基于EOF分析和GAMLSS模型的淮河流域极端气候事件非平稳特征

王怀军; 曹蕾; 俞嘉悦; 陆源源; 冯如; 杨雅雪; 叶正伟

引用本文:	王怀军,曹蕾,俞嘉悦,等.基于EOF分析和GAMLSS模型的淮河流域极端气候事件非平稳特征[J].灌溉排水学报,0,():-.
	Wang huaijun,Cao lei,YU jiayue,et al.基于EOF分析和GAMLSS模型的淮河流域极端气候事件非平稳特征[J].灌溉排水学报,0,():-.

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基于EOF分析和GAMLSS模型的淮河流域极端气候事件非平稳特征
王怀军¹, 曹蕾¹, 俞嘉悦¹, 陆源源¹, 冯如¹, 杨雅雪², 叶正伟¹
1.淮阴师范学院城市与环境学院;2.北京师范大学地理学部

摘要:

【目的】将经验正交函数分解(EOF)时间系数作为广义可加模型(GAMLSS)的分析对象,揭示流域尺度极端气候事件的非平稳性特征。【方法】以EOF和GAMLSS为基础,将时间、气候指数和CO2浓度为协变量,分析淮河流域极端气候事件的时空分布特征和非一致性规律。【结果】降水极值为平稳时间序列,无显著变化趋势。气温极值表现为显著变化,暖极值以1985年为分界,先下降后上升,冷极值中冷夜日数和霜冻日数呈持续下降趋势。气温极值为非平稳性变化,且CO2的变化是导致非平稳变化的原因。气温极值的非平稳变化导致不同重现期下重现水平发生显著变化。【结论】GAMLSS模型可以模拟气候极值序列位置、尺度参数的平稳和非平稳性变化,与EOF结合,可以减少站点尺度非平稳分析的不确定性。

关键词: 极端气候事件;EOF;GAMLSS;淮河流域

DOI：

分类号:P467

基金项目:国家自然科学基金(41701034,41830863)

Non-stationary characteristics of extreme climate events in Huaihe River Basin based on EOF and GAMLSS model

Wang huaijun¹, Cao lei², YU jiayue², Pan Yingping², Feng Ru², Yang Yaxue², Ye Zhengwei²

1.Wang Huaijun;2.Huaiyin Normal University

Abstract:

【Objective】Global changes lead to non-stationarity in extreme climate event, which make a great challenge for consistency assumption in traditional frequency analysis of climate variables. The time coefficient of empirical orthogonal function method (EOF) is used as the analysis object of the generalized additive model (GAMLSS),which will reveal the non-stationary characteristics of extreme climate events at the baisn scale.【Method】This paper takes the Huaihe River Basin as an example, the EOF and GAMLSS was used to analyze the non-stationary characteristics of climate extremes. And covariates of the GAMLSS were time, climate indices and CO2 concentration. 【Result】 The first loading of EOF1 of climate extremes reflects the overall change of the river basin, and the first corresponding principal components (PC1) of precipitation extreme has no significant trend. The PC1 of temperature extremes showed a significant change, with the warm extremes first decreased and then increased. The cold extremes of cold night and frost days continued to decrease; The correlation coefficients between precipitation extremes and the influencing factors are relative low, indicated the stationary change for precipitation extremes. The temperature extremes were non-stationary change, which is significantly related to time and CO2 concentration, and the change of CO2 is the reason of non-stationary change of temperature extreme; The GAMLSS can simulate the stationary and non-stationary changes of the location and scale parameters of the climate extremes. The precipitation extremes is the stationary time series, and Lognormal is its best fitting distribution. Temperature extremes showed non-stationary changes, and CO2 concentration have a significant effect on GAMLSS model parameters; Quintile analysis showed that the precipitation extremes in the Huaihe River Basin showed stationary changes. The warm extremes divided by 1985, and decline first and then rise. For cold extremes, the number of cold nights and frost days showed a continuous downward trend. The non-stationary changes in temperature extremes have led to significant changes in the return level under different return periods. 【Conclusion】The GAMLSS can simulate the stationary and non-stationary changes of the location and scale parameters of the climate extremes. Combined with EOF, the GAMLSS can reduce the uncertainty of non-stationary analysis.

Key words: extreme extremes; EOF; GAMLSS; Huaihe River Basin