京津冀地区植被碳源/汇的时空变化特征及影响因素分析

doi:10.16258/j.cnki.1674-5906.2024.09.001

生态环境学报 ›› 2024, Vol. 33 ›› Issue (9): 1329-1338.DOI: 10.16258/j.cnki.1674-5906.2024.09.001

• 碳循环与碳减排专栏 • 下一篇

京津冀地区植被碳源/汇的时空变化特征及影响因素分析

侯金龙¹(), 马志强¹^,^*(), 杨澄², 葛双双¹, 何迪¹, 董璠¹

1.北京城市气象研究院，北京 100089
2.应急管理部森林防火预警监测信息中心，北京 100054

收稿日期:2024-03-19 出版日期:2024-09-18 发布日期:2024-10-18
通讯作者: ^*马志强。E-mail: zqma@ium.cn
作者简介:侯金龙（1997年生），男，硕士，助理工程师，主要从事生态气象研究。E-mail: jlhou@ium.cn
基金资助:
国家自然科学基金项目(42275188);北京市科技新星计划资助(20220484209);中央级公益性科研院所基本科研业务费专项基金项目(IUMKY202311)

Analysis of Spatio-temporal Variation of Vegetation Carbon Sources and Sinks in the Beijing-Tianjin-Hebei Region and Influencing Factors

HOU Jinlong¹(), MA Zhiqiang¹^,^*(), YANG Cheng², GE Shuangshuang¹, HE Di¹, DONG Fan¹

1. Institute of Urban Meteorology, CMA, Beijing 100089, P. R. China
2. Forest Fire Prevention Early Warning and Monitor Center of Ministry of Emergency Management, Beijing 100054, P. R. China

Received:2024-03-19 Online:2024-09-18 Published:2024-10-18

摘要/Abstract

摘要：

植被净生态系统生产力（Net Ecosystem Productivity，NEP）在碳循环中起着关键作用，是生态系统碳预算的重要指标。基于MOD17A3数据、气象数据，结合土壤呼吸模型，对京津冀地区植被碳源/汇时空分布特征展开研究，并利用趋势分析、相关性分析等方法探究植被NEP与气象要素、植被归一化指数（Normalized Difference Vegetation Index，NDVI）及土地利用变化的关系。结果表明，1）2000－2022年京津冀地区NEP呈波动上升趋势，年增长率5.65 g·m⁻²，年均NEP为108 g·m⁻²，碳汇区面积也逐步增加，2022年占比达到最大值95.0%；NEP空间格局呈“北高南低”特征，与区域高程一致，并且有明显的空间异质性。2）近20年来，京津冀植被NEP呈上升趋势的面积占98.3%，且显著上升的区域占比为85.9%，在承德、张家口和北京较为集中，NEP为下降趋势的面积仅占1.72%；承德植被NEP呈显著上升的区域占比最多，为98.2%，而邯郸显著下降的区域占比最多，为1.03%。3）京津冀大部分地区植被NEP都与降水、气温呈正相关，与降水、气温的相关性均值分别为0.500、0.160，NEP与降水、气温呈显著正相关的面积占比分别为78.2%、13.9%，降水是影响京津冀地区NEP变化的关键气象因子；NDVI与NEP的平均相关系数为0.430，呈极显著正相关的面积占比为58.1%，其中正相关性较高的区域集中在京津冀西北部山区。土地利用变化结果显示近20年间京津冀林地面积大幅增加，3个研究时段内分别增加了31.4%、24.0%和11.9%，是驱动该地区植被NEP上升的重要因子。研究结果为京津冀地区植被碳源/汇的准确评估及“双碳”目标的实现提供了参考依据。

关键词: 净生态系统生产力（NEP）, 京津冀地区, 碳源/汇, 气象要素, 时空变化, 土壤异养呼吸

Abstract:

Net Ecosystem Productivity (NEP) of vegetation is a crucial component of the carbon cycle and a significant indicator of an ecosystem's carbon budget. This study analyzes the spatial and temporal distribution characteristics of vegetation carbon sources and sinks in the Beijing-Tianjin-Hebei region, based on MOD17A3 and meteorological data, combined with a soil respiration model. The relationships between vegetation NEP and meteorological factors, the Normalized Difference Vegetation Index (NDVI), and land-use change were explored using trend analysis, correlation analysis, and other methods. The results showed that: 1) From 2000 to 2022, NEP in the Beijing-Tianjin-Hebei region showed a fluctuating upward trend, with an annual growth rate of 5.65 g·m⁻² and an average annual NEP of 108 g·m⁻². The area of carbon sinks increased gradually, reaching a maximum of 95.0% in 2022, and the spatial pattern of NEP was characterized by a “high in the north and low in the south”, which was consistent with the regional elevation and obvious spatial heterogeneity. 2) Over the past 20 years, 98.3% of the area of Beijing-Tianjin-Hebei was characterized by an upward trend in vegetation NEP, and 85.9% of the area was characterized by a significant increase in NEP, which was more concentrated in Chengde, Zhangjiakou, and Beijing. The area with a downward trend in NEP accounted for only 1.72%. Chengde had the highest percentage of area with a significant increase in NEP of 98.2%, whereas Handan had the highest percentage of area with a significant decrease of 1.03%. 3) Vegetation NEP in most areas of the Beijing-Tianjin-Hebei region was positively correlated with precipitation and air temperature, and the mean values of correlation with precipitation and air temperature were 0.500 and 0.160, respectively. The percentage of the area where NEP was significantly positively correlated with precipitation and air temperature was 78.2% and 13.9%, respectively, and precipitation was the key meteorological factor influencing the changes in NEP in the Beijing-Tianjin-Hebei region. The average correlation coefficient between NDVI and NEP was 0.430, and the area with a very significant positive correlation accounted for 58.1%, and the area with a high positive correlation was concentrated in the mountainous areas of northwest Beijing-Tianjin-Hebei. The land use change results showed a significant increase in the area of forested land in Beijing-Tianjin-Hebei during the last 20 years, with increases of 31.4%, 24.0%, and 11.9%, respectively, over the three study periods, which is an important factor driving the rise in vegetation NEP in the region. This study provides a reference basis for the accurate assessment of vegetation carbon sources/sinks in the Beijing-Tianjin-Hebei region and the realization of the “double carbon” goal.

Key words: Net Ecosystem Productivity (NEP), Beijing-Tianjin-Hebei region, carbon sink/source, meteorological element, temporal-spatial variation, soil heterotrophic respiration

中图分类号:

X171.1

侯金龙, 马志强, 杨澄, 葛双双, 何迪, 董璠. 京津冀地区植被碳源/汇的时空变化特征及影响因素分析[J]. 生态环境学报, 2024, 33(9): 1329-1338.

HOU Jinlong, MA Zhiqiang, YANG Cheng, GE Shuangshuang, HE Di, DONG Fan. Analysis of Spatio-temporal Variation of Vegetation Carbon Sources and Sinks in the Beijing-Tianjin-Hebei Region and Influencing Factors[J]. Ecology and Environment, 2024, 33(9): 1329-1338.

图/表 13

图1 京津冀地理位置及高程示意图

Figure 1 Beijing-Tianjin-Hebei geographic location and elevation map

图2 2000－2022年京津冀年均气温及年降水量变化

Figure 2 Variations in mean annual temperature and precipitation in Beijing-Tianjin-Hebei during 2000-2022

图3 2000－2022年京津冀植被NEP的时间变化

Figure 3 Temporal variations of vegetation NEP in Beijing-Tianjin-Hebei during 2000-2022

图4 2000－2022年京津冀碳源/汇面积占比

Figure 4 Proportional area of carbon sources and sinks in Beijing-Tianjin-Hebei during 2000-2022

图5 2000－2022年京津冀NEP空间分布状况

Figure 5 Spatial distribution of NEP in Beijing-Tianjin-Hebei during 2000-2022

图6 植被NEP变化趋势占比统计

Figure 6 Statistics on the proportion of vegetation NEP change trend

图7 2000－2022年京津冀NEP与气温相关性

Figure 7 Correlation between NEP and temperature in Beijing-Tianjin-Hebei during 2000-2022

图8 2000－2022年京津冀NEP与降水相关性

Figure 8 Correlation between NEP and precipitation in Beijing-Tianjin-Hebei during 2000-2022

图9 2000－2022年京津冀NEP与NDVI相关性及显著性检验

Figure 9 Correlation and significance test between NEP and NDVI in Beijing-Tianjin-Hebei during 2000-2022

图10 京津冀土地利用现状图

Figure 10 Land use map of Beijing-Tianjin-Hebei

图11 京津冀土地利用转移桑基图

Figure 11 Sangji map of land use transfer in Beijing-Tianjin-Hebei

表1 研究时段内中国发生的拉尼娜事件及其特征值

Table 1 La ni?a events and characteristic values occurring in China during the study period

序号	起止年月	时间长度/ Month	峰值时间	峰值强度/ ℃	程度	类型
1	2000年10月− 2001年2月	5	2000年 12月	−0.8	弱	中部型
2	2007年8月− 2008年5月	10	2008年 1月	−1.7	中等	东部型
3	2010年6月− 2011年5月	12	2010年 12月	−1.6	中等	东部型
4	2011年8月− 2012年3月	8	2011年 12月	−1.1	弱	中部型
5	2017年10月− 2018年3月	6	2018年 1月	−0.8	弱	东部型
6	2020年8− 2021年3月	8	2020年 11月	−1.3	中等	东部型
7	2021年9月− 2023年1月	17	2022年 4月	−1.2	弱	东部型

表2 已有研究中NEP与气温、降水显著正相关面积占比

Table 2 Proportion of areas with significant positive correlation of NEP with temperature and precipitation in existing studies

区域	NEP与气温显著正相关面积占比/%	NEP与降水显著正相关面积占比/%	文献来源
华北平原	4.90	44.7	曹云等, 2023
黄河流域	2.00	47.2	曹云等, 2022
新疆	40.8	52.6	Lu et al., 2023
青藏高原	38.5	11.7	冶晓娟等, 2022
西南地区	10.7	16.5	徐勇等, 2023

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京津冀地区植被碳源/汇的时空变化特征及影响因素分析

Analysis of Spatio-temporal Variation of Vegetation Carbon Sources and Sinks in the Beijing-Tianjin-Hebei Region and Influencing Factors

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