Calibrating and validating bacterial water quality models: A Bayesian approach

• Published in: Water research (Oxford) 2009-06, Vol.43 (10), p.2688-2698
• Main Authors: Gronewold, Andrew D., Qian, Song S., Wolpert, Robert L., Reckhow, Kenneth H.
• Format: Article
• Language: English
• Subjects: Applied sciences; Bacteria; Bacteria die-off; Bayes Theorem; Bayesian analysis; Bayesian theory; calibration; environmental indicators; Environmental Monitoring - methods; Exact sciences and technology; feces; hydrologic models; indicator species; least squares; Least-Squares Analysis; MCMC; microbial contamination; Models, Theoretical; Monte Carlo method; Other industrial wastes. Sewage sludge; Parameter estimation; Pollution; pollution load; prediction; probabilistic models; Wastes; Water Microbiology; water pollution; water quality; Water quality modeling; Water treatment and pollution
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2009.02.034

Water resource management decisions often depend on mechanistic or empirical models to predict water quality conditions under future pollutant loading scenarios. These decisions, such as whether or not to restrict public access to a water resource area, may therefore vary depending on how models reflect process, observation, and analytical uncertainty and variability. Nonetheless, few probabilistic modeling tools have been developed which explicitly propagate fecal indicator bacteria (FIB) analysis uncertainty into predictive bacterial water quality model parameters and response variables. Here, we compare three approaches to modeling variability in two different FIB water quality models. We first calibrate a well-known first-order bacterial decay model using approaches ranging from ordinary least squares (OLS) linear regression to Bayesian Markov chain Monte Carlo (MCMC) procedures. We then calibrate a less frequently used empirical bacterial die-off model using the same range of procedures (and the same data). Finally, we propose an innovative approach to evaluating the predictive performance of each calibrated model using a leave-one-out cross-validation procedure and assessing the probability distributions of the resulting Bayesian posterior predictive p-values. Our results suggest that different approaches to acknowledging uncertainty can lead to discrepancies between parameter mean and variance estimates and predictive performance for the same FIB water quality model. Our results also suggest that models without a bacterial kinetics parameter related to the rate of decay may more appropriately reflect FIB fate and transport processes, regardless of how variability and uncertainty are acknowledged.