Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River basin

Open access


This paper investigates the impact of surface soil moisture assimilation on the estimation of both parameters and states in the Soil and Water Assessment Tool (SWAT) model using the ensemble Kalman filter (EnKF) method in upper Huai River basin. The investigation is carried out through a series of synthetic experiments and real world tests using a merged soil moisture product (ESA CCI SM) developed by the European Space Agency, and considers both the joint state-parameter updating and only state updating schemes. The synthetic experiments show that with joint state-parameter update, the estimation of model parameter SOL_AWC (the available soil water capacity) and model states (the soil moisture in different depths) can be significantly improved by assimilating the surface soil moisture. Meanwhile, the runoff modeling for the whole catchment is also improved. With only state update, the improvement on runoff modeling shows less significance and robustness. Consistent with the synthetic experiments, the assimilation of the ESA CCI SM with joint state-parameter update shows considerable capability in the estimation of SOL_AWC. Both the joint state-parameter update and the only state update scheme could improve the streamflow modeling although the optimal model and observation error parameters for them are quite different. However, due to the high vegetation coverage of the study basin, and the strong spatial mismatch between the satellite and the model simulated soil moisture, it is still challenging to significantly benefit the runoff estimates by assimilating the ESA CCI SM.


  • Abbaspour, K., Johnson, C., Van Genuchten, M.T., 2004. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal, 3, 4, 1340–1352.

  • Aksoy, A., Zhang, F., Nielsen-Gammon, J.W., 2006. Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model. Monthly Weather Review, 134, 10, 2951–2970.

  • Alvarez-Garreton, C., Ryu, D., Western, A., Crow, W.T., Robertson, D.E., 2014. The impacts of assimilating satellite soil moisture into a rainfall–runoff model in a semi-arid catchment. Journal of Hydrology, 519, 2763–2774.

  • Alvarez-Garreton, C., Ryu, D., Western, A., Su, C.H., Crow, W.T., Robertson, D.E., Leahy, C., 2015. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrology and Earth System Sciences Discussions, 11, 9, 10635–10681.

  • Aubert, D., Loumagne, C., Oudin, L., 2003. Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model. Journal of Hydrology, 280, 1, 145–161.

  • Barre, H.M.J., Duesmann, B., Kerr, Y.H., 2008. SMOS: The Mission and the System. Geoscience and Remote Sensing, IEEE Transactions on 46(3): 587–593.

  • Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., Hahn, S., 2012. Assimilation of Surface-and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling. Geoscience and Remote Sensing, IEEE Transactions on 50(7): 2542–2555.

  • Brocca, L., Moramarco, T., Dorigo, W., Wagner, W., 2013. Assimilation of satellite soil moisture data into rainfall-runoff modelling for several catchments worldwide. Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, IEEE.

  • Chen, F., Crow, W.T., Starks, P.J., Moriasi, D.N., 2011. Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture. Advances in Water Resources, 34, 4, 526–536.

  • Chen, W., Huang, C., Shen, H., Li, X., 2015. Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation. Advances in Water Resources, 86, 425–438.

  • Clark, M.P., Rupp, D.E., Woods, R.A., Zheng, X., Ibbitt, R.P., Slater, A.G., Schmidt, J., Uddstrom, M.J., 2008. Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources, 31, 10, 1309–1324.

  • Crosson, W.L., Laymon, C.A., Inguva, R., Schamschula, M.P., 2002. Assimilating remote sensing data in a surface flux–soil moisture model. Hydrological processes, 16, 8, 1645–1662.

  • Crow, W.T., Ryu, D., 2009. A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals. Hydrology and Earth System Sciences, 13, 1, 1–16.

  • Crow, W.T., Wood, E.F., 2003. The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97. Advances in Water Resources, 26, 2, 137–149.

  • Das, N.N., Entekhabi, D., Njoku, E.G., 2011. An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval. Geoscience and Remote Sensing, IEEE Transactions on 49, 5, 1504–1512.

  • Das, N.N., Entekhabi, D., Njoku, E.G., Shi, J.J. C., Johnson, J.T., Colliander, A., 2014. Tests of the SMAP Combined Radar and Radiometer Algorithm Using Airborne Field Campaign Observations and Simulated Data. Geoscience and Remote Sensing, IEEE Transactions on 52, 4, 2018–2028.

  • Entekhabi, D., Njoku, E.G., O'Neill, P.E., Kellogg, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D., Jackson, T.J., Johnson, J., Kimball, J., Piepmeier, J.R., Koster, R.D., Martin, N., McDonald, K.C., Moghaddam, M., Moran, S., Reichle, R., Shi, J.C., Spencer, M.W., Thurman, S.W., Leung, T., Van Zyl, J. 2010. The Soil Moisture Active Passive (SMAP) Mission. Proceedings of the IEEE 98, 5, 704–716.

  • Evensen, G., 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99, 10143–10162.

  • Han, E., Merwade, V., Heathman, G.C., 2012. Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model. Journal of Hydrology, 416–417, 98–117.

  • Heathman, G.C., Starks, P.J., Ahuja, L.R., Jackson, T.J., 2003. Assimilation of surface soil moisture to estimate profile soil water content. Journal of Hydrology, 279, 1–4, 1–17.

  • Lü, H., Yu, Z., Zhu, Y., Drake, S., Hao, Z. and Sudicky, E.A., 2011. Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation. Advances in Water Resources, 34, 3, 395–406.

  • Laiolo, P., Gabellani, S., Campo, L., Silvestro, F, Delogu, F, Rudari, R., Pulvirenti, L, Boni, G, Fascetti, F., Pierdicca, N., 2015. Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model. International Journal of Applied Earth Observation and Geoinformation.

  • Lee, H., Seo, D.-J., Koren, V., 2011. 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. Advances in Water Resources, 34, 12, 1597–1615.

  • Lievens, H., Tomer, S.K., Al Bitar, A., De Lannoy, G.J.M., Drusch, M., Dumedah, G., Hendricks Franssen, H.J. Hendricks, Kerr, Y.H., Martens, B., Pan, M., Roundy J.K., Vereecken, H., Walker, J.P., Wood E.F., Verhoest, N.E.C., Pauwels, V.R.N., 2015. SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia. Remote sensing of environment, 168, 146–162.

  • Lunt, I., Hubbard, S., Rubin, Y., 2005. Soil moisture content estimation using ground-penetrating radar reflection data. Journal of Hydrology, 307, 1, 254–269.

  • Massari, C., Brocca, L., Tarpanelli, A., Moramarco, T., 2015. Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe? Remote Sensing, 7, 9, 11403–11433.

  • McKay, M.D., Beckman, R.J., Conover, W.J., 1979. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21, 2, 239–245.

  • Monteith, J.L., 1965. Evaporation and the environment. In: 19th Symposia of the Society for Experimental Biology: The state and movement of water in living organisms. Cambridge Univ. Press, London, pp. 205–234.

  • Moradkhani, H., Sorooshian, S., Gupta, H.V., Houser, P.R., 2005. Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Advances in Water Resources, 28, 2, 135–147.

  • Morris, M.D., 1991. Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 2, 161–174.

  • Nash, J., Sutcliffe, J., 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10, 3, 282–290.

  • Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams, J.R., 2011. Soil and Water Assessment Tool Theoretical Documentation Version 2009. TR-406, Texas Water Resources Institute Technical Report No.406. Texax A&M University. (available at

  • Njoku, E.G., Jackson, T.J., Lakshmi, V., Chan, T.K., Nghiem, S.V., 2003. Soil moisture retrieval from AMSR-E. Geoscience and Remote Sensing, IEEE Transactions on 41, 2, 215–229.

  • Reichle, R.H., Koster, R.D., 2004. Bias reduction in short records of satellite soil moisture. Geophysical Research Letters, 31, 19. DOI:10.1029/2004GL020938.

  • Reichle, R.H., Crow, W.T., Keppenne, C.L., 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation. Water resources research, 44, 3.

  • Smith, P.J., 2010. Joint state and parameter estimation using data assimilation with application to morphodynamic modelling. University of Reading, Reading.

  • Troch, P.A., Paniconi, C., McLaughlin, D., 2003. Catchment-scale hydrological modeling and data assimilation. Advances in Water Resources, 26, 2, 131–135.

  • Walker, J.P., Willgoose, G.R., Kalma, J.D., 2001. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: A comparison of retrieval algorithms. Advances in Water Resources, 24, 6, 631–650.

  • Wang, D., Chen, Y., Cai, X., 2009. State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter. Water Resources Research, 45, 11.

  • Williams, J., 1969. Flood routing with variable travel time or variable storage coefficients. Trans. ASAE, 12, 1, 100–103.

  • Xie, X., Zhang, D., 2010. Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter. Advances in Water Resources, 33, 6, 678–690.

  • Xie, X., Zhang, D., 2013. A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter. Water Resources Research, 49, 11, 7350–7365.

  • Xie, X., Meng, S., Liang, S., Yao, Y., 2014. Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy. Hydrology and Earth System Sciences, 18, 10, 3923–3936.

  • Yang, X., Delsole, T., 2009. Using the ensemble Kalman filter to estimate multiplicative model parameters. Tellus A 61, 5, 601–609.

  • Yu, Z., Liu, D., Lü, H., Fu, X., Xiang, L., Zhu, Y., 2012. A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter. Journal of Hydrology, 475, 53–64.

  • Yu, Z., Fu, X., Luo, L., Lü, H., Ju, Q., Liu, D., Kalin, A.D., Huang, D., Yang, C., Zhao, L., 2014. One-dimensional soil temperature simulation with Common Land Model by assimilating in situ observations and MODIS LST with the ensemble particle filter. Water Resources Research, 50, 8, 6950–6965.

Journal of Hydrology and Hydromechanics

The Journal of Institute of Hydrology SAS Bratislava and Institute of Hydrodynamics CAS Prague

Journal Information

IMPACT FACTOR 2016: 1.654

CiteScore 2016: 1.72

SCImago Journal Rank (SJR) 2016: 0.440
Source Normalized Impact per Paper (SNIP) 2016: 0.969

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 46 46 31
PDF Downloads 15 15 9