On constraining TOPMODEL hydrograph simulations using partial saturated area information

Blažková, Šárka; Beven, Keith; Kulasova, Alena

doi:10.1002/hyp.331

Cited by 69 publications

(60 citation statements)

References 21 publications

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“…In the original study of Beven and Kirkby [1979], similarity in pattern of observed and predicted saturated areas was demonstrated for a small catchment in the UK. Other similarities in predicted and observed saturated areas and patterns of the topographic index have been demonstrated by Kirkby [1978] for Sleepers River, Vermont; Merot and Bruneau [1993] and Franks et al [1998] for the bocage landscape of Britanny, France; Ambroise et al [1996] in the Vosges, France; Güntner et al [1999] in the Brugga catchment in Germany; and Blazkova et al [2002] in the same Uhlirska catchment in the Czech Republic studied here.…”

Section: Introductionsupporting

confidence: 85%

“…This assumption, together with the quasi-steady state and hydraulic gradient assumptions noted above, leads to a topographic similarity index of the logarithmic form ln(a/tanb) Beven, 2001a]. This version of the model has previously been shown to produce good hydrograph simulations for the Uhlirska catchment [Blazkova and Beven, 1997;Blazkova et al, 2002]. The catchment has moderate side slopes, a wet valley bottom and wet climate and should be well suited to the application of TOPMODEL.…”

Section: Topmodelmentioning

confidence: 99%

“…[12] Following Blazkova et al [2002], the model has been applied within a generalized likelihood uncertainty estimation (GLUE) framework Beven, 2001a;Beven and Freer, 2001a] (see http://www.es.lancs.ac.uk/ hfdg/glue.html). GLUE is based on the rejection of the aim of trying to find an optimal model, in favor of a concept of equifinality of models and parameter sets.…”

Section: Topmodelmentioning

confidence: 99%

“…However, knowledge of the pattern of the topographic index allows the predictions to be mapped back into space. This has been shown to be a very useful property in evaluating the model simulations and calibrating the model parameters Franks et al, 1998;Quinn et al, 1998;Lamb et al, 1998;Blazkova et al, 2002]. In addition the fast run time of the model has allowed the uncertainty associated with the model predictions to be estimated using the GLUE methodology [see Beven, 1993Beven, , 2001aBeven et al, 2000;Beven and Freer, 2001a].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionsupporting

confidence: 85%

Section: Topmodelmentioning

confidence: 99%

Section: Topmodelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…They concluded that the use of groundwater data helped to reduce the parameter uncertainty but that the groundwater table uncertainty bounds were still wide, without reproducing the temporal variation. Franks et al [1998] and Blazkova and Beven [2002] analyzed the impact of introducing estimates of the saturated areas on the parameter uncertainty. They concluded that despite the uncertainty in estimation of the saturated areas, multiresponse calibration was useful to reduce significantly the uncertainty of one model parameter, but the effect of such constraining on the prediction bounds for the simulated runoff was much less important.…”

Section: Introductionmentioning

confidence: 99%

Bayesian multiresponse calibration of TOPMODEL: Application to the Haute‐Mentue catchment, Switzerland

Talamba

Parent

Musy

2010

Water Resources Research

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[1] This paper introduces a general framework that evaluates a numerical Bayesian multiresponse calibration approach based on a Gibbs within Metropolis searching algorithm and a statistical likelihood function. The methodology has been applied with two versions of TOPMODEL on the Haute-Mentue experimental basin in Switzerland. The approach computes the following: the parameter's uncertainty, the parametric uncertainty of the output responses stemming from parameter uncertainty, and the predictive uncertainty of the output responses stemming from an error term including, indiscriminately in a lumped way, model structure and input and output errors. Two case studies are presented: The first one applies this methodology with the classical TOPMODEL to assess the role of two-response calibration (observed discharge and soil saturation deficits) on model parameters and output uncertainty. The second one uses a three-response calibration (observed discharge, silica, and calcium stream water concentrations) with a modified version of TOPMODEL to study the uncertainty of the parameters and of the simulated responses. Despite its limitations, the present multiresponse Bayesian approach proved a valuable tool in uncertainty analyses, and it contributed to a better understanding of the role of the internal variables and the value of additional information for enhancing model structure robustness and for checking the performance of conceptual models.

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Model Calibration and Uncertainty Estimation

Gupta

Beven

Wagener

2005

Encyclopedia of Hydrological Sciences

195

185

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All rainfall-runoff models are, by definition, simplifications of the real-world system under investigation. The model components are aggregated descriptions of real-world hydrologic processes. One consequence of this is that the model parameters often do not represent directly measurable entities, but must be estimated using measurements of the system response through a process known as model calibration. The objective of this calibration process is to obtain a model with the following characteristics: (i) the input-state-output behavior of the model is consistent with the measurements of catchment behavior, (ii) the model predictions are accurate (i.e. they have negligible bias) and precise (i.e. the prediction uncertainty is relatively small), and (iii) the model structure and behavior are consistent with current hydrologic understanding of reality. This article describes the historic development leading to current views on model calibration, and the algorithms and techniques that have been developed for estimating parameters, thereby enabling the model to mimic the behavior of the hydrologic system. Manual techniques as well as automatic algorithms are addressed. The automatic approaches range from purely random techniques, to local and global search algorithms. An overview of multiobjective and recursive algorithms is also presented. Although it would be desirable to reduce the total output prediction error to zero (i.e. the difference between observed and simulated system behavior) this is generally impossible owing to the unavoidable uncertainties inherent in any rainfall-runoff modeling procedure. These uncertainties stem mainly from the inability of calibration procedures to uniquely identify a single optimal parameter set, from measurement errors associated with the system input and output, and from model structural errors arising from the aggregation of real-world processes into a mathematical model. Some commonly used approaches to estimate these uncertainties and their impacts on the model predictions are discussed. The article ends with a brief discussion about the current status of calibration and how well we are able to represent the effects of uncertainty in the modeling process, and some potential directions. THE NATURE OF RAINFALL-RUNOFF MODELSThe hydrology of any catchment involves complex interactions driven by a number of spatially distributed and highly interrelated water, energy, and vegetation processes. Any computer-based model intended to represent the behavior of a catchment must, therefore, conceptualize this reality using relatively simple mathematical equations that involve parameters to be specified for any particular application. Two characteristics of the modeling process are relevant to our discussion. First, all rainfall-runoff (RR) models, regardless of how spatially explicit, are to some degree lumped, so that their equations and parameters describe the processes as aggregated in space and time. As a consequence, the model parameters are typically not directly measurable, and...

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On constraining TOPMODEL hydrograph simulations using partial saturated area information

Cited by 69 publications

References 21 publications

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

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

Bayesian multiresponse calibration of TOPMODEL: Application to the Haute‐Mentue catchment, Switzerland

Model Calibration and Uncertainty Estimation

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