# Geomorphic activity and related large wood recruitment during debris flows and debris Floods: Storm Alex in the Vésubie valley (France)

Marco Martini, Vincenzo D'agostino, Guillaume Piton

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Marco Martini, Vincenzo D'agostino, Guillaume Piton. Geomorphic activity and related large wood recruitment during debris flows and debris Floods: Storm Alex in the Vésubie valley (France). CATENA, 2025, 257, pp.109180. 10.1016/j.catena.2025.109180. hal-05083475

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# Geomorphic activity and related large wood recruitment during debris flows and debris Floods: Storm Alex in the Vésubie valley (France)

Marco Martini<sup>a, *</sup>, Vincenzo D'Agostino<sup>a, b</sup>, Guillaume Piton<sup>b, c</sup>

## ARTICLE INFO

Dataset link: Active Reaches Vésubie Storm Alex (Original data)

**Keywords:**

Riparian forest

Large wood

Extreme flood

Debris flow

Sediment dynamics

Storm Alex

## ABSTRACT

Extreme floods in forested catchments cause geomorphic changes and large wood recruitment, worsening flood hazards. The study examines the geomorphic response and large wood recruitment in the Vésubie River catchment (392 km², southeast France) during Storm Alex in October 2020. Using high-resolution LiDAR-derived DEM, the research quantifies geomorphic changes in the main branches and 43 active tributaries, estimating sediment mobilization and channel dynamics at (sub-)catchment and 100-meter reach scales. Forest cover changes were analyzed by comparing pre- and post-event canopy height models (CHM), allowing for the estimation of large wood recruitment. The results show significant geomorphic changes, with extreme erosion and deposition rates observed at both the (sub-)catchment and reach scales. A total forest cover loss of 121 ha was estimated, with recruited large wood volumes per unit of catchment surface ranging from 4 – 445 m³/km² in the tributaries and 45 – 95 m³/km² in the main branches. A positive correlation was identified between forest cover loss and both erosion and deposition rates, suggesting that forest loss resulted from trees being scoured by erosion or buried by deposition. An empirical approach was developed to predict forest cover loss based on total sediment mobilization by implementing different equations for optimistic, intermediate, and catastrophic scenarios. The research highlights the interconnected nature of geomorphic processes and their role in large wood dynamics. The findings provide valuable insights into large wood recruitment and the impact of intense geomorphic activity on channel dynamics, demonstrating complex and tight interactions between sediment transport, forest cover, and flood hazards in mountain catchments.

## 1. Introduction

Wood dynamics in fluvial corridors and channels have gained increasing attention in recent decades. Its critical role in riparian vegetation and aquatic habitats ecology, and in the evolution of channel and floodplain morphology, as well as in regulating water and sediment fluxes, is well recognized (Kramer and Wohl, 2017; Swanson et al., 2021; Wohl et al., 2019). However, large in-stream wood pieces (>0.1 m in diameter and > 1 m long, Braudrick et al., 1997) recruited and transported by floods can exacerbate flooding, thus becoming a potential hazard factor to populations and infrastructure. Mobilized large wood can deposit at critical cross sections (e.g., bridges, culverts, hydraulic structures) by forming log jams, causing the clogging of these structures and leading to reduced conveyance or accelerating other phenomena such as channel bed aggradation, structure scouring or channel avulsion (Gschnitzer et al., 2017; Mazzorana et al., 2011; Piton et al., 2020; Ruiz-

Villanueva et al., 2014). Understanding the large wood dynamics, particularly during floods, debris flows, and debris floods, is a complex yet crucial task for enhancing hazard assessments and risk mitigation (Baggio et al., 2024; Jakob et al., 2022; Mazzorana et al., 2013). In this optic, the prediction of the large wood loads recruited and deposited during floods, should flank hydrological analyses as well as the prediction of the geomorphic activity (i.e., magnitude and intensity of mobilized sediment) of mountain river channels (Comiti et al., 2016; Ruiz-Villanueva et al., 2016).

During extreme floods in mountain catchments, forested channel banks may be affected by erosion and channel migration processes (Brenna et al., 2021; Liébault et al., 2024; Pellegrini et al., 2021; Ruiz-Villanueva et al., 2023), affecting especially the most recent alluvial terraces. This may cause the undermining and the uprooting of riparian trees, which then fall into the flow, are transported on some distance, and form large accumulations inside the active channels after the flood.

<footnote><sup>a</sup> Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy</footnote>

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

<footnote>* Corresponding author at: Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell'Università, 16, 35020 Legnaro, Italy. E-mail address: marco.martini.9@phd.unipd.it (M. Martini).</footnote>
Furthermore, large wood can be delivered from tributaries experiencing debris floods or debris flows as well as from hillslope mass movements (Comiti et al., 2008; Galia et al., 2018; Lucía et al., 2018; May and Gresswell, 2003; Rigon et al., 2012) and, more marginally, snow avalanches (Bebi et al., 2009; Gasser et al., 2019; Kemper and Scamardo, 2023). Human activities can further contribute to large wood recruitment (e.g. forestry operation residues and sawmills deposits).

In the last decades the knowledge on the recruitment process has improved thanks to its conceptualization (see Comiti et al., 2016), to several monitoring campaigns (e.g., Iroumé et al., 2024, 2020; Picco et al., 2023) and to a few studies providing estimations of large wood supplies during major floods (Comiti et al., 2016; Lucía et al., 2015; Piton et al., 2024; Rickenmann, 1997; Steeb et al., 2017). These contributions helped to improve the assessment of the large wood dynamics during floods. Several methodologies were proposed aiming to predict the volume of large wood recruited from the riparian banks during floods. Comiti et al., (2016), Gasser et al. (2019) and Steeb et al. (2023) reviewed comprehensively large wood recruitment prediction models. These models estimate the large wood supply at catchment- or reach-scale referring both at long- (e.g., 50-100 years) or short- term (i.e. single event) dynamics. In essence, most of the models focus on the definition of landsliding areas and buffer areas that may contribute to the large wood channel supply. They consider several parameters related to the forest cover types (e.g., tree species), the morphology of the supply area and the hillslope-channel coupling (e.g., slope, connectivity), and the hydrological forcing driving the mass wasting or sediment transport, channels shifting and widening. Several models were proposed to analyse the large wood supply from hillslope failures, while other models are dedicated to the estimate that from fluvial bank erosion. How much large wood can be delivered by debris flow and debris flood channels remains a challenge, as these processes behave differently from both hillslope failures or pure flood flows. Steeb et al. (2023), in their comprehensive approach of large wood supply to mountain rivers, estimate this contribution on mere hypotheses of channel widening and ratio of wood exported out the catchment.

Indeed, the relationship between the geomorphic activity (e.g., volume and rates of sediment eroded and deposited, depth of mobilized sediment) are not considered to help predicting the large wood recruitment. Meanwhile, assessing or predicting this geomorphic activity is a key step of debris flow and debris flood hazard assessments (Baggio et al., 2024). The definition of the process type and its hydrological characterization (flood volume, duration and peek discharge) are typically analysed in conjunction with estimates of the sediment volume, sediment transport rates, morphological changes and preferential erosion and deposition areas (Jakob et al., 2022). Since large wood in such systems is strongly recruited on the eroded banks and hillslopes, the present work aims to bridge this gap and verify the linkage between steep channel geomorphic activity and forest cover loss. To achieve this objective, we analysed 150 km of steep river channels distributed in 43 (sub-)catchments of the Vésubie River (surface 392 km²) during an extremely severe storm (Storm Alex) in October 2020. The large-scale post event analysis quantifies the geomorphic activity in terms of sediment magnitudes and intensities at (sub-)catchment and river reach scale for the active valley bottoms and the tributaries and jointly estimates the relative forest cover loss and large wood recruitment. Furthermore, the results are used to fit an empirical equation for the prediction of forest cover loss within the channel network. Since this type of assessment has not, to our knowledge, been systematically conducted for debris flow and debris flood prone channels, this work provides a novel set of data (see also the supplement section) and following new insights on the large wood recruitment along with an intense geomorphic channel activity. The final conclusion highlights the interest of facing this topic through a quantitative after event back-analysis and also the possibility to predict these combined processes through of a set of probable scenarios account for features of both the channel and its riparian forest.

## 2. Study area

From October 2nd to October 3rd, 2020, the Alpes-Maritimes department in France was affected by an extreme rainfall event caused by warm, moist air coming from the Mediterranean due to a rapid cyclonic depression named Storm Alex. Hundreds of millimetres of rain were measured over the Tineé, the Roya and the Vésubie catchments located on the southeastern flank of the Mercantour-Argettera massif (Alpes-Maritimes), in the hinterland of Nice, southeastern France. Radar data recorded up to 500 mm of rainfall in 24 h in the Vésubie catchment, corroborated by an EDF rain gauge at Saint-Martin-Vésubie, which measured 502 mm, while 580 mm were observed near the Tende Pass (Carrega and Michelot, 2021). The return period of these rainfall event estimated is > 1,000 years (ONF-RTM et al., 2023). Rivers experienced extreme floods with very high peak discharges and intense sediment transport, causing extensive geomorphological changes and leaving an aftermath of ten fatalities, eight missing people, as well as massive damage, to both buildings and linear infrastructure (roads, bridges, railways) concentrated in the valley floors (Carrega and Michelot, 2021; Martins et al., 2024; Rey et al., 2022). Peak discharge at the Vésubie's mouth, estimates based on flood marks and hydrological models, ranged from 700 to 1100 m3/s (Payrastre et al., 2022), with return periods between 100 and 500 years. Furthermore, the prolonged duration of the flood, lasting 18-24 h (ONF-RTM et al., 2023), exacerbated the geomorphological effects. The Vésubie catchment has been strongly affected, with several debris floods and flows observed in the tributaries and on the hillslopes, as well as the flooding of the valley bottoms (Liébault et al., 2024). The Vésubie River flows for 45 km, with a drained area of 392 km², before joining the Var River and flowing into the Mediterranean Sea near Nice. This study does not cover the last 6 km that are confined by narrow gorges and has only minor tributaries but rather focuses on the upstream 39-km long reach, divided into four sub-reaches called hereafter the "main branches" (Fig. 1):

(1) the Upper Boréon, beginning at approximately 1660 m a.s.l. where the channel widens and the slope decrease, extending downstream to the reservoir dam at the Vallon du Cavalet (#32) confluence (2.9 km in length, 6.5 % mean slope);

(2) the Lower Boréon, continuing from downstream the reservoir dam through its confluence with the Vallon de Salèse (#38) to the confluence with the Vésubie at Saint-Martin-Vésubie (7.2 km, 6.7 % mean slope);

(3) the Upper Vésubie, also referred to as Madone de Fenestre by the locals, spanning from the confluence with the Vallon de Prals to the Boréon confluence (8.9 km, 8.7 % mean slope); and

(4) the Vésubie, from Saint-Martin-Vésubie to the upstream end gorges in the lower valley (19.8 km, exhibits an average slope of 3 %) (Fig. 1);

43 tributaries showed significant geomorphological activity during Storm Alex, contributing to each sub-reach. A list of them, as well as a description of the main morphometric characteristics is presented in Table 1. Most of the tributaries (n = 27) are small, very steep, sub-catchments and hillslopes of limited extent (drainage area < 1 km², mean channel slope > 33 %) prone to debris flows and debris floods (Melton Index > 1). The others are medium size subcatchments (n = 10 with surface 1 km² and 10 km² with mean channel slope > 24 % (Melton index between 0.5 and 1.1) and also 6 sub catchments that are vaster (drainage area between 10 km² and 59 km²), less steep (mean channel slope between 9 % and 24 %) and more prone to bedload and debris flood processes (Melton Index < 0.5). The Vésubie catchment covers an elevation range from 140 to 3100 m.a.s.l. within which the climate shift from temperate with dry summer to cold without dry summer in the headwaters (sensu Beck et al., 2018). The annual rainfall shifts from a bit less than 1000 mm/year to more than 1200 mm/year at the confluence between the Boréon and the Madone de Fenestre and
<figure><img src="image_3.png" /><figcaption>Fig. 1. Vésubie Valley (Alpes-Maritimes department, Nice, France). The main branches of the valley bottom courses are highlighted in colours, in orange the tributaries experiencing geomorphic activity during Storm Alex.</figcaption></figure>

possibly more on the mountain summits. The mean annual temperature at the same confluence is 11 °C (mean monthly being 4 °C during winter and 20 °C during summer). Most of the surface of the Vésubie catchment is covered by closed forest (59 % of the drainage area) of coniferous (34 %), deciduous (13 %) and mixed species (12 %), open forests (11 %) of coniferous (6 %), deciduous (3 %) and mixed species (2 %), and herbaceous vegetation (14 %), with moorlands covering only limited extensions (7 %). Furthermore, most of the tributaries and the valley bottom channels are surrounded by riparian vegetation and high forests.

### 3. Materials and methods

The analyses described in this Section were conducted on the four main branches (Upper Boréon, Lower Boréon, Madone de Fenestre, Vésubie), and 43 tributaries that have been considered as significant sediment suppliers to the mainstem according to geomorphic activity detected by topographic differencing (Liébault et al., 2024). In essence, all tributaries were the DEM of Difference analysis (DoD) showed only random noise and consistent geomorphic patterns of debris flow or debris flood activity was lacking were considered geomorphologically inactive. Geomorphic activity was considered as significant when widespread erosion and deposition zones were detected from the DoD analysis. The areas where significant geomorphic activity was observed were digitized and used to define what we refer as “geomorphologically active channels” (i.e., portion of the catchments were geomorphic activity occurred during Storm Alex). The length of the thalweg and the surface of the active channels were used to normalized data in the following analysis, allowing for comparison between channels.

#### 3.1. Remote sensing data

Three sets of high-resolution IGN's orthophotos (BD Ortho) were used in this study. The 2020 campaign was used to define the pre-event state (0.20 m resolution). The post-event conditions were defined using

the orthophotos (0.10 m resolution) derived from the emergency express aerial survey performed by IGN three days after Storm Alex (between the 5th and 7th October 2020), where aerial images combined with airborne LiDAR data were captured to evaluate damages and impacted areas. The emergency express aerial survey did not extend over the entire Vésubie valley, leaving some tributaries (n = 13) partially uncovered. The 2023 campaign (0.20 m resolution) was used to integrate the post Storm Alex orthophotos dataset. The usage of these images was considered reliable because the 2023 aerial survey was conducted before Storm Aline (October 2023), which was the first notably hydrogeological events occurring after Storm Alex. Since only the upstream inaccessible and uninhabited part of the catchments was not covered by the emergency aerial survey, indeed it was reasonable to assume that no changes due to anthropic activities could have occurred. In case we would have doubts, we could have used satellite analysis to corroborate this hypothesis, but our close collaboration with the National Forest Office and local Torrent Control Service makes us confident in assuming the absence of significant floods or anthropogenic operations (e.g., timbering, earth moving) between the two surveys. BD Ortho datasets are in open-access from the web servers of data providers (BD Ortho, IGN), as well as remote sensing data from the emergency express aerial survey (https://www.geop ortail.gouv.fr/donnees/ortho-express-alex). Airborne near-infrared LiDAR surveys of November 2018 (LiDAR2018@MCNA obtained cour tesy of the Métropole de Nice) and October 2020 (captured during the IGN's emergency express aerial surveys) were used to undertaking a topographic differencing analysis and compute mobilized sediment volumes during Storm Alex along the study reaches. Near-infrared LiDAR data are restricted to terrestrial portions of the surveyed areas and cannot provide bathymetric topography. However, the low pro portion of water surfaces in the post-flood active channel and the magnitude of topographic changes observed after the storm lead to think that this limitation should have a low effect on computed erosion and deposition volumes. Similarly to the Orthophotos, 13 tributaries were only partially covered by the October 2020 LiDAR survey, so
<caption>**Table 1**</caption>

Main physical feature of the 43 subcatchments experiencing geomorphic activity during Storm Alex. Relief features are extracted from the 1 m resolution DEM from LiDAR2018@MCNA provided by Métropole Nice Côte d'Azur. Land-use features are extracted from the IGN BD Forêt V2 providing a classification of the vegetation cover in France (the forest cover include all polygons classified as closed forest in the IGN nomenclature).

<table><thead><tr><th rowspan="2">ID</th><th rowspan="2">Tributaries</th><th rowspan="2">System</th><th rowspan="2">Surface [km²]</th><th colspan="2">Elevation [m a.s.l.]</th><th rowspan="2">Channel length [m]</th><th rowspan="2">Mean channel slope [m/m]</th><th rowspan="2">Melton Index</th><th rowspan="2">Surface covered by closed forest [%]</th><th rowspan="2">Surface covered by open forest [%]</th></tr><tr><th>min</th><th>max</th></tr></thead><tbody><tr><td>#1</td><td>Collet colignol 1</td><td>Madone de Fenestre</td><td>0,06</td><td>1167</td><td>1759</td><td>529</td><td>74 %</td><td>2,4</td><td>100</td><td>0</td></tr><tr><td>#2</td><td>Cime pisset 1</td><td>Madone de Fenestre</td><td>0,07</td><td>1591</td><td>2135</td><td>742</td><td>54 %</td><td>2,0</td><td>74</td><td>7</td></tr><tr><td>#3</td><td>Collet colignol 2</td><td>Madone de Fenestre</td><td>0,07</td><td>1144</td><td>1905</td><td>671</td><td>81 %</td><td>2,8</td><td>100</td><td>0</td></tr><tr><td>#4</td><td>Pelago 1</td><td>Upper Boréon</td><td>0,10</td><td>1618</td><td>2450</td><td>747</td><td>69 %</td><td>2,6</td><td>47</td><td>53</td></tr><tr><td>#5</td><td>Bausset 1</td><td>Lower Boréon</td><td>0,12</td><td>1246</td><td>1920</td><td>382</td><td>74 %</td><td>2,0</td><td>100</td><td>0</td></tr><tr><td>#6</td><td>Rouchas</td><td>Madone de Fenestre</td><td>0,12</td><td>1097</td><td>1722</td><td>639</td><td>68 %</td><td>1,8</td><td>98</td><td>0</td></tr><tr><td>#7</td><td>Cime pisset 2_2</td><td>Madone de Fenestre</td><td>0,13</td><td>1572</td><td>2157</td><td>750</td><td>48 %</td><td>1,6</td><td>65</td><td>23</td></tr><tr><td>#8</td><td>Adousse 2</td><td>Lower Boréon</td><td>0,13</td><td>1313</td><td>1896</td><td>546</td><td>63 %</td><td>1,6</td><td>100</td><td>0</td></tr><tr><td>#9</td><td>Cime pisset 5</td><td>Madone de Fenestre</td><td>0,16</td><td>1462</td><td>2118</td><td>652</td><td>68 %</td><td>1,7</td><td>94</td><td>1</td></tr><tr><td>#10</td><td>Pelago 2</td><td>Upper Boréon</td><td>0,16</td><td>1603</td><td>2616</td><td>836</td><td>67 %</td><td>2,6</td><td>40</td><td>60</td></tr><tr><td>#11</td><td>Collet colignol 3</td><td>Madone de Fenestre</td><td>0,17</td><td>1122</td><td>1903</td><td>618</td><td>68 %</td><td>1,9</td><td>100</td><td>0</td></tr><tr><td>#12</td><td>Cime pisset 3</td><td>Madone de Fenestre</td><td>0,36</td><td>1539</td><td>2251</td><td>1145</td><td>47 %</td><td>1,2</td><td>39</td><td>33</td></tr><tr><td>#13</td><td>Cime pisset 4</td><td>Madone de Fenestre</td><td>0,36</td><td>1482</td><td>2194</td><td>644</td><td>67 %</td><td>1,2</td><td>45</td><td>54</td></tr><tr><td>#14</td><td>Serre long</td><td>Lower Boréon</td><td>0,38</td><td>1271</td><td>2165</td><td>1134</td><td>59 %</td><td>1,4</td><td>96</td><td>2</td></tr><tr><td>#15</td><td>Cime pisset 2_1</td><td>Madone de Fenestre</td><td>0,39</td><td>1576</td><td>2350</td><td>1344</td><td>51 %</td><td>1,2</td><td>53</td><td>43</td></tr><tr><td>#16</td><td>Saut</td><td>Lower Boréon</td><td>0,47</td><td>1304</td><td>2301</td><td>572</td><td>63 %</td><td>1,5</td><td>95</td><td>5</td></tr><tr><td>#17</td><td>Marre inferieur</td><td>Madone de Fenestre</td><td>0,53</td><td>1298</td><td>2175</td><td>1025</td><td>52 %</td><td>1,2</td><td>96</td><td>3</td></tr><tr><td>#18</td><td>Pelago 3</td><td>Upper Boréon</td><td>0,53</td><td>1582</td><td>2768</td><td>1811</td><td>63 %</td><td>1,6</td><td>20</td><td>80</td></tr><tr><td>#19</td><td>Nid de l'ane</td><td>Madone de Fenestre</td><td>0,58</td><td>1165</td><td>2165</td><td>1228</td><td>68 %</td><td>1,3</td><td>88</td><td>12</td></tr><tr><td>#20</td><td>Adousse 1</td><td>Lower Boréon</td><td>0,62</td><td>1329</td><td>2338</td><td>608</td><td>57 %</td><td>1,3</td><td>86</td><td>13</td></tr><tr><td>#21</td><td>Champet</td><td>Lower Boréon</td><td>0,65</td><td>1293</td><td>2253</td><td>1216</td><td>52 %</td><td>1,2</td><td>83</td><td>16</td></tr><tr><td>#22</td><td>Pounche</td><td>Madone de Fenestre</td><td>0,73</td><td>1665</td><td>2653</td><td>1659</td><td>51 %</td><td>1,2</td><td>12</td><td>86</td></tr><tr><td>#23</td><td>Pontets</td><td>Madone de Fenestre</td><td>0,80</td><td>1427</td><td>2289</td><td>1262</td><td>55 %</td><td>1,0</td><td>63</td><td>37</td></tr><tr><td>#24</td><td>Madame</td><td>Madone de Fenestre</td><td>0,83</td><td>1330</td><td>2338</td><td>1639</td><td>58 %</td><td>1,1</td><td>58</td><td>38</td></tr><tr><td>#25</td><td>Touron</td><td>Vésubie</td><td>0,86</td><td>852</td><td>2040</td><td>2171</td><td>37 %</td><td>1,3</td><td>85</td><td>14</td></tr><tr><td>#26</td><td>Pomeiras</td><td>Madone de Fenestre</td><td>0,87</td><td>1041</td><td>2132</td><td>2050</td><td>41 %</td><td>1,2</td><td>93</td><td>1</td></tr><tr><td>#27</td><td>Erps 2</td><td>Upper Boréon</td><td>0,87</td><td>1495</td><td>2558</td><td>1697</td><td>33 %</td><td>1,1</td><td>73</td><td>21</td></tr><tr><td>#28</td><td>Lapasse</td><td>Madone de Fenestre</td><td>1,21</td><td>1554</td><td>2351</td><td>1028</td><td>36 %</td><td>0,7</td><td>86</td><td>11</td></tr><tr><td>#29</td><td>Pierre du villar</td><td>Vésubie</td><td>1,30</td><td>788</td><td>2051</td><td>2484</td><td>37 %</td><td>1,1</td><td>76</td><td>21</td></tr><tr><td>#30</td><td>Archas</td><td>Lower Boréon</td><td>1,81</td><td>1202</td><td>2527</td><td>2462</td><td>47 %</td><td>1,0</td><td>61</td><td>39</td></tr><tr><td>#31</td><td>Champouns</td><td>Vésubie</td><td>2,18</td><td>867</td><td>1776</td><td>2359</td><td>26 %</td><td>0,6</td><td>45</td><td>55</td></tr><tr><td>#32</td><td>Cavalet</td><td>Lower Boréon</td><td>2,54</td><td>1361</td><td>2774</td><td>3546</td><td>29 %</td><td>0,9</td><td>42</td><td>33</td></tr><tr><td>#33</td><td>Cougne</td><td>Vésubie</td><td>2,94</td><td>693</td><td>2176</td><td>4296</td><td>31 %</td><td>0,9</td><td>73</td><td>20</td></tr><tr><td>#34</td><td>Erps 1</td><td>Upper Boréon</td><td>2,96</td><td>1504</td><td>2883</td><td>3479</td><td>26 %</td><td>0,8</td><td>15</td><td>64</td></tr><tr><td>#35</td><td>Vernet</td><td>Lower Boréon</td><td>3,38</td><td>959</td><td>2243</td><td>1770</td><td>26 %</td><td>0,7</td><td>58</td><td>36</td></tr><tr><td>#36</td><td>Villars</td><td>Lower Boréon</td><td>8,16</td><td>1095</td><td>2671</td><td>3892</td><td>29 %</td><td>0,6</td><td>24</td><td>70</td></tr><tr><td>#37</td><td>Cervagne</td><td>Vésubie</td><td>8,35</td><td>564</td><td>2066</td><td>5574</td><td>24 %</td><td>0,5</td><td>66</td><td>27</td></tr><tr><td>#38</td><td>Salese</td><td>Lower Boréon</td><td>12,38</td><td>1350</td><td>2701</td><td>5405</td><td>13 %</td><td>0,4</td><td>55</td><td>35</td></tr><tr><td>#39</td><td>Boreon amont</td><td>Upper Boréon</td><td>13,23</td><td>1661</td><td>2982</td><td>5219</td><td>21 %</td><td>0,4</td><td>18</td><td>52</td></tr><tr><td>#40</td><td>Venanson</td><td>Vésubie</td><td>14,59</td><td>788</td><td>2101</td><td>5486</td><td>18 %</td><td>0,3</td><td>76</td><td>23</td></tr><tr><td>#41</td><td>Espaillart</td><td>Vésubie</td><td>15,14</td><td>653</td><td>2495</td><td>6851</td><td>23 %</td><td>0,5</td><td>59</td><td>39</td></tr><tr><td>#42</td><td>Figaret</td><td>Vésubie</td><td>22,20</td><td>211</td><td>2084</td><td>11,398</td><td>12 %</td><td>0,4</td><td>77</td><td>20</td></tr><tr><td>#43</td><td>Gordolasque</td><td>Vésubie</td><td>59,20</td><td>521</td><td>3051</td><td>18,630</td><td>9 %</td><td>0,3</td><td>33</td><td>51</td></tr></tbody></table>

topographic differencing was obtained using the 2021 LiDAR survey from IGN. Each forest loss and geomorphic activity evidence in these areas were manually checked to remove eventual measurement of anthropogenic operations.

## 3.2. Segmentation of the study zone

A GIS-based spatial segmentation procedure was implemented to conduct a longitudinal analysis, as in classic river geomorphology and longitudinal analyses (Roux et al., 2015). This segmentation followed the classic steps: (i) verification of the polygon delineating the
geomorphological active areas derived from the co-registered DoD, (ii) extraction of the centreline of each polygon, with manual verification and correction, if necessary, (iii) plot of the 100 m, regularly spaced, transects orthogonal to the centerline, with manual verification and correction if needed, and (iv) definition of 100 m long reaches based on these transects.

### 3.3. Geomorphic changes and sediment budget analysis

Geomorphic changes occurred during Storm Alex were assessed using the DEM of Difference (DoD) technique starting from the airborne LiDAR surveys conducted in November 2018 and October 2020 along the Vésubie main branches and the most active tributaries (see details in Liébault et al., 2024; Tacon et al., 2023). The state-of-the-art approach was followed (Pfeifer et al., 2014; Wheaton et al., 2010), namely, LiDAR point clouds were co-registered to minimize the systematic error of topographic differencing (Anderson, 2019; Cucchiaro et al., 2018; Lallias-Tacon et al., 2014). Digital terrain models (DTMs) with 0.5 m resolution were then created from the co-registered point clouds for both pre- and post-event campaigns employing the TIN approach using QGIS. The results of the DoD analysis were used to compute erosion and deposition volumes as well as the net balance, by subtracting the two co-registered DEM exclusively inside the active zones. The DoD was thresholded considering a spatially uniform minimum level of detection (minLoD) equal to 0.13 m for the calculation of erosion and deposition volumes and not for the net sediment budget computation, as thresholding can be a source of bias and error in net change analysis (Lane et al., 2003; Anderson, 2019). Further details on the co-registration process and the assessment of errors and uncertainties of the DoD analysis are provided in Supplement Information. Sediment budgeting was computed both at valley bottom courses/sub catchments scales as well as at reach scales.

### 3.4. Empirical estimation of the return periods based on the sediment budget

To give a sense of more or less frequent features of the geomorphological activity that occurred during Storm Alex at each tributary, we applied the methodology proposed by Morel et al. (2023). This method predicts the expected sediment volume mobilized during debris flows or debris floods (V) using empirical equations derived from field observations in the French Alps. Morel et al. (2023) proposed several equations based on $R_{sp}$, the ratio of sediment-contributing surface area to catchment surface area, further integrated with parameters such as the Melton Index (M), Geological Index, and Connectivity Index. These equations estimate notably two event volumes: (i) the specific sediment volume for a 10-year return period event ($V_{10}$), and (ii) the specific reference sediment volume ($V_{ref}$), which refers to either the largest documented event or a theoretical 100-year return period event, whichever is larger. By comparing the sediment volumes observed from DoD analysis during Storm Alex with those predicted by the empirical equations, we classified the volumetric sediment budget produced within 43 tributaries of the Vésubie according to the following grading: "low magnitude", i.e. $V < V_{10}$; "intermediate magnitude", i.e. $V_{10} \le V < 3 V_{ref}$ or "extreme magnitude", i.e. $3 V_{ref} \le V$. This classification is based on the relationship existing between the volumes predicted by the two equations, with $V_{10}$ being approximately 1/3 $V_{ref}$, and therefore, we assumed $V \le V_{10}$ for $V < 0.3 V_{ref}$. Consistently, we define the upper threshold $3 V_{ref} \le V$ to discern between "intermediate" and "extreme" magnitude as a ratio between observed and predicted volumes equal to a factor 3 corresponds to very different events in terms of theoretical probability of occurrence. The sediment-contributing area was delineated using the French GIS database of forest inventory (BD Forêt® V2), which accurately maps vegetation types. Potential sediment sources, including bare soil areas, some vegetated areas like moorlands, and bare bedrock (which typically erodes slower than bare soil), were visually

identified. Connectivity to the channel network was assessed using orthophotos and the BD TOPO® database. Areas were considered connected if bare soil was visible between the hillslope and the channel or if a watercourse (permanent or ephemeral) was present on the hydrographic network maps obtained from the BD TOPO® database (base on detailed hiking maps usable down to a scale of 1:2000; see http://s://geoservices.ign.fr/bdtopo). For catchments without bare soil, sediment sources were assumed to come from streambed remobilization. Following Morel et al. (2023) recommendations, channel width was estimated using a regional hydraulic geometry model developed in France (Gob et al., 2014). The bankfull width ($W_{bf}$) for each tributary of the Vésubie was calculated as $W_{bf} = 5.06A^{0.27}$, where A is the catchment surface area. The channel bed area was computed by multiplying the bankfull width by the channel bed length of the main channels in the catchment, then added to the sediment-contributing area. The final proportion of sediment-contributing area to catchment area was calculated for each basin, with only five catchments identified as having no bare-soil area according to the protocol. Table 2 presents the equations used to predict $V_{10}$ and $V_{ref}$ (considered as the theoretical 100-year return period) as a function of the sediment-contributing surface alone ($f(R_{sp})$) and integrating the Melton Index ($f(R_{sp}, M)$).

### 3.5. Estimation of forest cover loss

To define the surface of the forest cover inside the study area before Storm Alex, the 2018 and 2020 airborne LiDAR surveys point cloud were used to derive a 1 m resolution Canopy Height Model (CHM) inside the geomorphologically active areas by subtracting the DTM from the DSM. As for the geomorphological analysis, the areas not covered by the 2020 emergency aerial survey (e.g., most of the Venanson catchment, #40) were integrated using the 2021 airborne LiDAR survey. The CHM was thresholded at 7 m to exclude low vegetation cover and anthropic constructions (see how this value was selected based on forestry data in Supplement Information). The forest cover loss $\Delta S_f$ [m$^2$] was than quantified as the difference between the pre-event forest cover surface ($S_{fpre}$) and the post-event forest cover surface ($S_{fpost}$):

$$ \Delta S_f = S_{fpre} - S_{fpost} \qquad (5) $$

### 3.6. Estimation of recruited large wood volumes

The volume of large wood recruited inside the active channels of the Vésubie during Storm Alex was estimated for each at (sub-)catchment following the methodology applied by Piton et al. (2024) for the neighbouring Roya catchment. Because pre-event Lidar data were not available, they rather measured forest cover loss by photo-interpretation of pre- and post-event areal pictures, and the recruited volume of large wood was obtained by multiplying the contributing surface by the large wood density parameters (Table 3). Same researchers also provided a discussion of the uncertainties related to the estimation of the areas contributing to large wood and the definition of the forest stand parameters. In our approach, the areas of forest loss were obtained with the differential analysis of diachronic Canopy Height Models (CHM) thresholded based on the forestry parameter extracted from the sample of 129 plots extracted from the IFN database and validated via photo-interpretation (see Supplementary Information). We are confident that this approach increases the accuracy of the definition of the contributing areas, avoiding taking into account those vegetated areas occupied by

<table><caption>**Table 2**
Empirical equations to predict the sediment volume production after Morel et al., (2023).</caption><thead><tr><th>Model</th><th>V<sub>10</sub> [m<sup>3</sup>]</th><th>Eq.</th><th>V<sub>ref</sub> [m<sup>3</sup>]</th><th>Eq.</th></tr></thead><tbody><tr><td>f(R<sub>sp</sub>)</td><td>168AR<sub>sp</sub><sup>0.88</sup></td><td>(1)</td><td>475AR<sub>sp</sub><sup>0.94</sup></td><td>(2)</td></tr><tr><td>f(R<sub>sp</sub>, M)</td><td>125AR<sub>sp</sub><sup>0.81</sup> 10<sup>0.2M</sup></td><td>(3)</td><td>363AR<sub>sp</sub><sup>0.87</sup> 10<sup>0.19M</sup></td><td>(4)</td></tr></tbody></table>
<table><caption>**Table 3**
Forest density data used for the analysis.</caption><thead><tr><th rowspan="2">Parameter</th><th colspan="3">Estimations</th></tr><tr><th>Low<br>(quantile 25<br>%)</th><th>Intermediate<br>(median)</th><th>High (quantile<br>75 %)</th></tr></thead><tbody><tr><td>Volume of wood per hectare<br>[m³/ha]</td><td>89</td><td>168</td><td>342</td></tr></tbody></table>

shrubby vegetation that would not supply large wood, which is difficult to identify by photo-interpretation. Despite that, the pre-event LiDAR survey refers to the conditions of the catchment in 2018, and that might be different from those of the 2020 before Storm Alex (e.g. trees growth), despite no major events occurred during this period. The recruited volume of large wood was then obtained by multiplying the contributing surface by the large wood density.

# 4. Results

## 4.1. Geomorphic response of the Vésubie to Storm Alex

The pre- and post- event LiDAR data were analysed to assess the geomorphological response of the Vésubie catchment to Storm Alex. The DoD analysis was computed considering a minLoD equal to 0.13 m, resulting from the quadratic sum of the RMSE of the LiDAR point cloud errors. This threshold showed significant topographic changes within the four valley bottom branches and the 43 tributaries. The DoD analysis shows that Storm Alex caused intense geomorphic changes inside the active channels, corresponding to total volumes of sediment erosion and deposition equal to 7.04 Mm³ ± 26,739 m³ (mean erosion rate per unit of active channel surface of 1.06 m³/m²) and 4.90 Mm³ ± 22,246 m³ (mean deposition rate of 0.73 m³/m²), respectively. Fig. 2 presents the sediment budgets inside the tributaries (Fig. 2A-C) and the main branches (Fig. 2D-F) of the Vésubie valley. About 38 % of the total volume of erosion (2.64 Mm³ ± 13,086 m³) and 20 % of the total volume of deposition (0.99 Mm³ ± 6,690 m³) were mobilized inside the 43 active tributaries. The sediment yield of the tributaries has a mean erosion volume 61,328 m³ but varies over three orders of magnitudes, from 1,408 m³ ± 17 m³ (subcatchment #2) to 688,301 m³ ± 2,581 m³ (subcatchment #40) (Fig. 2). In essence, our dataset gathers examples of rather strong to very strong debris-flows and debris-flood events.

At catchment scale, the relevance of the geomorphic activity triggered by Storm Alex is also highlighted by the high mean sediment yield rates per unit of active channel length (Fig. 2B, E) and surface (Fig. 2C, F) equal to 22.5 m³/m (range from 3.5 m³/m in subcatchment #5 and 151.6 m³/m in subcatchment #12) and 0.9 m³/m² (range between 0.15 m³/m² in subcatchment #5 and 3.78 m³/m² in subcatchment #16), respectively. The volumes of depositions range between 105 m³ ± 3 m³ (subcatchment #13) and 347,177 m³ ± 1,044 m³ (subcatchment #40) with a mean value among tributaries of 23,039 m³. The relatively limited mean sediment storage per meter of active channel equal to 7.0 m³/m (range 0.2–68.1 m³/m) and 0.2 m³/m² (range 0.01–1.18 m³/m²) highlighted a limited sediment storage capacity inside the tributaries during the Storm, resulting in negative net sediment balances for all the tributaries (Fig. 2A) that massively exported sediment to the main branches (range between –669 m³ ± 36 m³ (subcatchment #6) and –341,575 m³ ± 3,625 m³ (subcatchment #40), tributary mean value: –38,350 m³).

These amounts suggest that the delivery of about two thirds of the mobilized sediment to the valley bottom (62.5 % of the total volume of erosion observed inside the tributaries) has increased the availability of loose sediment inside the main branches of the Vésubie. Consequently, the volumes of deposition inside the valley bottom channels, equal to 3.91 Mm³ ± 15,556 m³ (79.8 % of the total volume of deposition), do not only include the sediments mobilized inside these main channels

(volume of erosion equal to 4.40 Mm³ ± 13,653 m³, 62.5 % of the total eroded volume) but also a fraction of sediments delivered by the tributaries. The estimated amount of sediment exported from the studied area of the Vésubie valley and delivered to the Var River after Storm Alex is estimated to (–2.14 Mm³ ± 48,985 m³). It is important to point out that within this sediment yield we do not differentiate bedload from suspended load and assume an equal porosity between the eroded and deposited areas.

The positive net sediment balance observed in the Upper Boréon is due to the presence of a reservoir dam that acted as sediment trap for bedload during the storm (Fig. 2D), retaining the sediment eroded within the valley bottom and the one delivered by the tributaries reach. The reservoir dam was very partially filled during the flood allowing to assume bedload transport disconnectivity at the dam. This is also reflected by an average aggradation rate equal to 1.14 m³/m² in the Upper Boréon reach. Despite that, the suspended fraction partially passed through the reservoir dam, contributing to the budget of the downstream reaches. The also positive sediment net balance of the Vésubie reach is instead the result of the combination of multiple factors (Fig. 2D). The reach shows a relative moderate sediment erosion (rate per unit of active channel surface equal to 0.97 m³/m²) compared to the others, mostly occurring on the banks as a consequence of the strong widening of the channel over the flood plain. Meanwhile, sediments mobilized inside the Lower Boréon, Madone de Fenestre and the tributaries were delivered to the Vésubie (all the mentioned channels showed an extremely negative net sediment balance). The sediment eroded on hillslopes, banks and in tributaries deposited inside the active channel and resulted in a general aggradation of the valley bottom.

## 4.2. Return period of the subcatchments sediment magnitude

To gather a sense of the magnitude of geomorphological changes occurred during Storm Alex, the sediment erosion volumes observed inside the tributaries of the Vésubie were compared with theoretical volumes predicted using the empirical equations proposed by Morel et al. (2023) (Eq. 1-4). The volumes predicted by the two models (volume as function of sediment contributing area only or with the integration of the Melton Index) resulted in slightly different values both for $V_{10}$ and $V_{ref}$. Despite that, the difference between the values predicted by the two models is very limited, therefore we decide to refer only to the simpler model ($f(R_{2p})$) for sake of simplicity. The comparison of predicted and observed sediment volumes (Fig. 3) reveals that 40 out of the 43 active tributaries experienced erosion volumes bigger than those predicted by the empirical equations for 100- years return period event ($V_{ref}$), with exception of the subcatchments #37, #27 and #39, as well as all of the inactive catchments that are not studied within this work (see them not shaded in orange in Fig. 1). The headwater part of the Boréon (subcatchment #39) experienced a low magnitude compared to the empirical predictions, with the observed erosion volumes being more than three time smaller the predicted ones approximating the volume predicted for a 10-year return period $V_{10}$ ($V < 0.3 V_{ref}$, i.e. $V < V_{10}$). Eight tributaires (#27, #32, #34, #36, #37, 38#, #41 and #43) produced an intermediate magnitude. The remaining 34 tributaries experienced extreme magnitudes, with observed volumes being at least three times higher the predicted one (3 $V_{ref} \le V$). Despite this approach provides only an estimation of the expected magnitude for given return period (10- and 100- years return periods) without defining the actual recurrence interval of these processes, it is possible to appreciate the exceptionality of the geomorphic processes triggered inside the Vésubie catchment as a response to Storm Alex. Tributaries showing extreme magnitudes are typically smaller compared to those experiencing low and intermediate magnitudes, and with absolute sediment budgets generally lower or equal to the seconds. In essence, during Storm Alex several small to intermediate size catchments (i.e. $2–3 < A < 8–20 \text{ km}^2$) experienced a rather strong event with a magnitude that could be attributed to a 100- years return period events while dozens of small
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Sediment budget of the tributaries (top row) and the main branches (bottom row) of the Vésubie after Storm Alex. Data are shown in absolute value (A and D), as a catchment-scale averaged normalized by the stream length and expressed in m³/m of channel (B and E), as well as another catchment-scale averaged normalized by the active area surface in m³/m², i.e. a proxy of the catchment-scale mean depth of erosion or thickness of deposition.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Comparison between the sediment volumes predicted by the empirical equation proposed by Morel et al. (2023) and the sediment volumes observed from the co-registered DoD after Storm Alex and. Observed volumes equal to 1/3 the predicted volumes ($V_{ref}$) correspond approximately to the predicted volume for a 10-years return period event ($V_{10}$).</figcaption></figure>

catchments ($A < 2–3 \text{ km}^2$) experienced an extreme event with a much strong return period.

### 4.3. Geomorphic changes at reach scale

The previous results address the catchment scale, however, the data resolution enabled to zoom at reach scale. The geomorphic response of the Vésubie catchment to Storm Alex was very intense, both in terms of topographic changes and volumes of sediment mobilised, and showed significant variability of intensity between tributaries and valley bottom channels. This section examines the variability in erosion and deposition within 100-m long reaches. Fig. 4 illustrate the variability of erosion, deposition, and net sediment balance rates at reach scale, normalized per length and surface unit of reach, for tributaries (1209 reaches) and main branches (380 reaches). Significant geomorphic changes occurred throughout most of the reaches of the tributaries. In general, the volumes of erosion observed in reaches within a tributary are high and vary roughly by an order of magnitude, while depositional processes show a smaller magnitude, although with wider ranges of variability. The overall consistency of erosion or deposition patterns within each tributaries means that the geomorphological intensity tended to be spatially consistent, rather than randomly distributed. Valley bottom channels, instead, show more uniform yet pronounced variability, especially in the

Upper Boréon. This branch indeed was more variably active than the other main branches where consistent sediment cascade triggered a dramatic geomorphological change (Liébault et al., 2024). The difference in the ranges of erosion and deposition absolute volumes among channels results mainly from the different width of the reaches. In fact, the high variability observed inside reaches of the Upper Boréon reduces significantly by considering erosion and deposition rates per unit of reach surface (Fig. 4F). This seems to be true also for most of the tributaries, however for some of them the variability remains high (e.g., #14, #18, #21, #34), highlighting the exceptional intensity of the process irrespective of the scale of analysis (Fig. 4C).

The characteristics values of volumes of erosion, deposition, net balance, and normalized rates per unit of reach length and surface together with their probability density functions (PDFs) are presented in Table 4 and Fig. 5, respectively. Both erosion and deposition follow almost a log-normal distribution, with high variability (large interquartile ranges, IQR) and occasional extreme values. These extremes skew mean values, making the median a more representative descriptor of the distributions. Furthermore, small erosion and deposition rates ($<0.1 \text{ m}^3/\text{m}$ and $<0.01 \text{ m}^3/\text{m}^2$) are included in the PDFs but should be interpreted with caution, as they are likely insignificant. Erosion rates in tributaries are high ($Q_{25\%}$ equal to 3.62 $\text{m}^3/\text{m}$ and 0.26 $\text{m}^3/\text{m}^2$) (Fig. 5A, D), driven by intense and valley confined processes affecting channel beds, banks, and surrounding hillslopes (e.g., landslides, gulles) (Fig. 6 top row). The limited sediment storage ($Q_{75\%}$ of 5 $\text{m}^3/\text{m}$ and 0.18 $\text{m}^3/\text{m}^2$) (Fig. 5B, E) suggests that most eroded material is transported downstream, resulting in negative sediment budgets for most of the reaches (Fig. 5C, F). More specifically, for 33 tributaries all the reaches showed a negative sediment net balance, while in the remaining channels only a few reaches accumulate sediments. Higher deposition volumes are tied to larger reach, typically on their debris or alluvial fan, near confluences with valley-bottom channels, and less frequently in upstream segments, as consequences of hillslope erosion process. Valley bottom channels, instead, consistently exhibit high erosion and deposition rates (Fig. 5G, H, J, K). The exceptional rates result from widespread channel widening, floodplain collapse, channel bed aggradation (Fig. 6 bottom row), and from the sediment supply coming from the tributaries. In contrast to the tributaries, main branch reaches exhibit mixed behaviour, with a more balanced distribution between zones with a positive balance and those with a negative balance. It is interesting to point out that erosion volumes and normalized volumes per unit of reach length in valley floors are roughly an order of magnitude higher than in tributaries at $Q_{25\%}$ and $Q_{95\%}$. However, this difference diminishes when volumes are normalized per unit of reach surface, where erosion rates become comparable between tributaries and valley bottoms: the width of the geomorphological activity has obviously a dominant effect on the volume normalized by channel length. Overall, Fig. 5 confirms that our dataset of more than 1500 reaches pools a widespread sample of weakly to very strongly active reaches that experienced rather erosion but deposition too.

To understand the local variability of geomorphic processes, sediment volumes per unit of reach surface ($\text{m}^3/\text{m}^2$) were analysed against mean channel slope (m/m, computed as the difference in elevation of the thalweg over the reach length) (Fig. 7). To make the analysis independent from the reach width and the overall scale of the channel, we considered erosion and deposition rates per unit of reach surface. Overall, erosion rates show a scattered pattern across slopes ranging from 1 % to 100 %, with high erosion volumes present even at low slopes without clear trend (p = 0.06). This lack of a distinct trend might be due to both the different nature of the erosive processes occurring inside tributaries and valley bottom, as well as the higher drainage areas of the reaches with milder slopes (see colour gradient in Fig. 7). The high magnitude of Storm Alex may have made the geomorphic effects less “slope-selective”, as these effects are not limited by flow/stream power (QS). In other words, the slope may have played a less crucial role because the water discharge was extremely high, maintaining a high
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Box plots of sediment volumes per reach active channel length (A, C) and sediment volume per reach active channel surface (B, D) for erosion (orange), deposition (blue) and net balance (green) inside the tributaries (top) and the main branches (bottom) of the Vésubie catchment.</figcaption></figure>

stream power even at lower slopes. Indeed, high erosion rates observed for low channel gradient occurred inside the valley bottom reaches (i.e. where high runoff converged, since Q5% of the drainage area is 16.8 km²), where the geomorphic processes during Storm Alex were likely debris floods, and thus increasing channel slopes (Q95% = 15.1 %)

could increase the erosive process (p < 0.001 in the sub-sample). Conversely, inside the tributaries, the channel gradient is generally high (Q25% = 16.5 %), here also highly varied erosion mean depth occurred for a wide range of channel slope, although a very slight positive correlation is observed for increasing slope (p = 0.01). As opposed
<table><caption>**Table 4**

Characteristic values of the distribution of erosion, deposition, net balance and relative rates per unit of reach length and surface inside tributaries and valley bottoms.</caption><thead><tr><th></th><th>Process</th><th>Q5</th><th>Q25</th><th>Q50</th><th>Q75</th><th>Q95</th><th>IQR</th><th>Mean</th></tr></thead><tbody><tr><td rowspan="10">Tributaries n = 1209</td><td>Erosion [m³]</td><td>119.79</td><td>381.23</td><td>958.35</td><td>2212.72</td><td>8208.77</td><td>1831.48</td><td>2181.27</td></tr><tr><td>Erosion [m³/m]</td><td>1.12</td><td>3.62</td><td>9.36</td><td>21.64</td><td>81.55</td><td>18.01</td><td>21.55</td></tr><tr><td>Erosion [m³/m²]</td><td>0.10</td><td>0.26</td><td>0.52</td><td>0.96</td><td>2.46</td><td>0.70</td><td>0.79</td></tr><tr><td>Deposition [m³]</td><td>2.99</td><td>22.27</td><td>104.84</td><td>514.17</td><td>3038.34</td><td>491.90</td><td>822.08</td></tr><tr><td>Deposition [m³/m]</td><td>0.03</td><td>0.22</td><td>1.01</td><td>5.00</td><td>30.41</td><td>4.79</td><td>8.19</td></tr><tr><td>Deposition [m³/m²]</td><td>0.00</td><td>0.02</td><td>0.06</td><td>0.18</td><td>0.71</td><td>0.16</td><td>0.20</td></tr><tr><td>Net Balance [m³]</td><td>-7434.36</td><td>-1706.57</td><td>-619.67</td><td>-164.91</td><td>1401.92</td><td>1541.66</td><td>-1361.38</td></tr><tr><td>Net Balance [m³/m]</td><td>-73.18</td><td>-16.93</td><td>-5.87</td><td>-1.55</td><td>14.02</td><td>15.38</td><td>-13.38</td></tr><tr><td>Net Balance [m³/m²]</td><td>-2.39</td><td>-0.87</td><td>-0.40</td><td>-0.12</td><td>0.41</td><td>0.75</td><td>-0.59</td></tr><tr><td rowspan="10">Main Branches n = 380</td><td>Erosion [m³]</td><td>302.22</td><td>2743.31</td><td>6980.65</td><td>14751.90</td><td>38143.03</td><td>12008.59</td><td>11588.62</td></tr><tr><td>Erosion [m³/m]</td><td>3.02</td><td>27.43</td><td>69.81</td><td>147.52</td><td>381.43</td><td>120.09</td><td>115.83</td></tr><tr><td>Erosion [m³/m²]</td><td>0.05</td><td>0.36</td><td>0.87</td><td>1.81</td><td>3.85</td><td>1.45</td><td>1.27</td></tr><tr><td>Deposition [m³]</td><td>224.57</td><td>2042.97</td><td>6338.59</td><td>13431.38</td><td>37659.42</td><td>11388.41</td><td>10290.01</td></tr><tr><td>Deposition [m³/m]</td><td>2.25</td><td>20.43</td><td>63.39</td><td>134.31</td><td>376.59</td><td>113.88</td><td>102.74</td></tr><tr><td>Deposition [m³/m²]</td><td>0.06</td><td>0.36</td><td>0.73</td><td>1.32</td><td>2.93</td><td>0.96</td><td>1.01</td></tr><tr><td>Net Balance [m³]</td><td>-33228.75</td><td>-7329.19</td><td>-614.10</td><td>6312.32</td><td>31404.34</td><td>13641.51</td><td>-1296.44</td></tr><tr><td>Net Balance [m³/m]</td><td>-332.29</td><td>-73.29</td><td>-6.14</td><td>63.12</td><td>314.04</td><td>136.42</td><td>-13.07</td></tr><tr><td>Net Balance [m³/m²]</td><td>-3.50</td><td>-1.16</td><td>-0.14</td><td>0.71</td><td>2.72</td><td>1.87</td><td>-0.26</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Probability density functions (PDF) of erosion, deposition, a net balance volumes per unit of active reach length (first and third rows) and surface (second and forth rows) inside the tributaries (n = 1209, A-F) and the main branches (n = 380, G-L).</figcaption></figure>

to the valley bottoms, here the limiting factors to a strong increase of erosion with channel gradient could be (i) the decreasing availability of material at steeper slopes that approach the angle of repose, and also (ii)

the runoff intensity, which most likely tended to decrease with slope since steep reaches are closer from headwaters.

Regarding deposition rate, intuitively, a significant increase is
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Examples of erosion and deposition patterns inside tributaries (top row) (Erps #27 & #34, Cime Pisset 3 #12 and Venanson #40, left to right) and valley bottoms (bottom row) upstream (left) and downstream (right) the confluence of the Lower Boréon and Madone de Fenestre. Background Image: IGN.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Relationship between sediment volume per unit of reach surface and channel slope inside 100-m reaches.</figcaption></figure>

related to decreasing channel slope (p < 0.001), highlighting the key role of channels slope in affecting sediment connectivity, although this effect seems to diminish as the slope decreases (p = 0.34 for valley bottoms), where other factors come into play (i.e. extreme magnitude of the processes) (p < 0.001 for contributing area).

In essence, our dataset covers a widespread extension of reach configurations with some of them belonging to tiny headwater gullies with catchment of a few hectares and slopes near the 100 %, down to their fans, receiving larger torrents and even down to the Vésubie main stem, which drains about 400 km² and has gradient slightly above 1 %.
Throughout this whole cascading network, the erosion mean depth varies typically between 0.1 and 2.5 m figuring the varied morphological responses of our sample. The weak correlation between erosion and slope is peculiar and likely results from the extremely efficient connectivity (due to generalized flow rates) that characterized this event. Conversely to erosion that occurred up to the steep headwaters, deposition is varied for slopes < 20–25 % but it drops above this threshold to become generally negligible. This is also a footprint of the high sediment mobility that was unable to deposit on the steepest hillslope channels.

### 4.4. Forest cover loss at reach scale

By analysing the pre- and post- event LiDAR datasets it was possible extract two canopy height models (CHM) and quantify the surface covered by forest inside the geomorphologically active areas of the tributaries and main branches of the Vésubie. The loss of forested cover inside these was quantified at reaches scales as absolute loss of forested surface (ΔS<sub>f</sub>, Eq. 5). ΔS<sub>f</sub> was analysed together with the sediment volumes rates, per length and surface unit, inside reaches to understand how erosion and deposition patterns affect the reduction of the forest

cover extent. Sediment volume variation per reach unit length (m³/m) represents an average cross-sectional surface affected by geomorphic changes. In cases of lateral erosion, such as observed in the Vésubie valley and discussed by Liébault et al. (2024), sediment rates per length unit of reach can serve as a proxy for channel widening. The reduction in the extent of forest cover was estimated in the areas affected by erosion and deposition by overlapping the pre- and post- event CHMs at the DoD. It is important to point out that the deposition observed from the DoD represent the morphology of the areas after the event and it is not known what was occurring during the event (e.g., possible erosion occurred at early stages of the flood, later filled by sediments delivered from upstream).

The reaches where the estimated value of forest cover reduction was < 1 m² (but not null) were removed from the analysis. We choose the limit of 1 m² (equal to a single cell of size 1 m) as it corresponds to the smallest surface considered meaningful to characterize the process and appropriate for the uncertainties of the method of estimation. These small values might be due to the segmentation process or to some random noise in the extraction of the CHM due to trees hanging on the cliffs above the channels.

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Reduction of the forest cover extent per length unit of reach for different geomorphic change intensities: A) intensity of erosion (values increasing towards left to ease comparison with panel C); B) intensity of deposition; C) sediment net balance per length unit of reach (negative values indicate net erosion, positive values net deposition).</figcaption></figure>
As expected, increasing loss values of $\Delta S_f$ were observed for increasing erosion and deposition intensities (p-value < 0.001) (Fig. 8A, B), with maximum forest loss of 79 m²/m and 72 m²/m, respectively, corresponding to extreme erosion and deposition of 964 m³/m and 413 m³/m, respectively. The forest cover loss, intuitively, is also related to the extension of the initial forest cover inside the erosional and depositional areas, with high $\Delta S_f$ values occurring where the initial forest cover was high (see colour gradient in Fig. 8). Increasing values of sediment rates per length unit of reach is consequently clearly associated to the increase of an average active cross-sectional area and thus to the widening of the active channels at the expense of the forest cover. Conversely, for the reaches where the extension of the initial forest cover was limited – probably because of more frequent past geomorphic activity – the observed $\Delta S_f$ values were substantially lower compared to those observed for similar intensities inside areas with higher initial forest cover extent (lighter colour in Fig. 8). In these initially partially forested areas, the forest cover reduction seems also less predictable, varying among three orders of magnitudes for similar intensities. We guess that this is the footprint of channels where relatively intense sediment transport is frequently observed (thus the weak forest cover) and some wooded patches are only randomly eroded for intense events. Furthermore, inside the depositional areas this behaviour seems slightly less pronounced.

Before performing this analysis, we expected a progressive continuous shift from strongly erosional reaches toward strongly depositional reaches passing by weakly active reaches. This continuum would be associated with high forest cover loss for high geomorphic intensity and vice-versa (low forest loss) for weak process intensity. In Fig. 8C the forest cover loss $\Delta S_f$ is plotted against the net sediment balance. The mean trend for the whole dataset indeed shows an expected intermediate minimum of forest cover loss for weak geomorphic activity (that is close to zero net balance) after which a further increase of forest

reduction corresponds to an increasing of the net deposition. However, maybe surprisingly, very high forest cover losses occurred also in reaches with -10 < sediment budget < 10 m³/m, such values could indeed let suppose a very small activity. Actually, the mean trend for forested reaches does not drop to negligible forest loss for net sediment balance close to zero because erosion and deposition can co-exist at reach scale, sometime with very strong intensity for both, especially in the main stems (triangles in Fig. 8C).

In fact, over the more than 1500 reaches, forest cover reduction was observed only inside the erosional areas but not inside the depositional ones for only 149 reaches (<10 %), regardless the intensity of the process. Similarly, in only 126 reaches was observed the opposite process, i. e. forest loss only in depositional areas but not in erosional areas. This highlight how, at reach scale, a high forest cover reduction can be related to the simultaneous occurrence of erosion and deposition (Fig. 8A, B), as exemplified in more than 1200 reaches. The sediment budget (Fig. 8C) correctly captures the predominance of one process over the other. However, in reaches where both erosion and deposition were intense and redistributed the sediment within the cross section, very strong forest loss might occur, but the sediment budget can be very low. That is why high values of $\Delta S_f$ were observed along the entire range of the sediment net balance in Fig. 8C. Of course, sparely wooded reaches could not lose many trees and thus initial forest cover limits the potential loss.

Consequently, it seems that the overall forest cover loss is a cumulated result of trees scoured and lost due to erosion and trees crushed and buried due to deposition. Rather than the sediment net balance, we suggest computing the sum of both absolute erosion and deposition volumes to get a sense of the total mobilized sediment volume per length unit of reach ($V_{tot}$). Fig. 9A shows it against the forest cover reduction with colour shading according to the initial forest cover extent. The trends are very clear with increase in forest cover loss when overall

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** A: relationship between the total mobilized sediment volume per length unit of reach and the forest cover reduction inside the 100-m long reaches; B: comparison between the ratio $\Delta S_f/V_{tot}^{0.68}$ and the total mobilized sediment volume per length unit of the reach. The power-law model resulted adequate to predict the forest cover loss from the total mobilized sediment volume per length unit of reach.</figcaption></figure>
mobilized sediment volume increases, with correlation coefficients equal to 0.69 and 0.73 for Spearman and Pearson respectively. The good degree of correlation suggests that it is possible to estimate the reduction of the forest cover at 100-m reach scale based on the total mobilized sediment in our dataset covering a wide range of channel slope, drainage area and geomorphic activity. For this purpose, a power-law equation was fitted from our dataset. In this case, we considered only the reaches where the forest cover loss is higher than 1 m², avoiding also those reaches where no forest cover loss was observed. By doing so, the degree of correlation slightly increases to 0.77 for Spearman correlation, while it remains equal for Pearson correlation.

Fig. 9B shows how the ratio between the observed values of forest cover loss ($\Delta S_f$) and those predicted by the model ($V_{tot}^{0.68}$), shows no trend compared to the total mobilized volume per reach unit length, highlighting the adequacy of the model in capturing the relationship between the two variables. The relationships between $V_{tot}$ and other variables that might play a role in the process are shown in Figure S3 and considered in the Discussion.

Although this analysis clearly demonstrates that throughout a wide range of slope, drainage area and total geomorphic activity, the latter can be used to predict the forest cover loss, a wide scattering around the mean trend is obvious on Fig. 9. To give a sense of this variability several other equations are provided as the 10%, 50%, and 90% quantiles of the ratio between $\Delta S_f$ and $V_{tot}^{0.68}$ representing an optimistic, intermediate, and catastrophic forest cover loss, respectively (Eqs. 7–9, Table 5). The practical application of these models to predict catchment scale forest cover loss is discussed in the Discussion.

On Fig. 9, it appears that small forest cover loss, i.e. near or below quantile 10%, tends to disappear for high overall geomorphic activity ($V_{tot} > 50–200 m^3/m$). This seems to suggest that for sufficiently high geomorphic activity, the overall activated area grows sufficiently to interact with forest patches of the Véubie catchment. In less initially forested areas, low forest cover loss even in strongly active reaches would most probably appear.

## 4.5. Forest cover loss and recruited large wood volumes

The loss of forested cover was quantified at (sub-)catchment scale as absolute loss of forested surface ($\Delta S_f$, Eq. 5). The volume of large wood recruited during Storm Alex inside each (sub-)catchment were estimated by multiplying the extension of the forest cover loss by the forest parameters (i.e., volume of wood per hectare) derived from the National Forest Inventory. A resulting summary of the forest cover loss and associated large wood volumes in each (sub-)catchment is reported in Table 6. The interpretation of these results and a comparison with other case studies in the literature is presented in the Discussion Section.

# 5. Discussion

## 5.1. (Sub-)catchment analysis: Geomorphic changes

The exceptionality of Storm Alex and its effects both on reshaping the morphology of the channels and the riparian land cover in the Véubie and other neighbour catchments (e.g. Roya catchment) are well described in the literature (Liébault et al., 2024; ONF-RTM et al., 2023; Piton et al., 2024). Based on the comparison between the erosion

<caption>**Table 5**</caption>

Power law model for the prediction of the forest cover loss from the total mobilized sediment per length unit of reach.

<table><thead><tr><th>Model</th><th>Equations</th><th></th></tr></thead><tbody><tr><td>Best fit (quantile 68 %)</td><td>ΔS<sub>f</sub> = 0.57 • V<sub>tot</sub><sup>0.68</sup></td><td>(6)</td></tr><tr><td>Low forest cover loss (quantile 10 %)</td><td>ΔS<sub>f</sub> = 0.03 • V<sub>tot</sub><sup>0.68</sup></td><td>(7)</td></tr><tr><td>Intermediate forest cover loss (quantile 50 %)</td><td>ΔS<sub>f</sub> = 0.34 • V<sub>tot</sub><sup>0.68</sup></td><td>(8)</td></tr><tr><td>High forest cover loss (quantile 90 %)</td><td>ΔS<sub>f</sub> = 1.04 • V<sub>tot</sub><sup>0.68</sup></td><td>(9)</td></tr></tbody></table>

magnitudes observed inside the 43 active tributaries of the Véubie catchment and those predicted with the empirical equation proposed by Morel et al. (2023), we hypothesize that most of the tributaries experienced debris flow/debris flood events with return periods equal or higher than 100-years. Consistently, Liébault et al., (2024) described how extreme were the geomorphological changes in the valley bottoms, with the consequent widening of the active channels and the destruction of the riparian forests. The catchment-averaged erosion volume per length unit of channel observed inside the 43 tributaries analysed in these studies resulted very variable, from moderate intensities (a few $m^3/m$) to very high intensities as observed in subcatchment #12 (i.e., 152 $m^3/m$). There are 39 tributaries showing erosion volumes per length unit of the channel in the range 3.5 $m^3/m$ – and 47.5 $m^3/m$, while only 4 showed higher intensities (i.e., subcatchments #12, #16, #21 and #34). These values are consistent with the range of 0 $m^3/m$ – 50 $m^3/m$ found by Hungr et al. (2005) reviewing the erosion rates for more than 500 events around the world. Moreover, Marchi and D'Agostino (2004) analysed the magnitude of 127 debris flow event in the Eastern Italian Alps, observing that most of the studied events observed erosion rates per unit of channel length up to 10 $m^3/m$ and seldom exceed 50 $m^3/m$. In our sample, erosion rates > 10 $m^3/m$ were observed in 27 tributaries, highlighting the exceptionality of the intensities of the geomorphic changes triggered by Storm Alex. The higher erosion rates observed are in line with the value of 110 $m^3/m$ observed by Marchi and Cavalli (2007) and of 119 $m^3/m$ observed by Baggio and D'Agostino (2022) inside the Rio Rotian (Eastern Italian Alps) after the very intense Storm Vaia (October 2018), but far below the extreme value of 300 $m^3/m$ observed by Rickenmann et al. (2003). Inside the valley bottoms courses erosion rates > 100 $m^3/m$ were observed in all the main branches, except for the Upper Boréon (33 $m^3/m$), consequently to the widening of the active channel as described by Liébault et al., (2024). While very high values of erosion rate are usually attributed to debris flows, the main stems of the Véubie were rather affected by debris floods: such process can thus trigger dramatic geomorphic changes during extreme events involving overall sediment volumes generally exceeding those of debris flow (D'Agostino, 2013).

## 5.2. (Sub-)catchment analysis: Large wood recruitment

The volumes of large wood recruited during Storm Alex inside the Véubie catchment is compared in Fig. 10 with the large wood supply mobilized in the Roya catchment during the same event (Piton et al. 2024) and in other catchments impacted by storms in the Alps and Japan (Lucía et al., 2018; Rickenmann, 1997; Schmocker and Weitbrecht, 2013; Steeb et al., 2023, 2017). It is worth stressing that our values are likely slightly overestimated: indeed, differently from the other studies considered, our analysis focuses on the volume of large wood recruited and it neglects the deposited logs thus not fully quantifying the wood budgets. However, Piton et al. (2024), found that only about 10% of the total mobilized volume of large wood deposited inside the active channels of the Roya catchment. The estimated volumes of large wood recruited inside the tributaries and the (sub-) catchments of the Véubie are comparable to the values of large wood supply of the data available from other catchments of similar size (Fig. 10A). The smaller tributaries (<1 $km^2$) show higher variability of supply (up to two orders of magnitude), while bigger tributaries are more aligned with the higher values of supply reported in the literature. The supplies of the Boréon, Madone de Fenestre and Véubie (sub-)catchments are aligned with the highest values of the other datasets, and above the values observed by Piton et al. (2024) inside the Roya catchment after the same event. For comparable sediment load, instead, the large wood supplies inside the tributaries align with the lower values observed in the other data available, while the supplies inside the (sub-) catchments are aligned with the few values with comparable sediment loads. (Fig. 10B). Four tributaries, which did not faced recruitment of large wood, are thus not shown on Fig. 10 (#8, #13, #22 and #29). These are either sub-
<caption>**Table 6**</caption>

Parameters describing the forest cover before and after Storm Alex inside the geomorphologically active areas of the tributaries and main branches of the Vésubie catchment, as well as the loss of forested surface (ΔS<sub>f</sub>) and the volume of large wood (LW) recruited: mid value (lower range – upper range).

<table><thead><tr><th rowspan="2">ID</th><th rowspan="2">Channel</th><th rowspan="2">System</th><th>Forest cover2018</th><th>Forest cover2020</th><th>ΔS<sub>f</sub></th><th>Volume of LW</th><th>Volume of LW per unit of catchment surface</th></tr><tr><th>[ha]</th><th>[ha]</th><th>[ha]</th><th>[m<sup>3</sup>]</th><th>[m<sup>3</sup>/km<sup>2</sup>]</th></tr></thead><tbody><tr><td>#1</td><td>Collet colignol 1</td><td>Madone de Fenestre</td><td>0.34</td><td>0.21</td><td>0.13</td><td>21 (11–44)</td><td>336 (176–703)</td></tr><tr><td>#2</td><td>Cime pisset 1</td><td>Madone de Fenestre</td><td>0.34</td><td>0.27</td><td>0.07</td><td>11 (6–22)</td><td>153 (83–306)</td></tr><tr><td>#3</td><td>Collet colignol 2</td><td>Madone de Fenestre</td><td>0.18</td><td>0.15</td><td>0.03</td><td>5 (3–11)</td><td>67 (40–148)</td></tr><tr><td>#4</td><td>Pelago 1</td><td>Upper Boréon</td><td>0.34</td><td>0.26</td><td>0.08</td><td>14 (7–29)</td><td>139 (70–289)</td></tr><tr><td>#5</td><td>Bausset 1</td><td>Lower Boréon</td><td>0.42</td><td>0.31</td><td>0.11</td><td>18 (10–37)</td><td>154 (85–316)</td></tr><tr><td>#6</td><td>Rouchas</td><td>Madone de Fenestre</td><td>0.20</td><td>0.11</td><td>0.09</td><td>15 (8–31)</td><td>123 (66–254)</td></tr><tr><td>#7</td><td>Cime pisset 2_2</td><td>Madone de Fenestre</td><td>2.55</td><td>2.20</td><td>0.34</td><td>57 (30–117)</td><td>445 (234–913)</td></tr><tr><td>#8</td><td>Adousse 2</td><td>Lower Boréon</td><td>0.07</td><td>0.26</td><td>0.00</td><td>0</td><td>0</td></tr><tr><td>#9</td><td>Cime pisset 5</td><td>Madone de Fenestre</td><td>0.46</td><td>0.38</td><td>0.08</td><td>14 (8–29)</td><td>90 (51–187)</td></tr><tr><td>#10</td><td>Pelago 2</td><td>Upper Boréon</td><td>0.11</td><td>0.09</td><td>0.02</td><td>3 (2–6)</td><td>19 (13–38)</td></tr><tr><td>#11</td><td>Collet colignol 3</td><td>Madone de Fenestre</td><td>0.28</td><td>0.08</td><td>0.20</td><td>34 (18–69)</td><td>199 (105–403)</td></tr><tr><td>#12</td><td>Cime pisset 3</td><td>Madone de Fenestre</td><td>1.90</td><td>1.81</td><td>0.09</td><td>16 (8–32)</td><td>45 (23–90)</td></tr><tr><td>#13</td><td>Cime pisset 4</td><td>Madone de Fenestre</td><td>0.07</td><td>0.14</td><td>0.00</td><td>0</td><td>0</td></tr><tr><td>#14</td><td>Serre long</td><td>Lower Boréon</td><td>0.73</td><td>0.39</td><td>0.34</td><td>57 (30–116)</td><td>149 (78–302)</td></tr><tr><td>#15</td><td>Cime pisset 2_1</td><td>Madone de Fenestre</td><td>1.03</td><td>0.86</td><td>0.17</td><td>28 (15–57)</td><td>72 (39–147)</td></tr><tr><td>#16</td><td>Saut</td><td>Lower Boréon</td><td>0.17</td><td>0.08</td><td>0.09</td><td>15 (8–30)</td><td>32 (17–63)</td></tr><tr><td>#17</td><td>Marre inferieur</td><td>Madone de Fenestre</td><td>0.37</td><td>0.29</td><td>0.08</td><td>13 (7–27)</td><td>25 (13–51)</td></tr><tr><td>#18</td><td>Pelago 3</td><td>Upper Boréon</td><td>0.10</td><td>0.07</td><td>0.03</td><td>5 (3–10)</td><td>9 (6–19)</td></tr><tr><td>#19</td><td>Nid de l'apos;ane</td><td>Madone de Fenestre</td><td>1.07</td><td>0.49</td><td>0.59</td><td>98 (52–200)</td><td>168 (89–343)</td></tr><tr><td>#20</td><td>Adousse 1</td><td>Lower Boréon</td><td>0.35</td><td>0.32</td><td>0.03</td><td>5 (3–10)</td><td>8 (5–16)</td></tr><tr><td>#21</td><td>Champet</td><td>Lower Boréon</td><td>0.82</td><td>0.39</td><td>0.42</td><td>71 (37–144)</td><td>104 (54–212)</td></tr><tr><td>#22</td><td>Pouche</td><td>Madone de Fenestre</td><td>0.00</td><td>0.05</td><td>0.00</td><td>0</td><td>0</td></tr><tr><td>#23</td><td>Pontets</td><td>Madone de Fenestre</td><td>0.31</td><td>0.21</td><td>0.10</td><td>17 (9–34)</td><td>21 (11–42)</td></tr><tr><td>#24</td><td>Madame</td><td>Madone de Fenestre</td><td>0.93</td><td>0.41</td><td>0.52</td><td>87 (46–176)</td><td>105 (55–212)</td></tr><tr><td>#25</td><td>Touron</td><td>Vésubie</td><td>0.06</td><td>0.06</td><td>0.01</td><td>0</td><td>0</td></tr><tr><td>#26</td><td>Pomeiras</td><td>Madone de Fenestre</td><td>1.97</td><td>1.36</td><td>0.61</td><td>102 (54–207)</td><td>117 (62–237)</td></tr><tr><td>#27</td><td>Erps 2</td><td>Upper Boréon</td><td>0.28</td><td>0.23</td><td>0.05</td><td>8 (4–17)</td><td>9 (5–19)</td></tr><tr><td>#28</td><td>Lapasse</td><td>Madone de Fenestre</td><td>0.85</td><td>0.40</td><td>0.44</td><td>74 (39–151)</td><td>61 (32–125)</td></tr><tr><td>#29</td><td>Pierre du villar</td><td>Vésubie</td><td>0.41</td><td>0.47</td><td>0.00</td><td>0</td><td>0</td></tr><tr><td>#30</td><td>Archas</td><td>Lower Boréon</td><td>0.42</td><td>0.26</td><td>0.16</td><td>28 (15–56)</td><td>15 (8–31)</td></tr><tr><td>#31</td><td>Champouns</td><td>Vésubie</td><td>2.66</td><td>1.40</td><td>1.26</td><td>211 (112–430)</td><td>97 (51–197)</td></tr><tr><td>#32</td><td>Cavalet</td><td>Lower Boréon</td><td>2.08</td><td>1.31</td><td>0.77</td><td>129 (68–263)</td><td>51 (27–104)</td></tr><tr><td>#33</td><td>Cougne</td><td>Vésubie</td><td>6.74</td><td>5.33</td><td>1.41</td><td>236 (125–481)</td><td>80 (43–164)</td></tr><tr><td>#34</td><td>Erps 1</td><td>Upper Boréon</td><td>3.27</td><td>1.30</td><td>1.98</td><td>332 (176–676)</td><td>112 (60–229)</td></tr><tr><td>#35</td><td>Vernet</td><td>Lower Boréon</td><td>0.84</td><td>0.46</td><td>0.39</td><td>65 (35–133)</td><td>19 (10–39)</td></tr><tr><td>#36</td><td>Villars</td><td>Lower Boréon</td><td>4.01</td><td>2.04</td><td>1.97</td><td>331 (175–673)</td><td>41 (21–82)</td></tr><tr><td>#37</td><td>Cervagne</td><td>Vésubie</td><td>3.19</td><td>2.60</td><td>0.59</td><td>99 (53–202)</td><td>12 (6–24)</td></tr><tr><td>#38</td><td>Salese</td><td>Lower Boréon</td><td>6.54</td><td>4.41</td><td>2.13</td><td>358 (189–728)</td><td>29 (15–59)</td></tr><tr><td>#39</td><td>Boreon amont</td><td>Upper Boréon</td><td>2.91</td><td>2.00</td><td>0.91</td><td>153 (81–312)</td><td>12 (6–24)</td></tr><tr><td>#40</td><td>Venanson</td><td>Vésubie</td><td>12.26</td><td>6.75</td><td>5.52</td><td>927 (491–1886)</td><td>64 (34–129)</td></tr><tr><td>#41</td><td>Espaillart</td><td>Vésubie</td><td>9.71</td><td>7.09</td><td>2.62</td><td>440 (233–896)</td><td>29 (15–59)</td></tr><tr><td>#42</td><td>Figaret</td><td>Vésubie</td><td>2.74</td><td>2.16</td><td>0.58</td><td>97 (51–198)</td><td>4 (2–9)</td></tr><tr><td>#43</td><td>Gordolasque</td><td>Vésubie</td><td>21.74</td><td>11.88</td><td>9.85</td><td>1655 (877–3370)</td><td>28 (15–57)</td></tr><tr><td>Main Branches</td><td>Upper Boréon</td><td></td><td>6.29</td><td>3.32</td><td>2.96</td><td>498 (264–1014)</td><td>/</td></tr><tr><td>Main Branches</td><td>Lower Boréon</td><td></td><td>29.50</td><td>5.99</td><td>23.51</td><td>3950 (2092–8040)</td><td>/</td></tr><tr><td>Main Branches</td><td>Madone de Fenestre</td><td></td><td>28.13</td><td>10.85</td><td>17.27</td><td>2902 (1537–5908)</td><td>/</td></tr><tr><td>Main Branches</td><td>Vésubie</td><td></td><td>61.90</td><td>19.20</td><td>42.69</td><td>7172 (3800–14601)</td><td>/</td></tr><tr><td>Catchment</td><td>Upper Boreon</td><td></td><td>13.30</td><td>7.27</td><td>6.03</td><td>1013 (537–2063)</td><td>45 (24–92)</td></tr><tr><td>Catchment</td><td>Boreon</td><td></td><td>59.24</td><td>23.49</td><td>35.95</td><td>6039 (3199–12294)</td><td>93 (49–190)</td></tr><tr><td>Catchment</td><td>Madone de Fenestre</td><td></td><td>40.97</td><td>20.29</td><td>20.80</td><td>3495 (1852–7115)</td><td>95 (50–193)</td></tr><tr><td>Catchment</td><td>Vésubie</td><td></td><td>221.62</td><td>100.72</td><td>121.27</td><td>20,374 (10793–41476)</td><td>63 (33–128)</td></tr></tbody></table>

catchments that have been very active over time, where riparian vegetation has no time to develop, or where the geomorphological response to the Storm Alex was not particularly intense.

The BAFU guidelines (2019) suggests the implementation of empirical formulas with one or two parameters to predict the supply of large wood in relation to catchment morphometric parameters (e.g. surface, channel length) or parameters related to the magnitude of the flood (e. g., total rainfall and flow volumes, peak discharges, sediment load). Due to the stochastic nature of the process, the guidelines systematically propose four equations: a median adjustment representative of “moderate production”, adjustments > 10 % and > 90 % of data, representative of relatively “low” and “high production” volumes, as well as an envelope curve, representative of extreme supply.

The adjustments to the catchment size-dependent and sediment load equations of the BAFU guidelines (2019) are illustrated in Fig. 10A and B, respectively. The recruited large wood volume related to the

catchment size inside the tributaries vary between “low production” and “high production”, with only one tributary (i.e. #25 Touron) below the “low production”. The variability is pronounced for the smaller tributaries; however, it reduces for the bigger sub-catchments.

Compared to the sediment load, instead, most of the tributaries recruited a volume of large wood between “low production” and “moderate production”, with 9 sub-catchments below the “low production” adjustment. The dots associated with the (sub-)catchments of the Vésubie system are between “moderate production” and the “high production” adjustments, with the Vésubie showing a supply slightly higher than the “high production”. These observations highlight how, given the exceptional nature of the Storm Alex, the recruitment of large wood volume inside the tributaries was generally relatively moderate, with exception of the bigger sub-catchments, while exceptional large wood volumes were recruited inside the three main steams of the Vésubie system. This result might be due to the exceptionality of the
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Catchment area (panle A) and sediment supply (panel B) versus large wood supply for the studied reaches and data from the literature (Lucía et al., 2018; Rickenmann, 1997; Schmocker and Weitbrecht, 2013; Steeb et al., 2023, 2017).</figcaption></figure>

geomorphic activity that occurred inside the Vésubie catchment as response to Storm Alex. Namely, very high sediment production was observed in most catchments (recall Fig. 3), while typical large wood amount was observed (Fig. 10A) so the relative ratio of wood to sediment is lower than usually. In essence, at (sub-)catchment scale these simple empirical approaches determine reasonable values for events where substantial large wood supply occurred.

It is interesting to notice that the exponent the power-law functions for the prediction of the “high”, “moderate” and “low production” of large wood supply from the sediment load proposed by the BAFU guidelines (2019) (i.e. 0.65), are very close to the exponent the power-law functions for the prediction of the “high”, “intermediate” and “low” forest cover loss from the total mobilized sediment volume at reach scale proposed in this study. Considering that the loss of the forest cover is a proxy of the large wood supply, this suggest that there is some kind of scale independent relationship that should be investigated in future studies.

### 5.3. Reach scale analysis: Geomorphic changes

As highlighted in the above mentioned studies and described by Comiti et al. (2016), large wood supply, as well as the intensity of the geomorphic processes (Hungr et al., 2005), varies inside catchments depending on multiple factors, such as for instance the channel topography, drainage area, channel slope, type of the geomorphic processes, rainfall patterns, and initial forest cover. Catchment-scale empirical estimates are useful for preliminary hazard assessment, but if more detailed studies must be produced, an analysis of the stream network and the reach potential productivity can be useful (Hübl, 2018). Actually, even within a given channel branch, a high degree of variability of erosion or deposition, up to several orders of magnitude, can be observed. Among tributaries and main branches respectively, 119 and 225 reaches exceeded geomorphic changes of 50 m³/m and 6 and 34

reaches exceeded 300 m³/m (up to 663 m³/m in the Venanson (#40) and 1044 m³/m in the Vésubie main channel).

To study the variability of the erosional rates, we analysed the average erosion depth inside the reaches as the erosion volume per surface unit of reach (m³/m²) and compared it with the mean slope gradient of the reaches (Fig. 7A). The maximum mean erosion depth values observed, up 7.5 m³/m² in the tributaries and 7.2 m³/m² in the main branches, are above the maximum values of 3 m and 5 m observed in the Swiss alps by Berger et al. (2011) and Rickenmann and Zimmermann (1993) respectively and those observed in the Eastern Italian Alps by (Bertoldi et al., 2023) after two very erosive debris flow events (4.9 m and 3.7 m), but are in line with what found Marchi et al. (2009) and Baggio et al. (2021) in the Eastern Italian Alps (up to 9 m and 8 m respectively).

Within a given catchment, it is well known that debris flow and debris flood erosion intensity grows with the channel gradient (e.g., Baggio et al., 2021; Hungr et al., 2005; Kronfellner-Kraus, 1984; Takahashi, 2007). Our dataset did not show a strong correlation between the reach mean slope and the mean erosion depth. Inside the reaches of the tributaries, erosion depth reaching a few meters (actually m³/m²) were observed from low to high gradients, and values > 1 m³/m² were observed starting from gradients around 5 %. It is only within the sub-samples of the main stems or the tributaries that trends appear. This is mostly likely because the whole dataset covers more than four orders of magnitude of drainage area and two order of magnitude of slope: at such a scale, processes are changing from minor debris flows in gullies to valley scale devastating debris floods and the transition between debris flows and debris floods as well as many different drainage areas and slopes hide the gradient effect on the processes. The deposition rates inside the tributaries seem to be more responsive to the slope gradient, as reaches with mean slope gradients higher than around 20 % experienced decreasing mean deposition depth consistently with values reported by Hungr et al. (2005).
## 5.4. Reach scale analysis: Forest cover loss

For detailed debris-flow and debris-flood hazard assessment, it is common to perform an analysis of the stream network assigning reach-scale erosion rate that cumulate to predict the possible volumes supplied at a given site (Lièbault et al., 2013). Taking into account this within-catchment variability makes sense: in our dataset too, geomorphic intensity and forest cover loss varied greatly along each tributary (Figure S 4).

Summing up reach production of large wood is also a way to compute the overall production (Mazzorana et al., 2009). The reach scale production is usually estimated by multiplying the forest stand density with the forest cover loss. Estimating the latter is always an uncertain task. Steeb et al. (2023) for instance compared two methods that, in essence, rely on more or less sophisticated estimates of buffer strip widths (e.g. 5 m, 10 m and 15 m for due to debris flow scenarios, with medium, large and very large intensity, respectively) and ratios of these strips being recruited (e.g. 5 %, 10 % and 30 % for the same three scenarios). Such methods are relatively quick to apply but consider only a magnitude class (medium to very large) independent of the erosion rate. In weakly active streams, such a method will overestimate the forest cover loss while in reaches where dramatic erosion of wooded banks might occur, it will underestimate it.

To overcome this limitation, three simple equations (Eqs. 7–9) are proposed within this investigation to predict a low, intermediate or high forest cover loss from the total mobilized sediment volume per unit length of channel. Once erosion rates are assigned, buffers where forest could be recruited can be defined along the stream network. The buffer width should account for the erosion rate, e.g. by assuming a typical erosion depth. Whether erosion occurs rather vertically by channel incision or laterally by channel widening will vary between sites depending, for instance, of the process type, the soil depth, the slope and the geology. We assume that it is not required to assume a precise estimate of this depth: the point is only to get a sense of a possible geomorphic extent of the activated areas along the channel to check whether they are wooded or not.

In unforested buffers, no large wood can be recruited. With the initial forest cover increasing in the buffers, an increase of forest cover loss will appear (Figure S 3D), essentially driven by the increase in total geomorphic activity (erosion plus deposition). End-users may select one or several empirical equations (Eqs. 7–9) according to this wood availability and apply them to each reach of the stream network. Estimates of the wood production for several optimistic or pessimistic scenarios can be produced, which are driven by the geomorphic process intensity that is predicated.

Such methods cannot be accurate due to the strong random variability of forest erosion by geomorphic processes (also visible at catchment scale in Fig. 10). Steeb et al. (2023) applied a simple GIS method as described above, as well as a more sophisticated method using fuzzy logic and better accounting for the landslide, debris flow stream and river bed channel features (e.g. connectivity to the stream network, slope, channel width). They showed that the ratio between observed and predicted volume of large wood was usually, but not systematically, within one order of magnitude. These findings support the consistency of using the new estimative equations (Eqs. 7–9) informed by the expected scenarios.

# 6. Conclusion

The present article explores the geomorphic response of 150 km of river channels located within the Vésubie river catchment (south-east France) and the consequent recruitment of large wood after a large storm (Storm Alex) in October 2020. Starting from high resolution topographic data (LiDAR derived DEM), it was possible to assess the geomorphic changes inside the main branches of the Vésubie and 43 active tributaries affected by debris flows and debris floods and estimate

the magnitudes and intensities of these processes both at (sub-)catchment and reach scale. Furthermore, the loss of forest cover was estimated by comparing the pre- and post-event extensions of the forest cover obtained from diachronic CHM at (sub-)catchment and reach scales.

The results show Storm Alex triggered exceptional debris flows and debris floods mobilizing significant amounts of sediments and widening the active channels, with the consequent recruitments of noticeable volumes of large wood. The collected and processed post-event data evidence that this Storm, affecting the Vésubie river catchment, has offered an exemplary and almost unique case to jointly deal with the chain of interconnected processes: flood, sediment dynamics, riverine forest cover loss, large wood recruitment.

Most of the active tributaries experienced events with an empirically estimated return period higher than 100- years, resulting in high connectivity between the tributaries and the valley bottoms, as shown by the extremely negative net sediment balances observed for all the active tributaries. The high geomorphic activity resulted in extreme channel widening, with substantial recruitment of large wood. However, the recruited volumes are similar with the highest value of large wood supply reported in other studies in the literature.

At reach scale, the intensities of the geomorphic activity resulted very variable, with local values exceeding the typical erosion rates reported in the literature. The forest cover loss also showed high variability along channels and prove to have a positive correlation both with erosion and, quasi unexpectedly, with deposition rates. Since both processes occurred inside most of the reaches (especially inside the valley bottoms), we hypothesize that the overall forest cover loss is a cumulated result of trees scoured and lost due to erosion and trees crushed and buried due to deposition. This assumption is corroborated by the good positive correlation between forest cover loss and the total mobilised sediment volumes per reach length. Finally, thanks to the novel data set, we proposed an empirical equation to predict at reach-scale the forest cover loss per reach length from the total mobilised sediment volume per reach length. To overcome limited accuracy of such method due to the high variability related to this process, a set of equations predicting optimistic, intermediate, and catastrophic forest cover loss scenarios were proposed.

# Fundings

The research is supported by the funds of the University of Padova (Italy): BIRD-DOR 2024-2025, Prof. Vincenzo D'Agostino and by the EU Interreg-Alcotra RITA project (grant agreement n.8504) for the DTM coregistration.

# CRediT authorship contribution statement

**Marco Matini:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Vincenzo D'Agostino:** Writing – review & editing, Funding acquisition, Formal analysis, Supervision. **Guillaume Piton:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Investigation, Formal analysis, Conceptualization.

# 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.

# Acknowledgement

The research is supported by the funds of the University of Padova (Italy): BIRD-DOR 2024-2025, Prof. Vincenzo D'Agostino and by the EU
Interreg-Alcotra RITA project (grant agreement n.8504) for the DTM coregistration. The authors would like to thank the metropole Nice Cote d'Azur for providing the 2018 LiDAR point cloud (LIDAR25cm@ SIGMNCA). Finally, the authors wish to thanks Editor Alexandre Wadoux and three anonymous rewievors for the helpful suggestions and comments.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.catena.2025.109180.

## Data availability

Active Reaches Vésubie Storm Alex (Original data) (Dataverse)

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