4.7 Article

Role of remotely sensed leaf area index assimilation in eco-hydrologic processes in different ecosystems over East Asia with Community Land Model version 4.5-Biogeochemistry

期刊

JOURNAL OF HYDROLOGY
卷 594, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.jhydrol.2021.125957

关键词

Community land model; Data assimilation; Leaf area index; Gross primary production; Evapotranspiration; Plant-available soil water

资金

  1. National Research Foundation of Korea - Ministry of Science, ICT & Future Planning [2020R1A2C2007670, 2016M1A5A1901769]
  2. Korea Environmental Industry & Technology Institute through the Climate Change R&D Program - Ministry of Environment [2018001310001]
  3. Korea Meteorological Administration RD Program [KMIPA 2015-6180]
  4. National Research Foundation of Korea [2020R1A2C2007670, 2016M1A5A1901769] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study demonstrated how assimilating Leaf Area Index (LAI) into a model improved carbon and water fluxes in different ecosystems across East Asia, particularly showing significant improvement in temperate needleleaf forests.
The leaf area index (LAI) is a key variable for representing vegetation state, and it is closely related to simulating carbon and water exchanges between land and the overlying atmosphere in land surface and terrestrial ecosystem models. Model simulations are still limited in their representation of vegetation phenological processes and capturing the resulting LAI seasonality. Therefore, this study demonstrated how LAI assimilation into the model improved carbon and water fluxes in different ecosystems over East Asia. We assimilated LAI derived from Moderate Resolution Imaging Spectroradiometer data for seven years (2004-2010) over East Asia into the Community Land Model version 4.5 with a biogeochemistry module (CLM4.5-BGC) by employing the ensemble adjustment Kalman filter method. Results showed that LAI assimilation remarkably improved estimated gross primary production (GPP). In particular, the root mean square error decreased from 97.12 to 48.63 gC/m(2)/ month across the region for June-August. Additionally, while evapotranspiration (ET) was less sensitive to LAI than GPP, the ET components of ground evaporation, canopy evaporation, and canopy transpiration significantly changed after assimilation. The analysis of plant-available soil water showed that LAI assimilation has unique effects on soil moisture depending on the soil layer, climate, and ecosystems. In general, the improvement in ecological prediction skill by LAI assimilation was particularly evident in temperate needleleaf forests where LAI was overestimated most distinctly. This study improves our understanding of the role of LAI assimilation in ecohydrological processes in different ecosystems with CLM4.5-BGC, which allows for the improvement of model forecasting and more accurate simulation of the effects of LAI state evolution.

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