Phosphorus load estimation in the Saginaw River, MI using a Bayesian hierarchical/multilevel model

Yoon Kyung Cha, Craig A. Stow, Kenneth H. Reckhow, Carlo DeMarchi, Thomas H. Johengen

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

We propose the use of Bayesian hierarchical/multilevel ratio approach to estimate the annual riverine phosphorus loads in the Saginaw River, Michigan, from 1968 to 2008. The ratio estimator is known to be an unbiased, precise approach for differing flow-concentration relationships and sampling schemes. A Bayesian model can explicitly address the uncertainty in prediction by using a posterior predictive distribution, while in comparison, a Bayesian hierarchical technique can overcome the limitation of interpreting the estimated annual loads inferred from small sample sizes by borrowing strength from the underlying population shared by the years of interest. Thus, by combining the ratio estimator with the Bayesian hierarchical modeling framework, long-term loads estimation can be addressed with explicit quantification of uncertainty. Our study results indicate a slight decrease in total phosphorus load early in the series. The estimated ratio parameter, which can be interpreted as flow-weighted concentration, shows a clearer decrease, damping the noise that yearly flow variation adds to the load. Despite the reductions, it is not likely that Saginaw Bay meets with its target phosphorus load, 440 tonnes/yr. Throughout the decades, the probabilities of the Saginaw Bay not complying with the target load are estimated as 1.00, 0.50, 0.57 and 0.36 in 1977, 1987, 1997, and 2007, respectively. We show that the Bayesian hierarchical model results in reasonable goodness-of-fits to the observations whether or not individual loads are aggregated. Also, this modeling approach can substantially reduce uncertainties associated with small sample sizes both in the estimated parameters and loads.

Original languageEnglish
Pages (from-to)3270-3282
Number of pages13
JournalWater Research
Volume44
Issue number10
DOIs
StatePublished - May 2010

Keywords

  • Annual riverine load
  • Bayesian hierarchical modeling
  • Predictive uncertainty
  • Ratio estimation
  • Total phosphorus

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