黄河三角洲芦苇湿地生态系统碳通量动态特征及其影响因素

doi:10.16258/j.cnki.1674-5906.2021.05.007

生态环境学报 ›› 2021, Vol. 30 ›› Issue (5): 949-956.DOI: 10.16258/j.cnki.1674-5906.2021.05.007

黄河三角洲芦苇湿地生态系统碳通量动态特征及其影响因素

王永志¹^,³(), 刘胜林²

1.河南科技大学应用工程学院,河南三门峡 472000
2.四川农业大学管理学院,四川成都 611130
3.三门峡职业技术学院,河南三门峡 472000

收稿日期:2019-10-17 出版日期:2021-05-18 发布日期:2021-08-06
作者简介:王永志（1981年生）,男,讲师,硕士,研究方向为生态系统碳固定。E-mail:wzy_81@126.com
基金资助:
2017年江苏省大创项目(201712808008Y)

Dynamic Characteristics and Influencing Factors of Carbon and Water Flux in Reed Wetland Ecosystem in the Yellow River Delta

WANG Yongzhi¹^,³(), LIU Shenglin²

1. School of Life Sciences, Henan University, Kaifeng 475004, China
2. College of Management, Sichuan Agricultural University, Chengdu 611130, China
3. Sanmenxia College of Vocational Technology, Sanmenxia, 472000, China

Received:2019-10-17 Online:2021-05-18 Published:2021-08-06

摘要/Abstract

摘要：

利用涡度相关法,对黄河三角洲芦苇湿地生态系统进行了连续2年的通量观测,分析了2017—2018年生长季芦苇湿地生态系统碳交换量（NEE）及其影响因素,为区域的碳收支预算和为全球碳循环模型的进一步完善提供理论基础。结果表明,在季节尺度上,芦苇湿地生长季具有明显的碳汇功能,生态系统呼吸（R_s）随着月份的增加呈倒“V”型变化特征,在8月达到最高;生态系统碳交换（NEE）和生态系统总初级生产力（GPP）随着月份的增加呈“V”型变化特征。2018年不同月份生态系统碳交换（NEE）、生态系统总初级生产力（GPP）、生态系统呼吸（R_s）均高于2017年,局部有所差异,其变化趋势与2017年总体保持一致。在日尺度上,2017—2018年芦苇湿地NEE日变化特征表现为两个CO₂吸收高峰,分别出现在11:00和16:00左右,其特点是在午间出现了碳交换通量的降低,CO₂排放的日最大值两个生长季均出现在8月。2017—2018年NEE_night随着月份的增加呈倒“V”型变化特征,在8月达到最高;而NEE_total和NEE_day随着月份的增加呈“V”型变化特征,在8月达到最高,局部有所差异。芦苇湿地生态系统的CO₂交换受到光合有效辐射（PAR）、土壤温度（t_s）和土壤体积含水量（T_a）的共同影响,生长季NEE通量与5 cm土壤温度和土壤湿度呈显著或极显著的指数关系（P<0.05,P<0.01）,同时生长季NEE通量与5 cm土壤温度和土壤湿度的R²均高于NEE通量与10 cm土壤温度和土壤湿度的R²,由此说明5 cm土壤温度和湿度能够更好的指示NEE通量的变化。

关键词: 黄河三角洲, 芦苇湿地, 涡度相关, 生态系统碳通量

Abstract:

By using the vorticity correlation method, the flux of reed wetland ecosystem in the Yellow River delta was observed for two consecutive years, and the carbon exchange capacity (NEE) of reed wetland ecosystem in the growing season of 2017?2018 and its influencing factors were analyzed. The results showed that, on the seasonal scale, reed wetland had obvious carbon sink function in the growing season, and the ecosystem respiration (R_s) showed an inverted “V” shape with the increase of months, reaching the highest in August.Ecosystem carbon exchange (NEE) and ecosystem total primary productivity (GPP) showed a v-shaped change with the increase of the month, reaching the highest level in August. Ecosystem carbon exchange (NEE), total primary productivity (GPP) and ecosystem respiration (R_s) in different months in 2018 were all higher than that in 2017, with partial differences, and the change trend was generally consistent with that in 2017. On the daily scale, the NEE diurnal change of reed wetland in 2017?2018 is characterized by two CO₂ absorption peaks, which appear at about 11:00 and 16:00 respectively. The characteristics are that the carbon exchange flux decreases at noon, and the maximum daily CO₂ emission occurs in August in both growing seasons. In 2017, with the increase of months, NEE_night showed an inverted “V” shape, reaching the highest in August. The NEE_total and NEE_day show a v-shaped change with the increase of months, and reach the highest in August. In 2018, NEE_night, NEE_total and NEE_day in different months were all higher than that in 2017, with local differences, and the trend was consistent with that in 2017. Reed wetland ecosystem CO₂ exchange by photosynthetic active radiation (PAR), soil temperature (t_s) and soil volumetric water content (T_a) from the combined impact of the growing season NEE flux with 5 cm soil temperature and soil moisture were significantly or extremely significantly relationship between index (P<0.05, P<0.01), at the same time, the growing season NEE flux with 5 cm soil temperature and soil moisture R² were higher than NEE flux and 10 cm soil temperature and soil moisture of R², so 5 cm soil temperature and humidity can better instructions NEE flux changes.

Key words: Yellow River Delta, reed wetlands, vorticity correlation, ecosystem carbon flux

中图分类号:

王永志, 刘胜林. 黄河三角洲芦苇湿地生态系统碳通量动态特征及其影响因素[J]. 生态环境学报, 2021, 30(5): 949-956.

WANG Yongzhi, LIU Shenglin. Dynamic Characteristics and Influencing Factors of Carbon and Water Flux in Reed Wetland Ecosystem in the Yellow River Delta[J]. Ecology and Environment, 2021, 30(5): 949-956.

图/表 7

参考文献 25

[1]	CAO S K, CAO G C, FENG Q, et al., 2017. Alpine wetland ecosystem carbon sink and its controls at the Qinghai Lake[J]. Environmental Earth Sciences, 76(5): 210. DOI URL
[2]	CHU X J, HAN G X, XING Q H, et al., 2018. Dual effect of precipitation redistribution on net ecosystem CO₂ exchange of a coastal wetland in the Yellow River Delta[J]. Agricultural and Forest Meteorology, 249: 286-296. DOI URL
[3]	DUMAN T, SCHAFER K V R, 2018. Partitioning net ecosystem carbon exchange of native and invasive plant communities by vegetation cover in an urban tidal wetland in the New Jersey Meadowlands (USA)[J]. Ecological Engineering, 114: 16-24. DOI URL
[4]	FANG Q Q, WANG G Q, LIU T X, et al., 2018. Controls of carbon flux in a semi-arid grassland ecosystem experiencing wetland loss: Vegetation patterns and environmental variables[J]. Agricultural and Forest Meteorology, 259: 196-210. DOI URL
[5]	FELLMAN J B, D’AMORE D V, HOOD E, et al., 2017. Vulnerability of wetland soil carbon stocks to climate warming in the perhumid coastal temperate rainforest[J]. Biogeochemistry, 133(2): 165-179. DOI URL
[6]	FLEISCHER E, KHASHIMOV I, HOLZEL N, et al., 2016. Carbon exchange fluxes over peatlands in Western Siberia: Possible feedback between land-use change and climate change[J]. Science of the Total Environment, 545-546: 424-433.
[7]	HAN G X, CHU X J, XING Q H, et al., 2015. Effects of episodic flooding on the net ecosystem CO₂ exchange of a supratidal wetland in the Yellow River Delta[J]. Journal of Geophysical Research: Biogeosciences, 120(8): 1506-1520. DOI URL
[8]	HANIS K L, AMIRO B D, TENUTA M, et al., 2015. Carbon exchange over four growing seasons for a subarctic sedge fen in northern Manitoba, Canada[J]. Arctic Science, 1(2): 27-44. DOI URL
[9]	KELSEY K C, LEFFLER A J, BEARD K H, et al., 2016. Interactions among vegetation, climate, and herbivory control greenhouse gas fluxes in a subarctic coastal wetland[J]. Journal of Geophysical Research: Biogeosciences, 121(12): 2960-2975. DOI URL
[10]	LI X L, JIA Q Y, LIU J M, 2016. Seasonal variations in heat and carbon dioxide fluxes observed over a reed wetland in northeast China[J]. Atmospheric Environment, 127: 6-13. DOI URL
[11]	MENG L, ROULET N, ZHUANG Q, et al., 2016. Focus on the impact of climate change on wetland ecosystems and carbon dynamics[J]. Environmental Research Letters, 11(10): 100201. DOI URL
[12]	MIAO G F, NOORMETS A, DOMEC J C, et al., 2017. Hydrology and microtopography control carbon dynamics in wetlands: Implications in partitioning ecosystem respiration in a coastal plain forested wetland[J]. Agricultural and Forest Meteorology, 247: 343-355. DOI URL
[13]	NEUBAUER S C, VERHOEVEN J T A, 2019. Wetland effects on global climate: Mechanisms, impacts, and management recommendations[M]//Wetlands: Ecosystem Services,Restorationand Wise Use.Springer, Cham,5: 39-62.
[14]	PUGH C A, REED D E, DESAI A R, et al., 2018. Wetland flux controls: how does interacting water table levels and temperature influence carbon dioxide and methane fluxes in northern Wisconsin?[J]. Biogeochemistry, 137(1-2): 15-25. DOI URL
[15]	RANKIN T, STRACHAN I B, STRACK M, 2018. Carbon dioxide and methane exchange at a post-extraction, unrestored peatland[J]. Ecological Engineering, 122: 241-251. DOI URL
[16]	SHARIFI A, HANTUSH M M, KALIN L, 2016. Modeling Nitrogen and Carbon Dynamics in Wetland Soils and Water Using Mechanistic Wetland Model[J]. Journal of Hydrologic Engineering, 22(1): D4016002.
[17]	STRACHAN I B, NUGENT K A, CROMBIE S, et al., 2015. Carbon dioxide and methane exchange at a cool-temperate freshwater marsh[J]. Environmental Research Letters, 10(6): 065006. DOI URL
[18]	WILSON B J, SERVAIS S, CHARLES S P, et al., 2018. Declines in plant productivity drive carbon loss from brackish coastal wetland mesocosms exposed to saltwater intrusion[J]. Estuaries and Coasts, 41(8): 2147-2158. DOI URL
[19]	ZHANG Q, SUN R, JIANG G Q, et al., 2016. Carbon and energy flux from aPhragmites australis wetland in Zhangye oasis-desert area, China[J]. Agricultural and Forest Meteorology, 230-231: 45-57.
[20]	ZHONG Q C, WANG K Y, LAI Q F, et al., 2016. Carbon dioxide fluxes and their environmental control in a reclaimed coastal wetland in the Yangtze Estuary[J]. Estuaries and Coasts, 39(2): 344-362. DOI URL
[21]	ZHOU L Y, YIN S L, AN S Q, et al., 2015. Spartina alterniflora invasion alters carbon exchange and soil organic carbon in eastern salt marsh of China[J]. Clean-Soil, Air, Water, 43(4): 569-576. DOI URL
[22]	ZHUANG Q L, ZHU X D, HE Y J, et al., 2015. Influence of changes in wetland inundation extent on net fluxes of carbon dioxide and methane in northern high latitudes from 1993 to 2004 [J]. Environmental Research Letters, 10(9): 095009. DOI URL
[23]	韩广轩, 2017. 潮汐作用和干湿交替对盐沼湿地碳交换的影响机制研究进展[J]. 生态学报, 37(24): 1024-1027.
	HAN G X, 2017. Effect of tidal action and drying-wetting cycles on carbon exchange in a salt marsh: Progress and prospects[J]Acta Ecologica Sinica, 37(24): 1024-1027.
[24]	陈小平, 刘廷玺, 王冠丽, 等, 2018. 温度和水分对科尔沁草甸湿地净生态系统碳交换量的影响[J]. 应用生态学报, 29(5): 1523-1534.
	CHEN X P, LIU Y X, WANG G L, et al., 2018. Effects of temperature and moisture on net ecosystem CO₂ exchange over a meadow wetland in the Horqin, China[J]. Chinese Journal of Applied Ecology, 29(5): 1523-1534.
[25]	罗琪, 文军, 王欣, 等, 2017. 黄河源高寒湿地-大气间水热和碳交换通量日变化特征的观测分析[J]. 高原气象, 36(3): 667-674.
	LUO Q, WEN J, WANG X, et al., 2017. Analysis of the Diurnal Characteristics of Water and Heat & CO₂ Exchanges at the Alpine Wetland in the Source Region of the Yellow River[J]. Plateau Meteorology, 36(3): 667-674.

月份 Month	5 cm土壤温度Soil temperature/℃			10 cm土壤温度Soil temperature/℃
月份 Month	拟合方程 Fitting equation	P	R²	拟合方程 Fitting equation	P	R²
1	y=0.015e^0.123^x	>0.05	0.32	y=0.056e^0.145^x	>0.05	0.21
2	y=0.034e^0.321^x	>0.05	0.34	y=0.021e^0.166^x	>0.05	0.25
3	y=0.056e^0.455^x	>0.05	0.41	y=0.059e^0.334^x	>0.05	0.32
4	y=0.027e^0.298^x	<0.05*	0.56	y=0.034e^0.216^x	>0.05	0.35
5	y=0.038e^0.317^x	<0.01**	0.62	y=0.012e^0.312^x	<0.01**	0.51
6	y=0.018e^0.894^x	<0.01**	0.67	y=0.019e^0.511^x	<0.01**	0.55
7	y=0.051e^0.109^x	<0.01**	0.76	y=0.027e^0.235^x	<0.01**	0.62
8	y=0.029e^0.215^x	<0.01**	0.77	y=0.021e^0.623^x	<0.01**	0.66
9	y=0.037e^0.245^x	<0.05*	0.54	y=0.038e^0.557^x	<0.05*	0.43
10	y=0.041e^0.317^x	<0.05*	0.31	y=0.042e^0.109^x	>0.05	0.28
11	y=0.026e^0.645^x	>0.05	0.27	y=0.035e^0.115^x	>0.05	0.15
12	y=0.052e^0.388^x	>0.05	0.23	y=0.029e^0.210^x	>0.05	0.17

月份 Month	5 cm土壤温度Soil temperature/℃			10 cm土壤温度Soil temperature/℃
月份 Month	拟合方程 Fitting equation	P	R²	拟合方程 Fitting equation	P	R²
1	y=0.015e^0.123^x	>0.05	0.32	y=0.056e^0.145^x	>0.05	0.21
2	y=0.034e^0.321^x	>0.05	0.34	y=0.021e^0.166^x	>0.05	0.25
3	y=0.056e^0.455^x	>0.05	0.41	y=0.059e^0.334^x	>0.05	0.32
4	y=0.027e^0.298^x	<0.05*	0.56	y=0.034e^0.216^x	>0.05	0.35
5	y=0.038e^0.317^x	<0.01**	0.62	y=0.012e^0.312^x	<0.01**	0.51
6	y=0.018e^0.894^x	<0.01**	0.67	y=0.019e^0.511^x	<0.01**	0.55
7	y=0.051e^0.109^x	<0.01**	0.76	y=0.027e^0.235^x	<0.01**	0.62
8	y=0.029e^0.215^x	<0.01**	0.77	y=0.021e^0.623^x	<0.01**	0.66
9	y=0.037e^0.245^x	<0.05*	0.54	y=0.038e^0.557^x	<0.05*	0.43
10	y=0.041e^0.317^x	<0.05*	0.31	y=0.042e^0.109^x	>0.05	0.28
11	y=0.026e^0.645^x	>0.05	0.27	y=0.035e^0.115^x	>0.05	0.15
12	y=0.052e^0.388^x	>0.05	0.23	y=0.029e^0.210^x	>0.05	0.17

月份 Month	5 cm土壤湿度 Soil moisture/%			10 cm土壤湿度 Soil moisture/%
月份 Month	拟合方程 Fitting equation	P	R²	拟合方程 Fitting equation	P	R²
1	y=0.023x^0.212+5.212	>0.05	0.32	y=0.025x^0.167+3.134	>0.05	0.21
2	y=0.016x^0.154+6.123	>0.05	0.34	y=0.031x^0.233+3.233	>0.05	0.25
3	y=0.034x^0.267+5.176	>0.05	0.41	y=0.025x^0.345+4.157	>0.05	0.32
4	y=0.028x^0.324+4.245	<0.05	0.56	y=0.019x^0.278+5.098	>0.05	0.35
5	y=0.041x^0.612+7.234	<0.05	0.62	y=0.033x^0.177+6.224	<0.05	0.51
6	y=0.035x^0.245+7.256	<0.01	0.67	y=0.056x^0.356+7.314	<0.01	0.55
7	y=0.027x^0.344+6.218	<0.01	0.76	y=0.062x^0.318+7.567	<0.01	0.62
8	y=0.044x^0.309+4.223	<0.01	0.77	y=0.034x^0.366+5.645	<0.01	0.66
9	y=0.038x^0.278+6.356	<0.05	0.54	y=0.051x^0.226+6.238	<0.05	0.43
10	y=0.056x^0.156+5.546	<0.05	0.31	y=0.066x^0.189+5.423	>0.05	0.28
11	y=0.031x^0.267+4.378	>0.05	0.27	y=0.038x^0.455+3.724	>0.05	0.15
12	y=0.027x^0.198+3.277	>0.05	0.23	y=0.017x^0.377+2.610	>0.05	0.17

月份 Month	5 cm土壤湿度 Soil moisture/%			10 cm土壤湿度 Soil moisture/%
月份 Month	拟合方程 Fitting equation	P	R²	拟合方程 Fitting equation	P	R²
1	y=0.023x^0.212+5.212	>0.05	0.32	y=0.025x^0.167+3.134	>0.05	0.21
2	y=0.016x^0.154+6.123	>0.05	0.34	y=0.031x^0.233+3.233	>0.05	0.25
3	y=0.034x^0.267+5.176	>0.05	0.41	y=0.025x^0.345+4.157	>0.05	0.32
4	y=0.028x^0.324+4.245	<0.05	0.56	y=0.019x^0.278+5.098	>0.05	0.35
5	y=0.041x^0.612+7.234	<0.05	0.62	y=0.033x^0.177+6.224	<0.05	0.51
6	y=0.035x^0.245+7.256	<0.01	0.67	y=0.056x^0.356+7.314	<0.01	0.55
7	y=0.027x^0.344+6.218	<0.01	0.76	y=0.062x^0.318+7.567	<0.01	0.62
8	y=0.044x^0.309+4.223	<0.01	0.77	y=0.034x^0.366+5.645	<0.01	0.66
9	y=0.038x^0.278+6.356	<0.05	0.54	y=0.051x^0.226+6.238	<0.05	0.43
10	y=0.056x^0.156+5.546	<0.05	0.31	y=0.066x^0.189+5.423	>0.05	0.28
11	y=0.031x^0.267+4.378	>0.05	0.27	y=0.038x^0.455+3.724	>0.05	0.15
12	y=0.027x^0.198+3.277	>0.05	0.23	y=0.017x^0.377+2.610	>0.05	0.17

黄河三角洲芦苇湿地生态系统碳通量动态特征及其影响因素

Dynamic Characteristics and Influencing Factors of Carbon and Water Flux in Reed Wetland Ecosystem in the Yellow River Delta

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 25

相关文章 6

编辑推荐

Metrics

本文评价

[1]	刘紫薇, 葛继稳, 王月环, 杨诗雨, 姚东, 谢金林. 大九湖泥炭湿地甲烷通量变异特征及影响因素[J]. 生态环境学报, 2023, 32(4): 706-714.
[2]	王洁, 单燕, 马兰, 宋延静, 王向誉. 秸秆/生物质炭协同还田措施对黄河三角洲盐碱土壤的改良效果研究[J]. 生态环境学报, 2023, 32(1): 90-98.
[3]	张楷悦, 刘增辉, 王颜昊, 王敬宽, 崔德杰, 柳新伟. 黄河三角洲自然保护区土壤PAHs的风险评估和空间特征[J]. 生态环境学报, 2022, 31(11): 2198-2205.
[4]	王瑞, 宋祥云, 柳新伟. 黄河三角洲不同植被类型土壤酶活性的季节变化[J]. 生态环境学报, 2022, 31(1): 62-69.
[5]	王浩, 陈永金, 刘加珍, 万波, 张丽. 黄河三角洲新生湿地3种柽柳灌丛对土壤有机碳空间分布的影响研究[J]. 生态环境学报, 2022, 31(1): 9-16.
[6]	闫振宁, 梅宝玲, 张桂萍, 韩广轩, 谢宝华, 张树岩, 周英锋, 刘展航. 高程对盐沼湿地互花米草生长与扩散的影响[J]. 生态环境学报, 2021, 30(6): 1183-1191.