文摘
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Summary
Success in the use of computer models for simulating environmental variables and processes requires objective model calibration and verification procedures. Several methods for quantifying the goodness-of-fit of observations against model-calculated values have been proposed but none of them is free of limitations and are often ambiguous. When a single indicator is used it may lead to incorrect verification of the model. Instead, a combination of graphical results, absolute value error statistics (i.e. root mean square error), and normalized goodness-of-fit statistics (i.e. Nash-Sutcliffe Efficiency coefficient, NSE) is currently recommended. Interpretation of NSE values is often subjective, and may be biased by the magnitude and number of data points, data outliers and repeated data. The statistical significance of the performance statistics is an aspect generally ignored that helps in reducing subjectivity in the proper interpretation of the model performance. In this work, approximated probability distributions for two common indicators (NSE and root mean square error) are derived with bootstrapping (block bootstrapping when dealing with time series), followed by bias corrected and accelerated calculation of confidence intervals. Hypothesis testing of the indicators exceeding threshold values is proposed in a unified framework for statistically accepting or rejecting the model performance. It is illustrated how model performance is not linearly related with NSE, which is critical for its proper interpretation. Additionally, the sensitivity of the indicators to model bias, outliers and repeated data is evaluated. The potential of the difference between root mean square error and mean absolute error for detecting outliers is explored, showing that this may be considered a necessary but not a sufficient condition of outlier presence. The usefulness of the approach for the evaluation of model performance is illustrated with case studies including those with similar goodness-of-fit indicators but distinct statistical interpretation, and others to analyze the effects of outliers, model bias and repeated data. This work does not intend to dictate rules on model goodness-of-fit assessment. It aims to provide modelers with improved, less subjective and practical model evaluation guidance and tools.
