The Real-Time Assimilation and Prediction System for Terrestrial Ecosystem Carbon Cycling Based on Workflow

doi:10.11871/jfdc.issn.2096-742X.2026.01.014

Abstract

Abstract:

[Objective] The feedback effects of terrestrial ecosystem carbon cycling on climate change are central to global change research. Currently, there is a lack of real-time, efficient, and automated carbon source-sink assessment and prediction systems, making it difficult to quickly and accurately quantify the carbon sink size, stability, and sustainability. This limitation affects the formulation of carbon sequestration strategies and the implementation of carbon neutrality initiatives. [Methods] This study develops a real-time assimilation and prediction system for terrestrial ecosystem carbon cycling, comprising multiple core modules such as data collection, transmission, analysis, workflow management, scheduling, prediction, and visualization. By integrating deep learning-based meteorological models, carbon cycle process models, data assimilation algorithms, and ecological iterative prediction methods, the system continuously assimilates real-time station observation data to enable short-term carbon sink predictions, which serves as a paradigm for transitioning from observation to prediction in field station research. [Results] Since its deployment in February 2023, the system has successfully integrated with four stations, including Dinghushan, Qianyanzhou, and Huitong, accumulating over 110,000 data records to date. [Conclusion] The system significantly improves the timeliness and efficiency of carbon cycle prediction, providing reliable data support and observable real-time retrieval services for ecological research and environmental management decision-making.

Key words: climate change, carbon cycle, assimilation prediction, deep learning, ecosystem, data assimilation, short-term prediction

WAN Meng, HE Honglin, REN Xiaoli, NIE Ningming, CAO Rongqiang, WANG Zongguo, LI Kai, WANG Xiaoguang, WANG Yangang, WANG Jue, GAO Chao. The Real-Time Assimilation and Prediction System for Terrestrial Ecosystem Carbon Cycling Based on Workflow[J]. Frontiers of Data and Computing, 2026, 8(1): 168-182, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.01.014.

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名称	数据类型	单位	英文全称	缩写	时间分辨率（真实值）
日均气温	碳循环驱动数据	℃	Air Temperature	Ta	天
光合有效辐射		mol day^-1	Photosynthetically Active Radiation	PAR	天
相对湿度		%	Relative Humidity	Rh	天
饱和水汽压差		kPa	Vapor Pressure Deficit	VPD	天
土壤含水量		m³m^-3	Soil Water Content	SWC	天
净生态系统生产力	碳通量数据	g C m^-2 day^-1	Net Ecosystem Productivity	NEP	5年
总初级生产力		g C m^-2 day^-1	Gross Primary Productivity	GPP	5年
净初级生产力		g C m^-2 day^-1	Net Primary Productivity	NPP	5年
叶碳分配量	碳循环全组分数据（碳储量、关键参数等）	g C m^-2 day^-1	Leaf Carbon Allocation	Af	5年
叶碳密度		g C m^-2	Leaf Carbon Density	Cf	5年
自养呼吸		g C m^-2 day^-1	Autotrophic Respiration	Ra	5年
植被碳密度		g C m^-2	Vegetation Carbon Density	Cveg	5年
植被碳周转时间		yr	Vegetation Carbon Turnover Time	τveg	5年
木质部碳分配量		g C m^-2 day^-1	Wood Carbon Allocation	Aw	5年
木质部碳密度		g C m^-2	Wood Carbon Density	Cw	5年
根碳分配量		g C m^-2 day^-1	Root Carbon Allocation	Ar	5年
异养呼吸		g C m^-2 day^-1	Heterotrophic Respiration	Rh	5年
根碳密度		g C m^-2	Root Carbon Density	Cr	5年
土壤碳密度		g C m^-2 day^-1	Soil Organic Carbon Density	Csom	5年
土壤碳周转时间		yr	Soil Carbon Turnover Time	τsoil	5年

data	MSE (鼎湖山)	MAPE (鼎湖山)	MSE (千烟洲)	MAPE (千烟洲)	MSE (会同)	MAPE (会同)	MSE (尖峰岭)	MAPE (尖峰岭)
Ta	0.045	0.165	0.040	0.152	0.043	0.158	0.038	0.149
PAR	0.055	0.185	0.050	0.179	0.052	0.182	0.048	0.175
RH	0.042	0.158	0.038	0.150	0.040	0.153	0.036	0.146
VPD	0.062	0.201	0.057	0.196	0.060	0.198	0.054	0.190
SWC	0.051	0.160	—	—	—	—	—	—