Evaluation of the carbon sequestration of Zhalong Wetland under climate change

doi:10.1007/s11442-021-1879-z

Abstract

Abstract:

Wetland ecosystems are crucial to the global carbon cycle. In this study, the Zhalong Wetland was investigated. Based on remote sensing and meteorological observation data from 1975-2018 and the downscaled fifth phase of the coupled model intercomparison project (CMIP5) climate projection dataset from 1961-2100, the parameters of a net primary productivity (NPP) climatic potential productivity model were adjusted, and the simulation ability of the CMIP5 coupled models was evaluated. On this basis, we analysed the spatial and temporal variations of land cover types and landscape transformation processes in the Zhalong Nature Reserve over the past 44 years. We also evaluated the influence of climate change on the NPP of the vegetation, microbial heterotrophic respiration (Rh), and net ecosystem productivity (NEP) of the Zhalong Wetland and predicted the carbon sequestration potential of the Zhalong Wetland from 2019-2029 under the representative concentration pathways (RCP) 4.5 and RCP 8.5 scenarios. Our results indicate the following: (1) Herbaceous bog was the primary land cover type of the Zhalong Nature Reserve, occupying an average area of 1168.02 ± 224.05 km ², equivalent to 51.84% of the total reserve area. (2) Since 1975, the Zhalong Nature Reserve has undergone a dry-wet-dry transformation process. Excluding several wet periods during the mid-1980s to early 1990s, the reserve has remained a dry habitat, with particularly severe conditions from 2000 onwards. (3) The 1975-2018 mean NPP, Rh, and NEP values of the Zhalong Wetland were 500.21±52.76, 337.59±10.80, and 162.62±45.56 gC·m^-2·a^-1, respectively, and an evaluation of the carbon balance indicated that the reserve served as a carbon sink. (4) From 1975-2018, NPP showed a significant linear increase, Rh showed a highly significant linear increase, while the increase in the carbon absorption rate was smaller than the increase in the carbon release rate. (5) Variations in NPP and NEP were precipitation-driven, with the correlations of NPP and NEP with annual precipitation and summer precipitation being highly significantly positive (P < 0.001); variations in Rh were temperature-driven, with the correlations of Rh with the average annual, summer, and autumn temperatures being highly significantly positive (P < 0.001). The interaction of precipitation and temperature enhances the impact on NPP, Rh and NEP. (6) Under the RCP 4.5 and RCP 8.5 scenarios, the predicted carbon sequestration by the Zhalong Wetland from 2019-2029 was 2.421 (± 0.225) × 10¹¹ gC·a^-1 and 2.407 (± 0.382) × 10¹¹ gC·a^-1, respectively, which were both lower than the mean carbon sequestration during the last 44 years (2.467 (± 0.950) × 10¹¹ gC·a^-1). Future climate change may negatively contribute to the carbon sequestration potential of the Zhalong Wetland. The results of the present study are significant for enhancing the abilities of integrated eco-meteorological monitoring, evaluation, and early warning systems for wetlands.

Key words: net ecosystem productivity, CMIP5, Zhalong Wetland

YU Chenglong, LIU Dan, ZHAO Huiying. Evaluation of the carbon sequestration of Zhalong Wetland under climate change[J].Journal of Geographical Sciences, 2021, 31(7): 938-964.

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Time	Classification accuracy (%)	Kappa coefficient	Time	Classification accuracy (%)	Kappa coefficient
1975	90.44	0.8756	1997	90.80	0.8916
1976	90.46	0.8778	1998	90.10	0.8711
1977	90.48	0.8824	1999	91.02	0.8985
1978	90.83	0.8920	2000	92.15	0.9153
1979	91.15	0.9022	2001	91.46	0.9074
1980	91.61	0.9125	2002	90.98	0.8968
1981	91.65	0.9131	2003	92.19	0.9156
1982	90.36	0.8711	2004	91.01	0.8981
1983	90.43	0.8735	2005	91.25	0.9051
1984	90.47	0.8801	2006	90.55	0.8841
1985	90.85	0.8929	2007	90.46	0.8771
1986	90.86	0.8959	2008	91.57	0.9086
1987	90.88	0.8965	2009	92.41	0.9178
1988	91.10	0.9005	2010	90.01	0.8701
1989	91.21	0.9043	2011	91.48	0.9076
1990	91.45	0.9063	2012	92.26	0.9171
1991	91.00	0.8974	2013	92.35	0.9174
1992	91.04	0.8985	2014	91.57	0.9086
1993	91.17	0.9025	2015	91.99	0.9142
1994	91.19	0.9036	2016	92.22	0.9162
1995	91.23	0.9046	2017	91.56	0.9082
1996	91.44	0.9057	2018	90.80	0.8916

Meteorological element	Emission scenarios	Pattern	Distance from model point to observation point	Meteorological element	Emission scenarios	Pattern	Distance from model point to observation point
Maximum temperature	RCP4.5	CESM1-BGC	1.06	Minimum temperature	RCP4.5	GFDL-ESM2G	1.13
		CMCC-CM	1.07			GFDL-ESM2M	1.13
		GISS-E2-H	1.04			MIROC5	1.10
		GISS-E2-H-CC	1.03			NorESM1-M	1.10
		GISS-E2-R	1.03			Multimodel set	0.96
		GFDL-ESM2M	1.07		RCP8.5	CanESM2	1.12
		MIROC5	1.03			GFDL-ESM2G	1.11
		Multimodel set	0.79			NorESM1-M	1.13
	RCP8.5	CESM1-BGC	1.12			Multimodel set	0.97
		FIO-ESM	1.12	Precipitation	RCP4.5	CMCC-CM	141.98
		GISS-E2-H	1.11			EC-EARTH	141.53
		GISS-E2-H-CC	1.09			GFDL-ESM2G	141.72
		GISS-E2-R	1.08			Multimodel set	117.88
		INM-CM4	1.13		RCP8.5	CESM1-BGC	140.94
		Multimodel set	0.86			GFDL-ESM2G	137.90
						MPI-ESM-LR	140.68
						Multimodel set	116.40

NPP acquisition method	R	Sig.	Independent-sample t-test
CASA model	0.229	0.049	Sig.=0.969
TEC model	-0.134	0.329	Sig.=0.002
Beijing model	0.049	0.722	Sig.<0.001
Chikugo model	0.055	0.692	Sig.<0.001
Thornthwaite Memorial model	0.135	0.327	Sig.<0.001
Miami model	0.134	0.329	Sig.<0.001
Zhou Guangsheng-Zhang Xinshi model	0.117	0.396	Sig.=0.077

Meteorological factors	Season	Mean value	Maximal value	Minimum value	Standard deviation	Propensity of change Air temperature (℃/10a), Precipitation (mm/10a)	P-value
Temperature (℃)	Spring	5.88	3.40	8.54	1.30	0.33	0.031
	Summer	21.67	19.65	23.49	0.85	0.30	0.002
	Autumn	4.36	2.13	6.43	1.13	0.35	0.008
	Winter	-16.27	-20.10	-11.53	1.81	0.25	0.246
Precipitation (mm)	Spring	57.31	16.47	135.56	29.28	3.80	0.279
	Summer	317.10	159.56	526.33	85.01	7.89	0.441
	Autumn	69.01	20.40	194.45	35.73	2.86	0.506
	Winter	8.27	2.64	25.61	4.69	1.72	0.001

Items	NPP	Rh	NEP
Mean value (gC·m^-2·a^-1)	500.21	337.59	162.62
Standard deviation (gC·m^-2·a^-1)	52.76	10.80	45.56
Linear regression correlation coefficient	0.319	0.650	0.155
Normalised tendency rate (gC·m^-2·10a^-1)	0.06	0.14	0.003
Sample number	44	44	44
Significance	0.034	<0.001	0.314