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Open AccessJournal ArticleDOI

Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation

David R. Legates, +1 more
- 01 Jan 1999 - 
- Vol. 35, Iss: 1, pp 233-241
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TLDR
In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.
Abstract
Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness-of-fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation-based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation-based measures should not be used to assess the goodness-of-fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.

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Citations
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Hydrological modelling using artificial neural networks

TL;DR: A template is proposed in order to assist the construction of future ANN rainfall-runoff models and it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
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Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure

TL;DR: In this paper, the authors describe parameter uncertainties using uniform distributions and fit these distributions iteratively within larger absolute intervals such that two criteria are met: (i) bracketing most of the measured data (>90%) within the 95% prediction uncertainty (95PPU) and (ii) obtaining a small ratio (<1) of the average difference between the upper and lower 95PPU and the standard deviation of measured data.
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A review and evaluation of catchment transit time modeling

TL;DR: In this article, the authors present an evaluation and review of the transit time literature in the context of catchments and water transit time estimation and provide a critical analysis of unresolved issues when applied at the catchment-scale.
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A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

TL;DR: Developing a hydrological forecasting model based on past records is crucial to develop a water quality forecasting model that can be applied to the Yangtze River basin.
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Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments

TL;DR: In this paper, the authors proposed to use a combination of graphical results, absolute value error statistics (i.e. root mean square error), and normalized goodness-of-fit statistics (e.g. Nash-Sutcliffe Efficiency coefficient, NSE) for quantifying the goodness of observations against model-calculated values.
References
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Journal ArticleDOI

River flow forecasting through conceptual models part I — A discussion of principles☆

TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Journal ArticleDOI

On the validation of models

TL;DR: In this paper, it is suggested that the correlation coefficieness between observed and simulated variates is not as good as observed variates, and that correlation can be improved.
Journal ArticleDOI

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.
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