Introduction
The quantity of sediment exported from large mountainous catchments is a
fundamental control on downstream river morphology
,
the advance and retreat of coastlines and the
growth of deltas
. How
sediment flux varies over thousand-year timescales reflects changes in
upstream landscape evolution which is set by climatic and tectonic conditions
in active orogenic settings .
Quantification
of sediment flux from large, tectonically active catchments is challenged by
the nature of the river channels (e.g. size and access), the stochastic
nature of sediment inputs
and highly variable water discharge regimes (e.g.
).
Constraining sediment fluxes at intermediate timescales of
102–104 years has been significantly
improved through the development of detrital 10Be cosmogenic
radionuclide (CRN) analysis (e.g. ).
The concentration of 10Be recorded in quartz-rich river sediments
is assumed to reflect the rate of upstream landscape lowering, assuming
steady-state denudation averaged over the entire upstream catchment. Based on
this approach, catchment-averaged denudation rates can be calculated and
converted into CRN-derived sediment fluxes which are typically averaged over
hundred- to thousand-year timescales
. These timescales are a
function of the landscape denudation rate (i.e. the time taken to erode to a
depth equivalent to the cosmic-ray attenuation length in that landscape)
.
Sediment production, delivery and transport out of large mountain catchments
is heavily influenced by stochastic inputs such as hillslope mass wasting
generated by earthquakes or intense storms, or glacial lake outburst floods
. In small catchments (<100 km2) that are susceptible to such events, stochastic
controls on sediment release may significantly perturb the 10Be
signal measured in sediment samples at the catchment outlet
. In
particular, deep-seated landslides excavate sediment from depths greater than
the attenuation length of cosmic rays. This addition of 10Be-poor
landslide material dilutes 10Be concentrations recorded in fluvial
sediments sampled at the catchment outlet
resulting in an over-estimation
of the long-term erosion rate . The timescales
over which these stochastic inputs influence downstream 10Be
concentrations are related to the time taken to evacuate the sediment input
from the impacted reach, and also depend on patterns of intermediate
sediment storage and release (recycling) upstream of the sampling locality
.
However, even in regions dominated by high rates of landslide occurrence, it
is commonly assumed that given sufficiently large catchment areas and
sufficient sediment mixing, the imprint of mass wasting processes on
10Be concentrations measured at the outlet should be negligible
.
The gross sediment flux from the Himalaya is the largest out of any mountain
range on the planet and provides fertile soils for ∼10 % of the global
population. The vast majority of this sediment flux is sequestered in the
Indus and Ganga–Brahmaputra delta and submarine fans
. Sediment volumes in the Ganga–Brahmaputra
delta imply that overall sediment flux from these two major Himalayan river
systems has halved due to the reduction in monsoon rainfall since the early
Holocene . Our current
understanding of how sediment flux from tributaries of the Ganga River into
the Himalayan foreland basin varies is primarily from suspended sediment and
detrital 10Be concentration data collected over the last 20 years
.
Suspended sediment data are generally based on a single daily measurement and
are difficult to scale up spatially and temporally. Under these
circumstances, 10Be concentrations in modern river sands can be
used to generate sediment flux estimates with the advantage of temporal and
spatial averaging. However, substantial variations in 10Be
concentrations from repeat river sand samples at the catchment outlets of
major Himalayan rivers have been documented
. Concentrations measured
on the Ganga River close to the mountain front (near Rishikesh) vary from
9.2±1.0 to 19.5±4.1×103 atoms g-1 over a 13-year time period based on three samples
; at the Kosi River near
Chatara, measurements vary between 26.7±3.4 and 54.4±2.9×103 atoms g-1 for three samples
collected in August 2007 and November 2009, respectively
. Measurement uncertainty on Ganga River
samples records a 1σ of around 10–20 % of the measured concentration,
whereas the measured variability from the repeat samples is >100 %.
Similar observations were made along main stem samples on the Yamuna River,
where discrepancies of up to ∼60 % between samples were observed
, and also along the
Marshyangdi River in Nepal . This degree of variability
could suggest that stochastic controls on sediment release may influence the
10Be signal, yet this is at odds with previous modelling and
analysis of large catchments which has proposed that catchments of this size
should be buffered against variations in detrital 10Be
concentrations induced by individual hillslope events
.
Well-preserved and dated river terraces
associated with the Ganga River in the west Ganga Plain present a unique
opportunity to test for variations in 10Be concentrations in both
ancient (i.e. independently dated terrace and floodplain deposits) and modern
fluvial sediments at the Himalayan mountain front. The half-life of
10Be (∼1.36 Myr) implies that any post-burial decay during the
last 0.01 Myr is minimal and can be accounted for, making it the ideal
technique for this approach. We analyse 18 samples of river sands from
near the outlet of the Ganga River as it crosses the mountain front. Samples
are taken from modern river gravel bars, recent sand deposits of the 2013
Alaknanda floods
and dated terrace and floodplain deposits ranging in age from ∼200 to
23 500 years. Using these data, we evaluate the short-term variability in
10Be concentrations and test for longer-term changes that are
expected to reflect variations in the strength of the Indian Summer Monsoon
(ISM)
.
Motivated by the results, we examine the impact of stochastic inputs of
sediment from the upstream mountain catchment on 10Be
concentrations close to the mountain front (herein referred to as the Ganga
outlet). We conclude by combining field observations, data and numerical
analyses' results to synthesise potential drivers of 10Be
concentration variability in large tectonically active catchments.
Study area and context
The Ganga River is a glacially fed perennial river rising in the High
Himalaya (Fig. ). The Ganga has two major tributaries, the
Bhagirathi and Alaknanda, which join near the village of Devprayag. Further
downstream, the Ganga flows through the eastern end of the Dehra Dun, an
intermontane valley in the Sub-Himalaya, prior to passing through the Mohand
Anticline, exiting the mountains at Haridwar before reaching the Ganga Plain
(Fig. ). This study focuses on the portion of the Ganga
catchment upstream of the Himalayan mountain front, the most downstream
extent of which we also term the catchment outlet. The Ganga catchment, like
other Himalayan rivers such as the Marshyangdi River in Nepal
, is characterised by a number of broad geomorphic
process domains. These process domains can be related to the spatial
distribution of tectonic structures, glacial cover, topographic relief and
climatic influences which vary across the catchment (Fig. ).
Upstream of the mountain front, down cutting by the Ganga River has left
behind a series of strath terraces cut into Lesser Himalayan or Siwalik
rocks, and cut-and-fill terraces in Quaternary alluvial fan deposits
. A number of these terraces have been dated
using optically stimulated luminescence (OSL) to reveal terrace ages of up to
∼14 ka . During the transition from the Late
Pleistocene to the Holocene, an intensification of the ISM is observed in a
number of proxy records
, which
is believed to have driven a period of intense fluvial incision across much
of the Himalaya . Erosion
of pre-Holocene sedimentary records during this period of intensified monsoon
is proposed as one mechanism to explain the notable absence of older terraces
. Further changes in the intensity of the ISM
during the Holocene have been inferred from marine sediments in the Bay of
Bengal and Arabian Sea, and speleothems from Oman and China
.
Limited terrestrial records from the Indian subcontinent
suggest a period of intensified ISM during the
early Holocene in response to changes in summer insolation forcing, which is
consistent with terrace formation driven by enhanced fluvial incision during
the early Holocene
.
Mean sediment flux to the lower Ganga Plains during the period 11–7 ka is
estimated to have increased by over 2-fold
, which is in good
agreement with stalagmite δ18O profiles in Oman
which indicate a rapid increase in ISM precipitation between ∼10.6 and
9.2 ka . Arabian Sea records further indicate
an earlier period of monsoon intensification at ∼13 ka, representing the
major transition between the glacial and Holocene periods, although
smaller-magnitude changes in climate are observed even earlier
. These phases of incision during the early
Holocene are punctuated by minor depositional events that form sequences of
fill terraces close to the mountain front. Slip on the underlying Himalayan
Frontal Thrust (HFT) produces vertical displacement rates of 4 to 6.9 mm yr-1 and may result in terrace abandonment
. During the mid-Holocene, stalagmite
records in Oman and Yemen suggest that the ISM has been gradually weakening
since ∼7.6 ka in response to a progressive decrease in summer insolation
. Evidence presented by
suggests that the ISM entered a more arid phase at
∼5 ka, although a number of abrupt events punctuate the mid-Holocene to late
Holocene record. For example, speleothem evidence from caves in central Nepal
has suggested that between 2300 and 1500 years BP there was a significant drop in
monsoon precipitation
. In general,
however, the ISM appears to have been relatively stable over the last 1.5–2 ka.
The 30 m Shuttle Radar Topography
Mission (SRTM) digital elevation model (DEM) of the Ganga catchment.
Coordinates are projected in Universal Transverse Mercator (UTM) zone 44N.
Glacier coverage as documented in the Global Land Ice Measurements from Space
(GLIMS) database is also shown in white. The red box represents the spatial
area shown in more detail in Fig. . “D.D” refers to the
Dehra Dun region which is delineated by the grey striped area.
Broad distribution of geomorphic process domains across the Ganga
catchment. The approximate positions of the Main Boundary Thrust (MBT), Main
Central Thrust (MCT) and South Tibetan Detachment Zone (STDZ) are shown by
red dashed lines following . Relative landslide
density was determined by manual mapping of >400 landslides across the
Ganga catchment using Google Earth imagery, where landslides in glacially
influenced parts of the catchment were excluded. ISM denotes the Indian
Summer Monsoon.
Sample information
A number of slack water and flood deposits in the Ganga valley record rapid
sediment accumulation over the Ganga floodplain during high flow events in
the late Holocene . Seven of these flood units
have been dated between ∼280 and 600 years old by OSL and calibrated
with 14C ages from preserved charcoal fragments
. These deposits are preserved in a slightly
wider part of the bedrock gorge upstream of the mountain front, where flood
waters would have backed up as the river enters the narrower gorge
immediately downstream. Additional deposits were studied by
at Devprayag and Raiwala
(Fig. ) although they recorded small flood couplets as
opposed to single flood event deposits. Stacked sand–silt couplets
representing phases of persistent flooding were also identified between
2500–1200 and 320–209 years BP at Devprayag and were attributed to changes
in the spatial extent of the ISM based on geochemical evidence
.
During 2013, heavy rainfall between the 15 and
17 June was centred over the Alaknanda and Bhagirathi
catchments and generated significant flash flooding and numerous landslides,
causing notable damage to the Kedarnath region in the Alaknanda catchment
(Fig. ). A moraine dammed lake (Chorabari) had formed
northwest of the Kedarnath region in response to the elevated levels of
snowmelt runoff in the preceding month, which is also understood to have
burst on the morning of 17 June 2013, releasing water
with a peak discharge estimated at 783 m3 s-1 into the Alaknanda valley
. Flash flooding is not an uncommon
phenomenon in the Ganga basin; other large-magnitude events were documented
in 1894 and 1970 . Both of these flood events were
attributed to the breaching of dams created by landslides on the tributaries
of the Alaknanda River, following unusually high rainfall events. Sediment
deposited following the 2013 floods upstream of Devprayag (Fig. ) over-topped the 1970 flood sediment deposits (thought to
be the largest flood during the last 600 years), suggesting that the 2013
flood water levels were the highest in the Alaknanda valley during at least
the last 600 years , and
possibly since the Last Glacial Maximum . The
2013 event also presents a rare opportunity to resample 10Be
concentrations following an extreme flood event in the modern Ganga River, to
compare against pre-event concentrations as documented by
.
Results
The 10Be concentrations of the two modern samples near the mountain
front (GAPUB and RAEM) are 17.70 and 15.53×103 atoms g-1,
respectively. When combined with sample BR924 from
which was similarly collected near the
mountain front, an average concentration of 14.1×103 atoms g-1 is estimated for modern samples. The concentration
of modern sample BGM taken from further upstream of the Alaknanda–Bhagirathi
confluence is 13.56×103 atoms g-1 which is comparable to
the average modern concentration of samples close to the mountain front which
integrates the full Bhagirathi catchment. 10Be concentrations of
the majority of samples, both from ancient terraces and recent flood
deposits, largely fall within the error of modern detrital samples
(Fig. and Table ). Only three samples
(BG1.8, DVDF and CDT4) display 10Be concentrations considerably
greater than the upper error bound (19.1×103 atoms g-1) of
modern river samples; the average concentrations of these terrace samples are
in excess of 20×103 atoms g-1. Only one sample, DVTT2, has
an average concentration (6.66×103 atoms g-1) notably
below the lower error bound of the modern samples (8.20×103 atoms g-1). Samples taken from flood deposits associated with
the 2013 Alaknanda flood (DV2013 and RFLO) reveal concentrations of 16.06 and
12.85×103 atoms g-1, respectively, which fall well within
the error of modern river sediment samples.
Measured modern river (red) and terrace or flood/floodplain (black)
10Be concentrations relative to their depositional age. Horizontal
error bars represent the published age error associated with the
independently dated deposit, and vertical error bars represent error in
10Be concentrations determined in this study. Sample BR924 from
is also included and labelled.
In a frequency histogram of 10Be concentration data
(Fig. a), the three samples with the highest
concentrations (BG1.8, DVDF and CDT4) produce a positively skewed
distribution. These samples represent a fine-grained ∼300-year flood
deposit , ∼10000-year old terrace fill
and ∼11000-year old terrace fill
, respectively (see Table A1 for further
sample details). With the removal of samples BG1.8 and CDT4 from the
frequency histogram, the 10Be concentration data generate a
near-normal distribution (Fig. a).
Results from CAIRN modelling of all concentrations suggest that
catchment-averaged denudation rates for each sample largely lie within the
variability of modern detrital samples (Fig. b). Based on
the measured concentrations, these samples correspond to integration
timescales of ∼500 years, representing the average time period when the
erosion rate is considered to be constant, based on the time needed to erode
one mean attenuation path length (approximately 60 cm/erosion rate)
. There does not appear to be a spatial trend between
10Be concentration and upstream catchment area, even downstream of
large tributary confluences (Fig. ). The impact of high
10Be concentration samples on the frequency histogram of erosion
rates calculated using CAIRN modelling is less apparent (Fig. b), but the distribution shows significant spread.
Calculating sediment flux estimates from a single erosion rate at the upper
end of the distribution could result in sediment flux estimate being up to
7 times larger than one based on a sample at the lower end of the
distribution.
(a) Frequency histogram of mean 10Be
concentrations shown in Fig. . (b) Frequency histogram
of mean erosion rates calculated using the CAIRN method.
Impact of stochastic inputs on 10Be variability and sediment flux estimates
CRN sample interpretation
Possible explanations for the high-concentration measurement at BG1.8 may
include insufficient shielding since deposition, resulting in 10Be
enrichment of the deposit. Unlike other samples analysed here, the event bed
associated with this sample was only ∼0.5 m thick so burial (and
therefore complete shielding) was unlikely to be instantaneous. Whilst a
number of additional samples were taken from this exposure to try and produce
depth-concentration profiles, their grain size was too fine for
10Be analysis. However, the maximum 10Be enrichment at
the site during burial is likely to only be ∼1650 atoms g-1 based on local CRN production rates and sample depth,
which is less than the measurement uncertainty. With respect to the two
terrace deposits (DVDF and CDT4), high concentrations could also have been
produced if the samples were overwhelmed by locally derived, high-concentration hillslope sediment which was not well mixed. Samples with the
largest 10Be concentration variability also seem to focus around
10–15 ka (Fig. ), which may represent a period of post-glacial
conditions where a combination of low 10Be concentration material
(generated by glacial erosion) and high 10Be concentration sediment
(due to lower precipitation rates and therefore slower erosion of
non-glaciated landscapes) generated during the Last Glacial Maximum may have
been mobilised as the ISM intensified during the early Holocene.
Modern river (red) and terrace or flood/floodplain (black)
catchment-averaged erosion rates with respect to distance downstream, sample
elevation (grey shaded region) and upstream catchment area (blue line).
Vertical error bars represent error associated with the modelled erosion rate
and propagated 10Be concentration errors used to derive the erosion
rate. The red shaded area represents erosion rates within the error of modern
samples. Outliers BG1.8 and CDT4 are labelled.
Impact of landslides on 10Be variability
A range of processes are likely to drive temporal variability in
10Be concentrations in sand sampled close to the outlet of large
Himalayan catchments. The most obvious process is stochastic inputs generated
by mass wasting of hillslopes, which generates large quantities of sediment
with relatively low 10Be concentrations. Frequency histograms
presented in Fig. suggest that such stochastic
processes may form part of the natural background variability, as
low-concentration values tend not to skew the distributions. More samples would
be needed to draw a clearer picture on this. Below, we examine how different
erosional processes may drive the types of temporal variability in
10Be concentrations measured close to the Ganga outlet. This is
approached using a numerical analysis of catchment-averaged 10Be
concentrations derived under varying background erosion rates, landslide
depth, surface 10Be production rates and degrees of event buffering
(i.e. varying proportions of “event” sediments are mixed into the fluvial
network). Given the complexity of this type of landscape (e.g. multiple
geomorphic process domains, climatic variability), we do not attempt to mimic
these processes and reproduce measured concentrations or erosion rates (e.g.
), nor do we use this analysis to determine
the relative contributions required from stochastic processes (e.g. area and
depth of landsliding) to produce our observed concentrations. Instead, this
numerical analysis is used to explore the sensitivity of outlet
10Be concentrations to a range of parameters and scenarios that may
drive variability. The analysis considers the impact of a single sediment-generating event, as opposed to the evolution of catchment-averaged
concentrations which occur in response to a distribution of landslides
occurring over timescales of hundreds to thousands of years across a landscape
(e.g. ).
The relative 10Be contribution by landsliding can be approximated
to first order by calculating the volume of material generated by the event
and the average concentration of that material. The concentration of
landslide material is strongly controlled by the local surface 10Be
production rate and depth of the landslide. 10Be production rates
rapidly diminish in the upper few metres of the Earth's surface
following
P(z)=P0e-zρΛ,
where z is the depth below the surface (cm), Λ is the
attenuation length (g cm-2), ρ is rock density (g cm-3), and P0 is the surface
nuclide production rate (atoms g-1 yr-1).
At depths greater than ∼2 m the CRN production rate (by spallation
reactions) is negligible, as is muon production, as atoms generated by muon
interactions represents a small proportion relative to those produced by
spallation reactions in the upper 1–2 m of the Earth's surface (e.g.
). Here, we calculate the average concentration
of landslide material by integrating the surface production rate within the
upper 2 m; we find that the depth-averaged production rate of the upper 2 m
(Pd) is ∼30 % of P0.
This was converted into a 10Be concentration (C) in atoms g-1 using
C=PdΛρϵ+Λλ/ρ,
from , where we assume that the 10Be
decay constant (λ) is equal to 0 over the timescales we are concerned
with (<103 years) relative to the half-life of 10Be. We use
ρ=2.7 g cm-3 and Λ=160 g cm-2. We also assume
a steady-state erosion rate (ϵ) across the upstream catchment. For
landslide depths of less than 2 m, the average concentration was calculated
based on the production rate integral specific to that depth. For simplicity,
we initially assume that the rest of the catchment is eroding uniformly at a
background erosion rate, with a catchment average 10Be production
rate of 35 atoms g-1 yr-1 which is comparable to the
catchment-averaged production rate calculated for the Ganga catchment in
CAIRN. The concentrations calculated at the Ganga outlet also assume complete
sediment mixing. The 10Be concentration at the catchment outlet
(αevent+uniform) is then calculated using
αevent+uniform=αuniformϕuniform+αeventϕeventϕuniform+ϕevent,
where ϕuniform and αuniform are the
background sediment flux and 10Be concentration, respectively.
ϕevent and αevent are the event- or
landslide-generated sediment flux and 10Be concentration, respectively. A
series of sub-catchments was then selected to examine the influence of
spatial variability in surface production rates across the Ganga basin, to
provide a realistic range of values in the numerical analysis
(Fig. ). Average shielding factors (snow and topographic
shielding) were first calculated for each of these sub-catchments using the
CAIRN method , which were then used in the online
CRONUS v2.3 calculator to calculate production
rates, using a constant production rate model with a Lal–Stone scaling scheme
for spallation (Fig. and Table ).
The default landslide surface production rates were initially set to the same
as the catchment-averaged production rate. The landslide surface production
rates were then varied based on realistic production rates derived from
sub-catchments across the Ganga catchment (Table ).
Earthquake-induced landsliding datasets from the 1999 Chi-Chi (Taiwan) and
2015 Gorkha (Himalaya) earthquakes
state that the total
landslide areas were ∼128 and 87–90 km2, respectively. Areas of
these sizes represent approximately 0.5 % of the Ganga catchment area. We
therefore use the value of 0.5 % as an approximation of the proportion of
the hypothetical catchment to have been impacted by landsliding. In the
analysis, the average depth of the landslides was varied from 0.5 to 5 m,
the average background erosion rate from 0.2 to 2.0 mm yr-1 and the
average landslide surface production rate from 10 to
60 atoms g-1 yr-1. We use an average landslide depth where, in
reality, the depths of individual landslides occurring in response to an
earthquake or intense storm are likely to fit a power–law distribution
. However, at any point in time it is unlikely that
the full power–law distribution of landslide depths is sampled or integrated
into the catchment wide signal, due to the recurrence interval and amount of
time taken to evacuate larger and deeper co-seismic landslides. Sediment
generated by inter-seismic landsliding is assumed to be represented in the
background erosion rate imposed across the catchment, whilst the sediment
generated by the landslide event is assumed to reflect a large co-seismic
event (i.e. the tail-end of landslide frequency distribution). We also assume
that the 10Be concentration profile in the upper 2 m of the
landscape is in steady state before landsliding. This assumption is more
important in slowly eroding landscapes, where it may take tens of thousands
of years to reach secular equilibrium . This may
result in over-estimated landslide 10Be concentrations in our
analysis, if the 10Be concentration profile is not in equilibrium.
Similarly, landsliding is more likely to occur in parts of the landscape
undergoing faster erosion rates where, above a certain hillslope gradient,
erosion rate becomes less closely correlated (to hillslope gradient) as the
main mechanism of erosion changes from transport-limited to
detachment-limited processes . It might therefore
be expected that these regions have initially lower 10Be
concentrations. By increasing the average landslide erosion rate (relative to
the catchment-average erosion rate applied across the rest of the catchment)
in our analysis, we indirectly assess the importance of such effects.
Location of sub-catchments used to determine the variability in
production rate across the Ganga catchment (presented in
Table ).
Catchment area, mean catchment elevation and average 10Be
surface production rate for sub-catchments in the Ganga catchment.
Catchment area
Mean catchment
Surface production
(km2)
elevation (m)
rate (atoms g-1 yr-1)
Sub-catchment 1
1955
1606
11.08
Sub-catchment 2
4635
4716
56.02
Sub-catchment 3
1801
5033
70.51
Sub-catchment 4
1449
1642
24.28
Sub-catchment 5
169
4483
49.13
Sub-catchment 6
181
1868
12.82
Sub-catchment 7
253
1404
9.57
Sub-catchment 8∗
39
4806
49.61
Ganga (whole)
23 038
3560
33.16
∗ This sub-catchment represents the area upstream of
Kedarnath during the 2013 Alaknanda flooding.
We calculate “volumetric sediment flux” by combining the flux derived from
background erosion rates with the calculated landslide flux and compared
these to sediment flux estimates derived from the 10Be
concentration at the catchment outlet (which we term the “CRN-derived
sediment flux”). For a catchment eroding at a uniform rate (ϵ in mm yr-1), the CRN-derived sediment flux is the product of the
erosion rate, catchment area (A in km2) and
average rock density (ρ in kg m-3).
(a) Variations in 10Be concentration predicted at
the outlet in response to increasing landslide depth and as a function of
background erosion rates (represented by coloured lines). (b) Outlet
10Be concentration as a function of background erosion rate (where
all other parameters are constant at default values – see
Table ), for a system undergoing no landsliding (red line –
where erosion is driven purely by background erosion) and another with 2 m
deep landsliding over 0.5 % of the catchment area (black line).
(c) Outlet 10Be concentration under varying average
landslide 10Be surface production rates (based on
Table ) and background erosion rates (coloured
lines). The black vertical line represents the whole Ganga-catchment-averaged
production rate of ∼33 atoms g-1 yr-1.
(d) Comparison of volumetric and CRN-derived sediment fluxes from
analysis in panels (a)–(c). The blue arrow labelled 1
shows the effect of decreasing background erosion rate, and the blue arrow
labelled 2 shows the effect of increasing landslide depth and/or landslide
10Be production rate. The black dots in panels (a) and
(d) represent scenarios A and B which are discussed in more detail
later and in Fig. .
In this analysis, we assume that sediment storage between the region affected
by landslides and the outlet is small relative to the total sediment flux of
the catchment. Unlike the eastern and western Himalaya, the central Himalaya
(which is largely drained by tributaries of the Ganga River) is comparatively
void of large valley fills , which is likely to
limit large volumes of sediment storage and sediment residence times. Recent
modelling has also suggested that approximately 50 % of coarse material
generated by post-seismic landsliding is evacuated within 5 to 25 years
. In our scenarios, we initially assume complete
evacuation of material to the outlet within a year. We then run additional
analysis where much smaller proportions of the event material are mixed into
the fluvial network in this first year (3, 5, 10 and 20 % of the event
sediment). The default and range of values tested for each parameter in the
analysis are shown in Table .
Based on the above calculations, our results suggest that increasing the
average landslide depth results in a marked decrease in outlet 10Be
concentration, most notably between depths of 0.5 and 3 m (Fig. a). This can be explained through the exponential decay in
10Be production rates in the upper 2 m of the landslide
. This reduction in
concentration is greatest under lower background erosion rates. Increasing
background erosion rates from 0.2 to 2.0 mm yr-1 also reduces
the effect of landsliding on outlet 10Be concentrations (Fig. b). Under lower background erosion rate, landslide material
represents a greater proportion of the total sediment flux, so the system has
less capacity to buffer the landslide input and the 10Be
concentration is more sensitive to deeper landslides. We also find that
outlet 10Be concentrations are sensitive to the average landslide
surface production rate. Where the average surface production rate of the
landsliding is increased (e.g. comparable to that expected in high-altitude
sub-catchments of the Ganga – see Table ), predicted
outlet 10Be concentrations also increase relative to scenarios with
otherwise identical parameter values (Fig. c). Interestingly,
we also find that volumetric sediment flux estimates are consistently higher
than CRN-derived fluxes (Fig. d). Increasing background
erosion rates increases both CRN-derived and volumetric sediment flux
estimates, but increasing average landslide depth or landslide 10Be
production rate can reduce CRN-derived sediment flux estimates to a much
greater degree than volumetric flux estimates.
(a) Effect of increasing average landslide erosion rate to
3.0 mm yr-1on outlet 10Be concentrations in response to
varying landslide depths and catchment background erosion rates. The overall
range in outlet concentrations is notably lower than in
Fig. a. Increasing the catchment-averaged erosion rate only
has an impact on outlet concentrations where the input of landslide material
is smaller, suggesting that the outlet concentration is dominated by
landslide-derived material. (b) Comparison of volumetric and
CRN-derived sediment fluxes for the same model conditions, where marker
colour corresponds to background erosion rate shown in panel (a).
The difference in volumetric and CRN-derived fluxes is much less than
scenarios shown in Fig. d. In general, the volumetric flux is
approximately double the CRN-derived sediment flux. By increasing and
decreasing the average landslide erosion rate to 4.0 and 2.0 mm yr-1
as shown by the smaller black markers, this relationship varies slightly.
(a) Effect of event buffering on outlet 10Be
concentrations, where smaller fractions (3, 5, 10 and 20 %) of the event
sediment are mixed into the fluvial network based on two background erosion
rates of 0.6 and 2.0 mm yr-1 shown in blue and red, respectively. The
event proportions are represented by the different dashed lines. The average
landslide surface erosion rate is set to 3.0 mm yr-1. Under faster
background erosion rates, the effect of larger landsliding events is more
easily buffered in outlet 10Be concentrations.
(b) Comparison of volumetric and CRN-derived sediment fluxes for
event buffering scenarios. Under these conditions, volumetric and CRN-derived
sediment flux estimates are much more comparable. As the amount of
landslide-derived material mixed into the system increases, volumetric
sediment fluxes become slightly larger than CRN-derived sediment fluxes.
Default and range of parameter values used in numerical analysis.
Parameter
Default
Range of
value
modelled values
Landslide depth (m)
2
0.5–5.0
Catchment area (km2)
23 000
–
Percent of catchment impacted by landsliding
0.5
–
Catchment-averaged surface production rate (atoms g-1 yr-1)
35
–
Background erosion rate (mm yr-1)
0.5
0.2–2.0
Landslide surface production rate (atoms g-1 yr-1)
35
10–60
Proportion of event sediment mixed into fluvial network (%)
100
3–20
The average landslide erosion rate was increased to 3.0 mm yr-1, based on estimates in , to
mimic the effects of faster erosion rates in regions more prone to
landsliding and landscapes without steady-state concentration profiles.
ran a series of numerical modelling scenarios to
explore the ratio of landslide to bedrock weathering (background) erosion
rates needed to reproduce measured CRN erosion rates in the Khudi catchment
in Nepal. The best fit model runs were found to have landslide erosion rates
of 3.35 mm yr-1. By applying a comparable value of 3.0 mm yr-1 to our calculations, a reduction in the absolute
values and range of outlet 10Be concentrations is produced. The
initial maximum outlet concentration of ∼70000 (in Fig. a) is reduced to 12 000 atoms g-1 under the
lowest background erosion rate scenarios (Fig. a).
This range of outlet 10Be variability is more comparable to that
observed at the Ganga outlet, although outlet concentrations appear less
sensitive to background erosion rates applied across the rest of the
catchment. Furthermore, the difference in volumetric and CRN-derived sediment
fluxes is also reduced (Fig. b). By reducing the
proportion of event sediment mixed into the fluvial network, similar
reductions in the amount of 10Be concentration variability
generated at the outlet are also observed (Fig. a), and
outlet concentrations are more sensitive to changes in catchment background
erosion rates. Under faster background erosion rates (2.0 mm yr-1), the variability generated by events of all depths can
be effectively masked by background variability where only 10 % of the event
sediment is mixed in (i.e. such that the outlet concentration lies within
100 % of the maximum value). Similarly, under lower background erosion rates
of 0.6 mm yr-1, the fraction of event sediment needed to
generate variability within 100 % of the highest concentration is slightly
lower at 3 %.
Our analysis generates variability in 10Be concentrations that is
considerably larger than what we document in the Ganga catchment (Fig. ), suggesting that buffering of stochastic inputs must occur
. The evacuation time of fine-grained sediment
(sand and finer) is likely to be fast relative to the coarse fraction, as the
fine-grained fraction is annually entrained and transported downstream during
months impacted by the ISM. This is supported by grain size analysis
along a number of exposed gravel bars within the
Ganga catchment, which demonstrate that the channel bed is comprised largely
of grain sizes >1 mm, even beneath the surface armour layer. Typically,
grain sizes <1 mm represent less than ∼15 % of the grain size
distribution (Fig. ) which is also observed across other
catchments of the Ganga River. This suggests that there is relatively little
in-channel storage (or mixing) of finer grained sediments relative to the
large fluxes of these river systems, which on entering the Ganga Plain, are
thought to be largely dominated (>90 %) by sand-sized (and finer)
sediments . However, the majority of landslide
deposits are likely to be made of coarser material
which will take longer to be
evacuated or abraded into smaller and more easily transportable grain sizes.
Whilst landsliding may generate the quantities and 10Be
concentrations of sediment required to drive significant changes in
concentration at the outlet, the evacuation timescales of these event
sediments buffers their impact. Evacuation of event deposits over decadal to
centennial timescales will reduce the ratio of background to event sediment
fluxes and likely limit the impact on
10Be concentrations documented at the outlet.
Other potential sources of variability in 10Be concentration
Whilst landsliding with different depths and from different parts of the
Ganga catchment is likely to represent a key component in 10Be
variability, a number of other factors may also contribute, which are
discussed below. Firstly, spatially variable distributions of quartz-rich
lithologies across the Ganga catchment may lead to over- and under-estimation
of denudation rates in specific lithological settings. However, potential
variations in sediment quartz content have been assessed by
in the Ganga catchment, who concluded that the
correction due to the dilution of quartz from sediments sourced from
carbonate-rich series in the catchment is of a similar magnitude (maximum of
∼9 % change in erosion rate for sub-catchments in the High Himalaya) to
the production rate estimates and analytical errors. Recent studies have also
highlighted the effect of grain-size-dependent 10Be enrichment,
where coarser gravel-sized fractions have been documented to yield higher
apparent denudation rates than the medium sand-sized fraction which is
typically sampled
as a result of
the process through which the different grain size fractions are generated
(e.g. reworked hillslope material, landsliding), or differing sediment source
elevations. Similarly, downstream lags in 10Be denudation rate
spikes have been observed along the Tsangpo–Brahmaputra River in the eastern
Himalayan syntax , due to the distance which sediment
generated in the rapidly uplifting Namcha Barwa – Gyala Peri massif must travel
before being abraded into the grain size fraction used for sampling. However,
modern samples collected close to the Ganga outlet are not likely to be
influenced by either process, as the majority of sediment has already been
abraded into sand by this point . Similarly, a
number of the floodplain and terrace deposits sampled were entirely sand.
Exceptions to this include terrace deposits CDT3, CDT4, DVDF, DVMT2, DVTT2
and RLB, where sand samples were taken from poorly consolidated fluvial
deposits containing imbricated and well-rounded quartzite cobbles and
pebbles. However, additional 10Be samples were not run on
individual clasts in these deposits to determine whether the coarser fraction
yielded higher apparent denudation rates.
Volumetric sand (grain size <1 mm) proportions in sub-surface
sediment samples along major tributaries of the Ganga River from
.
Glacial lake outburst floods (GLOFs) are not uncommon across the Himalaya
(e.g. ) and have the
potential to generate and mobilise large quantities of sediment. Geomorphic
analysis following the 1977 and 1985 GLOFs in the Mount Everest region
suggested that much of the sediment eroded from
the upper 10–16 km of the GLOF route was unconsolidated sediment (glacial
till, colluvium, glaciofluvial terraces). Erosion was typically found to be
limited in valleys with resistant bedrock or consolidated side walls.
Similarly, the availability of unconsolidated material is also thought to be
a key limiting factor in the volume of debris flows triggered following
GLOFs, which can limit the erosive potential of the flow
. In the absence of existing studies which document
10Be concentrations in proglacial lake sediments, we cannot infer
how sediment released from the glacial lake may contribute to downstream
variations in 10Be concentration. Geomorphological evidence in
reaches downstream of GLOFs suggests that much of the sediment eroded by the
flood is largely unconsolidated (glacially influenced) material from
relatively shallow depths (<3 m; ) which is
likely to have a complex exposure history. Given the relatively short length
of the reach impacted downstream of the GLOF (relative to the full length of
a system such as the Ganga), and the likely 10Be-enriched nature of
surface deposits reworked by GLOFs, it seems unlikely that these types of
events drive significant change in outlet 10Be concentrations. This
is supported by work in the Marshyangdi River catchment in Nepal, which
suggested that localised erosion in the upper glaciated catchment is almost
an order of magnitude lower than fluvial incision rates in the upper
Marshyangdi River . An analysis of the evolution
of detrital 10Be concentrations along the Marshyangdi River suggested
that low-concentration 10Be inputs from glaciated tributaries
dilute main stem 10Be concentrations . In
this instance, glacial erosion was averaged at ∼5 mm yr-1 in the High and Tethyan Himalayan portions of the
catchment, suggesting that glacially derived sediments may complicate
detrital 10Be concentrations and interpretation of
catchment-averaged denudation rates.
Extreme monsoonal storms, such as the one that generated the 2013 Alaknanda
flooding, also have the potential to generate 10Be variability if
hillslope runoff mobilises large quantities of unconsolidated sediment on
valley sides and initiates mass wasting of hillslopes
. Sample DV2013 was
collected from a thick sand unit at the Ganga channel margins (∼18 m
above the modern channel) near Devprayag, known locally to have been
deposited following the 2013 Alaknanda flood. We find that the 10Be
concentration of this deposit (16.06×103 atoms g-1) also
lies within the error of modern samples at the outlet. One interpretation is
that the sediment generated by this event was sufficiently well mixed: upon
reaching the Ganga outlet, it had minimal impact on the outlet 10Be
concentration. Material mobilised by the Alaknanda flooding was largely
unconsolidated, surficial hillslope material . As
such, the 10Be concentration of these sediments will reflect their
local production rate (∼50 atoms g-1 yr-1 – see
Table ) and background erosion rate. If erosion in
the Alaknanda valley is driven primarily by large storm and flood events,
unconsolidated surface sediments could have been accumulating 10Be
since as early as the Last Glacial Maximum (LGM) , with very low
background erosion rates. As such, this type of erosive event may have
generated sediment with a higher-than-expected 10Be concentration
(given the depth of material removed) as a result of this
10Be-enriched surface layer.
Annual monsoonal storms may also contribute to the observed variability where
storms tap into localised parts of the catchment. The hillslope sediments and
reworked deposits these storms mobilise could vary in 10Be
concentration in the different geomorphic process domains, as they will have
variable 10Be production rates (which is a function of elevation),
background erosion rates and deposit characteristics (e.g. deep-seated
landslide). Background erosion rates in particular are likely to vary
dramatically across the Ganga catchment as a result of spatially variable
rock uplift, lithology, rainfall and vegetation cover
.
Earthquake-induced landsliding, GLOFs and extreme storm events are all likely
to generate large quantities of sediment with 10Be concentrations
that would be sufficient to drive significant change in the 10Be
concentration recorded at the Ganga outlet. However, the impact that these
processes have is limited by the ability of the river to entrain and
transport this sediment out of the catchment. The evacuation timescales of
sediment generated by these processes will likely vary as a function of the
frequency and magnitude of localised storm events which mobilise mass-flow
deposits from hillslopes into rivers sediment.
If this sediment is sourced close to the sampling location, it is also
unlikely to be fully homogenised. The distance required to fully mix
localised hillslope or tributary inputs has been shown to be as much as
several kilometres , which may induce variability
in 10Be concentrations recorded at the outlet. In terms of modern
river samples, a number of small ephemeral streams drain directly in the main
Ganga channel near the outlet. During the monsoon season when these channels
are active, sediment of differing 10Be concentrations will be
transported to the main channel and may not be sufficiently mixed on reaching
the outlet sampling locations. High-concentration samples documented close to
the Ganga outlet could therefore represent locally derived and poorly mixed
sediments, which reflect the erosional processes specific to a small frontal
region of the catchment.
Suitability of 10Be as a proxy for sediment flux in large catchments
Our analysis of outlet 10Be concentrations suggests that the
observed doubling in sediment delivery to the Bengal fan during the early
Holocene may have been masked by the natural variability in palaeo-erosion
rate or 10Be concentration data preserved close to the Himalayan
mountain front. Whilst changes in the amount of sediment being delivered into
the fluvial network may have occurred, the natural variability in
10Be concentrations delivered to the mountain front is sufficiently
high that a doubling in volumetric flux (and therefore catchment-averaged
erosion rate) cannot be clearly identified using detrital sampling. This is
consistent with previous work using repeat 10Be samples from
tectonically active watersheds in China, where it was concluded that
replicability of data in these types of landscapes is likely to be poor, and
that larger sample populations are needed to better represent upstream
denudation rates . Results from our study also
support this finding, where we demonstrate that multiple samples are required
to better characterise the temporal variability in 10Be
concentrations at the Himalayan mountain front.
Using the approximate range of concentrations documented at the Ganga outlet
(5000–30 000 atoms g-1) as an example of natural
variability, we can statistically constrain the number of samples required to
capture this variability with repeat sampling. We proceed as follows: we
produce a population of concentrations by choosing, at random, x
values from a Gaussian distribution with a mean of 17 500 atoms g-1 and a standard deviation of 4000 atoms g-1, based on the values from the Ganga River samples. We
repeat this procedure 100 times for each value of x, with x
(the number of samples in a population) varying between 3 and 50. If we
assume that the standard deviation of the concentrations for each population
is a proxy for concentration variability within a set of samples, then the
mean standard deviation of the 100 populations for a given number of samples
x, and the standard deviation around this mean, give an indication
as to whether the variability is well constrained. This is exemplified in
Fig. : with increasing number of samples x within a
population, the mean standard deviation increases and converges
asymptotically towards the true value of 4000 atoms g-1.
The standard deviation around the mean for the 100 populations generated for
each number of samples x (error bars on the figure) reduces with
increasing sample number; i.e. the variability becomes better constrained.
With 18 samples, the mean standard deviation is within 10 % of the true
standard deviation; more importantly, increasing the number of samples beyond
18 leads to minimal improvement, with the mean increasing by less than
0.3 % per additional sample. We therefore suggest that 18 samples represent a good
balance between cost and performance when trying to characterise the natural
10Be concentration variability of a river system similar to the
Ganga River. It is important however to note that the error bars around the
mean standard deviation are large. Even with 50 samples, 68 % of the
concentration populations (within 1 standard deviation of the mean assuming
a Gaussian distribution of values – error bars in figure) will have a
standard deviation within 23 % of the true value (in the range
∼3100–4500 atoms g-1); nearly a third of the
populations will therefore have a standard deviation beyond this bound. This
figure is 35 and 44 % for 18 and 5 samples, respectively (with standard
deviations of ∼3610±1020 and 3000±1250 atoms g-1, respectively). These numbers may be influenced by the
shape of the concentration distribution.
Number of 10Be samples required to capture the natural
concentration variability of the Ganga River. Approximately 18 samples are
required to be within 10 % of the true standard deviation (or
variability) of the system. Blue dots represent the mean standard deviation
of 100 populations of concentrations for a given sample group size (between 3
and 50). Error bars represent the standard deviation of the mean standard
deviation of those 100 populations per sample group size. The solid
horizontal red line represents the mean standard deviation value that the
sample group sizes converge towards (4000 atoms g-1). The two dashed
red lines represent the number of samples required to be within 10 % of
the true standard deviation (labelled 90 %) and the standard deviation
expected from a set of five samples (labelled 78 %).
Schematic of how comparable mean CRN concentrations in river sand
can be derived under two different end-member erosion scenarios with
different volumetric sediment fluxes. In these instances, slow background
erosion rates and deep landsliding (Model A) result in comparable CRN
concentrations to landscapes dominated by faster background erosion rates and
shallow landsliding (Model B). If Model A is set with a background erosion
rate of 0.4 mm yr-1 and 5 m deep landsliding over 0.5 % of the
catchment, and Model B with 2 mm yr-1 background erosion rates and
1 m deep landsliding (over the same area), comparable CRN concentrations
(see black dots marked in Fig. a) and CRN-derived sediment
fluxes are generated, but volumetric sediment fluxes are over 3 times
larger in Model A. This is due to the relative enrichment of 10Be
in the upper 2 m of the landscape with low background erosion rates, which
when combined with low CRN concentration material from depth, results in two
distinct CRN concentration populations. Where erosion is generally more
homogeneous (Model B) and CRN concentrations are distributed more uniformly,
comparable mean CRN concentrations are derived between the two models. Both
scenarios assume complete mixing of the event sediment, hence why these are
considered end-member or extreme scenarios.
Our results also suggest that, for 10Be concentrations within a
natural degree of system variability, the volumetric sediment flux could
theoretically differ from that calculated directly from 10Be
concentrations (Fig. d and Table ). Similar
outlet 10Be concentrations could be derived from landscapes
dominated by different erosional processes within large catchments. For
example, our analysis suggests that a “fast-eroding” landscape experiencing a
background erosion rate of 2.0 mm yr-1 and 1 m deep
landslides over 0.5 % of the catchment (e.g. a landscape dominated by
shallow landsliding or debris flows) could produce comparable outlet
10Be concentrations to a “slow-eroding” landscape experiencing 0.4 mm yr-1 background erosion and 5.0 m deep landslides over
the same area (e.g. a landscape experiencing deep earthflows) (Fig. ). The CRN-derived sediment fluxes between these two
landscapes may be comparable, but the volumetric flux from the landscape with
lower background erosion (and deeper landsliding) is considerably larger than
from the landscape with higher background erosion (and shallower
landsliding). Halving the area affected by landsliding in only the lower
background erosion scenario (with deeper landsliding) still yields comparable
CRN-derived fluxes (within 15 % of each other, rather than 6 %), but the
volumetric flux is double that generated under higher background erosion
rates (with shallower landsliding over a larger area). These types of “slow-eroding” landscapes which experience episodes of mass wasting are exemplified
by arid parts of the northwest Himalaya, which generally only experience high-intensity rainstorms during abnormal monsoon years where the ISM can
penetrate north of the orographic barrier formed by the Higher Himalaya
(Fig. ). Similarly, slow-moving earthflows in parts of the Eel River catchment in California which is
characterised by long and low-gradient hillslopes mobilise huge quantities of
sediment which contribute to the majority of the suspended sediment flux from
the catchment . The two end-member models presented
in Fig. suggest that, under different geomorphic process
domains, comparable mean 10Be concentrations could theoretically be
produced through different 10Be concentration populations.
CRN-derived sediment fluxes are based on an average landscape lowering rate
and thus fail to incorporate the effects of spatially limited deeper inputs
of sediment which are characterised by much lower 10Be
concentrations. Lower rates of background erosion mean that sediment eroded
off the surface is enriched in 10Be (as sediment residence times in
the upper 1–2 m of the Earth's surface are longer as a function of lower
background erosion rates). This effectively averages out the influence of
lower concentration input from deeper inputs and results in near identical
10Be concentrations at the mountain front to a system undergoing
only a slightly faster (or more uniform) rate of background erosion. Thus,
considerably different volumetric fluxes can be obtained for the same
10Be concentration. However, our analysis has also shown that
spatially variable erosion rates and event buffering can alter this
relationship, such that CRN-derived and volumetric sediment fluxes can be
comparable. Furthermore, under particular conditions, it is possible to
generate systems where the effects of large sediment-generating events are
lost within the natural variability of the system. This may explain the
absence of a 10Be concentration signature of Holocene climate
change.