Daily streamflow prediction with uncertainty in ephemeral catchments using the GLUE methodology

Viola, Francesco; Noto, L. V.; Cannarozzo, Marcella; Loggia, Goffredo La

doi:10.1016/j.pce.2009.06.006

Cited by 31 publications

(21 citation statements)

References 16 publications

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“…The selection of the threshold value is an entirely arbitrary choice that affects the prediction uncertainty (Montanari, 2005;Mantovan and Todini, 2006) and probably is the most important concern for the GLUE method. A small cutoff threshold will lead to larger "behavioral" simulations and larger uncertainty bands, while larger threshold value will decrease the numbers of "behavioral" models and will reduce the uncertainty interval width (Xiong and O'Connor, 2008;Blasone et al, 2008;Viola et al, 2009). As the GLUE method is dependent on all these subjective decisions that influence the final uncertainty prediction, it has been deeply criticized and its several drawbacks have been well pointed out and discussed in the literature (Montanari, 2005;Mantovan and Todini, 2006).…”

Section: Uncertainty Analysis (Ua)mentioning

confidence: 99%

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Sellami

Jeunesse

Benabdallah

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. In this study a method for propagating the hydrological model uncertainty in discharge predictions of ungauged Mediterranean catchments using a model parameter regionalization approach is presented. The method is developed and tested for the Thau catchment located in Southern France using the SWAT hydrological model. Regionalization of model parameters, based on physical similarity measured between gauged and ungauged catchment attributes, is a popular methodology for discharge prediction in ungauged basins, but it is often confronted with an arbitrary criterion for selecting the "behavioral" model parameter sets (Mps) at the gauged catchment. A more objective method is provided in this paper where the transferrable Mps are selected based on the similarity between the donor and the receptor catchments. In addition, the method allows propagating the modeling uncertainty while transferring the Mps to the ungauged catchments. Results indicate that physically similar catchments located within the same geographic and climatic region may exhibit similar hydrological behavior and can also be affected by similar model prediction uncertainty. Furthermore, the results suggest that model prediction uncertainty at the ungauged catchment increases as the dissimilarity between the donor and the receptor catchments increases. The methodology presented in this paper can be replicated and used in regionalization of any hydrological model parameters for estimating streamflow at ungauged catchment.

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Section: Uncertainty Analysis (Ua)mentioning

confidence: 99%

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Sellami

Jeunesse

Benabdallah

et al. 2014

Hydrol. Earth Syst. Sci.

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“…Viola et al, 2009;Rankinen et al, 2006). Part of the problem is that parameter-rich models allow for equifinality, levelling out the impact of certain parameters.…”

Section: Parameter Uncertaintymentioning

confidence: 99%

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

et al. 2014

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Abstract.Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures -for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil-plant interface.We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten-Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation.The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 10 6 model runs randomly drawn from a uniform distribution.The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model.

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“…A small threshold leads to larger uncertainty interval, while a larger threshold value reduces the uncertainly interval width (Viola et al 2009, Li et al 2010.…”

Section: Glue Generalized Likelihood Uncertaintymentioning

confidence: 99%

“…This equifinality (Beven and Freer 2001) originates from the imperfect knowledge of the system under consideration and from different error sources (errors in input and boundary conditions, errors in using an approximate model structure of the real system and error in the observation variable being modelled) that can interact in a nonlinear way (Beven 2006). In addition, the subjective choice of the threshold value and the likelihood function can lead to additional uncertainty in the simulation results (Viola et al 2009, Jin et al 2010.…”

Section: Figmentioning

confidence: 99%

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Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments

Sellami

Jeunesse

Benabdallah

et al. 2013

Hydrological Sciences Journal

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The SWAT model was tested to simulate the streamflow of two small Mediterranean catchments (the Vène and the Pallas) in southern France. Model calibration and prediction uncertainty were assessed simultaneously by using three different techniques (SUFI-2, GLUE and ParaSol). Initially, a sensitivity analysis was conducted using the LH-OAT method. Subsequent sensitive parameter calibration and SWAT prediction uncertainty were analysed by considering, firstly, deterministic discharge data (assuming no uncertainty in discharge data) and secondly, uncertainty in discharge data through the development of a methodology that accounts explicitly for error in the rating curve (the stage−discharge relationship). To efficiently compare the different uncertainty methods and the effect of the uncertainty of the rating curve on model prediction uncertainty, common criteria were set for the likelihood function, the threshold value and the number of simulations. The results show that model prediction uncertainty is not only case-study specific, but also depends on the selected uncertainty analysis technique. It was also found that the 95% model prediction uncertainty interval is wider and more successful at encompassing the observations when uncertainty in the discharge data is considered explicitly. The latter source of uncertainty adds additional uncertainty to the total model prediction uncertainty.Keywords Mediterranean catchments; parameter uncertainty; uncertainty analysis; model calibration; SWAT; rating curve uncertainty Analyse de la propagation des incertitudes de la courbe de tarage et des paramètres du modèle SWAT pour deux petits bassins versants de la région méditerranéenne Résumé Dans cet article le modèle SWAT a été testé pour simuler l'écoulement de deux petits bassins versants (la Vène et le Pallas) en région méditerranéenne dans le sud de la France. Le calage du modèle et l'incertitude des prévisions ont été évalués simultanément en utilisant trois techniques différentes (SUFI-2, COLLE et Parasol). Tout d'abord, une analyse de sensibilité a été réalisée en utilisant la méthode de LH-OAT. Le calage des paramètres sensibles et l'incertitude des prévisions de SWAT ont été analysés en considérant, dans un premier temps, un débit déterministe (en supposant l'absence d'incertitude dans les données de débit), et dans un second temps, en considérant l'incertitude liée aux données de débit à travers le développement d'une méthodologie qui tient compte explicitement des erreurs dans la courbe de tarage (relation hauteur d'eau et vélocité d'écoulement). Pour comparer efficacement les différentes méthodes d'estimation de l'incertitude et l'effet de l'incertitude de la courbe de tarage sur l'incertitude des prévisions du modèle, des critères communs ont été définis pour la fonction de vraisemblance, la valeur seuil et le nombre de simulations. Les résultats montrent que l'incertitude des prévisions du modèle est non seulement spécifique au cas d'étude mais dépend aussi de la technique d'analyse d'incertitude sélec...

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Daily streamflow prediction with uncertainty in ephemeral catchments using the GLUE methodology

Cited by 31 publications

References 16 publications

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments

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