Advertisement

Journal of Meteorological Research

, Volume 33, Issue 2, pp 190–205 | Cite as

Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model

  • Xiaolei FuEmail author
  • Zhongbo Yu
  • Ying Tang
  • Yongjian Ding
  • Haishen Lyu
  • Baoqing Zhang
  • Xiaolei Jiang
  • Qin Ju
Special Collection on Development and Applications of Regional and Global Land Data Assimilation Systems
  • 3 Downloads

Abstract

Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter (EnKF) and Simple Biosphere Model (SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation, and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.

Key words

soil moisture Ensemble Kalman Filter (EnKF) Simple Biosphere Model (SiB2) prediction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Balsamo, G., J. F. Mahfouf, S. Bélair, et al., 2007: A land data assimilation system for soil moisture and temperature: An information content study. J. Hydrometeorol., 8, 1225–1242, doi: 10.1175/2007JHM819.1.CrossRefGoogle Scholar
  2. Dai, Y. J., X. B. Zeng, R. E. Dickinson, et al., 2003: The common land model. Bull. Amer. Meteor. Soc., 84, 1013–1024, doi: 10.1175/BAMS-84-8-1013.CrossRefGoogle Scholar
  3. Decker, M., and X. B. Zeng, 2009: Impact of modified Richards equation on global soil moisture simulation in the Community Land Model (CLM3.5). J. Adv. Model. Earth Syst., 1, 5, doi: 10.3894/JAMES.2009.1.5.CrossRefGoogle Scholar
  4. Dumedah, G., and P. Coulibaly, 2013: Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation. Adv. Water Resour., 60, 47–63, doi: 10.1016/j.advwatres.2013.07.007.CrossRefGoogle Scholar
  5. Dumedah, G., and J. P. Walker, 2014: Assessment of land surface model uncertainty: A crucial step towards the identification of model weaknesses. J. Hydrol., 519, 1474–1484, doi: 10.1016/j.jhydrol.2014.09.015.CrossRefGoogle Scholar
  6. Dumedah, G., and J. P. Walker, 2017: Assessment of model behavior and acceptable forcing data uncertainty in the context of land surface soil moisture estimation. Adv. Water Resour., 101, 23–36, doi: 10.1016/j.advwatres.2017.01.001.CrossRefGoogle Scholar
  7. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Oceans, 99, 10143–10162, doi: 10.1029/94JC00572.CrossRefGoogle Scholar
  8. Famiglietti, J. S., and E. F. Wood, 1994: Multiscale modeling of spatially variable water and energy balance process. Water Resour. Res., 30, 3061–3078, doi: 10.1029/94WR01498.CrossRefGoogle Scholar
  9. Fu, X. L., Z. B. Yu, L. F. Luo, et al., 2014: Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter. Stoch. Environ. Res. Risk Assess., 28, 681–693, doi: 10.1007/s00477-013-0781-3.CrossRefGoogle Scholar
  10. Fu, X. L., L. F. Luo, M. Pan, et al., 2018a: Evaluation of TOPMODEL-based land surface–atmosphere transfer scheme (TOPLATS) through a soil moisture simulation. Earth Interact., 22, 1–19, doi: 10.1175/EI-D-17-0037.1.CrossRefGoogle Scholar
  11. Fu, X. L., Z. B. Yu, Y. J. Ding, et al., 2018b: Analysis of influence of observation operator on sequential data assimilation through soil temperature simulation with common land model. Water Sci. Eng., 11, 196–204, doi: 10.1016/j.wse.2018.09.003.CrossRefGoogle Scholar
  12. Han, X. J., and X. Li, 2008: An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation. Remote Sens. Environ., 112, 1434–1449, doi: 10.1016/j.rse.2007.07.008.CrossRefGoogle Scholar
  13. Han, X. J., H. J. H. Franssen, C. Montzka, et al., 2014: Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations. Water Resour. Res., 50, 6081–6105, doi: 10.1002/2013WR014586.CrossRefGoogle Scholar
  14. Heathman, G. C., P. J. Starks, L. R. Ahuja, et al., 2003: Assimilation of surface soil moisture to estimate profile soil water content. J. Hydrol., 279, 1–17, doi: 10.1016/S0022-1694(03)00088-X.CrossRefGoogle Scholar
  15. Heemink, A. W., M. Verlaan, and J. Segers, 2001: Variance reduced ensemble Kalman filtering. Mon. Wea. Rev., 129, 1718–1728, doi: 10.1175/1520-0493(2001)129<1718:VREKF>2.0.CO;2.CrossRefGoogle Scholar
  16. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123–137, doi: 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2.CrossRefGoogle Scholar
  17. Huang, C. L., and X. Li, 2004: A review of land data assimilation system. Remote Sens. Technol. Appl., 19, 424–430, doi: 10.3969/j.issn.1004-0323.2004.05.026. (in Chinese)Google Scholar
  18. Huang, C. L., X. Li, L. Lu, et al., 2008: Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter. Remote Sens. Environ., 112, 888–900, doi: 10.1016/j.rse.2007.06.026.CrossRefGoogle Scholar
  19. Jackson, T. J., D. M. Le Vine, A. Y. Hsu, et al., 1999: Soil moisture mapping at regional scales using microwave radiometry: The southern great plains hydrology experiment. IEEE Trans. Geosci. Remote Sens., 37, 2136–2151, doi: 10.1109/36.789610.CrossRefGoogle Scholar
  20. Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82, 35–45, doi: 10.1115/1.3662552.CrossRefGoogle Scholar
  21. Koster, R. D., P. A. Dirmeyer, Z. C. Guo, et al., 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140, doi: 10.1126/science.1100217.CrossRefGoogle Scholar
  22. Lai, X., J. Wen, S. X. Cen, et al., 2014: Numerical simulation and evaluation study of soil moisture over China by using CLM4.0 model. Chinese J. Atmos. Sci., 38, 499–512, doi: 10.3878/j.issn.1006-9895.1401.13194. (in Chinese)Google Scholar
  23. Li, F. Q., W. T. Crow, and W. P. Kustas, 2010: Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Adv. Water Resour., 33, 201–214, doi: 10.1016/j.advwatres.2009.11.007.CrossRefGoogle Scholar
  24. Liang, X., D. P. Lettennmaier, E. F. Wood, et al., 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos., 99, 14415–14428, doi: 10.1029/94JD00483.CrossRefGoogle Scholar
  25. Lievens, H., G. J. M. De Lannoy, A. Al Bitar, et al., 2016: Assimilation of SMOS soil moisture and brightness temperature products into a land surface model. Remote Sens. Environ., 180, 292–304, doi: 10.1016/j.rse.2015.10.033.CrossRefGoogle Scholar
  26. Liu, D., A. K. Mishra, and Z. B. Yu, 2016: Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering. J. Hydrol., 538, 243–255, doi: 10.1016/j.jhydrol.2016.04.021.CrossRefGoogle Scholar
  27. Liu, H. R., F. Y. Lu, Z. Y. Liu, et al., 2016: Assimilating atmosphere reanalysis in coupled data assimilation. J. Meteor. Res., 30, 572–583, doi: 10.1007/s13351-016-6014-1.CrossRefGoogle Scholar
  28. Luo, L. F., A. Robock, K. E. Mitchell, et al., 2003: Validation of the North American land data assimilation system (NLDAS) retrospective forcing over the southern Great Plains. J. Geophys. Res. Atmos., 108, 8843, doi: 10.1029/2002JD003246.Google Scholar
  29. Luo, S. Q., X. W. Fang, S. H. Lyu, et al., 2017: Improving CLM4.5 simulations of land–atmosphere exchange during freeze–thaw processes on the Tibetan Plateau. J. Meteor. Res., 31, 916–930, doi: 10.1007/s13351-017-6063-0.CrossRefGoogle Scholar
  30. Lyu, H. S., Z. B. Yu, R. Horton, et al., 2011a: Multi-scale assimilation of root zone soil water predictions. Hydrol. Processes, 25, 3158–3172, doi: 10.1002/hyp.8034.CrossRefGoogle Scholar
  31. Lyu, H. S., Z. B. Yu, Y. H. Zhu, et al., 2011b: Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation. Adv. Water Resour., 34, 395–406, doi: 10.1016/j.advwatres.2010.12.005.CrossRefGoogle Scholar
  32. Milly, P. C. D., J. Betancourt, M. Falkenmark, et al., 2008: Stationarity is dead: Whither water management? Science, 319, 573–574, doi: 10.1126/science.1151915.CrossRefGoogle Scholar
  33. Monteith, J. L., 1973: Principles of Environmental Physics. Edward Arnold, London, 242 pp.Google Scholar
  34. Moradkhani, H., S. Sorooshian, H. V. Gupta, et al., 2005: Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour., 28, 135–147, doi: 10.1016/j.advwatres.2004.09.002.CrossRefGoogle Scholar
  35. Oleson, K. W., Y. J. Dai, G. B. Bonan, et al., 2004: Technical Description of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-461+STR, National Center for Atmospheric Research, Boulder, CO, doi: 10.5065/D6N877R0.Google Scholar
  36. Rodell, M., P. R. Houser, U. Jambor, et al., 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381–394, doi: 10.1175/BAMS-85-3-381.CrossRefGoogle Scholar
  37. Schaake, J. C., Q. Y. Duan, V. Koren, et al., 2004: An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res. Atmos., 109, D01S90, doi: 10.1029/2002JD003309.CrossRefGoogle Scholar
  38. Sellers, P. J., D. A. Randall, G. J. Collatz, et al., 1996: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation. J. Climate, 9, 676–705, doi: 10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2.CrossRefGoogle Scholar
  39. Shi, J. C., L. M. Jiang, L. X. Zhang, et al., 2006: Physically based estimation of bare-surface soil moisture with the passive radiometers. IEEE Trans. Geosci. Remote Sens., 44, 3145–3153, doi: 10.1109/TGRS.2006.876706.CrossRefGoogle Scholar
  40. Vrugt, J. A., H. V. Gupta, B. O. Nualláin, et al., 2006: Real-time data assimilation for operational ensemble streamflow forecasting. J. Hydrometeorol., 7, 548–565, doi: 10.1175/JHM504.1.CrossRefGoogle Scholar
  41. Wang, G. J., D. Chyi, L. Wang, et al., 2016: Soil moisture retrieval over Northeast China based on microwave brightness temperature of FY3B satellite and its comparison with other datasets. Chinese J. Atmos. Sci., 40, 792–804, doi: 10.3878/j.issn.1006-9895.1509.15207. (in Chinese)Google Scholar
  42. Weerts, A. H., and G. Y. H. El Serafy, 2006: Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models. Water Resour. Res., 42, W09403, doi: 10.1029/2005WR004093.CrossRefGoogle Scholar
  43. Western, A. W., and G. Blöschl, 1999: On the spatial scaling of soil moisture. J. Hydrol., 217, 203–224, doi: 10.1016/S0022-1694(98)00232-7.CrossRefGoogle Scholar
  44. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924, doi: 10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.CrossRefGoogle Scholar
  45. Xiang, L., W. W. Ling, Y. S. Zhu, et al., 2016: Self-adaptive Green-Ampt infiltration parameters obtained from measured moisture processes. Water Sci. Eng., 9, 256–264, doi: 10.1016/j.wse.2016.05.001.CrossRefGoogle Scholar
  46. Xie, X. H., and D. X. Zhang, 2010: 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.CrossRefGoogle Scholar
  47. Yeh, T. C., R. T. Wetherald, and S. Manabe, 1984: The effect of soil moisture on the short-term climate and hydrology change—A numerical experiment. Mon. Wea. Rev., 112, 474–490, doi: 10.1175/1520-0493(1984)112<0474:TEOSMO>2.0.CO;2.CrossRefGoogle Scholar
  48. Yu, Z. B., T. N. Carlson, E. J. Barron, et al., 2001: On evaluating the spatial–temporal variation of soil moisture in the Susquehanna River Basin. Water Resour. Res., 37, 1313–1326, doi: 10.1029/2000WR900369.CrossRefGoogle Scholar
  49. Yu, Z. B, X. L. Fu, L. F. Luo, et al., 2014a: One-dimensional soil temperature simulation with Common Land Model by assimilating in situ observations and MODISLST with the ensemble particle filter. Water Resour. Res., 50, 6950–6965, doi: 10.1002/2012WR013473.CrossRefGoogle Scholar
  50. Yu, Z. B., X. L. Fu, H. S. Lyu, et al., 2014b: Evaluating ensemble Kalman, particle, and ensemble particle filters through soil temperature prediction. J. Hydrol. Eng., 19, 0414027, doi: 10.1061/(ASCE)HE.1943-5584.0000976.Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Xiaolei Fu
    • 1
    • 2
    Email author
  • Zhongbo Yu
    • 3
  • Ying Tang
    • 4
  • Yongjian Ding
    • 1
  • Haishen Lyu
    • 3
  • Baoqing Zhang
    • 5
  • Xiaolei Jiang
    • 3
  • Qin Ju
    • 3
  1. 1.State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina
  2. 2.College of Civil EngineeringFuzhou UniversityFuzhouChina
  3. 3.State Key Laboratory of Hydrology–Water Resources and Hydraulic EngineeringHohai UniversityNanjingChina
  4. 4.Department of GeographyMichigan State UniversityEast LansingUSA
  5. 5.College of Earth and Environmental SciencesLanzhou UniversityLanzhouChina

Personalised recommendations