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Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison
Richard J Cooper1, Tobias Krueger2, Kevin M Hiscock1
1School of Environmental Sciences, University of East Anglia, Norwich Research Park Norwich, Norfolk, UK.
Water Resources Research
|November 28, 2015
Summary
Mixing models are sensitive to structural choices and error assumptions, impacting sediment source apportionment results. Careful model selection is crucial for accurate fluvial sediment load investigations.
Area of Science:
- Environmental Science
- Geosciences
- Hydrology
Background:
- Mixing models are widely used for sediment source apportionment.
- Bayesian and frequentist approaches are common in these models.
- Model setup choices can influence apportionment outcomes.
Purpose of the Study:
- To assess the impact of different mixing model setups on sediment source apportionment estimates.
- To conduct a one-factor-at-a-time (OFAT) sensitivity analysis within a Bayesian framework.
- To compare Bayesian and frequentist approaches for sediment source apportionment.
Main Methods:
- Formulated 13 versions of a mixing model with varied error assumptions and structural choices.
- Applied models to sediment geochemistry data from the River Blackwater, UK.
- Performed an OFAT sensitivity analysis to evaluate model sensitivity.
Main Results:
- All models identified subsurface sources as the largest contributor to suspended particulate matter (SPM) (median ~76%).
- Apportionment estimates varied significantly (up to 21%) based on model structure and error assumptions.
- Differences observed between Bayesian and frequentist, and full vs. empirical Bayesian setups.
Conclusions:
- Mixing model structural choices and error assumptions significantly impact sediment source apportionment.
- Bayesian models show high sensitivity to error assumptions and structural decisions.
- Sediment source apportionment results differ between Bayesian and frequentist methods.
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