• Brutsaert, W. A., 1982: Evaporation into the Atmosphere. D. Reidel, 299 pp.

  • Chen, Y., K. Yang, W. Tang, J. Qin, and L. Zhao, 2012: Parameterizing soil organic carbon’s impacts on soil porosity and thermal parameters for eastern Tibet grasslands. Sci. China Earth Sci., 55, 10011011, https://doi.org/10.1007/s11430-012-4433-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., K. Yang, J. Qin, L. Zhao, W. Tang, and M. Han, 2013: Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J. Geophys. Res. Atmos., 118, 44664475, https://doi.org/10.1002/jgrd.50301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clapp, R. B., and G. M. Hornberger, 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14, 601604, https://doi.org/10.1029/WR014i004p00601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn, 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20, 682690, https://doi.org/10.1029/WR020i006p00682.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, Y., N. Wei, H. Yuan, S. Zhang, W. Shangguan, S. Liu, and X. Lu, 2019a: Evaluation of soil thermal conductivity schemes for use in land surface modelling. J. Adv. Model. Earth Syst., 11, 34543473, https://doi.org/10.1029/2019MS001723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, Y., and Coauthors, 2019b: A global high-resolution data set of soil hydraulic and thermal properties for land surface modeling. J. Adv. Model. Earth Syst., 11, 29963023, https://doi.org/10.1029/2019MS001784.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, Y., T. Yao, L. Wang, X. Li, and X. Zhang, 2020: Contrasting roles of a large Alpine lake on Tibetan Plateau in shaping regional precipitation during summer and autumn. Front. Earth Sci., 8, 358, https://doi.org/10.3389/feart.2020.00358.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • FAO, 1971: South America. Vol. IV, Soil Map of the World (1:5,000,000), UNESCO, 193 pp., http://www.fao.org/3/as361e/as361e.pdf.

  • FAO, 1974: Legend. Vol. I, Soil Map of the World (1:5,000,000), UNESCO, 59 pp., http://www.fao.org/3/as360e/as360e.pdf.

  • FAO, 1975a: North America. Vol. II, Soil Map of the World (1:5,000,000), UNESCO, 210 pp., http://www.fao.org/3/as359e/as359e.pdf.

  • FAO, 1975b: Mexico and Central America. Vol. III, Soil Map of the World (1:5,000,000), UNESCO, 96 pp., http://www.fao.org/3/as358e/as358e.pdf.

  • FAO, 1977a: Africa. Vol. VI, Soil Map of the World (1:5,000,000), UNESCO, 340 pp., http://www.fao.org/3/as357e/as357e.pdf.

  • FAO, 1977b: South Asia. Vol. VII, Soil Map of the World (1:5,000,000), UNESCO, 144 pp., http://www.fao.org/3/as352e/as352e.pdf.

  • FAO, 1978a: North and Central Asia. Vol. VIII, Soil Map of the World (1:5,000,000), UNESCO, 180 pp., http://www.fao.org/3/as356e/as356e.pdf.

  • FAO, 1978b: Australasia. Vol. X, Soil Map of the World (1:5,000,000). UNESCO, 236 pp., http://www.fao.org/3/as355e/as355e.pdf.

  • FAO, 1979: Southeast Asia. Vol. IX, Soil Map of the World (1:5,000,000) UNESCO, 179 pp., http://www.fao.org/3/as353e/as353e.pdf.

  • FAO, 1981: Europe. Vol. V, Soil Map of the World (1:5,000,000), UNESCO, 225 pp., http://www.fao.org/3/as354e/as354e.pdf.

  • FAO, 1991: The digitized soil map of the world. World Soil Resources Rep. 67, accessed 16 April 2019, https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/home.

  • Gao, Y., K. Li, F. Chen, Y. Jiang, and C. Lu, 2015: Assessing and improving Noah-MP land model simulations for the central Tibetan Plateau. J. Geophys. Res., 120, 92589278, https://doi.org/10.1002/2015JD023404.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, J., K. Yang, W. Tang, H. Lu, J. Qin, Y. Chen, and X. Li, 2020: The first high-resolution meteorological forcing dataset for land process studies over China. Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jarvis, P. G., 1976: The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. Roy. Soc., B273, 593610, https://doi.org/10.1098/rstb.1976.0035.

    • Search Google Scholar
    • Export Citation
  • Koike, T., 2004: The coordinated enhanced observing period—An initial step for integrated global water cycle observation. WMO Bull., 53, 115121.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and A. G. Slater, 2008: Incorporating organic soil into a global climate model. Climate Dyn., 30, 145160, https://doi.org/10.1007/s00382-007-0278-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Letts, M. G., N. T. Roulet, N. T. Comer, M. R. Skarupa, and D. L. Verseghy, 2000: Parametrization of peatland hydraulic properties for the Canadian land surface scheme. Atmos.–Ocean, 38, 141160, https://doi.org/10.1080/07055900.2000.9649643.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, W., and Coauthors, 2012: Storage, patterns, and control of soil organic carbon and nitrogen in the northeastern margin of the Qinghai–Tibetan Plateau. Environ. Res. Lett., 7, 3540135412, https://doi.org/10.1088/1748-9326/7/3/035401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Q., K. Yang, Y. Chen, and X. Zhou, 2020: Method development for estimating soil organic carbon content in an alpine region using soil moisture data. Sci. China Earth Sci., 63, 591601, https://doi.org/10.1007/s11430-019-9554-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, S., X. Fang, S. Lyu, Y. Zhang, and B. Chen, 2017: Improving CLM4.5 simulations of land–atmosphere exchange during freeze–thaw processes on the Tibetan Plateau. J. Meteor. Res., 31, 916930, https://doi.org/10.1007/s13351-017-6063-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, Y. M., S. C. Kang, L. P. Zhu, B. Q. Xu, L. D. Tian, and T. D. Yao, 2008: Tibetan Observation and Research Platform: Atmosphere–land interaction over a heterogeneous landscape. Bull. Amer. Meteor. Soc., 89, 14871492, https://doi.org/10.1175/2008BAMS2545.1.

    • Search Google Scholar
    • Export Citation
  • Mueller, B., and S. I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys. Res. Lett., 41, 128134, https://doi.org/10.1002/2013GL058055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Z.-L. Yang, 2006: Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale. J. Hydrometeor., 7, 937952, https://doi.org/10.1175/JHM538.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, L. E. Gulden, and H. Su, 2007: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. J. Geophys. Res., 112, D07103, https://doi.org/10.1029/2006JD007522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-461+STR, 174 pp., https://www.cgd.ucar.edu/tss/clm/distribution/clm3.0/TechNote/CLM_Tech_Note.pdf.

  • Saxton, K. E., and W. J. Rawls, 2006: Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Amer. J., 70, 15691578, https://doi.org/10.2136/sssaj2005.0117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shangguan, W., Y. Dai, Q. Duan, B. Liu, and H. Yuan, 2014: A global soil data set for earth system modeling. J. Adv. Model. Earth Syst., 6, 249263, https://doi.org/10.1002/2013MS000293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, F., X. Duan, D. Chen, Z. Hao, and L. Cuo, 2013: Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau. J. Climate, 26, 31873208, https://doi.org/10.1175/JCLI-D-12-00321.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J., K. Yang, W. Guo, Y. Wang, J. He, and H. Lu, 2020: Why has the inner Tibetan Plateau become wetter since the mid-1990s? J. Climate, 33, 85078522, https://doi.org/10.1175/JCLI-D-19-0471.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, S., and Coauthors, 2016: Improving soil organic carbon parameterization of land surface model for cold regions in the northeastern Tibetan Plateau, China. Ecol. Modell., 330, 115, https://doi.org/10.1016/j.ecolmodel.2016.03.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verseghy, D. L., 1991: CLASS-A Canadian land surface scheme for GCMS: I. Soil model. Int. J. Climatol., 11, 111133, https://doi.org/10.1002/joc.3370110202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, K., and J. He, 2011: China meteorological forcing dataset (1979–2015). National Tibetan Plateau Data Center, accessed 19 March 2019, https://doi.org/10.3972/westdc.002.2014.db.

    • Crossref
    • Export Citation
  • Yang, K., T. Koike, B. Ye, and L. Bastidas, 2005: Inverse analysis of the role of soil vertical heterogeneity in controlling surface soil state and energy partition. J. Geophys. Res., 110, D08101, https://doi.org/10.1029/2004JD005500.

    • Search Google Scholar
    • Export Citation
  • Yang, K., Y. Y. Chen, and J. Qin, 2009: Some practical notes on the land surface modeling in the Tibetan Plateau. Hydrol. Earth Syst. Sci., 13, 687701, https://doi.org/10.5194/hess-13-687-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, K., and Coauthors, 2013: A multiscale soil moisture and freeze–thaw monitoring network on the third pole. Bull. Amer. Meteor. Soc., 94, 19071916, https://doi.org/10.1175/BAMS-D-12-00203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., Y. Chen, and J. Li, 2021: Effects of organic soil in the Noah-MP land-surface model on simulated skin and soil temperature profiles and surface energy exchanges for China. Atmos. Res., 249, 105284, https://doi.org/10.1016/j.atmosres.2020.105284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., M. G. Schaap, and Z. Wei, 2020: Development of hierarchical ensemble model and estimates of soil water retention with global coverage. Geophys. Res. Lett., 47, e2020GL088819, https://doi.org/10.1029/2020GL088819.

    • Search Google Scholar
    • Export Citation
  • Zhao, L., and Coauthors, 2013: Spatiotemporal analysis of soil moisture observations within a Tibetan mesoscale area and its implication to regional soil moisture measurements. J. Hydrol., 482, 92104, https://doi.org/10.1016/j.jhydrol.2012.12.033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, D., R. van der Velde, Z. Su, X. Wang, J. Wen, M. J. Booij, A. Y. Hoekstra, and Y. Chen, 2015a: Augmentations to the Noah model physics for application to the Yellow River source area. Part I: Soil water flow. J. Hydrometeor., 16, 26592676, https://doi.org/10.1175/JHM-D-14-0198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, D., R. van der Velde, Z. Su, X. Wang, J. Wen, M. J. Booij, A. Y. Hoekstra, and Y. Chen, 2015b: Augmentations to the Noah model physics for application to the Yellow River source area. Part II: Turbulent heat fluxes and soil heat transport. J. Hydrometeor., 16, 26772694, https://doi.org/10.1175/JHM-D-14-0199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, D., R. van der Velde, Z. Su, J. Wen, X. Wang, and K. Yang, 2017: Evaluation of Noah frozen soil parameterization for application to a Tibetan meadow ecosystem. J. Hydrometeor., 18, 17491763, https://doi.org/10.1175/JHM-D-16-0199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 252 252 35
Full Text Views 89 89 30
PDF Downloads 109 109 39

Influence of Organic Matter on Soil Hydrothermal Processes in the Tibetan Plateau: Observation and Parameterization

View More View Less
  • 1 a Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
  • | 2 b National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
  • | 3 c Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China
  • | 4 d School of Geographical Sciences, Southwest University, Chongqing, China
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

In the central-eastern Tibetan Plateau (TP) there is abundant organic matter in topsoils, which plays a crucial role in determining soil hydraulic properties that need to be properly described in land surface models. Limited soil parameterizations consider the impacts of soil organic matter (SOM), but they still show poor performance in the TP. A dedicated field campaign is therefore conducted by taking undisturbed soil samples in the central TP to obtain in situ soil hydraulic parameters and to advance SOM parameterizations. The observed findings are twofold: 1) The SOM pore-size distribution parameter, derived from measured soil water retention curves, has been demonstrated to be much underestimated in previous studies. 2) SOM saturated hydraulic conductivity is overestimated. Accordingly, a new soil hydraulic parameterization is established by modifying a commonly used one based on observations, which is then evaluated by incorporating it into Noah-MP. Compared with the original ones, the new parameterization significantly improves surface soil liquid water simulations at stations with high surface SOM content, especially in the warm season. A further application with the revised Noah-MP indicates that SOM can enhance sensible heat flux but decrease evaporation and subsurface soil temperature in the warm season and tends to have a much weak effect in the cold season. This study provides insights into the role of SOM in modulating soil state and surface energy budget. Note that, however, there are many other factors at play and the new parameterization is not necessarily applicable beyond the TP.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Kun Yang, yangk@tsinghua.edu.cn; Yingying Chen, chenyy@itpcas.ac.cn

Abstract

In the central-eastern Tibetan Plateau (TP) there is abundant organic matter in topsoils, which plays a crucial role in determining soil hydraulic properties that need to be properly described in land surface models. Limited soil parameterizations consider the impacts of soil organic matter (SOM), but they still show poor performance in the TP. A dedicated field campaign is therefore conducted by taking undisturbed soil samples in the central TP to obtain in situ soil hydraulic parameters and to advance SOM parameterizations. The observed findings are twofold: 1) The SOM pore-size distribution parameter, derived from measured soil water retention curves, has been demonstrated to be much underestimated in previous studies. 2) SOM saturated hydraulic conductivity is overestimated. Accordingly, a new soil hydraulic parameterization is established by modifying a commonly used one based on observations, which is then evaluated by incorporating it into Noah-MP. Compared with the original ones, the new parameterization significantly improves surface soil liquid water simulations at stations with high surface SOM content, especially in the warm season. A further application with the revised Noah-MP indicates that SOM can enhance sensible heat flux but decrease evaporation and subsurface soil temperature in the warm season and tends to have a much weak effect in the cold season. This study provides insights into the role of SOM in modulating soil state and surface energy budget. Note that, however, there are many other factors at play and the new parameterization is not necessarily applicable beyond the TP.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Kun Yang, yangk@tsinghua.edu.cn; Yingying Chen, chenyy@itpcas.ac.cn

Supplementary Materials

    • Supplemental Materials (PDF 488.76 KB)
Save