# Extreme Mediterranean rainfall impact on sedimentary routing systems: what can we learn from Storm Alex using in situ detrital $10^6$Be?

Apolline Mariotti, Pierre-Henri Blard, Julien Charreau, Samuel Toucanne, Stephan Jorry, Olivier Joseph, Team Aster

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Apolline Mariotti, Pierre-Henri Blard, Julien Charreau, Samuel Toucanne, Stephan Jorry, et al. Extreme Mediterranean rainfall impact on sedimentary routing systems: what can we learn from Storm Alex using in situ detrital $10^6$Be?. Geomorphology, 2026, 492, pp.110061. 10.1016/j geomorph.2025.110061. hal-05463310

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# Extreme Mediterranean rainfall impact on sedimentary routing systems: what can we learn from Storm Alex using in situ detrital $^{10}$Be?

Apolline Mariotti<sup>a,b,*</sup>, Pierre-Henri Blard<sup>a,c</sup>, Julien Charreau<sup>a</sup>, Samuel Toucanne<sup>d</sup>, Stephan Jorry<sup>d</sup>, Olivier Joseph<sup>e</sup>, ASTER Team<sup>f</sup>

## ARTICLE INFO

### Keywords:

- Heavy precipitation events

- Cosmogenic $^{10}$Be

- Sediment transport

- Storm Alex

## ABSTRACT

Understanding how extreme meteorological events influence sediment transport is critical for predicting landscape evolution under a changing climate. Detrital cosmogenic $^{10}$Be can provide insights into sediment dynamics following extreme rainfall, but high-resolution datasets tracking $^{10}$Be variations before and after a storm, alongside long-term records, remain rare.

The Var catchment (French Southern Alps) presents a unique case study, as its $^{10}$Be signal was well-documented before the October 2020 Storm Alex (>500 mm of rainfall/24 h), which triggered flash floods, mobilized large sediment volumes, and formed a 10 km-long sediment plume in the Mediterranean Sea. We compare $^{10}$Be concentrations in river sediments collected pre-storm (2016–2018), and at +7 days, +21 days, +4 months, and +7 months post-storm. We also use a historical offshore sample and contextualize these results with a 75 ka-long $^{10}$Be record from deep-sea sediment cores.

At the Var outlet, $^{10}$Be concentrations initially increased by ~25 % at +7 and +21 days, attributed to the mobilization of $^{10}$Be-rich sediments from the upstream Var and Tinée sub-catchments. Concentrations returned to pre-storm levels within four months, primarily due to dilution with $^{10}$Be-poor sediments from the Vésubie sub-catchment fluvioglacial terraces. While short-term $^{10}$Be fluctuations at the Var outlet reflect complex sediment sourcing, our comparison with the 0–75 ka record confirms that major glaciation events and potential anthropic influences remain distinguishable, demonstrating that $^{10}$Be is a robust proxy of denudation changes, even when extreme events are involved.

## 1. Introduction

The relative contribution of infrequent extreme catastrophic events vs. smaller, more frequent magnitude events in shaping the landscape, particularly in terms of geomorphic change, sediment flux, and overall landscape modification, has long been debated. In their pioneer study, Wolman and Miller (1960) proposed that moderate-sized events accounted for the largest portion of these processes within a landscape (i.e., the total sediment eroded, transported, or deposited). Conversely, Hovius et al. (1997) showed that large-magnitude, low-frequency landslides explained much of the sediment discharge in the Southern

Alps of New Zealand. Similarly, Lavé et al. (2023) recently reported geological evidence for a giant rockslide in the Annapurna massif (central Nepal) that collapsed a paleo-summit (> 8000 m), leaving a profound and long-lived imprint in the Himalayan landscape. Recent catastrophic events, such as the 2021 Chamoli rock-ice avalanche in India (Cook et al., 2021; Shugar et al., 2021), alongside other studies demonstrating the significant denudational efficiency of large mass movements (Korup et al., 2007; Marc et al., 2019), further suggest that catastrophic mega-rockslides can be dominant agents of erosion.

This debate is further complicated by the potentially diverse geomorphic responses to extreme events, which can range from systems

<footnote><sup>a</sup> CRPG, CNRS - Université de Lorraine, UMR 7358, 54500, Vandoeuvre-les-Nancy, France</footnote>

<footnote><sup>b</sup> GFZ Helmholtz Centre for Geosciences, 14473, Potsdam, Germany</footnote>

<footnote><sup>c</sup> Laboratoire de Glaciologie, DGES-IGEOS, Université Libre de Bruxelles, 1050, Bruxelles, Belgium</footnote>

<footnote><sup>d</sup> Geo-Ocean, Univ. Brest, CNRS, Ifremer, UMR6538, F-29280, Plouzane, France</footnote>

<footnote><sup>e</sup> Roudoule Écomusée, Musée de France, 06260, Puget-Rostang, France</footnote>

<footnote><sup>f</sup> Aix-Marseille Univ., CNRS, IRD, INRA, Coll. France, UM 34 CEREGE, Technopôle de l'Environnement Arbois-Méditerranée, BP80, 13545, Aix-en-Provence, France</footnote>

<footnote>* Corresponding author at: Trinity College Dublin, School of Natural Sciences, Dublin 2, Ireland.
E-mail address: apolline.mariotti@tcd.ie (A. Mariotti).</footnote>
primarily undergoing ‘flood-cleaning’ (net erosion and export of sediment) to ‘flood-depositing’ (net accumulation of sediment within the channel or system) behaviors (Turowski et al., 2013). These observed contradictory findings are likely attributable to the complex interplay of spatially variable catchment characteristics, including local climate, vegetation, and lithology, as well as morphometric factors, regional geodynamics, and fluvial transport capacity. Nevertheless, to better assess the relative importance of catastrophic versus smaller-magnitude events in geomorphic processes, it is crucial to quantify both the volume of material mobilized and transferred through the sedimentary system during extreme events and their frequency over geological timescales (St. George et al., 2020). This is particularly true for Heavy Precipitation Events (HPEs), which can produce high river discharge, flash floods, rapid transfer of large sediment volumes, and trigger various mass wasting events like landslides and rock falls. Despite those visible and instantaneous marks on the landscape, HPEs traces often quickly vanish on land at the geological timescale, leading to large unknowns regarding their frequency and long-term role in shaping the landscape. Moreover, in a warming climate, the expected increase in frequency of such events is also a major concern (Intergovernmental Panel on Climate Change, 2023; Nissen and Ulbrich, 2017). To improve our understanding of the impact of HPEs on landscapes, it is crucial to accurately quantify the sediment dynamics and characterize the geomorphic changes induced by these extreme events. These changes can then be traced back in the sedimentary archive, enabling the quantification of their frequency over time.

In situ cosmogenic nuclides, such as ¹⁰Be, are one of the most used tools to quantify denudation rates (i.e., removal of the upper meters of soil by physical erosion and chemical weathering) (Bierman and Steig, 1996; Brown et al., 1995; Granger et al., 1996; von Blanckenburg, 2005), sediment fluxes (Lupker et al., 2012; Wang et al., 2017; Wittmann et al., 2020) and sediment transfer processes (Fülöp et al., 2020; Jautzy et al., 2024; Wittmann et al., 2011). This method has been successfully applied in various climatic, tectonic, and topographic environments (e.g., Derrieux et al., 2014; Madella et al., 2018; Regard et al., 2016). The most common application of this method requires that certain conditions be satisfied, notably the steady state denudation assumption (von Blanckenburg, 2005). During catastrophic events, if large mass wasting episodes or fast river bank erosion occur, the near-instantaneous mobilization of sizeable volume of long-stored sediments and/or hillslope materials over thicknesses on the order of a few meters may, in theory, disturb the in situ ¹⁰Be concentrations of sediments usually exported to and by rivers from the catchments under steady state conditions (Dingle et al., 2018; Niemi et al., 2005; Schide et al., 2022). This occurs because mass wasting events can mobilize material from depths beyond those affected by steady-state surface denudation processes. Consequently, these deeper sediments, having been shielded from cosmic rays, typically exhibit low ¹⁰Be concentrations. When these sediments enter the sedimentary system, they therefore have the potential to lower the overall ¹⁰Be signal (Puchol et al., 2014). This was for example the case during the 2008 Mw 7.9 Wenchuan Earthquake, which generated the catastrophic release of deep hillslope material and diluted the ¹⁰Be signal in the rivers, with post-event concentrations up to 5 times lower than pre-event measures (Wang et al., 2017). In contrast, numerical modeling of the 2015 Mw 7.8 Gorkha earthquake suggested no clear dilution of the ¹⁰Be signal (Schide et al., 2022). This is primarily because the modeling for the Gorkha region accounted for pre-existing high background denudation rates in the central Himalaya, meaning most of the landscape was already actively denuding and producing relatively low ¹⁰Be concentrations. Consequently, even with the input of landslide material, the additional mobilized sediment after the earthquake did not consistently introduce sufficient “¹⁰Be-poor” sediment to significantly alter the existing fluvial ¹⁰Be signal (Schide et al., 2022). Moreover, an increase of ¹⁰Be concentrations has instead been observed in a few cases following a perturbation event (Foster and Anderson, 2016; Quock et al., 2022;

Wilkinson et al., 2022). This dynamic has been explained by an abrupt and anomalous export of “¹⁰Be-rich” sediments from parts of the catchment that are exposed to lower denudation rates over the long term (Schide et al., 2022). Like earthquakes, HPEs also have the potential to create large landslides and flash floods that can erode sediment and therefore can potentially affect the ¹⁰Be signature in stream sediments. Once again, contradictory results were found in the literature. Foster and Anderson (2016) measured no significant change in ¹⁰Be concentrations in the Colorado Front Range in some catchments after a HPE, while Kober et al. (2012) found that the ¹⁰Be concentrations measured in river sand decreased by a factor of 1.5 following debris-flow events triggered by snowmelt and prolonged precipitations. Grande et al. (2021) also found contradictory results in two nested catchments in Puerto Rico: while in the Icacos catchment the ¹⁰Be concentrations remained steady over time, they increased in the Guabá catchment for two years after Hurricane Maria.

Numerical models suggest that the likelihood of the ¹⁰Be signal being impacted during extreme events depends on multiple factors. These include the ¹⁰Be concentration of the source area sediments, as well as the average depth and thickness of the material mobilized (Schide et al., 2022). Additionally, the volume of material mobilized during the extreme event relative to the long-term flux also plays a significant role (Grande et al., 2021). Overall, the precise impact of major storms and flash floods on the ¹⁰Be signal is uncertain but could reveal various geomorphological processes occurring during the event. It is therefore critical to better understand the factors conditioning the cosmogenic signature of sediments. This requires high-resolution time series datasets, monitoring ¹⁰Be evolution before and after a storm (Foster and Anderson, 2016). Moreover, knowing how ¹⁰Be perturbation could be induced by HPEs could open the door to the recognition of such catastrophic events in the sedimentary record (Mariotti et al., 2021; St. George et al., 2020). Furthermore, if an HPE is able to disturb the ¹⁰Be signature in rivers, the typical time required by the sedimentary system to get back to pre-storm ¹⁰Be values also needs to be constrained so the perturbation can be identified in the sedimentary archives. Yet, very few studies have provided quantitative estimates for this recovery time. For the 2008 Mw 7.9 Wenchuan Earthquake, Wang et al. (2017) suggested it could take a few years to several decades for fluvial sediment fluxes to go back to pre-event values, while a recent study by Li et al. (2025) estimated that landslides sediments would be removed over centennial timescales. In contrast, in Puerto Rico, the recovery time after Hurricane Maria was estimated to be less than two years (Grande et al., 2021).

In this study, we investigate the “¹⁰Be signature” of Storm Alex that occurred in the Var catchment (Southern Alps, France) on October 2nd and 3rd 2020, characterized as a HPE (rainfall accumulation >500 mm in 24 h). To better understand how this storm has impacted the ¹⁰Be inventory of the Var catchment, we document ¹⁰Be concentrations in sediments exported by the Var River and its two main tributaries (the Tinée and the Vésubie Rivers), comparing samples taken at variable moments before and after Storm Alex, across multiple timescales (from a few days to several months and years).

## 2. Settings

### 2.1. The Var catchment

The Var catchment is a steep mountainous catchment located at the edge of the Southern French Alps. It is relatively small (2800 km²) and has elevation ranging from 3143 m a.s.l. (Mont Gélas) to sea level, with a main stream length shorter than 100 km and a steep average slope of 23°. The Var catchment is characterized by a fast transfer of sediment from the mountain source to the deep-sea sink with minimal storage time and is hence considered a reactive sedimentary system (Bonneau et al., 2017; Mariotti et al., 2021; Mulder et al., 1998). The Var catchment is divided into three sub-catchments: the upstream Var, the Tinée, and the Vésubie sub-catchments (Fig. 1A). The geology of the Var
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (A) Simplified geological map of the Var catchment and its sub-catchments. Geological data is from the French Geological Survey (Kergkhove and Montjuvent, 1977; Roure et al., 1976). White lines delimit the upstream Var, Tinée and Vésubie sub-catchments. Colored dots represent sampling points. (B) 24 h rainfall during Storm Alex from Chochon et al. (2021). Weather stations are from Météo France: (1) Saint-Martin-Vésubie, (2) Roquesteron, (3) Piera Cava, (4) Nice, (5) Guillaumes, and (6) Ascros. Hydrological station data are from the HYDRO database (http://www.hydro.eaufrance.fr/): (I) Entrevaux, (II) Nice Pont Napoléon, (III) Saint-Sauveur-sur-Tinée and (IV) Utelle.</figcaption></figure>

catchment is composed of two main domains: a crystalline basement in the North and highest parts of the catchment (Mercantour massif in the high Tinée and Vésubie), and a sedimentary cover made of marl, lime-stones, and sandstones in the South. Quartz-bearing rocks are present in both domains, albeit much more abundant in the crystalline zone, and represent a total of 26 % of the Var catchment (Fig. 1A).

The sedimentary dynamics in the Var catchment have been extensively studied, using both modern river sands (Bonneau et al., 2017; Mariotti et al., 2019; Thollon et al., 2023) and sediment cores spanning the last 75 kyr (Bonneau et al., 2017, 2014; Jorry et al., 2011; Mariotti et al., 2021). ¹⁰Be has been investigated in the Var catchment from river sand samples collected in 2016 and 2017 from the Var outlet and major tributaries (Mariotti et al., 2019). These results showed that the measurement of ¹⁰Be in fine-grained quartz sand (50–250 µm) was suitable to detect spatial and temporal changes in denudation rates. Based on the 2016 and 2017 samples, the ¹⁰Be-derived average denudation rate for the entire Var catchment over the last 2 ka is 0.24 ± 0.04 mm.yr⁻¹ (Mariotti et al., 2019), a value consistent with the estimates from sediment fluxes of 0.22 mm. yr⁻¹ (Bonneau et al., 2017).

The Var catchment is characterized by contrasted climatic conditions: a mountainous climate predominates the upstream reaches of the catchment, while a Mediterranean climate prevails in the southern low-elevation area. Precipitation and temperatures are partly controlled by elevation: mean annual precipitations recorded between 1981 and 2010 (Météo France database: https://donneespubliques.meteofrance.fr) are 733 mm.yr⁻¹ in Nice (2 m a.s.l.), and 1139 mm.yr⁻¹ in Saint-Martin-Vésubie (1064 m a.s.l.) (Figs. 1B, 2A). Half of the total precipitations occurs during the fall and winter months (September–December) (Kougkoulos et al., 2021). The hydrological regime of the Var is characterized by a “ torrent-like ” behavior, typical of steep mountainous catchments with significant seasonal discharge variations, the flux recorded during the spring snow melting period being twice the summer values (Fig. 2B). Additionally, high-frequency discharge maxima occur during intense rainfalls of the autumn season, resulting in a very large discharge variability observed in November (Fig. 2B) (Anthony and Julian, 1999).
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** (A) Mean annual precipitations in six major weather stations of the Var catchment (1981–2010) (Meteo France database; https://donneespubliques.meteo.fr). (B) Monthly discharge at the Var outlet (station Pont Napoléon) (2008–2016) (www.hydro.eaufrance.fr/). Each box indicates the interquartile range, the inner line is the median, and whiskers show the data range excluding outliers.</figcaption></figure>

## 2.2. Heavy precipitation events in the Var: the specificity of Storm Alex

Due to its location between the Alps and the Mediterranean Sea, the Var catchment is regularly affected by HPEs which are locally called “Mediterranean episodes”. These episodes are caused by a massive upwelling of warm, humid air that rises from the sea along the orographic barrier of the Maritime Alps. The warm, saturated air then cools down with elevation and condenses, leading to heavy and stationary rainfall events (Carrega, 2016; Carrega and Michelot, 2021; Rysman et al., 2016; Vigoureux et al., 2024). HPEs in the Var often lead to flash floods, with several events well reported. The last major flood before 2020 dates back to 1994 (Anthony and Julian, 1999). On November 5th 1994, a peak discharge of $3770 m^3.s^{-1}$ was recorded close to Nice, at the Var outlet (compared to a daily average of $50 m^3.s^{-1}$ - www.hydro.eaufrance.fr/), caused by heavy rainfall in the upstream catchment over the previous three days (155 mm recorded in Saint-Martin-Vésubie over a 48 h period - http://pluiesextremes.meteo.fr). The total amount of mobilized sediments by this flood has been estimated at around 18 million tons (Mulder et al., 1996). This storm caused up to 23 million euros in damages to infrastructure and housing (Ma et al., 2016) and has been characterized as a 100-year return event. Several other floods of lower magnitude have been recorded over the last 30 years, notably in November 2002 (discharge of $1100 m^3.s^{-1}$ at Nice), November 2011 (discharge of $910 m^3.s^{-1}$ at Nice), and November 2016 (discharge of $830 m^3.s^{-1}$ at Nice) (all discharge data from www.hydro.eaufrance.fr/).

On October 1st 2020, an extratropical cyclone named “Alex” originating from the northern Atlantic Ocean passed over the English Channel and Brittany, impacting the Jet Stream circulation and other air masses over Western Europe. In the Maritime Alps, this system generated a Mediterranean episode on the night of October 2nd - 3rd which, following several previous publications (Brigode et al., 2022; Chmiel et al., 2022; Liébault et al., 2024), will be called “Storm Alex” throughout this study. However, the meteorological event described here is specific to the Maritimes Alps and differs from the other meteorological phenomena that occurred during the full lifespan of the storm, notably in the United Kingdom. The specificity of this Mediterranean episode was three-fold: disrupted air masses due to the storm, unusually warm sea surface temperatures, and the orographic effect of the Alps emphasized by the north-south orientation of the impacted valleys (Carrega and Michelot, 2021). Although Mediterranean episodes are not rare in the region (Carrega, 2016), Storm Alex had an exceptional intensity, with strong winds ($> 100 km.h^{-1}$) and intense rainfall localized in the lower Tinée, Vésubie, and Roya catchments (Carrega and Michelot, 2021) (Fig. 1B).

In the Var catchment, the maximum rainfall value recorded during Storm Alex was 513 mm in 24 h in the village of Saint-Martin-Vésubie, representing roughly 6 months of yearly rainfall and the highest daily

value ever recorded for this weather station (Carrega and Michelot, 2021). Radar analysis estimated that the highest rainfall value for the storm is 585 mm, located in the lower Tinée valley (Chochon et al., 2021) (Fig. 1B). This extreme rainfall was concentrated in mountainous valleys with steep slopes (average slopes of the Vésubie and Tinée valleys: 30°), impermeable igneous and metamorphic rocks (gneiss, granites, schists) (Carrega and Michelot, 2021), low vegetation and thin or absent soil, resulting in high runoff that reached the rivers quickly. The precise hydrological signature of these flash floods is difficult to constrain because monitoring stations were damaged or swept away, and the morphology of river channels was significantly modified (Fig. 3). Additionally, there is a lack of reliable reference datasets for extreme events, which complicates the analysis and the modeling of the few available measured values. In 2021, an expert panel mandated by the French government published a report on Storm Alex and its impact in the Var catchment (Arbizzi et al., 2021). They estimated that the discharge in the lower Tinée rose from $15 m^3.s^{-1}$ (yearly average value - https://www.hydro.eaufrance.fr/stationhydro/Y623402401/synthese) to $>900 m^3.s^{-1}$ in the span of a few hours during the night of October 2nd - 3rd (Payrastre et al., 2022). In the lower Vésubie River, the discharge rose from $8 m^3.s^{-1}$ to $>700 m^3.s^{-1}$ (Carrega and Michelot, 2021), corroborated by observations that the water level rose from less than a meter to $>8 m$ under the bridge housing the monitoring station (station of Utelle-pont) (Arbizzi et al., 2021). In the downstream Var River, close to the outlet into the Mediterranean Sea, water level rose in one hour from 2.5 m to 5 m and discharge values from $<100 m^3.s^{-1}$ to $\sim 3000 m^3.s^{-1}$, a value comparable to the 1994 flood (Carrega and Michelot, 2021).

These extreme rainfall-induced flash floods rapidly became loaded with sediment, which in turn increased their erosive power, resulting in significant changes in channel morphology induced by bank erosion (Fig. 3). Aerial photographs taken by the Institut National de l'Information Géographique et Forestière (IGN) show that the Vésubie river channel widened from 10 to 20 m to over 100 m in its upstream section near Saint-Martin-Vésubie following Storm Alex (Fig. 3C/D). High volumes of sediments, including boulders, have deeply eroded the valley flanks and the fluvioglacial terraces, impacting the morphology of the riverbed for years to come (Arbizzi et al., 2021). The new landscape shows a soil and vegetation deficit in several upstream areas (Fig. 3D) and significant sediment aggradation in the channels. In the Tinée and upstream Var sub-catchments, while the rivers were also heavily loaded with sediments, lateral erosion of the river banks has been minimal compared to the Vésubie. Infrastructures at the valley bottom were mostly unaffected (Fig. 3A/B).

The total volume of sediment exported to the sea during by Storm Alex is unknown, but a recent study in the Vésubie sub-catchment used differential LiDAR along the valley bottom to constrain sediment erosion
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Photographs of the impact of Storm Alex on the landscape in the Var catchment. The inset map shows the picture locations in the Tinée (A/B in orange) and Vésubie (C/D/E in purple). (A) Aerial photograph of the Bancairon power plant (Tinée) from IGN before Alex (summer 2020). (B) Aerial photograph of the Bancairon power plant (Tinée) from IGN showing the river heavily loaded with sediments and minimal damage to infrastructure after Alex (5–7 October 2020). (C) Aerial photograph of Saint-Martin-Vésubie village from IGN before Alex (summer 2020). (D) Aerial photograph of Saint-Martin-Vésubie village from IGN showing significant landscape modification via bank erosion, stream widening and sediment surplus after Alex (5–7 October 2020). (E) Field photograph of a destroyed house sitting on a terrace in Saint-Martin-Vésubie village, taken by the Apolline Mariotti on May 5th, 2021. (F) Satellite imagery of the sediment export caused by Storm Alex at the Var outlet (close to Nice) and the Roya outlet (close to Ventimiglia). Imagery from the European Union, Copernicus Sentinel-2 imagery.</figcaption></figure>

and deposition (Liébault et al., 2024). They estimated a total sediment erosion of $4.1 \times 10^6 \text{ m}^3$ and a sediment accumulation at the Vésubie outlet of $2.1 \times 10^6 \text{ m}^3$. The latter was measured along the valley floor (roughly 100–200 m width) along 35 km of the Vésubie River. This study highlights that following Storm Alex, the Vésubie River's active channel surface significantly increased from $0.68 \text{ km}^2$ (in 2017) to $2.74 \text{ km}^2$ (in 2020), accompanied by an average channel bed aggradation of 2.4 m (ranging from -3.2 m to 11.2 m). While the Boréon and upper Vésubie (upstream of Saint-Martin-Vésubie) experienced predominant sediment erosion, the lower Vésubie (downstream of Saint-Martin-Vésubie) largely saw sediment deposition (Liebault et al., 2024). Considering the Var catchment as a whole, the sediment production and export during this event was nevertheless tremendous, enough to create a distinct

sediment plume in the Mediterranean Sea (Fig. 3F).

Overall, Storm Alex caused damages estimated at 1 billion euros (Arbizzi et al., 2021). It has also been estimated that in the coming decades, there will be a heightened risk of significant sediment transport during rainfall events with potential for damages downstream to infrastructure (Arbizzi et al., 2021).

### 3. Methods

In this study, we report $^{10}$Be concentrations measured in river sands collected throughout the Var catchment and its main tributaries at different dates (Fig. 1). We utilized three categories of samples.

First, six samples were collected at the outlet of the Var River at the
same location as Mariotti et al. (2019): two in 2018 (VAR-2018-5 I and VAR-2018-5 II) and four after Storm Alex at +7 days (VAR-A-5), +21 days (VAR-B-5), +4 months (VAR-C-5), and +7 months (VAR-D-5). These temporal replicates allowed us to determine the background and variability of $^{10}$Be concentrations before the storm, evaluate the impact of the storm on the $^{10}$Be signal and, finally, assess the time required to return to pre-storm conditions.

Second, given the heterogeneous impact of Storm Alex across the Var catchment (Fig. 1B), we sampled the upstream Var, Tinée, and Vésubie sub-catchments after the storm to gain insights into each sub-catchment's dynamic, and facilitate interpretation of the $^{10}$Be concentrations observed at the Var outlet (Fig. 1). The Var River was sampled at two different locations: upstream of its junction with the Tinée River (at +21 days: VAR-B-2 and at +7 months: VAR-D-2), and near the Var outlet in the Mediterranean Sea (samples described in the previous paragraph). The Tinée River was sampled upstream of its junction with the Var River (at +21 days: TIN-B-2 and at +7 months: TIN-D-2). The Vésubie River was similarly sampled upstream of its junction with the Var River (at +21 days: VES-B-2 and at +7 months: VES-D-2).

Finally, a historical offshore sediment sample was collected from the top of marine core ES-CS05, less than 1 km away from the riverine sampling site where the Var outlet samples were collected. This 10.5 m long core, retrieved on the upper continental shelf in shallow waters (20 m water depth, ~500 m south of the Nice airport) by the R/V Pourquoi Pas? during the ESTIME cruise (Dussud, 2014; ESTIME cruise, R/V Pourquoi Pas? doi:10.17600/14000400), is composed of silty clay Holocene deposits which can locally reach a thickness of 50 m (Garziglia et al., 2021). The core's mineralogy is primarily composed of calcite (36 %), clay (31 %) and quartz (22 %) sediments, and the bulk density varies between 1.5 and $2\,g.cm^{-3}$ (Garziglia et al., 2021). The sample material, collected between 6 and 16 cm depth from the top of the sediment core, consisted of fine-grained sand with quartz representing 25 % of the 50–250 µm grain size fraction. Given its grain size and shallow water depth, this sample is interpreted as a flood-related deposit. This interpretation is also supported by: (1) historical maps showing the Var outlet located in closer proximity to the core site, before its later diversion to the west; (2) satellite imagery showing flood-induced sediments plumes reaching the sampling site; and (3) an X-ray scan of the core showing multiple sand-grained layers interbedded within the silty clays, which are very likely also flood-related deposits (Supplementary Materials). The sample was dated by $^{14}$C on a *Turitella* sp. shell at 440 ± 30 BP collected exactly at 6 cm depth (laboratory number: Beta-505,191, $\delta^{13}$C: +3.1 ‰). After applying a local Holocene marine reservoir age correction ($\Delta R = -170 \pm 45 \,^{14}C$ years), calculated based on 15 local pre-bomb $\Delta R$ values from locations within 350 km around the core location using http://calib.org/marine (Reimer and Reimer, 2001), the radiocarbon date was calibrated to calendar ages using the Marine20 curve (Heaton et al., 2020). The resulting calibrated age is cal 1713–1950 CE (2σ).

Following the protocol from Mariotti et al. (2019), approximately 3 kg of wet sand were collected at each location and sieved to extract the 50–250 µm grain size fractions. Quartz was then isolated by magnetic separation and acid leaching using a mixture of $H_2SiF_6$ (3/4) + HCl (1/4). Quartz grains were then purified by successive HF leaching to remove 30 % of the total weight (Brown et al., 1991). Samples were then spiked using the Centre de Recherches Pétrographiques et Géochimiques (CRPG) $^{9}$Be carrier, corresponding to an average of $3.3 \times 10^{19}$ atoms of $^{9}$Be per sample. The samples were completely dissolved in HF and Be was isolated using anion and cation exchange columns and pH-sensitive precipitations. Following oxidation, samples were mixed with Nb powder, loaded into cathodes and the $^{10}$Be/$^{9}$Be ratios were measured at the ASTER facilities of the Centre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement (CEREGE). Measured ratios were reported against the CEREGE STD11 standard, with a certified value similar to the KNSTD07 normalization (Nishiizumi et al., 2007), and assume a $^{10}$Be/$^{9}$Be ratio of $(2.79 \pm 0.03) \times 10^{-11}$ for the SRM4325 standard material (Braucher et al., 2015). Three chemical blanks were

processed with the samples and yielded an average $^{10}$Be/$^{9}$Be ratio of $1.2 \times 10^{-15}$ (standard deviation of $1.8 \times 10^{-16}$).

While $^{10}$Be concentrations in river sands typically yield catchment-averaged denudation rates, we refrain from calculating them here. Samples collected post-storm violate the steady-state assumption, rendering denudation rate derivations hazardous. Instead, we use $^{10}$Be concentration variations as a tracer for sediment dynamics in time and space.

## 4. Results

We obtained 13 new $^{10}$Be measurements from samples collected in the Var catchment. Blank corrected $^{10}$Be concentrations are reported in Table 1 and range from $(1.5 \pm 0.2) \times 10^4$ at.g$^{-1}$ to $(9.5 \pm 0.7) \times 10^4$ at. g$^{-1}$. For the samples taken from the 2019 study, two grain sizes were initially analyzed (50–100 and 100–250 µm); therefore, we calculated the average $^{10}$Be concentration weighted by the quartz mass for these samples.

The two new outlet samples collected before the storm (VAR-2018-5 I and VAR-2018-5 II) have $^{10}$Be concentrations of $(3.4 \pm 0.3) \times 10^4$ at. g$^{-1}$ and $(3.1 \pm 0.3) \times 10^4$ at.g$^{-1}$ respectively, with a mean value of $(3.3 \pm 0.4) \times 10^4$ at.g$^{-1}$. Following Storm Alex, the +7 days sample (VAR-A-5) exhibited the highest post-storm $^{10}$Be concentration $(4.0 \times 10^4$ at. g$^{-1}$) (Fig. 4). Subsequent post-Storm Alex samples show a clear decrease from this peak (Table 1 and Fig. 4). The last sample collected at the outlet +7 months after Storm Alex (VAR-D-5) has a $^{10}$Be concentration of $(3.2 \pm 0.3) \times 10^4$ at.g$^{-1}$. This decrease in $^{10}$Be concentrations was steep within the first 4 months after Storm Alex and then leveled off between the samples at +4 months and +7 months (Fig. 4). Overall, both pre- and post-Storm Alex samples collected at the Var outlet are within the same range of values.

The upstream Var sub-catchment shows a wide range of exported $^{10}$Be concentrations, both before and after Alex (Fig. 5A). Before Alex, the $^{10}$Be concentration drops by ~25 % between 2016 and 2017, which could be correlated to a drop in discharge values. After Alex, the sub-catchment shows a sharp increase of 120 % in its $^{10}$Be concentration, reaching $9.5 \times 10^4$ at.g$^{-1}$ at +21 days; this is the highest value measured across the entire Var catchment. The second sample after Alex shows a steep drop in $^{10}$Be concentration at +7 months, decreasing to $3.8 \times 10^4$ at.g$^{-1}$, the lowest value recorded in this sub-catchment. The Tinée sub-catchment exhibits similar dynamics to the upstream Var: after Alex, a sharp increase of $^{10}$Be concentration at +21 days is recorded (a doubling of the $^{10}$Be value), followed by a 30 % drop at +7 months (Fig. 5B). Conversely, the Vésubie sub-catchment displays an opposite dynamic to the upstream Var and the Tinée sub-catchments: after the storm, the $^{10}$Be concentration shows a sharp decrease at +21 days (from $4.2 \times 10^4$ at.g$^{-1}$), and, after 7 months, it remains below pre-Storm Alex values (Fig. 5C).

Finally, the ES-CS05 historical sample, dated at cal 1713–1950 CE has the lowest $^{10}$Be concentration reported in this study: $(1.5 \pm 0.2) \times 10^4$ at.g$^{-1}$ (Table 1).

## 5. Discussion

### 5.1. Impact of Storm Alex on the $^{10}$Be concentrations and geomorphological implications

#### 5.1.1. $^{10}$Be concentrations at the Var outlet before Alex

At the scale of the whole catchment, including samples previously published in Mariotti et al. (2019), we have four data points establishing a 1.5-year record of $^{10}$Be concentrations before Storm Alex at the Var outlet (Fig. 4). Interestingly, a clear temporal decrease in these pre-Storm Alex $^{10}$Be concentrations is observed from 2016 to 2018 (Fig. 4). This trend suggests the influence of a prior transient sedimentary perturbation. However, current monitoring data (rainfall and discharge records) indicate no major flood events occurred during this
<caption>**Table 1**</caption>

¹⁰Be blank-corrected concentrations measured in the Var catchment and its sub-catchments before and after Storm Alex. * denotes samples from Mariotti et al. (2019).

<table><caption>**Fig. 4.** ¹⁰Be concentrations measured at the Var outlet (representative of the whole catchment) and discharge values. The dashed line marks Storm Alex. Discharge values from www.hydro.eaufrance.fr/ (station: Nice Pont Napoléon). Discharge values during and after Storm Alex are not shown as the morphometry of the measuring section has been strongly modified by the storm.</caption><thead><tr><th>River</th><th>Name</th><th>Sampling date</th><th>Time before/after Storm Alex (03.10.2020)</th><th>Lat (°N)</th><th>Long (°E)</th><th>Quartz-bearing rocks area (km²)</th><th>Concentration <sup>10</sup>Be (at·g<sup>-1</sup>)</th><th>1 σ concentration <sup>10</sup>Be (at·g<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="10">Var downstream</td><td>VAR-16-5*</td><td>16.11.16</td><td>-4 years</td><td>43.672</td><td>7.195</td><td>730.3</td><td>4.25E+04</td><td>6.77E+03</td></tr><tr><td>VAR-17-5*</td><td>10.11.17</td><td>-3 years</td><td></td><td></td><td></td><td>3.80E+04</td><td>6.09E+03</td></tr><tr><td>VAR-18-5I</td><td>01.02.18</td><td>-2.5 years</td><td></td><td></td><td></td><td>3.39E+04</td><td>2.53E+03</td></tr><tr><td>VAR-18-5</td><td>01.09.18</td><td>-2 years</td><td></td><td></td><td></td><td>3.14E+04</td><td>2.57E+03</td></tr><tr><td>II</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>VAR-A-5</td><td>10.10.20</td><td>+7 days</td><td></td><td></td><td></td><td>3.98E+04</td><td>2.72E+03</td></tr><tr><td>VAR-B-5</td><td>24.10.20</td><td>+21 days</td><td></td><td></td><td></td><td>3.73E+04</td><td>2.74E+03</td></tr><tr><td>VAR-C-5</td><td>11.02.21</td><td>+4 months</td><td></td><td></td><td></td><td>3.26E+04</td><td>2.44E+03</td></tr><tr><td>VAR-D-5</td><td>03.05.21</td><td>+7 months</td><td></td><td></td><td></td><td>3.24E+04</td><td>2.61E+03</td></tr><tr><td rowspan="6">Var upstream</td><td>VAR-16-2*</td><td>16.11.16</td><td>-4 years</td><td>43.931</td><td>7.115</td><td>83.8</td><td>6.62E+04</td><td>8.70E+03</td></tr><tr><td>VAR-17-2*</td><td>10.11.17</td><td>-3 years</td><td></td><td></td><td></td><td>5.03E+04</td><td>1.34E+04</td></tr><tr><td>VAR-B-2</td><td>24.10.20</td><td>+21 days</td><td></td><td></td><td></td><td>9.48E+04</td><td>6.84E+03</td></tr><tr><td>VAR-D-2</td><td>03.05.21</td><td>+7 months</td><td></td><td></td><td></td><td>3.83E+04</td><td>2.59E+03</td></tr><tr><td>TIN-16-1*</td><td>16.11.16</td><td>-4 years</td><td>43.933</td><td>7.177</td><td>365.1</td><td>2.84E+04</td><td>4.58E+03</td></tr><tr><td>TIN-B-2</td><td>24.10.20</td><td>+21 days</td><td></td><td></td><td></td><td>6.00E+04</td><td>3.66E+03</td></tr><tr><td rowspan="3">Tinée</td><td>TIN-D-2</td><td>04.05.21</td><td>+7 months</td><td></td><td></td><td></td><td>4.84E+04</td><td>3.23E+03</td></tr><tr><td>VES-16-2*</td><td>16.11.16</td><td>-4 years</td><td>43.879</td><td>7.231</td><td>184.0</td><td>4.30E+04</td><td>7.40E+03</td></tr><tr><td>VES-B-2</td><td>24.10.20</td><td>+21 days</td><td></td><td></td><td></td><td>2.68E+04</td><td>2.03E+03</td></tr><tr><td rowspan="2">Vésubie</td><td>VES-D-2</td><td>04.05.21</td><td>+7 months</td><td></td><td></td><td></td><td>2.66E+04</td><td>2.43E+03</td></tr><tr><td>ES-CS05</td><td>2014</td><td>1713–1950 CE</td><td>43.644</td><td>7.218</td><td>730.3</td><td>1.53E+04</td><td>1.97E+03</td></tr></tbody></table>

timeframe that might explain a decreasing ¹⁰Be trend. Furthermore, no significant landslides affecting quartz-bearing lithologies have been documented in the catchment during this period that could introduce large volumes of low-¹⁰Be material. Despite the absence of documented major events during 2016–2018, we can hypothesize the impact of a “sediment wave” propagating through the catchment. This implies a disruption to the sedimentary system that occurred prior to this monitoring window, with remobilized material gradually entering and progressing through the fluvial network.

Under this “sediment wave” scenario, two mechanisms could account for the observed decrease between 2016 and 2018. One possibility is that a “low-¹⁰Be wave” transported material with an overall low ¹⁰Be concentration. Such material could originate from deeper, shielded sources, such as deep-seated landslides or large-scale hillslope failures (Dingle et al., 2018; Puchol et al., 2014; Tofelde et al., 2018). In this case, we would then be observing the “front” of this “low-¹⁰Be wave” in late 2017 (VAR-17-5), subsequently lowering the catchment-average ¹⁰Be concentration. Alternatively, the observed decrease could

represent the “tail end” of a “high-¹⁰Be wave” caused by an initial perturbation (that occurred before 2016). This “high-¹⁰Be wave” would have delivered ¹⁰Be-enriched material, potentially sourced from the mobilization of older, exposed deposits that had accumulated high ¹⁰Be concentrations due to long-term cosmic ray production. As the sedimentary system gradually purges this ¹⁰Be-enriched material, it would slowly return to a steady-state. This observed pre-storm trend, while currently speculative regarding its precise origin, highlights the complex behavior of the Var’s sediment system even in the absence of major events like Storm Alex.

To interpret the observed ¹⁰Be variations at the outlet, we use the average daily discharge values from hydrological stations measured close to each sampling point as a proxy for the hydrological conditions and sediment transport dynamics (Figs. 4 and 6). While ¹⁰Be concentrations in river sediments represent a spatially and temporally integrated signal, instantaneous discharge is a critical control on which specific sediment sources within the catchment are actively eroded. As discharge varies, the river’s capacity to mobilize sediment changes, allowing it to access different sediment sources. For instance, higher discharge conditions can selectively mobilize material from hillslopes, river banks, or upstream storage areas, all of which may have distinct ¹⁰Be concentrations compared to “background” in-channel sediment. Thus, examining ¹⁰Be concentrations in relation to discharge provides insights into how hydrological forcing influences the provenance and characteristics of the transported sediment. During the four years of the 2016–2020 period, there is however no record of a major climatic perturbation that could have impacted the ¹⁰Be signal in the Var catchment, with the exception of high discharge values in November 2016 (Fig. 4), which peaked a few days after the first sample was collected. This event was due to thunderstorms, mainly in the Esteron sub-catchment (southwestern part of the Var catchment), with a maximum of 152 mm over 48 h recorded in Ascros (http://pluiesextremes.meteo.fr). This sub-catchment is mainly composed of carbonates with few sandstones (only 9 % of quartz-bearing rocks); it is thus unlikely that this event impacted the ¹⁰Be signal in the Var catchment. In contrast, 2017 was particularly dry, characterized by low discharge values at the outlet (Fig. 4). Rainfall during 2018, 2019 and the first 9 months of 2020 (before Alex) were characteristic of “standard” years, with yearly average, minimum, and maximum discharge values within

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** $^{10}$Be concentrations and discharge values for the upstream Var, Tinée and Vésubie sub-catchments. Discharge values from Météo France (www.hydro.eaufrance.fr/).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** $^{10}$Be concentration at the Var outlet between 2016 and 2018 as a function of discharge of the Var River measured at the Pont Napoleon station over four averaging periods (daily average discharge in yellow, weekly average discharge in orange, monthly average discharge in red and yearly average discharge in purple).</figcaption></figure>

the range of discharge values monitored since 2008 (Fig. 4).

Regarding landslides, a systematic analysis of the BRGM BDMvt database revealed 624 mapped landslides in the Var catchment. However, of the 292 landslides that occurred before Storm Alex, only 58 were located on quartz-bearing lithologies. Furthermore, most of these 58 landslides were too small to be accurately assessed on satellite imagery and thus unlikely to produce enough sediment to significantly impact the $^{10}$Be signal at the outlet. Modeling studies from Niemi et al. (2005) and Yanites et al. (2009) regarding catchment size and landslide magnitude further support that only exceptionally large events on quartz-bearing lithologies would likely impact the integrated outlet signal. Among the pre-Alex landslides on relevant lithologies, only the La Clapière landslide (upstream Tinée) stands out as potentially having the scale and lithological characteristics to influence the $^{10}$Be signal at the Var outlet. Its toe is located close to the river, suggesting a possible pathway for sediment reworking into the fluvial network. However, satellite imagery from 2005 and 2019 does not show obvious signs of substantial sediment reworking from La Clapière into the river during

the pre-Alex observational period. This lack of clear evidence, combined with the uncertainty surrounding the exact timing and cause of the $^{10}$Be signal shift observed during that specific timeframe, makes it difficult to attribute the observed pre-Alex $^{10}$Be trend solely to this landslide.

Moreover, we found no correlation between $^{10}$Be concentrations and discharge values measured at the Var outlet, regardless of the averaging period considered (daily, weekly, monthly, or yearly; Fig. 6). This lack of relationship, whether comparing high/low $^{10}$Be concentrations to high/low discharge values, strongly indicates that short-term weather forcing did not drive the $^{10}$Be variations observed prior to Storm Alex.

For the pre-Storm Alex period (2016–2018), we can consider the four samples to be representative of a dynamic steady-state. While the $^{10}$Be signal showed variability, it was not due to major stochastic perturbations (e.g., extreme floods, landslides) capable of fundamentally altering long-term denudation. Rather, these natural variations likely stem from the catchment’s inherent heterogeneity (topography, climate, sediment sources), an interpretation supported by the lack of recorded major climatic or sedimentary disruptions during this timeframe. Furthermore, the observed range of pre-Storm Alex $^{10}$Be variability (3.2 to $4.3 \times 10^4$ at.g$^{-1}$) is comparable to $^{10}$Be values from Holocene sediments sampled in sedimentary cores (Mariotti et al., 2021), which reflect similar climatic conditions to the present day and can thus be considered representative of a similar dynamic steady-state.

There are currently few publications that explore the temporal variability of the $^{10}$Be signal in catchments without major perturbations in the sedimentary system (in this case, a major perturbation would be any event characterized by a > 100-year return period) (Fig. 7). A compilation of $^{10}$Be concentration from 65 catchments worldwide demonstrates that in 37 % of these catchments, temporal replicate samples exhibited statistically significant differences in $^{10}$Be concentrations, quantified by ratios higher than 1.2 or lower than 0.8. Average uncertainties on published $^{10}$Be concentrations are approximately 6 % of the measured values (see Supplementary Materials).

We should note that global data indicate that larger catchments are more prone to display a temporal $^{10}$Be variability (Fig. 7). This observation is surprising, as it is also reported that larger basins buffer perturbations and thus are more likely to export uniform $^{10}$Be concentrations (Lupker et al., 2012; Niemi et al., 2005; Yanites et al., 2009). This observed variability of the $^{10}$Be concentrations between temporal replicates has been largely attributed to stochastic processes,
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Temporal replicability of $^{10}$Be concentrations in catchments without major perturbation of the sedimentary system. Published data from Kober et al. (2015), Lupker et al. (2012), Mariotti et al. (2019), Matmon et al. (2003), Portenga et al. (2015), Reusser et al. (2017), Sosa Gonzalez et al. (2017). Several samples are displayed on top of each other because they drain the same catchment area and have similar $^{10}$Be ratios: VAR-16-5 to VAR-17-5 = 1.1, VAR-17-5 to VAR-18-5 I = 1.1, VAR-18-5 I to VAR-18-5 II = 1.1.</figcaption></figure>

reflecting changes in the sediment sources (Himalayas - Lupker et al., 2012) and/or denudation processes (Yunnan Province, China - Sosa Gonzalez et al., 2017 and Alps - Mariotti et al., 2019).

In the Var, only the upstream Var sub-catchment between 2016 and 2017 exhibits a $^{10}$Be ratio with high variability before Storm Alex (Fig. 7), most likely due to a change in sediment source (Mariotti et al., 2019). Overall, while the cause of the observed $^{10}$Be variations at the Var outlet before Storm Alex remains uncertain, we note that the $^{10}$Be ratios from one sample to the next are well within the low range of the observed variability in the literature (Fig. 7).

### 5.1.2. $^{10}$Be concentration at the Var outlet after Alex

Although there is no $^{10}$Be data between November 2018 and October 2020, the impact of Storm Alex on the $^{10}$Be signal exported at the outlet of the whole catchment is clearly visible (Fig. 4), marked by a positive spike of approximately 25 % with respect to the last sample from 2018. It is also necessary to consider that the first post-Storm Alex sample was collected 7 days after the storm (VAR-A-5), so the exported $^{10}$Be concentration at that time may not represent the exact value of the sediment pulse synchronous to the storm. The $^{10}$Be values recorded at the Var outlet after Storm Alex exhibit a decreasing trend that, when fitted to the four post-storm samples, follows a power law model (Fig. 8). If true, this empirical power law fit (y = 44,772 x$^{-0.0602}$, R$^2$: 0.99) suggests a peak $^{10}$Be concentration of approximately ~4.5 x 10$^4$ at.g$^{-1}$ right after the storm (Fig. 8).

Overall, the immediate impact of a perturbation on the $^{10}$Be concentrations measured in river sediments is not systematic. While most studies report a dilution of the $^{10}$Be signal (Fig. 9 - ratios >1), other studies observe, conversely, an increase of the exported $^{10}$Be concentrations (Fig. 9 - ratios <1). The range of $^{10}$Be ratios observed after a perturbation (Fig. 9: ~0.5 to ~3.5) is higher than $^{10}$Be ratios observed without a perturbation (Fig. 7: ~0.5 to ~2.5), which indicates that $^{10}$Be in sediments can indeed be used in some settings to trace a perturbation. Out of the different types of perturbations, it seems that earthquakes are more likely to induce a dilution of the $^{10}$Be signal than other events. This could, however, be due to the fact that both studies looking at the impact of earthquakes (Wang et al., 2017; West et al., 2014) are reporting large co-seismic landslides. Therefore, in settings where earthquakes do not trigger significant landslides or mobilize quartz-bearing material, their

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Power law fit to estimate the original $^{10}$Be concentration of sediments at the Var outlet.</figcaption></figure>

impact on the $^{10}$Be signal might differ. Similar to Fig. 7, we observe that smaller catchments tend to have a more stable $^{10}$Be signal, while larger catchments exhibit higher variability. This is likely due to the homogeneity in small catchments of multiple variables that can impact the $^{10}$Be signal (elevation, slope, climate, anthropogenic impact, lithology, and different types of sediment sources). Conversely, in larger catchments, the potential for a change in sediment dynamics before and after a perturbation is much greater.

### 5.1.3. Sub-catchments $^{10}$Be data

In this section, we investigate the spatial and temporal variations of the $^{10}$Be signal within the Var sub-catchments. Because Storm Alex was characterized by significant spatial rainfall variations in the Var catchment (Fig. 1), it is important to consider each sub-catchment's dynamics. This allows us to explore the various landscape responses to the perturbation and the mechanisms that modified the $^{10}$Be concentrations observed in the sub-catchments and at the Var outlet.

Even though we lack a large dataset before and after the storm, the variations in $^{10}$Be concentrations observed in the sub-catchments show clear and contrasting signals. While the upstream Var and the Tinée sub-catchments exhibit a marked increase in the $^{10}$Be concentration after Storm Alex, the Vésubie sub-catchment conversely yielded a decrease. Thus, $^{10}$Be records show that the regional climatic event can yield
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Ratio of $^{10}$Be concentrations in catchments right before and after a major perturbation of the sedimentary system. Each data point from literature represents the ratio of post-event to pre-event $^{10}$Be concentrations (Cpost/Cpre). Chen et al. (2020): Typhoon Morakot, 2009; Cpre 3 years before, Cpost 3 years after event. Foster and Anderson (2016): 2013 storm-induced floods; Cpost 1 month to 1 year after event. Kober et al. (2012): debris flows; Cpre/Cpost sampled within weeks-months of events. Quock et al. (2022): Hurricane Maria, 2017; Cpre 2 months before, Cpost 4 months after. Wang et al. (2017): Wenchuan earthquake, 2008; 3–8 year interval. West et al. (2014): Wenchuan earthquake, 2008; 5–6 year interval. Wilkinson et al. (2022): Cyclone Gita & extreme rainfall, 2018; few months interval.</figcaption></figure>

opposite impacts on the $^{10}$Be signal from one sub-catchment to the next.

Indeed, during a climatic perturbation, various geomorphological
processes with contrasting consequences on $^{10}$Be concentration may
occur. Firstly, intense rainfall during a storm can promote the mobili-
zation of sediment previously disconnected from the river network,
significantly impacting the $^{10}$Be measured in river sands. In moun-
tainous catchments, the coupling between sediment sources and the
main river network is often poor, meaning that sediment produced on
hillslopes does not directly reach the river network (Ardizzone et al.,
2024; Heckmann and Schwanghart, 2013; Himmelstoss et al., 2024;
Messenzehl et al., 2014). These poorly-connected areas, which can
include talus slopes, glacial deposits, and plateau surfaces, function as
sediment sinks where denudation is much lower than on the connected
hillslopes. Due to their limited interaction with the fluvial system, these
areas can accumulate significant $^{10}$Be concentrations over pluri-
millennial timescales, resulting in $^{10}$Be concentrations that are higher
than those of the hillslopes at steady-state denudation ($>10^5$ at/g vs few
$10^4$ at/g in the case of the Var catchment). Studies from high-elevation
catchments across the Alps (Ardizzone et al., 2024; Cavalli et al., 2013;
Heckmann and Schwanghart, 2013; Messenzehl et al., 2014) consis-
tently report low sediment connectivity, suggesting that significant
portions of the Var catchment may be disconnected from the fluvial
network under “normal” hydrological conditions. During extreme rain-
fall events, stored sediments from these areas may be potentially
remobilized into the fluvial system (Rainato et al., 2021; Scorpio et al.,
2022, 2018), and consequently increase the $^{10}$Be concentrations of river
sands. Other processes can also modify this signal. Heavy rainfalls can
affect slope stability and may promote deep landslides and the flushing
of buried sediments with lower $^{10}$Be concentrations, which, if sampled at
sufficient depth ($> $ few meters), could dilute the downstream signal
(Puchol et al., 2014). Furthermore, heavy rainfalls also lead to an in-
crease in discharge values and sediment loads that can enhance river
erosion, leading to the remobilization of older fluvial or glacial

sediments stored in alluvial plains for several thousand years. These
reworked deposits could be incorporated in larger proportion than their
standard contribution in modern rivers and thus also contributing to
post-storm changes of the $^{10}$Be signal.

During Storm Alex, heavy rainfalls were observed in all sub-
catchments: on average, the upstream Var sub-catchment received
112 mm of rain over a 24 h period, the Tinée 182 mm, and the Vésubie
254 mm (Chochon et al., 2021). These values characterize the hetero-
geneous impact of the storm, and we note that the Vésubie sub-
catchment, where the heaviest rainfall was observed, is also the sub-
catchment whose $^{10}$Be signature evolved differently from the other
sub-catchments, with a $^{10}$Be concentration decrease.

Sediment connectivity and its variation during a storm are notably
hard to constrain (Hooke and Souza, 2021), and usually require high-
resolution imagery such as LiDAR or ortho-photography to map and/
or model sediment sources and sinks. However, most studies report an
increase in sediment connectivity during storm events, either by mass
wasting movements (landslides or debris flows) (Scorpio et al., 2022,
2018; Spiekermann et al., 2022; Surian et al., 2016) or by increased
runoff (Wainwright et al., 2011). In the case of our study area, we use a
Normalized Difference Vegetation Index (NDVI) map, calculated using
satellite imagery to constrain changes in vegetation cover over time as a
proxy of sediment connectivity. We used the website https://hazma
pper.org/ developed by Scheip and Wegmann (2021) to constrain the
change in NDVI one year before and after Storm Alex (Fig. 10). Our
assumption is that a decrease in vegetation cover detected after Storm
Alex would be associated with an increase in sediment connectivity due
to surface processes. The relative surface area exhibiting a decrease of
NDVI is roughly the same across the Var sub-catchment (6.6 %), the
Tinée sub-catchment (4.9 %), and the Vésubie sub-catchment (4.9 %). A
more in-depth analysis of Sentinel images was used to build an inventory
of landslides triggered by Storm Alex in the region (Prakash and Man-
coni, 2021). Most landslides occurred in the high Vésubie, in the area
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Geomorphic changes induced by Storm Alex in the study area. (A) is the whole Var catchment, (B) is zoomed-in on the village of Saint-Martin-Vésubie. Hashed lines mark the area where flooding occurred. We note the good overlap of the flooding zone at the valley bottom with areas with negative NDVI (orange and red). Purple polygons are landslides. Satellite imagery from IGN.</figcaption></figure>

around the village of Saint-Martin-Vésubie, and in the lower Tinée, close to the confluence with the Var (Prakash and Manconi, 2021), where rainfall accumulation was the strongest. In total, 0.8 km² of landslides were mapped in the Var sub-catchment (or >0.1 % of the area), 1.1 km² in the Tinée sub-catchment (0.3 % of the area), and 0.7 km² in the Vésubie sub-catchment (or 0.4 % of the area). These landslides were mostly observed in shallow soils or in channelized flows (Prakash and Manconi, 2021), where the potential for deeply buried sediments to be remobilized is low. Both the NDVI variation and the mapping of land-slides suggested that the sediment connectivity very likely increased during the storm, although no significant variations were observed be-tween sub-catchments. Therefore, it is likely that increased connectivity and mass wasting processes are not the primary geomorphological controls on the conflicting ¹⁰Be signal between the Var/Tinée sub-catchments and the Vésubie sub-catchment.

The discrepancy between the upstream Var and Tinée sub-catchments on one side (showing an increase in ¹⁰Be concentrations after Storm Alex) and the Vésubie sub-catchment on the other side (where the ¹⁰Be concentration drops) is more likely associated with the third process: the remobilization of fluvial and glacial/post-glacial sediments with low ¹⁰Be concentration stored in the alluvial plain.

In the upstream Var and lower Tinée (close to the confluence with the Var) valleys, the absence of major bank erosion (Fig. 3A/B) in the two sub-catchments suggests that the rivers did not incorporate a lot of reworked sediment from the valley bottom. Instead, it is very likely that the majority of sediments exported after the storm originated from sediment already connected to the fluvial network, or from sediments on disconnected slopes and plateaus. The increased input from poorly connected sediments during and after the storm is thus the most probable mechanism driving the increase of ¹⁰Be concentrations observed in

the upstream Var and Tinée sub-catchments +21 days after Storm Alex. Once this transient supply of sediments with high ¹⁰Be concentration is transported downstream, the ¹⁰Be signal returns to its previous state and the ¹⁰Be concentrations decrease, as observed at +7 months after the storm in the upstream Var and Tinée sub-catchments (Fig. 5).

In contrast, in the Vésubie valley and especially close to Saint-Martin-Vésubie, the river reworked large amounts of materials by cutting deep into the fluvioglacial terraces (Fig. 3C/D/E). A previous study indeed estimated that the active channel surface area in the Vésubie increased by a factor of 4 after Alex (from 0.68 km² in 2017 to 2.74 km² in 2020) (Liévault et al., 2024). As seen in the photograph (Fig. 3E), a large portion of the reworked sediments in the Vésubie valley was deeply buried under several meters of material and therefore shielded from cosmic rays. Moreover, most of the superficial formations that were reworked by Storm Alex are estimated to be from the last glacial period (Liévault et al., 2024). While these fluvioglacial deposits can contain a range of ¹⁰Be concentrations due to varied origins, it is plausible that a significant fraction of the remobilized material had relatively low ¹⁰Be concentration. This aligns with observations that glacial material, particularly subglacial sediments, typically exhibit very low ¹⁰Be concentrations (e.g. ~2.5 × 10³ at.g⁻¹ measured in sediments exported by the Bossons Glacier in the Alps; Guillon et al., 2015). Therefore, is it a reasonable working assumption that the average ¹⁰Be concentration of the fluvioglacial deposits remobilized during Storm Alex was significantly lower than the ¹⁰Be concentration measured in the Vésubie before the storm. Because of the amount of material leftover after the storm at the valley bottom, the ¹⁰Be signal exported by the Vésubie river will be impacted until the sediment surplus is purged (Arbizzi et al., 2021; Liévault et al., 2024). This dynamic explains the sustained low ¹⁰Be concentrations observed in the Vésubie at +21 days and +7 months
(Fig. 5).

### 5.1.4. Mixing model for sediment provenance during Storm Alex

We developed a mixing model to interpret the observed $^{10}$Be signatures, differentiating three distinct end-members representing the sediment sources mixed within the Vésubie sub-catchment during Storm Alex. (Table 2). The theoretical $^{10}$Be concentration resulting from the sediment mixing can then be calculated as follows (Binnie et al., 2006; Clapp et al., 2002; Matmon et al., 2003; Neilson et al., 2017; Savi et al., 2014):

$$C_{\text{mixed}} = \frac{\sum (C_i \times A_i \times z_i)}{\sum (A_i \times z_i)}$$

where $C_i$, $A_i$ and $z_i$ represent the $^{10}$Be concentration, area and eroded thickness for end-member i (1 = flooded areas, 2 = connected hillslopes, 3 = disconnected areas), respectively.

The three end-members are defined as follows: (1) Flooded area: this end-member represents sediments originating from the remobilization of former fluvioglacial deposits, characterized by rapid erosion during Storm Alex (Liébault et al., 2024). Due to their significant thickness of the deposits (> 3 m) (Fig. 3E) and the glacial origin (Liébault et al., 2024), these sediments have mostly been shielded from cosmic rays and are assumed to have low $^{10}$Be. For this end-member, we use the $^{10}$Be concentration of glacial sediments of the Bossons Glacier in the Alps (~2.5 × 10³ at.g⁻¹; Guillon et al., 2015). We estimated a surface of 5 km² for this sediment source using the impact maps produced by the SERTIT lab (https://sertit.unistra.fr/rms/?action=706). The average thickness of eroded sediment for this end-member is derived from the “mean unit erosion rate” of 1.27 m determined by Liébault et al. (2024) using LiDAR surveys in the Vésubie valley. (2) Connected hillslopes: this end-member represents sediments originating from connected hillslopes within quartz-bearing lithologies. These areas are assumed to have experienced denudation rates similar to pre-Storm Alex conditions (Mariotti et al., 2019). The area of this end-member corresponds to the surface area of quartz-bearing rocks within the Vésubie sub-catchment, excluding the areas designated for end-members 1 and 3. For the $^{10}$Be concentration and eroded sediment thickness, we used the pre-Storm Alex $^{10}$Be measurements in the Vésubie (4.12 × 10⁴ at.gr⁻¹) and the corresponding yearly denudation rate reported by Mariotti et al. (2019) (0.28 mm.yr⁻¹). (3) Disconnected area: this end-member represents sediments originating from previously disconnected areas of the Vésubie sub-catchment that were remobilized during Storm Alex. The precise contribution of these areas is uncertain; therefore, we considered two scenarios to bracket the potential impact: a low-input scenario and a high-input scenario. The minimum area of disconnected slopes was estimated using a 5 m DEM and geological map to identify quartz-bearing lithologies with slopes less than 5°, indicative of gentler terrains potentially prone to disconnection. For the maximum area of disconnected slopes, we adopted a conservative estimate of 20 % of the catchment’s total area (35.70 km²), based on studies on sediment connectivity in similar high alpine catchments (e.g., Messenzehl et al., 2014; Heckmann and Schwanghart, 2013). The thickness of eroded sediments for these disconnected slopes was allowed to vary between 0 and 2 m. The maximum volume of sediment was constrained by using the total volume of sediment exiting the tributaries into the Vésubie channel, which was calculated by Liébault et al. (2024) as 1.51 × 10⁶ m³ ± 25,600 m³. The $^{10}$Be concentration of these sediments is unknown but was bracketed by two plausible limit values. First, the $^{10}$Be concentration of these disconnected areas must be higher than that of sediments originating from connected hillslopes (4.12 × 10⁴ at.gr⁻¹) under the assumption that sediment on disconnected slopes is exposed for a much longer time to cosmic rays. Second, the maximum reasonable $^{10}$Be concentration was computed assuming that the maximum duration of surface exposure of the zone is lower than 20 ka, corresponding to the Last Glacial Maximum (LGM), which corresponds to a $^{10}$Be

concentration of 3.6 × 10⁵ at.gr⁻¹. While the exact contribution of each end-member cannot be precisely known, this approach allows us to constrain the unknown volume ($A_3 \times z_3$) and $^{10}$Be concentration ($C_3$) of sediments derived from disconnected hillslopes. We achieve this by exploring scenarios that force the mixing model to match the observed $^{10}$Be mixed concentration at the Vésubie outlet (VES-B-2: (2.68 ± 0.2) × 10⁴ at.gr⁻¹). In our model, the parameters for end-members 1 and 2 ($A_1, A_2, z_1, z_2, C_1, C_2$) are fixed, while those for end-member 3 ($A_3, z_3, C_3$) are considered unknown.

Our modeling results reveal an inverse relationship between the remobilized sediment volume from disconnected areas and its required $^{10}$Be concentration to match the observed mixed signal downstream (C₃). Specifically, a smaller remobilized sediment volume ($A_3 \times z_3$) necessitates a higher $^{10}$Be concentration (C₃) to match this signal, while a large remobilized volume allows for a lower $^{10}$Be concentration. The model thus demonstrates a trade-off: if disconnected slope sediments have accumulated $^{10}$Be to the maximum of the modeled range (3.60 × 10⁵ at.gr⁻¹), then a relatively small remobilized volume of sediment of 4.59 × 10⁵ m³ would be needed to reproduce the observed downstream signal. Conversely, if we constrain the sedimentary volume for these disconnected slopes using the value calculated by Liébault et al. (2024), this material would need to have a lower $^{10}$Be concentration of 1.29 × 10⁵ at.gr⁻¹ (Supplementary Materials).

The results of this mixing model, while quite broad, consistently indicate that to match the observed downstream $^{10}$Be signature, an input of sediment with significantly higher $^{10}$Be concentration than pre-Storm Alex (4.12 × 10⁴ at.gr⁻¹) is required. This implies that the sediment dynamics of the Vésubie sub-catchment are complex, with sediment sources characterized by a high spatial variability in $^{10}$Be concentration. This high variability has been previously observed in the Alps by Delunel et al. (2014), where they sampled various sediment sources within a mountainous catchment (periglacial deposits, colluvial and glacial material, as well as stream sediments). They found that the $^{10}$Be concentration can vary by a factor > 10 across different sources while maintaining a consistent $^{10}$Be concentration per sediment source. This suggests that it is indeed possible for different sediment sources within one catchment to have significantly contrasting $^{10}$Be signatures.

While each sub-catchments export a $^{10}$Be signature that is reflective of the sedimentary processes that occurred during the storm, the average $^{10}$Be concentration at the Var outlet is complexified by the fact that sub-catchments are producing contradictory $^{10}$Be signatures (high $^{10}$Be concentrations in the upstream Var and Tinée, low $^{10}$Be concentration in the Vésubie), and by the differences in surface areas between sub-catchments. The upstream Var (84 km² of quartz-bearing rocks) and Tinée (365 km²) sub-catchments are much bigger than the Vésubie sub-catchment (184 km²) and thus can export enough sediments to overprint the Vésubie $^{10}$Be signature. This likely explains why the $^{10}$Be signature at the Var outlet also shows a strong increase of the concentrations after the storm (the first two samples collected after the storm at +7 days (VAR-A-5) and +21 days (VAR-B-5)), followed by a progressive decrease (Fig. 4). Whereas the high $^{10}$Be signal of Storm Alex in the upstream Var and Tinée sub-catchments appears to decrease within a few months (Fig. 5), the low signal in the Vésubie sub-catchment remains stable for a more extended period, persisting for up to 7 months post-storm. Thus, after this initial spike driven by the upstream Var and Tinée sub-catchments, the $^{10}$Be concentration at the Var outlet decreases due to both the lowering of the $^{10}$Be concentrations exported by the upstream Var and the Tinée sub-catchments, as well as the continued low-concentration contribution of the Vésubie sub-catchment.

### 5.1.5. Timescale of recovery of the $^{10}$Be signal

To quantify how quickly a system can recover to pre-perturbation $^{10}$Be levels, we calculated a $^{10}$Be ratio defined as the $^{10}$Be concentration of the last sample before the perturbation divided by the $^{10}$Be concentration of a sample after the perturbation (Fig. 11). A ratio close to 1 indicates a return to pre-perturbation levels while values higher
<table><caption>**Table 2**

Parameters for the sediment mixing model in the Vésubie sub-catchment during Storm Alex.</caption><thead><tr><th>Area</th><th>Area (km²)</th><th>Average erosion thickness during Alex (m)</th><th>Eroded volume (m³)</th><th><sup>10</sup>Be concentration of sediments (at·g<sup>-1</sup>)</th></tr></thead><tbody><tr><td>(1) Flooded area</td><td>5<sup>a</sup></td><td>1.27<sup>b</sup></td><td>6.34 × 10<sup>6</sup></td><td>2.53 × 10<sup>3c</sup></td></tr><tr><td>(2) Connected hillslopes under steady-state denudation</td><td>134 to 178<sup>d</sup></td><td>2.8 × 10<sup>-4e</sup></td><td>From 3.75 × 10<sup>4</sup> to 5.00 × 10<sup>4</sup></td><td>4.12 × 10<sup>4f</sup></td></tr><tr><td>(3) Disconnected areas: Low input</td><td>0.27<sup>g</sup></td><td>From 0 to 2</td><td>From 0 to 1.51 × 10<sup>6i</sup></td><td>From 4.12 × 10<sup>4</sup> at·g<sup>-1</sup> to 3.6 × 10<sup>5</sup> at·g<sup>-1h</sup></td></tr><tr><td>(3) Disconnected areas: High input</td><td>35.70<sup>g</sup></td><td></td><td></td><td></td></tr></tbody></table>

a Calculated value from SERTIT.

b Calculated value from Liébault et al. (2024).

c Value from Guillon et al. (2015).

d Calculated value from QGIS.

e Value from Mariotti et al. (2019).

f Value from Mariotti et al. (2019).

g Calculated value from QGIS.

h Minimum value from Mariotti et al. (2019), maximum value calculated for an apparent exposure age of 20 ka and a local production rate of 17.9 at·g⁻¹·yr⁻¹.

i Value from Liébault et al. (2024).

than 1.2 or lower than 0.8 indicate respectively ¹⁰Be enrichment or depletion. A rapid change in the ¹⁰Be ratio following a perturbation has been observed by Kober et al. (2012) following debris flow events in the Aare catchment (Swiss Alps) (Fig. 11A). In this case, the small catchment reacted quickly to the debris flow before the ¹⁰Be concentration came back to pre-perturbation level in ~50 days (catchment Al) or ~350 days (catchment Al2). This disparity in timing could be due to a difference in event magnitude. Indeed, the Al samples were collected after “small to medium debris flow activity” whereas the Al2 samples were collected after “very large debris-flow activity” which produced a larger volume of sediment (Kober et al., 2012) that took longer to purge. In the Var catchment, the outlet signal comes back to pre-perturbation level in ~125 days (Fig. 11B). The Tinée sub-catchment maintains a low ¹⁰Be

ratio (0.5 to 0.6) while the Vésubie sub-catchment maintains a high ¹⁰Be ratio of 1.6 over 7 months after Alex. The ¹⁰Be ratio of the upstream Var sub-catchment is actually shifting from 0.5 to 1.3 in roughly ~200 days, which would indicate a shorter-lived storm-induced perturbation in this sub-catchment compared to the rest of the Var. While this demonstrates a relatively rapid recovery, other extreme events reveal much longer lasting impacts. For example, the effect of Typhoon Morakot in Taiwan (Fig. 11C: Chen et al., 2020) shows persisting perturbation of the ¹⁰Be signal. At ~1200 days (~3 years and 4 months) after the typhoon, the measured ¹⁰Be ratios range from 0.5 to 3.4 and at ~2400 days (~6 years and 6 months), the range of ¹⁰Be ratios has narrowed from 0.4 to 2.2. There is no clear individual trend after the typhoon, but we can observe an overall tendency of the ¹⁰Be ratio getting closer to the pre-

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Temporal signal for the ¹⁰Be concentration after a perturbation of the sedimentary system. The ¹⁰Be ratio is measured as the ¹⁰Be concentration of the last sample before the perturbation divided by the ¹⁰Be concentration of a sample after the perturbation.</figcaption></figure>
perturbation level over time. The 2008 Wenchuan earthquake (Fig. 11D) significantly diluted the ¹⁰Be signal, with ¹⁰Be ratios between 1.9 and 4.0 for the first ~1000 days after the earthquake. Over the next 1000 days, the ¹⁰Be ratios are slowly getting closer to pre-perturbation level, and we can note that catchment size does not seem to be a determining factor for the speed at which the ¹⁰Be ratio adjusts to the perturbation. This sustained perturbation of the ¹⁰Be signal following the 2008 Wenchuan earthquake is consistent with recent findings indicating a decadal increase in total sediment flux, which remobilized ~9% of the sediment mass from earthquake-triggered landslides within ten years (Li et al., 2025). This prolonged export of shielded, ¹⁰Be-poor material would therefore be consistent with the observed prolonged ¹⁰Be dilution.

## 5.2. Comparison with longer term ¹⁰Be dynamics

To contextualize the short-term ¹⁰Be signature observed during Storm Alex, we now compare it against a longer-term sedimentary archive of the Var catchment. These archives provide a baseline against the storm's impact, which is of great importance for hydrology and hazard management, where constraining the occurrence of a devastating event can help land management decisions and improve community resilience (St. George et al., 2020).

To achieve this, we have analyzed a historical offshore sediment sample, from cal 1713–1950 CE and additionally, we have included data from the 0–75 ka sediment cores record collected on the Var submarine ridge (Mariotti et al., 2021) (Fig. 12). These cores provide a continuous and high-resolution ¹⁰Be record from sand turbidites, allowing us to constrain the longer-term denudation dynamics of the Var catchment, covering the end of the last glacial period, the LGM, deglaciation and the Holocene. This approach aims to establish if the magnitude of the Storm Alex perturbation is comparable (or not) with catchment denudation processes over longer timescales.

The very low ¹⁰Be concentration from this historical sample ((1.5 ± 0.2) × 10⁴ at.g⁻¹, Table 1 and Fig. 12) contrasts with the increase in ¹⁰Be concentrations observed at the Var outlet following Storm Alex. Such a low ¹⁰Be signal, comparable to LGM values in the sediment cores record (Mariotti et al., 2021), points to a period of exceptionally high sediment flux and denudation within the Var catchment, characterized by the mobilization and export of significant volumes of ¹⁰Be-poor sediments.

This interpretation is in line with historical records indicating low forest cover in France during the 18th and 19th centuries (Mather et al., 1999; Thibaut et al., 2022). The military map of the Var catchment established by Bourcet in 1749–1754 (Fig. 13) further illustrates extensive deforestation across the region, which would have significantly favored hillslope erosion and sediment supply to the river system. This anthropogenic landscape modification could have profoundly impacted the Var's denudational regime and promoted the export of sediment with overall low ¹⁰Be concentration.

As observed in the Vésubie sub-catchment after Storm Alex, this low ¹⁰Be concentration could also point to the mobilization of sediments with low ¹⁰Be, such as fluvioglacial deposits from the high Tinée or Vésubie sub-catchments during periods of enhanced transport. Supporting this interpretation, a high-resolution paleoclimatic study based on sediment cores from Lake Allos (upstream Var sub-catchment) found that the period 1750–1900 experienced frequent floods, mostly triggered by autumnal intense precipitation events (Wilhelm et al., 2012). In combination with a reduced forest cover (Fig. 13), these regular flood events provide a plausible mechanism for an increase of ¹⁰Be-poor sediment export in the Var catchment during the 18th century.

The ¹⁰Be concentrations measured in these sediment cores have a much larger variability than what is observed in modern Var sediments, both before and after Storm Alex. The most notable period of deviation is during the LGM, where significantly lower ¹⁰Be concentrations reflect high denudation rates driven by extensive glacial erosion in the Tinée and Vésubie valleys. Beyond the LGM, the ¹⁰Be concentrations during the older glacial period (30–70 ka) and the Holocene (11.7 ka - present) mostly overlap and range between 2.7 and 4.3 × 10⁴ at.g⁻¹, despite covering very different climatic conditions. This suggests a complex non-linear relationship between climate and denudation dynamics, driven by paleoglaciers (Mariotti et al., 2021). Interestingly, the modern ¹⁰Be concentrations measured at the Var outlet, both pre- and post-Storm Alex, are in the same range of values (Fig. 12). This indicates that the variability of the modern ¹⁰Be signal, even if impacted by an extreme event such as Storm Alex, remains within the Holocene range (2.7 and 4.3 × 10⁴ at.g⁻¹). Furthermore, evidence for increased flooding occurrences during the Holocene in Europe after 5000 BP (Benito et al., 2015), indicates that the high ¹⁰Be variability observed in the sediment cores during that period could be reflective of storm-induced floods affecting various parts of the Var catchment.

<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Comparison between the modern ¹⁰Be values measured at the Var outlet and the paleo ¹⁰Be values measured on sediment cores, representative of the Var catchment. Discharge values from www.hydro.eaufrance.fr/ station: Nice Pont Napoléon. Oxygen data from Bonneau et al. (2014, 2017); Jorry et al. (2011).</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Fig. 13.** (A), (C), (F) Bourcet map (1749–1754) showing a landscape with spare vegetation and bare hillslopes. Map from Service Historique de La Défense, GR 6 M, J 10 C 1101, sheet n°8 (B), (D), (F) Google Earth capture of the same area at the present day. (E) Google Earth capture of Saint-Martin-Vésubie after Storm Alex. eInset map shows the location of A/B in the Var catchment.</figcaption></figure>
# 6. Conclusion

This study provides a detailed, high-resolution analysis of how an extreme precipitation event, Storm Alex, impacted the ¹⁰Be signal across the Var catchment, with implications for interpreting both modern and paleo-records. Our key findings are:

- The impact of Storm Alex (a 100-year flood) on the ¹⁰Be signal in the Var catchment sediments varied significantly both in space and time. At the Var outlet, this event initially led to an increase in ¹⁰Be concentration of ~25 % from the last pre-storm sample, which tapered off within 4 months. This outlet-scale pattern masked conflicting sediment contributions from individual sub-catchments. Specifically, the upstream Var and Tinée exported initial pulses of sediment with high ¹⁰Be concentrations (e.g., >6.0 × 10⁴ at.g⁻¹) shortly after the storm (+21 days). Conversely, the Vésubie sub-catchment sustained a steady export of sediment with significantly lower ¹⁰Be concentrations (e.g., 2.7 × 10⁴ at.g⁻¹) for at least seven months post-storm. These divergent responses are attributed to (1) the heterogeneous impact of the storm across each sub-catchment and (2) the varying nature and duration of sediment remobilization: short-lived, high ¹⁰Be pulses from the upstream Var and Tinée sub-catchments, versus a major, sustained export due to shielded ¹⁰Be-poor fluvioglacial deposits from the Vésubie sub-catchment.

- Comparing these ¹⁰Be variations over different time scales reveals the superimposition of forcings across varying amplitudes and periods. While ¹⁰Be concentrations at longer timescales are controlled by major climatic variability, including glaciations (Mariotti et al., 2021), short-term fluctuations are often driven by sub-catchment dynamics that can produce a complex and "noisy" signal at the outlet. This complexity means that despite Storm Alex's exceptional magnitude, it did not generate a ¹⁰Be "spike" or "dip" at the Var outlet, clearly distinguishable from the broader range of modern or Holocene variability. This inherent variability complicates the unequivocal use of ¹⁰Be in sedimentary archives to precisely document the occurrence, magnitude, and frequency of past extreme hydrological events or paleo-storms. Further sampling over the upcoming years along the Var source-to-sink system is therefore crucial to fully constrain the long-term signature of Storm Alex and better understand how Heavy Precipitation Events (HPEs) contribute to the long-term ¹⁰Be signal.

- Our study underscores the critical importance of detailed, high-resolution pre- and post-event surveying within catchments and their tributaries following HPEs. Such analyses are essential for identifying specific sediment sources, understanding their mobilization dynamics, and determining the recovery time of sedimentary systems. By demonstrating that HPEs can produce highly variable and even conflicting ¹⁰Be signatures within a single catchment, this study highlights the scarcity of such detailed post-event records. Expanding these types of high-resolution investigations across diverse catchment settings would significantly enhance our understanding of how HPEs influence sedimentary dynamics and contribute to more refined predictions of sedimentary system responses to future extreme weather events under a changing climate.

## CRediT authorship contribution statement

**Apolline Mariotti:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. **Pierre-Henri Blard:** Writing – review & editing, Investigation, Funding acquisition, Conceptualization. **Julien Charreau:** Writing – review & editing, Investigation, Funding acquisition, Conceptualization. **Samuel Toucanne:** Writing – review & editing, Investigation. **Stephan Jorry:** Writing – review & editing, Investigation. **Olivier Joseph:** Writing – review & editing, Investigation.

# Funding

This work was supported by funding from OTELO (Université de Lorraine, CNRS), INSU and the BRGM.

# Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

# Data availability

All ¹⁰Be data used in this study are available in the Supplementary data file.

# Acknowledgements

Carole Petit from Géoazur (University of Nice) for collecting several samples and scientific discussions. Sebastien Garziglia from IFREMER for sharing the sediment core. Yannick Thiery (BRGM) Denis Thiéblemont (BRGM), Marine Boulanger (CRPG - University of Lorraine), and Romain Bessos (Géoazur - University of Nice) for field assistance. Oliver Evrard, Vincent Godard, and Maarten Lupker for scientific discussions. We also thank Mikaël Attal and an anonymous reviewer for their constructive comments that greatly improved the manuscript.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j geomorph.2025.110061.

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