Quality assurance in model based water management – review of existing practice and outline of new approaches

Refsgaard, Jens Christian; Henriksen, Hans Jørgen; Harrar, William G.; Schölten, H.; Kassahun, Ayalew

doi:10.1016/j.envsoft.2004.07.006

Cited by 144 publications

(91 citation statements)

References 7 publications

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“…; see, for example, Jakeman & Letcher, 2003;Refsgaard et al, 2005). Existing sensitivity analysis studies, involving various RR models, mostly deal with: (a) the sensitivity to rainfall input data (see, for example, the review by Andréassian et al, 2001); (b) the sensitivity to potential evapotranspiration input data (see Andréassian et al, 2004;Oudin et al, 2005); and (c) the sensitivity to model structure and parameter values (see Kim & Delleur, 1997;Kumar et al, 2002;Butts et al, 2004;Merritt et al, 2005;Sieber & Uhlenbrook, 2005).…”

Section: Sensitivity Analysis Of Rainfall-runoff Models To the Amountmentioning

confidence: 99%

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Perrin¹,

Oudin²,

Andréassian³

et al. 2007

Hydrological Sciences Journal

171

149

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Streamflow data are essential for the calibration of continuous rainfall-runoff (RR) models. The quantity and quality of streamflow data can significantly influence parameter calibration and thus model robustness. Most existing sensitivity analysis studies on the role of streamflow data have used continuous periods to calibrate model parameters, with a minimum of one year, though ideally much longer periods are generally advised. However, in practical model applications, streamflow data series available for model calibration may be rather short or non-continuous. This study aims at assessing the sensitivity of continuous RR models to the quantity of information used during model calibration when it is randomly sampled in the observed hydrograph, i.e. using non-continuous calibration periods. This sampling provides less auto-correlated streamflow information for model calibration than continuous records. Two daily RR models with four and six free parameters were tested on a sample of 12 basins in the USA to obtain more general conclusions. The results showed that, in general, 350 calibration days sampled out of a longer data set including dry and wet conditions are sufficient to obtain robust estimates of model parameters. The more parsimonious model requires fewer calibration data to obtain stable and robust parameter values. Stable parameter values prove more difficult to reach in the driest catchments.Key words rainfall-runoff modelling; calibration; sampling; sensitivity analysis; streamflow data Impact d'une limitation de données de débit sur l'efficacité et les paramètres de modèles pluie-débit Résumé Les données de débit sont essentielles pour caler les modèles pluie-débit continus. La quantité et la qualité des données peuvent influencer de manière significative le calage des paramètres et donc la robustesse du modèle. La plupart des analyses de sensibilité ayant abordé le rôle des données de débit ont utilisé des périodes continues pour le calage des paramètres des modèles, avec un minimum d'une année, bien qu'idéalement des chroniques beaucoup plus longues soient généralement conseillées. Cependant, dans des applications pratiques de modélisation, les séries de données disponibles pour le calage des modèles sont courtes ou non-continues. L'objectif de cette étude est d'évaluer la sensibilité des modèles pluie-débit continus à la quantité d'information utilisée pour leur calage lorsqu'elle est échantillonnée aléatoirement dans l'hydrogramme observé, c'est-à-dire en utilisant des périodes de calage non-continues. Cet échantillonnage fournit une information de débit moins corrélée qu'une série continue pour le calage. Nous avons utilisé ici deux modèles pluie-débit avec quatre et six paramètres, et nous les avons testés sur un échantillon de 12 bassins aux Etats-Unis, pour obtenir des conclusions plus générales. Les résultats montrent qu'en général, 350 jours de calage tirés dans une série plus longue comprenant des conditions sèches et humides sont suffisants pour obtenir des valeurs robustes des...

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Section: Sensitivity Analysis Of Rainfall-runoff Models To the Amountmentioning

confidence: 99%

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Perrin¹,

Oudin²,

Andréassian³

et al. 2007

Hydrological Sciences Journal

171

149

View full text Add to dashboard Cite

show abstract

“…This will require defining limits of acceptability or benchmarks that can help reject or accept these hypotheses. Following good modelling practice guidelines for model verification, testing and validation could also help increase the confidence in the results and models we develop and then transfer to end-users (Refsgaard & Henriksen, 2004;Refsgaard et al, 2005).…”

Section: Implementing Severe Testsmentioning

confidence: 99%

The Court of Miracles of Hydrology: can failure stories contribute to hydrological science?

Andréassian¹,

Perrin²,

Parent

et al. 2010

Hydrological Sciences Journal

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“…Adopting good modelling practices improves the chances for the reusability and extendibility of models. The Position Paper [4] on the development and evaluation of environmental models is just one example of a host of literature and recommended practice that includes Refsgaard [5], Van der Sluijs et al [6] and Lee [7].…”

Section: Protocols and Guidelinesmentioning

confidence: 99%

Making Progress in Integrated Modelling and Environmental Decision Support

Jakeman

Sawah

Guillaume

et al. 2011

IFIP Advances in Information and Communication Technology

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Abstract. Integrated modelling and environmental decision support are increasingly important as society tackles some of the most complex challenges of our generation, with impacts on future generations. When integrated modelling is successful, the results can be transformational yet the core elements for generating that success are not always clear. There is an elusive element to finding the best mix of methods, models and approaches for any given problem. This raises issues for repeatability and questions regarding how the emerging metadiscipline will converge in order to consistently achieve quality results or increased understanding of the processes that lead to success. Key challenges include the need to diagnose elements that lead to successful process, training for professional and technical competencies, and increased access to stable platforms and interchangeable models and modelling tools. This paper aims to summarize some of the key process and product related challenges of integrated modelling and environmental decision support.

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Quality assurance in model based water management – review of existing practice and outline of new approaches

Cited by 144 publications

References 7 publications

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

The Court of Miracles of Hydrology: can failure stories contribute to hydrological science?

Making Progress in Integrated Modelling and Environmental Decision Support

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