Use of Selected Goodness-of-Fit Statistics to Assess the Accuracy of a Model of Henry Hagg Lake, Oregon

Assessing a model's ability to reproduce field data is a critical step in the modeling process. For any model, some method of determining goodness-of-fit to measured data is needed to aid in calibration and to evaluate model performance. Visualizations and graphical comparisons of model output are an excellent way to begin that assessment. At some point, however, model performance must be quantified. Goodness-of-fit statistics, including the mean error (ME), mean absolute error (MAE), root mean square error, and coefficient of determination, typically are used to measure model accuracy. Statistical tools such as the sign test or Wilcoxon test can be used to test for model bias. The runs test can detect phase errors in simulated time series. Each statistic is useful, but each has its limitations. None provides a complete quantification of model accuracy. In this study, a suite of goodness-of-fit statistics was applied to a model of Henry Hagg Lake in northwest Oregon. Hagg Lake is a man-made reservoir on Scoggins Creek, a tributary to the Tualatin River. Located on the west side of the Portland metropolitan area, the Tualatin Basin is home to more than 450,000 people. Stored water in Hagg Lake helps to meet the agricultural and municipal water needs of that population. Future water demands have caused water managers to plan for a potential expansion of Hagg Lake, doubling its storage to roughly 115,000 acre-feet. A model of the lake was constructed to evaluate the lake's water quality and estimate how that quality might change after raising the dam. The laterally averaged, two-dimensional, U.S. Army Corps of Engineers model CE-QUAL-W2 was used to construct the Hagg Lake model. Calibrated for the years 2000 and 2001 and confirmed with data from 2002 and 2003, modeled parameters included water temperature, ammonia, nitrate, phosphorus, algae, zooplankton, and dissolved oxygen. Several goodness-of-fit statistics were used to quantify model accuracy and bias. Model performance was judged to be excellent for water temperature (annual ME: -0.22 to 0.05 ° C; annual MAE: 0.62 to 0.68 ° C) and dissolved oxygen (annual ME: -0.28 to 0.18 mg/L; annual MAE: 0.43 to 0.92 mg/L), showing that the model is sufficiently accurate for future water resources planning and management.