1959—2019年辽河流域气象干旱时空特征分析

Spatio-temporal characteristics of meteorological drought in the Liaohe River Basin from 1959 to 2019

【目的】在全球气候变化的背景下,干旱问题日趋明显,对辽河流域干旱特征进行分析可为当地的防旱抗旱工作提供指导。【方法】基于1959—2019年辽河流域40个气象站点数据,应用标准化降水蒸散指数(SPEI),结合Copula函数、重现期计算探究流域气象干旱特征,并利用交叉小波分析探究SPEI与遥相关因子的相关关系。【结果】结果显示:1959—2019年,夏、秋季流域SPEI呈下降趋势;下辽河平原、东辽河平原、辽东丘陵南部以及西北、西南山地地区气象干旱同现重现期较短,在3~10 a之间;大兴安岭山地地区、下辽河平原、东辽河平原和辽东丘陵南端联合重现期较低,在1.5~3.5 a之间;SPEI与太阳黑子(SN)、北极涛动(AO)、太平洋年代际涛动(PDO)、南方涛动指数(SOI)、北大西洋涛动(NAO)在不同时间尺度的共振周期上,有较好的相关关系。【结论】结果表明:夏、秋季流域气象干旱呈加剧的趋势;基于Copula函数进行联合概率分析,更利于全面表征干旱事件的发生和发展规律,其中Gumbel Copula和Frank Copula函数能更好地拟合辽河流域绝大多数站点干旱烈度及干旱历时的联合分布;SN、AO、PDO、SOI、NAO均为辽河流域气象干旱变化的重要驱动因素。

[Objective] Global climate change in the background, the drought is becoming increasingly severe. The analysis of drought characteristics in the Liaohe River Basin can provide guidance for local drought prevention and mitigation. [Methods] Based on the data of 40 meteorological stations in the Liaohe River Basin from 1959 to 2019, the standardized precipitation evapotranspiration index(SPEI) was applied, combined with Copula function, and recurrence period calculation to explore the meteorological drought characteristics, and the correlation between SPEI and remote correlation factors was explored by cross-wavelet transform. [Results] From 1959 to 2019, the summer and autumn SPEI showed a decreasing trend. The co-occurrence return period in the Lower Liaohe Plain, the Eastern Liaohe Plain, the southern of Liaodong Hills and the northwest and southwest mountainous areas is relatively short, between 3 and 10 years. The joint recurrence period in the Greater Hinggan Mountains, the Lower Liaohe Plain, the Eastern Liaohe Plain and the southern end of the Liaodong Hills is short, between 1.5 and 3.5 years. SPEI has a good correlation with the sunspot(SN), the Arctic Oscillation(AO), the Pacific Interdecadal Oscillation(PDO), the Southern Oscillation Index(SOI), and the North Atlantic Oscillation(NAO) in terms of resonance periods on different time scales. [Conclusion] There has been a prevalent drying trend in summer and autumn. Joint probability analysis based on Copula function, the occurrence and development pattern of drought events can be more comprehensively characterized, and Gumbel Copula and Frank Copula functions can better fit the joint distribution of drought intensity and drought duration at most stations in the Liaohe River Basin. SN, AO, PDO, SOI and NAO are important driving factors of meteorological drought change.