2006
DOI: 10.1029/2005wr004820
|View full text |Cite
|
Sign up to set email alerts
|

Ignorance is bliss: Or seven reasons not to use uncertainty analysis

Abstract: [1] Uncertainty analysis of models has received increasing attention over the last two decades in water resources research. However, a significant part of the community is still reluctant to embrace the estimation of uncertainty in hydrological and hydraulic modeling. In this paper, we summarize and explore seven common arguments: uncertainty analysis is not necessary given physically realistic models; uncertainty analysis cannot be used in hydrological and hydraulic hypothesis testing; uncertainty (probabilit… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
371
0
1

Year Published

2009
2009
2016
2016

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 463 publications
(373 citation statements)
references
References 48 publications
(57 reference statements)
1
371
0
1
Order By: Relevance
“…This simple formulation of evapotranspiration has been found to be robust when applied in climate impact studies (Oudin et al, 2005). The HBV model (as all other hydrological models) is best used together with an uncertainty analysis framework (Seibert, 1997;Pappenberger and Beven, 2006). The model has a number of free parameters (Table 2).…”
Section: Hydrological Modelling and Parameter Uncertaintymentioning
confidence: 99%
“…This simple formulation of evapotranspiration has been found to be robust when applied in climate impact studies (Oudin et al, 2005). The HBV model (as all other hydrological models) is best used together with an uncertainty analysis framework (Seibert, 1997;Pappenberger and Beven, 2006). The model has a number of free parameters (Table 2).…”
Section: Hydrological Modelling and Parameter Uncertaintymentioning
confidence: 99%
“…Uncertainties in model parameterization and measurement have to be considered, but in practice rigorous uncertainty analysis is still rare (Stow et al, 2007). Model applications often fail to do an uncertainty assessment because many "competing methods" make it difficult to choose the most appropriate method and interpret the results (Pappenberger and Beven, 2006). Stochastic optimization can help to diminish the difficulties in terms of parameter estimation.…”
Section: Discussionmentioning
confidence: 99%
“…Only robust parameterization techniques allow for plausible explanations of system behavior in physically based models. Due to complex transformations of soil N and carbon, many models express this complexity by a high number of input parameters, which implicates the need for a careful model optimization (Pappenberger and Beven, 2006). Until recently, calibration of highly parameterized models was performed by an intensive sensitivity analysis and fitting modeling results to measurements by "trial and error" procedure until observed values were reproduced well.…”
Section: Introductionmentioning
confidence: 99%
“…Currently, research on uncertainty analysis has significantly increased (Refsgaard and Henriksen, 2004;Rosbjerg and Madsen, 2005). Pappenberger and Beven (2006) have furthermore emphasized that "uncertainty assessment is today a mandatory element of a good modeling practice." In order to better meet that need, MIKE SHE provides several alternative physical and conceptual approaches for each component of the model (Butts et al, 2004).…”
Section: Model Uncertaintymentioning
confidence: 99%