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Open AccessArticle

Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach

by Longwei Hu 1, Honglin He 2,3,4,*, Yan Shen 1, Xiaoli Ren 2,3, Shao-kui Yan 5, Wenhua Xiang 1,6, Rong Ge 2,3,7, Zhongen Niu 2,3,7, Qian Xu 2,3,7 and Xiaobo Zhu 2,3,8,9
1
Central South University of Forestry & Technology, Changsha 410004, China
2
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
National Ecosystem Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
5
Huitong Experimental Station of Forest Ecology, State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China
6
National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Changsha 410004, China
7
University of Chinese Academy of Sciences, Beijing 100049, China
8
Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem, Ministry of Education, Chongqing 400715, China
9
Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Forests 2020, 11(4), 369; https://doi.org/10.3390/f11040369
Received: 22 February 2020 / Revised: 20 March 2020 / Accepted: 21 March 2020 / Published: 26 March 2020
(This article belongs to the Section Forest Ecology and Management)
Process-based terrestrial ecosystem models are increasingly being used to predict carbon (C) cycling in forest ecosystems. Given the complexity of ecosystems, these models inevitably have certain deficiencies, and thus the model parameters and simulations can be highly uncertain. Through long-term direct observation of ecosystems, numerous different types of data have accumulated, providing valuable opportunities to determine which sources of data can most effectively reduce the uncertainty of simulation results, and thereby improve simulation accuracy. In this study, based on a long-term series of observations (biometric and flux data) of a subtropical Chinese fir plantation ecosystem, we use a model–data fusion framework to evaluate the effects of different constrained data on the parameter estimation and uncertainty of related variables, and systematically evaluate the uncertainty of parameters. We found that plant C pool observational data contributed to significant reductions in the uncertainty of parameter estimates and simulation, as these data provide information on C pool size. However, none of the data effectively constrained the foliage C pool, indicating that this pool should be a target for future observational activities. The assimilation of soil organic C observations was found to be important for reducing the uncertainty or bias in soil C pools. The key findings of this study are that the assimilation of multiple time scales and types of data stream are critical for model constraint and that the most accurate simulation results are obtained when all available biometric and flux data are used as constraints. Accordingly, our results highlight the importance of using multi-source data when seeking to constrain process-based terrestrial ecosystem models. View Full-Text
Keywords: process-based terrestrial ecosystem model; model–data fusion; multi-source data; carbon cycle; plantation process-based terrestrial ecosystem model; model–data fusion; multi-source data; carbon cycle; plantation
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Hu, L.; He, H.; Shen, Y.; Ren, X.; Yan, S.-K.; Xiang, W.; Ge, R.; Niu, Z.; Xu, Q.; Zhu, X. Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach. Forests 2020, 11, 369.

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