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Improving the vegetation dynamic simulations in a land surface model by using a statistical-dynamic canopy interception scheme. Miaoling Liang Zhenghui xie Institute of Atmospheric Physics, Chinese Academy of Sciences E-mail: [email protected]. Outline. Introduction. - PowerPoint PPT Presentation
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Improving the vegetation dynamic simulations in a Improving the vegetation dynamic simulations in a land surface model by using a statistical-dynamic land surface model by using a statistical-dynamic
canopy interception schemecanopy interception scheme
Miaoling Liang Zhenghui xieMiaoling Liang Zhenghui xie
Institute of Atmospheric Physics, Chinese Academy of SciencesInstitute of Atmospheric Physics, Chinese Academy of Sciences
E-mail: [email protected]: [email protected]
IntroductionIntroduction
Outline
effect of soil moisture on vegetation effect of soil moisture on vegetation growthgrowth effect of canopy interception on soil effect of canopy interception on soil moisturemoisture importance of canopy interception on soil importance of canopy interception on soil moisture moisture
Model descriptionModel description
Climatic forcing dataClimatic forcing data
SimulationsSimulations
ConclusionsConclusions
original canopy interception scheme statistical-dynamic scheme based on LAI
IntroductionIntroduction
Interactions between soil moisture and vegetation Soil moisture affects vegetation growth by controlling vegetation transpiration
Vegetation influences soil moisture via evapo-transpiration: canopy interception, throughfall, transpiration
Importance of soil moisture and vegetation in climate model (determines the albedo and thermal capacity of land surface)
Surface runoff
How does canopy interception influences soil water availability and thus control the soil moisture?
Canopy interception accounts for about 10~30% of the annual precipitation
Introduction of CLM-DGVM
Community Land Model is enabled to simulate vegetation dynamics coupled with LPJ Dynamic Global Vegetation Model
Previous work has observed that:
CLM-DGVM underestimates the forest coverage and vegetation production in favor of grass coverage than LPJ does due to its lower predictions of soil moisture.
Excessive canopy interception results in the lower Excessive canopy interception results in the lower soil moisture:soil moisture:
In CLM-DGVM, the fraction of precipitation intercepted by canopy is presented as:
[ 0.5( )]1 LAI SAIpif e
Accordingly, the model allows more than 90% of precipitation to be intercepted by canopy when LAI and SAI is greater than 4.6m2 m-2
here, LAI and SAI is leaf area index and stem area index respectively.
ObjectiveObjective
Canopy interception scheme of CLM allows Canopy interception scheme of CLM allows
unreasonable interception amount of precipitation unreasonable interception amount of precipitation
Present a statistical-dynamical canopy interception Present a statistical-dynamical canopy interception
scheme to improve the vegetation simulation scheme to improve the vegetation simulation
performance of CLM-DGVM.performance of CLM-DGVM.
Statistically dynamic interception scheme based on
LAI and SAI is proposed:
( )pif a LAI SAI
Where a is PFT-dependent parameter, obtained based on the statistical canopy interception amount.
Interception fraction as functions of the sum of LAI and SAI based on different canopy interception mechanisms
Data sets
Data: 40-year (1961-2000) climatic forcing data with 3 –hour, 0.5°× 0.5°temporal - spatial resolution
NCEP reanalysis data (4-times a day, 2.5 2.5 lat x 2.5 lonlat x 2.5 lon ) was regridded to 0.5°grids and averaged over the 6-hour to 3-hour interval (including: surface pressure, temperature, solar radiation, humidity and wind) Daily observed precipitation data from 676 normal meteorology stations are linearly interpolated to 0.5°× 0.5°and 3-hour frequency based on the diurnal variations of NCEP precipitation rate data
Study domain: China
Simulations
Two sets of paired simulations :
Initialization A: 200-year initialized run with the standard
CLM-DGVM forced with the climatic data from 1961-1990
repeatedly, followed with a 20-year simulation (1981-2000)
with standard CLM-DGVM (SA1) and modified CLM-DGVM
with new canopy interception scheme(SA2), respectively;
Initialization B: 200-year initialized run with the modified
CLM-DGVM, and 20-year simulation SB1 and SB2.
Equilibrium vegetation distribution
Comparisons of grasses coverage and trees coverage
Initialization B – Initialization A
SA1
SA2
Vegetation dynamics of simulations SA1 &
SA2
SB1
SB2Vegetation dynamics
of simulations SB1 & SB2
Area change of PFT(data are from the average of 20-year simulation)
Percent coverage of trees (a) and grasses (b) as well as net primary production (c) estimated from different simulations
Difference of soil moisture (%) in the top 50cm in summer and winter: (a) SA2-SA1 for summer, (b) SA2-SA1 for winter, (c) SB2-SB1 for summer, and (c) SB2-SB1 for winter. Data are averages from the 20-year simulations.
Model predicted (a) interception loss and (b) soil moisture of the top 50cm for the transition zone.
Conclusions The new canopy interception scheme allows
more water falling on the ground and
subsequently increases soil water availability for
vegetation growth which is especially the case in
semi-arid vegetation transition zone;
The statistical-dynamic interception scheme help
increase the predicted soil moisture and improve
the vegetation simulation performance of the
model.
Thanks for your attention!