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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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Automatic Calibration and Predictive Uncertainty Analysis of a Semidistributed Watershed Model

TL;DR: In this article, a two-stage routine for automatic calibration of the semidistributed watershed model Soil and Water Assessment Tool (SWAT) that finds the best values for the model parameters, preserves spatial variability in essential parameters, and leads to a measure of the model prediction uncertainty is presented.
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Hydrological analysis of the Upper Tiber River Basin, Central Italy: a watershed modelling approach

TL;DR: In this article, a physically based watershed model called Soil Water Assessment Tool was used to understand the hydrologic behaviour of the Upper Tiber River Basin, Central Italy, which was successfully calibrated and validated using observed weather and flow data for the period of 1963-1970 and 1971-1978, respectively.
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Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions

TL;DR: In this paper, the authors investigated the potential of Singular Spectral Analysis (SSA), Seasonal-Trend decomposition using Loess (STL) and attribute selection pre-processing approaches with the neural network methods in predicting monthly river streamflows in the Nallihan stream, Turkey.
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How well can we estimate error variance of satellite precipitation data around the world

TL;DR: In this paper, a method of estimating the square difference prediction of satellite precipitation using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate was presented.
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Improving the predictions of a MIKE SHE catchment‐scale application by using a multi‐criteria approach

TL;DR: Application of the suggested protocol, and in particular the use of the filtered flow-components in model calibration, enhances the physical consistency of model predictions, adding considerable value to the calibration process.
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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