Abstract
End-member mixing is a method in hydrology for attempting to define the runoff sources in river catchments. It involves estimation of the relative proportions of water from different sources, commonly producing a time series. Given regular measurements of a chemical tracer on the target water body and, in addition, corresponding measurements for samples of known sources, it is possible to perform end-member mixing using Bayesian models taking (essentially) a random effects approach in a hierarchical framework, including covariates if appropriate. This paper considers the case where there are no separate data available for the source components, and develops a model for source distributions via nonlinear regression on the tracer/flow relationship and nonparametric density estimation. We allow these source component distributions to vary from year to year and apply the model to a data set from two streams in central Scotland, comprised of weekly or fortnightly readings over seventeen years. We conclude there is evidence of a change in source distribution over time; that corresponding to low flow conditions exhibits a gradual increase in alkalinity for both of two streams studied, whereas for high flow conditions alkalinity appeared to be rising for only one stream. Copyright (C) 2011 John Wiley & Sons, Ltd.
Original language | English |
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Pages (from-to) | 921-932 |
Number of pages | 12 |
Journal | Environmetrics |
Volume | 22 |
Issue number | 8 |
Early online date | 25 Mar 2011 |
DOIs | |
Publication status | Published - Dec 2011 |
Keywords
- compositional analysis
- hierarchical Bayesian model
- MCMC
- WinBUGS
- chemical tracers
- multivariate receptor models
- kernel density-estimation
- mesoscale catchment
- residence times
- uncertainty
- stream
- water
- chemistry
- quality
- salt