Flash floods and muddy floods in Wallonia: recent temporal trends, spatial distribution and reconstruction of the hydrosedimentological fluxes using flood marks and sediment deposits

Campenhout, Jean Van; Hallot, Éric; Houbrechts, Geoffrey; Peeters, Alexandre; Levecq, Yannick; Gérard, Pierre; Petit, François

doi:10.4000/belgeo.16409

Cited by 9 publications

(9 citation statements)

References 45 publications

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“…This leads to a mismatch between the length of the flood records and the need for an adequate estimate of the return period, in order to achieve effective and efficient infrastructure design [10]. Increased imperviousness of the landscape tends to increase watershed response to rainfall [102] and heightens the risk of extreme flash-floods [88].…”

Section: Discussionmentioning

confidence: 99%

“…Figure 6 also shows the extreme discharges estimated during catastrophic flash-flood events in ungauged catchments [88] utilizing a range of methods (specific stream power deducted from mobilized bed load, maximum water level in channel, . .…”

Section: Discharge and Return Period Of Extreme Floodsmentioning

confidence: 99%

“…From a dataset of peak discharge of extreme floods observed in the last two centuries in 1400 watersheds in the entire world, Francou and Rodier [97] presented an envelope curve based on the given catchment area [88]. Their formula (Equation 16) gives the expected peak flow rate Q (in m 3 •s −1 ) with A, the area of the watershed (in km 2 ), Q 0 = 10 6 and A 0 = 10 8 .…”

Section: Discharge and Return Period Of Extreme Floodsmentioning

confidence: 99%

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Return Period of Characteristic Discharges from the Comparison between Partial Duration and Annual Series, Application to the Walloon Rivers (Belgium)

Campenhout

Houbrechts

Peeters

et al. 2020

Water

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The determination of the return period of frequent discharges requires the definition of flood peak thresholds. Unlike daily data, the volume of data to be processed with the generalization of hourly data loggers or even with an even finer temporal resolution quickly becomes too large to be managed by hand. We therefore propose an algorithm that automatically extracts flood characteristics to compute partial series return periods based on hourly series of flow rates. Thresholds are defined through robust analysis of field observation-independent data to obtain five independent flood peaks per year in order to bypass the 1-year limit of annual series. Peak over thresholds were analyzed using both Gumbel’s graphical method and his ordinary moments method. Hydrological analyses exhibit the value in the convergence point revealed by this dual method for floods with a recurrence interval around 5 years. Pebble-bedded rivers on impervious substratum (Ardenne rivers) presented an average bankfull discharge return period of around 0.6 years. In the absence of field observation, the authors have defined the bankfull discharge as the Q0.625 computed with partial series. Annual series computations allow Q100 discharge determination and extreme floods recurrence interval estimation. A comparison of data from the literature allowed for the confirmation of the value of Myer’s rating at 18, and this value was used to predict extreme floods based on the area of the watershed.

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Section: Discussionmentioning

confidence: 99%

Section: Discharge and Return Period Of Extreme Floodsmentioning

confidence: 99%

Section: Discharge and Return Period Of Extreme Floodsmentioning

confidence: 99%

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Return Period of Characteristic Discharges from the Comparison between Partial Duration and Annual Series, Application to the Walloon Rivers (Belgium)

Campenhout

Houbrechts

Peeters

et al. 2020

Water

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“…As precipitation reaches the land surface, different runoff generation mechanisms determine whether water starts to pond and whether overland flow is generated (e.g., Fiener et al, 2013). The water may then take several routes towards the stream channels (Ward and Robinson, 2000), as depicted in Fig. 1.…”

Section: Terminologymentioning

confidence: 99%

“…1. The flow path along the land surface is sometimes ambiguously referred to as "surface runoff" but is better defined by the widely-used term "overland flow" (Ward and Robinson, 2000). However, in the literature, this distinction is inconsistently made, whereas either of the terms or even both are used.…”

Section: Terminologymentioning

confidence: 99%

Surface water floods in Switzerland: what insurance claim records tell us about the caused damages in space and time

Bernet¹,

Prasuhn²,

Weingartner³

2017

Preprint

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Abstract. Surface water floods (SWFs) have received increasing attention in the recent years. Nevertheless, we still know relatively little about where, when and why such floods occur and cause damages, largely owed to a lack of data, but to some degree also because of terminological ambiguities. Therefore, in a preparatory step, we summarize related terms and identify the need for unequivocal terminology across disciplines and international boundaries in order to bring the science together. Thereafter, we introduce a large (n = 63'117), long (10–33 years) and representative (48 % of all Swiss buildings covered) data set of spatially explicit Swiss insurance flood claims. Based on registered flood damages to buildings, the main aims of this study are twofold: First, we introduce a methodology to differentiate damages caused by SWFs and fluvial floods based on the geographical location of each damaged object in relation to flood hazard maps and the hydrological network. Second, we analyze the data with respect to their spatial and temporal distributions aimed at quantitatively answering the fundamental questions of how relevant SWF damages really are, as well as where and when they occur in space and time. This study reveals that SWFs are responsible for at least 45 % of all flood damages to buildings and 23 % of the associated direct tangible losses, whereas lower losses per claim are responsible for the lower loss share. The Swiss lowlands are affected more heavily by SWFs than the alpine regions. At the same time, the results show that the damages are not evenly distributed within each region either. By far the most SWF damages occur during summer in almost all regions. The normalized damages of all regions show no significant upward trend of SWF damages between 1993–2013. We conclude that SWFs are in fact a highly relevant process in Switzerland that should receive similar attention like fluvial flood hazards. Moreover, as SWF damages almost always coincide with fluvial flood damages, we suggest to consider SWFs, just as fluvial floods, as integrated processes of our catchments.

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Prototype of a LPWA Network for Real-Time Hydro-Meteorological Monitoring and Flood Nowcasting

Douinot

Torre²,

Martin³

et al. 2019

Ad-Hoc, Mobile, and Wireless Networks

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Flash floods and muddy floods in Wallonia: recent temporal trends, spatial distribution and reconstruction of the hydrosedimentological fluxes using flood marks and sediment deposits

Cited by 9 publications

References 45 publications

Return Period of Characteristic Discharges from the Comparison between Partial Duration and Annual Series, Application to the Walloon Rivers (Belgium)

Return Period of Characteristic Discharges from the Comparison between Partial Duration and Annual Series, Application to the Walloon Rivers (Belgium)

Surface water floods in Switzerland: what insurance claim records tell us about the caused damages in space and time

Prototype of a LPWA Network for Real-Time Hydro-Meteorological Monitoring and Flood Nowcasting

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