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

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, 1340–1352.10.2136/vzj2004.1340Search in Google Scholar

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, 2951–2970.10.1175/MWR3224.1Search in Google Scholar

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, 2763–2774.10.1016/j.jhydrol.2014.07.041Search in Google Scholar

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, 10635–10681.10.5194/hessd-11-10635-2014Search in Google Scholar

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, 145–161.10.1016/S0022-1694(03)00229-4Search in Google Scholar

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): 587–593.10.1109/TGRS.2008.916264Search in Google Scholar

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): 2542–2555.10.1109/TGRS.2011.2177468Search in Google Scholar

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.10.1109/IGARSS.2013.6723273Search in Google Scholar

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, 526–536.10.1016/j.advwatres.2011.01.011Search in Google Scholar

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, 425–438.10.1016/j.advwatres.2015.08.003Search in Google Scholar

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, 1309–1324.10.1016/j.advwatres.2008.06.005Search in Google Scholar

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, 1645–1662.10.1002/hyp.1051Search in Google Scholar

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, 1–16.10.5194/hess-13-1-2009Search in Google Scholar

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, 137–149.10.1016/S0309-1708(02)00088-XSearch in Google Scholar

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, 1504–1512.10.1109/TGRS.2010.2089526Search in Google Scholar

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, 2018–2028.10.1109/TGRS.2013.2257605Search in Google Scholar

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.Search in Google Scholar

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, 10143–10162.10.1029/94JC00572Search in Google Scholar

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, 98–117.10.1016/j.jhydrol.2011.11.039Search in Google Scholar

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, 1–17.10.1016/S0022-1694(03)00088-XSearch in Google Scholar

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, 395–406.10.1016/j.advwatres.2010.12.005Search in Google Scholar

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.10.1016/j.jag.2015.06.002Search in Google Scholar

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, 1597–1615.10.1016/j.advwatres.2011.08.012Search in Google Scholar

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, 146–162.10.1016/j.rse.2015.06.025Search in Google Scholar

Lunt, I., Hubbard, S., Rubin, Y., 2005. Soil moisture content estimation using ground-penetrating radar reflection data. Journal of Hydrology, 307, 1, 254–269.10.1016/j.jhydrol.2004.10.014Search in Google Scholar

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, 11403–11433.10.3390/rs70911403Search in Google Scholar

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, 239–245.10.1080/00401706.1979.10489755Search in Google Scholar

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

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, 135–147.10.1016/j.advwatres.2004.09.002Search in Google Scholar

Morris, M.D., 1991. Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 2, 161–174.10.1080/00401706.1991.10484804Search in Google Scholar

Nash, J., Sutcliffe, J., 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10, 3, 282–290.10.1016/0022-1694(70)90255-6Search in Google Scholar

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 http://swat.tamu.edu/media/99192/swat2009-theory.pdf)Search in Google Scholar

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, 215–229.10.1109/TGRS.2002.808243Search in Google Scholar

Reichle, R.H., Koster, R.D., 2004. Bias reduction in short records of satellite soil moisture. Geophysical Research Letters, 31, 19. DOI:10.1029/2004GL020938.10.1029/2004GL020938Search in Google Scholar

Reichle, R.H., Crow, W.T., Keppenne, C.L., 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation. Water resources research, 44, 3.10.1029/2007WR006357Search in Google Scholar

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

Troch, P.A., Paniconi, C., McLaughlin, D., 2003. Catchment-scale hydrological modeling and data assimilation. Advances in Water Resources, 26, 2, 131–135.10.1016/S0309-1708(02)00087-8Search in Google Scholar

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, 631–650.10.1016/S0309-1708(00)00043-9Search in Google Scholar

Wang, D., Chen, Y., Cai, X., 2009. State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter. Water Resources Research, 45, 11.10.1029/2008WR007401Search in Google Scholar

Williams, J., 1969. Flood routing with variable travel time or variable storage coefficients. Trans. ASAE, 12, 1, 100–103.10.13031/2013.38772Search in Google Scholar

Xie, X., Zhang, D., 2010. Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter. Advances in Water Resources, 33, 6, 678–690.10.1016/j.advwatres.2010.03.012Search in Google Scholar

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, 7350–7365.10.1002/2012WR012853Search in Google Scholar

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, 3923–3936.10.5194/hess-18-3923-2014Search in Google Scholar

Yang, X., Delsole, T., 2009. Using the ensemble Kalman filter to estimate multiplicative model parameters. Tellus A 61, 5, 601–609.10.1111/j.1600-0870.2009.00407.xSearch in Google Scholar

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, 53–64.10.1016/j.jhydrol.2012.08.034Search in Google Scholar

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, 6950–6965.10.1002/2012WR013473Search in Google Scholar