A Soil Moisture Data Assimilation System for Pakistan Using PODEn4DVar and CLM4.5

+ Author Affiliations + Find other works by these authors
Funds: 
Supported by the National Key R&D Program of China (2018YFC1506602), National Natural Science Foundation of China (41830967), and Key Research Program of Frontier Sciences, Chinese Academy of Sciences (QYZDY-SSW-DQC012)
DOI: 10.1007/s13351-019-9020-2

PDF

    Abstract

  • Abstract

    Soil moisture is an important state variable for land–atmosphere interactions. It is a vital land surface variable for research on hydrology, agriculture, climate, and drought monitoring. In current study, a soil moisture data assimilation framework has been developed by using the Community Land Model version 4.5 (CLM4.5) and the proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation (PODEn4DVar) algorithm. Assimilation experiments were conducted at four agricultural sites in Pakistan by assimilating in-situ soil moisture observations. The results showed that it was a reliable system. To quantify further the feasibility of the data assimilation (DA) system, soil moisture observations from the top four soil-depths (0–5, 5–10, 10–20, and 20–30 cm) were assimilated. The evaluation results indicated that the DA system improved soil moisture estimation. In addition, updating the soil moisture in the upper soil layers of CLM4.5 could improve soil moisture estimation in deeper soil layers [layer 7 (L7, 62.0 cm) and layer 8 (L8, 103.8 cm)]. To further evaluate the DA system, observing system simulation experiments (OSSEs) were designed for Pakistan by assimilating daily observations. These idealized experiments produced statistical results that had higher correlation coefficients, reduced root mean square errors, and lower biases for assimilation, which showed that the DA system is able to produce and improve soil moisture estimation in Pakistan.
    • FullText(HTML)

  • Fig.  1.   Flow chart of data assimilation system.

    Download: Larger image PowerPoint slide

    Fig.  2.   Location map of the study sites in Pakistan.

    Download: Larger image PowerPoint slide

    Fig.  3.   Assimilation of in-situ soil moisture observations for two soil layers (0–5 and 20–30 cm) at different sites. Red line: simulated soil moisture (without DA); blue line: assimilation; green dot: assimilated observed soil moisture; and black dot: observed soil moisture value for evaluation.

    Download: Larger image PowerPoint slide

    Fig.  4.   Statistical analysis (R, RMSE, and BIAS) of simulated (without DA) and assimilated soil moisture against in-situ observations for different soil layers at different sites in Pakistan.

    Download: Larger image PowerPoint slide

    Fig.  4.   (Continued).

    Download: Larger image PowerPoint slide

    Fig.  5.   Effects of assimilation on two soil layers (40–50 and 50–70 cm) at different sites. Red line: simulated soil moisture (without DA); blue line: assimilation; and black dot: observed soil moisture.

    Download: Larger image PowerPoint slide

    Fig.  6.   Differences between simulated (without DA) and assimilated daily soil temperatures in the top four soil layers of CLM4.5 at four different experimental sites.

    Download: Larger image PowerPoint slide

    Fig.  7.   Differences between simulated (without DA) and assimilated daily surface latent and sensible heat fluxes at four experimental sites.

    Download: Larger image PowerPoint slide

    Fig.  8.   Time series for the soil moisture observations, true fields, assimilation, and simulation (without DA) for the (a) 1st, (b) 3rd, (c) 6th, and (d) 8th soil layers of CLM4.5 for Pakistan.

    Download: Larger image PowerPoint slide

    Table  1   Assimilating soil depths and the corresponding CLM4.5 layers

    In-situ soil depth (cm)CLM4.5 layer (cm)
    5L3 (~6.2)
    10L4 (~11.9)
    20L5 (~21.2)
    30L6 (~36.6)
    Download: Download as CSV

    Table  2   Evaluating soil depths and the corresponding layers of CLM4.5

    In-situ soil depth (cm)CLM4.5 layer (cm)
    40L7 (~62.0)
    50L7 (~62.0)
    70L8 (~103.8)
    90L8 (~103.8)
    Download: Download as CSV

    Table  3   Comparison of the root mean square errors (RMSE) and the correlation coefficients (R) for assimilation and simulation (without DA) in OSSEs

    1st layer3rd layer6th layer8th layer
    RMSE (sim)0.300.210.490.23
    RMSE (ass) 0.075 0.046 0.035 0.030
    R (sim)0.700.720.800.74
    R (ass)0.980.970.960.94
    Download: Download as CSV
    • References (35)
  • Cai, X. T., Z.-L. Yang, Y. L. Xia, et al., 2014: Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed. J. Geophys. Res. Atmos., 119, 13751–13770. doi: 10.1002/2014JD022113
    Chahine, M. T., 1992: The hydrological cycle and its influence on climate. Nature, 359, 373–380. doi: 10.1038/359373a0
    Chaudhry, Q.-Z., and G. Rasul, 2004: Agroclimatic classification of Pakistan. Science Vision, 9, 59–66.
    Crow, W. T., A. A. Berg, M. H. Cosh, et al., 2012: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev. Geophys., 50, RG2002. doi: 10.1029/2011RG000372
    Dai, A. G., K. E. Trenberth, and T. T. Qian, 2004: A global dataset of Palmer drought severity index for 1870–2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeor., 5, 1117–1130. doi: 10.1175/JHM-386.1
    De Lannoy, G. J. M., P. R. Houser, V. R. N. Pauwels, et al., 2007: 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
    Dirmeyer, P. A., A. J. Dolman, and N. Sato, 1999: The pilot phase of the global soil wetness project. Bull. Amer. Meteor. Soc., 80, 851–878. doi: 10.1175/1520-0477(1999)080<0851:TPPOTG>2.0.CO;2
    Evensen, G., 2004: Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn., 54, 539–560. doi: 10.1007/s10236-004-0099-2
    Houser, P. R., W. J. Shuttleworth, J. S. Famiglietti, et al., 1998: Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour. Res., 34, 3405–3420. doi: 10.1029/1998WR900001
    Hurrell, J. W., M. M. Holland, P. R. Gent, et al., 2013: The community earth system model: A framework for collaborative research. Bull. Amer. Meteor. Soc., 94, 1339–1360. doi: 10.1175/BAMS-D-12-00121.1
    Koster, R. D., P. A. Dirmeyer, et al., 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140. doi: 10.1126/science.1100217
    Kumar, S. V., R. H. Reichle, R. D. Koster, et al., 2009: Role of subsurface physics in the assimilation of surface soil moisture observations. J. Hydrometeor., 10, 1534–1547. doi: 10.1175/2009JHM1134.1
    Liu, D., and A. K. Mishra, 2017: Performance of AMSR_E soil moisture data assimilationin CLM4.5 model for monitoring hydrologic fluxes at global scale. J. Hydrol., 547, 67–79. doi: 10.1016/j.jhydrol.2017.01.036
    Long, D., B. R. Scanlon, L. Longuevergne, et al., 2013: GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas. Geophys. Res. Lett., 40, 3395–3401. doi: 10.1002/grl.50655
    Mao, J. F., P. E. Thornton, X. Y. Shi, et al., 2012: Remote sensing evaluation of CLM4 GPP for the period 2000–09. J. Climate, 25, 5327–5342. doi: 10.1175/JCLI-D-11-00401.1
    Mao, J. F., X. Y. Shi, P. E. Thornton, et al., 2013: Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982–2009. Remote Sens., 5, 1484–1497. doi: 10.3390/rs5031484
    Mitchell, K. E., D. Lohmann, P. R. Houser, et al., 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res. Atmos., 109, D07S90. doi: 10.1029/2003JD003823
    Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol., 25, 693–712. doi: 10.1002/joc.1181
    Oleson, K. W., Y. J. Dai, G. Bonan, et al., 2004: Technical description of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-461+STR, NCAR, Boulder Colorado, 173 pp, doi: 10.5065/D6N877R0.
    Oleson, K. W., D. M. Lawrence, G. B. Bonan, et al., 2010: Technical Description of Version 4.0 of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-478+STR, NCAR, Boulder Colorado, 266 pp, doi: 10.5065/D6FB50WZ.
    Oleson, K. W., D. M. Lawrence, G. B. Bonan, et al., 2013: Technical Description of Version 4.5 of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-503+STR, NCAR, Boulder Colorado, 171 pp, doi: 10.5065/D6RR1W7M.
    Piao, S., A. Ito, S. Li, et al., 2012: The carbon budget of terrestrial ecosystems in East Asia over the last two decades. Biogeo. Discuss., 9, 4025–4066. doi: 10.5194/bgd-9-4025-2012
    Qian, T. T., A. G. Dai, K. E. Trenberth, et al., 2006: Simulation of global land surface conditions from1948 to 2004. Part I: Forcing data and evaluations. J. Hydrometeor., 7, 953–975. doi: 10.1175/JHM540.1
    Robinson, D. A., C. S. Campbell, J. W. Hopmans, et al., 2008: Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review. Vad. Zon. J., 7, 358–389. doi: 10.2136/vzj2007.0143
    Robock, A., K. Y. Vinnikov, G. Srinivasan, et al., 2000: The global soil moisture data bank. Bull. Amer. Meteor. Soc., 81, 1281–1300. doi: 10.1175/1520-0477(2000)081<1281:TGSMDB>2.3.CO;2
    Sheffield, J., and E. F. Wood, 2008: Global trends and variability in soil moisture and drought characteristics, 1950–2000, from observation-driven simulations of the terrestrial hydrologic cycle. J. Climate, 21, 432–458. doi: 10.1175/2007JCLI1822.1
    Shi, C. X., Z. H. Xie, H. Qian, et al., 2011: China land soil moisture EnKF data assimilation based on satellite remote sensing data. Sci. China Earth Sci., 54, 1430–1440. doi: 10.1007/s11430-010-4160-3
    Shi, X. Y., J. F. Mao, P. E. Thornton, et al., 2013: Spatiotemporal patterns of evapotranspiration in response to multiple environmental factors simulated by the Community Land Model. Environ. Res. Lett., 8, 024012. doi: 10.1088/1748-9326/8/2/024012
    Sun, Q., Z. H. Xie, and X. J. Tian, 2015: GRACE terrestrial water storage data assimilation based on the ensemble four-dimensional variational method PODEn4DVar: Method and validation. Sci. China Earth Sci., 58, 371–384. doi: 10.1007/s11430-014-4978-1
    Tian, X. J., Z. H. Xie, and A. G. Dai, 2008a: A land surface soil moisture data assimilation system based on the dual-UKF method and the Community Land Model. J. Geophys. Res. Atmos., 113, D14127. doi: 10.1029/2007JD009650
    Tian, X. J., Z. H. Xie, and A. G. Dai, 2008b: An ensemble-based explicit four-dimensional variational assimilation method. J. Geophys. Res. Atmos., 113, D21124. doi: 10.1029/2008JD010358
    Tian, X. J., Z. H. Xie, and Q. Sun, 2011: A POD-based ensemble four-dimensional variational assimilation method. Tellus A, 63, 805–816. doi: 10.1111/j.1600-0870.2011.00529.x
    Wang, B., J. J. Liu, S. D. Wang, et al., 2010: An economical approach to four-dimensional variational data assimilation. Adv. Atmos. Sci., 27, 715–727. doi: 10.1007/s00376-009-9122-3
    Zhang, S. L., J. C. Shi, and Y. J. Dou, 2012: A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model. Int. J. Remote Sens., 33, 2770–2797. doi: 10.1080/01431161.2011.620032
    Zreda, M., W. J. Shuttleworth, X. Zeng, et al., 2012: COSMOS: The cosmic-ray soil moisture observing system. Hydrol. Earth Syst. Sci., 16, 4079–4099. doi: 10.5194/hess-16-4079-2012
    • Relative Articles

  • Related Articles

    Xiaolei FU, Haishen LYU, Zhongbo YU, Xiaolei JIANG, Yongjian DING, Donghai ZHENG, Jinbai HUANG, Hongyuan FANG. 2023: Effects of Soil Hydraulic Properties on Soil Moisture Estimation. Journal of Meteorological Research, 37(1): 58-74. DOI: 10.1007/s13351-023-2049-2
    Weilin LIAO, Dagang WANG, Guiling WANG, Youlong XIA, Xiaoping LIU. 2019: Quality Control and Evaluation of the Observed Daily Data in the North American Soil Moisture Database. Journal of Meteorological Research, 33(3): 501-518. DOI: 10.1007/s13351-019-8121-2
    Feimin ZHANG, Zhaoxia PU, Chenghai WANG. 2019: Impacts of Soil Moisture on the Numerical Simulation of a Post-Landfall Storm. Journal of Meteorological Research, 33(2): 206-218. DOI: 10.1007/s13351-019-8002-8
    Xiaolei FU, Zhongbo YU, Ying TANG, Yongjian DING, Haishen LYU, Baoqing ZHANG, Xiaolei JIANG, Qin JU. 2019: Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model. Journal of Meteorological Research, 33(2): 190-205. DOI: 10.1007/s13351-019-8138-6
    Youlong XIA, Zengchao HAO, Chunxiang SHI, Yaohui LI, Jesse MENG, Tongren XU, Xinying WU, Baoqing ZHANG. 2019: Regional and Global Land Data Assimilation Systems: Innovations, Challenges, and Prospects. Journal of Meteorological Research, 33(2): 159-189. DOI: 10.1007/s13351-019-8172-4
    ZHANG Shuwen, LIU Yanhua, ZHANG Weidong. 2013: Ensemble Square Root Filter Assimilation of Near-Surface Soil Moisture and Reference-Level Observations into a Coupled Land Surface-Boundary Layer Model. Journal of Meteorological Research, 27(4): 541-555. DOI: 10.1007/s13351-013-0402-6
    WAN Wei, LI Huang, CHEN Xiuwan, LUO Peng, WAN Jiahuan. 2013: Preliminary Calibration of GPS Signals and Its Effects on Soil Moisture Estimation. Journal of Meteorological Research, 27(2): 221-232. DOI: 10.1007/s13351-013-0207-7
    ZHANG Shuwen, ZHANG Weidong, QIU Chongjian. 2007: A Variational Method for Estimating Near-Surface Soil Moisture and Surface Heat Fluxes. Journal of Meteorological Research, 21(4): 476-486.
    Zhao Zongci, M. E. Schlesinger. 1992: SEASONAL CHANGES OF SOIL MOISTURE INDUCED BY INCREASED CO2 AS SIMULATED BY THE ATMOSPHERIC GENERAL CIRCULATION AND MIXED LAYER OCEAN MODEL. Journal of Meteorological Research, 6(2): 170-178.
    Liu Yongqiang, Ye Duzheng, Ji Jinjun. 1992: INFLUENCE OF SOIL MOISTURE AND VEGETATION ON CLIMATE CHANGES INDUCED BY THERMAL FORCING*. Journal of Meteorological Research, 6(1): 58-69.
    • Supplements (0)

  • Cited by

    Periodical cited type(6)

    1. Galina Gordiyenko, Artur Yakovets, Yuriy Litvinov, et al. The Influence of Sudden Stratospheric Warming on the Development of Ionospheric Storms: The Alma-Ata Ground-Based Ionosonde Observations. Atmosphere, 2024, 15(6): 626. DOI:10.3390/atmos15060626
    2. Chris Kent, Adam A. Scaife, William J. M. Seviour, et al. Identifying Perturbations That Tipped the Stratosphere Into a Sudden Warming During January 2013. Geophysical Research Letters, 2023, 50(24) DOI:10.1029/2023GL106288
    3. Yingxian Zhang, Dong Si, Yihui Ding, et al. Influence of Major Stratospheric Sudden Warming on the Unprecedented Cold Wave in East Asia in January 2021. Advances in Atmospheric Sciences, 2022, 39(4): 576. DOI:10.1007/s00376-022-1318-9
    4. R. Roy, J. Kuttippurath. The dynamical evolution of Sudden Stratospheric Warmings of the Arctic winters in the past decade 2011–2021. SN Applied Sciences, 2022, 4(4) DOI:10.1007/s42452-022-04983-4
    5. Fen Xu, X. San Liang. On the Generation and Maintenance of the 2012/13 Sudden Stratospheric Warming. Journal of the Atmospheric Sciences, 2017, 74(10): 3209. DOI:10.1175/JAS-D-17-0002.1
    6. Chunhui Lu, Yihui Ding. Analysis of isentropic potential vorticities for the relationship between stratospheric anomalies and the cooling process in China. Science Bulletin, 2015, 60(7): 726. DOI:10.1007/s11434-015-0757-4

    Other cited types(0)

Catalog

    Authors

    About the Journal

    Links

    WeChat

    Domestic Purchase

    京ICP备09060741号-1   Copyright©Journal of Meteorological Research  Contact: jmr@cms1924.org; 86-10-68408571  Total visits: 785899

    Supported by Beijing Renhe Information Technology Co. Ltd.    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return