Do higher data frequency and Bayesian auto-calibration lead to better model calibration? Insights from an application of INCA-P, a process-based river phosphorus model.

L.A. Jackson-Blake; J. Starrfelt

doi:10.1016/j.jhydrol.2015.05.001

Do higher data frequency and Bayesian auto-calibration lead to better model calibration? Insights from an application of INCA-P, a process-based river phosphorus model.

L.A. Jackson-Blake
, J. Starrfelt

Environmental & Biochemical Sciences

Norwegian Institute for Water Research

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	641-655
Journal	Journal of Hydrology
Volume	527
DOIs	https://doi.org/10.1016/j.jhydrol.2015.05.001
Publication status	Published - 1 Aug 2015

Keywords

Catchment modelling
Data frequency
INCA
MCMC-DREAM
Phosphorus

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10.1016/j.jhydrol.2015.05.001Licence: Unspecified licence

Cite this

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title = "Do higher data frequency and Bayesian auto-calibration lead to better model calibration? Insights from an application of INCA-P, a process-based river phosphorus model.",

keywords = "Catchment modelling, Data frequency, INCA, MCMC-DREAM, Phosphorus",

author = "L.A. Jackson-Blake and J. Starrfelt",

year = "2015",

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doi = "10.1016/j.jhydrol.2015.05.001",

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AU - Starrfelt, J.

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KW - Data frequency

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KW - MCMC-DREAM

KW - Phosphorus

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