1970—2020年黄土高原水蚀风蚀交错区极端降水时空变化研究及驱动因素分析

doi:10.12118/j.issn.1000－6060.2023.194

摘要/Abstract

摘要：

黄土高原水蚀风蚀交错区作为中国北方典型的生态脆弱区，因其独特地形和气候条件，极端降雨事件对其环境和生态系统的影响更加突出。选取水蚀风蚀交错区28个气象站点，结合RClimDex模型计算11个极端降水指数，采用线性相关分析法、Mann-Kendall趋势检验法和小波交叉法，分析了1970—2020年黄土高原水蚀风蚀交错区极端降水事件时空分布特征，探讨了极端降水事件的驱动因素。结果表明：（1） 1970—2020年水蚀风蚀交错区持续干燥日数（CDD）呈下降趋势，其余10个指数呈上升趋势，反映出近50 a研究区极端降水事件的频率、量级和强度不断增加。交错区年降水量增加和极端降水事件增加具有密切关系，且极端降水事件增加主要是由中雨日数（R10）和大雨日数（R20）引起。（2） 1970—2020年极端降水事件在全区整体为增加趋势，交错区中部和西南部极端降水事件显著发生，陕西段极端降水量和强度呈显著增加趋势且极端化程度更显著。（3）湿日总降水量（PRCPTOT）、暴雨日数（R25）、5 d最大降水量（R5d）3个极端降水指数，与影响因子厄尔尼诺-南方涛动（ENSO）、东亚夏季风（EASM）和太阳黑子（SN）具有不同的功率，与SN的交叉小波变换功率最大，说明影响因子中SN和极端降水指数的相关性最高，SN对极端降水事件的影响最大。

关键词: 水蚀风蚀交错区, 极端降水, 时空特征, 交叉小波, 黄土高原

Abstract:

As a typical ecologically vulnerable area in northern China, the ecologically vulnerable area of the Loess Plateau is significantly affected by extreme rainfall events because of its unique topography and climatic conditions. Twenty-eight meteorological stations in the water-wind erosion crisscross region were selected, and eleven extreme precipitation indices were calculated using the RClimDex model. Linear correlation analysis, the Mann-Kendall trend test, and the wavelet crossing method were used to analyze the spatial-temporal distribution characteristics of extreme precipitation events in the water-wind erosion crisscross region of the Loess Plateau from 1970 to 2020. Driving factors for extreme precipitation events are discussed. The results are as follows: (1) The number of continuous dry days in the water and wind erosion interlacing zone from 1970 to 2020 shows a decreasing trend, whereas the other 10 indices show an increasing trend, reflecting the increasing frequency, magnitude, and intensity of extreme precipitation events in the study area during the past 50 years. There is a close relationship between increases in annual precipitation and extreme precipitation events, and an increase in extreme precipitation events is mainly caused by the number of moderate and heavy rain days. (2) Extreme precipitation events show an increasing trend in the entire region from 1970 to 2020, with significant extreme precipitation events occurring in the central and southwest parts of the crisscross region. The extreme precipitation and intensity in the Shaanxi section show a significant increasing trend, and the extreme degree is very significant. (3) The three extreme precipitation indices, total wet day precipitation, number of heavy rain days, and 5-day maximum precipitation, have varying degrees of influence from El Nino-Southern Oscillation, East Asian Summer Monsoon, and sunspot number (SN), with cross wavelet transform with SN having the greatest influence. This shows that the correlation between SN and extreme precipitation index is the highest among the influencing factors, and SNs have the greatest influence on extreme precipitation events.

Key words: water-wind erosion crisscross region, extreme precipitation, temporal and spatial characteristics, cross wavelet, Loess Plateau

黎珩, 朱冰冰, 边熇, 王蓉, 唐馨怡. 1970—2020年黄土高原水蚀风蚀交错区极端降水时空变化研究及驱动因素分析[J]. 干旱区地理, 2024, 47(4): 539-548.

LI Heng, ZHU Bingbing, BIAN He, WANG Rong, TANG Xinyi. Temporal and spatial changes in extreme precipitation and its driving factors in the water-wind erosion crisscross region of the Loess Plateau from 1970 to 2020[J]. Arid Land Geography, 2024, 47(4): 539-548.

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类型	指数名称	指数代码	定义	单位
持续指数	持续湿润日数	CWD	日降水量≥1 mm的最长连续日数	d
	持续干燥日数	CDD	日降水量<1 mm的最长连续日数	d
	湿日总降水量	PRCPTOT	年全部雨（雪）日降水量之和	mm
绝对指数	中雨日数	R10	每年日降水量≥10 mm的总日数	d
	大雨日数	R20	每年日降水量≥20 mm的总日数	d
	暴雨日数	R25	每年日降水量≥25 mm的总日数	d
相对指数	强降水量	R95P	每年日降水量>95%分位值的降水量之和	mm
相对指数	极强降水量	R99P	每年日降水量>99%分位值的降水量之和	mm
强度指数	1日最大降水量	R1d	年内1日最大降水量	mm
	5日最大降水量	R5d	年内5日最大降水量	mm
	降水强度	SDII	年内日降水量≥1 mm的总量与总日数的比值	mm·d^-1

参数	Pre	CDD	CWD	PRCPTOT	R10	R20	R25	R95P	R99P	R1d	R5d
CDD	-0.24
CWD	0.54^**	-0.16
PRCPTOT	0.99^**	-0.24	0.54^**
R10	0.96^**	-0.20	0.60^**	0.96^**
R20	0.88^**	-0.12	0.39^**	0.89^**	0.86^**
R25	0.83^**	-0.08	0.36^**	0.84^**	0.79^**	0.96^**
R95P	0.78^**	-0.01	0.29^*	0.78^**	0.76^**	0.88^**	0.89^**
R99P	0.69^**	-0.01	0.25	0.70^**	0.64^**	0.73^**	0.77^**	0.90^**
R1d	0.68^**	-0.07	0.09	0.69^**	0.57^**	0.69^**	0.73^**	0.70^**	0.79^**
R5d	0.79^**	-0.08	0.42^**	0.80^**	0.74^**	0.81^**	0.82^**	0.76^**	0.78^**	0.84^**
SDII	0.77^**	0.01	0.29^*	0.77^**	0.76^**	0.89^**	0.91^**	0.82^**	0.75^**	0.77^**	0.85^**