A flash flood hazard assessment in dry valleys (northern France) by cellular automata modelling
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This paper presents an application of a cellular automaton-based run-off model (RUICELLS) to a series of small dry valleys in the Seine-Maritime department, northern France, to better assess their susceptibility to flash flood. These muddy floods shortly follow high rainfall (50–100 mm in less than 6 h) and occur in very small areas (<20 km2). A surge generally rushes down through the main valley just a few minutes after rains have peaked. Previous events (n = 69, in the period 1983–2005) have occasionally threatened human lives and have caused significant damage to property and infrastructure. Nonetheless, given the variation among the valleys and the infrequency of events, these floods have not been numerous enough to permit a statistical analysis. Instead, we numerically simulate the possible future flash floods using RUICELLS, a cellular automaton model driven by a set of three deterministic hydrological rules. Simulations have been conducted for 148 basins, each subject to 16 different rainfall scenarios (2.368 simulations in total) to (1) estimate the peak flow discharges (Q), the specific peak flows (Qs), and the lag times (T) of the flash floods and (2) detect the critical rainfall intensities that would trigger warnings and increased vigilance. Our simulations indicate that the number of basins susceptible to flash flooding greatly increases with the higher rainfall intensity, the distribution of sensitive crops (sugar beet, corn, maize, and flax) and the basin morphology. Several small basins could also induce by convergence a bigger flood in the downstream humid valleys. The location of the highest simulated discharges is aligned with observed events, and this comparison provides an evaluation of the modelling performance and of the credibility of the results.
KeywordsFlash flood Dry valleys Susceptibility assessment Northern France
Flash floods in northern France (Masson 1987; Devaud 1995; Merle et al. 2001; Arnaud-Fassetta et al. 2011) induce serious risk conditions on populated outlets, especially in the Seine-Maritime department (Delahaye et al. 2001; Douvinet 2008, 2014; Douvinet et al. 2013). These hazards are generated shortly after rains ranging from 50 to 100 mm in less than 6 h and occur in small dry valleys (<20 km2). Such flash flood presents distinct features: a violent onset, a rapid rising time, and a surge rushing down just a few minutes after rainfall peaked. Previous floods have occasionally threatened human lives (11 persons died over the period 1983–2005 in this department), and caused significant damage to property and infrastructure (ranging from 0.05 to 14 million Euro for the 1997, June 16th event). The hydrological and geomorphological characteristics are quite similar to others occurring in other sedimentary areas, in western France (Auzet et al. 1995) or in Flanders (Evrard et al. 2007) for example, but are notably different to French Mediterranean floods. The latter occur in basins with higher slope gradients, larger basin area (ranging from 50 to 300 km2) and are typically associated with higher rainfall intensities ( et al. 2001; Collier and Fox 2003; Reid 2004; Barrera et al. 2006; Ruin et al. 2007; Ortega and Heydt 2009; Gaume et al. 2009; Morin et al. 2009; Marchi et al. 2010).
Predicting the time of occurrence and the intensity of northern flash floods remains difficult at larger scales for several reasons: measurements and field-based experimentations are rarely conducted in small dry valleys; these phenomena are insufficiently documented and remain difficult to monitor as they produce destructive effects to measuring devices; the rarity of events and the long recurrence intervals hamper statistical analysis and calibration of models (Ferraris et al. 2002); the short distances between source areas (run-off production) and risk zones (i.e. settlements) frequently surprise inhabitants in a few minutes; changes in velocity, roughness, and water height introduce uncertainties in the estimation of peaks discharge (Gaume et al. 2009; Douvinet and Delahaye 2010) and strongly hamper the classical hydrological approaches (Anquetin et al. 2009; Lumbroso and Gaume 2012).
Because of the statistical models’ reliance on extensive inventories of location-specific past events, they are less transferable between different areas. Furthermore, they only implicitly represent the impacts of processes, rather than the processes themselves (Kappes et al. 2011). Conversely, the physically based models, including cellular automaton (CA) models, consider common physical characteristics of salient processes and are more readily transferable between sites (Coulthard and Van De Wiel 2006; Ménard and Marceau 2006; Van de Wiel et al. 2007). Consequently, we propose applying a CA model, i.e. RUICELLS (Delahaye et al. 2001; Douvinet et al. 2013), to anticipate the spatial occurrence and areas at risk. The RUICELLS model requires fewer parameters than most other physically based models, as distributed (or semi-distributed) hydrological models or soil erosion models (SEMs). Although a few similarities exist between these models (e.g. SEMs need to determine surface run-off before they can calculate soil erosion), the main difficulties are the greater number of environmental factors required (Jetten et al. 1996; Nearing et al. 2005).
By means of a combination of environmental parameters, chosen on the basis of previous experiences, three variables (peak flow, specific flow, and lag time) were calculated for 16 rainfall scenarios on 148 basins (2,386 simulations in total). The methodology applied here simplifies rainfall inputs to 16 rainfall scenarios. The reason for this is twofold. First, even though the recent efforts in meteorological observations provide relevant details on timing and location of convective storms (Collier and Fox 2003), the existing models (e.g. AROME or PANTHERE) do not yet predict rainfall intensities either with sufficient precision at fine scales (<10 km2) or with sufficient advance warning (1 h). Second, this simplified rainfall approach allows us to control the end-to-end simulation process (Fonstad 2006), measuring the transformation of input to output data, testing the sensitivity of basins to initial conditions, and defining their reactivity to different rainfalls with which we cannot experiment in reality.
2 Study sites
Physiographic and land-use characteristics for 25 (of 38) basins (classified by increasing size), on which one flash flood has occurred over the period 1983–2005 (from Douvinet 2008)
Name of basins (25)
Date of eventsa
Average height (m)
3 Materials and methods
3.1 CA background for hydrological modelling
Cellular automaton models increasingly contribute to hydrological or geomorphological studies over the last decade (e.g. Ménard and Marceau 2006; Coulthard et al. 2007; Van de Wiel et al. 2007, 2011). In these dynamic models, the global properties arise from the local and spatial interactions of cellular entities (Wolfram 2002; Fonstad 2006). A lattice on which each cell possesses its own state characterizes these models. The time is discrete and the state of cells updated through the application of a set of predefined rules (Phipps and Langlois 1997). These rules, either expert-based, deterministic or probabilistic, dictate how the cells interact with their neighbors. The CA modelling approach is decades old, introduced by Von Neumann in 1951 (Gardner 1970), made famous by Conway’s Game of Life (1970) and has supported an array of advances in many fields since the 1980s in physics, mathematics, chemistry, and ecology, and since the mid-1990s also in geomorphology (Douvinet et al. 2013). For example, the CA models have been used to study aeolian ripples (Anderson 1990), forest fires (Clarke et al. 1994), debris flows (Di Gregorio et al. 1998), debris-laden floods (Bursik et al. 2003), lava dynamics (Avolio et al. 2006), channel meandering (Coulthard and Van de Wiel 2006), the evolution of coasts (Dearing et al. 2005), and the response modelling of river systems (Van de Wiel et al. 2011) among others.
Murray and Paola (1994)’s braided river model was the first CA including hydrological and geomorphological processes, although their representations of river processes did not include explicit time and real physical scaling (Parsons and Fonstad 2007). Thomas and Nicholas (2002) extended the Murray-Paola model to simulate more realistic flow dynamics in braided river systems. Other water flow models have been developed, e.g. to simulate the growth of small rills in response to hillslope erosion (Favis-Mortlock 1998), to measure soil erosion at microscopic scales in SoDa (Valette et al. 2006), or to simulate basin responses using a wave approximation for in-channel flows (De Roo et al. 1996). Coulthard et al. (2007) and Van de Wiel et al. (2007) recently introduce a gradually varied CA for catchment evolution modelling that includes sediment transport dynamics. A more recent version (Coulthard et al. 2013) includes unsteady catchment hydrology. Although all these models differ considerably in their aims and implementation details, they share a common conceptual design in which a link is established between topographic variables, such as the elevation and its derivative, and hydraulic variables, such as water fluxes and flow velocity. The rules of each CA model describe the precise nature of that link.
Cellular automaton models can also be linked with smoothed particle approaches (e.g. Drogoul 1993) to better assess generic dynamics or hydrological fluxes. In recent years, agent-based modelling (ABM) has been tested in hydrology and geomorphology after first initiatives in ecology, sociology, or human geography. These models may provide alternative approach to CA modelling. For example, CATCHSCAPE allows simulating the hydrological system with its distributed water balance or to irrigate schemes management, crop and vegetation dynamics (Bécu et al. 2003). ABMs can be used in alluvial plains where processes between independent interacting entities behave according to the local environment (Teles et al. 1998). But the agent-based modelling applications remain less used than CA in geomorphology as the attention is more drawn on interactions between human or autonomous entities, more than on physic components, and because CA conveniently have an inherent spatial structure.
Another modelling approach is distributed modelling, improving the lumped models that only predict discharges at final outlets. However, even though distributed hydrological models, also based on the Digital Elevation Maps (Moussa and Bocquillon 1996; Cudennec et al. 2002; Kirkby et al. 2005), are supposed to be spatially explicit over the entire basin, they are usually validated and uniquely calibrated at the outlet. None of them allow for the estimation of potential surface flow concentration in all parts of a basin since the drainage limit divide (Douvinet et al. 2013). Previous studies are also focused on the relation between the global catchment morphology and its hydrological response measured at the final outlet. These studies underlined the difficulties encountered when linking local responses (sub-basins or hillslopes) to this global behaviour, and this aim has been one of the main issues for geomorphologists since the 1970s (Veltri et al. 1996; Rodriguez-Iturbe and Rinaldo 1997; Schmitz and Cullmann 2008). A few studies have successfully shown that the network organization plays a key role on hydrological functionality (Dietrich et al. 1993; Vogt et al. 2003). The CA RUICELL partially overcomes such difficulties and also implicitly captures the channel network structure and its influence on flood through scales.
3.2 The RUICELLS’ spatial structure and conceptual design
Similar to other hydrological or geomorphological CA models, the RUICELLS model establishes a link between topographic variables and hydraulic variables. In this sub-section, we focus on RUICELLS’ spatial structure and conceptual design, more than on all the mathematical relations underlying the process representation that is too cumbersome and too space consuming. A full description of the RUICELLS model, including its mathematical structure, can be found in Delahaye et al. (2001), Langlois and Delahaye (2002), Jaziri 2004 and Douvinet et al. (2013).
The diversity of the topography and the variety of the mechanisms involved precludes a global modelling of the run-off process (Mita et al. 2001; Palacios-Vélez et al. 1998; Tucker et al. 2001) and it requires a sharp division of the concerned area into homogeneous and interconnected cells. In RUICELLS, the original CA concept is generalized to incorporate the variety of the topographical conditions: elementary surfaces on hillslopes, linear portions of thalwegs, and local depressions. The spatial dimensions of cells thus are 0, 1, or 2 (point, line, or surface). Moreover, the connections of the automata are directed only by the neighbourhood topology of cells, but also by morphological links organizing the space: the links of discharge between the cells and the links of overflow between the sub-basins.
In order to access the geometric information, the topological graph applied on the TIN structure is composed of three main features: node, arc, or triangle (Fig. 2d), inducing the following relational tables. The arcs play a major role: each arc is connected to two nodes and two triangles, and a morphological attribute may be given to it by the relative heights of the former and the relative slope angles of the latter. Comparing the heights of two nodes, we can see if the connecting arc is downhill, uphill, or flat. As for the triangles, two of them (side by side) may be also, individually, downhill, uphill, or flat. An arc whose final node is lower than the initial one is downhill but if its two neighboring triangles are downhill towards it, it equals a downhill thalweg (Fig. 2d). The typology gives 33 = 27 theoretical possibilities. After eliminating some rare and specific situations, we have kept several attributes for the arcs. The “external limit” has been introduced to handle with the limits of the studied area. The “flat” is attributed to the limit between two flat triangles. It must be stressed that these attributes are purely local: if an arc equals a downhill thalweg, there is no continuity for the downstream arcs (Douvinet et al. 2009, 2013). Yet its knowledge is important to determine the run-off process, which is linear along this arc, while if the arc attribute is left slope, the run-off is a sheet flow and its direction transversal.
Six flow parameters are defined in RUICELLS (Fig. 2h): the water height required to maintain a constant flow when the slope angle is negligible (k00 = 1); the water height needed for a constant flow if slopes are higher (k01 = 0.1); the water height threshold up to which flow speed attempts v0 (k10 = 50) or v1 (k11 = 100); the maximum speed if slope angles are negligible (v0 = 0.2) or higher (v1 = 60). All these parameters have been calibrated on the basin of Saint-Martin-de-Boscherville (13.4 km2), partly by comparing with simulations results of the STREAM model (Merle et al. 2001) and partly by comparing with flow estimations derived from the maximum slack water deposits observed after the June 16th, 1997 flash flood event (Delahaye et al. 2001).
3.3 Data acquisition and chosen parameters
To simulate potential hydrological responses to various rainfall intensities, three types of input data are needed, aside from the DEM (source: IGN; resolution of 25 meters in this study): (1) a relevant land-use map (LUM); (2) water infiltration capacities; (3) the definition of rainfall intensities (with real data or not).
Water infiltration capacities (in mm h−1) in Seine-Maritime, according to various studies, and minimal values chosen for the flash flood assessment in this study
Name of land-use type in the CLC–GPBF database
Cerdan et al. (2002)
Delahaye et al. (2001)
Final parameters used in this study
Temporary or permanent grass
To account for rainfall variability, we assess the susceptibility of basins playing with different rainfall intensities. Two choices were possible: either we implement rains according to the frequency analysis methods for extremes, i.e. the SHYREG database (Renard et al. 2013), or we consider project rainfall scenarios for all basins. The first choice was not appropriate for this study for two reasons: (1) the statistic calculation of rains presenting small probability and large return periods introduce high uncertainties; (2) earlier research (Douvinet et al. 2009) underlined differences between measurements by official stations, radar, and volunteer stations. For the storm event of July 25th, 2000, for example, Neuville-sur-Dieppe has officially measured 33.2 mm in 24 h, whereas the radar pixelated 50–75 mm in 2 h (at a distance of 2 km from the station) and a volunteer cumulated 78 mm in 1 h 15 min. Thus, even if rains are not representative of the extreme possible events on each basin, we created a set of potential rainfall scenarios of different intensity and duration: 20, 30, 40, and 50 mm in 1 h; 30, 40, 50, and 60 mm in 2, 3, and 6 h, Even though this flood susceptibility is likely overestimated in these worst-case scenarios, the highest intensities (50 mm in 1 h) could locally happen.
3.4 Simulation set-up and outputs
3.5 Limits for the modelling performance assessment
Normally, after a model is developed, it is tested before being put to use as a predictive or explanatory tool. This is a form of quality assurance, and it involves the simulation of a situation for which observed data are available (Van de Wiel et al. 2011). For this instance, the model parameters have been only calibrated for the 1997, June 16th event (Delahaye et al. 2001), through the simulation of the diffusion of the run-off process in two basins. In these two cases, the major simulated areas sensitive to the run-off processes equal to the real production areas and the divergence with the observations is only important in the upstream southern part of a basin. The simulation, indeed, locates a major flow, which has not been observed in this area; this is due to the fact that the simulation has not taken into account the influence of the highway crossing the upstream part of the basin. This highway stopped the flow and produced a flood leveling, generating retentions of water along many embankments. The observation shows the limits of the model, but stresses also the efficiency of such a tool to evaluate the incidence of an implement on the hydrological behaviour of a basin. On the other hand, these results show the accuracy of this approach and how, starting from a simple data set, it is possible to set-up a cartographic presentation of the run-off dynamics. Simulations also give a good agreement in comparison with estimations proposed by more complex hydrological models (GR4J, STREAM, and LISEM) and those derived from water deposits (Merle et al. 2001). Our simulations cannot be verified quantitatively due to a lack of independent data. Validation occurs only on a scenario basis, and it is always possible to attribute errors of the simulations to inaccuracy of the initial or external forcing conditions, rather than to inaccuracy of the model’s hypotheses (Van de Wiel et al. 2011). Even though Begueria (2006) use, for example, confusion matrices to compare modelling and recorded events in true or false positives or negatives information (Kappes et al. 2011), the low availability of hydrological data of events renders such approach impractical. Lacking an independent quantitative validation, these first modelling results are only evaluated in a qualitative way, which necessitates a careful interpretation (see Sect. 5.1).
4 Results and susceptibility assessment
Simulations are analyzed for three main variables that characterize basin susceptibility of flash flooding: peak flow discharge (Q), peak unit discharge (Qs), calculated by dividing Q by the basin size and lag time (T), i.e. the duration between the beginning of rainfall and the onset of peak discharge (and not the time between the onset of peak rainfall and the onset of peak discharge, due to the simulation configuration), for each of the 148 studied basins and each of the 16 rainfall intensities. Even though results are available on each basin, they are presented here in aggregated form, i.e. at large scale, to facilitate the susceptibility analysis.
4.1 Peak flow discharges
Similarly, the susceptibility decreases for rainfalls more spread over time. For example, for storm with 50 mm in 2 h, only 33 basins have Q > 4 m3/s (Fig. 5d) and 6 basins have values up to 7 m3/s. In terms of land use, susceptibility to flash flooding is higher in basins where percentages of sugar beet, corn, maize, and flax are important. These basins are subject to flash flooding at 30 mm in 1 h, and even react to lower intensity storm events of longer duration (e.g. 40 mm in 2 h or 50 mm in 3 h). Conversely, the peaks of discharges of other basins, in which cultivated areas are more dispersed, suddenly increase (>7 m3/s) for more intense storm event of 50 mm in 1 h. Grasslands are sufficient to reduce the run-off production coming from upstream parts for gentle rainfall intensities (<40 mm.h−1), but become inefficient for more intense rains. Basins with other dominant land use present intermediary behaviours between these extremes.
At larger scales, several basins with high responses are spatially concentrated, especially along the coastal areas along The Channel, the Seine River, or along a few tributaries (Scie, Durdent or Saâne rivers). In this case, several floods can arrive at the same moment and generate high-risk levels in case of an extended thunderstorm (>10 km2). Flash floods from similar events, but occurring over more isolated basins (such as in the eastern part of the department), are easier to manage and to prevent. In a qualitative way, the comparison with historic flash floods occurrences (over the period 1983–2005) shows a good correlation with the highest Q values: the three basins identified as the most susceptible in our simulations for storm events of 40 mm in 1 h, also have historically observed flood events). However, the validations are not systematic. For example, flood events have been observed in 12 of the 21 basins identified, as the most sensitive for 50 mm in 1 h, while the results for 30 mm in 1 h, as well as for 50 mm in 2 h, are a less successful indicator. Therefore, the Q values need to be divided by the basin size, since weak peak flow discharges do not have the same hydrological significance in small and large basins.
4.2 Peak unit discharges
Clear trends are observable, with, especially high Qs values at the outlets of dry valleys recorded to bigger rivers (Durdent, Valmont and Bolbec) and with the highest peak unit discharges occurring in the smallest basins. The basin size increases more quickly than Q and this explains why high Qs values are rarely observable on “larger” basins (ranging from 10 to 20 km2). Even with the provision that these first modelling results need to be treated with care, three points are important: (1) small basins can produce high Qs values, independently of their land use, and rainfall-discharge models are insufficient to manage their susceptibility; (2) basins combining large basin area (>10 km2) and a Qs value greater than 1 m3/s/km2 are the most sensitive since they can produce the most damaging floods; (3) concentrated high Qs values induce high risk in several valleys (Lézarde, Valmont). In a qualitative way, the comparison with historic flash floods occurrences (over the period 1983–2005) shows a good relation with the highest Qs values (5 of the 9 basins identified as the most sensitive in our simulations for storm events of 40 mm in 1 h, and 17 of the 34 basins for 50 mm of rainfall in 1 h, had historic events).
4.3 Lag times
Several basins with peak flows ranging from 4 to 7 m3/s present the smallest lag times. The forecasters need to pay attention a greater attention on these as they can simultaneously produce several flash floods. Fortunately, all these identified basins very unlikely generate high flows at the same moment, since a storm event with 50 mm of rainfall in 1 h is very unlikely to occur over the entire Seine-Maritime. However, such storms can threaten this area in the future (following the predictive scenario 2.a; GIEC 2009) and can affect multiple basins locally if they are within close proximity. On the other basins, lag time increases with basin size, and forecasters should have more time (>3 h) for alert. In a qualitative way, the comparison with historic flash floods occurrences (over the period 1983–2005) shows bad correlations (whatever the rainfall intensities) because only a few number of basins with historical floods present small lag times. Hence, this parameter is of paramount importance for forecasters, but seems to be the less useful to explain the flash flooding susceptibility (Fig. 7).
The model’s success in identifying flash flooding over a majority of basins where historical flooding indeed was observed indicates that it can be used to anticipate the flash floods in the Seine-Maritime department. However, since the simulations cannot be completely validated, care must be taken in interpreting the results.
5.1 Validation efforts and limits
The modelling validation is a fundamental step because this determines both the quality of the approach and the credibility of simulation results. In this study, the validation remains difficult due to the relatively low number of basins (38) affected by previous flash flood events (over the period 1983–2005). If we focus on the simulations obtained on these 38 basins, 17 (46 %) have peak unit discharges up to 0.7 m3 s−1 km−2 and 24 (63 %) have a peak flow discharge up to 4 m3 s−1, for a rain of 50 mm in 1 h. Even if these results indicate that the model is successful in identifying flash flooding in most of these basins, this also indicates that a number of basins where historical flooding was observed did not experience flooding in simulations (14 out of 38, or 37 %). The identification of such differences can be explained by three arguments: (1) the real rains were more intense than our maximum intensity rainfall scenario (50 mm in 1 h)—by running simulations with higher intensities (i.e. from 60 to 100 mm in 1 h), we observe that all the 38 basins present high sensitivities for a rain up to 78 mm in 1 h; (2) a higher sensitivity to run-off and flash floods (even though the peak unit discharge does not exceed 0.7 m3 s−1 km−2) because of a strong human settlement in the outlets—this hypothesis is attested on 9 basins out of the 14 studied; (3) the simulations underestimate the impact of the “built” environment in LUM. Alternatively, how can we explain the identification of other basins for which the simulations indicate flash flood susceptibility, but where no historical observations are present? If we trust in local observations on “non-affected” basins, provided by stakeholders or risk managers, 35 basins (32 %) have known local problems (flooded roads, small erosions) after intense rains. If we consider this additional information, the flash flood susceptibility is confirmed on 59 basins (57 % of the 148 studied basins). Finally, if the critical rain is recorded in the future, we should survey the basin reactivity and then see if the simulation results can be validated a posteriori.
5.2 Advantages and limitations for anticipation
Anticipation of flash floods in small basins becomes urgent, since they induce rare, violent and sudden impacts on inhabited outlets. Furthermore, the local population is unaware of the possible flash flooding risk to which they are exposed. The other models developed earlier, such as STREAM, LISEM, or WATEM (De Vente and Poesen 2005; Nearing et al. 2005), permit to manage flash flooding susceptibility for a specific basin but not on many basins, since local to outlet scales, and by playing with different intensities. Therefore, these simulations proposed by RUICELLS can improve our knowledge without taking into account rains frequency. This approach consists of combining the most recent and available GIS data with the CA modelling. Results are discussed with local stakeholders and risk managers to verify whether the highest simulated susceptibilities have resulted in previous problems. Simulations obtained in many basins are not validated but several experimentations in real time should be planned over the next 10 years. Nonetheless, these preliminary investigations give promising results. We hope that this kind of work will serve not only to help farmers reducing soil losses, but also to help forecasters to define places or roads where potential high-level damage can be expected. There is a need to protect people if time to react does not exceed a few minutes, as it was the case in the basin of Saint-Martin. We could also diffuse a vigilance signal with colors ranging from green to red, as it already exists in France for floods over greater basins (www.vigicrues.fr).
Similar investigations may be carried out in other sedimentary areas where the flash floods also occurred with violence in the last years, as in the Sussex (Boardman et al. 2003) or in Flanders (Evrard et al. 2007). Alternatively, these maps also question the networks capacities and the socio-economic stakes to face to flash flood events, and they also necessitate the study of resilience of societies and the evaluation of economic losses (Douvinet et al. 2013). For this, we have to quantify the precise structural vulnerability to flash floods at the inhabited outlets and the time needed for the restoration post-event. We will work on it with the SCHAPI (the French forecasting official service) over the period 2013–2016).
The anticipation of flash floods in small and dry basins located in the Seine-Maritime is hampered by a lack of hydrological, meteorological, and geomorphological knowledge. The rareness and severity of such events make the measurement of hydrological responses and behavior after intense rains difficult. In this paper, we present the methodological investigations and the flash flooding susceptibility results obtained on the entire department of Seine-Maritime using the CA RUICELLS model. We rely on the hydrological estimations (peak discharges, specific peak discharges and lag time) and the critical rains to identify conditions, at local scales, which may necessitate increasing vigilance from hydrological and meteorological forecasters (like for the FFG, Flash Flood Guidances, created in USA, Estupina-Borell et al. 2005). We emphasize the need for a careful interpretation of simulation results, remaining conscious of inherent assumptions of the model used and of the quality of input data. Even though the information on a number of documented flash flood events exist (Douvinet 2008), records for some susceptible areas are missing, which impedes the validation of a deterministic modelling approach, as adopted in this study. However, validations efforts could provide levels at which to issue alerts and question the potential effectiveness of a specific flash flood alert system for this region. And for this, field experiments and surveys are expected during the next 2 years.
Two main questions should be addressed in these subsequent studies. First, these floods are associated with high sediment concentrations that remain difficult to define. Indeed, managers and official services clean the flooded urbanized areas and erase deposits before they can be surveyed and studied. Thus, even though sediment sources are well known (soil erosion, destabilization of slopes and mass movements, incision in road networks, overthrusting of debris, vegetal, and artificial elements adding to solid fluxes), a precise quantification of the sediment budget is delicate in these small ungauged areas (Douvinet et al. 2013). Second, the lack of knowledge on specific stream powers (measured only in a few cross sections) and on influential factors (links between land use, morphological features, and rainfall intensities) for flash flooding requires additional studies. Indeed, a better assessment of the minimum values needed to induce erosion, incision, and flash flood should help us for a further understanding of the emergence of a turbid wave observed in several thalwegs.
The authors are grateful to the Service Central d’Hydrométéorologie en Appui à la Prévision des Inondations (SCHAPI) for funding the RuRan project over the period 2011–2013 within which the study could be carried out. They also want to express their gratitude to the French Research Ministry and CNRS for funding the SYMBAD project (2004–2007), within which RUICELLS was developed and calibrated on a few basins. In addition, thanks are due to F. Mallet and A. Christol (for field experimentations and simulations), and to A. Escudier, B. Janet, A. Bachoc (SCHAPI), Y. Redor, K. Goncalvez (SPC-SACN) and P. Langlois (University of Rouen) for suggestions and comments, which helped to improve the quality of the article.
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