华北地区小时极端降水的非稳态研究

本文以华北五省为研究区，基于1960—2014年小时降水数据建立1、2、3、6、12和24 h极端降水序列，对比分析稳态和非稳态假设下极端降水重现期估计的差异。研究表明：1960―2014年华北不同时间极端降水的变化趋势略有不同，时间越短呈上升趋势的站点越多，1~3 h的极端降水呈上升趋势的站点较多，稳态和非稳态假设下的20~100 a一遇重现期平均差异较大，其中，1 h极端降水的显著上升站点中，二者的平均相对误差达30%~43%；而6~24 h极端降水中，呈下降趋势的站点增多，其中，24 h极端降水显著下降站点中，二者的平均相对误差达-43%~-32%；无显著趋势站点，二者的平均相对误差大部分介于-10%~10%。随着重现期增大，二者差异的不确定性区间增大，不同变化趋势站点表现一致。研究发现，华北地区短历时极端降水强度增加，稳态假设下极端降水的重现期会严重低估。因此，选用非稳态假设估计极端降水的重现期，将降低极端降水的灾害风险。

Non-stationary sub-daily extreme precipitation analysis in the North China

Based on hourly rainfall observational data of meteorological stations during 1960 to 2014, a non-stationary frequency analysis of the Annual Maxima (AM) sub-daily rainfall series (1, 2, 3, 6, 12 and 24 h rainfall, using a moving window approach) for North China was conducted, then the difference between stationary and non-stationary Intensity-Duration-Frequency (IDF) curves using Bayesian inference was estimated. Results show that the trends of 1, 2, 3, 6, 12 and 24 h annual maxima rainfall in North China from 1960 to 2014 are complex. While the shorter the duration, the more stations that show an upward trend; and for over 6 h extreme precipitation, the more stations that show a downward trend. For a 20 to 100 years 1 h extreme precipitation, the difference between the non-stationarity and stationarity extreme precipitation is larger, and at the station with the upward trend that a stationary climate assumption may lead to underestimation of extreme precipitation about 30%-43%; the average difference of 6 h to 24 h is relatively small, and the difference at station with downward trend is about -43%- -32%. The difference between the non-stationarity and stationarity extreme precipitation decreases with the extension of the duration, and the uncertainty increases as the return period increases in all conditions. Shorter precipitation events have been intensified more in the past decades in North China, the shorter the duration the larger the differences between the nonstationary and stationary extremes. Therefore, this non-stationary extreme value analysis can potentially reduce the risk of extreme precipitation.