Hydrology and Earth System Sciences (Dec 2020)

Last-decade progress in understanding and modeling the land surface processes on the Tibetan Plateau

  • H. Lu,
  • H. Lu,
  • D. Zheng,
  • K. Yang,
  • K. Yang,
  • F. Yang

DOI
https://doi.org/10.5194/hess-24-5745-2020
Journal volume & issue
Vol. 24
pp. 5745 – 5758

Abstract

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Land surface models (LSMs) that simulate water and energy exchanges at the land–atmosphere interface are a key component of Earth system models. The Tibetan Plateau (TP) drives the Asian monsoon through surface heating and thus plays a key role in regulating the climate system in the Northern Hemisphere. Therefore, it is vital to understand and represent well the land surface processes on the TP. After an early review that identified key issues in the understanding and modeling of land surface processes on the TP in 2009, much progress has been made in the last decade in developing new land surface schemes and supporting datasets. This review summarizes the major advances. (i) An enthalpy-based approach was adopted to enhance the description of cryosphere processes such as glacier and snow mass balance and soil freeze–thaw transition. (ii) Parameterization of the vertical mixing process was improved in lake models to ensure reasonable heat transfer to the deep water and to the near-surface atmosphere. (iii) New schemes were proposed for modeling water flow and heat transfer in soils accounting for the effects of vertical soil heterogeneity due to the presence of high soil organic matter content and dense vegetation roots in surface soils or gravel in soil columns. (iv) Supporting datasets of meteorological forcing and soil parameters were developed by integrating multi-source datasets including ground-based observations. Perspectives on the further improvement of land surface modeling on the TP are provided, including the continuous updating of supporting datasets, parameter estimation through assimilation of satellite observations, improvement of snow and lake processes, adoption of data-driven and artificial intelligence methods, and the development of an integrated LSM for the TP.