Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments

Ritter, Axel; Muñoz‐Carpena, Rafael

doi:10.1016/j.jhydrol.2012.12.004

Cited by 782 publications

(477 citation statements)

References 61 publications

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“…7). This model performance is very good according to the statistical assessment of hydrologic model performances by (Ritter and Muñoz-Carpena, 2013). …”

Section: Simulation With the Second Implementation Of The Discrete Ramentioning

confidence: 89%

Modelling stream flow with a discrete rainfall–runoff model and 37 GHz PDBT microwave observations: the Xiangjiang River basin case study

Shang

Menenti

2016

Preprint

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“…7). This model performance is very good according to the statistical assessment of hydrologic model performances by (Ritter and Muñoz-Carpena, 2013). …”

Section: Simulation With the Second Implementation Of The Discrete Ramentioning

confidence: 89%

Modelling stream flow with a discrete rainfall–runoff model and 37 GHz PDBT microwave observations: the Xiangjiang River basin case study

Shang

Menenti

2016

Preprint

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“…The evaluation of all the applied methods is based on the combination of graphical results, statistical evaluation metrics, and normalized goodness-of-fit statistics. Furthermore, a comprehensive procedure proposed by Ritter and Muñoz-Carpena (2013) for evaluating model performance is tested for all applied methods. Approximated probability distributions for NSE and root-mean-square error (RMSE) are derived with bootstrapping followed by bias correction and enhanced calculation of confidence intervals.…”

Section: Evaluation Of the Methodsmentioning

confidence: 99%

Streamflow simulation methods for ungauged and poorly gauged watersheds

Λουκάς

Vasiliades

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Rainfall-runoff modelling procedures for ungauged and poorly gauged watersheds are developed in this study. A well-established hydrological model, the University of British Columbia (UBC) watershed model, is selected and applied in five different river basins located in Canada, Cyprus, and Pakistan. Catchments from cold, temperate, continental, and semiarid climate zones are included to demonstrate the procedures developed. Two methodologies for streamflow modelling are proposed and analysed. The first method uses the UBC watershed model with a universal set of parameters for water allocation and flow routing, and precipitation gradients estimated from the available annual precipitation data as well as from regional information on the distribution of orographic precipitation. This method is proposed for watersheds without streamflow gauge data and limited meteorological station data. The second hybrid method proposes the coupling of UBC watershed model with artificial neural networks (ANNs) and is intended for use in poorly gauged watersheds which have limited streamflow measurements. The two proposed methods have been applied to five mountainous watersheds with largely varying climatic, physiographic, and hydrological characteristics. The evaluation of the applied methods is based on the combination of graphical results, statistical evaluation metrics, and normalized goodness-of-fit statistics. The results show that the first method satisfactorily simulates the observed hydrograph assuming that the basins are ungauged. When limited streamflow measurements are available, the coupling of ANNs with the regional, non-calibrated UBC flow model components is considered a successful alternative method to the conventional calibration of a hydrological model based on the evaluation criteria employed for streamflow modelling and flood frequency estimation.

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“…As single-objective calibration lowers the identifiability of model parameters and structural 10 elements (Efstratiadis and Koutsoyiannis, 2010) and often hide shortcomings of models (Ritter and Muñoz-Carpena, 2013), we pursued a multi-objective calibration procedure. Following the concept of Moriasi et al (2007), a model run was deemed behavioural, if the logarithmic Nash-Sutcliffe-Efficiency (logNSE) was >0.5, the percentage bias (PBIAS) was below/above ±25% and the ratio between root mean square error to the standard deviation of the measured data (RSR) was <0.7.…”

Section: Calibration and Validationmentioning

confidence: 99%

Incremental model breakdown to assess the multi-hypotheses problem

Jehn

Houska

Bestian

et al. 2017

Preprint

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Abstract.The ambiguous representation of hydrological processes have led to the formulation of the multiple hypotheses approach in hydrological modelling, which requires new ways of model construction. However, most recent studies focus 10 only on the comparison of predefined model structures or building a model step-by-step. This study tackles the problem the other way around: We start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash-Sutcliffe, percentage bias and the ratio between root mean square error to the standard deviation of the measured data). Through this 15 incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15 th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 with the final Model 15 and find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and less model parameters.

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Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments

Cited by 782 publications

References 61 publications

Modelling stream flow with a discrete rainfall–runoff model and 37 GHz PDBT microwave observations: the Xiangjiang River basin case study

Modelling stream flow with a discrete rainfall–runoff model and 37 GHz PDBT microwave observations: the Xiangjiang River basin case study

Streamflow simulation methods for ungauged and poorly gauged watersheds

Incremental model breakdown to assess the multi-hypotheses problem

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