Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-571-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-571-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Jan 2017
Research article |
| 27 Jan 2017
Quantifying uncertainty on sediment loads using bootstrap confidence intervals
Johanna I. F. Slaets
CORRESPONDING AUTHOR
Institute of Plant Production and Agroecology in the Tropics and
Subtropics, University of Hohenheim, Garbenstrasse 13, 70599 Stuttgart,
Germany
Hans-Peter Piepho
Biostatistics Unit, Institute of Crop Science, University of
Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany
Petra Schmitter
The International Water Management Institute, Nile Basin and East
Africa Office, Addis Ababa, Ethiopia
Thomas Hilger
Institute of Plant Production and Agroecology in the Tropics and
Subtropics, University of Hohenheim, Garbenstrasse 13, 70599 Stuttgart,
Germany
Georg Cadisch
Institute of Plant Production and Agroecology in the Tropics and
Subtropics, University of Hohenheim, Garbenstrasse 13, 70599 Stuttgart,
Germany
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Cited
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- Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields A. Rigden et al. 10.1038/s43016-020-0028-7
- The combined effects of VPD and soil moisture on historical maize yield and prediction in China F. Zhao et al. 10.3389/fenvs.2023.1117184
- Methods of yield stability analysis in long-term field experiments. A review M. Reckling et al. 10.1007/s13593-021-00681-4
- Can large language models effectively reason about adverse weather conditions? N. Zafarmomen & V. Samadi 10.1016/j.envsoft.2025.106421
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8 citations as recorded by crossref.
- A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R J. Li 10.3390/app9102048
- Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields A. Rigden et al. 10.1038/s43016-020-0028-7
- The combined effects of VPD and soil moisture on historical maize yield and prediction in China F. Zhao et al. 10.3389/fenvs.2023.1117184
- Methods of yield stability analysis in long-term field experiments. A review M. Reckling et al. 10.1007/s13593-021-00681-4
- Can large language models effectively reason about adverse weather conditions? N. Zafarmomen & V. Samadi 10.1016/j.envsoft.2025.106421
- Making management decisions in the face of uncertainty: a case study using the Burdekin catchment in the Great Barrier Reef P. Kuhnert et al. 10.1071/MF17237
- Assessing the likelihood of drought impact occurrence with extreme gradient boosting: a case study on the public water supply in South Korea J. Seo & Y. Kim 10.2166/hydro.2023.064
- Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction A. Fathabadi et al. 10.1016/j.scitotenv.2021.151760
2 citations as recorded by crossref.
Latest update: 24 Apr 2025
Short summary
Determining measures of uncertainty on loads is not trivial, as a load is a product of concentration and discharge per time point, summed up over time. A bootstrap approach enables the calculation of confidence intervals on constituent loads. Ignoring the uncertainty on the discharge will typically underestimate the width of 95 % confidence intervals by around 10 %. Furthermore, confidence intervals are asymmetric, with the largest uncertainty on the upper limit.
Determining measures of uncertainty on loads is not trivial, as a load is a product of...
