The exponential store: a correct formulation for rainfall—runoff modelling

Michel, Claude; Perrin, Charles; Andréassian, Vazken

doi:10.1623/hysj.48.1.109.43484

Cited by 48 publications

(42 citation statements)

References 14 publications

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“…This corroborates the finding of O'Connor (1998) that, in practical hydrology, the simpler models, based largely on exercises in pattern recognition and curve fitting through analysis of the available data, can still play a significant role as effective simulation tools, and that performance enhancement is not guaranteed by the adoption of complex model structures. Other authors such as Michel et al (2003), Wilby et al (2003), Campolo et al (2003), Chibanga et al (2003), Solomatine & Dulal (2003), Onyando et al (2003) and Güntner et al (2004) and have also attested to this observation.…”

Section: Resultssupporting

confidence: 57%

Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin, Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero, en Tanzanie

Yawson

Kongo

Kachroo

2005

Hydrological Sciences Journal

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Modelling of the rainfall-runoff transformation process and routing of river flows in the Kilombero River basin and its five sub-catchments within the Rufiji River basin in Tanzania was undertaken using three system (black-box) models-a simple linear model, a linear perturbation model and a linear varying gain factor model-in their linear transfer function forms. A lumped conceptual model-the soil moisture accounting and routing model-was also applied to the sub-catchments and the basin. The HEC-HMS model, which is a distributed model, was applied only to the entire Kilombero River basin. River discharge, rainfall and potential evaporation data were used as inputs to the appropriate models and it was observed that sometimes the system models performed better than complex hydrological models, especially in large catchments, illustrating the usefulness of using simple black-box models in datascarce situations.

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Section: Resultssupporting

confidence: 57%

Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin, Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero, en Tanzanie

Yawson

Kongo

Kachroo

2005

Hydrological Sciences Journal

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“…The TOPMO model is a six-parameter version derived from the TOPMODEL (Beven & Kirkby, 1979;Michel et al, 2003). In this version, the distribution of the topographic index was not computed using a DEM, but parameterized, as suggested by the results of Franchini et al (1996).…”

Section: Rainfall-runoff Modelsmentioning

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

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163

142

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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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“…This version uses a parameterized analytical expression of the soil-topographic index distribution (see Edijatno et al [1999] and Michel et al [2003] for more details). To avoid confusion with TOPMODEL, it will be denoted TOPMO hereinafter.…”

Section: Rainfall-runoff Modelsmentioning

confidence: 99%

Dynamic averaging of rainfall‐runoff model simulations from complementary model parameterizations

Oudin

Andréassian²,

Mathevet³

et al. 2006

Water Resources Research

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191

132

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[1] In this paper, we analyze how our evaluation of the capacity of a rainfall-runoff model to represent low or high flows depends on the objective function used during the calibration process. We present a method to combine models to produce a more satisfactory streamflow simulation, on the basis of two different parameterizations of the same model. Where we previously had to choose between a more efficient simulation for either high flows or low flows (but inevitably less efficient in the other range), we show that a balanced simulation can be obtained by using a seasonal index to weigh the two simulations, providing good efficiency in both low and high flows.

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The exponential store: a correct formulation for rainfall—runoff modelling

Cited by 48 publications

References 14 publications

Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin, Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero, en Tanzanie

Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin, Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero, en Tanzanie

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

Dynamic averaging of rainfall‐runoff model simulations from complementary model parameterizations

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