ; Bihan et al, ; Cui, Guo, et al, ). When discharge and water level increased in a steep mountain channel, supercritical flow could occur and even destabilize this channel (Church & Zimmermann, ; Ohtsu, Yasuda, & Takahashi, ; Stolle et al, ).…”

“…For example, traditional contact monitoring instruments, such as the propeller current metre, electric magnetic velocity metre, acoustic Doppler velocimeter, and acoustic Doppler current profile (Hong, Guo, Wang, & Yeh, ; Ran, Li, Liao, Hl, & Wang, ), were always installed into the river water to estimate these hydrological parameters and were easily destroyed by the devastating flash flood, especially in a steep watershed with rapid flows. Meanwhile, the extrapolated rating curve under common flow conditions was limited and not reliable (Bihan, Payrastre, Gaume, Moncoulon, & Pons, ). Therefore, the uncertainty and lack of reliable observed data in the field can further exaggerate the difficulty in understanding the hydrological processes of flash floods with rapid flows.…”

Impact Forecasting to Support Emergency Management of Natural Hazards

“…The literature describes impact forecasting approaches for near‐real‐time applications, that is, providing information at the same time as the event is happening (Kim et al, 2011; Kron et al, 2010) and for short‐term forecasts with lead times of a few hours to 1 day. Examples are found in Bihan et al (2017) and Ritter et al (2020) for flash floods and in Bhola et al (2018) and Fuchs et al (2017) for pluvial floods in urban areas. River flood impact forecasting systems with lead times of several days have been proposed by Dottori et al (2017), Bachmann et al (2016), or Brown et al (2016).…”

“…Quantitative impact forecasts include estimates of the number of people affected, economic damage, and infrastructure affected. The level of detail varies from the number of buildings affected (Bihan et al, 2017) to economic damage for residential buildings, commerce, agriculture, industry, and transport and infrastructure sectors (Dale et al, 2014; Dottori et al, 2017; Ritter et al, 2020) and economic damage to individual buildings (Dale et al, 2014; Fuchs et al, 2017).…”