Quantifying uncertainty on sediment loads using bootstrap confidence intervals
Load estimates are more informative than constituent concentrations alone, as they allow quantification of on- and off-site impacts of environmental processes concerning pollutants, nutrients and sediment, such as soil fertility loss, reservoir sedimentation and irrigation channel siltation. While s...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-01-01
|
| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/21/571/2017/hess-21-571-2017.pdf |
| Summary: | Load estimates are more informative than constituent
concentrations alone, as they allow quantification of on- and off-site
impacts of environmental processes concerning pollutants, nutrients and
sediment, such as soil fertility loss, reservoir sedimentation and irrigation
channel siltation. While statistical models used to predict constituent
concentrations have been developed considerably over the last
few years,
measures of uncertainty on constituent loads are rarely reported. Loads are
the product of two predictions, constituent concentration and discharge,
integrated over a time period, which does not make it straightforward to
produce a standard error or a confidence interval. In this paper, a linear
mixed model is used to estimate sediment concentrations. A bootstrap method
is then developed that accounts for the uncertainty in the concentration and
discharge predictions, allowing temporal correlation in the constituent data,
and can be used when data transformations are required. The method was tested
for a small watershed in Northwest Vietnam for the period 2010–2011. The
results showed that confidence intervals were asymmetric, with the highest
uncertainty in the upper limit, and that a load of 6262 Mg year<sup>−1</sup> had
a 95 % confidence interval of (4331, 12 267) in 2010 and a load of
5543 Mg an interval of (3593, 8975) in 2011. Additionally, the approach
demonstrated that direct estimates from the data were biased downwards
compared to bootstrap median estimates. These results imply that constituent
loads predicted from regression-type water quality models could frequently be
underestimating sediment yields and their environmental impact. |
|---|---|
| ISSN: | 1027-5606 1607-7938 |