中国气候舒适度时空分布特征分析

doi:10.12082/dqxxkx.2020.190513

摘要/Abstract

摘要：

气候舒适度对人类活动和地区适宜性评价等研究具有重要意义,而温湿指数是气候舒适度评价的一项重要指标。传统的温湿指数计算都是基于站点数据,无法获取大尺度区域舒适度的时空变化特征。本文利用2005—2018年MODIS地表温度、大气可降水量数据,结合地理加权回归方法对经典温湿指数模型进行改进,计算并分析中国年均和月均气候舒适度时空演变特征。结果如下：① 采用GWR方法进行地表温度和气温的拟合,拟合精度（Adjusted R²=0.9~0.98,RMSE=0.14~1.89 ℃）较为理想,说明采用LST、NDVI、DEM作为自变量的地理加权回归分析,能够较精确地拟合地面气温;② 2005—2018年年均温湿指数统计结果表示,云南省累计舒适月数最多,高达167个月,中部省份相对于东南沿海省市舒适时期较多,最高舒适月数差值可达到41个月。中国年均舒适度空间分布规律基本保持一致,除新疆、西藏和东北的部分区域以外,舒适度空间呈现从南到北,舒适度等级由舒适变寒冷。从舒适度等级面积变化情况看,2005—2018年全国舒适度等级呈现由寒冷变舒适的趋势;③ 2018年全年舒适面积最大的月份为5月,其次为10月,不舒适月份集中在1月和7月,全国呈现极冷或极热。春季和秋季空间分布特征较为相似,呈现由东南到西北逐渐递减的趋势;除青藏高原地区外,夏季和冬季呈现由南到北递减趋势。舒适区域主要集中在低纬、中海拔地区。

关键词: 气候舒适度, 时空分布特征, 温湿指数, 地理加权回归, 地表温度, 大气可降水量, MODIS, 中国

Abstract:

Climate comfort level has great significance to human activities and regional suitability assessment, and temperature humidity index is important for climate comfort evaluation. Traditional temperature-humidity index is obtained based on the observed data from some sations, which cannot reflect the spatio-temporal characteristics of climate comfort in large-scale areas. In this paper, the modified temperature humidity index model is proposed using Land Surface Temperature (LST) and Precipitable Water Vapor (PWV) from 2005 to 2018 retrieved from MODIS. Using this new index, the spatio-temporal characteristics of climate comfort level in China are calculated and analyzed. The results are shown as follows: (1) The GWR method is used to fit the surface temperature and air temperature. The fitting accuracy (Adjusted R² = 0.90~0.98, RMSE = 0.14~1.89 ℃) is ideal, which indicates that LST, NDVI, and DEM are used as the independent variables for geographical weight Regression analysis can more accurately fit the air temperature; (2) The statistical results of the annual average temperature and humidity index from 2005 to 2018 show that the cumulative number of comfortable months in Yunnan Province is the most, up to 167 months, and the central provinces are relatively comfortable compared to the southeast coastal provinces, and the difference between the highest comfort months can reach 41 months. The spatial distribution of China's average annual comfort level is basically the same. Except for parts of Xinjiang, Tibet, and the northeast, the comfort level in China is from south to north, and the comfort level changes from comfortable to cold. Judging from the changes in the area of each comfort level, the national comfort level showed a trend from cold to comfortable from 2005 to 2018; (3) The month with largest comfortable area in 2018 is May, followed by October. Uncomfortable months are concentrated in January and July when the country is extremely cold or hot. The spatial distribution characteristics of spring and autumn are similar, showing a gradual decreasing trend from southeast to northwest; except for the Qinghai-Tibet Plateau, summer and winter show a decreasing trend from south to north. The comfort zone is mainly concentrated in low-latitude and middle-altitude areas.

Key words: climate comfort level, spatiotemporal distribution, temperature-humidity index, geographically weighted regression, land surface temperature, precipitable water vapor, MODIS, China

刘艳霞, 冯莉, 田慧慧, 阳少奇. 中国气候舒适度时空分布特征分析[J]. 地球信息科学学报, 2020, 22(12): 2338-2347.DOI:10.12082/dqxxkx.2020.190513

LIU Yanxia, FENG Li, TIAN Huihui, YANG Shaoqi. Spatio-Temporal Distribution Analysis of Climate Comfort Level in China[J]. Journal of Geo-information Science, 2020, 22(12): 2338-2347.DOI:10.12082/dqxxkx.2020.190513

图/表 9

表1

图1

表2

表3

表4

2018年各月站点气温与拟合结果的相对误差"

站点名称	经度/°E	纬度/°N	高程/m	月份
站点名称	经度/°E	纬度/°N	高程/m	1	2	3	4	5	6	7	8	9	10	11	12
哈尔滨	126.94	44.42	161	0.47	0.41	0.48	0.34	0.22	0.41	0.19	0.04	0.14	0.08	0.22	0.40
乌鲁木齐	87.62	43.78	903	0.71	1.44	3.38	4.06	4.78	4.19	4.78	4.44	3.87	4.21	2.90	0.04
喀什	76.78	39.24	1204	0.47	0.77	2.30	0.77	0.07	0.39	0.28	0.40	2.44	2.40	0.37	0.64
额济纳旗	101.07	41.94	940	0.20	0.41	0.39	0.14	0.29	0.84	0.99	1.36	0.16	0.88	1.48	0.16
敦煌	94.68	40.14	1136	3.37	2.31	3.70	2.43	2.17	2.11	2.12	2.44	2.88	2.37	2.94	3.48
酒泉	98.48	39.77	1470	0.26	0.94	0.81	0.34	0.49	0.32	0.49	0.49	0.27	0.31	0.24	0.10
民勤	103.08	38.63	1369	0.26	0.49	1.08	1.22	1.72	2.26	2.34	1.76	1.62	1.04	0.68	0.20
格尔木	94.90	36.42	2816	1.79	1.40	1.14	1.42	0.87	0.23	0.00	0.02	0.67	1.04	1.11	1.74
二连浩特	111.97	43.64	964	0.11	0.63	0.44	0.74	0.11	0.11	0.10	0.20	0.30	0.14	0.64	0.94
太原	112.33	37.62	776	0.26	0.24	0.17	0.69	0.62	0.49	0.44	0.62	0.36	0.24	0.34	0.76
沈阳	122.72	41.42	16	1.40	0.71	0.34	0.12	0.03	0.11	0.36	0.19	0.40	0.29	0.44	0.74
北京	116.33	39.74	44	0.49	0.91	0.70	0.30	0.42	0.44	0.17	0.63	0.41	0.41	0.38	0.82
大连	121.63	38.90	82	0.48	1.19	0.39	0.42	0.47	0.48	0.14	0.32	0.64	1.09	0.82	0.20
济南	116.98	36.68	37	0.46	0.40	0.97	0.71	0.60	0.47	0.40	0.82	0.49	0.46	0.49	0.44
那曲	91.10	32.34	4699	1.12	1.47	0.48	0.47	0.39	1.13	0.34	0.91	0.07	0.40	1.04	1.02
拉萨	91.13	29.67	3642	0.68	0.89	1.26	1.78	2.11	2.47	1.64	1.86	2.33	1.07	1.42	0.88
昌都	97.17	31.14	3342	1.74	2.27	2.20	2.69	0.82	0.43	2.20	1.96	0.01	1.38	0.44	0.48
腾冲	98.40	24.02	1634	0.03	0.30	0.32	0.36	0.06	0.13	0.07	0.32	0.04	0.09	0.00	0.19
昆明	102.68	24.02	1889	4.77	4.42	4.98	3.39	3.43	3.09	3.28	4.04	2.92	4.13	4.71	4.84
西安	108.92	34.14	431	1.38	1.10	1.60	1.70	1.71	1.16	1.48	1.67	1.41	1.42	1.24	1.11
郑州	113.37	34.44	401	0.42	0.48	0.40	0.44	1.99	0.70	0.23	0.84	0.44	0.31	0.44	0.47
宜昌	111.00	30.80	294	0.34	0.38	0.32	0.46	0.34	0.42	0.37	0.32	0.04	0.04	0.14	0.37
贵阳	106.60	26.84	1243	1.46	1.92	1.84	2.39	2.96	2.38	3.22	3.09	2.31	3.14	2.28	2.10
桂林	110.30	24.32	149	2.20	0.49	0.49	0.60	0.70	0.37	0.47	0.07	0.28	0.24	0.47	0.94
上海	121.44	31.62	3	0.67	0.24	0.60	0.47	0.24	0.43	1.02	0.70	0.31	0.23	0.34	0.33
福州	119.28	26.08	16	0.44	0.09	0.29	0.41	0.24	0.47	0.37	0.24	0.12	0.18	0.20	0.27

表4

图2

图3

表5

图4

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温湿指数(THI)	人体感知	舒适感等级
<40	极冷,极不舒适	1
40~45	寒冷,不舒适	2
45~55	偏冷,较不舒适	3
55~60	较凉爽,舒适	4
60~65	非常凉爽,非常舒适	5
65~70	比较凉爽,舒适	6
70~75	偏热,较不舒适	7
75~80	闷热,不舒适	8
>80	极其闷热,极不舒适	9

数据类型	时间分辨率/d	空间分辨率	来源
地表温度(Land Surface Temperature,LST)	8	1 km	NASA官网
归一化植被指数(Normalized Difference Vegetation Index,NDVI)	16	500 m	NASA官网
大气可降水量(Precipitable Water Vapor,PWV)	1	5 km	NASA官网
数字高程模型(Digital Elevation Model,DEM)	-	90 m	CGIAR-CSI SRTM

月份	R²	AdjR²	AICc	Sigma	残差			标准误差
月份	R²	AdjR²	AICc	Sigma	范围/℃	-2~2 ℃误差站点所占百分比	-1~1 ℃站点所占百分比	标准误差
1	0.98	0.97	7689.73	1.28	-3.08~4.72	91.37	74.40	0.16~1.23
2	0.98	0.98	6707.40	1.38	-1.49~1.44	90.38	74.67	0.14~1.33
3	0.98	0.97	2678.30	1.44	-2.44~3.41	90.98	74.86	0.17~1.39
4	0.96	0.94	1397.08	1.68	-3.97~4.37	90.01	74.42	0.19~1.64
5	0.93	0.90	996.86	1.42	-3.19~2.34	90.07	73.43	0.18~1.47
6	0.91	0.90	419.44	2.44	-2.94~3.84	90.28	73.48	0.29~1.37
7	0.94	0.92	468.49	2.84	-2.42~4.73	91.64	72.98	0.34~1.76
8	0.96	0.94	246.68	2.68	-3.44~4.43	90.84	72.46	0.33~1.49
9	0.91	0.90	442.37	2.98	-2.24~3.71	90.22	73.23	0.39~1.89
10	0.96	0.94	994.03	1.66	-3.71~3.11	90.24	74.42	0.21~1.41
11	0.98	0.97	329.14	1.64	-2.94~3.46	91.47	77.92	0.18~1.49
12	0.98	0.98	7302.13	1.26	-1.67~3.46	91.64	74.74	0.16~1.22

年份	极冷-极不舒适		寒冷-不舒适		偏冷-不舒适		凉爽-舒适		非常凉爽-非常舒适		比较凉爽-舒适		偏热-不舒适		闷热-不舒适
年份	面积	百分比	面积	百分比	面积	百分比	面积	百分比	面积	百分比	面积	百分比	面积	百分比	面积	百分比
2005	200.65	20.91	144.93	15.10	293.44	30.58	145.11	15.12	120.79	12.59	41.73	4.35	12.67	1.32	0.17	0.02
2009	202.60	21.11	148.66	15.49	316.21	32.95	134.09	13.97	114.31	11.91	38.19	3.98	5.54	0.58	-	-
2014	165.23	17.22	119.95	12.50	345.07	35.96	133.78	13.94	132.75	13.84	49.48	5.16	13.21	1.38	0.03	-
2018	160.86	16.76	264.16	14.87	264.16	27.51	134.51	14.10	115.16	11.99	136.11	14.18	6.68	0.70	0.001	-