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

Geologi

22 Citationer (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.

Originalsprog	Engelsk
Tidsskrift	Advances in Water Resources
Vol/bind	86
Udgave nummer	Part B
Sider (fra-til)	400–413
ISSN	0309-1708
DOI	https://doi.org/10.1016/j.advwatres.2015.07.018
Status	Udgivet - 1 dec. 2015

Adgang til dokumentet

10.1016/j.advwatres.2015.07.018

Citationsformater

@article{83a41bbbb01f446db03b76bf64435baf,

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.",

author = "Donghua Zhang and Henrik Madsen and Ridler, {Marc E.} and Refsgaard, {Jens C.} and Jensen, {Karsten H{\o}gh}",

year = "2015",

month = dec,

day = "1",

doi = "10.1016/j.advwatres.2015.07.018",

language = "English",

volume = "86",

pages = "400–413",

journal = "Advances in Water Resources",

issn = "0309-1708",

publisher = "Pergamon Press",

number = "Part B",

}

TY - JOUR

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

Y1 - 2015/12/1

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.

U2 - 10.1016/j.advwatres.2015.07.018

DO - 10.1016/j.advwatres.2015.07.018

M3 - Journal article

SN - 0309-1708

VL - 86

SP - 400

EP - 413

JO - Advances in Water Resources

JF - Advances in Water Resources

IS - Part B

ER -