669 23,,,,, 20 90, [ 4, 5 ],, ASA ( GLDAS) (LDAS), ( ELDAS) (CLDAS) [ 6 ], [ 7 ], 2 ( ) ( ), ;,,,,,, 2. SHAW,,,/, [ 8 0 ] SHAW F ig. Fram ework of the

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1 ADVACES I EARTH SC IECE Vol. 23 o. 9 Sep., 2008 : (2008) , 3,, 2,, 3, (., ; 2., 6004; 3., ) :,,,(), SHAW, : (),, : ; ; ; SHAW ; : P : A,,,, /, [ ] 20 90,, (Climate and Cryo2 sphere, CliC) [ 2 ] ; 2007 ( Interna2 tional Polar Year, IPY) ; GT 2P ( Global Terrestrial et2 work for Permafrost, ) ; PACE ( Permafrost and Climate in Europe) CALM (Circumpolar Active LayerMo2 nitoring), [ 3 ], CoLM (Common Land Model) SHAW ( Simultaneous Heat and W ater) 3 : ; : : ( : 2007CB4504) ; ( : ). : (9792),,,,. E2mail : zhoujianmaomi@63. com

2 669 23,,,,, 20 90, [ 4, 5 ],, ASA ( GLDAS) (LDAS), ( ELDAS) (CLDAS) [ 6 ], [ 7 ], 2 ( ) ( ), ;,,,,,, 2. SHAW,,,/, [ 8 0 ] SHAW F ig. Fram ework of the a ssim ila tion flow SHAW 2.. C s 5T 5t - i L f 5q l T l c l 5z 5 i = 5 5t 5z K s 5T 5z 5q - L v v 5z + 5 v 5t - () : C s ( J / (m 3 K) ) ; T ( K) ; i l ( kg/ m 3 ) ; i (m 3 /m 3 ) ; L f L v (J /kg) ; K s (W / (m K) ) ; c l ( J / ( kg K) ) ; q l q v ( kg/ (m 2 s) ) ; v ( kg/m 3 ) l 5t + i 5 i 5t = 5 5z K 5 5z + l + 5q v +U (2) 5z : l (m 3 /m 3 ) ; K (m / s) ;(m ) ; U / (m 3 / s), : K = K s l s (2b +3) - ( 2 +3 / b) = K s e l (3) : s (m 3 /m 3 ) ; b, [ ]; e (m)

3 9 : SHAW Fuchs [ 2 ],, < = + = L f g T T k = L f T g ( T ) (4) : < (m ) ; (m ) ; ( ) ; T k ( K) : = - crt k / g : c ( eq /kg) ; R - b l : = e s,, : L f T + crt l = k s T k e g,: L l = f T s e g T k (2) 2. 2 b b (5) (6) Evensen [ 3, 4 ] 994 Ep stein, 0,, Burgers [ 5 ] Houtekamer [ 6, 7 ] Keppenne [ 8 ] M itch2 ell [ 9 ] W hitaker [ 20 ] B rushdal [ 2 ] ; Reichle [ ], ;Moradkhani [ 25 ],, : () X e = (X, X 2,, X n ) R n (7) : n, (2) k + X f i, k + X f i, k + = M (X a i, k ) + i, k i, k (0, W k ) (8) : X f i, k + i k +, X a i, k i k, M ( ), W k, i, k 0W k (3) k + K k + P f k + = K k + = P f k + H T (HP f k + H T + R ) - (9) - i = P f k + H T = HP f k + H T = (X f i, k + - X f k + ) (X f i, k + - X f k + ) T - i = [ X f i, k + - X f k + ] (0) [H (X f i, k + ) - H (X f k + ) ] T () - i = [ H (X f i, k + ) - H (X f k + ) ] [H (X f i, k + ) - H (X f k + ) ] T (2) : K k +, X f i, k + i k +, X f k + k +, R, H ( ), P f k + k + (4) k + X a k + P a k + X a i, k + = X f i, k + + K k + [ ( Y k + + v i, k + ) - P a k + = H (X f i, k + ) ] v i, k + (0, R ) (3) X a k + = X a i, k + (4) i = - i = (X a i, k + - X a k + ) (X a i, k + - X a k + ) T (5) : X a i, k + i k +, X a k + k +, Y k + k +, v i, k + 0 R (5), (2) 2. 3 RM S E = ( obs t - x t ) 2 (6) t =

4 ( ; E; m ) ,, ( ) () ; 20 cm 40 cm 65 cm 20 cm, 00 cm 50 cm, , 2 ( ) cm SHAW 30, cm{5%, 5%, 5%, 5% }, { 5%, 5%, 5%, 5% }, {2. 5%, 2. 5%, 2. 5%, 2. 5% }, : 2 5 SHAW,,, Table Root2m ean2square error of the sim ula ted and a ssim ila ted so il m o isture ( cm) RMSE RMSE

5 9 : 969, cm SHAW ( ) 30, cm { 5%, 5%, 5%, 5%, 5%, 5% }, { 5%, 5%, 5%, 5%, 5%, 5% }, { 2. 5%, 2. 5%, 2. 5%, 2. 5%, 2. 5%, 2. 5% }, : 6 SHAW,, (RMSE) 4. 3, (RMSE) 0. 2,,, 6 F ig. 6 Results of the a ssim ila tion of frozen2so il depth 3. 3,

6 SHAW : 7 8 :, H 2, 5 ( ), 2,6 7 H / R n < L E / R n,, 8 0, H / R n L E / R n, ; L E 3, 6 7, 6 7

7 9 : 79, 0,, ; G MJ / (m 2 d), 2, 2, 5 6,, 9,, 2,, 9 :SHAW, 4: (): 9, 25% 35% 7% 3% (2): 3,, 7% 2% (3): 4 5,, 65 cm 25% 30% (4) : 6 9, 20 cm,,, 20% 35% 9 F ig. 9 The com pare of the sim ula ted and observed so il prof ile wa ter con ten t 3. 4,,,,, : (),, (2),,,,, (3), (4),

8 279 23, ( References) : [ ] L i Xin, Cheng Guodong. Review of model of the permafrost - cli2 mate model. [ J ]. Journal of Glaciology and Geocryology, 2002, 24 (3) : [,. [ J ]., 2002, 24 (3) : ] [ 2 ] A llison I, Barry R G, Goodison B E. Climate and Cryosphere (CliC) project science and coordination p lan ( version ) [ Z ]. WCRP24 /WMO / TD o. 053, 200. [ 3 ] Jin H J, B rown J. Great challenges of and innovative solutions to the unstable permafrost in central and high A sia under a warm ing climate[ J ]. Episodes, 2007, 30 () : [ 4 ] Daley R. A tmospheric Data Analysis[M ]. ew York: Cambridge University Press, 99: [ 5 ] Talagrand O. A ssim ilation of observations, an introduction [ J ]. Journal of them eteorological Society of Japan, 997, 75 (B) : [ 6 ] L i Xin, Huang Chunlin, Che Tao, et al. The p rogress and outlook of China s land surface data assim ilation system [ J ]. Progress in ature Science, 2007, 7 ( 2) : [,,,. [ J ]., 2007, 7 (2) : ] [ 7 ] England A W, DeRoo R. Active layer thickness and moisture con2 tent of arctic tundra from SVAT/Radiobrightness models and as2 sim ilated. 4 or 6. 9 GHz brightness[ R ]. ward, ID , Final Report of SF A2 [ 8 ] Flerchinger G, Saxton K E. Simultaneous heat and water model of a freezing snow2residue 2soil system. theory and development [ J ]. Transactions of the ASAE, 989, 32 (2) : [ 9 ] assar I, Horton R, Flerchinger G. Simultaneous heat and mass transfer in soil columns exposed to freezing/ thawing condi2 tions[ J ]. Soil Science, 2000, 65 (3) : [ 0 ] Zheng Xiuqing, Fan Guisheng, Xing Shuyan. Moisture Content Movement in Seasonal on2saturated Freezing and Thawing Soil [M ]. Beijing: Geological Press, [,,. [M ]. :, ] [ ] Yang K, Koike T, Ye B, et al. Inverse analysis of the role of soil vertical hetero2geneity in controlling surface soil state and energy partition [ J ]. Journal of Geophysical Research, 2005, 0, D080, doi: /2004JD [ 2 ] FuchsM, Campbell G S, Papendick R I. An analysis of sensible and latent heat flow in a partially frozen unsaturated soil[ J ]. Soil Science Society of Am erica Journal, 978, 42 (3) : [ 3 ] Evensen G. Sequential data assim ilation with a nonlinear quasi2 geostrophic model usingmonte2carlo methods to forecast error sta2 tistics[ J ]. Journal of Geophysical R esearch, 994, 99 ( C5 ) : [ 4 ] Evensen G. The ensemble Kalman filter: Theoretical formulation and practical imp lementat2ion [ J ]. Ocean D ynam ics, 2003, 53: [ 5 ] Burgers G, van Leeuwen P J, Evensen G. Analysis scheme in the ensemble Kalman filter[ J ]. M onthly W eather Review, 998, 26: [ 6 ] Houtekamer P L, M itchell H L. Data assim ilation using an en2 semble Kalman filter technique [ J ]. M onthly W eather Review, 998, 26: [ 7 ] Houtekamer P L,M itchell H L. A sequential ensemble Kalman fil2 ter for atmospheric data assim ilation [ J ]. M onthly W eather Re2 view, 200, 29: [ 8 ] Keppenne C L. Data assim ilation into a p rim itive2equation model with a parallel ensemble Kalman filter[ J ]. M onthly W eather Re2 view, 2000, 28: [ 9 ] M itchell H L, Houtekamer P L. An adap tive ensemble Kalman filter[ J ]. M onthly W eather Review, 2000, 28: [ 20 ] W hitaker J S, Ham ill T M. Ensemble data assim ilation without perturbed observations[ J ]. M onthly W eather Review, 2002, 30: [ 2 ] B rusdal K, B rankart J M, Evensen G, et al. A demonstration of ensemble2based assim ilation methods with a layered OGCM from the perspective of operational ocean forecasting system s [ J ]. Journal of M arine System s, 2003, 40 /4: [ 22 ] Reichle R H, Entekhabi D. Downscaling of radio brightness measurements for soil moisture estimation: A four2dimensional variational data assim ilation approach [ J ]. W ater Resources Re2 search, 200, 37 (9) : [ 23 ] Reichle R H, W alker J P, Koster R D, et al. Extended versus ensemble filtering for land data assim ilation [ J ]. Journal of hydrom eteorology, 2002, 3: [ 24 ] Reichle R H, McLaughlin D B, Entekhabi D. Hydrologic data assim ilation with the ensemble Kalman filter[ J ]. M onthly W eath2 er Review, 2002, 30: [ 25 ] Moradkhani H, Sorooshian S, Gup ta H V, et al. Dual state2pa2 rameter estimation of hydrolog2ical models using ensemble kalman filter[ J ]. Advances in W ater Resources, 2005, 28: [ 26 ] Zhou Jian, L i Xin, W ang Genxu, et al. Coup led land surface p rocess pattern SIB2 with the unsaturated seepage model and its app lication[ J ]. Advances in Earth Science, 2008, 23 ( 6 ) : [,,,. SIB2 [ J ]., 2008, 23 ( 6) : ]

9 9 : 379 Data Assim ilation Algorithm Apply to Energy2Water Balance Analysis of the High Cold Ecosystem at Qingha i2tibet Pla in, orthwest China ZHOU J ian, 3, WAG Genxu, 2, L I Xin, YAG Yongm in 3, PA Xiaoduo (. Cold and A rid R egions Environm ental and Engineering R esearch Institute, Chinese Academ y of Sciences, L anzhou , China; 2. Institute of M oun tain Hazards and Environm ent, Ch inese A cadem y of Sciences, Chengdu 6004, Ch ina; 3. R esou rces and Environm en t Institute of L anzhou U niversity, L anzhou , China) Abstract: The frozen soil region of many years located in the Q inghai2tibet Plain hinterland, Changing of the W ater content of soil during freeze2thaw p rocess, intensely responds to climate change and brings remarkable change on land energy2water balance, then has the large feedback function to the global climate. In order to reveal this kind of change, we used the observation data from the w ind volcano testing field system, selected water 2 heat coup ling model SHAW as dynam ics restraint frame, which consider the influence of snow cover & the vegetation cover & the forest flooring to the soil freezing and thaw ing, and imp roved SHAW forecast capacity to the soil mois2 ture content and the frozen soil dep th by Ensemble Kalman Filter of data simulation method. The analysis based on data assim ilation theory indicate that the data assim ilation method may remarkably enhance the forecasting ability of water2heat coup ling model to state variables, and p rovide theory basis for monitor utilizing m ultip le source informa2 tion in frozen soil area. Key words: Frozen2soil; Soil moisture content; Land surface p rocess model; Land data assim ilation; Ensem2 ble Kalm an filter. ( ) ( ) ( ) ( ) /2 /3 /4 /3 /4 /2 /3 /4 ( ) ( ) ( ) ( ) : (), 5% 20%() (2) (3) (4) /2 /3 /4, : E2mail: adearth@ lzb. ac. cn

4 : 255 [ 2, 4 7 ] [ 8 11 ],,,, [ 12 ] Koldewey , [ 13 ],,, NCEP Koldewey, NCEP 2,, 1231km, 107km Koldewey ( N, E, 11m) A lfre

4 : 255 [ 2, 4 7 ] [ 8 11 ],,,, [ 12 ] Koldewey , [ 13 ],,, NCEP Koldewey, NCEP 2,, 1231km, 107km Koldewey ( N, E, 11m) A lfre 18 4 Vol. 18, No. 4 2006 12 CH INESE JOURNAL OF POLAR RESEARCH December 2006 (, 100081), Koldewey 1993 2003,,, 32. 2%,, ;, 10, NCEP /NCAR, NCEP Koldewey NCEP /NCAR 1,, [ 1 ],,, 90 1991,, [ 2 ] ; 1999 2003,