Results and Discussion
3.1 Comparison of laboratory evaluation of gully suspended
sediment monitoring methods
The laboratory evaluation of the various monitoring methods
(flow-proportional discrete manual sampling, simulated RS sampler, PASS
sampler, autosampler, and turbidity logger) demonstrated the
capabilities and limitations of the methods to provide representative
measurements of suspended sediment concentration and particle size
distribution (Table 2, Figure 3, SI-4) as discussed in relevant sections
below. The scientific literature considers discrete manual, isokinetic
depth and width integrated, sampling to be the most representative field
sample collection method (Horowitz et al., 2008; Perks, 2014; Ward et
al., 1990). For this reason, we argue that assessment of sampler
performance under laboratory conditions should be made by comparison to
the discrete manual sampling results. The flow-proportional discrete
manual samples collected during the laboratory evaluation are comparable
to what would be collected using isokinetic manual sampling techniques
in the field (Ward et al., 1990).
3.1.1 Autosampler
The time-weighted average suspended sediment concentration of the
samples collected using the autosampler underestimated the manual
discrete sample time-weighted average suspended sediment concentration
by 38% and was also lower than the other tested methods (Table 2). The
coarser sediment fraction (100-2000 µm) was also underrepresented in the
samples collected by the autosampler (Figure 3, Table 2). This is due to
increased head pressure and slower sampling velocity as a result of the
elevation difference between the autosampler and its sample intake.
Thus, heavier particles (i.e., sand) were under-represented in the
samples collected with the autosampler (Bent et al., 2003; Clark et al.,
2009; Fowler et al., 2009). These samples also had different suspended
sediment concentrations and particle size distribution to comparable
samples collected by the other methods (Figure 3). The finer fraction of
sediment (the 10th percentile
(d10 ) of the particle size distribution) within
the samples, collected using the autosampler, appears to be similar to
the discrete sample sediment d10 , however, the
two datasets were significantly different (Table 2). Additionally, the
median sediment particle size (d50 ) and
90th percentile (d90 ) of
samples collected using the autosampler were generally close to half or
less of those sediments collected by the other methods (Table 2). These
data indicate that unless an autosampler can be configured so that the
level of its intake is close to that of the sampling unit there will
likely be under-representation of larger suspended sediment particles
(>100 µm) and therefore also the suspended sediment
concentration in the collected samples. This limitation suggests that
suspended sediment data collected using an autosampler from a gully with
high channel banks should be corrected using comparable data from a more
representative method (e.g., manual sampling).
3.1.2 Rising stage sampler
The time-weighted average suspended sediment concentration derived from
RS sampler data was biased to a higher sediment concentration (32%)
compared to the time-weighted average suspended sediment concentration
of the manually collected samples (Table 2). This bias was expected as
samples were not collected after the simulated peak stage (i.e., 75
mins). The particle size distribution was not significantly different to
the discrete manual sample data, as previously discussed in the methods,
and it was also similar to the PASS sample data (Table 2, Figure 3).
The RS sampler provides representative individual sample data, however,
the often rapid sampling rate due to gullies having a fast rising stage
and lack of falling stage data will likely result in an overestimation
of suspended sediment concentration and a potentially unrepresentative
PSD for a flow event (García‐Comendador et al., 2017; Shellberg et al.,
2013). However, we note that this laboratory simulation represents only
one type of hydrograph that may occur in gully systems, so the
suitability of the RS sampling approach should be considered on a
case-by-case basis using available data on the relationship of suspended
sediment concentration and flow at a particular field site.
3.1.3 PASS sampler
The time-weighted average suspended sediment concentration of the
samples collected using both discrete and PASS sampling methods differed
by only 9% ± 5% (Table 2). The suspended sediment concentration of the
sample water expelled (i.e., water not retained) by the PASS sampler was
150 mg/L, which is equivalent to the sampler retaining 98.5 ± 1% of the
total sediment sampled. The modifications made to the PASS sampler,
therefore, have not hindered its ability to collect a representative
sample of time-weighted average suspended sediment concentration and
particle size distribution.
The particle size distribution statistics (i.e.,d10, d50, and
d90 ) of the suspended sediment collected using the PASS
and discrete sampling methods reveal generally good agreement between
the two methods (Table 2). The distribution of fine particles
< 10 µm were almost identical, whereas distributions of larger
(heavier) particles differed with increasing size (Figure 3). This
difference is likely due to the heterogeneity in sand particles in
suspension within the agitation vessel during the test. The continuous
collection of sediment by the PASS sampler should more accurately
incorporate this heterogeneity into the final measurement compared to
discrete sampling, which likely explains the difference in the coarser
sediment particle size fractions collected by the PASS and discrete
sampling methods (Figure 3).
Overall, our data suggests that the PASS sampler is capable of
collecting a time-integrated sediment sample that is comparable in
suspended sediment concentration and particle size distribution to that
collected by isokinetic manual sampling approaches, under controlled
laboratory conditions.
3.1.4 Turbidity Logger
Turbidity measurements and discrete sample suspended sediment
concentrations had a strong linear relationship (R2 =
0.97), indicating that a predictive relationship between turbidity and
suspended sediment could be used to estimate SSC from turbidity data
(SI-5). Simulated RS sampler sample suspended sediment concentrations
also had a strong linear relationship with turbidity
(R2 = 0.94), however, this was only for three paired
measurements, which is not sufficient to derive a predictive
relationship between turbidity and suspended sediment concentration
(Rasmussen et al., 2009). Suspended sediment concentrations of the
samples collected with the autosampler showed a more variable
relationship with turbidity measurements (R2 = 0.87)
(SI-5). The time-weighted average suspended sediment concentration
derived from turbidity data corrected with manually collected discrete
samples compared well to the PASS (within 11%) and RS samples (within
26%) (SI-4). These results suggest the turbidity logger may be a good
surrogate for the other monitoring methods provided a significant
relationship between suspended sediment concentration and turbidity can
be obtained under field conditions.
3.2 Field evaluation of gully monitoring methods
The two gullies at the field site were investigated over two wet seasons
(2017/2018 and 2018/2019). During this time several flow events of
different intensities were monitored (SI-6). Due to the remote locations
of gullies used in this study, samples were often only able to be
retrieved after multiple flow events had occurred, rather than after
individual flow events. As such, there were only a limited number of
single flow events that could be used to directly compare the
performance of the various monitoring methods.
3.2.1 Autosampler
The autosampler collected samples in gully-2 with suspended sediment
concentrations and particle size distributions that were similar to the
other methods. The lack of suspended sand in gully-2 (commonly less than
2% by sample volume) meant that samples were representative despite the
sampling unit being elevated (>1.5 m) relative to the
intake (Table 3 and Figure 4). In contrast, samples collected using the
autosampler from gully-1 had similar characteristics to those observed
in the laboratory test, where suspended sediment concentration and
particle size distribution were different to the PASS and RS samples
when a relatively large amount of suspended sand was present
(>20%) (Table 3). For example, during a short and intense
flow event during the Jan-18 to Feb-18 sampling period in gully-1,
samples collected by the autosampler underestimated the time-weighted
average suspended sediment concentration by ~30%
compared to the PASS sampler (Table 3, SI-6). Conversely, flow events
that had relatively lower proportions of suspended sand
(<10%) compared well to PASS sampler and RS sampler
estimates. These differences in sample suspended sediment concentration
and particle size distribution are consistent with observations from the
laboratory test where the autosampler was unable to collect
representative samples of the coarser sediment fractions due to the
vertical displacement between the sampler position and its inlet.
Additionally, the autosampler had several operational issues (e.g.,
insect infestation, sample intake blockages, and programming
malfunctions) that limited the number of samples it collected in these
specific field settings.
3.2.2 Rising Stage Sampler
The remote location of the study site meant the RS sampler arrays (i.e.,
six samplers) were only collected three times during the study period.
This highlights the challenge of gaining sufficient samples for more
than a small number of flow events from a gully using this method
compared to the autosampler and PASS sampler, which can sample multiple
flow events per deployment.
Based on the results of the laboratory evaluation, samples collected
using the RS sampler were expected to be more representative of actual
suspended sediment concentrations compared to samples collected by the
autosampler. This was valid for most samples, however, under the field
conditions prevailing at the study site some of the RS samplers were
observed to accumulate large quantities of water (between 25-35% of the
1 L sampler volume) due to condensation. This phenomenon was
unpredictable and resulted in suspended sediment samples being diluted
by unknown amounts of water, thus potentially introducing significant
error to the calculated SSC. Condensation in RS samplers has been noted
in previous studies (Edwards et al., 1999), however, these comparatively
large accumulations of condensate are likely caused by the high ambient
daytime air temperatures and relative humidity, followed by cooler night
time temperatures (a change of ~18°C), at the study
site. This is likely to be an issue at many sites located in tropical
regions and should be considered when designing monitoring programs in
such places.
Unfortunately, upon return to a remote site following a flow event,
there is no way of knowing which, if any, or to what degree individual
samples collected by the RS samplers were affected by condensation.
Considering this, it is best to interpret the RS sample suspended
sediment concentration data with approximately 25-30% uncertainty. The
RS samples had suspended sediment concentrations and particle size
distributions in the range of the autosampler and PASS sampler samples
(Table 3, Figure 4), although it is possible some of the suspended
sediment concentrations could be outside of that range if condensation
is considered. RS samples demonstrated the variability in particle size
distribution under different water depth conditions well. For example,
during a flow event in gully-1, the particle size distribution shifted
between being dominated by finer and coarser particle as the water level
increased (e.g., sample d50 andd90 ranged between 6.24 to 11.8 and 59.9 to 116,
respectively) (SI-7). This ability to obtain information on suspended
sediment particle size dynamics is a strength of the RS sampler
approach.
Overall, suspended sediment concentration (provided the sampler is not
compromised by condensation) and sediment particle size data of the RS
samples compared well with the PASS sampler in both gully types. The
development of a falling stage sampler has been recently reported,
although no assessment of its limitations or capabilities has been done
to date (DPI, 2017). Such a sampler could address a major limitation of
using RS samplers for monitoring sediment transport processes in
gullies.
3.2.3 PASS sampler
The particle size distribution of the samples collected from gully-2 by
the autosampler, RS sampler, and PASS sampler were all very similar for
all flow events (Table 3 and Figure 4). The average particle size
distribution of the samples collected by the autosampler and PASS
sampler were often within the uncertainty of their respective particle
size distribution statistics (d10,
d50, d90 ) (Table 3). This data confirms
the observations of the laboratory test in that the PASS sampler is
collecting a sample comparable to the other methods for both
time-weighted average suspended sediment concentration and particle size
distribution of fine suspended sediment (< 63 µm).
The PASS sampler, RS sampler, and autosampler data did not agree as well
for samples collected from gully-1, where the higher percentage of
suspended sand present during flows resulted in more variable suspended
sediment concentrations and particle size distributions (Table 3, Figure
4). Despite this, the range of time-weighted average suspended sediment
concentrations of PASS samples compared relatively well with the other
methods for flow events with less suspended sand (e.g., flow events
sampled between November 2017 and January 2018) (Table 3). The particle
size distribution of coarser sediment (i.e., thed90 ) measured for the PASS samples were typically
more than double those measured on the RS and autosampler samples, which
indicates that the latter methods likely under-represented the coarser
suspended sediment fraction in gully-1. The time-weighted average design
of the PASS sampler means it cannot provide information on suspended
sediment dynamics during a flow event. However, the PASS sampler is
well-suited for investigating long-term trends in suspended sediment
concentration and particle size distribution (e.g., several wet
seasons), and for assessing the effectiveness of gully remediation
works. Comparison of the laboratory and field data of the PASS sampler
to the autosampler and RS sampler shows the method provides the most
representative time-integrated suspended sediment data of the three
methods and because the PASS sampler data was most consistent with
manually collected samples.
3.2.4 Turbidity Logger
The turbidity logger can provide a high frequency of suspended sediment
concentration measurements over extended time periods (e.g., months),
provided there are sufficient comparable physical samples collected to
ensure accurate calibration of the method (Rasmussen et al., 2009).
There were some instances, at gully-2, where turbidity measurements
could have been corrected to suspended sediment concentration
measurements, using samples collected by the autosampler
(R2 > 0.83 (SI-8)). However, this
characteristic was not reflected in the measurements collected from
gully-1, where the relationship between the autosampler sample suspended
sediment concentrations and the turbidity logger measurements was poor
(R2=0.17 (SI-8)).
The lack of a relationship between turbidity and SSC at gully-1 was
likely due to the higher proportion of sand at this site. The turbidity
measurement method is based on the detection of light intensity,
originally emitted from the instrument, refracted from a particle back
to the instrument detector. A study by Rasmussen et al. (2009) found the
presence of fine to very coarse sand (125-2000 µm) can often a
negatively bias turbidity measurements because the larger particles do
not reflect light in a manner that is consistent with that used to
calibrate the instrument (Rasmussen et al., 2009). This measurement
characteristic often leads to an underestimation of the
turbidity-suspended sediment concentration relationship (Bent et al.,
2003; Clark et al., 2009; Fowler et al., 2009).
Without site-specific calibration, turbidity measurements are unlikely
to be suitable for even semi-quantitative investigations of suspended
sediment dynamics in gully systems. This is evidenced by the lack of
significant difference between the turbidity measurements of the loggers
located in the two studied gullies (SI-9), despite very different
suspended sediment concentration ranges and PSDs (Table 3; Figure 4).
For example, the mean turbidity of gully-1 (1250 (± 1173) NTU) and
gully-2 (1501 (± 994) NTU), for the 2017/2018 wet season, were not
significantly different, yet the SSCs measured by the other methods
differed by ~4 to 7-fold between these gullies (Table
3). This emphasises the importance of collecting representative
suspended sediment concentration samples in-order to calibrate the
turbidity measurement to a surrogate suspended sediment concentration.
Turbidity measurements alone do not provide useful information and thus
should only be relied upon as a complimentary addition to other
monitoring methods (e.g., RS or PASS
samplers).
3.2.5 Comparison to manual sampling
The collection of manual samples from gullies is often difficult due to
the remote location of the sites, safety concerns, and the
unpredictability of flow events. However, samples were able to be
collected from a single flow event in gully-1. Seven samples were
manually collected during this event using a DH-48 sampler, and one
time-integrated sample was collected over the same period by a PASS
sampler deployed in the gully. There was little difference between
average particle size distributions (Table 4, Figure 5) and the
time-weighted average suspended sediment concentrations of the manually
collected samples (6067 mg L-1) and PASS sample (6082
mg L-1), respectively. While these data are
preliminary, it further supports the ability of the PASS sampler to
collect representative samples of time-weighted average suspended
sediment concentration and particle size distribution in challenging
field settings.