Bias-aware data assimilation in integrated hydrological modelling
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Bias-aware data assimilation in integrated hydrological modelling. / Ridler, Marc-Etienne; Zhang, Donghua; Madsen, Henrik; Kidmose, Jacob; Refsgaard, Jens Christian; Jensen, Karsten Høgh.
In: Hydrology Research, Vol. 49, No. 4, 2018, p. 989-1004.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Bias-aware data assimilation in integrated hydrological modelling
AU - Ridler, Marc-Etienne
AU - Zhang, Donghua
AU - Madsen, Henrik
AU - Kidmose, Jacob
AU - Refsgaard, Jens Christian
AU - Jensen, Karsten Høgh
PY - 2018
Y1 - 2018
N2 - One of the major challenges in hydrological data assimilation applications is the presence of bias in both models and observations. The present study uses the ensemble transform Kalman filtering (ETKF) method and an observational bias estimation technique to estimate groundwater hydraulic heads. The study was carried out in a relatively complex, groundwater dominated, catchment in Denmark using the MIKE SHE model code. The method is implemented and evaluated using synthetic data and subsequently tested against real observations. The results from the synthetic experiments show that the bias-aware filter outperforms the standard filter, with improved state estimate and correct bias estimate. The assimilation using real observations further demonstrates the robustness of bias-aware ETKF, and the potential improvements using integrated hydrological modelling. Furthermore, the experiments with assimilating over different depths show that the state estimates depend on correlation across layers.
AB - One of the major challenges in hydrological data assimilation applications is the presence of bias in both models and observations. The present study uses the ensemble transform Kalman filtering (ETKF) method and an observational bias estimation technique to estimate groundwater hydraulic heads. The study was carried out in a relatively complex, groundwater dominated, catchment in Denmark using the MIKE SHE model code. The method is implemented and evaluated using synthetic data and subsequently tested against real observations. The results from the synthetic experiments show that the bias-aware filter outperforms the standard filter, with improved state estimate and correct bias estimate. The assimilation using real observations further demonstrates the robustness of bias-aware ETKF, and the potential improvements using integrated hydrological modelling. Furthermore, the experiments with assimilating over different depths show that the state estimates depend on correlation across layers.
U2 - 10.2166/nh.2017.117
DO - 10.2166/nh.2017.117
M3 - Journal article
VL - 49
SP - 989
EP - 1004
JO - Hydrology Research
JF - Hydrology Research
SN - 1998-9563
IS - 4
ER -
ID: 197004817