气候变暖背景下黄土高原≥0 ℃和≥10 ℃积温时空变化

doi:10.13866/j.azr.2025.06.03

摘要/Abstract

摘要：

黄土高原是中国气候变化敏感区，研究其积温时空变化特征有利于全面了解气候变暖背景下黄土高原温度资源状况。基于1960—2019年黄土高原55个气象站点逐日平均气温数据，利用线性拟合、突变检验及优势分析等研究方法，分析了黄土高原≥0 ℃、≥10 ℃积温初日、终日、持续日数和活动积温的时空变化特征。结果显示：（1） 1960—2019年黄土高原≥0 ℃和≥10 ℃积温各指标变化具有同步性，均表现出初日提前、终日推迟、持续日数延长，活动积温增加趋势（P<0.01），多数指标在1990s后期至2000s前期发生转变；两类积温各指标平均值的空间分布具有一致性，均表现出初日（终日/持续日数/活动积温）自西北向东南逐渐提前（推迟/延长/增加），各指标的变化趋势空间差异明显。（2）黄土高原≥0 ℃积温初日、终日和持续日数变化主要受纬度和海拔的共同影响，其他积温各指标变化主要受海拔的影响；≥0 ℃和≥10 ℃积温初日变化对持续日数变化的贡献率分别为65.1%、68.4%。（3） 1990—2019年与1960—1989年相比，≥0 ℃和≥10 ℃积温多数指标的趋势变化保持不变，初日变化对持续日数变化的贡献率分别下降2.3%、增加15.2%，分别表现为自南向西呈“偏高-偏低-偏高”相间分布和东南偏高、西北偏低。黄土高原≥0 ℃和≥10 ℃积温初日、终日、持续日数及活动积温对气候变暖响应显著，其变化特征具有明显的区域性和阶段性。

关键词: 活动积温, 优势分析法, 贡献率, 时空变化, 黄土高原

Abstract:

The Loess Plateau (LP) in China is highly sensitive to climate change, making it an ideal region for understanding temperature dynamics under global warming. This study analyzed the spatio-temporal variations of integrated temperature indicators for ≥0 ℃ and ≥10 ℃—including the first date (FD), ending data (ED), duration (DD), and active integrated temperature (AIT)—using daily average temperature data from 55 meteorological stations on the LP spanning 1960 to 2019. Methods such as linear fitting, mutation tests, and dominance analysis were employed. The results indicate that, from 1960 to 2019, the indicators for both thresholds changed synchronously, with an advancing FD, a delayed ED, a prolonged DD, and an increasing AIT (P<0.01). Notably, most interdecadal shifts occurred in the 1990s, with abrupt changes concentrated from the late 1990s to the early 2000s. The spatial distribution of mean values for both thresholds was similar, showing that FD advanced, ED delayed, DD prolonged, and AIT increased from northwest to southeast. However, the spatial trends differed: the magnitude of the ED delay followed an east-west pattern with alternating phases, while the increase in AIT was higher in the east and lower in the west. For the ≥0 ℃ threshold, changes in FD, ED, and DD were influenced jointly by latitude and altitude, whereas changes in the other indicators were mainly driven by altitude, with contribution rates between 65.59% and 72.17%. The contribution of FD changes to DD changes was 65.1% for ≥0 ℃ and 68.4% for ≥10 ℃, each exhibiting opposite spatial distribution patterns. Compared with 1960-1989, most indicators—except DD and AIT for ≥0 ℃ and FD for ≥10 ℃—showed significant shifts (in terms of earlier or delayed timing, extended duration, or increased magnitude) during 1990-2019, with more pronounced changes at the ≥0 ℃ threshold. Furthermore, the contribution of FD change to DD change decreased by 2.3% for ≥0 ℃ but increased by 15.2% for ≥10 ℃. Spatially, the variation in contribution rates exhibited a “higher-lower-higher” pattern along the south-to-west axis and a contrast with higher values in the southeast and lower in the northwest. Overall, the integrated temperature indicators for both thresholds on the LP show significant responses to climate warming, with distinct regional and temporal characteristics.

Key words: integrated temperature, dominance analysis, contribution rate, spatio-temporal variation, the Loess Plateau (LP)

安彬, 陈文靖, 肖薇薇. 气候变暖背景下黄土高原≥0 ℃和≥10 ℃积温时空变化[J]. 干旱区研究, 2025, 42(6): 981-992.

AN Bin, CHEN Wenjing, XIAO Weiwei. Spatio-temporal variation characteristics of integrated temperatures of ≥0 ℃ and ≥10 ℃ on the Loess Plateau under global climate warming[J]. Arid Zone Research, 2025, 42(6): 981-992.

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参考文献 43

[1]	中国气象局气候变化中心. 中国气候变化蓝皮书(2023)[M]. 北京: 科学出版社, 2023.
	[CMA Climate Change Centre. Blue Book on Climate Change in China (2023)[M]. Beijing: Science Press, 2023.]
[2]	王岩, 王昊, 崔鹏, 等. 气候变化的灾害效应与科学挑战[J]. 科学通报, 2024, 69(2): 286-300.
	[Wang Yan, Wang Hao, Cui Peng, et al. Disaster effects of climate change and the associated scientific challenges[J]. Chinese Science Bulletin, 2024, 69(2): 286-300.]
[3]	IPCC. Climate Change 2021: The Physical Science Basis[C]//Lee J Y, Marotzke J, Bala G, et al. Future global climate: Scenario-42 based projections and near-term information. Cambridge: Cambridge University Press, 2021.
[4]	IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability[R]. Cambridge, UK and New York, USA: Cambridge University Press, 2022.
[5]	姜彤, 翟建青, 罗勇, 等. 气候变化影响适应和脆弱性评估报告进展: IPCC AR5到AR6的新认知[J]. 大气科学学报, 2022, 45(4): 502-511.
	[Jiang Tong, Zhai Jianqing, Luo Yong, et al. Understandings of assessment reports on climate change impacts, adaptation and vulnerability: Progress from IPCC AR5 to AR6[J]. Transactions of Atmospheric Sciences, 2022, 45(4): 502-511.]
[6]	Tao F, Zhang S, Zhang Z. Spatiotemporal changes of wheat phenology in China under the effects of temperature, day length and cultivar thermal characteristics[J]. European Journal of Agronomy, 2012, 43: 201-212.
[7]	Liu Y J, Zhou W M, Ge Q S. Spatiotemporal changes of rice phenology in China under climate change from 1981 to 2010[J]. Climatic Change, 2019, 157(2): 261-277.
[8]	吕彤, 郭倩, 丁永霞, 等. 气候变化下中国小麦与玉米适生区研究[J]. 兰州大学学报(自然科学版), 2022, 58(5): 588-599.
	[Lü Tong, Guo Qian, Ding Yongxia, et al. Suitable planting areas of wheat and maize under climate changes over China[J]. Journal of Lanzhou University (Natural Sciences), 2022, 58(5): 588-599.]
[9]	杜军, 黄志诚, 次旺顿珠, 等. 气候变化背景下西藏高原地区界限温度时空变化特征[J]. 中国农业气象, 2024, 45(2): 111-123.
	[Du Jun, Huang Zhicheng, Tsewangthondup, et al. Impact of climate warming on the threshold temperature in Xizang under global climate change[J]. Chinese Journal of Agrometeorology, 2024, 45(2): 111-123.]
[10]	包光, 屈勇, 刘娜, 等. 延安市界限温度≥10 ℃的活动积温变化特征[J]. 地球环境学报, 2019, 10(2): 149-155.
	[Bao Guang, Qu Yong, Liu Na, et al. Accumulated temperature variation characteristics of the boundary temperature ≥10 ℃ in Yan’an[J]. Journal of Earth Environment, 2019, 10(2): 149-155.]
[11]	白磊, 张帆, 尚明, 等. 基于格点数据的1961—2018年中国多种积温时空变化研究[J]. 地球信息科学学报, 2021, 23(8): 1446-1460. doi: 10.12082/dqxxkx.2021.200500
	[Bai Lei, Zhang Fan, Shang Ming, et al. Evolution of the multiple accumulated temperature across China’s mainland in 1961-2018 with the gridded meteorological dataset[J]. Journal of Geo-information Science, 2021, 23(8): 1446-1460.]
[12]	孔锋, 王一飞. 中国正积温空间格局及时相差异特征研究[J]. 干旱区资源与环境, 2021, 35(11): 103-110.
	[Kong Feng, Wang Yifei. Temporal-spatial evolution and temporal difference of positive accumulated temperature in China[J]. Journal of Arid Land Resources and Environment, 2021, 35(11): 103-110.]
[13]	王智颖, 臧淑英, 周道玮, 等. 1957—2016年中国农业界限温度时空变化研究[J]. 地理科学, 2020, 40(1): 137-148. doi: 10.13249/j.cnki.sgs.2020.01.017
	[Wang Zhiying, Zang Shuying, Zhou Daowei, et al. Temporal and spatial variation of agricultural threshold temperature from 1957 to 2016[J]. Scientia Geographica Sinica, 2020, 40(1): 137-148.] doi: 10.13249/j.cnki.sgs.2020.01.017
[14]	张善红, 白怡, 宋连环, 等. 近60 a来秦岭山地≥10 ℃积温时空演变及亚热带-暖温带界线问题[J]. 生态与农村环境学报, 2022, 38(6): 755-764.
	[Zhang Shanhong, Bai Yi, Song Lianhuan, et al. Assessing the boundary between subtropical and warm temperate zones and the spatiotemporal evolution of the active accumulated temperature of ≥10 ℃ in the Qinling Mountains from 1960 to 2019[J]. Journal of Ecology and Rural Environment, 2022, 38(6): 755-764.]
[15]	和骅芸, 胡琦, 唐书玥, 等. 基于站点数据分析中国大陆区域喜凉/温作物界限温度的时空演变[J]. 中国农业气象, 2023 44(2): 85-95.
	[He Huayun, Hu Qi, Tang Shuyue, et al. Analysis of spatio-temporal evolution of the boundary temperature of chimonophilous/thermophilic crops in China’s Mainland based on site data[J]. Chinese Journal of Agrometeorology, 2023, 44(2): 85-95.]
[16]	时光训, 丁明军. 近40 a来长江流域≥10 ℃积温的时空变化特征[J]. 热带地理, 2016, 36(4): 682-691. doi: 10.13284/j.cnki.rddl.002833
	[Shi Guangxun, Ding Mingjun. Spatial and temporal variation of ≥10 ℃ accumulated temperature in the Yangtze River Basin for the past 40 years[J]. Tropical Geography, 2016, 36(4): 682-691.] doi: 10.13284/j.cnki.rddl.002833
[17]	王国强, 张勃, 张耀宗, 等. 甘肃省近55年来积温变化趋势特征分析[J]. 水土保持研究, 2016, 23(5): 193-198.
	[Wang Guoqiang, Zhang Bo, Zhang Yaozong, et al. Variation tendency of accumulated temperature in Gansu Province in recent 55 years[J]. Research of Soil and Water Conservation, 2016, 23(5): 193-198.]
[18]	孙艺杰, 刘宪锋, 任志远, 等. 1960—2016年黄土高原多尺度干旱特征及影响因素[J]. 地理研究, 2019, 38(7): 1820-1832. doi: 10.11821/dlyj020190088
	[Sun Yijie, Liu Xianfeng, Ren Zhiyuan, et al. Spatiotemporal variations of multi-scale drought and its influencing factors across the Loess Plateau from 1960 to 2016[J]. Geographical Research, 2019, 38(7): 1820-1832.]
[19]	安彬, 肖薇薇, 朱妮, 等. 近60 a黄土高原地区降水集中度与集中期时空变化特征[J]. 干旱区研究, 2022, 39(5): 1333-1344. doi: 10.13866/j.azr.2022.05.01
	[An Bin, Xiao Weiwei, Zhu Ni, et al. Temporal and spatial variations of precipitation concentration degree and precipitation concentration period on the Loess Plateau from 1960 to 2019[J]. Arid Zone Research, 2022, 39(5): 1333-1344.] doi: 10.13866/j.azr.2022.05.01
[20]	张琨, 吕一河, 傅伯杰, 等. 黄土高原植被覆盖变化对生态系统服务影响及其阈值[J]. 地理学报, 2020, 75(5): 949-960. doi: 10.11821/dlxb202005005
	[Zhang Kun, Lyu Yihe, Fu Bojie, et al. The effects of vegetation coverage changes on ecosystem service and their threshold in the Loess Plateau[J]. Acta Geographica Sinice, 2020, 75(5): 949-960.]
[21]	胥刚, 任继周, 冯琦胜, 等. 黄土高原农业溯源[J]. 草业科学, 2015, 32(10): 1695-1701.
	[Xu Gang, Ren Jizhou, Feng Qisheng, et al. Origin of agriculture in Loess Plateau[J]. Pratacultural Science, 2015, 32(10): 1695-1701.]
[22]	He G, Wang Z H, Shen J B, et al. Transformation of agriculture on the Loess Plateau of China towards green development[J]. Frontiers of Agricultural Science & Engineering, 2021, 8(4): 491-500.
[23]	王锡稳, 王毅荣. 黄土高原积温变化的敏感性研究[J]. 干旱区地理, 2006, 29(6): 817-822.
	[Wang Xiwen, Wang Yirong. Sensitivity of total temperature change in China Loess Plateau[J]. Arid Land Geography, 2006, 29(6): 817-822.]
[24]	姚玉璧, 王润元, 杨金虎, 等. 黄土高原半干旱区气候变化对春小麦生长发育的影响——以甘肃定西为例[J]. 生态学报, 2011, 31(15): 4225-4234.
	[Yao Yubi, Wang Runyuan, Yang Jinhu, et al. Impacts of climatic change on spring wheat growth in a semi-arid region of the Loess Plateau: A case study in Dingxi, Gansu Province[J]. Acta Ecologica Sinica, 2011, 31(15): 4225-4234.]
[25]	张维敏, 王景红. 黄土高原红枣种植区积温变化特征分析[J]. 水土保持研究, 2016, 23(6): 232-237.
	[Zhang Weimin, Wang Jinghong. Variation characteristics of the accumulated temperature of Jujube planting zones in the hilly region of the Loess Plateau over the past 40 years[J]. Research of Soil and Water Conservation, 2016, 23(6): 232-237.]
[26]	刘盼, 赵西宁, 高晓东, 等. 黄土高原极端气温变化特征及其与平均气温的相关性[J]. 应用生态学报, 2022, 33(7): 1975-1982. doi: 10.13287/j.1001-9332.202207.024
	[Liu Pan, Zhao Xining, Gao Xiaodong, et al. Characteristics of extreme temperature variation in the Loess Plateau and its correlation with average temperature[J]. Chinese Journal of Applied Ecology, 2022, 33(7): 1975-1982.] doi: 10.13287/j.1001-9332.202207.024
[27]	晏利斌. 1961—2014年黄土高原气温和降水变化趋势[J]. 地球环境学报, 2015, 6(5): 276-282.
	[Yan Libin. Characteristics of temperature and precipitation on the Loess Plateau from 1961 to 2014[J]. Journal of Earth Environment, 2015, 6(5): 276-282.]
[28]	郝睿超, 王淇葆, 王辰, 等. 气候变化背景下黄土高原干湿演变特征及成因分析[J]. 灌溉排水学报, 2024, 43(12): 74-83.
	[Hao Ruichao, Wang Qibao, Wang Chen, et al. Alterations in drying and wetting spells and their determinants in the Loess Plateau under the influence of climate change[J]. Journal of Irrigation and Drainage, 2024, 43(12): 74-83.]
[29]	张耀宗, 张勃, 刘艳艳, 等. 1960—2013年黄土高原地区气温变化对Hiatus现象的响应[J]. 水土保持研究, 2020, 27(4): 213-219.
	[Zhang Yaozong, Zhang Bo, Liu Yanyan, et al. Response of variability of temperature in the Loess Plateau to the Hiatus in the process of global warming in the period from 1960 and 2013[J]. Research of Soil and Water Conservation, 2020, 27(4): 213-219.]
[30]	刘闯, 石瑞香. 中国四大生态地理区的划分及其界线数据研究[J]. 全球变化数据学报, 2018, 2(1): 42-50.
	[Liu Chuang, Shi Ruixiang. GIS dataset of boundaries among four geo-eco regions of China[J]. Journal of Global Change Data & Discovery, 2018, 2(1): 42-50.]
[31]	Budescu D V. Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression[J]. Psychological Bulletin, 1993, 114: 542-551.
[32]	唐启义. DPS数据处理系统-实验设计、统计分析及数据挖掘[M]. 第2版. 北京: 科学出版社, 2010.
	[Tang Qiyi. DPS Data Processing System-Experimental Design, Statistical Analysis and Data Mining[M]. 2th ed. Beijing: Science Press, 2010.]
[33]	陈婷婷, 余文君, 李艳忠, 等. 中国1960—2019年体感温度的时空变化及其风险分析[J]. 气候变化研究进展, 2024, 20(3): 265-277.
	[Chen Tingting, Yu Wenjun, Li Yanzhong, et al. The spatiotemporal changes and risk analysis of apparent temperature in China from 1960 to 2019[J]. Climate Change Research, 2024, 20(3): 265-277.]
[34]	邓晨晖, 白红英, 高山, 等. 1964—2015年气候因子对秦岭地区植物物候的综合影响效应[J]. 地理学报, 2018, 73(5): 917-931. doi: 10.11821/dlxb201805011
	[Deng Chenhui, Bai Hongying, Gao Shan, et al. Comprehensive effect of climatic factors on plant phenology in Qinling Mountains region during 1964-2015[J]. Acta Geographica Sinice, 2018, 73(5): 917-931.]
[35]	Fu Y S H, Zhao H F, Piao S L, et al. Declining global warming effects on the phenology of spring leaf unfolding[J]. Nature, 2015, 526, 104-107.
[36]	张志高, 张秀丽, 贾梦薇, 等. 1960—2020年黄河流域农业热量资源时空演变特征[J]. 山西农业大学学报(自然科学版), 2024, 44(3): 119-130.
	[Zhang Zhigao, Zhang Xiuli, Jia Mengwei, et al. Spatio-temporal evolution of agricultural thermal resources in the Yellow River Basin during 1960-2020[J]. Journal of Shanxi Agricultural University (Natural Science Edition), 2024, 44(3): 119-130.]
[37]	Liu Y J, Qin Y, Ge Q S. Spatiotemporal variations in maize phenology of China under climate change from 1981 to 2010[J]. Journal of Geographical Sciences, 2019, 29(3): 351-362.
[38]	李宇, 邱炳文, 何玉花, 等. 基于MODIS数据的2001—2018年中国耕地复种指数反演研究[J]. 地理科学进展, 2020, 39(11): 1874-1883. doi: 10.18306/dlkxjz.2020.11.008
	[Li Yu, Qiu Bingwen, He Yuhua, et al. Cropping intensity based on MODIS data in China during 2010-2018[J]. Progress in Geography, 2020, 39(11): 1874-1883.] doi: 10.18306/dlkxjz.2020.11.008
[39]	Wang C Z, Wang X H, Jin Z L, et al. Large increase in crop pests and diseases over China since 1970[J]. Nature Food, 2022, 3: 57-65.
[40]	刘凯, 聂格格, 张森. 中国1951—2018年气温和降水的时空演变特征研究[J]. 地球科学进展, 2020, 35(11): 1113-1126. doi: 10.11867/j.issn.1001-8166.2020.102
	[Liu Kai, Nie Gege, Zhang Sen. Study on the spatiotemporal evolution of temperature and precipitation in China from 1951 to 2018[J]. Advances in Earth Science, 2020, 35(11): 1113-1126.] doi: 10.11867/j.issn.1001-8166.2020.102
[41]	李雯, 侯丽陶, 张世强. 1980—2019年西北地区海拔依赖性变暖与雪雨比的关系研究[J]. 地理研究, 2023, 42(7): 1957-1970. doi: 10.11821/dlyj020221182
	[Li Wen, Hou Litao, Zhang Shiqiang. Study on the relationship between elevation dependent warming and snowfall/rainfall ratio in Northwest China from 1980 to 2019[J]. Geographical Research, 2023, 42(7): 1957-1970.]
[42]	丁一汇, 柳艳菊, 徐影, 等. 全球气候变化的区域响应:中国西北地区气候“暖湿化”趋势、成因及预估研究进展与展望[J]. 地球科学进展, 2023, 38(6): 551-562. doi: 10.11867/j.issn.1001-8166.2023.027
	[Ding Yihui, Liu Yanju, Xu Ying, et al. Regional responses to global climate change: Progress and prospects for trend, causes, and projection of climatic warming-wetting in Northwest China[J]. Advances in Earth Science, 2023, 38(6): 551-562.] doi: 10.11867/j.issn.1001-8166.2023.027
[43]	李宇, 周德成, 闫章美, 等. 中国主要城市的城市化对局地增温的贡献[J]. 环境科学, 2022, 43(5): 2822-2830.
	[Li Yu, Zhou Decheng, Yan Zhangmei, et al. Contribution of urbanization to local warming in major cities of China[J]. Environmental Science, 2022, 43(5): 2822-2830.]

年代际	≥0 ℃积温				≥10 ℃积温
年代际	初日（FD）/d	终日（ED）/d	持续日数（DD）/d	活动积温（AIT）/（℃·d）	初日（FD）/d	终日（ED）/d	持续日数（DD）/d	活动积温（AIT）/（℃·d）
1960s	2.23	-2.68	-4.91	-117.73	3.50	-3.36	-6.86	-136.44
1970s	2.96	-2.02	-4.98	-123.03	2.31	-1.47	-3.79	-114.52
1980s	3.60	-1.46	-5.06	-132.71	1.68	-0.12	-1.81	-98.10
1990s	-0.27	0.59	0.87	8.10	0.49	-0.93	-1.41	-2.47
2000s	-4.85	1.18	6.03	162.02	-2.23	2.95	5.18	147.75
2010s	-3.66	4.39	8.05	203.35	-5.76	2.93	8.69	203.79

突变特征	≥0 ℃积温				≥10 ℃积温
突变特征	初日（FD）/d	终日（ED）/d	持续日数（DD）/d	活动积温（AIT）/（℃·d）	初日（FD）/d	终日（ED）/d	持续日数（DD）/d	活动积温（AIT）/（℃·d）
突变年份	1999年	2004年	1997年	1998年	2004年	1998年	1997年	1997年
突变前均值	68.46	320.75	252.80	3660.54	113.36	278.93	165.75	3053.40
突变后均值	61.54	325.81	263.92	3956.93	106.61	283.56	177.02	3345.03
突变前后变化	-6.91	5.06	11.12	296.39	-6.75	4.63	11.27	291.63

地区	时段	≥0 ℃积温				≥10 ℃积温				文献来源
地区	时段	初日（FD） /（d·a^-1）	终日（ED） /（d·a^-1）	持续日数（DD） /（d·a^-1）	活动积温（AIT） /（℃·d·a^-1）	初日（FD） /（d·a^-1）	终日（ED） /（d·a^-1）	持续日数（DD） /（d·a^-1）	活动积温（AIT） /（℃·d·a^-1）	文献来源
黄土高原	1960—2019年	-0.158	0.130	0.288	7.50	-0.186	0.128	0.314	7.55	本研究
黄土高原	1998—2012年	0.425	0.023	-0.401	-6.23	0.134	-0.087	-0.221	-5.37	本研究
黄土高原	1960—2004年	-	-	0.370	4.70	-	-	0.370	4.60	文献[23]
中国	1961—2020年	-0.156	0.078	0.234	7.28	-0.136	0.132	0.268	7.37	文献[15]
长江流域	1970—2013年	-	-	-	-	-0.153	0.130	0.318	9.41	文献[16]
汉江流域	1970—2013年	-	-	-	-	-0.085	0.146	0.228	10.35	文献[16]
黄河流域	1960—2020年	-0.216	0.116	0.333	7.02	-0.214	0.063	0.278	3.45	文献[36]
西藏高原	1981—2020年	-0.315	0.229	0.543	8.05	-0.158	0.348	0.505	8.61	文献[9]
延安市	1951—2012年	-	-	-	-	-0.185	0.244	0.419	10.66	文献[10]

积温类型	生态地理分区	平均值				变化趋势
积温类型	生态地理分区	初日（FD） /d	终日（ED） /d	持续日数（DD） /d	活动积温（AIT） /（℃·d）	初日（FD） /（d·a^-1）	终日（ED） /（d·a^-1）	持续日数（DD） /（d·a^-1）	活动积温（AIT） /（℃·d·a^-1）
≥0 ℃ 积温	东亚季风亚区	62.72	325.79	264.06	3901.06	-0.168^**	0.121^**	0.289^**	7.154^**
	西北干旱亚区	70.52	317.47	247.95	3698.78	-0.145^**	0.129^**	0.275^**	8.212^**
	青藏高原亚区	68.92	316.41	248.49	2902.12	-0.151^*	0.227^**	0.378^**	6.285^**
≥10 ℃ 积温	东亚季风亚区	109.47	283.07	174.60	3287.48	-0.202^**	0.124^**	0.327^**	7.459^**
	西北干旱亚区	112.26	278.54	167.28	3127.14	-0.180^**	0.133^**	0.314^**	8.058^**
	青藏高原亚区	128.14	269.92	142.78	2169.42	-0.056	0.133^**	0.189^**	5.066^**

积温类型	指标	相关系数			贡献率/%
积温类型	指标	经度	纬度	海拔	经度	纬度	海拔
≥0 ℃积温	初日（FD）	-0.199	0.595^***	0.661^***	5.73	42.33	51.94
	终日（ED）	0.228	-0.653^***	-0.655^***	5.08	51.76	43.16
	持续日数（DD）	0.214	-0.626^***	-0.662^***	5.35	46.73	47.91
	活动积温（AIT）	0.504^***	-0.230	-0.906^***	20.76	11.09	68.15
≥10 ℃积温	初日（FD）	-0.495^***	0.156	0.901^***	21.24	6.59	72.17
	终日（ED）	0.410^**	-0.351^**	-0.838^***	15.06	19.35	65.59
	持续日数（DD）	0.461^***	-0.250	-0.881^***	18.44	11.68	69.87
	活动积温（AIT）	0.539^***	-0.146	-0.929^***	23.54	6.58	69.88