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Constraining Dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures.

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Constraining Dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures. / Freer, Jim E.; Beven, Keith J.; McDonnell, J. J. et al.
In: Journal of Hydrology, Vol. 291, No. 3-4, 01.06.2004, p. 254-277.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Freer JE, Beven KJ, McDonnell JJ, McMillan H. Constraining Dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures. Journal of Hydrology. 2004 Jun 1;291(3-4):254-277. doi: 10.1016/j.jhydrol.2003.12.037

Author

Freer, Jim E. ; Beven, Keith J. ; McDonnell, J. J. et al. / Constraining Dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures. In: Journal of Hydrology. 2004 ; Vol. 291, No. 3-4. pp. 254-277.

Bibtex

@article{da5397e9436b490482d26adf43d0cc2b,
title = "Constraining Dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures.",
abstract = "Dynamic TOPMODEL is applied to the Maimai M8 catchment (3.8 ha), New Zealand using rainfall–runoff and water table information in model calibration. Different parametric representations of hillslope and valley bottom landscape units (LU's) were used to improve the spatial representation of the model structure. The continuous time series water table information is obtained from tensiometric observations from both near stream (NS) and hillslope (P5) locations having different responses to rainfall events. For each location, and within an area equivalent to an effective model gridscale (25 m2), a number of tensiometer readings at different depths were available (11 for the NS site and nine for the P5 site). Using this information a distribution of water table elevations for each time step at each location was calculated. The distribution of water table elevations was used to derive fuzzy estimates of the water table depth for the whole time series that includes the temporal variability of the uncertainty in the observations. These data were used to constrain the spatial representation of the model having previously conditioned the model using the rainfall–runoff data. Model conditioning was assessed using the Generalised Likelihood Uncertainty Estimation procedure. Results show that many combinations of parameter values for the two LU's were able to simulate the rainfall–runoff data. Further constraining of the model responses using the fuzzy water table elevations at both locations considerably reduced the number of behavioural parameter sets. An evaluation of the distributions of behavioural parameter sets showed that improvements to the model structure for the two LU's were required, especially for simulations of the response at the hillslope location.",
keywords = "Dynamic TOPMODEL, Generalised likelihood uncertainty estimation, Water table uncertainty, Parameter constraining, Fuzzy rules, Multicriteria calibration",
author = "Freer, {Jim E.} and Beven, {Keith J.} and McDonnell, {J. J.} and H. McMillan",
note = "JEF supervised the MSc thesis of HMcM, which used the thesis data collected by JJMcD in Maimai, New Zealand. The first paper to include the use of fuzzy performance measures in GLUE for time series data to express the variability in the information content of data over different events. RAE_import_type : Journal article RAE_uoa_type : Earth Systems and Environmental Sciences",
year = "2004",
month = jun,
day = "1",
doi = "10.1016/j.jhydrol.2003.12.037",
language = "English",
volume = "291",
pages = "254--277",
journal = "Journal of Hydrology",
publisher = "Elsevier Science B.V.",
number = "3-4",

}

RIS

TY - JOUR

T1 - Constraining Dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures.

AU - Freer, Jim E.

AU - Beven, Keith J.

AU - McDonnell, J. J.

AU - McMillan, H.

N1 - JEF supervised the MSc thesis of HMcM, which used the thesis data collected by JJMcD in Maimai, New Zealand. The first paper to include the use of fuzzy performance measures in GLUE for time series data to express the variability in the information content of data over different events. RAE_import_type : Journal article RAE_uoa_type : Earth Systems and Environmental Sciences

PY - 2004/6/1

Y1 - 2004/6/1

N2 - Dynamic TOPMODEL is applied to the Maimai M8 catchment (3.8 ha), New Zealand using rainfall–runoff and water table information in model calibration. Different parametric representations of hillslope and valley bottom landscape units (LU's) were used to improve the spatial representation of the model structure. The continuous time series water table information is obtained from tensiometric observations from both near stream (NS) and hillslope (P5) locations having different responses to rainfall events. For each location, and within an area equivalent to an effective model gridscale (25 m2), a number of tensiometer readings at different depths were available (11 for the NS site and nine for the P5 site). Using this information a distribution of water table elevations for each time step at each location was calculated. The distribution of water table elevations was used to derive fuzzy estimates of the water table depth for the whole time series that includes the temporal variability of the uncertainty in the observations. These data were used to constrain the spatial representation of the model having previously conditioned the model using the rainfall–runoff data. Model conditioning was assessed using the Generalised Likelihood Uncertainty Estimation procedure. Results show that many combinations of parameter values for the two LU's were able to simulate the rainfall–runoff data. Further constraining of the model responses using the fuzzy water table elevations at both locations considerably reduced the number of behavioural parameter sets. An evaluation of the distributions of behavioural parameter sets showed that improvements to the model structure for the two LU's were required, especially for simulations of the response at the hillslope location.

AB - Dynamic TOPMODEL is applied to the Maimai M8 catchment (3.8 ha), New Zealand using rainfall–runoff and water table information in model calibration. Different parametric representations of hillslope and valley bottom landscape units (LU's) were used to improve the spatial representation of the model structure. The continuous time series water table information is obtained from tensiometric observations from both near stream (NS) and hillslope (P5) locations having different responses to rainfall events. For each location, and within an area equivalent to an effective model gridscale (25 m2), a number of tensiometer readings at different depths were available (11 for the NS site and nine for the P5 site). Using this information a distribution of water table elevations for each time step at each location was calculated. The distribution of water table elevations was used to derive fuzzy estimates of the water table depth for the whole time series that includes the temporal variability of the uncertainty in the observations. These data were used to constrain the spatial representation of the model having previously conditioned the model using the rainfall–runoff data. Model conditioning was assessed using the Generalised Likelihood Uncertainty Estimation procedure. Results show that many combinations of parameter values for the two LU's were able to simulate the rainfall–runoff data. Further constraining of the model responses using the fuzzy water table elevations at both locations considerably reduced the number of behavioural parameter sets. An evaluation of the distributions of behavioural parameter sets showed that improvements to the model structure for the two LU's were required, especially for simulations of the response at the hillslope location.

KW - Dynamic TOPMODEL

KW - Generalised likelihood uncertainty estimation

KW - Water table uncertainty

KW - Parameter constraining

KW - Fuzzy rules

KW - Multicriteria calibration

U2 - 10.1016/j.jhydrol.2003.12.037

DO - 10.1016/j.jhydrol.2003.12.037

M3 - Journal article

VL - 291

SP - 254

EP - 277

JO - Journal of Hydrology

JF - Journal of Hydrology

IS - 3-4

ER -