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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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Quantifying uncertainty from large-scale model predictions of forest carbon dynamics

TL;DR: In this paper, the authors linked the biogeochemical Forest-DNDC model to a GIS database to predict growth of Eucalyptus globulus plantations at two different scales (0.045 ha plot and 100 ha grid � 1 scale) across Victoria, in south-eastern Australia.
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Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions.

TL;DR: Assessment of the ability of several state of the art gridded precipitation products to reproduce monthly precipitation values at climate stations in the Pamir mountains during two 15 year periods shows error patterns and magnitudes show that in complex terrain, evaluation of temporal and spatial variations of included observations is a prerequisite.
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Inverse Dual-Permeability Modeling of Preferential Water Flow in a Soil Column and Implications for Field-Scale Solute Transport

TL;DR: In this paper, a DPM based on two coupled Richards9 equations was applied to compare the performance of inverse and forward simulations of laboratory preferential flow data. But the results showed that a hydrograph alone is not sufficient for inverse identification of soil hydraulic DPM parameters.
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Determination of compound channel apparent shear stress: application of novel data mining models

TL;DR: In this article, the authors investigated various data mining models to estimate apparent shear stress in a symmetric compound channel with smooth and rough floodplains, including random forest (RF), random tree (RT), reduced error pruning tree (REPT), M5P, and the distinguished hybrid bagging-M5P model.
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Influence of river discharge patterns on the hydrodynamics and potential contaminant dispersion in the Douro estuary (Portugal).

TL;DR: Stable flows seem to be the most effective in dispersing contaminants eventually introduced into the estuary through its small river tributaries, and flow management may have important effects on estuarine hydrodynamics through non-linear interactions between flow magnitude and variability.
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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