Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system

Beven, Keith

doi:10.1002/hyp.343

Cited by 328 publications

(276 citation statements)

References 50 publications

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“…The modeled fluxes were then used in model state equations to compute the time derivative of model states, where again FORTRAN-90 case statements were used to distinguish between the different model architectures. FUSE differs from other modular hydrological models [e.g., Leavesley et al, 1996;2002] because it modularizes individual flux equations, rather than linking existing submodels. Imposing a modular structure at the level of individual flux equations greatly simplifies adding new modeling options; the only real constraint is the computing resources required to run the large number (>1000) of possible model structures.…”

Section: Multimodel Configurationmentioning

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

546

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: Multimodel Configurationmentioning

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

546

635

View full text Add to dashboard Cite

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“…For example, Beven (2002), Kirchner (2006), McDonnell et al (2007), Todini (2007) and, recently, Wagener et al (2010) provided a list of promising leads for observational networks and model developments. Here we suggest that further progress could be made by paying more attention to anomalies.…”

Section: How Could We Better Learn From Anomalies?mentioning

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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“…[36] It is expected that similar problems of local parameters will affect any distributed modeling structure when compared with observations of local state variables [Beven, 1996[Beven, , 2000[Beven, , 2002. The dynamics of each model structure will be different, but the need for local parameters to improve the predictions will remain.…”

Section: Resultsmentioning

confidence: 99%

“…The justification for allowing such local variations is that a model with global parameters might result in distributed predictions that are approximately right, but cannot be expected to predict the effects of local heterogeneities on local responses. Similar arguments can be applied to any distributed model that will rely on some degree of model calibration of catchment averaged parameters [Beven, 2000[Beven, , 2001a[Beven, , 2001b[Beven, , 2002.…”

Section: Allowing Local Effective Parameter Adjustmentsmentioning

confidence: 96%

Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL?

Blažková

Beven

Tachecí

et al. 2002

Water Resources Research

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[1] The distributed predictions of the original version of TOPMODEL are here compared with distributed observations of water table levels in the Uhlirska catchment in the Jizera Mountains, Czech Republic. The calibration of the model has been carried out within the GLUE framework, which allows the estimation of uncertainties in predicting the distributed patterns of the water table at different times. Many of the water table levels are predicted within the limits of uncertainty, but it is shown that the predictions could be improved by the calculation of a local effective transmissivity value (or local upslope contributing areas) at each observation site. These effective transmissivities show a similar relationship to the topographic index as found in a previous study of a small catchment in Norway. Some of the anomalies can be explained by deficiencies in the topographic analysis but this may also be an indication of possible structural deficiencies in the model. Interpretation is, however, difficult, and it remains to be seen whether these anomalies they might be avoided in more dynamic distributed models.

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Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system

Cited by 328 publications

References 50 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

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

Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL?

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