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Sciences in Cold and Arid Regions ›› 2019, Vol. 11 ›› Issue (2): 93-115.doi: 10.3724/SP.J.1226.2019.00093.

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Review on simulation of land-surface processes on the Tibetan Plateau

Rui Chen1,2,MeiXue Yang1(),XueJia Wang1,GuoNing Wan1   

  1. 1. State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-01-24 Accepted:2019-04-02 Online:2019-04-01 Published:2019-04-29
  • Contact: MeiXue Yang E-mail:mxyang@lzb.ac.cn
  • About author:MeiXue Yang, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. No. 320, West Donggang Road, Lanzhou, Gansu 730000, China. Tel: +86-931-4967376; E-mail: mxyang@lzb.ac.cn

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Abstract:

The Tibetan Plateau (TP) has powerful dynamics and thermal effects, which makes the interaction between its land and atmosphere significantly affect climate and environment in the regional or global area. By retrospecting the latest research progress in the simulation of land-surface processes (LSPs) over the past 20 years, this study discusses both the simulation ability of land-surface models (LSMs) and the modification of parameterization schemes from two perspectives, the models' applicability and improved parameterization schemes. Our review suggests that different LSMs can well capture the spatiotemporal variations of the physical quantities of LSPs; but none of them can be fully applied to the plateau, meaning that all need to be revised according to the characteristics specific to the TP. Avoiding the unstable iterative computation and determining the freeze?thaw critical temperature according to the thermodynamic equilibrium equation, the unreasonable freeze?thaw parameterization scheme can be improved. Due to the complex underlying surface of the TP, no parameterization scheme of roughness length can well simulate the various characteristics of the turbulent flux over the TP at different temporal scales. The uniform soil thermodynamic and hydraulic parameterization scheme is unreasonable when it is applied to the plateau, as a result of the strong soil heterogeneity. There is little research on the snow-cover process so far, and the improved scheme has no advantage over the original one due to the lack of some related physical processes. The constant interaction among subprocesses of LSPs makes the improvement of a multiparameterization scheme yield better simulation results. According to the review of existing research, adding high-quality observation stations, developing a parameterization scheme suitable for the special LSPs of the TP, and adjusting the model structures can be helpful to the simulation of LSPs on the TP.

Key words: Tibetan Plateau, land?atmosphere interaction, land-surface models, model applicability, parameterized modification

Figure 1

Location of study area"

Table 1

Major land-surface-processes experiments of the TP"

Experiments name Primary objective Dates Reference
QXPMEX To study the diurnal variation, seasonal variation, geographical distribution, and heating effect of the plateau radiation balance and heat balance on the TP; the effect on the seasonal variation of planetary-scale circulation; the occurrence, development, and structure of the summer weather system on the TP 1979.5?1979.8 Xu and Chen (2006)
TIPEX To reveal the physical processes of ground?atmosphere interaction, the plateau atmospheric boundary layer and troposphere structure, cloud-radiation processes, and the effects of plateau motions and thermal forces on the formation of atmospheric circulation, monsoons, climate change, and disastrous weather 1996?2000 Xu and Chen (2006)
GAME-Tibet To improve the quantitative understanding of the land?atmosphere interaction on the TP, develop land models and methods, and apply them to larger spatial scales. To develop and test methods for estimating land-surface parameters using satellite data 1996?2000 Ma et al. (2006)
CAMP-Tibet To improve the quantitative understanding of the land?atmosphere interaction and hydrological cycle on the TP, to develop tests of land-surface-processes models, and to calculate land-surface parameters using satellite data 2001?2006 Ma et al. (2006, 2009a)
JICA To understand the impact of the TP on the disastrous weather in East Asia, the impact of the TP on the global and regional energy and water cycle, and the impact of the TP on global climate change 2005?2009 Xu et al. (2008b);Zhang et al. (2012)
TORP To study the multilayer interaction on the TP (mainly land?atmosphere interaction) 2005 up to now Ma et al. (2008, 2009b)

Figure 2

Comparison of simulated soil moisture of four LSMs in GLDAS with the in situ soil moisture from two observation networks at different soil layers: (a) first layer and (b) second layer in the Nagqu network region; (c) first layer, (b) second layer, and (c) third layer in the Maqu network region (Bi et al., 2016)"

Figure 3

Simulated hourly near-surface soil moisture, monthly mean diurnal variations of surface-skin temperature, sensible-heat flux, and latent-heat flux at Amdo, 1998 (Yang et al., 2009)"

Table 2

Error statistic of GLDAS simulated soil moisture during the unfrozen season for the Nagqu network (Chen et al., 2013b)"

Area Depth (cm) LSMs BIAS (m3/m3) RMSE (m3/m3) R 2 No.
Coarse network 0?5 CLM ?0.08 0.09 0.473 1878
Noah ?0.06 0.08 0.483 1878
VIC ?0.02 0.05 0.358 1878
Mosaic ?0.13 0.14 0.491 1878
10?40 CLM 0.01 0.02 0.609 1878
Noah 0.00 0.02 0.585 1878
Dense network 0?5 Noah ?0.07 0.09 0.466 1878
10?40 Noah ?0.04 0.04 0.528 1878

Figure 4

Comparison of observed and simulated soil liquid-water content at six soil depths at the DY station. The black dashed line denotes observation data, the blue line denotes results from the Hydro-SiB2 model, and the red line denotes results from the revised model (Bao et al., 2016)"

Figure 5

Comparison of the diurnal variation of the (a) surface temperature (°C), (b) net radiation (W/m2), (c) sensible-heat flux (W/m2), and (d) soil-heat flux (W/m2). Circles represent the observations, the dashed line represents simulations by the original models, and the solid line represents simulations by revised roughness parameterization (Chen et al., 2010)"

Table 3

Parameterization scheme of roughness"

Formula Reference Abbreviation
Z 0 h = Z 0 m / ( P r R e * ) Sheppard, 1958 S58
Z 0 h = Z 0 m e x p [ - k α ( 8 R e * ) 0.45 P r 0.8 ] Owen and Thomson, 1963 OT63
Z 0 h = Z 0 m e x p ( 2.0 - 2.46 R e * 0.25 ) Brutsaert, 1982 B82
Z 0 h = Z 0 m e x p ( - 0.1 k R e * 0.5 ) Zilitinkevich, 1995 Z95
Z 0 h = Z 0 m e x p ( - k α R e * 0.45 ) Zeng and Dickinson, 1998 Z98
Z 0 h = Z 0 m e x p ( 2 - 1.29 R e * 0.25 ) Kanda et al., 2007 K07
Z 0 h = ( 70 v / u * ) e x p ( - β u * 0.5 T * 0.25 ) Yang et al., 2008 Y08
Z 0 h = Z 0 m e x p - 10 - 0.4 Z 0 m 0.07 k R e * 0.5 Chen and Zhang, 2009 CZ09
Z 0 h = Z 0 m e x p ( - 0.32 R e * 0.5 ) Zeng et al., 2012 Z12
Z 0 h = Z 0 m ' e x p [ - 0.8 1 - G V F 2 k R e * 0.5 ] Zheng et al., 2012 ZH12

Table 4

Parameterization scheme of soil thermal conductivity and hydraulic conductivity"

Parameterization schemes References
λ = K e λ s a t + ( 1 - K e ) λ d r y S r > 1 × 10 - 5 λ d r y S r ≤ 1 × 10 - 5 λ s a t = ( λ q q λ o 1 - q ) 1 - θ s a t λ l i q θ s a t T ≥ T f ( λ q q λ o 1 - q ) 1 - θ s a t λ l i q θ s a t λ i c e θ s a t - θ l i q T ≤ T f Johansen, 1975
λ d r y = ( 0.135 ρ d + 64.7 ) / ( 2700 - 03947 ρ d ) K e = 0.7 l o g S r + 1 T ≥ T f l o g ( S r ) T ≤ T f

λ = K e λ s a t + 1 - K e λ d r y S r > 1 × 10 - 5 λ d r y S r ≤ 1 × 10 - 5 λ s a t = 8.80 % s a n d + 2.92 % d r y % s a n d + % d r y 1 - θ s a t λ l i q θ s a t T ≥ T f 8.80 % s a n d + 2.92 % d r y % s a n d + % d r y 1 - θ s a t λ l i q θ s a t λ i c e θ s a t - θ l i q T ≤ T f

λ d r y = ( 0.135 ρ d + 64.7 ) / ( 2700 - 03947 ρ d ) K e = l o g S r + 1 T ≥ T f S r T ≤ T f

Farouki, 1986

λ = K e λ s a t + ( 1 - K e ) λ d r y S r > 1 × 10 - 5 λ d r y S r ≤ 1 × 10 - 5 λ s a t = ( λ q q λ o 1 - q ) 1 - θ s a t λ l i q θ s a t T ≥ T f ( λ q q λ o 1 - q ) 1 - θ s a t λ l i q θ s a t λ i c e θ s a t - θ l i q T ≤ T f

λ d r y = ( 0.135 ρ d + 64.7 ) / ( 2700 - 03947 ρ d ) K e = e x p [ K T 1 - 1 / w ]

Yang et al., 2005

λ = K e λ s a t + ( 1 - K e ) λ d r y S r > 1 × 10 - 5 λ d r y S r ≤ 1 × 10 - 5 λ s a t = [ ∏ j λ m j j ] 1 - θ s a t λ l i q θ s a t T ≥ T f [ ∏ j λ m j j ] 1 - θ s a t λ l i q θ s a t λ i c e θ s a t - θ l i q T ≤ T f

λ d r y = χ × 10 - η θ s a t K e = K S r / 1 + ( K - 1 ) S r

Cote and Konrad, 2005

λ = K e λ s a t + ( 1 - K e ) λ d r y S r > 1 × 10 - 5 λ d r y S r ≤ 1 × 10 - 5 λ s a t = ( λ q q λ o 1 - q ) 1 - θ s a t λ l i q θ s a t T ≥ T f ( λ q q λ o 1 - q ) 1 - θ s a t λ l i q θ s a t λ i c e θ s a t - θ l i q T ≤ T f

λ d r y = χ × 10 - η θ s a t K e = K S r / 1 + ( K - 1 ) S r

Luo et al., 2009b

λ = K e λ s a t + ( 1 - K e ) λ d r y S r > 1 × 10 - 5 λ d r y S r ≤ 1 × 10 - 5 λ s a t = ( λ q q λ S O C V S O C λ o 1 - q - V S O C ) 1 - θ s a t λ l i q θ s a t T ≥ T f ( λ q q λ S O C V S O C λ o 1 - q - V S O C ) 1 - θ s a t λ l i q θ s a t λ i c e θ s a t - θ l i q T ≤ T f

λ d r y = 1 - V S O C λ m , d r y + V S O C λ S O C , d r y K e = e x p [ K T 1 - 1 / w ]

Chen et al., 2012

λ = K e λ s a t + ( 1 - K e ) λ d r y S r > 1 × 10 - 5 λ d r y S r ≤ 1 × 10 - 5 λ s a t = ( λ q q λ S O C V S O C λ o 1 - q - V S O C ) 1 - θ s a t λ l i q θ s a t T ≥ T f ( λ q q λ S O C V S O C λ o 1 - q - V S O C ) 1 - θ s a t λ l i q θ s a t λ i c e θ s a t - θ l i q T ≤ T f

λ d r y = χ × 10 - η θ s a t K e = K S r / 1 + ( K - 1 ) S r

Pan et al., 2017
Ψ = Ψ S ( θ θ S ) - b K = K S ( θ S ) ( θ θ S ) 2 b + 3 Clapp and Hornberger, 1978
Ψ = 1 α ( S e - 1 m - 1 ) 1 - m K = K S S e 1 2 ( 1 - ( 1 - s e 1 / m ) m ) 2 Van Genuchten, 1980

Figure 6

Variations of daily mean soil temperature and moisture at 5 cm (a, e), 25 cm (b, f), 75 cm (c, g), and 150 cm (d, h). CTL denotes the original model, LAY denotes the revised model of soil vertically heterogeneous, SOM denotes the revised model of soil organic matter, and root 1?3 denotes the different saturated conductivities at the rhizosphere, respectively (Gao et al., 2015)"

Figure 7

Observed and simulated snow depth (m), soil temperature (°C), soil moisture (m3/m3), and soil-ice content at 5 cm at the DY site (Wang et al., 2017)"

Figure 8

Comparisons of monthly (a) averaged downward shortwave radiation; (b) accumulated precipitation; (c) LE, soil temperature, and moisture from depths of (d, f) 5 cm and (e, g) 25 cm; and simulated results from five Noah experiments for the period of November 2009 to November 2010 (Zheng et al., 2016a)"

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