Distribution Characteristics of Extreme Precipitation and Design Rainfall Patterns in Guangzhou
-
摘要: 利用广州市80个省级气象观测站2012—2021年降水资料及五个国家气象站近60 a的分钟级降水资料，本文分析了极端降水空间分布特征，采用芝加哥雨型法和同频率分析法构建短历时与长历时设计暴雨雨型。结果表明，（1）广州市1 h最大降水呈“中部高、东北和西南低”的空间分布，最大值位于增城南部，24 h最大降水则呈东北高、西南低的空间分布，高值中心位于从化中部至增城交界，低值区在番禺至南沙，降雨空间差异显著。（2）短历时设计暴雨雨型显示，广州、花都站雨峰位置随历时变化显著，雨峰系数波动大，从化、增城站雨峰系数稳定，番禺站次之，峰值雨量以增城站最大，番禺、从化站次之，广州、花都站较小。（3）长历时设计暴雨雨型显示，最大1 h累积降水速率从大到小依次为广州站＞花都站＞增城站＞番禺站＞从化站，到达峰值时刻累积雨量占比从大到小依次为番禺站＞广州站＞增城站＞花都站＞从化站。Abstract: Guangzhou has experienced frequent extreme rainfall events with increasing urban flood risks in recent decades. Based on the rainfall data from 80 provincial meteorological stations in Guangzhou during 2012—2021, and the minutes-level rainfall records from five national meteorological stations over the past 60 years, this study investigates the spatiotemporal distribution characteristics of extreme precipitation. The Chicago rainfall pattern (storm hyetograph) method and equal frequency analysis method were employed to synthetically construct design rainfall patterns for both short- and long-duration rainfall events, respectively. The main findings are as follows: (1) The spatial distribution of the maximum 1-hour precipitation in Guangzhou exhibits a pattern of higher values in central Cuangzhou and lower values in the northeast and southwest, with the highest value observed in southern Zengcheng. In contrast, the maximum 24-hour precipitation is higher in northeast-high and lower in the southwest, with a high-value center located at the junction of Conghua and Zengcheng, while low-value areas extended from Panyu to Nansha, Indicating a notable spatial heterogeneity. (2) For short-duration design rainfall patterns, the peak positions at Guangzhou and Huadu stations vary considerably with rainfall duration, accompanied by large fluctuations in the peak position coefficient. In comparison, The peak position coefficients at Conghua and Zengcheng stations remain relatively stable, followed by those at Panyu station. The peak rainfall intensity is the strongest at the Zengcheng station, followed by the Panyu and Conghua stations, whereas Guangzhou and Huadu stations exhibit comparatively lower peak rainfall amounts. (3) For long-duration design rainfall patterns, the maximum 1-hour cumulative rainfall intensity ranks as follows: Guangzhou > Huadu > Zengcheng > Panyu > Conghua. In terms of the proportion of accumulated rainfall occurring at the peak time, the order is Panyu > Guangzhou > Zengcheng > Huadu > Conghua.
-
[1] 中国气象局气候变化中心. 中国气候变化蓝皮书-2021[M]. 北京: 科学出版社, 2021. [2] 王萃萃, 翟盘茂. 中国大城市极端强降水事件变化的初步分析[J]. 气候与环境研究, 2009, 14(5): 553-560. [3] 翟志宏, 何健. 华南区域1961—2008年暴雨变化趋势统计特征[J]. 广东气象, 2011, 33(1): 24-27. [4] 徐晓明, 张雪芹. 1961—2019年深圳市极端降水变化[J]. 自然灾害学报, 2021, 30(3): 43-51. [5] 胡畔, 陈波, 史培军. 中国暴雨洪涝灾情时空格局及影响因素[J]. 地理学报, 2021, 76(5): 1148-1162. [6] 吴迎旭, 周一, 孟莹莹, 等. 2008—2016年黑龙江省短时强降水分布特征及影响系统[J]. 自然灾害学报, 2017, 26(6): 175-183. [7] 孙喜艳, 伍志方, 梁巧倩, 等. 广东省不同等级小时强降水时空特征分析[J]. 自然灾害学报, 2017, 26(5): 102-109. [8] 陆苗, 高超, 苏布达, 等. 淮河流域极端降水空间分布及概率特征[J]. 自然灾害学报, 2015, 24(5): 160-168. [9] 靳奎峰, 巢惟忐, 陈虹杏, 等. 双偏振天气雷达在灾害性降水估测中的应用——以广州"5.22"特大暴雨灾害为例[J]. 灾害学, 2021, 36(3): 140-146. [10] 严志涛, 李谢辉, 刘子堂, 等. 气候变暖背景下西南四省市暴雨洪涝灾害风险变化[J]. 灾害学, 2021, 36(2): 200-207. [11] 田付友, 杨舒楠, 郑永光, 等. 北京地区两次极端特大暴雨过程中短时强降水环境条件对比分析[J]. 暴雨灾害, 2021, 40(1): 27-36. [12] 庄晓翠, 李博渊, 秦榕, 等. 新疆东部一次区域极端暴雨环境场特征[J]. 高原气象, 2020, 39(5): 947-959. [13] 岑国平, 沈晋, 范荣生. 城市设计暴雨雨型研究[J]. 水科学进展, 1998, 9(1): 41-46. [14] 李文涛, 隋军, 刘成林, 等. 设计降雨雨峰系数对排水管网设计流量的影响分析[J]. 净水技术, 2015, 34(5): 100-103, 111. -
下载:
点击查看大图
图(7)
计量
- 文章访问数: 3
- HTML全文浏览量: 1
- PDF下载量: 0
- 被引次数: 0
粤公网安备 4401069904700003号