Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution
Abstract
The efficacy of vegetation dynamics simulations in offline land surface models (LSMs) largely depends on the quality and spatial resolution of meteorological forcing data. In this study, the Princeton Global Meteorological Forcing Data (PMFD) and the high spatial resolution and upscaled China Meteorological Forcing Data (CMFD) were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (SSiB4/TRIFFID) and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau (TP), a region with complex topography and sparse observations. By comparing the monthly Leaf Area Index (LAI) and Gross Primary Production (GPP) against observations, we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP, reducing RMSEs by 24.3% and 20.5%, respectively. The multi-year averaged GPP decreased from 364.68 gC m–2 yr–1 to 241.21 gC m–2 yr–1 with the percentage bias dropping from 50.2% to –1.7%. When using the high spatial resolution CMFD, the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5% and 9.5%, respectively. This study highlights the importance of more realistic and high-resolution forcing data in simulatingmore »
- Authors:
-
- Nanjing Univ. (China)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Chengdu University of Information Technology (China)
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE; National Natural Science Foundation of China (NSFC); Collaborative Innovation Center for Climate Change, Jiangsu Province, China
- OSTI Identifier:
- 1873290
- Report Number(s):
- PNNL-SA-174212
Journal ID: ISSN 0256-1530
- Grant/Contract Number:
- AC05-76RL01830; 42130602; 42175136
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Advances in Atmospheric Sciences
- Additional Journal Information:
- Journal Volume: 39; Journal Issue: 7; Journal ID: ISSN 0256-1530
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; SSiB4; meteorological forcing data; vegetation dynamics; spatial resolution; Tibetan Plateau
Citation Formats
Kang, Zhijie, Qiu, Bo, Xiang, Zheng, Liu, Ye, Lin, Zhiqiang, and Guo, Weidong. Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution. United States: N. p., 2022.
Web. doi:10.1007/s00376-022-1426-6.
Kang, Zhijie, Qiu, Bo, Xiang, Zheng, Liu, Ye, Lin, Zhiqiang, & Guo, Weidong. Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution. United States. https://doi.org/10.1007/s00376-022-1426-6
Kang, Zhijie, Qiu, Bo, Xiang, Zheng, Liu, Ye, Lin, Zhiqiang, and Guo, Weidong. Fri .
"Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution". United States. https://doi.org/10.1007/s00376-022-1426-6. https://www.osti.gov/servlets/purl/1873290.
@article{osti_1873290,
title = {Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution},
author = {Kang, Zhijie and Qiu, Bo and Xiang, Zheng and Liu, Ye and Lin, Zhiqiang and Guo, Weidong},
abstractNote = {The efficacy of vegetation dynamics simulations in offline land surface models (LSMs) largely depends on the quality and spatial resolution of meteorological forcing data. In this study, the Princeton Global Meteorological Forcing Data (PMFD) and the high spatial resolution and upscaled China Meteorological Forcing Data (CMFD) were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (SSiB4/TRIFFID) and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau (TP), a region with complex topography and sparse observations. By comparing the monthly Leaf Area Index (LAI) and Gross Primary Production (GPP) against observations, we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP, reducing RMSEs by 24.3% and 20.5%, respectively. The multi-year averaged GPP decreased from 364.68 gC m–2 yr–1 to 241.21 gC m–2 yr–1 with the percentage bias dropping from 50.2% to –1.7%. When using the high spatial resolution CMFD, the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5% and 9.5%, respectively. This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.},
doi = {10.1007/s00376-022-1426-6},
journal = {Advances in Atmospheric Sciences},
number = 7,
volume = 39,
place = {United States},
year = {Fri Jun 03 00:00:00 EDT 2022},
month = {Fri Jun 03 00:00:00 EDT 2022}
}
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