Combining Model Results and Monitoring Data for Water Quality Assessment

Abstract

A Bayesian approach is used to update and improve water quality model predictions with monitoring data. The objective of this work is to facilitate adaptive management by providing a framework for sequentially updating the assessment of water quality status, to evaluate compliance with water quality standards, and to indicate if modification of management strategies is needed. Currently, most water quality or watershed models are calibrated using historical data that typically reflect conditions different from those being forecast. In part because of this, predictions are often subject to large errors. Fortunately, in many instances, postmanagement implementation monitoring data are available, although often with limited spatiotemporal coverage. These monitoring data support an alternative to the one-time prediction: pool the information from both the initial model prediction and postimplementation monitoring data. To illustrate this approach, a watershed nutrient loading model and a nitrogen-chlorophyll a model for the Neuse River Estuary were applied to develop a nitrogen total maximum daily load program for compliance with the chlorophyll a standard. Once management practices were implemented, monitoring data were collected and combined with the model forecast on an annual basis using Bayes Theorem. Ultimately, the updated posterior distribution of chlorophyll a concentration indicated that the Neuse River Estuary achieved compliance with North Carolina's standard.