基于地理探测器分析青藏高原降水δ18O空间分异特征

doi:10.13866/j.azr.2021.05.01

摘要/Abstract

摘要：

地理探测器是研究地理现象的空间分异性,并且定量分析其驱动因子的一种统计方法。利用地理探测器方法对青藏高原24个站点的降水δ¹⁸O年均值进行了分析,该方法可在一定程度上反映青藏高原降水δ¹⁸O年均值的空间分异性,得出纬度、海拔、经度和降水量对青藏高原降水δ¹⁸O年均值空间分异的解释力分别为0.82、0.71、0.57和0.49,温度对青藏高原降水δ¹⁸O年均值空间分异的解释力不显著;因子之间的共同作用增强了降水δ¹⁸O年均值在空间上的分异性。讨论了青藏高原站点降水δ¹⁸O年均值与纬度、经度、海拔、年降水量和年均温之间的关系,并对降水δ¹⁸O主控因子的季节变化进行分析,得出纬度对青藏高原降水δ¹⁸O年均值、夏季均值和冬季均值空间分异的解释力均为最强。

关键词: 青藏高原, 降水δ¹⁸O, 空间分异性, 地理探测器

Abstract:

The annual precipitation δ¹⁸O from 24 stations over the Tibetan Plateau was analyzed based on the use of a geographical detector, which was applied to probe the spatial heterogeneity of precipitation δ¹⁸O and to quantitatively understand the drivers. This method was used to successfully reflect the spatial heterogeneity of the annual precipitation δ¹⁸O over the Tibetan Plateau. The latitude, altitude, longitude, and precipitation amounts respectively explained 0.82, 0.71, 0.57, and 0.49 of the spatial variation in annual precipitation δ¹⁸O. Additionally, the combined effects of different factors enhanced the explanation of spatial variation. The possible influences of latitude, precipitation amount, altitude, and temperature on annual precipitation δ¹⁸O and seasonal variations in influential factors that cause the spatial heterogeneity of precipitation δ¹⁸O were discussed. We conclude that latitude has the strongest explanatory power in relation to the spatial heterogeneity of annual, summer, and winter mean precipitation δ¹⁸O over the Tibetan Plateau.

Key words: Tibetan Plateau, precipitation δ¹⁸O, spatial heterogeneity, geographical detector

席文涛,高晶. 基于地理探测器分析青藏高原降水δ¹⁸O空间分异特征[J]. 干旱区研究, 2021, 38(5): 1199-1206.

XI Wentao,GAO Jing. Spatial heterogeneity of annual precipitation δ¹⁸O over the Tibetan Plateau based on the use of a geographical detector[J]. Arid Zone Research, 2021, 38(5): 1199-1206.

图/表 8

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站点名称	纬度/（°）	经度/（°）	海拔/m	降水量/mm	年均温/℃	样本量（n）	观测期	数据来源
樟木	28.0	86.0	2239	1318	16.3	71	2005年	TNIP
玉树	33.0	97.0	3682	386	3.6	536	2000—2004年	TNIP
羊村	29.9	91.9	3500	296	10.4	57	2005年	TNIP
翁果	28.9	90.4	4500	253	4.0	90	2004—2007年	TNIP
沱沱河	34.2	92.4	4533	204	-1.3	1022	1991—2005年	TNIP
塔什库尔干	37.8	75.3	3100	115	1.6	143	2003—2005年	TNIP
狮泉河	32.5	80.1	4278	82	0.5	96	1999—2002年	TNIP
奴下	29.5	94.6	2780	347	11.9	88	2005年	TNIP
聂拉木	28.2	86.0	3810	590	3.3	776	1996—2006年	TNIP
那曲	31.5	92.1	4508	500	-0.3	1132	1999—2005年	TNIP
鲁朗	29.8	94.7	3327	467	5.7	119	2007年	TNIP
拉孜	29.1	87.7	4000	216	13.7	41	2005年	TNIP
拉萨	29.7	91.1	3658	417	6.3	1041	1994—2006年	TNIP
改则	32.3	84.1	4430	229	-1.0	322	1998—1999年	TNIP
堆村	28.6	90.5	5030	290	-0.9	152	2004—2007年	TNIP
定日	28.7	87.1	4330	265	7.1	285	2000—2006年	TNIP
德令哈	37.4	97.4	2981	186	2.2	115	1992—2006年	TNIP
波密	29.9	95.8	2737	431	8.4	111	2007—2008年	TNIP
白地	29.1	90.4	4430	313	1.2	171	2004—2007年	TNIP
张掖	38.9	100.4	1483	154	7.8	86	1986—2003年	GNIP
乌鲁木齐	43.8	87.6	918	304	7.4	131	1986—2003年	GNIP
兰州	36.1	103.9	1517	322	10.4	41	1985—1999年	GNIP
喀布尔	34.7	69.1	1860	330	11.6	109	1962—1991年	GNIP
和田	37.1	79.6	1375	209	9.1	47	1988—1992年	GNIP

判断依据	交互作用结果
q(X₁∩X₂)<min[q(X₁),q(X₂)]	非线性减弱
min[q(X₁),q(X₂)]<q(X₁∩X₂)<max[q(X₁),q(X₂)]	单因子非线性减弱
q(X₁∩X₂)>max[q(X₁),q(X₂)]	双因子增强
q(X₁∩X₂)=q(X₁)+q(X₂)	独立
q(X₁∩X₂)>q(X₁)+q(X₂)	非线性增强

q统计量	影响因子
q统计量	纬度	经度	海拔	降水量	温度
夏季	0.89	0.52	0.52	0.26	0.40
冬季	0.34	0.12	0.05	0.14	0.27

基于地理探测器分析青藏高原降水δ¹⁸O空间分异特征

Spatial heterogeneity of annual precipitation δ¹⁸O over the Tibetan Plateau based on the use of a geographical detector

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	影响因子
	海拔	经度	纬度	年均温	年降水量
q统计量	0.71	0.57	0.82	0.17	0.49
P值	0.002	0.026	0.000	0.493	0.048

	影响因子交互作用的q统计量
	海拔	经度	纬度	年均温	年降水量
海拔	0.71
经度	0.87	0.57
纬度	0.91	0.85	0.82
年均温	0.86	0.83	0.86	0.17
年降水量	0.85	0.71	0.93	0.84	0.49