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A new technique, Bayesian Monte Carlo (BMC), is used to quantify errors in water quality models caused by uncertain parameters. BMC also provides estimates of parameter uncertainty as a function of observed data on model state variables. The use of Bayesian inference generates uncertainty estimates that combine prior information on parameter uncertainty with observed variation in water quality data to provide an improved estimate of model parameter and output uncertainty. It Mso combines Monte Carlo analysis with Bayesian inference to determine the ability of random selected parameter sets to simulate observed data. BMC expands upon previous studies by providing a quantitative estimate of parameter acceptabilty using the statistical likelih,'md function. The likelihood of each parameter set is employed to generate an n-dimensional hypercube describing a probability distribution of each parameter and the covariance among parameters. These distributions are utilized to estimate uncertainty in model predictions. Application of BMC to a dissolved oxygen model reduced the estimated uncertainty in model output by 72% compared with standard Monte Carlo techniques. Sixty percent of this reduction was directly attributed to consideration of covariauce between model parameters. A significant benefit of the technique is the ability to compare the reduction in total model output uncertainty corresponding to: (1) collection of more data on model state variables, and (2) laboratory or field studies to better define model processes. Limitations of the technique include computational requirements and accurate estimation of the joint probability distribution of model errors. This analysis was conducted assuming that model error is normally and independently distributed.

Abstraet-A budget model is developed to predict the long-term response of a lake to changes in its phosphorus loading. This model computes total phosphorus and hypolimnetic oxygen concentrations, taking sediment-water interactions into account. The lake is treated as two segments: the water and a surface sediment layer. A total phosphorus budget for the water accounts for inputs due to external loading and recycle from the sediments. It reflects losses due to flushing and settling. The sediment layer gains total phosphorus by settling and loses total phosphorus by recycle and burial. The recycle from the sediments to the water is dependent on the levels of sediment total phosphorus and hypolimnetic oxygen. Hypolimnetic oxygen concentration is estimated with a semi-empirical model. The model is applied to Shagawa Lake. An analysis is performed to demonstrate how its predictions replicate in-lake changes not possible with simpler phosphorus budget models.

The biology of the zebra mussel is reviewed as it relates to water‐quality problems in rivers. A relationship between population densities of the zebra mussel and their respiration demands for oxygen is developed, which can be used to support the analysis of the impact of zebra mussel infestation on the oxygen resources of streams and rivers. Dramatic changes in the water quality of the Seneca River, N.Y., a major tributary to Lake Ontario, have been brought about by zebra mussel infestation. These changes are documented from 3 years of monitoring data. The infestation converted the Seneca River from a turbid, phytoplankton‐rich, nutrient‐depleted system, with nearly saturated oxygen concentrations, to a river with high clarity, low‐phytoplankton concentrations, enriched in dissolved nutrients, with greatly undersaturated oxygen concentrations. The degradation of oxygen resources was severe enough to cause violations of New York State standards for daily minimum and daily average concentrations for a number of days in the late summer of 1993. The associated loss in the waste assimilative capacity of the river is confounding waste discharge management and planning efforts in the river basin. This form of degradation is expected for other systems as the zebra mussel infestation spreads. The impact is expected to be greatest in rivers and streams with high concentrations of phytoplankton, large areas of rock substrate, and limited reaeration capacity.

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