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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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Modeling reference evapotranspiration in island environments: assessing the practical implications.

TL;DR: In this paper, the authors used gene expressions programming (GEP) and k-fold testing to evaluate the performance of different ETo models in five island locations of Iran and found that the calibrated mass transfer-based equations and their corresponding GEP models generally surpassed the other applied models in the studied locations.
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Sensitivity Analysis and Calibration of Hydrological Modeling of the Watershed Northeast Brazil

TL;DR: In this paper, a distributed hydrological model (SWAT) was applied to the flows of water in the watershed of the Poxim River in order to conduct sensitivity analysis and calibration of a SWAT.
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Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

TL;DR: In this paper, the authors present the computational procedures relative to predicting the missing variables from temperature, namely, the PM temperature approach (PMT), and estimating ETo with the Hargreaves-Samani (HS) equation.
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Impacts of historic climate variability on seasonal soil frost in the midwestern United States.

TL;DR: In this paper, the effects of historic climate variability on cold-season processes, including soil temperature, frost depth, and the number of frost days and freeze-thaw cycles, were examined.
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Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine

TL;DR: This study attempts to address the issue of convergence to the local optima instead of the global optimum by hybridizing the ELM with three swarm-based optimization algorithms, namely the particle swarm optimization (PSO), the moth-flame optimization (MFO) and the whale optimization algorithm (WOA) with different fitness functions.
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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