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\begin{document}

\title{Anatomy of an Alpine bedload transport event: a watershed-scale
seismic-network perspective}



\author[1]{Gilles Antoniazza}%
\author[2]{Michael Dietze}%
\author[1]{Davide Mancini}%
\author[3]{Jens Martin Turowski}%
\author[4]{Dieter Rickenmann}%
\author[4]{Tobias Nicollier}%
\author[4]{Stefan Boss}%
\author[5]{Stuart N Lane}%
\affil[1]{University of Lausanne}%
\affil[2]{Georg-August-Universität Göttingen}%
\affil[3]{GFZ German Research Centre for Geosciences, Potsdam}%
\affil[4]{WSL}%
\affil[5]{Université de Lausanne}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
The way Alpine rivers mobilize, convey and store coarse material during
high-magnitude events is poorly understood, notably because it is
difficult to obtain measurements of bedload transport at the watershed
scale. Seismic sensor data, evaluated with appropriate seismic physical
models, can provide that missing link by yielding absolute time-series
of bedload transport. Low cost and ease of installation allows for
networks of sensors to be deployed, providing continuous,
watershed-scale insights into bedload transport dynamics. Here, we
deploy a network of 24 seismic sensors to capture the motion of coarse
material in a 13.4 km\textsuperscript{2} Alpine watershed during a
high-magnitude bedload transport event. First, we benchmark the seismic
inversion routine with an independent time-series obtained with a
calibrated acoustic system. Then, we apply the procedure to the other
seismic sensors across the watershed. Spatially-distributed time-series
of bedload transport reveal a relative inefficiency of Alpine watersheds
in evacuating coarse material, even during a relatively infrequent
high-magnitude bedload transport event. Significant inputs measured for
some tributaries were rapidly attenuated as the main river crossed less
hydraulically-efficient reaches, and only a comparatively negligible
proportion of the total amount of material mobilized in the watershed
was exported at the outlet. Cross-correlation analysis of the
time-series suggests that a faster moving water wave (re-)mobilizes
local material and bedload is expected to move slower, and over shorter
distances. Multiple periods of competent flows are likely to be
necessary to evacuate the coarse material produced throughout the
watershed during individual source-mobilizing bedload transport events.%
\end{abstract}%



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\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
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\emph{Journal of Geophysical Research JGR: Earth Surface}

Supporting Information for

\textbf{Anatomy of an Alpine bedload transport event: a watershed-scale
seismic-network perspective}

Gilles Antoniazza\textsuperscript{1,2}, Michael
Dietze\textsuperscript{3,4}, Davide Mancini\textsuperscript{1}, Jens M.
Turowski\textsuperscript{3}, Dieter Rickenmann\textsuperscript{2},
Tobias Nicollier\textsuperscript{2}, Stefan Boss\textsuperscript{2} and
Stuart N. Lane\textsuperscript{1}

\textsuperscript{1}University of Lausanne, Institute of Earth Surface
Dynamics (IDYST), Lausanne, Switzerland.

\textsuperscript{2}Swiss Federal Research Institute WSL, Mountain
Hydrology and Mass Movements, Birmensdorf, Switzerland.

\textsuperscript{3}German Research Center for Geosciences (GFZ), Section
4.6 Geomorphology, Potsdam, Germany.

\textsuperscript{4}Georg-August University G\selectlanguage{ngerman}öttingen, Faculty of
Geosciences and Geography, Göttingen, Germany.

\textbf{Contents of this file}

Text S1 to S5 Figures S2 to S3 Tables S1 to S5

\textbf{Text S1. Seismic sensor location and river morphology
parameters}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Sensor ID} & \textbf{X {[}m{]}} & \textbf{Y {[}m{]}} & \textbf{Z
{[}m a.s.l.{]}} & \(\mathbf{W}\) \textbf{{[}m{]}} &
\(\mathbf{r}_{\mathbf{0}}\) \textbf{{[}m{]}} & \(\mathbf{\theta}\)
\textbf{{[}radians{]}}\tabularnewline
\midrule
\endhead
S1 & 2'574'634.45 & 1'122'486.97 & 1'195.52 & 6 & 5 &
0.10\tabularnewline
S2 & 2'574'562.88 & 1'122'204.91 & 1'239.95 & 5 & 6 &
0.14\tabularnewline
S3 & 2'574'221.68 & 1'121'474.76 & 1'308.92 & 5 & 7 &
0.10\tabularnewline
S4 & 2'574'358.68 & 1'121'130.53 & 1'396.03 & 8 & 10 &
0.32\tabularnewline
S5 & 2'574'183.40 & 1'121'041.48 & 1'357.25 & 8 & 13 &
0.12\tabularnewline
S6 & 2'574'534.44 & 1'120'779.26 & 1'499.50 & 7 & 16 &
0.33\tabularnewline
S7 & 2'574'208.85 & 1'120'786.14 & 1'386.63 & 5 & 8 &
0.09\tabularnewline
S8 & 2'574'224.16 & 1'120'488.44 & 1'415.50 & 4 & 7 &
0.14\tabularnewline
S9 & 2'574'392.68 & 1'120'296.45 & 1'516.13 & 6 & 12 &
0.36\tabularnewline
S10 & 2'574'032.51 & 1'120'063.88 & 1'466.63 & 5 & 5 &
0.08\tabularnewline
S11 & 2'574'274.62 & 1'119'871.97 & 1'550.79 & 8 & 12 &
0.31\tabularnewline
S12 & 2'573'933.32 & 1'119'828.17 & 1'482.72 & 7 & 30 &
0.06\tabularnewline
S13 & 2'574'058.52 & 1'119'553.27 & 1'519.41 & 4 & 20 &
0.15\tabularnewline
S14 & 2'573'723.20 & 1'119'501.53 & 1'514.27 & 6 & 19 &
0.10\tabularnewline
S15 & 2'574'260.44 & 1'119'406.99 & 1'593.43 & 7 & 18 &
0.38\tabularnewline
S16 & 2'574'056.94 & 1'119'173.46 & 1'589.80 & 7 & 9 &
0.22\tabularnewline
S17 & 2'573'469.90 & 1'118'959.20 & 1'565.62 & 4 & 25 &
0.17\tabularnewline
S18 & 2'573'707.51 & 1'118'994.74 & 1'580.77 & 5 & 15 &
0.14\tabularnewline
S19 & 2'573'168.35 & 1'118'843.45 & 1770.45 & 4 & 10 &
0.41\tabularnewline
S20 & 2'574'118.72 & 1'118'844.82 & 1'689.67 & 8 & 26 &
0.31\tabularnewline
S21 & 2'573'263.68 & 1'118'679.25 & 1'707.30 & 6 & 11 &
0.31\tabularnewline
S22 & 2'573'771.07 & 1'118'623.49 & 1'665.49 & 7 & 25 &
0.24\tabularnewline
S23 & 2'573'679.65 & 1'118'370.85 & 1'757.85 & 5 & 25 &
0.41\tabularnewline
S24 & 2'573'411.55 & 1'118'360.45 & 1'808.75 & 8 & 28 &
0.48\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S1.} Seismic station coordinates (in CH1903+ Swiss
coordinate system) and river morphology parameters. \(W\)
{[}m{]} is the channel width, \(r_{0}\) {[}m{]} is the
sensor-to-river distance and\(\theta\) {[}radians{]} is the
channel average gradient.

\textbf{Text S2. Grain-size distribution parameters}

Line-by-number counts (Wolman, 1954) of b-axis mobile particles of the
bed (n = 100) were performed at each of the 24 seismic sensor locations.
Particles were partitioned into 10 classes (0.0001 -- 0.008 m, 0.008 -
0.038 m, 0.038 -- 0.08 m, 0.08 -- 0.13 m, 0.13 -- 0.192 m, 0.192 -- 0.28
m, 0.28 -- 0.4 m, 0.4 -- 0.55 m, 0.55 -- 0.7 m), and a best-fit
raised-log cosine function was fitted to each empirical sample (best of
10\textsuperscript{4} trials), following Tsai et al. (2012). Results of
the best-fit at each seismic sensor location is presented in Figure S2,
and the associated parameters \(D_{50}\) {[}m{]} and
\(\sigma_{g}\) used for the seismic inversion are provided in Table
S2.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Sensor ID} & \(\mathbf{D}_{\mathbf{50}}\) \textbf{{[}m{]}} &
\(\mathbf{\sigma}_{\mathbf{g}}\) \textbf{{[}-{]}}\tabularnewline
\midrule
\endhead
S1 & 0.06 & 0.9\tabularnewline
S2 & 0.06 & 1\tabularnewline
S3 & 0.06 & 0.8\tabularnewline
S4 & 0.06 & 0.4\tabularnewline
S5 & 0.06 & 0.5\tabularnewline
S6 & 0.06 & 0.4\tabularnewline
S7 & 0.06 & 1\tabularnewline
S8 & 0.06 & 1\tabularnewline
S9 & 0.06 & 0.9\tabularnewline
S10 & 0.05 & 0.9\tabularnewline
S11 & 0.06 & 0.7\tabularnewline
S12 & 0.06 & 0.5\tabularnewline
S13 & 0.05 & 0.7\tabularnewline
S14 & 0.06 & 0.8\tabularnewline
S15 & 0.05 & 0.7\tabularnewline
S16 & 0.06 & 0.5\tabularnewline
S17 & 0.05 & 0.8\tabularnewline
S18 & 0.06 & 0.9\tabularnewline
S19 & 0.06 & 0.4\tabularnewline
S20 & 0.06 & 0.5\tabularnewline
S21 & 0.05 & 0.5\tabularnewline
S22 & 0.06 & 0.5\tabularnewline
S23 & 0.06 & 0.9\tabularnewline
S24 & 0.06 & 0.5\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S2.} Parameters \(D_{50}\) and \(\sigma_{g}\)
of the best-fit log-raised cosine function at each of the 24 seismic
sensor location.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure S2.} Best-fit log-raised cosine distribution of 24
samples (n=100) of mobile particle of the bed at each seismic sensor
location. The thicker the line, the greater the number of seismic sensor
locations that share the same \(D_{50}\) and \(\sigma_{g}\)
values (i.e. Table S2), from a single seismic sensor location sharing
the same parameter values (i.e. thin line), up to six seismic sensor
locations sharing the same parameter values (i.e. thick line). Note that
seismic sensor locations presenting the same \(D_{50}\) and
\(\sigma_{g}\)values were estimated based on site-specific and
different line-by-number counts.

\textbf{Text S3. Ground seismic parameters}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Sensor ID} & \(\mathbf{v}_{\mathbf{p0}}\)
\selectlanguage{ngerman}\textbf{{[}m·s\textsuperscript{-1}{]}} & \(\mathbf{\xi}\)
\textbf{{[}-{]}} & \(\mathbf{K}_{\mathbf{0}}\) \textbf{{[}-{]}} &
\(\mathbf{\eta}\) \textbf{{[}-{]}}\tabularnewline
\midrule
\endhead
S1 & 482 & 0.25 & 17 & 0.14\tabularnewline
S2 & 131 & 0.27 & 10 & 0.01\tabularnewline
S3 & 447 & 0.21 & 11 & 0.15\tabularnewline
S4 & 368 & 0.34 & 8 & 0.31\tabularnewline
S5 & 430 & 0.18 & 30 & 0.07\tabularnewline
S6 & 452 & 0.08 & 6 & 0.53\tabularnewline
S7 & 397 & 0.27 & 15 & 0.17\tabularnewline
S8 & 370 & 0.30 & 15 & 0.17\tabularnewline
S9 & 400 & 0.15 & 14 & 0.18\tabularnewline
S10 & 400 & 0.30 & 11 & 0.15\tabularnewline
S11 & 660 & 0.30 & 14 & 0.14\tabularnewline
S12 & 400 & 0.30 & 11 & 0.15\tabularnewline
S13 & 447 & 0.21 & 11 & 0.15\tabularnewline
S14 & 300 & 0.35 & 10 & 0.18\tabularnewline
S15 & 576 & 0.19 & 9 & 0.38\tabularnewline
S16 & 389 & 0.12 & 10 & 0.21\tabularnewline
S17 & 420 & 0.26 & 10 & 0.15\tabularnewline
S18 & 405 & 0.28 & 16 & 0.24\tabularnewline
S19 & 359 & 0.27 & 5 & 0.49\tabularnewline
S20 & 389 & 0.12 & 10 & 0.21\tabularnewline
S21 & 359 & 0.27 & 5 & 0.49\tabularnewline
S22 & 350 & 0.42 & 11 & 0.30\tabularnewline
S23 & 350 & 0.42 & 11 & 0.30\tabularnewline
S24 & 350 & 0.42 & 11 & 0.30\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S3.} Ground seismic parameters estimated at the different
seismic station locations based on active seismic surveys.
\(v_{p0}\) is the phase velocity of the Raleigh wave at a
frequency \(f_{0}\) = 1 Hz and \(\xi\) {[}-{]} a
dimensionless exponent used to scale a frequency-dependent decay in
seismic signal (equations {[}1a{]} and {[}1b{]} in the main manuscript).
\(K_{0}\) is a dimensionless quality factor at a frequency
\(f_{0}\) = 1 Hz and \(\eta\) {[}-{]} a dimensionless
exponent that expresses the change in the quality
factor\selectlanguage{ngerman} K with frequency (equation {[}1c{]} in the main
manuscript). Note that a single active seismic experiment encompassing
multiple sensors has been performed for the group S10/S12, S16/S20 and
S22/23/24 thanks to their geographical proximity.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure S3a.} Best-fit regression of decay in wave group velocity
with frequency (equations {[}1a{]} and {[}1b{]} in the main manuscript)
based on active seismic experiments for each of the 24 seismic sensor
locations. Each line is fitted through the average value of 20 repeated
sledgehammer impacts (see also Figure 5b in the main manuscript). The
thicker the line, the greater the number of seismic sensor locations
that share the same \(v_{p0}\) and \(\xi\) values
(i.e. Table S3), from a single seismic sensor location sharing the same
parameter values (i.e. thinner line), up to three seismic sensor
locations sharing the same parameter values (i.e. thicker line). Note
that seismic sensor locations presenting the same \(v_{p0}\) and
\(\xi\) values were estimated based on a single active
seismic experiment encompassing multiple sensors thanks to their
geographical proximity. The parameters found by Bakker et al. (2020) for
an alluvial channel (in red), and proposed by Tsai et al. (2012) for a
generic bedrock site (in blue) are presented for comparative purpose.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure S3b.} Best-fit regression of decay in seismic power with
distance, and its frequency-depedency (equations {[}1a{]} and {[}1b{]}
in the main manuscript) based on active seismic surveys for each of the
24 seismic sensor locations. Each line is fitted through the average of
20 repeated sledgehammer impacts, (see also Figure 5d in the main
manuscript). The thicker the line, the greater the number of seismic
sensor locations that share the same \(K_{0}\) and
\(\eta\) values (i.e. Table S3), from a single seismic sensor
location sharing the same parameter values (i.e. thinner line), up to
three seismic sensor locations sharing the same parameter values (i.e.
thicker line). Note that seismic sensor locations presenting the
same\(\ K_{0}\) and\(\eta\) values were estimated based
on a single active seismic experiment encompassing the location of the
different sensors. The parameters found by Bakker et al. (2020) for an
alluvial channel (in red), and proposed by Tsai et al. (2012) for a
generic bedrock site (in blue) are presented for comparative purpose.

\textbf{Text S4. Channel gradient, contributing area and bedload
transport mass measured at each of the 24 seismic sensor locations}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Sensor ID} & \textbf{Type} & \textbf{Channel gradient {[}\%{]}}
& \textbf{Contributing area {[}km\textsuperscript{2}{]}} &
\textbf{Percentage contr. area {[}\%{]}} & \textbf{Bedload transp. mass
{[}kg{]}} & \textbf{Bedload transp. mass per unit area
{[}kg/km\textsuperscript{2}{]}}\tabularnewline
\midrule
\endhead
S1 & MC & 10 & 13.4 & 100 & 0.28\selectlanguage{ngerman}·10\textsuperscript{6} &
0.02\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S2 & MC & 14 & 13.3 & 99 & 0.11\selectlanguage{ngerman}·10\textsuperscript{6} &
0.008\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S3 & MC & 10 & 13.0 & 97 & 0.07\selectlanguage{ngerman}·10\textsuperscript{6} &
0.005\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S4 & TT & 33 & 0.41 & 3 & 0.28\selectlanguage{ngerman}·10\textsuperscript{6} &
0.26\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S5 & MC & 12 & 11.6 & 87 & 2.22\selectlanguage{ngerman}·10\textsuperscript{6} &
0.19\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S6 & TT & 34 & 0.65 & 5 & 0.74\selectlanguage{ngerman}·10\textsuperscript{6} &
1.14\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S7 & MC & 9 & 10.7 & 80 & 0.39\selectlanguage{ngerman}·10\textsuperscript{6} &
0.04\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S8 & MC & 14 & 10.2 & 76 & 0.09\selectlanguage{ngerman}·10\textsuperscript{6} &
0.009\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S9 & TT & 38 & 0.42 & 3 & 0 & 0\tabularnewline
S10 & MC & 8 & 9.0 & 67 & 0.23\selectlanguage{ngerman}·10\textsuperscript{6} &
0.03\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S11 & TT & 32 & 0.6 & 4 & 1.10\selectlanguage{ngerman}·10\textsuperscript{6} &
1.83\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S12 & MC & 6 & 7.8 & 58 & 0.90\selectlanguage{ngerman}·10\textsuperscript{6} &
0.12\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S13 & TT & 15 & 1.1 & 8 & 0.64\selectlanguage{ngerman}·10\textsuperscript{6} &
0.58\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S14 & MC & 10 & 6.3 & 47 & 0.40\selectlanguage{ngerman}·10\textsuperscript{6} &
0.06\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S15 & TT & 40 & 0.42 & 3 & 0 & 0\tabularnewline
S16 & TT & 22 & 0.33 & 2 & 4.59\selectlanguage{ngerman}·10\textsuperscript{6} &
13.92\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S17 & TT & 17 & 0.92 & 7 & 3.61\selectlanguage{ngerman}·10\textsuperscript{6} &
3.92\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S18 & MC & 14 & 2.9 & 22 & 0.11\selectlanguage{ngerman}·10\textsuperscript{6} &
0.04\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S19 & TT & 43 & 0.07 & 0.5 & 0.008\selectlanguage{ngerman}·10\textsuperscript{6} &
0.12\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S20 & TT & 32 & 0.29 & 2 & 0.67\selectlanguage{ngerman}·10\textsuperscript{6} &
2.33\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S21 & TT & 32 & 0.57 & 4 & 1.27\selectlanguage{ngerman}·10\textsuperscript{6} &
2.23\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S22 & TT & 24 & 0.39 & 3 & 0.43\selectlanguage{ngerman}·10\textsuperscript{6} &
1.10\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S23 & TT & 43 & 0.43 & 3 & 0.24\selectlanguage{ngerman}·10\textsuperscript{6} &
0.55\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
S24 & TT & 52 & 1.6 & 12 & 0.07\selectlanguage{ngerman}·10\textsuperscript{6} &
0.04\selectlanguage{ngerman}·10\textsuperscript{6}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\textbf{Table S4.} Channel type (MC = main channel, TT = torrential
tributary), channel gradient, contributing area, the bedload transported
mass and the bedload transported mass per unit area at each of the 24
seismic sensor locations.

\textbf{Text S5. Lag in bedload transport time-series signal,
inter-sensor distance and associated propagation velocity}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{S\textsubscript{x} S\textsubscript{y}} & \textbf{Lag {[}min{]}}
& \textbf{r {[}-{]}} & \textbf{Dist {[}m{]}} & \selectlanguage{ngerman}\textbf{Velocity
{[}m·min\textsuperscript{-1}{]}}\tabularnewline
\midrule
\endhead
S2 S1 & 38 & 0.81 & 268 & 7\tabularnewline
S3 S2 & -46 & 0.57 & 882 & -19\tabularnewline
S4 S3 & 47 & 0.48 & 457 & 10\tabularnewline
S5 S3 & 10 & 0.5 & 469 & 47\tabularnewline
S6 S5 & 26 & 0.73 & 476 & 18\tabularnewline
S7 S5 & -6 & 0.63 & 283 & -47\tabularnewline
S8 S7 & 10 & 0.96 & 314 & 31\tabularnewline
S10 S8 & 19 & 0.91 & 534 & 28\tabularnewline
S11 S10 & 4 & 0.62 & 364 & 91\tabularnewline
S12 S10 & 10 & 0.82 & 263 & 26\tabularnewline
S13 S12 & -32 & 0.66 & 291 & -9\tabularnewline
S14 S12 & 2 & 0.86 & 426 & 213\tabularnewline
S16 S13 & -3 & 0.8 & 411 & -137\tabularnewline
S17 S14 & 5 & 0.89 & 658 & 132\tabularnewline
S18 S14 & -3 & 0.88 & 556 & -185\tabularnewline
S19 S17 & 3 & 0.35 & 290 & 97\tabularnewline
S20 S16 & 6 & 0.89 & 343 & 57\tabularnewline
S21 S17 & 11 & 0.48 & 303 & 28\tabularnewline
S22 S18 & 10 & 0.72 & 409 & 41\tabularnewline
S23 S18 & 4 & 0.8 & 651 & 163\tabularnewline
S24 S18 & 73 & 0.78 & 716 & 10\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S5.} Lag {[}min{]} maximizing the cross-correlation r
{[}-{]}, inter-sensor distance {[}m{]} and associated propagation
velocity {[}m\selectlanguage{ngerman}·min\textsuperscript{-1}{]}.

\textbf{Hosted file}

\verb`essoar.10512845.1.docx`  available at \url{https://authorea.com/users/552096/articles/611025-anatomy-of-an-alpine-bedload-transport-event-a-watershed-scale-seismic-network-perspective}

\selectlanguage{english}
\FloatBarrier
\end{document}

