HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

By C. Dawson, Robert J. Abrahart, Linda M. See. Environmental Modelling & Software, 2007

516 citations recorded by the source corpus.

This paper presents details of an open access web site that can be used by hydrologists and other scientists to evaluate time series models. There is at present a general lack of consistency in the way in which hydrological models are assessed that handicaps the comparison of reported studies and hinders the development of superior models. The HydroTest web site provides a wide range of objective metrics and consistent tests of model performance to assess forecasting skill. This resource is designed to promote future transparency and consistency between reported models and includes an open forum that is intended to encourage further discussion and debate on the topic of hydrological performance evaluation metrics. It is envisaged that the provision of such facilities will lead to the creation of superior forecasting metrics and the development of international benchmark time series datasets.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b00e6e5d2e97c1ea867376bc47c1b1b1a8e4242a

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Citations in the corpus

A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, unknown): “They are the correlation coefficient (R), root-mean-square error (RMSE), and Nash–Sutcliffe model efficiency coefficient (NSE) [Nash and Shutcliff, 1970; Dawson et al., 2007; Bennett et al., 2013].”
Comparing expert judgement and numerical criteria for hydrograph evaluation (2015, methodology): “A number of studies, including those by Smith et al. (2004), Dawson et al. (2007, 2010), Krause et al. (2005), and references therein, were analysed to compile a list of 60 criteria (see Appendix).”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “Dawson et al. (2007) discussed 20 performance metrics used in hydrological models.”
Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry (2017, methodology): “Statistics chosen were mean absolute error (MAE), mean error (ME), index of agreement (IoAd), and Nash-Sutcliffe efficiency (NSE), which were calculated using the ‘HydroTest’ software (Dawson et al., 2007).”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, methodology): “) and therefore, based on the purpose of the model, different criteria should be selected (Dawson et al., 2007; Bennett et al., 2013).”
The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method (2016, background): “…error (RMSE) is selected as the performance criterion of level prediction, records in real units the level of overall agreement between the observed and modelled datasets (Dawson et al. 2007) and is indicative of the good measure of model performance for high flows (Karunanithi et al. 1994).”
Characterising performance of environmental models (2013, methodology): “Studies have also focused explicitly on performance criteria, such as Moriasi et al. (2007) and Dawson et al. (2007, 2010), who produced guidelines for systematic model evaluation, including a list of recommended evaluation techniques and performance metrics.”