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, https://doi.org/10.1029/2008WR007031, 2009.
Chen, H., Yang, D., Hong, Y., Gourley, J .J., and Zhang, Y.: Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting, Adv. Water Resour., 59, 209–220, https://doi.org/10.1016/j.advwatres.2013.06.010, 2013.
Drécourt, J.-P., Madsen, H., and Rosbjerg, D.: Bias aware Kalman filters: Comparison and improvements, Adv. Water Resour., 29, 707–718, https://doi.org/10.1016/j.advwatres.2005.07.006, 2005.
Fertig, E. J., Hunt, B. R., Ott, E., and Szunyogh, I.: Assimilating non-local observations with a local ensemble Kalman filter, Tellus A, 59, 719–730, https://doi.org/10.1111/j.1600-0870.2007.00260.x, 2007
Graham, D. N. and Butts, M. B.: Flexible, integrated watershed modelling with MIKE SHE, in: Watershed Models, edited by: Singh, V. P. and Frevert, D. K., 245–272, CRC Press, ISBN: 0849336090, Boca Raton, Florida, USA, 2005.
Greve, M. H., Greve, M. B., Bøcher, P. K., Balstrøm, T., Breuning-Madsen, H., and Krogh, L.: Generating a Danish raster-based topsoil property map combining choropleth maps and point information, Geografisk Tidsskrift, 107, 1–12, 2007.
Harlim, J. and Hunt, B. R.: Local Ensemble Transform Kalman Filter: An Efficient Scheme for Assimilating Atmospheric Data, preprint (2005).
Hendricks Franssen, H. J. and Kinzelbach, W.: Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem, Water Resour. Res., 44, W09408, https://doi.org/10.1029/2007WR006505, 2008.
Hendricks Franssen, H. J., Kaiser, H. P., Kuhlmann, U., Bauser, G., Stauffer, F., Muller, R., and Kinzelbach, W.: Operational real-time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream-aquifer interaction and parameter updating, Water Resour. Res., 47, W02532, https://doi.org/10.1029/2010WR009480, 2011.
Herschy, R. W.: Hydrometry – Principles and Practices, 2nd Edn., Wiley & Sons Ltd, Hoboken, New Jersey, USA, 1999.
Juston, J., Seibert, J., and Johansson, P.-O.: Temporal sampling strategies and uncertainty in calibrating a conceptual hydrological model for a small boreal catchment, Hydrol. Process., 23, 3093–3109, https://doi.org/10.1002/hyp.7421, 2009.
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, https://doi.org/10.1002/wrcr.20169, 2013.
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water Resour. Res., 43, W07401, https://doi.org/10.1029/2006WR005756, 2007.
Madsen, H.: Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives, Adv. Water Resour., 26, 205–216, https://doi.org/10.1016/S0309-1708(02)00092-1, 2003.
Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P.: Dual State-Parameter Estimation of Hydrological Models using Ensemble Kalman Filter, Adv. Water Resour., 28, 135–147, 2005.
Miyoshi, T.: An adaptive covariance localization method with the LETKF (presentation), 14th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS) (recorded presentation), 2010.
Nie, S., Zhu, J., and Luo, Y.: Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments, Hydrol. Earth Syst. Sci., 15, 2437–2457, https://doi.org/10.5194/hess-15-2437-2011, 2011.
Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza, M., Kalnay, E., Patil, D. J., and Yorke, J. A.: A local ensemble Kalman filter for atmospheric data assimilation, Tellus A, 56, 415–428, https://doi.org/10.1111/j.1600-0870.2004.00076.x, 2004.
Refsgaard, J. C.: Parametrisation, calibration and validation of distributed hydrological models, J. Hydrol., 198, 69–97, 1997.
Sakov, P. and Bertino, L.: Relation between two common localisation methods for the EnKF, Comput. Geosci., 15, 225–237, 2011.
Sakov, P., Evensen, G., and Bertino, L.: Asynchronous data assimilation with the EnKF, Tellus, 62A, 24–29, https://doi.org/10.1111/j.1600-0870.2009.00417.x, 2010.
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, Z.: Parameter Estimation of a Physically-Based Land Surface Hydrologic Model Using the Ensemble Kalman Filter: A Synthetic Experiment, Water Resour. Res., 50, 706–724, https://doi.org/10.1002/2013WR014070, 2014.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004WR003059, 2005.
Wanders, N., Karssenberg, D., de Roo, A., de Jong, S. M., and Bierkens, M. F. P.: The suitability of remotely sensed soil moisture for improving operational flood forecasting, Hydrol. Earth Syst. Sci., 18, 2343–2357, https://doi.org/10.5194/hess-18-2343-2014, 2014.
Xie, X. and Zhang, D.: Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter, Adv. Water Resour., 33, 678–690, https://doi.org/10.1016/j.advwatres.2010.03.012, 2010.
Zupanski, M.: All-Sky Satellite Radiance Data Assimilation: Methodology and challenges, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, edited by: Park, S. K. and Xu, L., Vol. II, https://doi.org/10.1007/978-3-642-35088-7_1, Springer-Verlag Berlin Heidelberg, 2013.