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

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Abstract

The Generalised Likelihood Uncertainty Estimation (GLUE) approach is presented here as a tool for estimating the predictive uncertainty of a rainfall-runoff model. The GLUE methodology allows to recognise the possible equifinality of different parameter sets and assesses the likelihood of a parameters set being acceptable simulator when model predictions are compared to observed field data. The results of the GLUE methodology depend greatly on the choice of the likelihood measure and on the choice of the threshold which determines if a parameters set is behavioural or not. Moreover the sampling size has a strong influence on the uncertainty assessment of the response of a rainfall-runoff model. This is one of the most controversial and criticized aspect of the GLUE methodology, because it seems that this procedure does not learn from observations. Following these premises, this paper investigated first on the effect of different likelihood measures on the uncertainty analysis in the rainfall-runoff modelling of a mediterranean catchment. Performance of individual parameter sets has been assessed using three likelihood measures with a shaping factor. The acceptability threshold influence on the uncertainty analysis has been also evaluated. Finally it can be demonstrated how, using the GLUE, the predictive uncertainty grows with the streamflow variance while remains almost the same with the sampling size. In order to overcome these inconsistencies, a new simple likelihood measure, which explicitly takes into account the sample variance and extension, is here proposed.
Lingua originaleEnglish
pagine (da-a)701-706
Numero di pagine6
RivistaPhysics and Chemistry of the Earth
Volume34
Stato di pubblicazionePublished - 2009

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Catchments
drainage
catchment
methodology
uncertainty analysis
prediction
predictions
Runoff
sampling
runoff
Rain
rainfall-runoff modeling
rainfall
thresholds
Uncertainty analysis
acceptability
simulators
simulator
streamflow
estimating

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Geochemistry and Petrology

Cita questo

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title = "Daily streamlow prediction with uncertainty in ephemeral catchments using the GLUE methodology",
abstract = "The Generalised Likelihood Uncertainty Estimation (GLUE) approach is presented here as a tool for estimating the predictive uncertainty of a rainfall-runoff model. The GLUE methodology allows to recognise the possible equifinality of different parameter sets and assesses the likelihood of a parameters set being acceptable simulator when model predictions are compared to observed field data. The results of the GLUE methodology depend greatly on the choice of the likelihood measure and on the choice of the threshold which determines if a parameters set is behavioural or not. Moreover the sampling size has a strong influence on the uncertainty assessment of the response of a rainfall-runoff model. This is one of the most controversial and criticized aspect of the GLUE methodology, because it seems that this procedure does not learn from observations. Following these premises, this paper investigated first on the effect of different likelihood measures on the uncertainty analysis in the rainfall-runoff modelling of a mediterranean catchment. Performance of individual parameter sets has been assessed using three likelihood measures with a shaping factor. The acceptability threshold influence on the uncertainty analysis has been also evaluated. Finally it can be demonstrated how, using the GLUE, the predictive uncertainty grows with the streamflow variance while remains almost the same with the sampling size. In order to overcome these inconsistencies, a new simple likelihood measure, which explicitly takes into account the sample variance and extension, is here proposed.",
keywords = "Ephemeral catchments, Generalized Likelehood Uncertainty Estimation, Predictive uncertainty, Rainfall-Runoff model",
author = "Marcella Cannarozzo and Leonardo Noto and {La Loggia}, Goffredo",
year = "2009",
language = "English",
volume = "34",
pages = "701--706",
journal = "Physics and Chemistry of the Earth",
issn = "1474-7065",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Daily streamlow prediction with uncertainty in ephemeral catchments using the GLUE methodology

AU - Cannarozzo, Marcella

AU - Noto, Leonardo

AU - La Loggia, Goffredo

PY - 2009

Y1 - 2009

N2 - The Generalised Likelihood Uncertainty Estimation (GLUE) approach is presented here as a tool for estimating the predictive uncertainty of a rainfall-runoff model. The GLUE methodology allows to recognise the possible equifinality of different parameter sets and assesses the likelihood of a parameters set being acceptable simulator when model predictions are compared to observed field data. The results of the GLUE methodology depend greatly on the choice of the likelihood measure and on the choice of the threshold which determines if a parameters set is behavioural or not. Moreover the sampling size has a strong influence on the uncertainty assessment of the response of a rainfall-runoff model. This is one of the most controversial and criticized aspect of the GLUE methodology, because it seems that this procedure does not learn from observations. Following these premises, this paper investigated first on the effect of different likelihood measures on the uncertainty analysis in the rainfall-runoff modelling of a mediterranean catchment. Performance of individual parameter sets has been assessed using three likelihood measures with a shaping factor. The acceptability threshold influence on the uncertainty analysis has been also evaluated. Finally it can be demonstrated how, using the GLUE, the predictive uncertainty grows with the streamflow variance while remains almost the same with the sampling size. In order to overcome these inconsistencies, a new simple likelihood measure, which explicitly takes into account the sample variance and extension, is here proposed.

AB - The Generalised Likelihood Uncertainty Estimation (GLUE) approach is presented here as a tool for estimating the predictive uncertainty of a rainfall-runoff model. The GLUE methodology allows to recognise the possible equifinality of different parameter sets and assesses the likelihood of a parameters set being acceptable simulator when model predictions are compared to observed field data. The results of the GLUE methodology depend greatly on the choice of the likelihood measure and on the choice of the threshold which determines if a parameters set is behavioural or not. Moreover the sampling size has a strong influence on the uncertainty assessment of the response of a rainfall-runoff model. This is one of the most controversial and criticized aspect of the GLUE methodology, because it seems that this procedure does not learn from observations. Following these premises, this paper investigated first on the effect of different likelihood measures on the uncertainty analysis in the rainfall-runoff modelling of a mediterranean catchment. Performance of individual parameter sets has been assessed using three likelihood measures with a shaping factor. The acceptability threshold influence on the uncertainty analysis has been also evaluated. Finally it can be demonstrated how, using the GLUE, the predictive uncertainty grows with the streamflow variance while remains almost the same with the sampling size. In order to overcome these inconsistencies, a new simple likelihood measure, which explicitly takes into account the sample variance and extension, is here proposed.

KW - Ephemeral catchments

KW - Generalized Likelehood Uncertainty Estimation

KW - Predictive uncertainty

KW - Rainfall-Runoff model

UR - http://hdl.handle.net/10447/37636

M3 - Article

VL - 34

SP - 701

EP - 706

JO - Physics and Chemistry of the Earth

JF - Physics and Chemistry of the Earth

SN - 1474-7065

ER -