Multivariate hydrological data assimilation of soil moisture and groundwater
head

Zhang, Donghua; Madsen, Henrik; Ridler, Marc E.; Kidmose, Jacob; Jensen, Karsten H.; Refsgaard, Jens C.

doi:https://doi.org/10.5194/hess-20-4341-2016

Articles | Volume 20, issue 10

https://doi.org/10.5194/hess-20-4341-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Modeling hydrological processes and changes

https://doi.org/10.5194/hess-20-4341-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 10

Research article

|

26 Oct 2016

Research article |

| 26 Oct 2016

Multivariate hydrological data assimilation of soil moisture and groundwater head

Donghua Zhang, Henrik Madsen, Marc E. Ridler, Jacob Kidmose, Karsten H. Jensen, and Jens C. Refsgaard

Data sets

HOBE project website HOBE http://hobe.dk/

HydroCast Hydrological Forecasting and Data Assimilation http://hydrocast.dhigroup.com/

Hydrological model forcing data Danish Meteorological Institute https://www.dmi.dk/vejr/arkiver/vejrarkiv/

Soil moisture data International Soil Moisture Network (ISMN) https://ismn.geo.tuwien.ac.at/networks/hobe/

Short summary

We present a method to assimilate observed groundwater head and soil moisture profiles into an integrated hydrological model. The study uses the ensemble transform Kalman filter method and the MIKE SHE hydrological model code. The proposed method is shown to be more robust and provide better results for two cases in Denmark, and is also validated using real data. The hydrological model with assimilation overall improved performance compared to the model without assimilation.