2.6 Uncertainty assessment
Uncertainties on suspended sediment load values were simulated by
considering the most critical sources of measuring errors. More
specifically, other potential SSL values were simulated using the
following equation, modified from Vanmaercke et al. (2015):
\(\text{SS}L_{\text{sim}}=SSL\times U_{\text{ME}}+SSL\times U_{\text{FF}}\),
(4)
where SSLsim is another potentially true value ofSSL after considering the various sources of uncertainty.UME reflects the uncertainties associated with
measuring errors, UFF represents the uncertainty
associated with the unmeasured finer fraction. In the original equation
from Vanmaercke et al. (2015), two more potential sources of uncertainty
are discussed: low sampling frequency and length of measuring period.
The latter is not applicable for our data as we are dealing with annual
values. We also assumed that sampling frequency might not be a source of
uncertainty in our case, as with daily sampling intervals, both bias and
imprecision tend to zero (Moatar et al. , 2006).
UME reflects the integrated effect of errors on
individual runoff discharge measurements, suspended sediment
concentration measurements, and uncertainties due to intra-daily
variation in runoff and sediment concentrations not captured by the
measurements. Previous studies reported that these errors are commonly
20-30% (Steegen and Govers, 2001; Harmel et al. , 2006;
Vanmaercke et al. , 2015). We, therefore, expected that 30%
provides a realistic and relatively conservative estimate of the
uncertainty on SY-values associated with measuring errors. Hence,UME was simulated as a random number from a
normal distribution with a mean of 1 and a standard deviation of 0.30.
However, SSL values derived from measurements at gauging stations
in Russia are subject to additional uncertainties associated with filter
type and may underestimate the actual suspended sediment load (Chalovet al. , 2019). At Russian gauging stations, suspended sediment
concentration is measured by the gravimetric method using paper filters
with pore sizes ranging from 2 to 3 µm (so-called «blue tape», de-ashed
filters, «ТУ 6-09-1678-86» specification) according to Handbook. One may
argue that our results are incomparable with other findings (Kasperet al. , 2018). However, from previous studies (Bogen, 1989;
Williams and Rosgen, 1989), we know that the > 5 µm
fraction constitutes most of the suspended load in the glacierized and
mountainous catchments. Therefore, we assume that our sediment data may
be a good indicator of the total sediment output from the study
catchments.
To estimate how pore size can impact total suspended sediment
concentration, we performed a brief exploratory data analysis of
particle size distribution from Williams and Rosgen (1989). We selected
only nine mountainous rivers flowing in similar environmental conditions
as those presented in this study from their dataset. We found that out
of 216 samples mean percent by weight finer than 4 µm is 24.7%, with a
corresponding standard deviation of 9.5%. The proportion of finer
fraction can vary from 8% to 43% (i.e., the 2.5% and 97.5% quantile)
depending on the season and river. Hence, UFF was
simulated as a random number from a normal distribution with a mean of
0.247 and a standard deviation equal to 0.095. Evidently,UFF values were restricted to values between 0.08
and 0.43.
Equation 4 was used to simulate respectively 1000 alternative SSLfor every year and every gauging station. From these values, we
calculated 95% confidence intervals on every SSL value (i.e.,
the difference between the 97.5% and 2.5% quantile of the 1000
simulated values).
Buchner et al. (2020) reported that the overall accuracy of the cropland
change map is 75.7%, of the forest change map is 90.2 %. Therefore,
uncertainties of the landcover change associated with measuring errors
were simulated as a random number from a normal distribution with a mean
of 1 and a standard deviation of 0.24 for cropland and 0.1 for a forest.
Various data sources (global satellite imagery, aerial photos, and
topographic maps) were used to create the Greater Caucasus glacier
inventory (Tielidze and Wheate, 2018), so the glacier area error varies
through time and between methods from 4.4% to 7.9%. We assumed that an
8% error provides a realistic estimate of the glacier area uncertainty.
In the result, the uncertainty was simulated as a random number from a
normal distribution with a mean of 1 and a standard deviation of 0.08.