1999-2008年间中国森林土壤碳汇功能初步估算

doi:10.5814/j.issn.1674-764x.2022.01.002

摘要/Abstract

摘要：

基于样地资料、文献资料和森林资源清查资料,以及不同森林类型蓄积、生物量、年凋落量和土壤呼吸之间的函数关系,估算1999-2008年间中国森林生态系统的NEP(净生态系统生产力)、△Cbiomass(现存森林植被碳储量增量)和NR(非呼吸代谢消耗光合产物),再根据森林生态系统碳平衡方程,初步估算中国森林土壤碳汇(△Csoil = NEP-△Cbiomass-NR)。研究结果表明：中国森林生态系统总的NEP、△Cbiomass、NR和△Csoil分别为157.530、48.704、31.033和77.793 Tg C yr^?1,单位面积NEP、△Cbiomass、NR和△Csoil分别为101.247、31.303、19.945和49.999 g C m^?2 yr^?1。中国森林土壤碳汇存在较大的空间差异,江西、湖南、浙江、福建、安徽、山西、陕西、广西和辽宁9省(区)森林土壤为碳源,释放的碳约为25.507 Tg C yr^-1。其他22个省(区)森林土壤为碳汇,吸收的碳约为103.300 Tg C yr^-1。本研究建立了基于森林资源清查资料的中国森林土壤碳汇评价方法,是对现有的基于统计资料进行森林生态系统碳循环研究的有益补充,将推动具有可比性的、按照一致性的研究方法开展的区域尺度森林土壤固碳功能研究。

关键词: 碳平衡, 森林生态系统, 森林资源清查, 土壤碳汇

Abstract:

The National Forest Inventory (NFI) is an important resource for estimating the national carbon balance (These data were unpublished data, and we could only obtain the data before 2008 through data search by now). Based on the data from sample plots, the literature, and NFI, as well as the relationships between volume, biomass, annual litterfall and soil respiration of different forest types, the net ecosystem production (NEP), changes in forest biomass carbon storage (△Cbiomass) and non-respiratory losses (NR) of China’s forests during 1999-2008 were estimated, and the forest soil carbon sequestration (△Csoil) was assessed according to the carbon balance principle of the forest ecosystem (△Csoil = NEP - NR - △Cbiomass). The results showed that the total NEP, △Cbiomass, NR and △Csoil values for China’s forests were 157.530, 48.704, 31.033 and 77.793 Tg C yr^-1 respectively, and average NEP, △Cbiomass, NR, and △Csoil values were 101.247, 31.303, 19.945 and 49.999 g C m^-2yr^-1 respectively. There were large spatial differences in forest soil carbon sequestration in different parts of China. The forest soil in Jiangxi, Hunan, Zhejiang, Fujian, Anhui, Shanxi, Shaanxi, Guangxi and Liaoning served as carbon sources and the carbon released was about 25.507 Tg C yr^-1. The other 22 provinces served as carbon sinks and the average carbon sequestration by forest soil came to 103.300 Tg C yr^-1. This research established a method for evaluating soil carbon sequestration by China’s forests based on the NFI, which is a useful supplement to current statistical data-based studies on the forest ecosystem carbon cycle, and can promote comparable studies on forest soil carbon sequestration with consistent research methods at the regional scale.

Key words: carbon balance, forest ecosystem, national forest inventory, soil carbon sequestration

王斌, 刘某承, 周志春. 1999-2008年间中国森林土壤碳汇功能初步估算[J]. 资源与生态学报, 2022, 13(1): 17-26.

WANG Bin, LIU Moucheng, ZHOU Zhichun. Preliminary Estimation of Soil Carbon Sequestration of China’s Forests during 1999-2008[J]. Journal of Resources and Ecology, 2022, 13(1): 17-26.

图/表 4

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Forest types	N	Biomass	r_Biomass	ABI	r_ABI	n	Annual litterfall	r_AL
Cupressus funebris, Keteleeria fortunei	10	B=V/(1.0202+0.0022V)	0.9605^a	P=B/(0.1132A+0.0745B)	0.9018^a	10	L=B/(9.8381+0.1337B)	0.7508^b
Larix	39	B=V/(1.1111+0.0016V)	0.9571^a	P=B/(0.1885A +0.0728B)	0.7980^a	39	L=B/(16.734+0.0577B)	0.9267^a
Pinus armandii, Pinus densata and other mountain pines	43	B=V/(1.2390+0.0013V)	0.9546^a	P=B/(0.3840A +0.0104B)	0.9475^a	43	L=B/(7.5272+0.1102B)	0.7469^a
Pinus massoniana	46	B=V/(1.4254+0.0004V)	0.9587^a	P=B/(0.4046A +0.0098B)	0.9674^a	46	L=B/(15.451+0.0225B)	0.9319^a
Pinus yunnanensis, Pinus khasya	41	B=V/(1.3624-0.0003V)	0.9951^a	P=B/(0.2423A +0.0581B)	0.9475^a	41	L=B/(18.905+0.0422B)	0.9847^a
Pinus tabulaeformis, Platycladus orientalis	147	B=V/(1.0529+0.0020V)	0.9679^a	P=B/(0.3520A+0.0161B)	0.9760^a	147	L=B/(11.177+0.1501B)	0.8689^a
Pinus sylvestris var. mongolica	7	B=V/(1.2544+0.0030V)	0.9129^b	P=B/(0.1405A+0.1203B)	0.9740^a	7	L=4.20±0.3538
Cunninghamia lanceolata	70	B=V/(1.2917+0.0022V)	0.9541^a	P=B/(0.4598A+0.0069B)	0.9691^a	48	L=B/(10.132+0.0874B)	0.7783^a
Cunninghamia lanceolata	70	B=V/(1.2917+0.0022V)	0.9541^a	P=B/(0.4598A+0.0069B)	0.9691^a	22	L=B/(8.7239+0.0418B)^d	0.9618^a
Picea, Abies, Tsuga	154	B=V/(1.3667+0.0012V)	0.9228^a	P=B/(0.2267A+0.0526B)	0.8482^a	35	L=B/(27.204+0.0812B)	0.9580^a
Picea, Abies, Tsuga	154	B=V/(1.3667+0.0012V)	0.9228^a	P=B/(0.2267A+0.0526B)	0.8482^a	119	L=3.34±0.9277^e
Temperate mixed coniferous-broadleaf forest	13	B=V/(1.1731+0.0018V)	0.9686^a	P=B/(0.1038A+0.0761B)	0.9087^a	13	L=3.46±0.9597
Temperate typical deciduous broadleaf forest	59	B=V/(0.6539+0.0038V)	0.9335^a	P=B/(0.2393A+0.0495B)	0.9565^a	59	L=B/(18.246+0.0366B)	0.8627^a
Subtropical evergreen broadleaf forest	222	B=V/(0.7883+0.0026V)	0.8567^a	P=B/(0.2503A+0.0226B)	0.8885^a	222	L=B/(20.507+0.0383B)	0.9104^a
Subtropical mixed evergreen-deciduous broadleaf forest	13	B=V/(0.5788+0.0020V)	0.9201^a	P=B/(0.3018A+0.0331B)	0.8219^a	13	L=B/(9.1028+0.0575B)	0.8746^a
Sclerophyllous evergreen Quercus forest	8	B=V/(0.7823+0.0014V)	0.9111^b	P=B/(0.2989A+0.0117B)	0.9469^a	8	L=B/(34.845+0.0283B)	0.9003^b
Betula and Populus	119	B=V/(0.8115+0.0019V)	0.9501^a	P=B/(0.3080A+0.0138B)	0.9429^a	119	L=B/(16.722+0.0324B)	0.9236^a
Tropical rain forest and monsoon forest	8	B=V/(0.6809+0.0006V)	0.9972^a	P=B/(0.1797A+0.0344B)	0.6499^c	8	L=B/(8.0976+0.0540B)	0.8118^b

Forest types	N	Biomass	r_Biomass	ABI	r_ABI	n	Annual litterfall	r_AL
Cupressus funebris, Keteleeria fortunei	10	B=V/(1.0202+0.0022V)	0.9605^a	P=B/(0.1132A+0.0745B)	0.9018^a	10	L=B/(9.8381+0.1337B)	0.7508^b
Larix	39	B=V/(1.1111+0.0016V)	0.9571^a	P=B/(0.1885A +0.0728B)	0.7980^a	39	L=B/(16.734+0.0577B)	0.9267^a
Pinus armandii, Pinus densata and other mountain pines	43	B=V/(1.2390+0.0013V)	0.9546^a	P=B/(0.3840A +0.0104B)	0.9475^a	43	L=B/(7.5272+0.1102B)	0.7469^a
Pinus massoniana	46	B=V/(1.4254+0.0004V)	0.9587^a	P=B/(0.4046A +0.0098B)	0.9674^a	46	L=B/(15.451+0.0225B)	0.9319^a
Pinus yunnanensis, Pinus khasya	41	B=V/(1.3624-0.0003V)	0.9951^a	P=B/(0.2423A +0.0581B)	0.9475^a	41	L=B/(18.905+0.0422B)	0.9847^a
Pinus tabulaeformis, Platycladus orientalis	147	B=V/(1.0529+0.0020V)	0.9679^a	P=B/(0.3520A+0.0161B)	0.9760^a	147	L=B/(11.177+0.1501B)	0.8689^a
Pinus sylvestris var. mongolica	7	B=V/(1.2544+0.0030V)	0.9129^b	P=B/(0.1405A+0.1203B)	0.9740^a	7	L=4.20±0.3538
Cunninghamia lanceolata	70	B=V/(1.2917+0.0022V)	0.9541^a	P=B/(0.4598A+0.0069B)	0.9691^a	48	L=B/(10.132+0.0874B)	0.7783^a
Cunninghamia lanceolata	70	B=V/(1.2917+0.0022V)	0.9541^a	P=B/(0.4598A+0.0069B)	0.9691^a	22	L=B/(8.7239+0.0418B)^d	0.9618^a
Picea, Abies, Tsuga	154	B=V/(1.3667+0.0012V)	0.9228^a	P=B/(0.2267A+0.0526B)	0.8482^a	35	L=B/(27.204+0.0812B)	0.9580^a
Picea, Abies, Tsuga	154	B=V/(1.3667+0.0012V)	0.9228^a	P=B/(0.2267A+0.0526B)	0.8482^a	119	L=3.34±0.9277^e
Temperate mixed coniferous-broadleaf forest	13	B=V/(1.1731+0.0018V)	0.9686^a	P=B/(0.1038A+0.0761B)	0.9087^a	13	L=3.46±0.9597
Temperate typical deciduous broadleaf forest	59	B=V/(0.6539+0.0038V)	0.9335^a	P=B/(0.2393A+0.0495B)	0.9565^a	59	L=B/(18.246+0.0366B)	0.8627^a
Subtropical evergreen broadleaf forest	222	B=V/(0.7883+0.0026V)	0.8567^a	P=B/(0.2503A+0.0226B)	0.8885^a	222	L=B/(20.507+0.0383B)	0.9104^a
Subtropical mixed evergreen-deciduous broadleaf forest	13	B=V/(0.5788+0.0020V)	0.9201^a	P=B/(0.3018A+0.0331B)	0.8219^a	13	L=B/(9.1028+0.0575B)	0.8746^a
Sclerophyllous evergreen Quercus forest	8	B=V/(0.7823+0.0014V)	0.9111^b	P=B/(0.2989A+0.0117B)	0.9469^a	8	L=B/(34.845+0.0283B)	0.9003^b
Betula and Populus	119	B=V/(0.8115+0.0019V)	0.9501^a	P=B/(0.3080A+0.0138B)	0.9429^a	119	L=B/(16.722+0.0324B)	0.9236^a
Tropical rain forest and monsoon forest	8	B=V/(0.6809+0.0006V)	0.9972^a	P=B/(0.1797A+0.0344B)	0.6499^c	8	L=B/(8.0976+0.0540B)	0.8118^b

Province	Area (10⁶ ha)		Carbon density (10⁶ g C ha^-1 yr^-1)			Biomass/ volume (10⁶ g m^-3)	Harvest volume (10⁶ m³ yr^-1)	Emission by fire^a(10⁶ g C ha^-1)	Fire area (10³ha yr^-1)	Total (10¹²g C yr^-1)
Province	6th NFI	7th NFI	6th NFI	7th NFI	Change	Biomass/ volume (10⁶ g m^-3)	Harvest volume (10⁶ m³ yr^-1)	Emission by fire^a(10⁶ g C ha^-1)	Fire area (10³ha yr^-1)	△Cbiomass	Harvest	Fire
Anhui	2.455	2.708	18.245	21.918	0.735	0.863	3.640	3.190	0.649	1.990	1.571	0.002
Beijing	0.234	0.356	19.378	16.313	-0.613	1.117	0.053	5.650	0.022	-0.218	0.030	0.000
Chongqing	1.532	1.820	21.959	23.220	0.252	0.746	0.102	13.420	0.242	0.459	0.038	0.003
Fujian	5.639	5.661	30.470	36.314	1.169	0.849	6.661	5.140	5.684	6.617	2.827	0.029
Gansu	1.921	2.134	39.801	40.250	0.090	0.887	0.047	13.550	0.004	0.192	0.021	0.000
Guangdong	6.606	6.788	19.529	20.017	0.097	0.900	4.108	2.350	1.584	0.662	1.849	0.004
Guangxi	7.475	8.067	20.893	26.843	1.190	0.924	7.072	3.290	2.501	9.599	3.267	0.008
Guizhou	3.443	3.981	21.609	24.949	0.668	0.827	1.070	7.680	2.431	2.659	0.443	0.019
Hainan	0.892	0.842	46.218	49.002	0.557	1.134	0.754	11.410	0.228	0.469	0.428	0.003
Hebei	2.065	2.882	16.671	16.247	-0.085	1.118	0.523	6.900	0.178	-0.244	0.292	0.001
Henan	1.977	2.834	24.415	25.555	0.228	1.120	1.123	6.330	0.553	0.646	0.628	0.003
Heilongjiang	17.922	19.126	37.906	40.818	0.582	1.027	7.057	13.930	84.107	11.138	3.622	1.172
Hubei	4.160	5.078	17.083	18.526	0.288	0.898	1.783	2.540	1.399	1.465	0.801	0.004
Hunan	6.091	7.265	17.208	19.157	0.390	0.797	6.242	2.790	11.912	2.833	2.489	0.033
Jilin	7.116	7.267	53.761	55.640	0.376	0.958	4.213	18.580	0.110	2.732	2.018	0.002
Jiangsu	0.444	0.744	25.416	23.909	-0.301	1.017	0.764	2.490	0.118	-0.224	0.388	0.000
Jiangxi	7.278	7.681	17.864	23.331	1.094	0.907	5.094	2.890	5.480	8.400	2.310	0.016
Liaoning	3.226	3.613	28.322	30.026	0.341	1.073	1.782	8.030	0.201	1.232	0.956	0.002
Inner Mongolia	16.082	16.813	32.173	32.734	0.112	0.935	4.103	11.060	14.765	1.884	1.918	0.163
Ningxia	0.092	0.111	21.664	22.831	0.233	1.029	0.002	10.260	0.002	0.026	0.001	0.000
Qinghai	0.342	0.355	40.628	42.473	0.369	0.770	0.019	16.550	0.073	0.131	0.007	0.001
Shandong	0.830	1.561	20.544	22.208	0.333	1.094	1.179	2.780	0.090	0.520	0.645	0.000
Shanxi	1.605	1.724	19.415	23.690	0.855	1.069	0.072	7.740	0.898	1.474	0.039	0.007
Shaanxi	5.086	5.670	30.513	30.607	0.019	1.026	0.293	11.080	0.132	0.107	0.150	0.001
Shanghai	0.006	0.034	22.009	12.193	-1.963	0.821	0.003	0.000	0.000	-0.067	0.001	0.000
Sichuan	11.036	11.653	47.540	46.563	-0.195	0.680	1.248	13.420	0.752	-2.276	0.424	0.010
Tianjing	0.046	0.055	17.579	20.040	0.492	1.100	0.019	4.150	0.008	0.027	0.011	0.000
Tibet	8.445	8.411	93.150	90.451	-0.540	0.678	0.205	17.300	0.062	-4.541	0.069	0.001
Xinjiang	1.562	1.692	61.874	61.526	-0.070	0.692	0.408	16.010	0.089	-0.118	0.141	0.001
Yunnan	13.566	14.727	42.958	41.827	-0.226	0.793	3.208	14.820	1.841	-3.332	1.272	0.027
Zhejiang	3.615	3.936	12.387	18.060	1.135	0.825	2.062	2.330	5.092	4.466	0.851	0.012
Total/Average	142.787	155.59	35.425	36.301	0.175	0.845	64.909	8.312	141.204	48.704	29.507	1.526

Province	Area (10⁶ ha)		Carbon density (10⁶ g C ha^-1 yr^-1)			Biomass/ volume (10⁶ g m^-3)	Harvest volume (10⁶ m³ yr^-1)	Emission by fire^a(10⁶ g C ha^-1)	Fire area (10³ha yr^-1)	Total (10¹²g C yr^-1)
Province	6th NFI	7th NFI	6th NFI	7th NFI	Change	Biomass/ volume (10⁶ g m^-3)	Harvest volume (10⁶ m³ yr^-1)	Emission by fire^a(10⁶ g C ha^-1)	Fire area (10³ha yr^-1)	△Cbiomass	Harvest	Fire
Anhui	2.455	2.708	18.245	21.918	0.735	0.863	3.640	3.190	0.649	1.990	1.571	0.002
Beijing	0.234	0.356	19.378	16.313	-0.613	1.117	0.053	5.650	0.022	-0.218	0.030	0.000
Chongqing	1.532	1.820	21.959	23.220	0.252	0.746	0.102	13.420	0.242	0.459	0.038	0.003
Fujian	5.639	5.661	30.470	36.314	1.169	0.849	6.661	5.140	5.684	6.617	2.827	0.029
Gansu	1.921	2.134	39.801	40.250	0.090	0.887	0.047	13.550	0.004	0.192	0.021	0.000
Guangdong	6.606	6.788	19.529	20.017	0.097	0.900	4.108	2.350	1.584	0.662	1.849	0.004
Guangxi	7.475	8.067	20.893	26.843	1.190	0.924	7.072	3.290	2.501	9.599	3.267	0.008
Guizhou	3.443	3.981	21.609	24.949	0.668	0.827	1.070	7.680	2.431	2.659	0.443	0.019
Hainan	0.892	0.842	46.218	49.002	0.557	1.134	0.754	11.410	0.228	0.469	0.428	0.003
Hebei	2.065	2.882	16.671	16.247	-0.085	1.118	0.523	6.900	0.178	-0.244	0.292	0.001
Henan	1.977	2.834	24.415	25.555	0.228	1.120	1.123	6.330	0.553	0.646	0.628	0.003
Heilongjiang	17.922	19.126	37.906	40.818	0.582	1.027	7.057	13.930	84.107	11.138	3.622	1.172
Hubei	4.160	5.078	17.083	18.526	0.288	0.898	1.783	2.540	1.399	1.465	0.801	0.004
Hunan	6.091	7.265	17.208	19.157	0.390	0.797	6.242	2.790	11.912	2.833	2.489	0.033
Jilin	7.116	7.267	53.761	55.640	0.376	0.958	4.213	18.580	0.110	2.732	2.018	0.002
Jiangsu	0.444	0.744	25.416	23.909	-0.301	1.017	0.764	2.490	0.118	-0.224	0.388	0.000
Jiangxi	7.278	7.681	17.864	23.331	1.094	0.907	5.094	2.890	5.480	8.400	2.310	0.016
Liaoning	3.226	3.613	28.322	30.026	0.341	1.073	1.782	8.030	0.201	1.232	0.956	0.002
Inner Mongolia	16.082	16.813	32.173	32.734	0.112	0.935	4.103	11.060	14.765	1.884	1.918	0.163
Ningxia	0.092	0.111	21.664	22.831	0.233	1.029	0.002	10.260	0.002	0.026	0.001	0.000
Qinghai	0.342	0.355	40.628	42.473	0.369	0.770	0.019	16.550	0.073	0.131	0.007	0.001
Shandong	0.830	1.561	20.544	22.208	0.333	1.094	1.179	2.780	0.090	0.520	0.645	0.000
Shanxi	1.605	1.724	19.415	23.690	0.855	1.069	0.072	7.740	0.898	1.474	0.039	0.007
Shaanxi	5.086	5.670	30.513	30.607	0.019	1.026	0.293	11.080	0.132	0.107	0.150	0.001
Shanghai	0.006	0.034	22.009	12.193	-1.963	0.821	0.003	0.000	0.000	-0.067	0.001	0.000
Sichuan	11.036	11.653	47.540	46.563	-0.195	0.680	1.248	13.420	0.752	-2.276	0.424	0.010
Tianjing	0.046	0.055	17.579	20.040	0.492	1.100	0.019	4.150	0.008	0.027	0.011	0.000
Tibet	8.445	8.411	93.150	90.451	-0.540	0.678	0.205	17.300	0.062	-4.541	0.069	0.001
Xinjiang	1.562	1.692	61.874	61.526	-0.070	0.692	0.408	16.010	0.089	-0.118	0.141	0.001
Yunnan	13.566	14.727	42.958	41.827	-0.226	0.793	3.208	14.820	1.841	-3.332	1.272	0.027
Zhejiang	3.615	3.936	12.387	18.060	1.135	0.825	2.062	2.330	5.092	4.466	0.851	0.012
Total/Average	142.787	155.59	35.425	36.301	0.175	0.845	64.909	8.312	141.204	48.704	29.507	1.526

Province	Area (10⁶ ha)	Total (10¹²g C yr^-1)							Average (g C m^-2 yr^-1)
Province	7th NFI	NPP	R_h	NEP	NR	NBP	△Cbiomas	△Csoil	NEP	NR	NBP	△Csoil
Anhui	2.708	10.256	8.806	1.450	1.573	-0.123	1.990	-2.113	53.533	58.071	-4.538	-78.005
Beijing	0.356	0.966	0.878	0.088	0.030	0.058	-0.218	0.276	24.680	8.407	16.273	77.573
Chongqing	1.820	6.572	5.905	0.667	0.041	0.626	0.459	0.167	36.628	2.260	34.368	9.156
Fujian	5.661	29.020	22.789	6.232	2.856	3.376	6.617	-3.241	110.090	50.456	59.634	-57.262
Gansu	2.134	9.330	8.072	1.259	0.021	1.237	0.192	1.046	58.968	0.990	57.978	48.993
Guangdong	6.788	36.642	21.679	14.963	1.853	13.111	0.662	12.449	220.447	27.298	193.149	183.402
Guangxi	8.067	41.723	29.478	12.244	3.275	8.970	9.599	-0.629	151.791	40.598	111.193	-7.803
Guizhou	3.981	17.276	13.728	3.548	0.461	3.087	2.659	0.428	89.138	11.587	77.552	10.758
Hainan	0.842	6.694	4.166	2.528	0.430	2.098	0.469	1.629	300.362	51.121	249.241	193.559
Hebei	2.882	7.979	7.193	0.787	0.294	0.493	-0.244	0.737	27.292	10.189	17.103	25.582
Henan	2.834	13.099	9.175	3.924	0.632	3.292	0.646	2.646	138.482	22.299	116.182	93.391
Heilongjiang	19.126	101.792	80.709	21.083	4.794	16.290	11.138	5.151	110.233	25.064	85.169	26.934
Hubei	5.078	21.931	15.700	6.230	0.804	5.426	1.465	3.962	122.699	15.838	106.861	78.017
Hunan	7.265	22.875	22.680	0.196	2.522	-2.326	2.833	-5.159	2.693	34.713	-32.020	-71.009
Jilin	7.267	44.561	35.295	9.266	2.020	7.246	2.732	4.514	127.504	27.800	99.704	62.117
Jiangsu	0.744	3.817	2.376	1.441	0.389	1.052	-0.224	1.277	193.567	52.220	141.346	171.493
Jiangxi	7.681	28.871	25.800	3.071	2.325	0.746	8.400	-7.654	39.983	30.273	9.710	-99.642
Liaoning	3.613	14.450	12.568	1.882	0.958	0.924	1.232	-0.307	52.074	26.501	25.573	-8.510
Inner Mongolia	16.813	69.169	59.223	9.946	2.081	7.864	1.884	5.981	59.157	12.380	46.777	35.573
Ningxia	0.111	0.430	0.322	0.108	0.001	0.107	0.026	0.081	97.600	0.965	96.635	73.286
Qinghai	0.355	1.645	1.321	0.324	0.009	0.315	0.131	0.184	91.209	2.430	88.779	51.881
Shandong	1.561	8.380	4.736	3.644	0.645	2.998	0.520	2.479	233.383	41.340	192.043	158.760
Shanxi	1.724	5.293	5.168	0.126	0.046	0.080	1.474	-1.394	7.283	2.648	4.634	-80.870
Shaanxi	5.670	18.534	18.994	-0.460	0.152	-0.612	0.107	-0.719	-8.113	2.677	-10.790	-12.672
Shanghai	0.034	0.097	0.084	0.013	0.001	0.012	-0.067	0.079	39.426	3.044	36.382	232.689
Sichuan	11.653	53.595	44.134	9.461	0.434	9.027	-2.276	11.303	81.193	3.728	77.465	96.999
Tianjing	0.055	0.256	0.152	0.104	0.011	0.094	0.027	0.067	191.215	19.566	171.650	122.423
Tibet	8.411	56.276	43.329	12.947	0.070	12.877	-4.541	17.417	153.925	0.837	153.088	207.069
Xinjiang	1.692	8.739	7.117	1.622	0.143	1.480	-0.118	1.597	95.851	8.429	87.422	94.380
Yunnan	14.727	85.799	58.003	27.796	1.299	26.497	-3.332	29.829	188.741	8.820	179.921	202.547
Zhejiang	3.936	13.144	12.104	1.039	0.863	0.177	4.466	-4.290	26.408	21.924	4.484	-108.988
Total/Average	155.590	739.213	581.683	157.530	31.033	126.497	48.704	77.793	101.247	19.945	81.301	49.999