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: mlliang@mail.iap.ac.cnE-mail: mlliang@mail.iap.ac.cn

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!