基于改进Biome-BGC模型的东南丘陵区茶园生态系统碳通量模拟

doi:10.13287/j.1001-9332.202505.001

应用生态学报 ›› 2025, Vol. 36 ›› Issue (5): 1339-1349.doi: 10.13287/j.1001-9332.202505.001

基于改进Biome-BGC模型的东南丘陵区茶园生态系统碳通量模拟

邵于洋^1,2,3, 李恒鹏^2*, 耿建伟², 于江华¹, 石运杰^2,3, 艾柯代·艾斯凯尔^2,3

¹南京信息工程大学环境科学与工程学院, 南京 210044;
²中国科学院南京地理与湖泊研究所, 流域地理学重点实验室, 南京 210008;
³中国科学院大学, 南京 210008

收稿日期:2024-11-09 修回日期:2025-02-13 出版日期:2025-05-18 发布日期:2025-11-18
通讯作者: *E-mail: hpli@niglas.ac.cn
作者简介:邵于洋, 男, 2000年生, 硕士研究生。主要从事生态系统碳循环模拟研究。E-mail: shaoyuyang22@mails.ucas.ac.cn
基金资助:
云南省科技厅科技计划项目(202202AE090034)、国家重点研发计划项目(2023YFD1702101)和国家自然科学基金项目(42201127)

Simulation of carbon flux in tea plantation based on an improved Biome-BGC model in hilly areas of Southeast China

SHAO Yuyang^1,2,3, LI Hengpeng^2*, GENG Jianwei², YU Jianghua¹, SHI Yunjie^2,3, AKIDA Askar^2,3

¹School of Environmental Science and Engineering, Nanjing University of Information Science and Techno-logy, Nanjing 210044, China;
²Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;
³University of Chinese Academy of Sciences, Nanjing 210008, China

Received:2024-11-09 Revised:2025-02-13 Online:2025-05-18 Published:2025-11-18

摘要/Abstract

摘要： 中国东南丘陵地区茶园的快速扩张对地区碳循环产生显著影响。Biome-BGC模型常被用于碳通量定量研究,但其对人工管理过程刻画不足。本研究结合实测与遥感叶面积指数(LAI)数据,改进了Biome-BGC模型,以增强其对茶园人工管理过程的模拟能力。结果表明: LAI是Biome-BGC模型中关键的中间变量,对LAI的准确模拟是提升模型对茶园碳通量模拟精度的关键。改进后的模型显著提升了对总初级生产力(GPP)和生态系统呼吸(RE)的模拟精度,5年平均GPP和RE值分别为1.26、1.19 kg C·m^-2,日尺度R²分别达到0.55和0.80,较改进前分别提升44.5%和降低0.9%,均方根误差(RMSE)分别为0.887和1.030 g C·m^-2·d^-1,较改进前分别降低50.3%和68.4%,月尺度的模拟效果更佳,显著改善了原始模型因未充分刻画人工修剪导致的碳通量高估问题。改进后的模型能够动态刻画修剪引起的LAI 波动对碳循环的影响,并验证了其在不同时间尺度下的适用性,为存在高强度人工管理的茶园生态系统碳循环定量研究提供了技术支撑。

关键词: Biome-BGC模型, 茶园, 碳通量模拟, 叶面积指数

Abstract: The rapid expansion of tea plantations in the hilly regions of southeastern China significantly impacts regional carbon cycle. The Biome-BGC model, commonly used to quantify carbon fluxes, lacks sufficient representation of artificial management processes. We integrated the measured and remote-sensed leaf area index (LAI) to improve the Biome-BGC model, enhancing its simulation capabilities for the artificial management processes in tea plantations. The results showed that LAI was a crucial intermediate variable in the Biome-BGC model. Accurate simulation of LAI was the key to improve the model’s precision in simulating carbon fluxes in tea plantations. The improved model significantly enhanced the simulation accuracy of gross primary productivity (GPP) and ecosystem respiration (RE), with 5-year average GPP and RE values of 1.26 and 1.19 kg C·m^-2, respectively. The daily-scale R² values reached 0.55 and 0.80, representing an increase of 44.5% for GPP and a decrease of 0.9% for RE compared to the original model. The root mean square error (RMSE) values were 0.887 and 1.030 g C·m^-2·d^-1, representing reductions of 50.3% for GPP and 68.4% for RE compared to the original model, respectively. At the month scale, the improved model significantly reduced the overestimation of original model resulted from insufficient representation of artificial pruning for tea plantations. The improved model could dynamically depict the impact of LAI fluctuations caused by pruning on the carbon cycle and its applicability across different time scales had been verified, which would provide technical support for quantitative research on carbon cycling in tea plantations with high-intensity anthropogenic management.

Key words: Biome-BGC model, tea plantation, carbon flux simulation, leaf area index

邵于洋, 李恒鹏, 耿建伟, 于江华, 石运杰, 艾柯代·艾斯凯尔. 基于改进Biome-BGC模型的东南丘陵区茶园生态系统碳通量模拟[J]. 应用生态学报, 2025, 36(5): 1339-1349.

SHAO Yuyang, LI Hengpeng, GENG Jianwei, YU Jianghua, SHI Yunjie, AKIDA Askar. Simulation of carbon flux in tea plantation based on an improved Biome-BGC model in hilly areas of Southeast China[J]. Chinese Journal of Applied Ecology, 2025, 36(5): 1339-1349.

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基于改进Biome-BGC模型的东南丘陵区茶园生态系统碳通量模拟

Simulation of carbon flux in tea plantation based on an improved Biome-BGC model in hilly areas of Southeast China

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