Please wait a minute...
img img
Adv. Search
Advances in Earth Science  2008, Vol. 23 Issue (9): 965-973    DOI: 10.11867/j.issn.1001-8166.2008.09.0965
Data Assimilation Algorithm Apply to Energy-Water Balance Analysis of the High Cold Ecosystem at Qinghai-Tibet Plain, Northwest China
Zhou Jian1,3,Wang Genxu1,2,Li Xin1,Yang Yongmin3,Pan Xiaoduo1
1.Cold and Arid Regions Environmental and Engineering Research Institute , Chinese Academy of Sciences, Lanzhou 730000, China; 2. Institute of Mountain Hazards and Environment,Chinese Academy of Sciences, Chengdu 610041, China; 3. Resources and Environment Institute of Lanzhou University, Lanzhou 730000, China
Download:  PDF (1962KB) 
Export:  BibTeX | EndNote (RIS)      

The frozen soil region of many years located in the Qinghai-Tibet Plain hinterland, Changing of the Water content of soil during freeze-thaw process, intensely responds to climate change and brings remarkable change on land energy-water 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 wind volcano testing field system, selected water-heat coupling model SHAW as dynamics restraint frame, which consider the influence of snow cover & the vegetation cover & the forest flooring to the soil freezing and thawing, and improved SHAW forecast capacity to the soil moisture content and the frozen soil depth by Ensemble Kalman Filter of data simulation method. The analysis based on data assimilation theory indicate that the data assimilation method may remarkably enhance the forecasting ability of water-heat coupling model to state variables, and provide theory basis for monitor utilizing multiple source information in frozen soil area.

Key words:  Frozen-soil      Soil moisture content      Land surface process model      Land data assimilation      Ensemble Kalman filter.     
Received:  02 January 2008      Published:  10 September 2008
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
Articles by authors
LI Xin
PAN Xiaoduo
YANG Yongmin
WANG Genxu

Cite this article: 

Zhou Jian,Wang Genxu,Li Xin,Yang Yongmin,Pan Xiaoduo. Data Assimilation Algorithm Apply to Energy-Water Balance Analysis of the High Cold Ecosystem at Qinghai-Tibet Plain, Northwest China. Advances in Earth Science, 2008, 23(9): 965-973.

URL:     OR

[1] Li Xin,Cheng Guodong. Review of model of the permafrost-climate model[J]. Journal of Glaciology and Geocryology,2002,243:315-321.[李新,程国栋.冻土气候关系模型评述[J].冰川冻土,2002,243:315-321.]

[2] Allison I,Barry R G,Goodison B E. Climate and CryosphereCliC project science and coordination plan version 1[Z]. WCRP-114/WMO/TD No.1053,2001.

[3] Jin H J,Brown J. Great challenges of and innovative solutions to the unstable permafrost in central and high Asia under a warming climate[J]. Episodes,2007,301:54-55.

[4] Daley R. Atmospheric Data Analysis[M]. New York:Cambridge University Press,1991:1-457.

[5] Talagrand O. Assimilation of observations,an introduction[J]. Journal of the Meteorological Society of Japan,1997,751B:191-209.

[6] Li Xin,Huang Chunlin,Che Tao,et al. The progress and outlook of China's land surface data assimilation system[J]. Progress in Nature Science,2007,172:163-173.[李新,黄春林,车涛,.中国陆面数据同化系统研究的进展与前瞻[J]. 自然科学进展,2007,172:163-173.]

[7] England A W,DeRoo R. Active layer thickness and moisture content of arctic tundra from SVAT/Radiobrightness models and assimilated 1.4 or 6.9 GHz brightness[R]. Final Report of NSF Award,ID 0240747,2006.

[8] Flerchinger G N,Saxton K E. Simultaneous heat and water model of a freezing snow-residue-soil system.theory and development[J]. Transactions of the ASAE,1989,322:565-571.

[9] Nassar I N,Horton R,Flerchinger G N. Simultaneous heat and mass transfer in soil columns exposed to freezing/thawing conditions[J]. Soil Science,2000,1653:208-216.

[10] Zheng Xiuqing,Fan Guisheng,Xing Shuyan. Moisture Content Movement in Seasonal Non-saturated Freezing and Thawing Soil[M]. Beijing: Geological Press,2002.[郑秀清,樊贵盛,邢述彦. 水分在季节性非饱和冻融土壤中的运动[M].北京:地质出版社,2002.]

[11] Yang K,Koike T,Ye B,et al. Inverse analysis of the role of soil vertical hetero-geneity in controlling surface soil state and energy partition[J]. Journal of Geophysical Research,2005,110,D08101,doi:10.1029/2004JD005500.

[12] Fuchs M,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 America Journal,1978,423:379-385.

[13] Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics[J]. Journal of Geophysical Research,1994,99C5:10 143-10 162.

[14] Evensen G.The ensemble Kalman filter: Theoretical formulation and practical implementat-ion[J]. Ocean Dynamics,2003,53:343-367.

[15] Burgers G,van Leeuwen P J,Evensen G. Analysis scheme in the ensemble Kalman filter[J]. Monthly Weather Review,1998,126:1 719-1 724.

[16] Houtekamer P L,Mitchell H L. Data assimilation using an ensemble Kalman filter technique[J]. Monthly Weather Review,1998,126:796-811.

[17] Houtekamer P L,Mitchell H L.A sequential ensemble Kalman filter for atmospheric data assimilation[J]. Monthly Weather Review,2001,129:123-137.

[18] Keppenne C L.Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter[J]. Monthly Weather Review,2000,128:1 971-1 981.

[19] Mitchell H L,Houtekamer P L. An adaptive ensemble Kalman filter[J]. Monthly Weather Review,2000,128:416-433.

[20] Whitaker J S,Hamill T M. Ensemble data assimilation without perturbed observations[J]. Monthly Weather Review,2002,130:1 913-1 924.

[21] Brusdal K,Brankart J M,Evensen G,et al. A demonstration of ensemble-based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems[J]. Journal of Marine Systems,2003,40/41:253-289.

[22] Reichle R H,Entekhabi D. Downscaling of radio brightness measurements for soil moisture estimation: A four-dimensional variational data assimilation approach[J]. Water Resources Research, 2001,379:2 353-2 364.

[23] Reichle R H,Walker J P,Koster R D,et al. Extended versus ensemble filtering for land data assimilation[J]. Journal of hydrometeorology,2002,3:728-740.

[24] Reichle R H,McLaughlin D B,Entekhabi D. Hydrologic data assimilation with the ensemble Kalman filter[J]. Monthly Weather Review,2002,130:103-114.

[25] Moradkhani H,Sorooshian S,Gupta H V,et al. Dual state-parameter estimation of hydrolog-ical models using ensemble kalman filter[J]. Advances in Water Resources,2005,28:135-147.

[26] Zhou Jian,Li Xin,Wang Genxu,et al. Coupled land surface process pattern SIB2 with the unsaturated seepage model and its application[J]. Advances in Earth Science,2008,236:570-579.[周剑,李新,王根绪,.陆面过程模式SIB2与包气带入渗模型的耦合及其应用[J].地球科学进展,2008,236:570-579.]

No Suggested Reading articles found!