Towards the characterization of streamflow simulation uncertainty through multimodel ensembles

Georgakakos, Konstantine P.; Seo, Dong Jun; Gupta, H. V.; Schaake, John C.; Butts, M. B.

doi:10.1016/j.jhydrol.2004.03.037

Cited by 327 publications

(266 citation statements)

References 16 publications

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“…At first glance this suggests there is actually limited potential to use outputs from our multimodel configuration as an estimate of model uncertainty. Ideally, simulations of streamflow from different model structures will bracket the observed streamflow [e.g., Georgakakos et al, 2004;Vrugt and Robinson, 2007]; when this does not occur (as in this study) it can be viewed as being indicative of a lack of independent information in different models. However, the consistency in model errors may also arise because errors in model inputs affect different models in similar ways.…”

Section: Summary and Discussionmentioning

confidence: 99%

Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models

Clark

Slater

Rupp³

et al. 2008

Water Resources Research

526

635

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[1] The problems of identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure remain outstanding research challenges for the discipline of hydrology. Progress on these problems requires understanding of the nature of differences between models. This paper presents a methodology to diagnose differences in hydrological model structures: the Framework for Understanding Structural Errors (FUSE). FUSE was used to construct 79 unique model structures by combining components of 4 existing hydrological models. These new models were used to simulate streamflow in two of the basins used in the Model Parameter Estimation Experiment (MOPEX): the Guadalupe River (Texas) and the French Broad River (North Carolina). Results show that the new models produced simulations of streamflow that were at least as good as the simulations produced by the models that participated in the MOPEX experiment. Our initial application of the FUSE method for the Guadalupe River exposed relationships between model structure and model performance, suggesting that the choice of model structure is just as important as the choice of model parameters. However, further work is needed to evaluate model simulations using multiple criteria to diagnose the relative importance of model structural differences in various climate regimes and to assess the amount of independent information in each of the models. This work will be crucial to both identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure. To facilitate research on these problems, the FORTRAN-90 source code for FUSE is available upon request from the lead author.

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Section: Summary and Discussionmentioning

confidence: 99%

Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models

Clark

Slater

Rupp³

et al. 2008

Water Resources Research

526

635

View full text Add to dashboard Cite

show abstract

“…Many authors have studied modelling errors arising from uncertainties in radar estimates of rainfall (Anderl et al, 1976;Borga, 2002;Quirmbach & Schultz, 2002). Some authors have examined the sensitivity of ensemble flow simulations produced by the distributed model with regard to uncertainty in parametric and radar rainfall input, by using Monte Carlo methods (Carpenter & Georgakakos, 2004;Georgakakos et al, 2004). Applications on extreme flash floods have been made (Zoccatelli et al, 2010), and have shown that the correct estimate of rainfall volume is not enough for the accurate reproduction of flash-flood events characterized by large and structured rainfall spatial variability (Sangati et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of hydrological models to uncertainty in rainfall input

Arnaud¹,

Lavabre²,

Fouchier³

et al. 2011

Hydrological Sciences Journal

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Different approaches used in hydrological modelling are compared in terms of the way each one takes the rainfall data into account. We examine the errors associated with accounting for rainfall variability, whether in hydrological modelling (distributed vs lumped models) or in computing catchment rainfall, as well as the impact of each approach on the representativeness of the parameters it uses. The database consists of 1859 rainfall events, distributed on 500 basins, located in the southeast of France with areas ranging from 6.2 to 2851 km 2 . The study uses as reference the hydrographs computed by a distributed hydrological model from radar rainfall. This allows us to compare and to test the effects of various simplifications to the process when taking rainfall information (complete rain field vs sampled rainfall) and rainfall-runoff modelling (lumped vs distributed) into account. The results appear to show that, in general, the sampling effect can lead to errors in discharge at the outlet that are as great as, or even greater than, those one would get with a fully lumped approach. We found that small catchments are more sensitive to the uncertainties in catchment rainfall input generated by sampling rainfall data as seen through a raingauge network. Conversely, the larger catchments are more sensitive to uncertainties generated when the spatial variability of rainfall events is not taken into account. These uncertainties can be compensated for relatively easily by recalibrating the parameters of the hydrological model, although such recalibrations cause the parameter in question to completely lose physical meaning.Key words rainfall distribution; radar rainfall; raingauge network; lumped modelling; distributed modelling; uncertainty Sensibilité des modèles hydrologiques aux incertitudes dues à l'information pluviométrique utiliséeRésumé Cette étude compare différentes approches utilisées en modélisation hydrologique, chaque approche étant différenciée par sa manière de prendre en compte l'information pluviométrique. Nous regardons les erreurs liées à la prise en compte de la variabilité spatiale de la pluie, à travers la modélisation hydrologique (modèles globaux ou distribués) ou à travers le calcul de la pluie de bassin. Nous regardons aussi l'impact de chaque approche sur la représentativité des paramètres qui lui sont associés. Ce travail porte sur 1859 événements pluvieux répartis sur 500 bassins versants situés dans le sud-est de la France, dans une gamme de surface allant de 6.2 à 2851 km 2 . On définit comme étant les hydrogrammes de référence, les crues modélisées par un modèle hydrologique distribué à partir de champs de pluies issus de radars météorologiques. Cela nous permet de travailler de façon relative, et de tester l'effet de différentes simplifications dans la prise en compte des processus, lors de la prise en compte de la pluie (champs de pluie complet ou échantillonnage de la pluie) et/ou lors de la modélisation hydrologique (globale ou distribuée). Les résultats montrent globale...

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“…However, although model parameter uncertainties for a single model can be minimized by simulation-optimization schemes (e.g., GA-SWAP, etc. ), bias due to different model structures still remain (considerably) in model outputs [Hoetting et al, 1999;Georgakakos et al, 2004;Ajami et al, 2007].…”

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confidence: 99%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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Various hydrological models have been developed for estimating root zone soil moisture dynamics. These models, however, incorporated their own parameterization approaches indicating that the output from the different model inherent structures might include uncertainties because we do not know which model structure is correct for describing the real system. More recently, multimodel approaches using a Bayesian Model Averaging (BMA) scheme can improve the overall predictive skill while individual models retain their own uncertainties for simulated soil moisture based on a single set of weights in modeling under different land surface wetness conditions (e.g., wet, moderately wet, and dry conditions). In order to overcome their limitations, we developed a BMA-based multimodel simulation approach based on various soil wetness conditions for estimating effective surface soil moisture dynamics (0-5 cm) and quantifying uncertainties efficiently based on the land surface wetness conditions. The newly developed approach adapts three different hydrological models (i.e., Noah Land Surface Model, Noah LSM; Soil-Water-Atmosphere-Plant, SWAP; and Community Land Model, CLM) for simulating soil moisture. These models were integrated with a modified-microGA (advanced version of original Genetic Algorithm (GA)) to search for optimized soil parameters for each model. Soil moisture was simulated from the estimated soil parameters using the hydrological models in a forward mode. It was found that SWAP performed better than others during wet condition, while Noah LSM and CLM showed a good agreement with measurements during dry condition. Thus, we inferred that performance of individual models with different model structures can be different with land surface wetness. Taking into account the effects of soil wetness on different model performances, we categorized soil moisture measurements and estimated different weights for each category using the BMA scheme. Effective surface soil moisture dynamics were obtained by aggregating multiple weighted soil moisture. Our findings demonstrated that the effective soil moisture estimates derived by this approach showed a better match with the measurements compared to the original models and single-weighted outputs. Multimodel simulation approach based on land surface wetness enhances the ability to predict reliable soil moisture dynamics and reflects the strengths of different hydrological models under various soil wetness conditions.

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Towards the characterization of streamflow simulation uncertainty through multimodel ensembles

Cited by 327 publications

References 16 publications

Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models

Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models

Sensitivity of hydrological models to uncertainty in rainfall input

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

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