Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model

Donghua Zhang; Henrik  Madsen; Marc E.  Ridler; Jens C. Refsgaard; Karsten Høgh Jensen

doi:10.1016/j.advwatres.2015.07.018

Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model

Donghua Zhang, Henrik Madsen, Marc E. Ridler, Jens C. Refsgaard, Karsten Høgh Jensen

Geology

22 Citations (Scopus)

Abstract

The ensemble Kalman filter (EnKF) is a popular data assimilation (DA) technique that has been extensively used in environmental sciences for combining complementary information from model predictions and observations. One of the major challenges in EnKF applications is the description of model uncertainty. In most hydrological EnKF applications, an ad hoc model uncertainty is defined with the aim of avoiding a collapse of the filter. The present work provides a systematic assessment of model uncertainty in DA applications based on combinations of forcing, model parameters, and state uncertainties. This is tested in a case where groundwater hydraulic heads are assimilated into a distributed and integrated catchment-scale model of the Karup catchment in Denmark. A series of synthetic data assimilation experiments are carried out to analyse the impact of different model uncertainty assumptions on the feasibility and efficiency of the assimilation. The synthetic data used in the assimilation study makes it possible to diagnose model uncertainty assumptions statistically. Besides the model uncertainty, other factors such as observation error, observation locations, and ensemble size are also analysed with respect to performance and sensitivity. Results show that inappropriate definition of model uncertainty can greatly degrade the assimilation performance, and an appropriate combination of different model uncertainty sources is advised.

Original language	English
Journal	Advances in Water Resources
Volume	86
Issue number	Part B
Pages (from-to)	400–413
ISSN	0309-1708
DOIs	https://doi.org/10.1016/j.advwatres.2015.07.018
Publication status	Published - 1 Dec 2015

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10.1016/j.advwatres.2015.07.018

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title = "Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model",

abstract = "The ensemble Kalman filter (EnKF) is a popular data assimilation (DA) technique that has been extensively used in environmental sciences for combining complementary information from model predictions and observations. One of the major challenges in EnKF applications is the description of model uncertainty. In most hydrological EnKF applications, an ad hoc model uncertainty is defined with the aim of avoiding a collapse of the filter. The present work provides a systematic assessment of model uncertainty in DA applications based on combinations of forcing, model parameters, and state uncertainties. This is tested in a case where groundwater hydraulic heads are assimilated into a distributed and integrated catchment-scale model of the Karup catchment in Denmark. A series of synthetic data assimilation experiments are carried out to analyse the impact of different model uncertainty assumptions on the feasibility and efficiency of the assimilation. The synthetic data used in the assimilation study makes it possible to diagnose model uncertainty assumptions statistically. Besides the model uncertainty, other factors such as observation error, observation locations, and ensemble size are also analysed with respect to performance and sensitivity. Results show that inappropriate definition of model uncertainty can greatly degrade the assimilation performance, and an appropriate combination of different model uncertainty sources is advised.",

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T1 - Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model

AU - Zhang, Donghua

AU - Madsen, Henrik

AU - Ridler, Marc E.

AU - Refsgaard, Jens C.

AU - Jensen, Karsten Høgh

PY - 2015/12/1

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N2 - The ensemble Kalman filter (EnKF) is a popular data assimilation (DA) technique that has been extensively used in environmental sciences for combining complementary information from model predictions and observations. One of the major challenges in EnKF applications is the description of model uncertainty. In most hydrological EnKF applications, an ad hoc model uncertainty is defined with the aim of avoiding a collapse of the filter. The present work provides a systematic assessment of model uncertainty in DA applications based on combinations of forcing, model parameters, and state uncertainties. This is tested in a case where groundwater hydraulic heads are assimilated into a distributed and integrated catchment-scale model of the Karup catchment in Denmark. A series of synthetic data assimilation experiments are carried out to analyse the impact of different model uncertainty assumptions on the feasibility and efficiency of the assimilation. The synthetic data used in the assimilation study makes it possible to diagnose model uncertainty assumptions statistically. Besides the model uncertainty, other factors such as observation error, observation locations, and ensemble size are also analysed with respect to performance and sensitivity. Results show that inappropriate definition of model uncertainty can greatly degrade the assimilation performance, and an appropriate combination of different model uncertainty sources is advised.

AB - The ensemble Kalman filter (EnKF) is a popular data assimilation (DA) technique that has been extensively used in environmental sciences for combining complementary information from model predictions and observations. One of the major challenges in EnKF applications is the description of model uncertainty. In most hydrological EnKF applications, an ad hoc model uncertainty is defined with the aim of avoiding a collapse of the filter. The present work provides a systematic assessment of model uncertainty in DA applications based on combinations of forcing, model parameters, and state uncertainties. This is tested in a case where groundwater hydraulic heads are assimilated into a distributed and integrated catchment-scale model of the Karup catchment in Denmark. A series of synthetic data assimilation experiments are carried out to analyse the impact of different model uncertainty assumptions on the feasibility and efficiency of the assimilation. The synthetic data used in the assimilation study makes it possible to diagnose model uncertainty assumptions statistically. Besides the model uncertainty, other factors such as observation error, observation locations, and ensemble size are also analysed with respect to performance and sensitivity. Results show that inappropriate definition of model uncertainty can greatly degrade the assimilation performance, and an appropriate combination of different model uncertainty sources is advised.

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