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Zhang, Donghua; Madsen, Henrik; Ridler, Marc-Etienne Francois; Kidmose, Jacob Baarstrøm; Jensen, Karsten Høgh; Refsgaard, Jens Christian (2016)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: T, G, GE1-350, Geography. Anthropology. Recreation, Environmental technology. Sanitary engineering, Environmental sciences, Technology, TD1-1066
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.
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    • 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: https://www.dmi.dk/vejr/arkiver/vejrarkiv/, 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.
    • Gharamti, M. E., Hoteit, I., and Valstar, J.: Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering, Adv. Water Resour., 60, 75-88, doi:10.1016/j.advwatres.2013.07.011, 2013.
    • Graham, D. N. and Butts, M. B.: Flexible, in tegrated watershed modelling with MIKE SHE, in: Watershed Models, edited by: Singh, V. P. Frevert, D. K., CRC Press, 245-272, 2005.
    • Gregersen, J. B., Gijsbers, P. J. A., and Westen, S. J. P.: OpenMI: Open modelling interface, J. Hydroinform., 9, 175- 191, doi:10.2166/hydro.2007.023, 2007.
    • Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776-2790, doi:10.1175/1520-0493(2001)129<2776:Ddfobe>2.0.Co;2, 2001.
    • Han, X., Li, X., He, G., Kumbhar, P., Montzka, C., Kollet, S., Miyoshi, T., Rosolem, R., Zhang, Y., Vereecken, H., and Franssen, H.-J. H.: DasPy 1.0 - the Open Source Multivariate Land Data Assimilation Framework in combination with the Community Land Model 4.5, Geosci. Model Dev. Discuss., 8, 7395-7444, doi:10.5194/gmdd-8-7395-2015, 2015.
    • HOBE: HOBE project website, available at: http://hobe.dk/, last access: 5 October 2016.
    • HydroCast - Hydrological Forecasting and Data Assimilation, available at: http://hydrocast.dhigroup.com/, last access: 5 October 2016.
    • International Soil Moisture Network (ISMN): Soil moisture data, available at: https://ismn.geo.tuwien.ac.at/networks/hobe/, last access: 5 October 2016.
    • Jensen, K. H. and Illangasekare, T. H.: HOBE: A Hydrological Observatory, Vadose Zone J., 10, 1-7, doi:10.2136/vzj2011.0006, 2011.
    • Kang, J.-S., Kalnay, E., Liu, J., Fung, I., Miyoshi, T., and Ide, K.: “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation, J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673, 2011.
    • Kurtz, W., Hendricks Franssen, H.-J., Kaiser, H.-P., and Vereecken, H.: Joint assimilation of piezometric heads and groundwater temperatures for improved modeling of riveraquifer interactions, Water Resour. Res., 50, 1665-1688, doi:10.1002/2013WR014823, 2014.
    • Lee, H., Seo, D.-J., and Koren, V.: Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states, Adv. Water Resour., 34, 1597-1615, doi:10.1016/j.advwatres.2011.08.012, 2011.
    • Li, Y., Ryu, D., Western, A. W., and Wang, Q. J.: Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags, Water Resour. Res., 49, 1887-1900, doi:10.1002/wrcr.20169, 2013.
    • Madsen, H.: Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives, Adv. Water Resour., 26, 205-216, doi:10.1016/S0309- 1708(02)00092-1, 2003.
    • Montzka, C., Pauwels, V., Franssen, H.-J., Han, X., and Vereecken, H.: Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review, Sensors, 12, 16291-16333, doi:10.3390/s121216291, 2012.
    • Rasmussen, J., Madsen, H., Jensen, K. H., and Refsgaard, J. C.: Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance, Hydrol. Earth Syst. Sci., 19, 2999-3013, doi:10.5194/hess-19-2999-2015, 2015.
    • Refsgaard, J. C.: Parameterisation, calibration and validation of distributed hydrological models, J. Hydrol., 198, 69-97, doi:10.1016/S0022-1694(96)03329-X, 1997.
    • Ridler, M. E., Madsen, H., Stisen, S., Bircher, S., and Fensholt, R.: Assimilation of SMOS-derived soil moisture in a fully integrated hydrological and soil-vegetation-atmosphere transfer model in Western Denmark, Water Resour. Res., 50, 8962-8981, doi:10.1002/2014wr015392, 2014a.
    • Ridler, M. E., van Velzen, N., Hummel, S., Sandholt, I., Falk, A. K., Heemink, A., and Madsen, H.: Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI), Environ. Modell. Softw., 57, 76-89, doi:10.1016/j.envsoft.2014.02.008, 2014b.
    • Sakov, P. and Bertino, L.: Relation between two common localisation methods for the EnKF, Comput. Geosci., 15, 225-237, doi:10.1007/s10596-010-9202-6, 2010.
    • Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X.: Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment, Water Resour. Res., 50, 706-724, doi:10.1002/2013WR014070, 2014.
    • Visser, A., Stuurman, R., and Bierkens, M. F. P.: Real-time forecasting of water table depth and soil moisture profiles, Adv. Water Resour., 29, 692-706, doi:10.1016/j.advwatres.2005.07.011, 2006.
    • Wang, X., Bishop, C. H., and Julier, S. J.: Which Is Better, an Ensemble of Positive-Negative Pairs or a Centered Spherical Simplex Ensemble?, Mon. Weather Rev., 132, 1590-1605, doi:10.1175/1520-0493(2004)132<1590:wibaeo>2.0.co;2, 2004.
    • Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913-1924, doi:10.1175/1520-0493(2002)130<1913:Edawpo>2.0.Co;2, 2002.
    • Xie, X. H. and Zhang, D. X.: Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter, Adv. Water Resour., 33, 678-690, doi:10.1016/j.advwatres.2010.03.012, 2010.
    • Zhang, D., Madsen, H., Ridler, M. E., Refsgaard, J. C., and Jensen, K. H.: Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model, Adv. Water Resour., 86, 400-413, doi:10.1016/j.advwatres.2015.07.018, 2015.
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