陕北黄土高原区极端降水时空变化特征及其影响因素

doi:10.11821/dlyj020181067

摘要/Abstract

摘要：

基于1970—2017年逐日降水数据,辅以趋势分析、空间分析和小波相干等气候诊断方法,对陕北黄土高原区极端降水时空变化特征进行分析,探讨不同海区海温异常与降水变化的响应关系。结果表明：① 1970—2017年,陕北地区气温波动上升,降水增加,半干旱界线明显向西北方向移动;② 1970—2017年,陕北地区降水呈现极端化。具体表现为,弱降水日数减少,强降水日数增加,降水持续时间呈现破碎化,1日最大降水量、5日最大降水量和降水强度均表现出显著的上升趋势;③ 在影响因素上,陕北地区极端降水变化受赤道太平洋中西部海温影响明显于东部,受赤道太平洋北侧影响明显于南侧,受海温年代周期变化影响（14~16a）明显于中长期周期（4~8a）。同时,NINO W区可作为区域极端降水响应的关键海区。当NINO W区海温异常偏高时,陕北地区降水普遍偏高,降水强度和持续时间增加,易发生雨涝灾害。

关键词: 极端降水, 时空分析, 厄尔尼诺, 陕北地区

Abstract:

The questions concerning how much precipitation is extreme and how extreme precipitation responds to warming are important to improving climate resiliency and achieving sustainable development of typical loess hills and gullies in China. The spatial-temporal variation of extreme precipitation is investigated on the Loess Plateau in northern Shaanxi province from 1970 to 2017. To understand the influence of El Ni?o on extreme climate variation, this study analyzes whether extreme precipitation events are linked to the SST in nine different sea regions (NINO 1+2, NINO 3, NINO 3.4, NINO 4, NINO W, NINO A, NINO B, NINO C, NINO Z). The result showed that from 1970 to 2017, an obvious warming-wetting trend occurred on the Loess Plateau in northern Shaanxi. The warming process can be divided into three periods: a rapid increase period (1970-1998), followed by a short hiatus (1999-2012), and finally, a warming rebound period (2013-2017). In addition, precipitation was dominated by interannual oscillation. However, after 2004, precipitation increased in northern Shaanxi, which was associated with semi-arid boundaries moving to the northwest. The result also showed that 16 extreme climate indices were quantified as precipitation changes that were more intense on the Loess Plateau in northern Shaanxi. Spatially, regarding the number of precipitation days when daily precipitation was above 1, 5, 10, or 20 mm, there was a clear increasing trend from south to north. Even so, the region showed a consistent and significant change in the number of decreasing rainy days and increasing SDII. Finally, in the past 48 years, the influence of SST on the central and western equatorial Pacific, for the decadal variation of extreme precipitation, was more obvious than that in the eastern equatorial Pacific. Additionally, while the influence of SST on the northern side of the equatorial Pacific was more obvious than that on the southern side, the effect by the SST periodic variation on the 14~16a time scale was more significant than that on the 4~8a time scale. It is worth mentioning that changes in nearly all indices had a strong correlation to NINO W, especially the intensity indices (Rx1day, Rx5day, and SDII) and the indices defined on a percentile-based threshold (R95, R99, R95P, and P99P). The anomaly of SST in the Nino W region served as a typical signal that there was an increase in occurrence of extreme precipitation in northern Shaanxi.

Key words: extreme precipitation, spatiotemporal analysis, El Ni?o, northern Shaanxi

李双双, 孔锋, 韩鹭, 延军平, 汪成博, 武亚群. 陕北黄土高原区极端降水时空变化特征及其影响因素[J]. 地理研究, 2020, 39(1): 140-151.

LI Shuangshuang, KONG Feng, HAN Lu, YAN Junping, WANG Chengbo, WU Yaqun. Spatiotemporal variability of extreme precipitation and influencing factors on the Loess Plateau in northern Shaanxi province[J]. GEOGRAPHICAL RESEARCH, 2020, 39(1): 140-151.

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序号	ENSO指数	定义
1	NINO 1+2区	10°S~0°、90°W~80°W区域内,海表温度距平的区域平均值
2	NINO 3区	5°S~5°N、150°W~90°W区域内,海表温度距平的区域平均值
3	NINO 4区	5°S~5°N、160°E~150°W区域内,海表温度距平的区域平均值
4	NINO 3.4区	5°S~5°N 、170°W~120°W区域内,海表温度距平的区域平均值
5	NINO W区	0°~10°N、140°E~180°E区域内,海表温度距平的区域平均值
6	NINO C区	10°S~0°、180°~90°W区域内,海表温度距平的区域平均值
7	NINO A区	25°N~35°N、130°E~150°E区域内,海表温度距平的区域平均值
8	NINO B区	0°~10°N、50°E~90°E区域内,海表温度距平的区域平均值
9	NINO Z区	NINO 1+2 区、NINO 3 区和NINO 4 区海表温度距平指数的面积加权平均值

极端降水指数	RD	Rr1	Rr5	Rr10	Rr25	Rx1day	Rx5day	SDII
相关系数	0.140	0.750	0.900	0.930	0.840	0.560	0.690	0.720
P-value	0.340	0.001	0.001	0.001	0.001	0.001	0.001	0.001
极端降水指数	R999	R95	R99P	R95P	CDD	CWD	PRCPTOT	GPRCP
相关系数	0.930	0.840	0.980	0.790	-0.130	0.580	1.000	0.930
P-value	0.001	0.001	0.001	0.001	0.380	0.001	0.001	0.001