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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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Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning

TL;DR: In this paper, a Gene Expression Programming (GEP) model was used for estimating evaporation through spatial and temporal data scanning techniques using leave-one-out procedures, where a complete scan of the possible train and test set configurations is carried out according to temporal and spatial criteria.
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Past, present and future land use changes and their impact on water balance

TL;DR: Assessment of the landuse change and its impact on the water balance of the Narmada river basin in Madhya Pradesh, India shows an increased water yield and surface runoff but decreased ET, which is due to change in the curve number (CN) values.
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Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling

TL;DR: In this article, the authors investigated the forecasting ability of a conceptual and a neural network model for real-time rainfall runoff forecasting, comparing the performances obtainable for increasing lead-times and analyzing the influence of the amount of the calibration data over two real-data case studies.
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Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model

TL;DR: In this paper, a CROPGRO-Cotton module within the Cropping System Model (CSM) that is distributed with the Decision Support System for Agrotechnology Transfer (DSSAT) was evaluated for the Texas High Plains (THP) region using measured data from cotton water use efficiency experiments at Halfway over a period of four years (2010-2013).
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Estimation of Soil Hydraulic Properties from Numerical Inversion of Tension Disk Infiltrometer Data

TL;DR: In this article, the authors compared Mualem-van Genuchten (MVG) soil hydraulic parameters obtained from direct laboratory and in situ unsaturated hydraulic conductivity measurements with estimates using numerical inversion of tension infiltration data of four coarse-to medium-textured soils in Alentejo (Portugal).
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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