headwater sediment dynamics in a debris ﬂow catchment ......activity from the middle and lower...

Earth Surf. Dynam., 4, 489–513, 2016www.earth-surf-dynam.net/4/489/2016/doi:10.5194/esurf-4-489-2016© Author(s) 2016. CC Attribution 3.0 License.

Headwater sediment dynamics in a debris flowcatchment constrained by high-resolution

topographic surveys

Alexandre Loye1, Michel Jaboyedoff1, Joshua Isaac Theule2, and Frédéric Liébault2

1Risk-group – ISTE – Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland2Université Grenoble Alpes, Irstea, UR ETNA, Saint-Martin-d’Hères, France

Correspondence to: Alexandre Loye ([email protected])

Received: 1 December 2015 – Published in Earth Surf. Dynam. Discuss.: 14 January 2016Revised: 19 May 2016 – Accepted: 20 May 2016 – Published: 28 June 2016

Abstract. Debris flows have been recognized to be linked to the amounts of material temporarily stored in tor-rent channels. Hence, sediment supply and storage changes from low-order channels of the Manival catchment,a small tributary valley with an active torrent system located exclusively in sedimentary rocks of the ChartreuseMassif (French Alps), were surveyed periodically for 16 months using terrestrial laser scanning (TLS) to studythe coupling between sediment dynamics and torrent responses in terms of debris flow events, which occurredtwice during the monitoring period. Sediment transfer in the main torrent was monitored with cross-section sur-veys. Sediment budgets were generated seasonally using sequential TLS data differencing and morphologicalextrapolations. Debris production depends strongly on rockfall occurring during the winter–early spring season,following a power law distribution for volumes of rockfall events above 0.1 m3, while hillslope sediment rework-ing dominates debris recharge in spring and autumn, which shows effective hillslope–channel coupling. Theoccurrence of both debris flow events that occurred during the monitoring was linked to recharge from previousdebris pulses coming from the hillside and from bedload transfer. Headwater debris sources display an ambigu-ous behaviour in sediment transfer: low geomorphic activity occurred in the production zone, despite rainstormsinducing debris flows in the torrent; still, a general reactivation of sediment transport in headwater channels wasobserved in autumn without new debris supply, suggesting that the stored debris was not exhausted. The seasonalcycle of sediment yield seems to depend not only on debris supply and runoff (flow capacity) but also on geo-morphic conditions that destabilize remnant debris stocks. This study shows that monitoring the changes withina torrent’s in-channel storage and its debris supply can improve knowledge on recharge thresholds leading todebris flow.

1 Introduction

In steep mountain catchments, rainfall intensity and duration(including snowmelt) are insufficient to predict debris flowoccurrence, even though the initiation of runoff-generateddebris flows requires significant water inflow (Van Dine,1985; Decaulne and Saemundsson, 2007; Guzzetti, 2008). Inmany cases, the properties of the channel reach which de-termine the amount of debris that can be entrained can beoften more important than the mechanisms of initiation in-duced by the hydrological or meteorological conditions prior

to the event (Hungr, 2011; Theule et al., 2015). The fre-quency and magnitude of debris flow have been recognizedto be linked to the amount of material temporarily storedin channel reaches (Van Steijn et al., 1996; Cannon et al.,2003; Hungr et al., 2005), such that hillside sediment deliv-ery, recharging those channels, represents a key factor for theoccurrence of debris flows (e.g. Benda and Dunne, 1997; Bo-vis and Jakob, 1999; Berti et al., 2000). This implies efficienthillslope–channel coupling (Hooke, 2003; Schlunegger et al.,2009; Johnson et al., 2010). Therefore, the rate of sedimentsupply needs to be considered for predicting debris flow haz-

Published by Copernicus Publications on behalf of the European Geosciences Union.

490 A. Loye et al.: Headwater sediment dynamics

Productionzone

Sediment trap

Zone of transfer

Chambery'

Figure 1. Inset: map of the study area; the Manival catchment is in solid red and the impressive debris fan is hatched. Main: aerial viewof the Manival catchment, draped over a topographic model; sediment supply is concentrated in the headwater (production zone) as erosionactivity from the middle and lower catchment is not connected to the torrent (zone of transfer) (image: Aerodata International Surveys; DEM:Irstea UR ETNA).

ards (Rickenmann, 1999; Jakob et al., 2005). However, thedifficulty results in quantifying sediment processes and ratesand volumes from hillslopes and in-channel debris storage(Peiry, 1990; Zimmermann et al., 1997).

The quantification of the overall sediment production andtransfer rate has increasingly relied upon multi-temporal dig-ital stereophotogrammetry (Coe et al., 1993; Chandler andBrunsden, 1995; Veyrat-Chavillon and Memier, 2006) andelevation difference from high-resolution digital elevationmodels (HRDEMs) (Smith et al., 2000; Wu and Cheng,2005; Roering et al., 2009; Theule et al., 2012). In terraindominated by steep slopes, traditional aerial-derived digitalelevation models (DEMs) are typically inappropriate to studygeomorphic processes. Limitations include the poor render-ing of small topographic changes (Perroy et al., 2010), thepoor representation of steep terrain with small curvature radiiand data gaps in vertically oriented and overhanging topog-raphy. Even on gentler gradients, the sharp breaks in slope,encountered in erosion scars for instance, are often insuffi-

ciently modelled by airborne HRDEMs, leading to erroneousvolume estimations (Bremer and Sass, 2011). This repre-sents a serious drawback in estimating the sediment budgetof steep terrain, where sediment activity comes mostly fromrock walls and rugged gullies. Because of these issues, manyhillslope and rock slope process studies have used terres-trial laser scanner (TLS) data to build the topographic model(Jaboyedoff et al., 2012). The recent development of long-range TLS devices provides an effective means of acquiringhigh-resolution topographic information that can adequatelyreflect the morphology of steep bedrock-dominated areas.The practical disadvantages in data acquisition inevitably re-lated to ground surveys can be compensated for by flexibilityin transport, ensuring a full coverage with minimal zones oftopographic shadowing.

This paper presents a quantitative study of sedimentrecharge and channel response leading to debris flow events,using 3-D digital terrain models acquired by TLS. This is il-lustrated on the Manival (French Alps), a torrent that experi-

Earth Surf. Dynam., 4, 489–513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 491

Figure 2. Geological map of the catchment headwater (production zone) after Gidon (1991) and location of first-order debris flow channels(thick blue line) and their respective watersheds (white lines). For the ease of analysis, the Roche Ravine and Col du Baure subcatchmentsin the east side were further subdivided according to their gully complex (dotted white lines).

ences runoff-generated debris flow almost every year (Péteuilet al., 2008). The surveys captured hillslope processes andsediment dynamics occurring throughout the system includ-ing the tributary channels down to the main torrent and wereperformed periodically over 16 months. The spatio-temporalvariability of debris production and subsequent transport andstorage of sediment are analysed on a seasonal timescale, inorder to discuss the debris supply dynamics and the implica-tions in debris flow initiation. This study also complementsa parallel investigation regarding the controls on debris flowerosion and bedload transport in the Manival torrent (Theuleet al., 2015).

2 Study site

2.1 General setting

The 3.9 km2 Manival catchment located at the edge of theChartreuse Massif (France) (Fig. 1) has a rugged, 1200 mrelief watershed, resulting from deep headward entrench-ment (Gidon, 1991). The topography consists of a narrowlyconfined head and a steep-sided colluvium-filled valley, de-limited in the west by a series of rock walls and scree-mantled deposits separated by rock couloirs and in the eastby steep rock and talus slopes divided by gullies. The lithol-ogy ranges in age from Late Jurassic to Early Cretaceous(Fig. 2) (Charollais et al., 1986). In the heart of the basin,thick sequences of calcareous marl interbedded with layersof marl predominate. Towards the ridge, the bedrock evolvesprogressively from stratified to more massive limestone. The

valley sides are formed by the fold limbs of an anticline,where secondary folding and minor faults induce local varia-tions in structure (Gidon, 1991). This tectonic setting and thevarying stratigraphic competence have strongly influencedthe topographic development of the catchment, providing adynamic geomorphic environment producing considerablerunoff as a response to heavy, frequent rainstorms (Fig. 3).

2.2 Characteristics of the headwater sediment dynamics

The contemporary geomorphic activity contributing to thetorrent’s recharge with debris is concentrated exclusively inthe headwater, where no remnant glacial deposits are found(Gruffaz, 1997). In the upper catchment, large old rock de-posits flooring the west side hillslope (Fig. 4) have dramati-cally influenced the bottom topography, and thus the channelnetwork, resulting in a conjunction of four first-order debrisflow channels deeply incised down to the bedrock in sev-eral reaches. The upper catchment can therefore be subdi-vided into five subcatchments in terms of sediment recharge(Fig. 2). Bed entrenchment is now constrained by checkdams. However, lateral erosion still occurs episodically byflooding and debris flow scouring.

The style of sediment production and delivery is some-what different throughout the headwater, according to the lo-cal morphology and the lithologic and structural setting. Themajor geomorphic processes, identified preliminarily by ob-servations from aerial photographs and field investigations,were initially characterized in a map (Fig. 4) describingthe spatial distribution of geomorphic features and sediment

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N o data (w in ter snow and ice)

N o data (w in ter snow and ice)

Debris Flow 01

Apr 09 May 09 Jun 09 Jul 09 Aug 09 Sep 09 Oct 09 Nov 09 Dec 09 Apr 10 May 10 Jun 10 Jul 10 Aug 10 Sep 10

Rai

nfal

l Int

ensi

ty (m

m

h )

0

10

20

30

40

50

60

70

80

90

Aug 09 Sep 09 Oct 09 No 09 Jun 10 Ju 10 Aug 10 Sep 10

Debris Flow 02

...

N o data

Bedloadtransport

-1

Figure 3. Maximum rainfall intensity over the monitoring period measured by a rain gauge located at the top of the torrent (see Fig. 4) andcalculated for a 5 min time interval. The mean annual precipitation is about 1500 mm in the headwater of the Manival (modified from Loye,2013).

transfer processes contributing to debris recharge in the first-order channels. The west and upper sides are dominated byrockfall. Large rock collapses delimited by persistent jointsoccur due to the progressive degradation of the slope under-neath (Loye et al., 2011). Where the slope gradient allowsscree and soil development, erosion scars can be observed;sediment sources are remobilized from discrete shallow land-slides. Depending on the location and size, rockfall can reachthe channels directly or accumulate on slopes or in ravines,before being subsequently routed to high-order segments bya combination of gravitational and hydrological processes.Towards the east, the erosion seems to be more progressivethrough the formation of gullies (Loye et al., 2012). Nearthe ridge, the slopes display mostly talus and scree depositslightly covered with vegetation, whereas the hillside belowexposes steepened rock slopes. Many active erosion scarscan be observed. They contribute debris into gullies and talusslope deposits that are subsequently entrained in channelsdownslope.

Historical records of debris flows since the 18th centuryshow a frequency of 0.3 events per year that reached the apexof the fan (Brochot et al., 2000). The largest event depositedapproximately 60 000 m3. However, the torrent experiencessmaller fluxes of debris (< 1000 m3) usually not reported inarchives. Such events can occur 2–3 times per year, wheninitiated by intense runoff (Veyrat-Charvillon, 2005). Vol-umes of debris deposited in the sediment trap for the last 25years are on average 2200 m3 yr−1, reaching a maximum of7000 m3 yr−1 in 2008 (RTM service, National Forests Office(France)).

3 Methods and data processing

3.1 Topographic monitoring using TLS

The terrain was surveyed with an ILRIS-3D laser scanner(Optech Inc.). This device provides a range of up to 1.2 kmfor 80 % reflectivity surface, and the instrumental precision

Table 1. Dates of TLS acquisitions. Note that for the analysis, thesecond survey was merged with the first one (see text for details).

Monitoring Start and end dates Period IDperiod (MP) of survey

First 01/04/2009–12/07/2009 MP1Second 12/07/2009–30/08/2009 merged with MP1Third 30/08/2009–11/11/2009 MP2Fourth 11/11/2009– 08/07/2010 MP3

is about 7 mm/100 m range for both distance and position(Optech Inc.). The overall coverage of the upper catchmentwith TLS point clouds required 50 scans using a 20 % surfaceoverlap. These scans were collected over a 5-day period fromnine individual viewpoints to ensure a full 3-D rendering ofthe topography. Particular attention was given to irregular re-gions and major breaks in slope, such as rock couloirs anddeep-cut gullies. Using multiple scanning locations allowedus to limit shadow zones and increase the point cloud densityof the scanned area. A series of four surveys was performedfor each season during 2009, and one extra survey was per-formed in July 2010 to analyse the effect of the precedingwinter period (Table 1). The monitoring setup remained sim-ilar for all surveys. Post-processing of the TLS raw data wasdone using Polyworks (InnovMetric). Erroneous points andvegetation were filtered manually, ensuring a total controlof the removed data to preserve a high density of pointsin topographic features with small radii curvature. Althoughthis procedure is time consuming, box (semi-)automatic ap-proaches to filter vegetation accurately still remain in a stageof development for dissected mountain morphology (Broduand Lague, 2012). Each of the multiple scans of a survey wasmerged with another one using common tie points of perma-nent topographic features and the dataset was processed as 12standalone sub-datasets, rather than all processed together.Given the size of the monitored area, dividing the point cloud



T

T

T

T

Uncovered

Covered with vegetation

Covered with forest

Scree-mantled hillslope

1st order debris flow channelT

Scouring / bank erosion

Debris flow deposit

Debris flow

FreshActive or dormant

Erosion scarOlder to ancient

Rock couloirs / scree hollow

Rock slopes

Cliffs (shaded relief )

Bedrock slopes

Debris deposit (active)

Rock collapse deposits

Debris slide (fresh)

0 125 250m

X

X Rain gauge

Figure 4. Geomorphic process map (contour interval: 20 m) illustrating the spatial pattern of sediment sources and transfer in the first-orderchannel complex. Note the impressive rock collapse deposits now crossed by four first-order debris channels. Their bed incision is stronglyconstrained by a series of check dams (marked as black “T” on the map), but erosion scars all along the deposit suggest that the reaches arestill subject to lateral erosion.

into smaller datasets avoids the propagation of inaccuracythrough large co-registered scan series. ICP (iterative clos-est point) algorithms (Besl and McKay, 1992), which min-imize the distance between two point clouds, were used todetermine the best alignment of subsets surveyed at differ-ent times in order to obtain the best co-registration within atime series. The same procedure was applied between subsetpoint clouds and a point cloud derived from a commercialairborne laser scanner (mean density: 6.9 pts m−2) and ac-quired in June 2009 to place the TLS data into the standard

Lambert projection coordinate system used in France. Theinitial survey point cloud data were set as the surface modelof reference. Each successive survey was georeferenced ontothis reference using ICP. The topographic change occurringbetween two successive surveys is too localized to influencethe global co-registration within two survey data subsets con-sisting of millions of data points, hence the alignment accu-racy. More details about multiple scan registration techniquesand point cloud time series comparison can be found in Op-pikofer (2009). The generated surface produced by the above



Table 2. TLS data and surface coverage characteristics of the five subcatchments from the first monitoring period (MP1). As the view pointsand parameters of acquiring remained similar, the values are essentially the same for all surveys.

Surface∗ Lidar data survey Scanned area∗

Subcatchment Total Vegetation Number Mean Mean Mean Total Percentage of the non-name (km2) cover (%) of points spacing (m) range (m) density (pts m−2) (km2) vegetated surface

Col du Baure 0.29 43.0 37 625 236 0.055 131 340 0.11 84Roche Ravine 0.30 20.5 43 736 412 0.071 278 251 0.17 79Manival 0.35 9.1 40 192 976 0.096 349 141 0.28 90Grosse Pierre 0.08 9.0 9 703 449 0.110 447 145 0.07 97Genièvre 0.35 26.6 19 886 472 0.108 311 109 0.18 79

Production zone 1.36 22.7 151 144 545 0.081 275 219 0.82 84∗ Topographic surface area.

procedure has a point spacing ranging from 2.5 to 18 cm ac-cording to the distance of acquisition. A maximum range ofabout 800 m was reached on the top peak of the catchmentwith a point cloud density of 25 pts m−2. The surface cov-erage of our surveys represents 84 % of the deforested areaunder investigation (Table 2).

3.2 Topographic change identification andcharacterization

The active geomorphic features within two successivedatasets were identified on a point-by-point basis using theshort-distance neighbouring point search algorithm (Bitelliet al., 2004) that computes, in 3-D, the shortest differencevectors between the points of two datasets. The vector signindicates the net change direction of topography, i.e. surfaceof erosion or deposition. A set of points (cluster) was con-sidered active if at least eight adjacent points of similar signdisplayed an absolute difference above the limit of detection(LoD, see Sect. 3.4). Each active feature was outlined visu-ally using the point cloud of difference (Fig. 5a). The pointclusters of both survey datasets, which correspond to the to-pography of the active features, were extracted according totheir spatial extend coordinates and each detected geomor-phic feature was labelled as follows:

1. rock slope erosion, characterized by rockfall or rock-slides;

2. hillslope erosion, specifically the reworking of loose orcompacted debris on slope, in gullies, and in channels;

3. deposition, including material aggradation initiated byboth rock slope failure (new production) and remobi-lization of debris.

Using the images captured by the TLS integrated camera,clusters of points not corresponding to geomorphic processactivity, such as snowmelt, were ignored.

3.3 Volume computation of each geomorphic feature

As the volume of active features cannot be directly computedby differencing TLS point datasets, the active features of twosuccessive point clouds must be interpolated into continuoussurfaces (DEM). Gridded model (or raster) is regarded as be-ing the most effective type of model to use for irregularly dis-tributed datasets, which sometimes contain few or no points(El-Sheimy et al., 2005), as can be the case for rockfall anderosion scars. The algorithm chosen for the interpolation ofthe DEM has little influence on the final result, as TLS dataprovide an extremely dense coverage of the detected objects(Anderson et al., 2005). Therefore, they were interpolated us-ing linear inverse distance weighting (Burrough and McDon-nell, 1998) and generated in a regular grid separately. Thegrid spacing and direction of interpolation were designed ina specific way for each feature: the coordinate system of ref-erence was replaced by a local orthogonal system where thex–y axes represent the average plane of topography nearby(Fig. 5b). This new reference frame was defined using eigen-value decomposition of the covariance matrix of the pointcloud of reference (Shaw, 2003). Interpolating the surface el-evation in the direction of local topography allows the gener-ation of a realistic DEM independent of slope steepness and,thus, a close realistic representation of topography in the caseof overhanging features. The cell size was defined accord-ing to the point spacing distribution of both datasets. A se-ries of tests revealed that setting the grid spacing at 68 % ofthe cumulative frequency distribution of point spacing pro-vides a continuous surface reconstruction while keeping ahigh degree of detail from the point cloud. This ensures anaccurate volume computation of geomorphic features. Thevolume was computed as the sum of the cell difference in el-evation (both positive and negative) between the successiveDEMs. Absolute cell differences lying below a given thresh-old (see Sect. 3.4) were not considered. This volume compu-tation using a local deterministic method of interpolation andan adaptive gridding approach was developed in the Matlabnumerical computing environment.



Volume computation=

sum of the cell difference in elevation between the

successive DEMs

Dataset 1 Dataset 2

-0.5

0

23

45

67

8

01

-1

0

1

32

-0.5

0

23

45

67

8

01

-1

01

23

0

- 0.005

- 0.01

- 0.015

Global coordinate

systemy

x

zLocal

coordinate system

x’

z’y’x

y

z

=>

Projection of the datasets into the local coordinates

system +

Regular grid interpolation using IDW

Coordinate system trans-formation into the average orientation of the slope

Active feature detection on the sequential point clouds using 3-D point clouds of difference

A

B

C

D

Data point extraction of the pre- and post-topography of the feature

Scene

TLS dataset

Point cloud of difference

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

-0.05

-0.10

-0.15

-0.20

-0.25

-0.30

-0.35

-5.00 m

5.00 m...

.

.

.

LoD

Hillslope erosion

Rockfall

Scale : wide = 200 m height = 300 m

(a0(a)

(b)

Figure 5. 3-D detection (a) and schematic illustration (b) of the extraction and volume computation method of an individual active featureprovided by two successive point cloud datasets.



Histogram statistics ICP : Georeferencing point cloud April 2009–July 2010

Subcatch. name Manival

Mean ( µ ) 0.000104

SD ( σ ) 0.060718

Minimum (min) -0.212658

Maximum (max) 0.212658

Peak 48593

Number of Points 3526850

µ σmin max

Figure 6. Distribution of the distance between two survey point clouds after the process of georeferencing using the ICP procedure. Thedistance approaches normal distribution with a zero mean, showing that errors generated by multiple scan registration and point cloud surveygeoreferencing are Gaussian, random, and independent. Data are given in metres.

Table 3. Registration and georeferencing standard deviations (in centimetres) of the position uncertainty on a point by point basis that wasused to derive the LoD at 95 % confidence interval and subsequently to detect topographic changes down to a certain minimum volume ofgeomorphic features.

Sub- 2σ co-registered 2σ co-georeferencing 2σ Taylor uncertainty∗

catchment (cm) (LoD) (cm) (cm)

name(σdreg =

√σ 2dPC1+ σ 2

dPC2

)Survey Monitoring period Monitoring period

First Second Third Fourth First Second Third First Second Third

Col du Baure 1.9 1.7 1.5 1.5 5.9 6.9 6.9 5.1 4.5 4.2Roche Ravine 3.2 2.9 2.6 2.7 8.4 9.4 9.0 8.6 7.7 7.5Manival 4.6 4.1 3.0 3.4 9.6 10.2 12.2 12.3 10.2 9.1Grosse Pierre 4.1 3.0 3.3 3.3 10.6 10.6 12.2 10.2 8.9 9.3Genièvre 3.7 3.6 3.2 3.6 6.7 7.6 8.3 10.3 9.6 9.6

∗ PC: point cloud used to generate the map (point cloud) of difference in 3-D.

3.4 Point cloud accuracy and limits of detection of thegeomorphic features

A reliable identification of erosion and deposition featuresrequires the definition of a LoD, where the change in eleva-tion between successive point clouds can be considered realas opposed to noise. Each TLS data point theoretically has aunique precision depending on the range and laser incidenceangle (Buckley et al., 2008). In practice, the individual pointprecision of a scan can be assumed to model a surface witha global uniform uncertainty, considering the very high pointdensity (Abellàn et al., 2009). Given the homogeneity of sur-face error and considering that the distance between sequen-tial points at a position (x, y) should tend to 0, the accuracyof TLS data can be estimated by substituting the precision ofeach data point by a singular measurement of the error as-sociated with the entire point distribution across the surface(Lane et al., 2003). Hence, the uncertainty related to bothscan registration and point cloud georeferencing, the instru-

mental error included, was defined by the standard deviationof the distance (σd) between the points (Fig. 6). The LoD wastherefore set at 2σ of the co-georeferencing and correspondsto the 95 % confidence limit (Table 3). Comparison with theapproach considering the error propagation for all uncertain-ties associated with each point cloud and assuming a normaldistribution of the error in distance (Taylor, 1997) shows thatthe uncertainties considered here are consistent.

In the case of volume computation, information on ele-vation uncertainty associated with each point cloud surveyneeds to be extended to the DEM on a cell-by-cell basis. Forany grid cell (i, j ) generated by the interpolation of adjacentpoints p with independent elevation, the uncertainty of a cellelevation can be considered the standard deviation (σe) of thedata points elevation, where σei, j = σep/

√n according to the

equation of standard error of the mean, n being the number ofpoints to define the cell elevation. The elevation uncertainty



for each cell in a DEM of difference is then expressed by

σ1ei, j =

√(σ1 ei, j

)2+(σ2ei, j

)2. (1)

The volume uncertainty is then calculated by summing upthe derived volume uncertainty of each cell of the feature asfollows:

1vfeature = a

√√√√ n∑i=1

n∑j=1

(σ1ei, j

)2 , (2)

with a= cell area. The smallest detectable volume is about10−3 m3 (10× 10× 10 cm) (Table 3) but can reach up to0.006 m3 (25× 25× 10 cm) depending on the point spacingat maximum range. Topographic change detection and vol-ume computation accuracy depend not only on the qualityof the TLS data, such as point density and post-processing-related inaccuracy. They also depend on the complexity ofthe surface geometry, like in our case, by integrating therange in position of all data points defining each grid cellvalue of a feature. Monitoring the hillslope activity is alsolimited by the ability of the process to create a distinct to-pographic change. Consequently, the deposition of individ-ual small rockfalls was not always detected, as detached rockmasses fragment into smaller pieces that are below the LoD.A similar issue was observed for erosion processes within de-bris. Nevertheless, most of the material accumulation couldbe related to upslope landslides or scouring. The sedimentbudgets were therefore kept in volumetric units, as they arecommensurate for a consistent analysis. They were not con-verted to mass, although this would make more sense forcomparing hillslope processes and rock slope yields. Suchconversion requires an accurate density value of each sur-face process, whose approximations introduce additional un-knowns. Deposition related to rock failures may therefore beslightly overrepresented in the sediment balance, althoughthis could be partly compensated for by a limited detectionof small features.

3.5 Sediment budgets of the Manival torrent

Monitoring of the coarse-sediment transfer has been per-formed all along the main torrent channel to the sedimenttrap located downstream on the alluvial fan. The in-channelstorage change was established after every noticeable flowevent, using the morphological approach based on cross-section survey techniques (Ashore and Church, 1998), andthe volume of sediment deposited in the sediment trap wasmeasured by TLS survey differencing. Sequential volumesof recharge enable us to study the influence of debris supplyfrom the production zone through the seasons. The character-istics and observational analysis of this event-based monitor-ing were documented in detail in Theule et al. (2012, 2015)and are therefore not described any further.

3.6 Estimation of debris production rate

A rate of debris production for the study period is obtainedfrom the total volume of rock slope erosion. An objectiveestimation can be deduced by characterizing the cumulativedistribution of rockfall volumes with a power law as follows(Gardner, 1970):

N (v > V )= aV −b. (3)

N is the rockfall frequency for a volume ν greater than Vand a and b are constants. a depends on the study size and onrock slope properties, whereas b tends to be rather site inde-pendent (Dussauge-Peisser et al., 2002; Dewez et al., 2011).Considering that rock slope process activity causing rockfalldoes not fluctuate much over time, the inventory analysis canbe used to infer the frequency of the occurrence of largerevents. This is done by integrating the rockfall frequencyderivative n(v)= dN

dV over the range of potential volumes.The estimation of the total volume Vt per unit time that canbe expected on average over a longer period of observationis therefore expressed by (modified from Hantz et al., 2002)

Vt =

n(Vmax)∫n(Vmin)

V dn=−ab

Vmax∫Vmin

V ×V −b−1dV =

− ab

Vmax∫Vmin

V −bdV =−ab

(1− b)V 1−b

∣∣∣∣∣∣∣∣Vmin

Vmax

. (4)

The goodness of fit of the power law was evaluated with theχ2 test (Taylor, 1997) and the standard deviation of values aand b was determined with the maximum likelihood estimate(Aki, 1965). The erosion rates are assessed by dividing Vtwith the surface prone to rockfall.

4 Results: hillslope process activity monitoring

4.1 First monitoring period (April–August 2009)

The topographic changes recorded from July to August 2009did not show any relevant geomorphic activity (only a fewsmall rockfalls). These results were therefore merged withthe preceding monitoring period.

Rock slope activity is dominated by individual small rock-falls distributed throughout the upper catchment. Only fewevents exceed 1 m3, such that contributions in terms of de-bris production are marginal in most parts of the catchment(Fig. 7). The most significant geomorphic activity was lo-cated almost exclusively in the major gullies of Baure andGrosse Pierre ravines and consists essentially of debris scour-ing of a few 100 m3 redeposited further down. Material re-entrainments were also observed in several other smaller gul-lies, but their volumes are relatively small. The rock couloirsof the Genièvre subcatchment and the scar of the old rock



deposit barely showed any geomorphic activity. The channelsdisplayed a net incision (−636 m3

± 43) in the upper reaches.Bedload aggradation remains very low (+90 m3

± 6). Belowthe upper confluence, the channel trunk exhibits a mixed pat-tern of zones of erosion (−60 m3

± 2), such as gravel-wedgescouring, and zones of redeposition of entrained material(+80 m3

± 4) induced by bedload transport.

4.2 Second monitoring period (September–November2009)

Rock slope activity remains similar in spatial extent andvolumes to the previous survey period, but rockfall fre-quency is higher (Fig. 8). Hillslope process activity was morewidespread on the east side but more localized on the west-ern valley walls, while the rock couloirs showed no geo-morphic activity. In the upper headwater, material rework-ing was concentrated almost exclusively in the steep trib-utary gullies. They displayed scouring of a relatively largevolume (−357 m3

± 12). Deposition features along the thal-weg were almost inexistent (+18 m3

± 1.3). In the south-east, not only the Baure Ravine (net erosion: −61 m3

± 8)but the whole series of hillside gullies exhibited signs of ac-tivity, such as erosional segments alternating with deposition.On scree slopes, several minor areas with erosional rills andtheir associated debris deposits were observed, some of themreaching the channel trunk (+42 m3

± 2). Such small hillsidedebris flows were probably triggered by sediment entrain-ments within the rills, as no evidence of sliding at their headwas observed. The channels show a net erosion upstream(−482 m3

± 18), whereas continuous incisions were morepronounced in the Manival channel (−443 m3

± 16) and alsoin the Roche Ravine (−40 m3

± 3). Deposition zones werealmost completely absent (15 m3

± 1.3). Towards the up-per confluence, the lower segments of the Manival channelexhibited continuous zones of aggradation (97 m3

± 6) thatwere scoured on one side. This morphology is characteristicof closed-process debris flow levees and run-up zones besidethe incised channel bed. Below the upper confluence, chan-nel bed cut (−40 m3

± 2) and fill (+16 m3± 1) was sparse

and concentrated at the junction with hillside gullies. Sucha pattern of bed reworking demonstrates the connectivity ofthe Baure gully series with the channel trunk.

4.3 Third monitoring period (November–July 2010)

This period showed an important increase in rock slope ero-sion, both in frequency and magnitude, resulting from theoccurrence of large slope failures and enhanced localizedrockfall activity, for instance in rock walls made of calcare-ous marl situated directly above the Manival (2035 m3

± 39)and the Roche Ravine (256 m3

± 17) channels (Fig. 9). Mostof the debris collapses supplied the channel directly; therest was temporarily deposited in breaks in the slope. Thelower headwater part showed a great fluctuation as well

(Genièvre: 116 m3; Grosse Pierre: 145 m3). At the top of theBaure Ravine, 816 m3

± 25 of rock fragments contributedsubstantially to recharge the sediment storage at the gullyhead. Below, debris infilling was continuously scoured. A1170 m3

± 18 rockslide is responsible for a large channelinfill in the Manival subcatchment. Several other smallerrockfalls contributed to the recharge of tributary gullies andscree hollows. In the Roche Ravine, debris deposits weresparse because rockfall remained of a low magnitude onaverage (571 events < 1 m3), although frequency was high(578 events). The large debris infill at the channel head wascaused by two erosion scars in the gullies (270 m3

± 14 and65 m3

± 4). In the rock couloirs of the Genièvre subcatch-ment, a significant accumulation of material from landslidesand rockfalls was observed (remnant volume: 204 m3

± 13),taking into account that the hillslope erosion represents450 m3 (±14). In the Grosse Pierre Ravine, 343 m3

± 17 ofdebris were accumulated at the rock couloir outlet, recharg-ing the scree slope above the channel head. In the Col duBaure, relatively large aggradation in the lower part of tribu-tary gullies was observed (remnant volume: +142 m3

± 2),resulting from material entrainment. Several debris slideswere also detected on scree slopes, without any contact withthe channel trunk.

The upper channel reaches were clearly depositional, asa consequence of large slope failures. The Manival chan-nel showed a continuous zone of remnant accumulation of948 m3 (±18) of which a portion was carried along down-stream as bedload. Towards the confluence, erosion domi-nated (−487 m3

± 19) over deposition (+25 m3± 3). In the

Roche Ravine, a continuous zone of erosion in the scar ofthe old rock deposit produced debris accumulation mostly onthe slope. But a landslide of 190 m3

± 9 reached the channel.Overall, aggradation was observed all along the channel head(+148 m3

± 18) and scouring was limited (−65 m3± 4).

From the confluence downstream, the channel behaviouris dominantly erosional (−97 m3

± 4) almost without anyaggradation (+3± 0.3 m3).

4.4 Rock slope production inventory

Over the 16 months, 1866 rockfalls with volumes rang-ing from 10−4 to 103 were recorded. This yields a total of3575 m3

± 30 and an erosion rate of 3.1 mm yr−1, given thetopographic surface area of rock faces. The inventory followsa power law (Fig. 10) with a 99 % confidence level for eventslarger than 3 m3 (χ2 value= 17.3). For events larger than1 m3, the power law is accepted at the 95 % confidence level(χ2 value= 5.89). Both threshold volumes provide a b valueclose to 0.81± 0.06. Considering only the volumes above10 m3 (25 events) gives a b value of 0.76. Below 0.1 m3,the observed frequency deviated clearly from the power lawregime until the rollover reached an approximately constantrate for the smallest volumes. According to our inventory,rockfall of more than 1 m3 is expected 153± 11 times per



1 monitoring periodst

April 2009–September 2009

±

0 250125Metres

Rock slope erosionVolume (m3)

LoD - 0.25

0.25 - 0.75

0.75 - 5.0

5.0 - 23

23 - 25

DepositionVolume (m3)

LoD - 2.0

2.0 - 5.0

5.0 - 15

15 - 60

60 - 270

Hillslope erosionVolume (m3)

LoD - 5

5 - 20

20 - 50

50 - 200

200 - 220

No lidar coverage

Figure 7. Geomorphic activity revealed by comparing the topographic differences of the two successive TLS surveys operated in April andAugust 2009. The sediment budgets for each subcatchment are detailed in Fig. 13.

2 nd monitoring periodSeptember–November 2009

±

0 250125Metres

Rock slope erosionVolume (m3)

LoD - 0.15

0.15 - 0.50

0.50 - 1.00

1.00 - 4.00

4.00 - 6.00


LoD - 0.25

0.25 - 2.0

2.0 - 5.0

5.0 - 15

15 - 25


LoD - 3.0

3.0 - 10

10 - 30

30 - 50

50 - 75

No lidar coverage

Figure 8. Geomorphic activity revealed by comparing the topographic differences of the two successive TLS surveys operated in Augustand November 2009. The sediment budgets for each subcatchment are detailed in Fig. 14.



3 monitoring periodrd

November 2009–July 2010

±

0 250125Metres

[m 3]

[m 3]

[m 3]

Volume (m3)LoD - 10

10 - 60

60 - 300

300 - 700

700 - 1100


LoD - 25

25 - 100

100 - 200

200 - 600

600 - 1200


LoD - 5

5 - 15

15 - 60

60 - 180

180 - 270

No lidar coverage

Rock slope erosion

Figure 9. Geomorphic activity revealed by comparing the topographic differences of the two successive TLS surveys operated in November2009 and July 2010. The sediment budgets for each subcatchment are detailed in Fig. 15.

Table 4. Sediment budget (in cubic metres) of the Manival torrent established after noticeable events using the morphological approach afterTheule et al. (2012). The torrent recharge (sediment input) is estimated from in-storage changes in channels and volumes deposited in thesediment trap (output).

Monitoring Survey dates in the torrent Sediment Storage Channel Channel Sediment Totalperiod output change erosion deposition input sediment Input

First no. 1 06/07/2009–28/08/2009 1873± 62 −2034± 559 5232± 136 3199± 63 0–63 0–63

Second no. 2 30/08/2009–07/10/2009 0 789± 84 1409± 31 2197± 53 736–842934–1102

no. 3 08/10/2009–12/11/2009 302± 36 −73± 66 1546± 36 1473± 31 198–260

Third no. 4 13/11/2009–01/06/2010 580± 45 −580± 81 1961± 45 1372± 36 0–36174–844∗no. 5 02/06/2010–08/06/2010 3320± 176 −3052± 272 7658± 178 4606± 93 0–537

no. 6 09/06/2010–08/10/2010 819± 46 −608± 82 2246± 46 1637± 36 174–246

∗ The TLS survey of the third monitoring period (MP3) lasted until 08/07/2010; no. 6 was not considered for the analysis of the sediment budgets.

year on average. The largest event (1170 m3) occurs every 2years, and the 1-year return period rockfall has a volume ofapproximately 465 m3. Considering only these classes of vol-umes of the inventory (see Table 6), the rock slope productionreaches a rate of 3678 m3 yr−1

± 210 (4 mm yr−1± 0.3).

4.5 Torrent in-channel storage changes

Two debris flows with multiple surges and several remark-able bedload transport events were observed in the main tor-rent during the survey period (Theule et al., 2012). A debrisflow occurred on the 25 August 2009, caused by a short-

duration rainstorm. The volume of sediment eroded in thetorrent (5232 m3

± 136) is equivalent to the volume that wasredeposited in both the torrent itself and the sediment trap(5072 m3

± 125), suggesting that the majority of entrainedmaterial was stored in the torrent (Table 4). Sediment inputfrom the headwater can be considered marginal. Before that,no significant torrent activity was observed, despite a seriesof rainfall events with low to moderate intensity. In Septem-ber 2009, a long period of moderate rainfall intensity causedmaterial reworking by bedload transport all along the torrent.However, no sediment was supplied to the sediment trap. A



10-4

10-2

100

102

104

10-1

10

101

102

103

104

105

106

Volume of events [m3]

a: 153.1 b: 0.81± 0.06

Cumul. volume distribution Fitted power law

Excluded data

Data of the overallsurvey period

0

10-4

10-2

100

102

104

10-1

100

101

102

103

104

105

106


Nor

mal

ized

ann

ual f

requ

ency

[num

ber

yea

r ]

a: 256.5b: 0.80 ± 0.07


Data of the 3rd monitoring period

10-4

10-3

10-2

10-1

100

101

10210

0

101

102

103

104

105


Nor

mal

ized

ann

ual f

requ

ency

[num

ber

yea

r ]

a: 62.9

b: 0.86 ± 0.1

Cumul. volume distribution

Data of the 2nd monitoring period

10-3

10-2

10-1

100

101

102

100

101

102

103

104

105


Nor

mal

ized

ann

ual f

requ

ency

[num

ber

yea

r ]

a: 34.1 b: 0.94 ± 0.11


Data of the 1st monitoring period

Nor

mal

ized

ann

ual f

requ

ency

[nu

mbe

r y

ear

]

Excluded data

Excluded data Excluded data

(a) (b)

(c) (d)

-1

-1

Fitted power law

-1

-1

Figure 10. Cumulative volume distribution of the rockfall observed during the first (a), the second (b), and the third monitoring period (c) andover the entire study time of 16 months (d). For each dataset, the power law is fitted for volumes larger than 0.1 m3. Below this thresholdvolume, the distribution exhibits a rollover that progressively reaches an almost constant frequency for the smallest detected volumes.

net gain of storage in the headwater was therefore inferred.In October, a succession of low-intensity rainfall events trig-gered sediment transport in the torrent that accumulated inthe sediment trap with a volume of at least 302 m3

± 36. Thesediment budget indicates clearly a recharge of 229 m3

± 31,a transfer of debris that was stored mostly in the distal part ofthe torrent. Throughout the winter, a gradual incision was ob-served all along the torrent, resulting from frequent periodsof low-intensity rainfall as well as snowmelt. Due to main-tenance (dredging), the sediment trap was disturbed and noreliable data were available. In any case, no sign of signif-icant sediment activity was detected. A new debris flow on6 June deposited 3320 m3

± 176 in the sediment trap. Thistime, a certain supply of sediment from the headwater wasobserved (∼ 270 m3). This event was followed by a series ofintense rainfall events without much reworking in the dis-tal part, suggesting that any significant transfer occurred intothe torrent downstream. The in-torrent storage changes andestimated recharge budgets are shown for each monitoringperiod in Fig. 11.

5 Synthesis

The overall transfer dynamics, from debris source zone tothe apex of the fan, are illustrated in Fig. 12. The vol-umes detected during the 16-month study period reveal anet export of 3378 m3

± 361 of sediment from the headwa-ter to the main torrent (Table 5). The overall rock slopeyield is 3575 m3

± 30 for a volume of erosion reaching3129 m3

± 150 on the hillside and 1809 m3± 92 in the chan-

nel complex. The volume of deposition, induced by both de-bris production and material reworking, yields a total vol-ume of 5135 m3

± 251, of which only 1382 m3± 56 (27 %)

is linked to the channel complex. In the main torrent, the sed-iment transfer was relatively large (∼ 20 000 m3; net storagechange −4950 m3

± 118) and essentially related to the oc-currence of two debris flows (Theule et al., 2012), deplet-ing significantly the in-torrent sediment storage of the distalparts (entrainment zone). Material deposited in the sedimenttrap for the survey period yields 6075 m3

± 45. During the



0

1000

2000

3000

4000

5000

3

0

1000

2000

3000

4000

5000

0 500 1000 1500

Downstream Distance from Alluvial Storage Apex (m)

3

1873 (±62)m 3

-20

-15

-10

-5

0

5

10

15

20


3

-1

-20

-15

-10

-5

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5

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-1

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0 500 1000 1500

3

-1

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-15

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-5

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-1

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1000

2000

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4000

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0

1000

2000

3000

4000

5000

3

0

1000

2000

3000

4000

5000

3

(06/07/2009 - 28/08/2009) (debris-flow)

(30/08/2009 - 07/10/2009)

(08/10/2009 - 12/11/2009)

(13/11/2009 - 01/06/2010)

(02/06/2010 - 08/06/2010) (debris-flow)

736-842 m 3

0 m 3

198-260 m 3

266-338 m 3

535-625 m 3

0-537 m 3 3320 (±176)m

3

Downstream distance from production zone Downstream distance from production zone

In -channe l s torage change per un it lengthBlue: eposition Red: rosion Black: alance

S edim ent transportCumulative olume

S edim ent deposition

0

1000

2000

3000

4000

5000

3

0

1000

2000

3000

4000

5000

0 500 1000 1500

3

1873 (±62)m 3

-20

-15

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-1

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-15

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0 500 1000 1500

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-1

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-5

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-1

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3

(06/07/2009 - 28/08/2009)

(30/08/2009 - 07/10/2009)

(08/10/2009 - 12/11/2009)

(13/11/2009 - 01/06/2010)

(02/06/2010 - 08/06/2010)

Entrainment zone

736-842 m 3

0 m 30 m 3

198-260 m 3

266-338 m 3266-338 m 3

535-625 m 3535-625 m 3

-3320 (±176)m3

3

Downstream

In -channe l s torage change per un it lengthBlue: deposition Red: erosion Black: balance

S edim ent ransportCumulative volume

S edim ent trap

1st

MP

2nd

MP

3rd

MP

April-August 2009

-7 0 0 0

-5 0 0 0

-3 0 0 0

-1 0 0 0

1 0 0 0

3 0 0 0

5 0 0 0

7 0 0 0

Hills ide 1 -2 OrderC hanne l

E ntra inm entZone

TransportZone

D epositionZone

Volu

me

Cha

nge

(m3)

Net B alanc e

Net E ros ion

Net Depos it ion

TorrentHeadw ater

September-November 2009

-7000

-5000

-3000

-1000

1000

3000

5000

7000

Hills ide 1 -2 OrderC hanne l

E ntra inm entZone

TransportZone

D epositionZone

Volum

e Cha

nge (

m3)

Net B alanc eNet E ros ionNet Depos it ion

He a dw a te r To rre nt

November 2009 - July 2010

-7 0 0 0

-5 0 0 0

-3 0 0 0

-1 0 0 0

1 0 0 0

3 0 0 0

5 0 0 0

7 0 0 0

Hills ide 1-2 O rderC hannel

E ntrainm entZone

Trans portZone

D epos itionZone

Volm

ue C

hang

e (m

3)

Net B alanc eNet E ros ionNet Depos it ion

Headw ater

Fig. 6 Storage changes for the three peri-ods showing sediment transfers from debris source zone to the apex of the fan.

Deposition zone

To rre nt

Headwateroutlet

Sedimenttrap

0 537 m 3

Transport zone

(debris flow)U

nit v

olum

e ch

ange

(m m

)U

nit v

olum

e ch

ange

(m m

)U

nit v

olum

e ch

ange

(m m

)U

nit v

olum

e ch

ange

(m m

)U

nit v

olum

e ch

ange

(m m

) (debris flow)

Sedi

men

t tra

nspo

rt (m

)Se

dim

ent t

rans

port

(m )

Sedi

men

t tra

nspo

rt (m

)Se

dim

ent t

rans

port

(m )

Sedi

men

t tra

nspo

rt (m

)

Figure 11. Torrent in-channel storage changes per unit length and sediment budgets of cumulative volumes transported in the torrent fromthe headwater outlet to the sediment trap downstream for each monitoring period (MP). The torrent recharge (sediment input) was estimatedgiven the in-storage change and the volume deposited in the sediment trap (see Table 4 for details on values) (modified from Theule et al.,2012).

autumn, bedload transport of hundreds of square metres con-tributed to sediment recharge throughout the torrent.

In the spring–midsummer period, the hillside sedimentbudget yields a total rock slope production of 99 m3

± 6,for a volume of erosion of −547 m3

± 50 and deposi-tion of +408 m3

± 35 (Table 5). This suggests that about238 m3

± 61 of material was supplied the channel complex,originating almost exclusively from material re-entrainmentin gullies (Fig. 13). The sediment budget of the channels indi-cates a significant reduction in storage (−487 m3

± 44), com-prising large and continuous incisions (−636 m3

± 43) in

the upper reaches and material aggradation (+149 m3± 11)

in the lower reaches resulting mostly from zones of tran-sient redeposition. This results in a recharge of the torrentof +726 m3

± 103 for this survey period.During the late summer–autumn season, the total volume

of hillside erosion is −640 m3± 27, due to a widespread

scouring of the tributary gullies located east and south-eastof the headwater (Fig. 14). The total volume of rock slopeproduction (50 m3

± 3) and deposition (+182 m3± 12) re-

mained low. Overall, the sediment budget indicates that thehillslope contributed about 510 m3

± 30 of sediment to the



Rock slope

Hillside

1–2 order channels

Torrent trunk

Sediment trap

3575 m3

R

3129 m3

ED

3753 m3

1809 m3

E

D1382 m3

- 4950 m3

ΔS

+ 3378 m3

ER

D

Rockfall Erosion

Deposition ΔS Storage change

Net headwater sed. input

Net torrent sed. output

(a) (b)April 2009–July 2010

+ 6075 m3

Headwater(production

zone)

Torrent(Zone of transfer)

33

April–August 2009

-7 0 0 0

-5 0 0 0

-3 0 0 0

-1 0 0 0

1 0 0 0

3 0 0 0

5 0 0 0

7 0 0 0

Hills ide 1–2 Orderchanne l

E ntra inm entzone

Transportzone

D epositionzone

Volu

me

chan

ge (m

)

Net balanc e

Net eros ion

Net depos it ion

TorrentHeadw ater

September–November 2009

-7000

-5000

-3000

-1000

1000

3000

5000

7000

Hills ide 1–2 Orderchanne l

E ntra inm entzone

Transportzone

D epositionzone

Volum

e cha

nge

(m )

Net balanc eNet eros ionNet depos it ion

He a dw a te r To rre nt

November 2009–July 2010

-7 0 0 0

-5 0 0 0

-3 0 0 0

-1 0 0 0

1 0 0 0

3 0 0 0

5 0 0 0

7 0 0 0

Hills ide 1–2 O rderchannel

E ntrainm entzone

Trans portzone

D epos itionzone

Volm

ue c

hang

e (m

)

Net balanc eNet eros ionNet depos it ion

Headw ater

Fig. 6 Storage changes for the three peri-ods showing sediment transfers from debris source zone to the apex of the fan.

To rre nt

33

3

Figure 12. Overall sediment budget (a) and net sediment balance (b) for each monitoring period showing the overall transfer dynamics fromdebris source zone in the headwater to the apex of the fan through the torrent observed during the period of investigation.

channel reaches (Table 5). The sediment budget of the chan-nels yields−522 m3

± 20 of erosion for+127 m3± 13 of de-

position. This is characterized by bedload reworking in bothlow-order and trunk channels and a progressive transfer of+904 m3

± 51 of material into the torrent.During winter–spring 2010, a total deposition volume of+3163 m3

± 147 is recorded on the hillside for an erodedvolume of −3129 m3

± 150. A relatively large produc-tion of debris (3424 m3

± 89) is observed (Table 5). Thenet sediment balance on the hillside yields a supply of+2203 m3

± 187 of sediment into the channels, and thenet sediment balance for the channel complex indicates anincrease in in-channel sediment storage of +455 m3

± 47for a total volume of deposition of 1105 m3

± 36 and ero-sion of 651 m3

± 29 due to large bed scouring zones in thedownstream reaches. Sediment transfer into the torrent is1749 m3

± 199 (Fig. 15).

6 Discussion

6.1 Debris supply through rock slope production

Debris production from rock walls shows a strong seasonalpattern. The great majority of recorded rock instabilities inboth magnitude (95 %) and frequency (75 %) occurred dur-ing the cold period. Previous studies of the calcareous cliffsnear Grenoble, which have a similar morphotectonic context,revealed that freeze–thaw cycles are the main triggering fac-tor of rockfall (Frayssines and Hantz, 2006). Ice jacking cancause microcrack propagation, leading to failure (Matsuokaand Sakai, 1999). Along the eastern ridge, the bedrock sur-face is often highly fractured, suggesting frost shattering. Thespatial pattern of rockfall also strongly suggests a tectonic-lithological influence that can be explained by differentialerosion between the successive limestone and marl beds. Inthe rock wall series on the west side, the monoclinal config-uration of the bedding, combined with a strong difference of



Table 5. Overall headwater sediment budget recorded during the three survey periods and net sediment balance of the 16 months of moni-toring. Sediment budgets for each catchment subsystem are detailed in the Supplement.

First monitoring Volume total (m3)period Hillside Channel Headwater

Rockfall 99.4± 5.9 99.4± 5.9Deposition 408.2± 35.4 149.2± 10.9 557.4± 46.3Erosion 547.2± 49.5 636.4± 43.3 1183.5± 92.8

Subtotal −238.3± 61.2 −487.2± 44.7 −725.6± 103.9

Second monitoring Volume total (m3)period Hillside Channel Headwater


Subtotal −508.5± 29.9 −395.3± 23.4 −903.7± 50.9

Third monitoring Volume total (m3)period Hillside Channel Headwater


Subtotal −2203.0± 187.4 454.7± 46.5 −1748.3± 199.2

Total Volume total (m3)monitoring Hillside Channel Headwater


Subtotal −2949.8± 264.9 −427.8± 106.9 −3377.6± 361.4

competency between stratigraphic sequences, gives rise to anoverhanging formation highly susceptible to failure. On theeast side, the bedding is mostly cataclinal and approaches dipslope, depending on the slope. Rock failures initiated by pla-nar sliding on bedding planes were observed.

The observed debris production follows a power law dis-tribution in a range covering at least 3 orders of magnitude[100–103]. The exponent b is slightly higher than the aver-age value reported for the Grenoble cliffs ([0.4–0.7]; Hantz,2011) but is in agreement with other short inventories cover-ing a lower range of volume ([10−2–102]; Hungr et al., 1999;Dussauge et al., 2003). Inventories dominated by small vol-umes tend to increase the b value, compared to the ones cov-ering rather large volumes (Stark and Hovius, 2001). Above100 m3, the deviation from the power law may be attributedto the short period of sampling for events of such a largemagnitude. The rollover encountered towards small volumesresults most likely in the under-detection of the number ofevents. This sampling bias is far above the minimum volumeof detection (0.006 m3); therefore, another behaviour char-acterizing the failure of small volumes cannot be excluded.

This may take the form of a physical erosion process thatdiffers from the one influencing larger instabilities, whichare controlled primarily by the geometrical and geomechan-ical properties of the rock mass (Selby, 1993; Sauchyn et al.,1998), and tectonic weakening (Cruden, 2003; Coe and Harp,2007). As observed here, low-magnitude rockfall events rep-resent a low proportion of overall debris supply, even thoughthey vary locally from 1 or 2 orders of magnitude in vol-ume over time. The total amount of sediment available isonly significantly influenced by high-magnitude instabilities(Fig. 16).

Previous sediment budgets derived from topographic mea-surement using stereophotogrammetry estimated the highesterosion rates over an average of 40 years to range from 10.8to 17.8 mm yr−1 in the headwater (Veyrat-Charvillon andMemier, 2006). Given the large uncertainty of the approach,and the fact that they measured the hillslope and thalweg geo-morphic activity, these values are broadly consistent with theerosion rate derived here from a short-period rockfall inven-tory by assuming the possible occurrence of rockslide mag-nitudes [106–107]. Considering that the power law is valid



Figure 13. Overall headwater sediment budget observed during the first monitoring period revealing the sediment dynamics through thespring–summer season and the net balance of sediment recharge in the downstream torrent for several months preceding the August 2009debris flow.

for larger slope failures, a 7500 m3 event can be expected ev-ery 10 years and a 120 000 m3 event every 100 years. Theaverage debris production ranges between 5587± 241 and12 903± 305 m3 yr−1, assuming a maximum potential ero-sion of 105 and 107 m3 respectively over several centuries(Table 6). No historical Manival rockslide exists to supportthis estimation. The large old rock deposit (∼ 6.1 Mm3) ofthe upper catchment is the largest detected event, but it mayhave formed from several rock collapses. The rockfall in-ventory of the Grenoble cliffs reports volumes smaller than105 m3 for the last century and 107 m3 since the 17th century(Hantz et al., 2003). Such a magnitude is also likely at theManival. A mean rate of rock slope erosion of approxima-tively 10 mm yr−1. 10 000 m3 yr−1 can be therefore expectedin the upper catchment over the century.

Upstream from the Manival channel, the scouring of debrisslopes and scree hollows triggered by rock slope productionaccounted for about 40 % of the net erosion recorded duringthe autumn period and 25 % in the Baure Ravine over theentire study period. The spatial pattern of geomorphic workshowed that hillslope process activity was observed princi-pally in gullies and scree slopes situated directly below ac-tive rock walls. The dominant mode of debris supply in theManival headwater is therefore highly episodic, implying agreat spatial heterogeneity in sediment recharge rates.

6.2 Debris supply through hillslope activity

As rock slope activity was very limited from spring toautumn, hillslope geomorphic activity dominated sediment



Figure 14. Overall headwater sediment budget observed during the second monitoring period revealing the sediment dynamics and the netbalance of sediment recharge in the downstream torrent during the autumn.

recharge during this period. Until the end of August, hill-side gullies and low-order channels remain almost inactivein terms of sediment delivery. Conversely, the autumn pe-riod was characterized by a general increase in the intensityof geomorphic activity. Continuous scouring and the relativepaucity of deposition features from hillside gullies as well asclear incisions and micro debris flows in channel reaches in-dicate that mobilized material was almost entirely entraineddownstream by runoff. For the entire area, the hillside contri-bution represents on average a volume 5 times larger than thevolume that was observed in spring and summer, and channelbed reworking was of a much larger magnitude as well.

During winter–spring 2010, the total volume of depositionrecorded on the hillside significantly exceeds the rate of de-position recorded so far, resulting from the huge increase in

debris production that can be attributed to the winter accord-ing to observations carried out in the preceding spring. Hill-slope and gully erosion remain on average comparable to thevolumetric transfer of sediment observed in the preceding au-tumn, implying a clear connectivity.

These negative sediment balances in all sediment cas-cade components suggest a very high degree of connectiv-ity between hillside and channels in autumn, and hillside fandeposits observed in early spring along low-order channelbanks reflect an effective hillslope–channel coupling. Thisdiffers from effective sediment transfer occurring mostly dur-ing the summer (e.g. Berger et al., 2011; Cavalli et al., 2013).



Figure 15. Overall headwater sediment budget observed during the third monitoring period revealing the sediment dynamics through thewinter–spring and the net balance of sediment recharge in the downstream torrent for the period preceding the June 2010 debris flow.

6.3 Sediment recharge of the torrent

The sediment input, back-calculated from the in-torrent stor-age changes, is consistent with the net sediment outputrecorded from the headwater for the first two survey periods.In the torrent, the morphological monitoring that started inJuly revealed almost no sediment recharge (< 70 m3) and iscoherent with observations made in the summer in the uppercatchment. The headwater sediment output must have accu-mulated before, probably mobilized as bedload by commonrunoff events in spring. In autumn, both budgets are approx-imately equal (1018± 84 m3 against 904 m3

± 51), consid-ering that few segments between both entities are missingand that both budgets were in volumetric units, despite hav-ing different sediment densities. The morphological budget

indicates that the torrent experienced a net recharge in thedistal part and emphasizes the clear connectivity from theproduction zones to the torrent, as mentioned before. In thethird survey period, the headwater sediment balance indi-cates a net export of debris (1749 m3

± 199), whereas themorphological monitoring detected no significant volumesof debris entering the main torrent. Even the recharge (sed-iment input, Fig. 11) measured during the June debris flowevents (< 600 m3) remains far below the transfer of sedi-ment recorded upstream in the headwater. This discrepancymay result from material deposition occurring in the non-monitored segments at the headwater outlet. But field stud-ies did not confirm this. The analysis of past series of sed-iment budgets performed in the upper Manival catchment



0 10000

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Figure 16. Continuous lines: erosion rate as function of size of events for a certain volume of production (potential maximum volume V1...9),considering that rockfall volume distribution observed at Manival follows power law behaviour (Table 6). Dashed lines: contribution of eachclass of volumes to the erosion rate showing the significant effect of large slope failures. For a maximum eroded volume of 3600 m3 yr−1

(V1), the 1000 m3 rockfall event contributes 60 %, while events less than 100 m3 induce less than 20 % of erosion, although they are of amuch higher frequency; a 100 000 m3 rockslide would generate 70 % of total eroded material of 500 000 m3 (V7) over a century.

(Veyrat-Charvillon, 2005) reveals that the spring–early sum-mer time currently exhibits a period of recharge followinga phase of discharge within a short time lapse dependingon the hydrometeorological and snowmelt conditions. Themost reasonable explanation is therefore the relatively longtime interval between measurements, such as the successivereworking of bedload transport suppressing the cut and fillpattern and masking the short-term behaviour of sedimenttransfer in the torrent. This is a well-known issue when work-ing with channelized hillslope processes (Fuller and Marden,2010). Although this monitoring aspect concerns the topo-graphic changes recorded by TLS in the headwater as well,geomorphic activity, such as micro debris flows and contin-

uous channel bed degradation, strongly suggests phases ofsediment recharge preceding the debris flow events, whichwould be consistent with other studies (e.g. Brayshaw andHassan, 2009; Marchi et al., 2002, Bennett et al., 2012).

6.4 Possible causes of seasonal fluctuations in debrissupply

The Manival headwater experienced low geomorphic ac-tivity through the summer, and consequently low sedimentrecharge of the torrent, even though rainstorms were of suf-ficiently high intensity to trigger debris flows of significantmagnitude in torrent. Considerations of the temporal pattern



Table 6. Rock slope debris production rate estimated from the inventory analysis using power law distribution of volume for potentialrockfall (Fig. 10).

Class of 10−3–10−2 10−2–10−1 10−1–1 1–10 101–102 102–103 103–104 104–105 105–106 106–107

volume (m3)

Measured 143 (112.5) 742 (583.7) 789 (620.7) 168 (132.2) 19 (14.95) 3 (2.36) 1 (0.79)frequency (per year)

Calculated 36 990± 5621± 854± 130± 19.7± 3.0± 0.46± 0.069± 0.011± 0.0016±frequency 4366 581 86 9.6 1.2 0.14 0.015 0.0013 1× 10−4 1.2× 10−5

Cumulative 1467 1355 772 152 19 3.1 0.79measured frequency

Cumulative 43 619± 6629± 1007± 153± 23± 3.5± 0.54± 0.08± 0.01± 0.0016±calculated 5043 677 97 11 1.58 0.198 0.018 0.0014 1.1× 10−4 1.2× 10−5

frequency

Fallen volume 102± 155± 236± 358± 544± 827± 1257± 1911± 2904± 4413±per year (m3) 12 16 19 26 32 37 39 32 8 51

Total fallen 298± 454± 689± 1047± 1592± 2419± 3676± 5587± 8491± 12 903±volume per 43 59 79 105 136 172 210 241 249 305year (m3)

Cliff area 826 804 m2 (only the topographic rock slope surface)

Erosion rate 0.36± 0.54± 0.83± 1.3± 1.9± 2.9± 4± 6.8± 10.2± 15.6±(mm) 0.05 0.07 0.1 0.1 0.2 0.2 0.3 0.3 0.3 0.4

of sediment transfer and the analysis of erosion features, likealternating areas of scouring and infilling in gullies, suggestthat runoff still has an important role in the headwater sed-iment dynamics. A clear relation between sediment trans-fer magnitude and precipitation remains complex, however(Fig. 3), as is often the case in mountainous catchments (VanSteijn, 1996; Bovis and Jakob, 1999; Pelfini and Santilli,2008). The enhanced geomorphic activity observed in thehillside of several headwater subsystems, for instance dur-ing the autumn period, induced a simultaneous yet highlyheterogeneous response in their channel reaches. A signifi-cant increase in bed incision and reworking similar to debrisflow was observed in the upper reaches of the Manival sub-catchment, implying an important sediment transfer. In con-trast, the activity of other channel reaches was reduced byhalf, e.g. in Roche Ravine, or even remained geomorphicallymuch less active, with only little sediment recharge.

Considering that meteorological conditions were similar,this opposite behaviour may only be explained by a certaindepletion of debris availability. This reduction in sedimentyield can come not only within a supply-limited regime ofthe contributing area (Jakob et al., 2005; Glade, 2005) butalso from the fact that check dams, like bedrock-dominatedreaches, inhibit channel bed incision. Hence, the sedimentstorage has to be refilled either from the contributing hill-side or from the upstream mass movement. A similar obser-vation can be drawn from the Grosse Pierre Ravine sedimentbudget, whose gully downslope remained completely discon-nected from the head of the subcatchment over the entirestudy period. Although this ravine is very steep and incises

the large old rock deposits, no geomorphic work was ob-served, resulting most likely from the absence of debris sup-ply from upstream. Hillside sediment delivery seems there-fore to be clearly a limiting factor to sediment yield fromlow- to high-order channels and thus to the sediment rechargerate of the debris flow torrent downstream. As the occurrenceof bedload transport and micro debris flows is controlled pre-dominantly by the availability of sediment, even very intenserainstorm-derived runoff does not automatically lead to a sig-nificant transfer of sediment from the hillside to low-orderchannels in the case of material depletion.

Nevertheless, this behaviour is somehow equivocal, con-sidering the fact that the transport capacity of ephemeralstream runoff and sheetwash related to high-intensity rain-storms is larger than the one generated by low-intensitylong-duration rainfall, above all, when gully material (likein Manival) can be characterized as coarse and poorly sortedrockfall-fragment-derived debris. Lenzi et al. (2003) inter-preted the annual fluctuation in sediment yield as the ef-fect of sediment source destabilization or reactivation fol-lowing a high-magnitude flow event, which facilitates mate-rial entrainment by subsequent runoff. Johnson and Warbur-ton (2006) refer to the influence of sediment source charac-teristics in the control of hillslope sediment discharge. Theexplanation may be that the 25 August rainstorm dramat-ically altered the debris sources in a way that the autumnrainfalls – which, although they were of lower intensity, hada longer flood time – were able to transfer sediment downs-lope. Excess pore-fluid pressure in debris deposits can per-sist for days to weeks after sediment emplacement (Major



and Iverson, 1999; Major, 2000), making debris depositsgeotechnically less stable.

Although they depend on the local geomorphological set-ting, such as slope gradient, local topographic hollow, anddegree of convergence (Reneau et al., 1990; Stock and Diet-rich, 2006; Mao et al., 2009), these observations tend to showthat long-lasting rainfall reduces the stability of the coarsesurface layer that armours the gullies and scree slopes. Thisin turn affects the amount of debris supply from the hillside,despite the flow capacity and sediment availability.

7 Conclusions

This investigation of a yearly pattern of sediment dynam-ics underlines the fact that the seasonal cycle of sedimentdischarge from the headwater supplying the Manival torrentwith debris consisted of two phases of recharge: one phase inearly spring, linked to enhanced debris production and runoffconditions, and a second phase in autumn, during long peri-ods of rainfall. Furthermore, the occurrence of the debris flowevents was conditional on a net sediment delivery toward thetorrent.

Overall, the torrent effectiveness seems to be controlledearly in the year, from winter to spring, by sediment pro-duction and later in the year by the ability of hydrologicaleffects to weaken the remnant debris sources, with debrisavailability being only one of the limiting factors at the Mani-val torrent. The rate of sediment delivery, directly recharg-ing both hillside and low-order channels, is controlled byhigh-magnitude slope failure of moderate frequency whichoccurred mostly during winter time. Consequently, materialre-entrainment concentrates locally in specific tributary gul-lies. The delivery of sediment to the torrent may be related tothe hydrometeorological conditions since the last rainstormrather than to flow capacity directly. Low-order reaches con-tribute significantly to the sediment delivery mechanism ofthe catchment headwater by controlling storage and routingprocesses. Hence, the recharge threshold required for a newdebris flow to occur at the Manival depends primarily on theshort-term debris supply, partly derived from the rate of rockslope sediment production and partly derived from mobiliz-ing debris on the hillside. The rate of sediment recharge inthe torrent is, however, greatly intermittent, since productionand entrainment are both highly stochastic processes. Thisregime of headwater sediment delivery may have been iden-tified in other nearby mountain environments, but very lit-tle literature exists (Alvarez and Garcia Ruiz, 2000; Veyrat-Charvillon, 2005; Berger et al., 2011) that has explored thetimescale of sediment discharge in sufficient detail, e.g. on aseasonal basis.

Debris flow magnitudes have so far been mostly deter-mined based on volume estimates derived from past events,reducing the susceptibility analysis to the known history.Monitoring of the in-storage changes within the torrentlinked to the debris supply can help to improve knowledge

on the recharge threshold leading to debris flow activity andtherefore on their prediction. According to the rock slopeproduction observed in this study, 10 000 m3 yr−1 of de-bris supplying the headwater channels can be expected inManival over a century. Despite the multiplicity of sedimentsources and the mode of transfer operating on different spa-tial and temporal scales, the pattern of processes govern-ing the sediment dynamics can be considered precisely ona seasonal basis using TLS techniques. Therefore, maximumsediment discharges from the torrent system can be speci-fied. Without direct measurement of the rate of sediment fluxand of the coupling between hillslope and channel processes,this cannot be rigorously determined. The timing of sedimentbudget monitoring is, however, a crucial aspect for their laterinterpretation.

The Supplement related to this article is available onlineat doi:10.5194/esurf-4-489-2016-supplement.

Acknowledgements. The authors would like to thank theircolleagues at IGAR and IRSTEA Grenoble (ex. CEMAGREF),in particular A. Pedrazzini and M.-H. Derron, for their valuablecomments during the preparation of this publication. This studywas entirely supported by the University of Lausanne, except forthe event-based cross-section surveys that were funded by thePôle Grenoblois d’étude et de recherche pour la prévention desrisques naturels. The ONF-RTM38 is acknowledged for makingthe access to the upper Manival catchment easier. This publicationbenefited from an interactive discussion with O. Sass and two otheranonymous reviewers and from proofreading by S. Conway.

Edited by: S. Conway

References

Abellàn, A., Jaboyedoff, M., Oppikofer, T., and Vilaplana, J. M.:Detection of millimetric deformation using a terrestrial laserscanner: experiment and application to a rockfall event, Nat.Hazards Earth Syst. Sci., 9, 365–372, doi:10.5194/nhess-9-365-2009, 2009.

Aki, K.: Maximum likelihood estimate of b in the formula logN= a-bM and its confidence limits, B. Earthq. Res. I. Tokyo, 43, 237–239, 1965.

Alvarez, B. and Garcia-Ruiz, J.: Variability of sediment yield froma high mountain catchment, central Spanish Pyrenees, Arct.Antarct. Alp. Res., 32, 478–484, 2000.

Anderson, E. S., Thompson, J. A., and Austin, R. E.: LiDAR den-sity and linear interpolator effects on elevation estimates, Int. J.Remote Sens., 26, 3889–3900, 2005.

Ashmore, P. E. and Church, M. A.: Sediment transport and rivermorphology: A paradigm for study, in: Gravel-Bed Rivers in theEnvironment, edited by: Klingeman, P. C., Beschta, R. L., Ko-mar, P. D., and Bradley, J. D., Water Resource Publications, LLC,Highlands Ranch, CO, USA, 115–148, 1998.


http://dx.doi.org/10.5194/esurf-4-489-2016-supplement

http://dx.doi.org/10.5194/nhess-9-365-2009



Benda, L. and Dunne, T.: Stochastic forcing of sediment supply tochannel networks from landsliding and debris flow, Water Re-sour. Res., 33, 2849–2863, 1997.

Bennett, G. L., Molnar, P., Eisenbeiss, H., and McArdell, B. W.:Erosional power in the Swiss Alps: characterizing slope failureat the head of the Illgraben, Earth Surf. Proc. Land. 37, 1627–1640, doi:10.1002/esp.3263, 2012.

Berger, C., McArdell, B. and Schlunegger, F.: Sediment transferpatterns at the Illgraben catchment, Switzerland: Implication forthe time scales of debris flow activities, Geomorphology, 125,421–432, 2011.

Berti, M., Genevois, R., LaHusen, R., Simoni, A., and Tecca,P. R.: Debris flow monitoring in the acquabona watershed on theDolomites (Italian alps), Phys. Chem. Earth. Pt. B, 25, 707–715,2000.

Besl, P. J. and McKay, N. D.: A method for registration of 3-Dshapes, IEEE T. Pattern Anal., 14, 239–256, 1992.

Bitelli, G., Dubbini, M. and Zanutta, A.: Terrestrial laser scanningand digital photogrammetry techniques to monitor landslide bod-ies, in: Proceedings of the XXth ISPRS Congress Geo-ImageryBridging Continents, XXXV, part B5, Istanbul, Turkey, 12–23July 2004, ISPRS, 246–251, 2004.

Bovis, M. J. and Jakob, M.: The role of debris supply conditions inpredicting debris flow activity, Earth Surf. Proc. Land., 24, 1039–1054, 1999.

Brayshaw, D. and Hassan, M. A.: Debris flow initiation and sedi-ment recharge in gullies, Geomorphology, 109, 122–131, 2009.

Bremer, M. and Sass, O.: Combining airborne and terrestrial laserscanning for quantifying erosion and deposition by a debris flowevent, Geomorphology, 138, 49–60, 2011.

Brochot, S., Coeur, D., Lang, M. and Naulet, R.: Historique – Isèreet torrents affluents. Utilisation de l’information historique pourune meilleur définition du risque d’inondation (rapport), Cema-gref/Achtys, Lyon–Grenoble, 248 pp., 2000.

Brodu, N. and Lague, D.: 3-D terrestrial lidar data classification ofcomplex natural scenes using a multi-scale dimensionality crite-rion: Applications in geomorphology, ISPRS J. Photogramm. 68,121–134, 2012.

Buckley, S., Howell, J., Enge, H., and Kurz T. : Terrestrial laserscanning in geology: data acquisition processing and accuracyconsiderations, J. Geol. Soc. London, 165, 625–638, 2008.

Burrough, P. and McDonnell, R.: Principals of Geographic Infor-mation Systems, Oxford University Press, Oxford, UK, 333 pp.,1998.

Cannon, S. H., Gartner, J. E., Parret, C., and Parise M.: Wildfire-related debris flow generation through episodic progressivesediment-bulking process, western USA. In: Debris-flow hazardmitigation: Mechanics, Prediction, and Assessment,edited by:Rickenmann, D. and Chen, L., Millpress, Rotterdam, the Nether-lands, 71–82, 2003.

Cavalli, M., Trevisani, S., Comiti, F., and Marchi, L.: Geomor-phometric assessment of spatial sediment connectivity in smallAlpine catchments, Geomorphology, 188, 31-41, 2013.

Chandler, J. H. and Brunsden, D.: Steady state behaviour of theBlack Ven mudslide : the application of archival analytical pho-togrammetry to studies of landform change, Earth Surf. Proc.Land., 20, 255–275, 1995.

Charollais, J., Dondey, D., Ginet, C., Lombard, A., Muller, J. P.,Rosset, J., and Ruchat, C.: Carte géol. France (1/50.000◦),Feuille Domène (33-34), Orléans, B.R.G.M, 1986.

Coe, J. A. and Harp, E. L.: Influence of tectonic folding on rockfallsusceptibility, American Fork Canyon, Utah, USA, Nat. HazardsEarth Syst. Sci., 7, 1–14, doi:10.5194/nhess-7-1-2007, 2007.

Coe, J. A., Whitney, J. W., and Harrington, C. D.: Photogrammet-ric analysis of Quaternary hillslope erosion at Yucca Mountain,Nevada, Geol. Soc. Am. Astr. Progr., 25, 1993.

Cruden, D. M.: The shapes of cold, high mountains in sedimentaryrocks, Geomorphology, 55, 249–261, 2003.

Decaulne, A. and Saemundsson, P.: Spatial and temporal diver-sity for debris-flow meterotological control in subarctic oceanicperiglacial environments in Iceland, Earth Surf. Proc. Land., 32,1971–1983, 2007.

Dewez, T. and Rohmer, J.: Probabilistic rockfall hazard: empir-ical computation based on ground-based lidar observations inMesnil-Val, Normandy. Journée Aléa Gravitaires, 7–8 Septem-bre 2011, Strasbourg, France, 104–115, 2011.

Dussauge, C., Grasso, J.-R., and Helmstetter, A.: Statistical Anal-ysis of Rock Fall Volume Distributions: Implications for RockFall Dynamics, J. Geophys. Res.-Sol. Ea., 108, 2–11, 2003.

Dussauge-Peisser, C., Helmstetter, A., Grasso, J.-R., Hantz, D.,Desvarreux, P., Jeannin, M., and Giraud, A.: Probabilistic ap-proach to rock fall hazard assessment: potential of histori-cal data analysis, Nat. Hazards Earth Syst. Sci., 2, 15–26,doi:10.5194/nhess-2-15-2002, 2002.

El-Sheimy, N., Valeo, C., and Habib, A.: Digital terrain model-ing: acquisition, manipulation, and applications, Artech House,Boston, MA, USA, 257 pp., 2005.

Frayssines, M. and Hantz, D.: Failure mechanisms and trigger-ing factors in calcareous cliffs of the Subalpine Ranges (FrenchAlps). Eng. Geol. 86: 256–270, 2006.

Fuller, I. C. and Marden, M.: Rapid channel response to variabil-ity in sediment supply: Cutting and filling of the Tarndale Fan,Waipaoa catchment, New Zealand, Mar. Geology, 270, 45–54,2010.

Gardner, J. S.: Rockfall: a geomorphic process in high mountainterrain, The Albertan Geographer, 6, 15–20, 1970.

Gidon, M.: Géologie de la Chartreuse – Sentiers de la Chartreuse:Circuit de la Dent de Crolles, Association “A la découverte duPatrimoine de Chartreuse”, publ. 1d, 1 edn., 20 pp., Fig. 9, avail-able at: www.Geol-alp.com (last access: 18 April 2014), 1991.

Glade, T.: Linking debris-flow hazard assessments with geomor-phology, Geomorphology, 66, 189-213, 2005.

Gruffaz, F.: Torrent du Manival (Isère), Etude de basin et de la plagede dépôts torrentiels (rapport), RTM/ONF, Grenoble, France,66 pp., 1997.

Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfallintensity–duration control of shallow landslides and debris flows:an update, Landslides, 5, 3–17, 2008.

Hantz, D.: Quantitative assessment of diffuse rock fall hazardalong a cliff foot, Nat. Hazards Earth Syst. Sci., 11, 1303–1309,doi:10.5194/nhess-11-1303-2011, 2011.

Hantz, D., Dussauge-Peisser, C., Jeannin M., and Vengeon, J.-M.:Rock fall Hazard: from expert opinion to quantitative evaluation.Symposium “Geomorphology: from expert opinion to modeling(April 2002)”, Strasbourg, France, 115–122, 2002.


http://dx.doi.org/10.1002/esp.3263



www.Geol-alp.com



Hantz, D., Vengeon, J. M., and Dussauge-Peisser, C.: An his-torical, geomechanical and probabilistic approach to rock-fallhazard assessment, Nat. Hazards Earth Syst. Sci., 3, 693–701,doi:10.5194/nhess-3-693-2003, 2003.

Hooke, J.: Coarse sediment connectivity in river channel systems:a conceptual framework and methodology, Geomorphology, 56,79–94, 2003.

Hungr, O.: Characterizing debris flows for design of hazard mit-igation, in: Proceedings of the 5th International Conferenceon Debris Flow Hazards Mitigation/Mechanics, Prediction, andAssessment, Padua, Italy, edited by: Genevois, R., Hamilton,D. L., and Prestininzi, A., Italian Journal of Engineering Ge-ology and Environment, Book of abstracts, Keynote lecture, 5,doi:10.4408/IJEGE.2011-03.B-001, 2011.

Hungr, O., Evans, S. G., and Hazzard, J.: Magnitude and frequencyof rockfalls along the main transportation corridors of southwest-ern British Columbia, Can. Geotech. J., 36, 224–238, 1999.

Hungr, O., McDougall, S., and Bovis, M.: Entrainment of materialby debris flows. Chapt. 7, in: Debris Flow Hazards and RelatedPhenomena, edited by: Jakob M. and Hungr O., Springer Verlag,Heidelberg, Germany, in association with Praxis Publishing Ltd,135–158, 2005.

Jaboyedoff, M., Oppikofer, T., Abellàn, A., Derron, M. H., Loye,A., Metzger R., and Pedrazzini, A.: Use of lidar in landslide in-vestigations: a review, Nat. Hazards., 61, 5–28, 2012.

Jakob, M., Bovis, M., and Oden, M.: The significance of chan-nel recharge rates for estimating debris-flow magnitude and fre-quency, Earth Surf. Proc. Land., 30, 755–766, 2005.

Johnson, R. M. and Warburton, J.: Variability in sediment supply,transfer and deposition in an upland torrent system: Iron Crag,northern England, Earth Surf. Proc. Land., 31, 844–861, 2006.

Johnson, R. M., Warburton, J., Mills, A. J., and Winter C.: Evaluat-ing the significance of event and post-event sediment dynamicsin a first order tributary using multiple sediment budgets, Geogr.Ann. A, 92, 189–209, 2010.

Lane, S. N., Westaway, R. M., and Murray Hicks, D.: Estimationof erosion and deposition volumes in a large, gravel-bed, braidedriver using sysnotpic remoe sensing, Earth Surf. Proc. Land., 28,249–271, 2003.

Lenzi, M. A., Mao, L., and Comiti, F.: Interannual varia-tion of suspended sediment load and sediment yieldin an alpine catchment, Hydrolog. Sci. J., 48, 899–915,doi:10.1623/hysj.48.6.899.51425, 2003.

Loye, A.: Budgeting rockfall and modeling sediment delivery in tor-rent systems, PhD Thesis, University of Lausanne, Switzerland,448 pp., 2013.

Loye, A., Jaboyedoff, M., Pedrazzini, A., Theule, J., Liébault, F.,and Metzger, R.: Morphostructural analysis of an alpine debrisflows catchment: implication for debris supply, in: Proceedingsof the 5th International Conference on Debris Flow Hazards Mit-igation/Mechanics, Prediction, and Assessment, Padua, Italy, 7–11 June 2011, edited by: Genevois, R., Hamilton, D. L., and Pres-tininzi, A., Italian Journal of Engineering Geology and Environ-ment, 115–126, doi:10.4408/IJEGE.2011-03.B-014, 2011.

Loye, A., Jaboyedoff, M., Pedrazzini, A., Theule, J., Liébault, F.,and Metzger, R.: Influence of bedrock structures on the spatialpattern of erosional landforms in small alpine catchments, EarthSurf. Proc. Land., 37, 1407–1423, 2012.

Major, J. J.: Gravity-driven consolidation of slurries – implicationsfor debris-flow deposition and deposit characteristics, J. Sedi-ment. Res., 70, 64–83, doi:1073-130X/00/070-64/03.00, 2000.

Major, J. J. and Iverson, R. M.: Debris-flow deposition – effectsof pore-fluid pressure and friction concentrated at flow margins,Geol. Soc. Am. Bull., 111, 1424–1434, 1999.

Mao, L., Cavalli, M., Comiti, F., Marchi, L., Lenzi, M. A., andAratto, M.: Sediment transfer processes in two Alpine catch-ments of contrasting morphological settings, J. Hydrol., 364, 88–98, 2009.

Marchi, L., Arattano, M., and Deganutti, A. M.: Ten years of debris-flow monitoring in the Moscardo Torrent (Italian Alps), Geomor-phology, 46, 1–17, 2002.

Matsuoka, N. and Sakai, H.: Rockfall activity from an alpine cliffduring thawing period., Geomorphology, 28, 309–328, 1999.

Oppikofer, T.: Detection, Analysis and monitoring of slope move-ments by high-resolution digital elevetion models. PhD Thesis,Inst. of Geomatics and Risk Analysis, University of Lausanne,Lausanne, Switzerland, 2009.

Peiry, J. L.: Les torrents de L’Arve: dynamique des sédiments etimpact de l’aménagement des bassins versants sur l’activité tor-rentielle, Rev. Geogr. Alp., 78, 25–58, 1990.

Pelfini, M. and Santilli, M. Frequency of debris flowsand their relation with precipitation: A case study inthe Central Alps, Italy, Geomorphology, 101, 721–730,doi:10.1016/j.geomorph.2008.04.002, 2008.

Perroy, R. L., Bookhagen, B., Asner, G. P., and Chadwick, O. A.:Comparison of gully erosion estimates using airborne andground-based LiDAR on Santa Cruz Island, California, Geomor-phology, 118, 288–300, 2010.

Péteuil, C., Maraval, C., Bertrand, C., and Monier, G.: Torrent duManival: Schéma d’aménagement et de gestion du basin ver-sant contre les crues, techn. report (unpubl.), Office Nationaldes Forêts, Service de Restauration des terrains en Montagnesde l’Isère, Grenoble, France, 107 pp., 2008.

Reneau, S. L., Dietrich, W. E., Donohue, D. J., Jull, A. J. T., andRubin, M.: Late Quaternary history of colluvial deposition anderosion in hollows, central California Coast Ranges, Geol. Soc.Am. Bull. 102, 969–982, 1990.

Rickenmann, D.: Empirical relationship for Debris Flows, Nat. Haz-ards, 19, 47–77, 1999.

Roering, J. J., Stimely, L. L., Mackey, G. H., and Schmidt, D. A.:Using DInSAR, airborne LIDAR and archival air photos to quan-tify landsliding and sediment transport, Geophys. Res. Lett., 36,L19402, 5 pp., doi:10.1029/2009GL040374, 2009.

Sauchyn, D. J., Cruden, D. M., and Hu, X. Q.: Structural control ofthe morphometry of open rock basins, Kananaski region, Cana-dian Rocky Mountains, Geomorphology, 22, 313–324, 1998.

Schlunegger, F., Badoux, A., McArdell, B. W., Gwerder, C., Schny-drig, D., Rieke-Zapp, D., and Molnar, P.: Limits of sedimenttransfer in an alpine debris-flow catchment, Illgraben, Switzer-land, Quaternary Sci. Rev., 28, 1097–1105, 2009.

Selby, M. J.: Hillslope Material and Processes, Oxford Univ. Press,Oxford, UK, 451 pp., 1993.

Shaw, P. J. A.: Multivariate statistics for the EnvironmentalSciences, Hodder-Arnold, ISBN 0-3408-0763-6, London, UK,2003.

Smith, L. C., Alsdorf, D. E., Magilligan, F. J., Gomez, B., Mertes,L. A. K., Smith, N. D., and Garvin, J. B.: Estimation of ero-



http://dx.doi.org/10.4408/IJEGE.2011-03.B-001

http://dx.doi.org/10.1623/hysj.48.6.899.51425

http://dx.doi.org/10.4408/IJEGE.2011-03.B-014

http://dx.doi.org/10.1016/j.geomorph.2008.04.002

http://dx.doi.org/10.1029/2009GL040374


sion, deposition and net volumetric change caused by the 1996Skeidararsandur Jökulhlaup, Iceland, from SAR interferometry,Water Resour. Res., 36, 1583–1594, 2000.

Stark, C. P. and Hovius, N.: The characterization of landslide sizedistributions, Geophys. Res. Lett., 28, 1091–1094, 2001.

Stock, J. D. and Dietrich, W. E.,: Erosion of steepland valleys bydebris flows, Geol. Soc. Am. Bull., 118, 1125–1148, 2006.

Taylor, J. R.: An introduction to error analysis, University scienceBooks, Sausalito, California, USA, 2nd Edn., 327 pp., 1997.

Theule, J. I., Liébault, F., Loye, A., Laigle, D., and Jaboyedoff, M.:Sediment budget monitoring of debris-flow and bedload trans-port in the Manival Torrent, SE France, Nat. Hazards Earth Syst.Sci., 12, 731–749, doi:10.5194/nhess-12-731-2012, 2012.

Theule, J. I., Liébault, F., Laigle, D., Loye, A., and Jaboyedoff ,M.: Channel scour and fill by debris flows and bedload transport,Geomorphology, 243, 92–105, 2015.

Van Dine, D. F.: Debris flow and debris torrents in the SouthernCanadian Cordillera, Can. Geotech. J., 22, 44–68, 1985.

Van Steijn, H.: Debris-flow magnitude-frequency relationships formountainous regions of Central and Northwest Europe, Geomor-phology, 15, 259–273, 1996.

Veyrat-Charvillon, S.: Elaboration d’une méthode de prédiction duvolume maximal d’une lave torrentielle (PREVENT), DoctorThesis, Université Blaise Pascal, Clermont-Ferrant, France, 2,354 pp., 2005.

Veyrat-Charvillon, S. and Memier, M.: Stereophotogrammetry ofarchive data and topographic approaches to debris-flow torrentmeasurements: Calculation of channel-sediment states and a par-tial sediment budget for Manival torrent (Isère, France), EarthSurf. Proc. Land., 31, 201–219, 2006.

Wu, Y. and Cheng, H.: Monitoring of gully erosion on the LoessPlateau of China using a global positioning system, Catena, 63,154–166, 2005.

Zimmermann, M., Mani, P., and Romang, H.: Magnitude–frequencyaspects of alpine debris flows, Eclogae Geol. Helv., 90, 415–420,1997.



headwater sediment dynamics in a debris ﬂow catchment ......activity from the middle and lower...

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