Ensemble evaluation of hydrological model hypotheses

Krueger, Tobias; Freer, J. E.; Quinton, John; Macleod, Christopher; Bilotta, Gary; Brazier, Richard E.; Butler, P. J.; Haygarth, P. M.

doi:10.1029/2009wr007845

Cited by 93 publications

(112 citation statements)

References 58 publications

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“…The diagnosis of these failures is often difficult without extra information (e.g. Tromp-van Meerveld and McDonnell, 2006;Krueger et al, 2010;Clark et al, 2011a;Renard et al, 2011, to mention a few). In addition, aggregate performance criteria such as the Nash-Sutcliffe efficiency index may hide internal model inconsistencies.…”

Section: The Fixed Modelling Approachmentioning

confidence: 99%

The influence of conceptual model structure on model performance: a comparative study for 237 French catchments

Esse

Perrin

Booij

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Models with a fixed structure are widely used in hydrological studies and operational applications. For various reasons, these models do not always perform well. As an alternative, flexible modelling approaches allow the identification and refinement of the model structure as part of the modelling process. In this study, twelve different conceptual model structures from the SUPERFLEX framework are compared with the fixed model structure GR4H, using a large set of 237 French catchments and discharge-based performance metrics. The results show that, in general, the flexible approach performs better than the fixed approach. However, the flexible approach has a higher chance of inconsistent results when calibrated on two different periods. When analysing the subset of 116 catchments where the two approaches produce consistent performance over multiple time periods, their average performance relative to each other is almost equivalent. From the point of view of developing a well-performing fixed model structure, the findings favour models with parallel reservoirs and a power function to describe the reservoir outflow. In general, conceptual hydrological models perform better on larger and/or wetter catchments than on smaller and/or drier catchments. The model structures performed poorly when there were large climatic differences between the calibration and validation periods, in catchments with flashy flows, and in catchments with unexplained variations in low flow measurements.

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Section: The Fixed Modelling Approachmentioning

confidence: 99%

The influence of conceptual model structure on model performance: a comparative study for 237 French catchments

Esse

Perrin

Booij

et al. 2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Recently, Liu et al (2009) have proposed a methodology for identifying behavioural models avoiding the subjective choice of a threshold based on a global goodness of fit index, replacing it by a condition for every time step based on an observation error set prior. An application of the GLUE methodology to account uncertainty in model parameter, model structure and data is presented by Krueger et al (2010), however, the understanding of data uncertainties often remains incomplete (e.g. rainfall input).…”

Section: Introductionmentioning

confidence: 99%

Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

Velázquez¹,

Anctil²,

Perrin³

2010

Hydrol. Earth Syst. Sci.

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Abstract. This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

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“…McMillan et al (2012) provide a list of typical quantitative results of combined flow rate uncertainty. In one studies this uncertainty is listed as high as 100% for low flows, 10% for medium flows, and 20% for high flows (Krueger et al, 2010;McMillan et al, 2012). Daily discharge uncertainty is listed with a range of ± 100 -200% for low flows and ±100% for high flows by Harmel and Smith (2007) and up to 50% by Hamilton and Moore (2012) for all magnitudes.…”

Section: Significance Of the Error Value Ementioning

confidence: 99%

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

Khan¹,

Valeo²

2016

J ENVIRON INFORM

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ABSTRACT.A fuzzy linear regression (FLR) method is proposed that uses real-time data to accurately predict daily peak flow rate for the Bow and Elbow Rivers in southern Alberta. FLR model performance was compared to a non-fuzzy, error-in-variables model (EIV). Mean daily flow rate, with a delay of one, two, three or seven days was used as the independent variable. In implementing the FLR, a unique hybrid modelling approach was devised that treated peak flow rate as probabilistic and mean daily flow rate as possibilistic. Three gauge errors, 5%, 10% and 20%, were tested and compared to quantify uncertainty in observed flow rate. The research proposed a new method of computing the exceedance probability of peak flow rate using fuzzy numbers. NSE, PBIAS and RSR and a proposed rating system were used to evaluate and compare the methods. Two different calibration schemes were used, including a quasi-real time system. The tests demonstrated that FLR with a one day lag was a very good predictor of peak flow rate and outperformed EIV for two stations on the Bow River. A test dataset from the floods of June 2013 in Calgary was used for risk assessment. The FLR results demonstrated higher flexibility and sensitivity to the flood as it approached Calgary. The fuzzy method was able to capture the peak flow rate for the majority of the high flow rate days, while the EIV model was unable to predict this data within the 95% confidence interval.

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Ensemble evaluation of hydrological model hypotheses

Cited by 93 publications

References 58 publications

The influence of conceptual model structure on model performance: a comparative study for 237 French catchments

The influence of conceptual model structure on model performance: a comparative study for 237 French catchments

Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

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