Assimilation of soil moisture in LPJ-DGVM

Xufeng Wang; Mingguo Ma; Xujun Han; Yi Song

doi:10.1117/12.830312

18 September 2009 Assimilation of soil moisture in LPJ-DGVM

Xufeng Wang, Mingguo Ma, Xujun Han, Yi Song

Proceedings Volume 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI; 747220 (2009) https://doi.org/10.1117/12.830312
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

Process-oriented dynamic vegetation models are effective tools to assess carbon and water exchanges between vegetation and environment for different scales. Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) is one of the well-established, process-oriented dynamic vegetation models. It can simulate seasonal trends of EvapoTranspiration (ET) and Net Ecosystem Exchange (NEE) forced by weather data. In this study, LPJ-DGVM was employed to simulate the ET and NEE in Yingke (YK) oasis station and A'Rou (AR) freeze/thaw observation station. The results indicate that LPJ-DGVM could not make good estimations in both YK station and AR station. The simulation results were validated with the water and CO₂ flux observation from Eddy Covariance (EC). The freeze-thaw phenomenon and irrigation have great impacts on soil water content dynamic in arid region, but they are not considered in LPJ-DGVM. In order to improve the simulation accuracy, a soil water content data assimilation scheme was designed. The observed soil water content was assimilated into LPJ-DGVM with Ensemble Kalman Filter (EnKF) algorithm. The simulation accuracy of LPJ-DGVM was improved obviously when soil water content was assimilated into LPJ-DGVM. The EnKF is effective for assimilating in situ observation.

Citation Download Citation

Xufeng Wang, Mingguo Ma, Xujun Han, and Yi Song "Assimilation of soil moisture in LPJ-DGVM", Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 747220 (18 September 2009); https://doi.org/10.1117/12.830312

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available