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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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Groundwater Recharge and Capillary Rise in Irrigated Areas of the Upper Yellow River Basin Assessed by an Agro‐Hydrological Model

TL;DR: In this paper, a mathematical model SWAP-EPIC was applied to analyse the processes of groundwater recharge and capillary rise in the Hetao Irrigation District of the upper Yellow River basin.
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Numerical study on the urbanisation of Putrajaya and its interaction with the local climate, over a decade

TL;DR: In this article, the authors investigate the chronological local urban climate changes that have taken place over a decade (1999-2011) of urbanisation using the NCAR Weather and Research Forecasting (WRF) model coupled to a numerically proven land surface and urban canopy model (NOAH LSM/UCM).
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Calibration of the Hargreaves–Samani method for the calculation of reference evapotranspiration in different Köppen climate classes

TL;DR: In this article, the Hargreaves-Samani (HS) method is used for the calculation of ETo since it is based on measurements of daily minimum and maximum air temperature alone.
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Exploring effective best management practices in the Miyun reservoir watershed, China

TL;DR: Wang et al. as mentioned in this paper developed a watershed model (the Soil and Water Assessment Tool) for the Miyun Reservoir Watershed and calibrated and validated it using long-term sediment, nitrogen (N) and phosphorus (P) data.
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Satellite gravity measurement monitoring terrestrial water storage change and drought in the continental United States

TL;DR: GHDI is established to represent the extent of GRACE-inferred TWS anomaly departing from its historical average and is calibrated to resemble traditional Palmer Hydrological Drought Index (PHDI) in the continental US, indicating its feasibility for drought monitoring.
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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