# Sediment yields during a catastrophic flood: Example of the Vésubie tributaries during Storm Alex

Théo Welfringer, Frédéric Liébault, Dominique Laigle, Damien Kuss

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Théo Welfringer, Frédéric Liébault, Dominique Laigle, Damien Kuss. Sediment yields during a catastrophic flood: Example of the Vésubie tributaries during Storm Alex. Géomorphologie: relief, processus, environnement, 2025, Torrents, 31 (4), pp.20319. 10.4000/14nmk. hal-05272207

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# Sediment yields during a catastrophic flood: Example of the Vésubie tributaries during Storm Alex

## Productions sédimentaires d'une crue extrême : exemple des affluents de la Vésubie au cours de la tempête Alex

Théo Welfringer<sup>a*, b</sup>, Frédéric Liébault<sup>b</sup>, Dominique Laigle<sup>b</sup>, Damien Kuss<sup>c, d</sup>

## ABSTRACT

On October 2, 2020, Storm Alex triggered one of the most extreme torrential floods in recent years in France, causing severe damage in the Vésubie and Roya valleys (southeastern France). In the Vésubie, the floods induced exceptional sediment yields both in the main river and its tributaries. High-resolution pre- and post-event LiDAR data enabled the construction of a detailed sediment budget at the catchment scale. This study (i) contextualizes the storm-induced torrential event with other documented intense floods across the Alps and worldwide, and (ii) analyses the variability and controlling factors of the specific sediment yield in the Vésubie tributaries. While Storm Alex ranks among the highest sediment yields recorded in the Alps, it is included in the variability domain of extreme headwater sediment production. Integrating these new data with existing Alpine records allows to refine an upper limit (the 99th percentile) for values of extreme sediment yields in the Alps. Variability in measurement methods across the literature underscores the suitability of systematically using high resolution topographic differencing for future extreme torrential events analyses. The observed specific sediment yield variability across the tributaries cannot be explained by rainfall forcings, making sediment availability the dominant control. We demonstrate that the specific sediment yield variability is best predicted by the proportion of sediment sources and the slope at the outlet measured on each catchment.

**Keywords:** extreme flood, extreme rainfall, sediment yield, sequential airborne LiDAR, sediment sources, mountain river.

## RÉSUMÉ

La tempête Alex du 2 octobre 2020 a été à l'origine de l'une des crues torrentielles les plus intenses du XXIᵉ siècle en France, causant d'importants dégâts, principalement dans les vallées de la Vésubie et de la Roya. Dans le bassin de la Vésubie, les crues torrentielles ont entraîné une production sédimentaire exceptionnelle sur les affluents et le fond de vallée. Des données LiDAR pré- et post-événement ont permis la construction d'un bilan sédimentaire détaillé à l'échelle de l'ensemble du bassin versant. Cette étude (i) replace cet événement torrentiel au regard des crues intenses documentées dans les Alpes et dans le monde, et (ii) analyse la variabilité et les facteurs de contrôle de la production sédimentaire spécifique des affluents de la Vésubie. Bien que la tempête Alex figure parmi les plus fortes productions sédimentaires documentées dans les Alpes, elle n'est pas pour autant sans précédent. L'intégration des données de la tempête Alex aux données de la littérature dans les Alpes nous permet d'établir une borne supérieure (99 percentile) robuste de la production sédimentaire des évènements extrêmes à l'échelle des Alpes. La variabilité des méthodes de mesure utilisées dans la littérature démontre la pertinence de généraliser l'utilisation des topographies différentes haute résolution pour l'étude des futurs évènements extrêmes. Les résultats montrent que la variabilité de la production sédimentaire ne s'explique pas par les forçages pluviométriques, mais est contrôlée par la disponibilité en sédiments de chaque bassin. Enfin, il ressort que la proportion des sources sédimentaires et la pente à l'exutoire calculées à l'échelle des bassins versants sont les meilleurs prédicteurs de la production sédimentaire spécifique.

**Mots-clés :** crue extrême, précipitations extrêmes, transport solide, acquisition LiDAR, sources sédimentaires, rivières de montagne.

## 1. Introduction

Extreme torrential events in headwaters of upland catchments can generate dramatic consequences for human settlements and infrastructures in alpine valleys. In the French Alps, the latest catastrophic event of June 2024 in the Ecrins massif has induced a massive sediment disaster on the entire alluvial fan

of the Etançon Torrent, and the subsequent devastation of the hamlet of La Bérarde (Blanc et al., 2024). On a wider scale, many well-documented extreme torrential floods occurred during the last 20 years in the Swiss, Austrian, and Italian Alps (e.g., Rickenmann and Koschni, 2010; Krapesch et al., 2011; Scorpio et al., 2022) and in the Pyrenees (Blanpied et al., 2018). Although the notion of extreme event remains relatively vague, as the link

<footnote><sup>a</sup> AgroParistech, 91120, Palaiseau, France.</footnote>

<footnote><sup>b</sup> Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France.</footnote>

<footnote><sup>c</sup> Office National des Forêts – Direction Forêt et Risques Naturels, 9 quai Créqui, 38000 Grenoble, France.</footnote>

<footnote><sup>d</sup> SYndicat Mixte des Bassins Hydrauliques de l'Isère, 9 rue Jean Bocq, 38022 Grenoble, France.</footnote>
between the return period of a flood event and its geomorphic consequences is not straightforward (Arnaud-Fassetta et al., 2005), extreme torrential events are systematically associated with massive sediment transport and intense geomorphic changes. Therefore, it is of uppermost importance to collect and analyse sediment yields associated with such catastrophic events (Krapesch et al., 2011; Korup, 2012). This kind of data, even if roughly estimated from rapid field surveys of deposited volumes, are crucial information for characterizing the event, since peak discharges are generally lacking or quite difficult to reconstruct with a good confidence interval.

A better characterization of extreme sediment yields of
torrents is not only important for the understanding of causes
and consequences of sediment disasters in mountains, but also
for the protection of populations against such events. Indeed,
major geomorphic processes occurring on alluvial fans, such
as channel avulsion, channel aggradation and widening, are all
strongly linked with the sediment yield at the fan apex (Pitlick,
1993; Korup, 2004; Arnaud-Fassetta et al., 2005). However,
prediction of event-based extreme sediment yields of upland
catchments is still a key challenge of torrent research, despite
many recent empirical appraisals based on regional datasets
of extreme events (Marchi and D'Agostino, 2004; Gertsch
et al., 2012; Peteuil et al., 2012; Altmann et al., 2021; Morel
et al., 2023). Simple scaling laws based on drainage area have
shown that extreme debris-flow volumes scale linearly with the
catchment size (isometric relation), emphasizing the effect of
widespread sediment source activation on the sediment yield of
major events (Marchi et al., 2019). These scaling laws have been
also regionally explored to show the importance of lithological
and glacial imprint (surficial deposits) controls on the specific
sediment yield related to debris flows, through their effect on
sediment supply (Brardinoni et al., 2012). The relative surface
of sediment sources in the catchment (active erosion cells
connected to the stream network) has also been identified as a
major controlling factor of extreme sediment yields in several
statistical studies of regional datasets (Peteuil et al., 2012;
Altmann et al., 2021; Morel et al., 2023). Sediment supply and
connectivity throughout the catchment are then recognized as
critical factors for the prediction of the sediment response of
upland catchments to hydro-meteorological forcings (Heckmann
et al., 2018; Scorpio et al., 2022). The effect of the magnitude of
hydro-meteorological forcings has been less investigated, since
rainfall and runoff data are generally missing in meta-analysis
of event-based sediment yields. However, the regional analysis
of sediment transport data during the extreme floods of August
2005 in Switzerland has shown that bedload yields were related
to runoff volumes derived from radar rainfall data, while debris-
flow yields were not, but rather mostly related to the drainage
area and channel slope (Rickenmann et Koschni, 2010).

Storm Alex is an extreme rainfall event that has induced catastrophic torrential floods in two alpine valleys of SE France (Vésubie and Roya) in October 2020 (Carrega et Michelot, 2021). An emergency LiDAR (Light Detection and Ranging) survey undertaken few days after the event by IGN (Institut national de l'information géographique et forestière) in these valleys has provided a dataset of high-quality for assessing catchment-

scale sediment transfers, notably in the Vésubie where a pre-
event LiDAR survey was available. In the Roya valley, the lack
of a pre-event LiDAR hinders a large-scale analysis of sediment
transfers, although some analyses have been reported on (i) the
geomorphic response of a tributary catchment (Viévola Torrent,
Kerverdo et al., 2024), and (ii) large in-stream wood yield of the
whole catchment (Piton et al., 2024). In the Vésubie valley, a
detailed analysis of the sediment cascade along mainstems has
been reported (Liébault et al. 2024), but sediment yields of the
tributaries have not been the scope of a specific investigation.
These have been analysed here with the objectives of (i) to
produce new data of extreme event-based sediment yields of
small alpine catchments and to compare them with previously
reported extreme events, and (ii) to analyse the inter-catchment
variability and the controlling factors of the tributary sediment
responses.

2. Study site and Storm Alex

2.1. Tributaries of the Vésubie River

The Vésubie is a gravel-bed river of the Southern French Alps, draining the southeastern part of the Argentera-Mercantour massif, in the Alpes-Maritimes County. The climate is strongly influenced by (i) the proximity to the Mediterranean Sea, with heavy rainfall events in autumn and convective storms during summer, and by (ii) the complex orography, leading to a strong spatial variability in rainfall distribution. The Vésubie drains a 392 km² catchment, with its highest point at the Cime du Gélas (3143 m asl). Its outlet is situated at 320 m asl, at the confluence with the Var River, which flows into the Mediterranean Sea near the city of Nice. The Vésubie catchment has a relatively low population, with just over 5000 inhabitants mainly concentrated in 3 municipalities (Lantosque, Roquebillière, Saint-Martin-Vésubie) located in the valley bottom (fig. 1).

The Vésubie catchment is divided into two major geological
units: to the north, the crystalline rocks of Argentera-
Mercantour (mainly granites, anatexites, and migmatites), and
to the south, the Mesozoic and Cenozoic sedimentary cover,
primarily composed of limestones, marls, and sandstones
(fig. 2A). Important glacial imprints from the last cold period are
present, like fluvio-glacial deposits in the valley bottom, glacial
cirques, and moraines mainly in the highest headwater basins.
The past obstruction of the Vésubie valley by the Gordolasque
glacier resulted in the accumulation of important fluvio-glacial
deposits between Saint-Martin-Vésubie and Roquebillère
(Julian, 1997).

This study focuses on 43 tributary catchments distributed
along the Boréon, the Upper Vésubie (also known as Madone
de Fenestre), and the Vésubie from the confluence with the
Boréon to the confluence with the Figaret (n°25, fig. 1). These
catchments include all the tributaries (n = 35) that have been
investigated in the study of Liébault et al. (2024), who identified
all tributaries that were considered as important sediment
sources to mainstems during the event. These include all the
tributaries located downstream of the Boréon reservoir (major
sediment trap, fig. 1) that underwent significant morphological
<figure><img src="image_2.png" /><figcaption>**Fig. 1** – The 47 studied catchments of the Vésubie River basin. A: location in France (Lambert 93); B: global view; C: zoom on the Madone de Fenestre and Boréon valleys. Numbers refer to catchments listed in Table S1.</figcaption></figure>

1. Main rivers; 2. Selected tributaries; 3. Other tributaries; 4. Catchments delineation; 5. Elevation (m); 6. Main city centers; 7. Boréon reservoir; 8. Cime du Gélas.

<caption>**Fig. 1** – *Emprise des 47 bassins versants d'étude de la vallée de la Vésubie.* A : localisation en France (Lambert 93) ; B : vue globale ; C : zoom sur les vallées de la Madone de Fenestre et du Boréon. Les numéros font référence aux bassins listés dans le Tableau S1.</caption>

1. Cours d'eau principaux ; 2. Affluents étudiés ; 3. Autres affluents ; 4. Empresses des bassins versants ; 5. Altitude (m) ; 6. Principaux centres- villes ; 7. Retenue du Boréon ; 8. Cime du Gélas.

changes on their alluvial fans during the Storm Alex, based on the visual inspection of ortho-images and topographic differencing data produced few days after the event (see section 3.1). This dataset was completed with 8 catchments located upstream of

the Boréon reservoir, for which a strong geomorphic activity has been detected with ortho-images and topographic differencing data. Additionally, four cumulative catchments are considered in the analysis related to comparison with previously reported
extreme events (see section 4.1): the Boréon upstream from the reservoir, the Boréon upstream from the confluence with the Madone de Fenestre, the Madone de Fenestre, and the Vésubie. The main characteristics of these catchments are presented in Table S1.

## 2.2. Storm Alex: hydrometeorological features and post-event emergency survey by IGN

Storm Alex was an extreme meteorological event (atmospheric river) induced by a cyclonic depression that formed in North Atlantic on October 1, 2020 (Davolio et al., 2023). Its arrival on metropolitan territory on October 2 brought a flow of warm and humid air from the Mediterranean, triggering extreme rainfall over the southeastern Argentera-Mercantour massif. The highest 24-hour cumulative rainfalls, with more than 80 % falling within just 12 hours, were observed in the Roya valley, exceeding 650 mm, while in the Vésubie valley, maximum cumulative rainfall was measured at around 500 mm (fig. 2B). Statistical analysis of 24 h cumulative rainfall have shown return periods exceeding 1000 years (Carrega et Michelot, 2021).

Hydrological and hydraulic studies have reached consensus on the estimates of peak discharges, notably 850 m³.s⁻¹ [650-1050]

for the Vésubie downstream of the confluence with the Figaret (Pons et al., 2024). However, these studies stressed out that the extreme nature of the event is not solely linked to the magnitude of peak discharges, but rather to the exceptional runoff volumes and sediment transport during the event.

Two days after the disaster, almost all affected basins were surveyed by IGN using airborne imagery and LiDAR. In 2021, IGN conducted a nationwide LiDAR survey, allowing to complete areas not covered in 2020. The availability of a LiDAR survey undertaken in November 2018, by the MNCA (Métropole Nice Côte d'Azur) on its territory, enabled the creation of a DoD (DEM of Difference) on the whole Vésubie valley, that has been invaluable for geomorphological studies (ONF-RTM et al., 2023; Liébault et al., 2024). In the Vésubie valley, the reconstruction of the sediment cascade allowed the estimation of sediment supply from tributaries at 1.5 Mm³; although these contributions were considerable, they remain far below the sediment recharge through the fuelling effect of alluvial storage along the valley floor, estimated at 4.1 Mm³ (Liébault et al., 2024). This dramatic sediment supply led to major morphological changes, with an increase of the mean active channel width by a factor of 4.65, resulting in the rapid formation of a braided channel over 35 km.

<figure><img src="image_3.png" /><figcaption>**Fig. 2 - Geology of the Vésubie valley and 24 h rainfall accumulation during Storm Alex.** A: geology of the Vésubie valley, based on the BRGM (Bureau de Recherches Géologiques et Minières) (BD Charm-50); B: the 24 h rainfall accumulation during Storm Alex, from 02/10/2020 06:00 UTC to 03/10/2020 06:00 UTC, based on the Antilope (Champeaux et al., 2009) rainfall data processed by Electricité De France.</figcaption></figure>

1. Alluvial deposits; 2. Colluvial deposits; 3. Sedimentary rocks; 4. Magmatic rocks; 5. Metamorphic rocks; 6. Glacial deposits; 7. Vésubie catchment; 8. Isohyets 100 mm; 9. Main city centers; 10. Stream network; 11. 24 h rainfall (mm).

<caption>**Fig. 2 - Géologie de la vallée de la Vésubie et cumul de pluie sur 24 h de la tempête Alex.** A : géologie de la vallée de la Vésubie, tirée de la BD Charm-50 du BRGM ; B : cumul de pluie sur 24 h de la tempête Alex, du 02/10/2020 06:00 UTC au 03/10/2020 06:00 UTC, d'après la lame d'eau Antilope (Champeaux et al., 2009) réanalysée par Electricité De France.</caption>

1. Dépôts alluviaux ; 2. Dépôts colluviaux ; 3. Roches sédimentaires ; 4. Roches magmatiques ; 5. Roches métamorphiques ; 6. Dépôts glaciaires ; 7. Bassin versant de la Vésubie ; 8. Isohyètes 100 mm ; 9. Principaux centres-villes ; 10. Cours d'eau ; 11. Précipitations sur 24 h (mm).
# 3. Material and methods

## 3.1. Event-based sediment yields of the tributary catchments

The quantification of sediment yields for the tributary catchments of the Vésubie is based on the use of LiDAR point clouds collected in November 2018, October 2020 (after the storm), and June 2021 (Liébault et al., 2024). The 2021 point clouds were used in areas not covered by the post-storm flight of 2020. A co-registration of sequential point clouds was performed with CloudCompare®, reducing the average systematic error of alignment from 10.8 cm to -0.69 cm (Tacon et al., 2023; Liébault et al., 2024).

Realigned point clouds were used to construct 0.5 m DEMs, and a classic topographic differencing (DoD) was used to calculate deposited and eroded volumes across all areas of interest (using *terra* package in R software; Hijmans et al., 2025), along with associated uncertainties. Areas of interest were manually digitized to systematically include all the active and connected geomorphic surfaces located upstream of the fans apex (fig. 3), since the objective is to evaluate the volume of sediment reaching the outlet of the sediment production zone. This volume is of interest for assessing torrential hazards on alluvial fans, related to sediment deposition and channel avulsion. Only a brief summary of the workflow is presented here, since it was described in details in Liébault

et al. (2024). A DoD thresholding using a 95 % confidence interval LoD (Level of Detection) was used to calculate eroded and deposited volumes (Lane et al., 2003). Following recommendations from Carley et al. (2012), the LoD was subtracted from all DoD cells for the computation of gross eroded and deposited volumes. For net balance calculations, DoD thresholding was not used to avoid additional biases and errors (Anderson, 2019; Lane et al., 2003). The uncertainty calculations for net volumes are based on the approach presented by Anderson (2019). Finally, sediment volumes, both eroded and deposited, and the overall balance were calculated for each area of interest using the *terra::extract* function (Hijmans et al., 2025). Sediment yields of some tributary catchments are therefore different from those reported in the previous study by Liébault et al. (2024) whose objective was to evaluate sediment supply from tributaries, and which systematically included alluvial fans in the computation of net sediment volumes. The tributaries sediment budget data are summarized in Table S2.

## 3.2. Geomorphological and rainfall parameters extraction

A 5 m DEM constructed from the 2018 LiDAR point cloud was used to automatically extract the 47 tributary catchments and their stream network. Extraction of stream networks was done with the «*Channel network and drainage basins*» module available in the

<figure><img src="image_4.png" /><figcaption>**Fig. 3 – Mapping of the areas of interest.** A: global view of the areas of interest; B: DoD on four tributary catchments of the Madone de Fenestre. October 2023 ortho-images from IGN in the background.

1. Catchments delineation; 2. Areas of interest. 3. DEM of Difference (DoD) (m).</figcaption></figure>

<caption>**Fig. 3 – Cartographie des emprises d'intérêt.** A : vue globale des emprises d'intérêt ; B : DoD sur l'emprise de 4 affluents de la Madone de Fenestre. L'orthophotographie du fond de carte est tirée de la campagne d'octobre 2023 de l'IGN.

1. Emprises des bassins versants; 2. Emprises d'intérêt; 3. Différentiel des MNT (m).</caption>
QGIS SAGA toolbox (Conrad et al., 2015). A Strahler order threshold of 4 was chosen for channel initiation. A manual correction of the resulting first- and second-order streams was carried out to better align with the pre-storm situation, on the basis of the 2020 ortho-image surveyed before the storm by the IGN between July and August and the 2018 DEM. The stream network of the 4 large cumulative catchments was extracted from the IGN BD TOPO. Catchment and stream network layers were used to calculate the geomorphological parameters presented in Table S1. The Melton index M (Melton, 1965) was calculated as:

$$M = \frac{Z_{\max} - Z_{\min}}{\sqrt{A}} \quad [1]$$

with $Z_{\max}$ (m) and $Z_{\min}$ (m) the maximum and minimum catchment elevations, respectively, and A (m²) the drainage area. The slope at the outlet ($S_o$ in %) was defined as the slope of the hydrographic network upstream of the fan apex, over a distance of 10 times the width of the channel at the fan apex. The mean catchment slope S was extracted from the 5 m DEM, and the mean channel slope $S_{Chan}$ was extracted from the previously constructed stream network. The index of connectivity (IC) (Borselli et al., 2008; Cavalli et al., 2013) was calculated for each tributary catchment outlet using the R code developed by Martini et al. (2022). Calculations were made with the 2018 filled 5 m DEM, a 5 m by 5 m moving window and the targets set at the fan apex of each catchment. In agreement with procedures proposed by Morel et al. (2023), the following variables were extracted: the mean value of the IC on each catchment and on their sediment sources (see section 3.3), $IC_{50}$ and $IC_{50Sed}$ respectively, and the 95th percentile of the IC on each catchment and on their sediment sources, $IC_{95}$ and $IC_{95Sed}$ respectively.

Regarding rainfall data, two datasets of cumulative hourly rainfall for the event are available, from Météo-France (MF) and Electricité De France (EDF). These two datasets are derived from the MF Antilope database, providing distributed rainfall at a time step of 15 min and at a spatial resolution of 1 km² on the basis of raw radar data and rain gauge data (Champeaux et al., 2009). In the case of Storm Alex, the Antilope data were re-processed by MF and EDF, respectively, who considered different set of additional rain gauges to refine the rainfall data. The resulting processed rainfall data are provided at a time step of 1 hour and at a spatial resolution of 1 km². Cumulative rainfalls provided by MF are lower than those from EDF. For each dataset, the 24 h rainfall accumulation precipitated over the catchments were computed ($R_{EDF}$ and $R_{MF}$, respectively for the rainfall accumulation calculated with the EDF and MF values, in m), as well as the maximum hourly intensity observed ($I_{EDF}$ and $I_{MF}$, in mm.h⁻¹). Although a significant spatial variability in daily rainfall depth is observed between catchments (fig. 4), the cumulative rainfall and intensities are considerable in both cases, with minimum 24-hour rainfall measured at 287 mm for EDF data and 166 mm for MF data.

### 3.3. Mapping of the sediment sources

A special attention was placed on mapping the sediment sources of the tributary catchments. The mapping is partly based on the bare soil mapping approach of Carlson and Ripley (1997) and the workflow is presented in Figure 5. This approach is based on the Normalized Difference Vegetation Index (NDVI) (Kriegler et al., 1969):

$$NDVI = \frac{IR - R}{IR + R} \quad [2]$$

<figure><img src="image_5.png" /><figcaption>Fig. 4 - Mean hourly rainfall intensities and mean rainfall accumulation computed for the 43 tributary catchments. A: Antilope data processed by EDF; B: Antilope data processed by MF.</figcaption></figure>

The yellow error bar represents the 5th and 95th percentiles of the hourly rain intensity. The blue line represents the mean cumulative rainfall curve computed for all the catchments. The blue envelope represents the evolution of cumulative rainfall in the catchments whose total cumulative rainfall corresponds to the 5th (lower limit) and 95th (upper limit) percentiles.

<caption>Fig. 4 - Evolution de l'intensité horaire moyenne et du cumul de pluie moyen pour l'ensemble des 43 bassins versants des affluents. A : Donnée Antilope réanalysée par EDF; B : Donnée Antilope réanalysée par MF.</caption>

Les barres d'erreurs jaunes représentent les 5e et 95e percentiles de l'intensité horaire. La ligne bleue correspond à l'évolution du cumul de pluie moyen au pas de temps horaire. L'enveloppe bleue correspond à l'évolution du cumul de pluie des bassins dont le cumul total correspond au 5e (borne inférieure) et 95e (borne supérieure) percentile.
where *IR* and *R* represent the infrared and red bands that have been extracted from the 2020 pre-storm IGN IRC (Infra-Red Color) ortho-images. The Carlson and Ripley (1997) method identifies a threshold *NDVI* value beyond which IRC pixel values correspond to vegetation cover. Pixels with *NDVI* values below this threshold represent either unvegetated surfaces (eroded terrains, rock outcrops, roads, constructions) or shaded pixels. The use of the *NDVI* index in order to map erosive patches has already been applied by Bertrand (2014). In this study, the *NDVI* threshold was manually calibrated using the pre-storm IRC ortho-image of 2020 resampled at a 1 m resolution for ease of calculation, and was set at -0.05. Shaded pixels, the nature of which cannot be precisely determined, were excluded by removing pixels where the sum of *R* and *IR* band values was below 120. The resulting raster of unvegetated surfaces, filtered for shaded pixels, was processed by excluding pixels with slopes below 20 %, as these correspond to relatively flat areas that cannot be considered as sediment sources. The resulting raster was then degraded to a resolution of 2.5 m and converted into polygons (shapefile), excluding isolated pixels.

The last step was to consider a buffer zone along the stream network, in order to isolate unvegetated and steep hillslope surfaces connected to stream channels, and also to consider the available sediments within the channel. The width of this buffer zone (*W*<sub>b</sub>) was considered as constant and was set at 2.5 m on either side of the channel axis at the catchment outlet, based on the common channel width observed in each catchment.

The union between the stream network buffer and the previously constructed shapefile of steep unvegetated zones provides an automated version of steep unvegetated hillslopes connected to the stream network. This automated version was manually refined using ortho-images and DEMs to remove remaining disconnected zones, such as sediment sources located upstream sediment traps (lakes, artificial reservoir), large scree slopes in glacial cirques, and anthropogenic infrastructures that were not filtered during automatic steps. The resulting shapefile (fig. 6) represents active sediment sources connected to the stream network, and allow to extract A<sub>sed</sub> (in km²) the area of sediment sources on each tributary catchment, and R<sub>sed</sub>, the ratio of A<sub>sed</sub> divided by the drainage area on each tributary.

### 3.4. Database of event-based sediment production of alpine torrents

A database of 432 event-based sediment yields of alpine headwater catchments has been compiled from five datasets reported in the scientific literature or academic reports (tab. 1). Each documented event is linked to a catchment of known size. The identified events correspond to maximal reported sediment yields during survey periods of heterogeneous duration. Estimates of sediment yield for each torrent are based on various methods. The majority of the sediment volumes are derived from estimates of volumes extracted during dredging of sediment retention basins after intense events, while just a few are estimated from topographic measurements taken

<figure><img src="image_6.png" /><figcaption>**Fig. 5 – Flowchart of the sediment sources mapping.**</figcaption></figure>

IR and R are the infrared and red bands from the IRC orthophoto. threshold_NDVI and threshold_SHAD are the thresholds applied to the NDVI raster and IRC orthophoto, respectively. PIXELS_GND and PIXELS_SHAD are the rasters of unvegetated surface pixels and shadow pixels, respectively. SHP_GND is the shape of the unvegetated and steep hillslope surfaces. SHP_STREAM is the shape of the stream network. SHP_GND_C is the shape of the steep unvegetated hillslope surfaces connected to the stream network. SHP_SED is the shape of the connected active sediment sources.

<caption>**Fig. 5 – Logigramme de la construction de l'emprise des sources sédimentaires.**</caption>

IR et R sont les bandes infrarouges et rouges de l'orthophotographie IRC. threshold_NDVI et threshold_SHAD sont respectivement les seuils appliqués au raster du NDVI et à l'orthophotographie IRC. PIXELS_GND et PIXELS_SHAD sont respectivement les rasters des surfaces non végétalisées et des pixels d'ombre. SHP_GND est le shapefile des zones non végétalisées sur les versants pentus. SHP_STREAM est le shapefile du réseau hydrographique. SHP_GND_C est le shapefile des zones non végétalisées des versants connectées au réseau hydrographique. SHP_SED est le shapefile des sources sédimentaires connectées au réseau hydrographique.
<figure><img src="image_7.png" /><figcaption>**Fig. 6** – Examples of sediment sources mapping (in red). A1: upper Pomeiras (n°36, fig. 1); A2: upper Venanson (n°43, fig. 1); A3: upper Salèse (n°40, fig. 1), it shows an example of manual correction to remove non-eroded (or disconnected) scree slopes.</figcaption></figure>

The background orthophoto is taken from the 2020 IGN campaign before Storm Alex. Blue lines represent the extracted stream network.

<caption>**Fig. 6** – *Exemples de l'emprise de sources sédimentaires (en rouge).* A1 : versants amont du Pomeiras (n°36, fig. 1) ; A2 : versants amont du Venanson (n°43, fig. 1) ; A3 : versants amont de la Salèse (n°40, fig. 1), ce dernier présente un exemple de zones d'égoulis non actives retirées manuellement de l'emprise des sources sédimentaires.</caption>

L'orthophotographie utilisée en fond de carte est tirée de la campagne de l'IGN de 2020, avant la tempête Alex. Les lignes bleues correspondent au réseau hydrographique.

directly in the field. Some volumes are also extracted from different
types of documentation, such as technical reports and archival
records. In addition, sediment yields of extreme debris-flow events
observed in Asia and South America, compiled by Marchi et al.
(2019), have also been integrated in the database. These catastrophic
events are specific to seismically active mountain ranges, and provide
a worldwide reference of paroxysmal headwater sediment yields.

**4. Results**

**4.1. Storm Alex versus extreme headwater sediment yields in the Alps**

The sediment yield (SY, in m³) is presented in Figure 7 as
a function of the drainage area (A, in km²) for both the Storm
Alex and the Alpine datasets presented in Table 1. The power

law regression of the Alex dataset shows a Pearson correlation
coefficient of 0.78 (R² = 0.61; *p*-value < 0.001), indicating a classic
and significant positive correlation between the two variables.
This correlation is characterized by a steeper slope and a higher
y-intercept compared to the power regression of the literature data
 tab. 2). However, the results exhibit significant scatter, and the
different percentiles from the Alpine dataset, calculated with the
quantile regressions method, are therefore displayed (Koenker et
Hallock, 2001). A covariance analysis (ANCOVA) was undertaken
to compare the two regression models by testing the effect of the
dataset source (binary categorical factor) on SY rating curves for
the Storm Alex and the Alpine dataset. Significant differences
in intercepts (F = 10.22; *p*-value < 0.01) and in regression slopes
(F = 322.7; *p*-value < 0.001) are obtained.

Since SY is strongly correlated to A, the specific sediment yield
(SSY, in m³.km⁻²), characterized as SY normalized by A, has also
**Tab. 1 – Datasets of extreme event-based sediment yields.**

Lines 1 to 5 refer to alpine headwaters and lines A to D refer to catastrophic debris flows in seismically active mountain ranges (Marchi et al., 2019). Historical data refers to scientific journals, technical reports and documents from the archives.

**Tab. 1 – Jeux de données des productions sédimentaires tirées d'évènements torrentiels extrêmes.**

Les lignes 1 à 5 correspondent aux données recensées sur les bassins alpins, et les lignes A à D aux phénomènes de laves torrentielles de régions à forte activité sismique (Marchi et al., 2019). « Historical data » fait référence à des données tirées de journaux scientifiques, de rapports techniques et de documents d'archives.

<table><thead><tr><th>Data set</th><th>Data acquisition</th><th>Survey period</th><th>Number of events</th><th>Sediment yield (Mm³)</th><th>Drainage area (km³)</th><th>Region</th><th>Sources</th></tr></thead><tbody><tr><td>1</td><td>Historical data and field surveys</td><td>1882-1996</td><td>142</td><td>0.0004 – 0.095</td><td>0.07 – 83</td><td>Italian Alps</td><td>D'Agostino, 1996<br>Marchi and D'Agostino, 2004</td></tr><tr><td>2</td><td>Post-event field surveys</td><td>August 2005</td><td>71</td><td>0.003 – 0.5</td><td>0.26 – 463</td><td>Swiss Alps</td><td>Rickermann and Koschni, 2010</td></tr><tr><td>3</td><td>Historical data and field surveys</td><td>1939-2008</td><td>78</td><td>0.0005 – 0.3</td><td>0.07 – 19</td><td>Swiss Alps</td><td>Gertsch et al., 2012</td></tr><tr><td>4</td><td>Historical data and sediment retention basins</td><td>18<sup>th</sup> to 21<sup>th</sup> centuries</td><td>71</td><td>0.000005 – 0.5</td><td>0.001 – 46</td><td>French Alps</td><td>Petuil et al., 2012</td></tr><tr><td>5</td><td>Sediment retention basins</td><td>18<sup>th</sup> to 21<sup>th</sup> centuries</td><td>70</td><td>0.00015 – 0.2</td><td>0.06 – 78</td><td>French Alps</td><td>Morel et al., 2023</td></tr><tr><td>A</td><td>Post-event field surveys</td><td>December 2019</td><td>12</td><td>0.4 – 2.6</td><td>0.16 – 18</td><td>Venezuela</td><td>Garcia-Martinez and López, 2005<br>Larsen et al., 2001</td></tr><tr><td>B</td><td>Post-event field surveys</td><td>1996 – 2009</td><td>67</td><td>0.001 – 0.6</td><td>21.4 – 52</td><td>Taïwan</td><td>Chang et al., 2001</td></tr><tr><td>C</td><td>Post-event field surveys</td><td>13/08/2010</td><td>20</td><td>0.006 – 0.3</td><td>0.12 – 8</td><td>Sichuan (China)</td><td>Tang et al., 2012</td></tr><tr><td>D</td><td>Post-event field surveys</td><td>10/07/2013</td><td>5</td><td>0.2 – 0.8</td><td>4.6 – 43</td><td>Sichuan (China)</td><td>Guo et al., 2016</td></tr></tbody></table>

**Tab. 2 – Summary statistics of power-law regressions of SY vs A using the different datasets.**

<sup>a</sup>: 95 % CI. N: number of data.

**Tab. 2 – Résumé statistique des régressions en loi puissance de la production sédimentaire vs la surface drainée en utilisant les différents jeux de données.**

<sup>a</sup>: IC 95 %. N: nombre de données.

<table><thead><tr><th></th><th>Storm Alex</th><th>Alpine dataset</th></tr></thead><tbody><tr><td>Best fit</td><td>SY = 19566.67A<sup>0.65</sup></td><td>SY = 7464.5A<sup>0.52</sup></td></tr><tr><td>R<sup>2</sup></td><td>0.61</td><td>0.24</td></tr><tr><td>p-value</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>N</td><td>47</td><td>432</td></tr><tr><td>Constant term<sup>a</sup></td><td>14021.26 – 27305.3</td><td>6312.07 – 8827.34</td></tr><tr><td>Exponent<sup>a</sup></td><td>0.49 – 0.80</td><td>0.43 – 0.61</td></tr></tbody></table>

been calculated on each watershed to eliminate the areal distortion (Church, 2017). Figure 8 is inspired by the representation of Brardinoni et al. (2012), and displays SSY of the Storm Alex dataset as a function of logarithmically equally-spaced bins of A. A general decrease of SSY is observed and characterized by a negative slope of -0.35. Boxplots displayed on Figure 8 as logarithmically equally-spaced bins of drainage area reveal a major decline of SSY around a catchment size of 1 km², and a stabilisation for drainage area above 10 km².

**4.2. Controlling factors of SY and SSY for Storm Alex**

All the variables tested are listed in Table 3 and their values are detailed in Table S1. Spearman correlations between log-transformed variables, except for IC variables, which were not transformed, extracted for the 43 catchments are presented in Figure 9. Data of the 4 cumulative catchments are not considered in this section, since

the analysis is restricted to the sediment production of headwaters. Correlations values for the index of connectivity are only displayed for the 95th percentile (i.e., IC<sub>95</sub> and IC<sub>95Sed</sub>) since the results presented below are consistent with the mean IC values. Considering SY, the positive correlation with A is already described in section 4.1. Additionally, a negative correlation is found with topographic metrics (i.e., M, S, Sc, So), themselves highly negatively correlated with A. Regarding the sediment availability and connectivity variables, a positive correlation is found with A<sub>Sed</sub>, while a negative correlation is found with IC<sub>95</sub> and IC<sub>95Sed</sub>. Finally, the rainfall variables are less or not correlated, with a negative correlation found with R<sub>EDF</sub> and R<sub>MF</sub> and no significant correlation found with I<sub>EDF</sub> and

**Tab. 3 – List of variables tested as potential controlling factors of SY and SSY.**

**Tab. 3 – Liste des variables testées comme facteurs de contrôle potentiels de la production sédimentaire et de la production sédimentaire spécifique.**

<table><thead><tr><th>Type of variable</th><th>Symbol</th><th>Description and units</th></tr></thead><tbody><tr><td rowspan="10">Geomorphological variables</td><td>A</td><td>Drainage area (km²)</td></tr><tr><td>M</td><td>Melton index (-)</td></tr><tr><td>S</td><td>Mean catchment slope (%)</td></tr><tr><td>S<sub>Chan</sub></td><td>Mean channel slope (%)</td></tr><tr><td>S<sub>o</sub></td><td>Outlet slope (%)</td></tr><tr><td>A<sub>Sed</sub></td><td>Active sediment sources area (km²)</td></tr><tr><td>R<sub>Sed</sub></td><td>Active sediment sources ratio (-)</td></tr><tr><td>IC<sub>50</sub> / IC<sub>95</sub></td><td>Index of connectivity (mean / 95th percentile) (-)</td></tr><tr><td>IC<sub>Sed50</sub> / IC<sub>Sed95</sub></td><td>Index of connectivity calculated on the sediment sources (mean / 95th percentile) (-)</td></tr><tr><td rowspan="2">Meteorological variables</td><td>I<sub>EDF</sub> / I<sub>MF</sub></td><td>Maximum hourly rainfall intensity (EDF data / MF data) (mm.h<sup>-1</sup>)</td></tr><tr><td>R<sub>EDF</sub> / R<sub>MF</sub></td><td>24 h rainfall accumulation (EDF data / MF data) (m)</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Fig. 7 – SY as a function of A using the different dataset.</figcaption></figure>

The blue triangles refer to the Storm Alex dataset, the red triangles refer to the Alpine dataset, and the green triangles refer to data collected in seismically active mountain ranges. Power-law regressions for the Storm Alex and the Alpine dataset are plotted in blue and red, respectively. The dashed lines represent the quantile regressions for the 5th, 95th, and 99th percentiles of the Alpine dataset.

<caption>Fig. 7 – Production sédimentaire en fonction de la surface drainée pour les différents jeux de données.</caption>

Les triangles bleus correspondent aux données de la tempête Alex, les triangles rouges correspondent aux données de la littérature alpine et les triangles verts correspondent aux données collectées dans les régions à forte activité sismique. Les régressions en loi puissance pour les données de la tempête Alex et de la littérature alpine sont respectivement tracées en bleu et rouge. Les droites en pointillées correspondent aux régressions quantiles des 5th, 95th et 99th percentiles pour les données de la littérature alpine.

I<sub>MF</sub>. Regarding SSY, no more significant correlations are found with IC<sub>Sed95</sub> and R<sub>EDF</sub>.

A stepwise multiple regression was carried out to build a model of log(SSY) and to understand the relative importance of each variable in relation to the others. Each variable, except for IC<sub>95</sub> and IC<sub>Sed95</sub>, was considered through a logarithmic transformation. First, each of the geomorphological and rainfall variables was included in a global linear model of log(SSY), and the variance inflation factor (VIF) was computed to remove variables that inflated the R<sup>2</sup> of the model due to the collinearity between some predictor variables. The set of variables was considered acceptable once the VIF of each variable was lower than 10 (James et al., 2013), and was composed of: M, S<sub>Chan</sub>, S<sub>O</sub>, R<sub>Sed</sub>, S, R<sub>EDF</sub> and I<sub>EDF</sub>. Finally, the best explanatory model was identified based on the Akaike information criterion (AIC). The obtained multiple regression model, summarized in Table 4, showed a statistically significant relation between SSY and R<sub>Sed</sub> and S<sub>O</sub>, exhibiting their crucial role in the explanation of SSY variability.

# 5. Discussion

## 5.1. Sediment yields of torrents during Storm Alex: an alpine perspective

Torrent sediment yields during Storm Alex rank among the highest ever recorded in the Alps (fig. 7). Only ten catchments fell below

the Alpine dataset power-law regression, and seven catchments exceeded the 95th percentile regression, illustrating the exceptional sediment transport during this event. The largest catchments showed considerable SY values, even though the main mode of transport of sediments was bedload. However, the 99th percentile regression of the Alpine dataset encompasses well Storm Alex sediment yields, with only one catchment exceeding it (Cime Pisset 3, 138,000 m<sup>3</sup> for a 0.34 km<sup>2</sup> drainage area) (fig. 10). This not only demonstrates the interest of this data compilation for the predetermination of event-based extreme sediment transport volumes in alpine environments, but also shows that Storm Alex sediment yields cannot be considered as unprecedented in the recent torrential history of the Alps. Additionally, comparing Storm Alex to seismically active regions allows further contextualization of its magnitude. For small catchments (< 1.0 km<sup>2</sup>), Alex sediment yields are

<caption>Tab. 4 – Summary results of the stepwise regression analysis of log(SSY).</caption>

Residual standard error: 0.435 on 40 degrees of freedom. Multiple R<sup>2</sup>: 0.505. Adjusted R<sup>2</sup>: 0.480. F-statistic: 20.39 on 2 and 40 DF. p-value: 7.85×10<sup>-6</sup>.

<caption>Tab. 4 – Résumé des résultats de la régression pas à pas de la transformation log de la production sédimentaire spécifique.</caption>

Ereur standard résiduelle : 0.435 sur 40 degrés de liberté. R<sup>2</sup>: 0.505. R<sup>2</sup> ajusté : 0.480. F: 20.39 sur 2 et 40 DF. p-value : 7.85×10<sup>-6</sup>.

<table><thead><tr><td>Variable</td><td>Coefficient</td><td>Standard error</td><td>t</td><td>p-value</td><td>VIF</td></tr></thead><tbody><tr><td>Intercept</td><td>3.851</td><td>0.562</td><td>6.857</td><td>&lt;0.01</td><td>-</td></tr><tr><td>log (R<sub>Sed</sub>)</td><td>0.863</td><td>0.282</td><td>3.056</td><td>&lt;0.01</td><td>3.645</td></tr><tr><td>log (S<sub>O</sub>)</td><td>0.664</td><td>0.152</td><td>4.359</td><td>&lt;0.01</td><td>3.223</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>**Fig. 8** – **SSY as a function of A for the Storm Alex dataset.** A: median, 1st and 3rd quartiles of SSY for logarithmically equally-spaced bins of drainage area. n is the number of catchments in each bin; B: all the values of SSY as a function of A with the power-law regression (R² = 0.31; p-value < 0.001).</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 9** – **Spearman rank correlation matrix for each pair of variables.**</figcaption></figure>

The non-significant correlations (p-value > 0.05) are crossed.

<caption>**Fig. 9** – **Matrice de corrélation de Spearman pour chaque couple de variable**</caption>

Les corrélations non significatives (p-value > 0.05) sont marquées d'une croix.

comparable to some of the most intense debris-flows events recorded in these regions. However, for larger drainage areas, Storm Alex yields are frequently surpassed. Events like, for instance, the Vargas disaster in Venezuela (García-Martínez et López, 2005), driven by extreme rainfall (1200 mm in 17 days, 911 mm in 3 days, and 72 mm.h⁻¹ peak intensity), surpass Storm Alex data but occurred under very different climatic conditions.

However, methodological differences for assessing sediment yields between the datasets (tab. 1) must be considered. Indeed, the use of a DoD at the catchment scale to measure sediment yields at the fan apex might always provide higher values compared to the deposited volume on a fan or in a retention basin, since the latter do not consider the volume that transits over the fan. It is well illustrated in the case of Storm Alex, where deposited volumes on the fans are most of the time significantly lower than the volumes passing through fan apex (tab. S2). The systematic availability of a DoD after every extreme torrential event might be a way to homogenize sediment yields data in the future. The recent event in the hamlet of La Bérarde (Blanc et

al., 2024) demonstrated the efficiency of having a DoD for retroactive analysis of the sediment cascade during the event, allowing a robust reconstruction of volumes passing through the fan apex that has been dramatically aggraded during the torrential flood. However, as a common limitation is generally the lack of LiDAR data prior to the event, large-scale LiDAR surveys (e.g., national or regional scale), as it was done in France in 2021 with the LiDAR HD campaign steered by the IGN, will be of great interest for standardization of event-based sediment yield reconstructions.

The Storm Alex dataset encompasses a wide range of drainage areas for a single event. Also, this dataset provides numerous information for very small catchments (drainage area < 0.2 km), which were poorly represented in the Alpine dataset. Inclusion of the 47 data of the Storm Alex dataset in the Alpine dataset allows to build an updated quantile regression of the 99th percentile, illustrated in Figure 10 and detailed in Table 5. The updated quantile regression of the 99th percentile shows a decrease in slope and a slight increase of the intercept compared to the quantile regression based on the Alpine dataset only, and it also appears
<figure><img src="image_11.png" /><figcaption>Fig. 10 – Representation of the updated 99th percentile of SY as a function of A for the combination of the Alpine and the Storm Alex datasets.</figcaption></figure>

The red triangles represent the Alpine dataset and the blue triangles represent the Storm Alex dataset. The dotted red and purple lines represent the quantile regression of the 99th percentile for the Alpine dataset only and for the combination of the Alpine and the Storm Alex datasets, respectively.

<caption>Fig. 10 – Représentation du 99e percentile actualisé de la production sédimentaire en fonction de la surface drainée, pour l'ensemble des données de la littérature alpine et des données de la tempête Alex.</caption>

Les triangles rouges représentent les données de la littérature alpine et les triangles bleus représentent les données de la tempête Alex. Les lignes pointillées rouges et violettes représentent la régression quantile du 99e percentile des données de la littérature alpine seulement et de la combinaison des données de la tempête Alex et des données de la littérature alpine, respectivement.

<caption>Tab. 5 – Summary statistics of the updated quantile regression of the 99th percentile of log(SY) as a function of log(A).</caption>

<table><caption>Tab. 5 – Résumé des statistiques de la mise à jour de la régression quantile du 99th percentile de la transformation en log de la production sédimentaire en fonction de la transformation en log de la surface drainée.</caption><thead><tr><th>Dataset</th><th>Variable</th><th>Coefficient</th><th>Standard error<sup>a</sup></th><th>t</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="2">Alpine dataset 99<sup>th</sup> percentile</td><td>Intercept</td><td>5.262</td><td>0.184</td><td>28.586</td><td>&lt;0.01</td></tr><tr><td>log (A)</td><td>0.472</td><td>0.194</td><td>2.434</td><td>&lt;0.01</td></tr><tr><td rowspan="2">Updated 99<sup>th</sup> percentile</td><td>Intercept</td><td>5.322</td><td>0.119</td><td>44.837</td><td>&lt;0.01</td></tr><tr><td>log (A)</td><td>0.396</td><td>0.115</td><td>3.450</td><td>&lt;0.01</td></tr></tbody></table>

a: standard error, calculated using bootstrap.

a : erreur standard, calculée par bootstrap.

to be more robust considering the lowest standard errors. This new equation provides a new way of easily identifying a consistent upper limit for the sediment yield of extreme torrential events in the Alps.

The Storm Alex dataset shows a classic SSY decrease with A (fig. 8), as previously reported for event-based debris-flow sediment yields in the northeastern Italian Alps (Brardinoni et al., 2012; Church, 2017). The exponent of this power law for the Storm Alex dataset is -0.35. This exponent can inform about sediment recruitment in the fluvial system at the event timescale. The higher value of this exponent as compared to that reported for debris-flow events of the Italian Alps, which are comprised between -1.02 and -0.46 (Brardinoni et al., 2012), shows a more effective sediment recruitment along the investigated stream network for Storm Alex. This observation first highlights that SSY decreases with drainage area, interpreted as a signature of a

degrading landscape (Church, 2017), remains valid for extreme events. It also confirms the crucial role played by the recruitment of available sediment along the stream network in sediment yields, as previously shown by the catchment-scale mass balance reconstruction (Liébault et al., 2024). Additionally, the effectiveness of the sediment recruitment was also enhanced by the large storm cell which, as well as being very intense, swept across a wide area, and resulted in considerable average rainfall accumulations even in the largest catchments.

Combination of sediment yields with similar contributing areas can be used to infer event-based sediment transfers across a range of spatial scales. The Storm Alex dataset (fig. 7) offers an extended view of sediment transfer during extreme events, as compared to that reported for debris-flow events of the northeastern Italian Alps (Brardinoni et al., 2012), which shows an increase in SSY up to drainage areas of 1 km², and a sharp decrease after, under the effect of sediment deposition on alluvial fans. Although the Storm Alex dataset cannot be used to document the trend for very small contributing areas (less than 1 km²), the sharp decline above 1 km² is also observed (fig. 8). However, SSY values are determined at the apex of the fan in the present study, and the effect of sediment deposition on the fans cannot be considered as an explanation of this sharp decline. In addition, alluvial fans of the Vésubie tributaries during Storm Alex constituted either large deposition zone (n°22, tab. S2), or new sources of sediment (n°24, tab. S2). These fluctuating observations cannot lead us to the same interpretation as Brardinoni et al. (2012). Our explanation of the decreasing SSY values between 1 and 10 km² relies on the sudden drop of both the mean catchment slope and the proportion of sediment sources
at the catchment scale for these drainage area values. Above 10 km², the decrease in SSY is interrupted, which can be interpreted as the effect of sediment recruitment from alluvial storages which starts to be effective, enabled by extreme runoffs. This fuelling effect of alluvial storage could not be documented in the dataset of Brardinoni et al. (2012), since this study was restricted to sediment transfers along headwater channels prone to debris flows, and it does not include sediment cascading effects in the downstream fluvial domain.

## 5.2. Controls on the Storm Alex sediment yields

The statistical analysis of rainfall variables reveals the absence of correlation between rainfall characteristics (cumulative rainfall and maximum intensity) and the variability of the net and specific sediment yield during Storm Alex. The negative correlation observed between the sediment yield (SY) and the 24h rainfall accumulation computed with the EDF values ($R_{EDF}$) is explained by the catchment size effect on rainfall accumulation. Indeed, as rainfall accumulation attenuates outside the storm cell's highest intensities (fig. 2B), the mean rainfall accumulation measured over larger catchments will decrease, whereas these catchments have the highest SY (fig. 7). The absence of significant correlation between the drainage area (A) and $R_{EDF}$ (fig. 9) should be taken with caution ($p$-value = 0.055), especially since a significant negative correlation is observed between A and $R_{MF}$ ($p$-value = 0.014). Higher and more intense rainfall did not necessarily trigger higher sediment transport, suggesting that due to the long and intense rainfall (fig. 4), water discharge was no longer a limiting factor for sediment yields, but rather the sediment availability within each catchment became the primary limitation. Therefore, supply-unlimited catchments have been much more active than supply-limited catchments. This is in agreement with studies on alpine torrents (Morel et al., 2023), which also stressed out rain-related variables to be poor predictors of the sediment yield. However, this observation is based on raw rainfall data and does not take into account runoff volumes or stream power. Rickenmann and Koschni (2010) did show a relation between the flood runoff volume and the transported bedload during August 2005 flood events in Switzerland, but the large uncertainties associated with discharge estimates (Pons et al., 2024) make it difficult to reproduce such an approach in the Storm Alex case study.

The performance of the slope at the outlet ($S_o$) in the regression model is consistent with its conception, as it is a great indicator of the sediment transport capacity. The strong positive correlation between the specific sediment yield (SSY) and the ratio of sediment sources ($R_{sed}$) further supports the fact that sediment availability is the most important controlling factor of the sediment response during the storm. Catchments with higher $R_{sed}$ values, and thus larger active sediment sources before the storm, were able to provide more material, resulting in higher SSY. These results are in good agreement with those of Peteuil et al. (2012), who also included a sediment sources index and an estimation of the slope at the outlet in their predictive equations of SY. Both of these parameters can be easily obtained, with a high-resolution DEM for $S_o$ and a high-resolution DEM and infrared orthophotos for $R_{sed}$, and they should systematically be extracted and tested in the analysis of specific sediment yields of extreme torrential events. In some of the different predictive models of SSY developed by Morel et al. (2023), an IC variable in addition to an indicator of the sediment sources ratio in different predictive models of SSY was selected, but the

slope at the outlet did not emerge as a great indicator. However, these differences should first be put into perspective with regard to the 25 m DEM used by Morel et al. (2023), which led to high uncertainties on the slope measurement. Also, the absence of an IC variable in our SSY predictive model should be regarded considering the variables selection using the VIF which led us to discard $IC_{Sed50}$ due to a VIF value of 10.45. The acknowledged interest of this type of variables (Heckmann et al., 2018) should still encourage their determination in the analysis of extreme sediment yields.

Additionally, an analysis of the catchments with the highest residuals allowed us to underscore some tributaries with a specific response. The highest negative residuals (i.e., catchments for which SSY is strongly over-estimated) belong mainly to catchments with a dense vegetation cover along the stream network, and to catchments where the mapped sediment sources possibly include many bedrock outcrops with reduced surficial deposits. The highest positive residuals belong to catchments exhibiting significant hillslope erosion.

For example, in the Venanson catchment (fig. 11A), a 110 % increase of the surface of active sediment sources has been observed after Storm Alex. In the Erps 1 catchment (fig. 11B), sediments produced by a massive mass movement have increased the channel width up to 20 times over a distance of almost 1 km, leading to a net erosion of 0,15 Mm³ and an increase in active sediment sources of 140 %. This is also the case of the Adousse 2 catchment where the triggering of a massive landslide during Storm Alex has induced a 230 % increase of active sediment sources. Such massive hillslope destabilizations are known to occur during extreme events (Rickenmann et Koschni, 2010; Ruiz-Villanueva et al., 2018), but statistical models based on pre-event sediment sources should systematically under-estimate sediment yields controlled by large incipient erosion landforms. Although landslide susceptibility analysis could be of interest for considering this issue, the spatio-temporal prediction of new sediment sources remains quite uncertain. However, the combination of active sediment source mapping with landslide susceptibility analysis could be an interesting approach to test for improving sediment yield models.

Some catchments have been strongly disturbed by Storm Alex, and it is reasonable to assume that an increase in the sediment yield is to be expected in catchments where massive hillslope destabilisation has occurred, particularly in the Madone de Fenestre tributaries where the bedrock is rarely visible, and in some tributaries where marked hillslope erosion created newly activated sediment sources. Catchments where significant channel aggradation has occurred in the valley floor (e.g., the Venanson) could also show an increase in sediment yield, under the effect of a classic post-event channel incision. Monitoring the sediment production in these newly activated sediment sources during future events would provide valuable insight into the amount of sediment stocks and whether they can be mobilized.

## 6. Conclusions

This investigation revealed that the Storm Alex sediment yields rank among the highest ever recorded in the Alps, reaching values similar to those observed in seismically active regions, although they cannot be considered as unprecedented in the recent torrential history of the Alps. The usual decrease of specific sediment yields with drainage area remains valid for Storm Alex, but with a lower slope, confirming the crucial role played by the fuelling effect of fluvio-glacial and alluvial
<figure><img src="image_12.png" /><figcaption>**Fig. 11** – Examples of new active sediment sources observed after Alex Storm. A: west of the upper Venanson catchment (n°44, fig. 1); B: location of the massive mass movements which occurred in the Erps 1 catchment (n°22, fig. 1).</figcaption></figure>

The left background orthophotos are taken from the 2020 IGN campaign before Storm Alex and the right background orthophotos are taken from the 2023 IGN campaign. The purple dotted line represents the catchments delineation.

<caption>**Fig. 11** – Exemples des nouvelles sources sédimentaires actives observées après la tempête Alex. A : secteur Ouest de l'amont du bassin versant du Venanson (n°44, fig. 1) ; B : localisation du mouvement gravitaire d'ampleur qui s'est déclenché dans le bassin des Erps 1 (n°22, fig. 1).</caption>

Les orthophotographies de gauche sont tirées de la campagne d'acquisition de l'IGN de 2020, avant la tempête Alex, et les orthophotos de droite sont tirées de la campagne d'acquisition de l'IGN de 2023. La ligne violette en pointillés représente les limites des bassins versants.

storages along the stream network, as previously shown by the catchment-scale mass balance reconstruction.

At the scale of the Vésubie catchment, geomorphic controls on event-based specific sediment yields were more important than rainfall controls. Sediment availability, quantified as the proportion of active sediment sources within each tributary, emerged as the dominant control. The slope at the outlet of the catchment also proved to be a good predictor, confirming the importance of sediment transfer efficiency for the prediction of sediment yields. Despite the limitations of the predictive equation, these results are consistent with previous studies, further demonstrating the key role of sediment availability and transport in sediment supply during extreme torrential events.

This study finally provides some new data of event-based extreme sediment yields for alpine environments, that contribute to improve the empirical assessment of maximum sediment transport volumes that could reach alluvial fans of headwaters during major flood events. The

systematic use of high-resolution topographic differencing after major torrential events is also strongly supported as a key methodological solution for a better standardization of event-based sediment yield reconstruction in headwater catchments.

## Supplementary material

Supplementary material to this article can be found below and online at https://doi.org/10.5281/zenodo.16421770 (Tab. S1) and https://doi.org/10.5281/zenodo.16531975 (Tab.S2).

## Acknowledgements

Acknowledgements are expressed to the French Ministry of Agriculture which funded the research activities. This investigation was also supported by the ANR project VIGIMONT (Project Reference
Number ANR-22-CE04-0021) and by the *Direction Générale de la Prévention des Risques* (DGPR) of the French Ministry of Environment.

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Version française abrégée

La tempête Alex du 2 octobre 2020 a été à l'origine de l'une des crues torrentielles les plus intenses du XXIᵉ siècle en France, causant d'importants dégâts, principalement dans les vallées de la Vésubie et de la Roya. Dans le bassin de la Vésubie, les crues torrentielles ont entraîné une production sédimentaire exceptionnelle sur les affluents, et une remobilisation en masse des sédiments de fond de vallée. Cette étude (i) replace cet événement torrentiel au regard des crues intenses documentées dans les Alpes et dans des régions de montagne à forte activité sismique, et (ii) analyse la variabilité et les facteurs de contrôle de la production sédimentaire spécifique des affluents de la Vésubie.

Le périmètre de la présente étude se concentre sur le bassin versant de la Vésubie à l'amont du Figaret (n° 25, fig. 1). Les bassins versants
des 43 affluents qui ont été actifs pendant la tempête Alex constituent le jeu de données étudié. Des données LiDAR pré- et post-événement ont permis la construction d'un bilan sédimentaire détaillé à l'échelle de l'ensemble du bassin versant. Les données pluviométriques disponibles pour la tempête Alex sont tirées de la lame d'eau Antilope de Météo-France qui a été réanalysée par Météo-France et Electricité de France, et permettent d'extraire sur chaque bassin versant le cumul moyen sur 24 h et l'intensité horaire maximale observée. Le modèle de terrain à 5 m de 2018 disponible sur le secteur d'étude permet d'extraire un certain nombre de paramètres géomorphologiques, résumés dans le Tableau S1. Une attention particulière est apportée à la cartographie des sources sédimentaires actives présentes avant la tempête. Ces sources sont cartographiées en exploitant l'indice différentiel normalisé de végétation (NDVI), qui permet d'identifier les surfaces non végétalisées, et le réseau hydrographique, en isolant les zones non végétalisées connectées au réseau hydrographique. La Figure 9 présente l'ensemble des corrélations entre les différents paramètres extraits sur les bassins versants ainsi qu'avec la production sédimentaire et la production sédimentaire spécifique. Un modèle prédictif de la production sédimentaire spécifique a été construit avec une méthode de régression pas à pas, permettant d'extraire les variables de contrôle de la production sédimentaire spécifique les plus représentatives, et est présenté dans le Tableau 4. Afin de situer la tempête Alex au regard d'autres évènements torrentiels intenses, plusieurs données de la littérature ont été compilées. Ces données, synthétisées dans le Tableau 2, ont permis de recenser des valeurs de productions sédimentaires (en m³) sur un grand nombre de bassins versants de surface connue. Un premier jeu de données concerne 432 évènements torrentiels intenses documentés dans les Alpes, et un second jeu de données concerne 114 évènements extrêmes de laves torrentielles documentés dans des zones du globe présentant une activité sismique marquée.

La Figure 7, qui représente la production sédimentaire en fonction de la surface de bassin versant, permet de situer la tempête Alex au regard des deux jeux de données tirés de la littérature. Il en ressort que les valeurs de production sédimentaire des affluents de la Vésubie au cours de la tempête Alex figurent parmi les plus fortes productions sédimentaires des Alpes, mais pour autant ne sont pas sans précédent à l'échelle du passé torrentiel récent des Alpes. L'intégration des données de la tempête Alex aux données de production sédimentaire observées dans les Alpes nous permet d'établir une borne supérieure robuste (le 99ᵉ percentile, tracé sur la Figure 10 et détaillé dans le Tableau 5) de la production sédimentaire des évènements extrêmes à l'échelle des Alpes. Au regard de la variabilité des méthodes de mesure de la production sédimentaire utilisées dans la littérature, la présente étude ainsi que le cas récent de la crue torrentielle qui a dévasté le hameau de La Bérarde (Blanc et al., 2024) démontrent la pertinence de généraliser l'utilisation des DoD pour l'étude des futurs évènements torrentiels extrêmes.

L'analyse des corrélations présentées dans la Figure 9 montre que la variabilité des productions sédimentaires ne peut pas s'expliquer par les paramètres pluviométriques de la tempête, ce qui laisse supposer que les conditions hydrauliques n'ont pas été limitantes à la production sédimentaire, contrairement à la disponibilité en sédiments de chaque bassin. Les bassins versants présentant des sources sédimentaires « illimitées » ont en effet montré les plus grosses productions sédimentaires spécifiques. Ces conclusions ressortent

des variables retenues par la régression pas à pas, qui sont d'une part le ratio de sources sédimentaires et d'autre part la pente à l'exutoire à l'échelle du bassin versant. Ces deux variables ressortent également du modèle prédictif de la production sédimentaire construit par Petuil et al. (2012) sur des bassins versant isérois, ce qui vient renforcer l'importance d'une caractérisation des sources sédimentaires et des paramètres liés à la capacité de transport des sédiments à l'échelle des bassins versants pour l'estimation des productions sédimentaires spécifiques. Il est toutefois important de souligner que certains bassins versants ont été sujets à des mouvements gravitaires très intenses lors de la tempête Alex, déclenchant des productions sédimentaires massives, alors même qu'ils présentaient initialement de faibles surfaces de sources sédimentaires, ce qui nécessite de prendre des précautions dans l'interprétation du modèle prédictif construit dans la présente étude.