山西北部极端降水时空演变规律及与大气环流因子的响应

doi:10.12118/j.issn.1000-6060.2023.440

干旱区地理 ›› 2024, Vol. 47 ›› Issue (3): 391-402.doi: 10.12118/j.issn.1000-6060.2023.440

山西北部极端降水时空演变规律及与大气环流因子的响应

蔡霞¹(), 梁桂花², 张冬峰³(), 蔡琳⁴, 白樱⁵, 李锐锋⁶

1.山西省朔州市气象局，山西朔州 036002
2.山西省朔州市朔城区气象局，山西朔州 036002
3.山西省气候中心，山西太原 030006
4.山东省菏泽学院，山东菏泽 274000
5.河北省邯郸市气象局，河北邯郸 056001
6.山西省朔州市平鲁区气象局，山西朔州 036002

收稿日期:2023-08-18 修回日期:2023-10-09 出版日期:2024-03-25 发布日期:2024-03-29
通讯作者: 张冬峰（1969-），女，博士，正研级高工，主要从事气候和气候变化等研究. E-mail: minczdf@hotmail.com
作者简介:蔡霞（1975-），女，高级工程师，主要从事气候变化研究、农业气象服务、气象灾害等研究. E-mail: caixiaqx@sina.com
基金资助:
山西省气象局课题(SXKMSQH20236329);山西省重点研发计划项目(201803D31219);菏泽学院科研基金计划项目（扶贫专项）(XY18FP08)

Temporal and spatial evolution of extreme precipitation and its response to atmospheric circulation factors in northern Shanxi Province

CAI Xia¹(), LIANG Guihua², ZHANG Dongfeng³(), CAI Lin⁴, BAI Ying⁵, LI Ruifeng⁶

1. Shuozhou Meteorological Bureau of Shanxi Province, Shuozhou 036002, Shanxi, China
2. Shuocheng Meteorological Bureau, Shuozhou City, Shanxi Province, Shuozhou 036002, Shanxi, China
3. Shanxi Climate Center, Taiyuan 030006, Shanxi, China
4. Heze University, Shandong Province, Heze 274000, Shandong, China
5. Handan Meteorological Bureau, Handan 056001, Hebei, China
6. Pinglu Meteorological Bureau, Shuozhou City, Shanxi Province, Shuozhou 036002, Shanxi, China

Received:2023-08-18 Revised:2023-10-09 Published:2024-03-25 Online:2024-03-29

摘要/Abstract

摘要：

基于山西北部28个国家气象站点，1972—2020年逐日降水资料，选用8个极端降水指数，采用线性回归、Pearson相关分析、连续小波和交叉小波变换分析等方法，研究了山西北部极端降水的时空变化及其与大气环流因子的相关性和周期特征。结果表明：（1）在时间上，山西北部8个极端降水指数都是在20世纪70年代后期和21世纪00年代后期到10年代，年总降水量（PRCPTOT）、中雨以上日数（R10mm）、强降水量（R95P）、极强降水量（R99P）、1 d最大降水量（Rx1day）、5 d最大降水量（Rx5day）均增多，日降水强度（SDII）显著增强，持续湿期日数（CWD）也略有增多。整个20世纪80年代降水异常偏少。（2）在空间上，极端降水指数呈从东北向西南地区逐步增加的态势。从站点趋势变化来分析，大多数站点的极端降水指数呈上升趋势，其中，上升趋势最显著的站点均位于忻州市境内西南部。朔州市境内和忻州市东南部站点PRCPTOT和SDII都呈增加趋势，但CWD则呈减少趋势，由此说明朔州市境内和忻州市东南部地区发生极端降水事件的概率较大。（3）通过小波变换分析发现，1990—2020年山西北部极端降水指数表现出约4 a左右的周期特征。在选取的大气环流指数中，北大西洋涛动指数（NAO）对山西北部极端降水影响最明显，NAO越大，PRCPTOT、R10mm、R95p、R99p、Rx1day、Rx5day、SDII则越小，同时CWD也越少，山西北部产生少雨干旱的几率较大，反之，易发生多雨、洪涝现象。研究结果可为山西北部气象灾害的防御提供科学理论依据。

关键词: 极端降水指数, 时空分布, 大气环流异常因子, 小波分析, 山西北部

Abstract:

Based on daily precipitation data from 28 national meteorological stations in northern Shanxi Province, China, from 1972 to 2020, temporal and spatial changes in extreme precipitation in northern Shanxi and their correlation with atmospheric circulation factors and periodic characteristics were studied using linear regression, Pearson correlation analysis, continuous wavelet analysis, and cross wavelet transform analysis. The results show the following: (1) In terms of time, the eight extreme precipitation indices in northern Shanxi increased significantly in the late 1970s and from the late 2010s to the early 2000s: annual total wet-day precipitation (PRCPTOT), number of heavy precipitation days (R10mm), soaked days (R95P), extremely wet days (R99P), maximum 1-day precipitation amount (Rx1day), and maximum 5-day precipitation amount (Rx5day). The simple daily intensity index (SDII) increased significantly, and consecutive wet days (CWD) also increased slightly. Precipitation was unusually low throughout the 1980s. (2) Spatially, the extreme precipitation indices gradually increased from northeast to southwest. From the analysis of the stations’ trend change, the extreme precipitation indices of most stations showed an upward trend, with the most significant upward trend of the stations located southwest of Xinzhou City. PRCPTOT and SDII in Shuozhou City and southeast of Xinzhou City showed an increasing trend. However, days of CWD showed a decreasing trend, indicating that the probability of extreme precipitation events in Shuozhou City and the southeast of Xinzhou City was high. (3) Through wavelet transform analysis, it was discovered that the extreme precipitation indices in northern Shanxi had a periodic feature of recurring in approximately 4 years in the past 30 years. Among the selected atmospheric circulation indices, the North Atlantic Oscillation Index (NAO) had the most obvious influence on extreme precipitation in northern Shanxi. The larger the NAO, the smaller the PRCPTOT, R10mm, R95p, R99p, Rx1day, Rx5day, and SDII, and the lower the CWD, the greater the probability of low rainfall and drought in northern Shanxi, which is prone to flash flooding. The research results can provide a scientific theoretical basis for the prevention of meteorological disasters in northern Shanxi.

Key words: extreme precipitation indices, spatiotemporal distribution, atmospheric circulation anomaly factor, wavelet analysis, northern Shanxi

蔡霞, 梁桂花, 张冬峰, 蔡琳, 白樱, 李锐锋. 山西北部极端降水时空演变规律及与大气环流因子的响应[J]. 干旱区地理, 2024, 47(3): 391-402.

CAI Xia, LIANG Guihua, ZHANG Dongfeng, CAI Lin, BAI Ying, LI Ruifeng. Temporal and spatial evolution of extreme precipitation and its response to atmospheric circulation factors in northern Shanxi Province[J]. Arid Land Geography, 2024, 47(3): 391-402.

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指数	上升趋势站点个数	显著上升站点个数	上升趋势站点占比/%	下降趋势站点个数	显著下降站点个数	下降趋势站点占比/%
PRCPTOT	23	2	82.1	5	0	17.9
Rx1day	16	3	57.1	12	0	42.9
Rx5day	21	0	75.0	7	0	25.0
R10mm	25	7	89.3	3	0	10.7
R95p	19	2	67.9	9	0	32.1
R99p	15	2	53.6	13	1	46.4
SDII	27	3	96.4	1	0	3.6
CWD	12	1	42.9	16	1	57.1

类型	NAO	AO	WP	NP	SOI	AMO	SN	PDO	亚洲区极涡面积指数
PRCPTOT	-0.17	0.01	-0.08	0.04	0.07	0.23	-0.16	0.04	-0.21
Rx1day	-0.35^**	-0.22	-0.14	-0.07	0.14	0.27^*	-0.13	-0.16	-0.24^*
Rx5day	-0.27^*	-0.23	-0.17	0.13	0.09	0.22	-0.02	-0.04	-0.23
R10mm	-0.14	0.10	-0.08	0.02	-0.01	0.28^*	0.18	0.09	0.27^*
R95p	-0.28^*	-0.23	-0.18	0.10	0.17	0.24^*	-0.21	-0.05	-0.20
R99p	-0.28^*	-0.28^*	-0.15	0.11	0.09	0.19	-0.17	-0.06	-0.17
SDII	-0.30^**	-0.15	-0.19	0.09	0.08	0.34^**	-0.23	-0.01	-0.29^**
CWD	0.06	0.13	-0.003	0.15	-0.05	-0.03	-0.03	0.07	-0.01

山西北部极端降水时空演变规律及与大气环流因子的响应

Temporal and spatial evolution of extreme precipitation and its response to atmospheric circulation factors in northern Shanxi Province

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指数	英文缩写	指数定义	单位
日降水强度	SDII	年降水总量与湿日日数（日降水量≥1 mm）的比值	mm·d^-1
年总降水量	PRCPTOT	日降水量≥1 mm年累计降水量	mm
1 d最大降水量	Rx1day	每月最大1 d降水量	mm
5 d最大降水量	Rx5day	每月连续5 d最大降水量	mm
强降水量	R95p	日降水量>95%分位值的年累计降水量	mm
极强降水量	R99p	日降水量>99%分位值的年累计降水量	mm
中雨以上日数	R10mm	日降水量≥10 mm的日数	d
持续湿期日数	CWD	日降水量≥1 mm的最大持续日数	d

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