Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model. (December 2015)

• Record Type: Journal Article
• Authors: Zhang, Donghua; Madsen, Henrik; Ridler, Marc E.; Refsgaard, Jens C.; Jensen, Karsten H.
• Is Part Of: Advances in water resources. Volume 86 Part B(2015)
• Page Start: 400
• Page End: 413
• Publication Date: 2015-12
• Subjects: Uncertainty -- Data assimilation -- Ensemble Kalman filter -- Hydrological modelling -- MIKE SHE; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2015.07.018
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: Systematic study for assessing uncertainties in hydrologic data assimilation. Incorrect definition of model uncertainty can lead to poor assimilation performance. Assimilation performance is robust to a large range of uncertainty magnitudes. Innovation statistic is a useful diagnostic to evaluate uncertainty estimation. 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.