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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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Modelling of the monthly and daily behaviour of the runoff of the Xallas river using Box-Jenkins and neural networks methods

TL;DR: In this article, a study of the hydrological behaviour of the Xallas river basin in the northwest of Spain, based on modelling the performance of the runoff produced by the river at different temporal scales, is presented.
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Comparison of four updating models for real-time river flow forecasting

TL;DR: It is recommended that, in the context of real-time river flow forecasting based on error-forecast updating, modellers should continue to use the single autoregressive (AR) model.
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Structural optimisation and input selection of an artificial neural network for river level prediction

TL;DR: A recently developed novel optimisation algorithm combining properties of simulated annealing and tabu search is used to arrive at an optimal ANN for the prediction of river levels 5 h in advance, representing an improvement over trial and error as a method of ANN structural optimisation and input selection.
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Short-term temporal changes of soil carbon losses after tillage described by a first-order decay model

TL;DR: In this article, a model was proposed to explain carbon dioxide (CO2) emission after tillage as a function of the no-till emission plus a correction due to the tillage disturbance.
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Modeling runoff–sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed

TL;DR: In this article, a watershed simulation using hydrological model integrated with GIS has been conducted for the Beressa watershed using a SWAT-CUP model, which has successfully simulated and calibrated runoff and sediment yield.
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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