Multivariate hydrological data assimilation of soil moisture and groundwater head

Other literature type, Article English OPEN
Zhang, Donghua ; Madsen, Henrik ; Ridler, Marc-Etienne Francois ; Kidmose, Jacob Baarstrøm ; Jensen, Karsten Høgh ; Refsgaard, Jens Christian (2016)

Observed groundwater head and soil moisture profiles are assimilated into an integrated hydrological model. The study uses the ensemble transform Kalman filter (ETKF) data assimilation method with the MIKE SHE hydrological model code. The method was firstly tested on synthetic data in a catchment of less complexity (the Karup catchment in Denmark), and later implemented using data from real observations in a larger and more complex catchment (the Ahlergaarde catchment in Denmark). In the Karup model, several experiments were designed with respect to different observation types, ensemble sizes and localization schemes, to investigate the assimilation performance. The results showed the necessity of using localization, especially when assimilating both groundwater head and soil moisture. The proposed scheme with both distance localization and variable localization was shown to be more robust and provide better results. Using the same assimilation scheme in the Ahlergaarde model, groundwater head and soil moisture were successfully assimilated into the model. The hydrological model with assimilation showed an overall improved performance compared to the model without assimilation.
  • References (36)
    36 references, page 1 of 4

    Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634-642, doi:10.1175/1520- 0493(2003)131<0634:ALLSFF>2.0.CO;2, 2003.

    Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects, Mon. Weather Rev., 129, 420- 436, doi:10.1175/1520-0493(2001)129<0420:Aswtet>2.0.Co;2, 2001.

    Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Comparison of Data Assimilation Techniques for a Coupled Model of Surface and Subsurface Flow, Vadose Zone J., 8, 837-845, doi:10.2136/vzj2009.0018, 2009a.

    Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow, Water Resour. Res., 45, W10421, doi:10.1029/2008wr007031, 2009b.

    Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter, Adv. Water Resour., 29, 1107-1122, doi:10.1016/j.advwatres.2005.09.007, 2006.

    Danish Meteorological Institute: Hydrological model forcing data, available at:, last access: 5 October 2016.

    De Lannoy, G. J. M., Houser, P. R., Pauwels, V. R. N., and Verhoest, N. E. C.: State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency, Water Resour. Res., 43, W06401, doi:10.1029/2006wr005100, 2007.

    DHI: The MIKE SHE user and technical reference manual (2016 version), DHI, 2015.

    Doherty, J.: PEST, Model-independent parameter estimation, User manual, 5th Edn., Watermark Numerical Computing, 2010.

    Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343-367, doi:10.1007/s10236-003-0036-9, 2003.

  • Metrics
    No metrics available
Share - Bookmark