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地理科学进展, 2023, 42(1): 145-160 doi: 10.18306/dlkxjz.2023.01.012

研究论文

江淮流域梅雨过程识别及梅雨期分级降水时空特征

姚飛,1,2, 杨秀芹,1,2,*, 刘慕嘉1,2, 张余庆3, 李华1,2

1.水利部水文气象灾害机理与预警重点实验室，南京信息工程大学，南京 210044

2.南京信息工程大学水文与水资源工程学院，南京 210044

3.淮阴师范学院城市与环境学院，江苏 淮安 223300

Identification of Meiyu process and spatiotemporal characteristics of different precipitation levels during the Meiyu period over the Yangtze-Huai River Basin

YAO Fei,1,2, YANG Xiuqin,1,2,*, LIU Mujia1,2, ZHANG Yuqing3, LI Hua1,2

1. Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

2. School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

3. School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, Jiangsu, China

通讯作者: *杨秀芹(1981— )，女，山东聊城人，博士，副教授，硕士生导师，研究方向为流域水文气候与水资源规划与管理。E-mail: young_sd@nuist.edu.cn

收稿日期: 2022-05-26   修回日期: 2022-08-24  

基金资助: 国家自然科学基金项目(41907384)

Received: 2022-05-26   Revised: 2022-08-24  

Fund supported: National Natural Science Foundation of China(41907384)

作者简介 About authors

姚飛(1993— )，男，四川攀枝花人，硕士生，研究方向为流域水文气候与水资源规划与管理。E-mail: yaofei@nuist.edu.cn

摘要

在全球气候变化背景下，近60 a江淮流域梅雨特征量及梅雨期分级降水的时空变化特征还不明晰。论文采用江淮流域1961—2020年239个气象站逐日降水、气温和NCEP/NCAR再分析资料识别梅雨过程，研究梅雨入出梅日期等特征量及梅雨期不同量级的雨日数等指标的时空特征，计算城市化对梅雨期强降水的贡献。结果表明：Ⅰ区(江南区)平均入出梅最早，Ⅱ区(长江中下游区)次之，Ⅲ区(江淮区)入出梅最晚，梅雨期长度依次为30、30和24 d，入出梅日和梅雨期长度趋势性均不明显。Ⅰ区平均梅雨雨强最大(367.6 mm)，Ⅱ区次之(298.4 mm)，Ⅲ区最小(253.5 mm)；Ⅱ区梅雨雨强显著增加、平均梅雨强度指数最大，最易发生暴力梅，Ⅲ区梅雨强度指数变化最剧烈。江淮流域梅雨量Ⅰ、Ⅱ区中部较大，Ⅰ区雨日数最多，Ⅱ区次之，Ⅲ区最少。梅雨期小雨日数最多、降水发生率最高，中雨、大雨和暴雨依次减少。绝大多数站点小雨、中雨日数趋势性不明显，Ⅱ区中东部大雨、暴雨日数显著增加。绝大多数站点大雨、暴雨降水发生率趋势性不明显，Ⅱ区较多站点小雨、中雨发生率显著下降是其东部梅雨期降水发生率显著减少的原因。暴雨量占梅雨量比例最大、降水贡献率也最大，大雨、中雨和小雨依次减小。Ⅰ、Ⅱ区东部站点大雨、暴雨量显著增加是该区梅雨量显著增加的原因。绝大部分站点的不同量级降水贡献率趋势性不明显，只有Ⅱ区中东部17个站点小雨、中雨贡献率显著降低。城市化加剧了强降水指标上升，城市化对R95P和R99P的贡献率分别为10.59%和17.39%。研究结果可为江淮流域梅雨期暴雨洪涝事件预警、水旱灾害防御、水资源调度提供参考。

关键词： 梅雨监测指标; 降水分级; 强降水; 江淮流域

Abstract

The spatiotemporal characteristics of Meiyu intensity index and different precipitation levels during the Meiyu period over the Yangtze-Huai River Basin are still unclear during recent 60 years under global climate change. This study used observed daily precipitation and temperature data from 239 meteorological stations over the Yangtze-Huai River basin from 1961 to 2020 combined with NCEP/NCAR reanalysis data to identify the Meiyu process and investigate the spatiotemporal characteristics of Meiyu intensity index, the number of rainy days, precipitation amount, precipitation incidence rate, and precipitation contribution rate of different precipitation levels during the Meiyu period, and calculate the contribution rate of urbanization to heavy precipitation indices during the Meiyu period. It aimed to provide references for early warning of rainstorm and flood events, flood and drought disaster prevention, and water resources regulation in the Yangtze-Huai River Basin during the Meiyu Period. The research results show that: The average Meiyu onset date and ending date were the earliest in area I (Jiangnan area), followed by area II (middle and lower reaches of the Yangtze River) and area III (Jianghuai area), and the length of the Meiyu period was 30 days, 30 days, and 24 days respectively. There was no obvious trend for onset date, ending date, and length of the Meiyu period. The average Meiyu rain intensity in area I was the highest, at 367.6 mm, followed by area II at 298.4 mm and area III at 253.5 mm; the Meiyu rain intensity index in area II showed a significant increasing trend in recent 60 years, and the average Meiyu intensity index was the highest in area Ⅱ. Violent Meiyu are most likely to occur in area II, and the inter-annual variation of the Meiyu intensity index was the largest in area III. Meiyu precipitation amount in the central part of areas Ⅰ and Ⅱ was relatively high, with the highest number of rainy days in area Ⅰ, followed by area Ⅱ and the lowest was found in area Ⅲ. During the Meiyu period, the number of light rain days was the largest and the incidence of precipitation was the highest, while the corresponding indices of moderate rain, heavy rain, and rainstorm decreased in turn. The trend of light rain days and moderate rain days was not obvious for most stations, but the number of heavy rain days and rainstorm days in the central and eastern parts of area II increased significantly. The trend of the incidence of heavy rain and rainstorm was not obvious for most stations, but the incidence of light rain and moderate rain for many sites in area II decreased significantly, which is the reason for the significant decrease of the incidence of precipitation during the Meiyu period in eastern area Ⅱ. Rainstorm amount accounted for the largest proportion of precipitation during the Meiyu period, the contribution rate of rainstorm was also the largest, and heavy rain, moderate rain, and light rain decreased in turn. The significant increasing trend of heavy rain and rainstorm precipitation at the eastern stations in areas I and II is the reason for the significant increasing trend of Meiyu precipitation in this area. The trend of precipitation contribution rates of different magnitudes of precipitation was not obvious for most stations, and only 17 stations in the central and eastern parts of area II showed a significant decreasing trend of the contribution rates of light rain and moderate rain. Urbanization aggravates the increasing trend of heavy precipitation, and the contribution rate of urbanization to R95P and R99P was 10.59% and 17.39%, respectively.

Keywords： Meiyu monitoring indices; precipitation classification; heavy precipitaion; Yangtze-Huai River Basin

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本文引用格式

姚飛, 杨秀芹, 刘慕嘉, 张余庆, 李华. 江淮流域梅雨过程识别及梅雨期分级降水时空特征[J]. 地理科学进展, 2023, 42(1): 145-160 doi:10.18306/dlkxjz.2023.01.012

YAO Fei, YANG Xiuqin, LIU Mujia, ZHANG Yuqing, LI Hua. Identification of Meiyu process and spatiotemporal characteristics of different precipitation levels during the Meiyu period over the Yangtze-Huai River Basin[J]. Progress in Geography, 2023, 42(1): 145-160 doi:10.18306/dlkxjz.2023.01.012

梅雨是东亚地区独特的天气气候现象，是东亚夏季风阶段性活动的产物，主要出现在6—7月中国江淮流域到韩国、日本一带[1⇓⇓-4]。梅雨天气有雨量大、雨期长、日照短、湿度大、云量多、风力小等特点，并有显著的年际和年代际变化特征，常导致江淮流域旱涝灾害[1,2,4⇓⇓-7]。

目前对梅雨发生的天气气候成因已经取得了较多认识[2,5,8]。东亚梅雨季节的气候背景主要为西太平洋副热带高压呈纬向分布，其脊线自日本南部延伸至中国南部，亚洲高纬度环流表现为从贝加尔湖以东至鄂霍次克海的单一阻塞型，中国江淮流域和日本南部一带受季风输送的暖湿水汽影响而形成梅雨带[2,9]。梅雨的季节内振荡受热带环流低频振荡和北纬冷空气入侵影响较多[2,10]，年际变化则与El Niño事件、对流层双年振荡(TBO)和青藏高原东南缘上游地表潜热通量有关[11⇓-13]，年代际变化受北大西洋多年代际涛动(AMO)和北太平洋年代际涛动(PDO)影响[14-15]，而且很大程度上梅雨与东亚夏季风的年代际变化有相反的趋势[13,16-17]，梅雨特征的长期变化还与城市化、大气气溶胶增多密切相关[2,6,18]。

20世纪60年代后期以来，国家气候中心和中央气象台主要采用长江中下游5个气象站(武汉、九江、芜湖、南京和上海)的降水资料和副高脊线位置识别梅雨过程[19-20]，但各地在确定区域入梅日和出梅日时所采用的指标和阈值不尽相同[8,21]。伴随着非典型梅雨的出现，梅雨的降水规律和监测范围发生变化，梅雨判别标准也随之变化[21-22]。中国气象局2014年印发的《梅雨监测业务规定(试行)》规范了国家级和省级梅雨监测业务，赵俊虎等[20]、陈旭等[23]和罗小杰等[24]基于此规定识别和分析江淮梅雨。国家气候中心2017年发布的《梅雨监测指标》(GB/T 33671—2017)(以下简称“国标”)统一了国家和地方的梅雨监测业务，国标采用逐日降水和气温数据，结合副高脊线位置识别梅雨过程，适用于梅雨监测、预报预测、评估及服务[1,6,25]。梅雨监测指标相关的研究一类为直接使用国家气候中心提供的梅雨监测数据分析梅雨异常特征及其成因[4⇓-6,26]，另一类依据国标识别某局部区域的梅雨过程，进而分析梅雨监测指标的特征[8,25,27⇓-29]。

在气候变化和城市化背景下，中国东部的降水特征发生了变化，主要表现为极端降水的频率和强度明显增加，连续降水及弱降水减少，强降水增加，空间异质性增强[2,30⇓⇓⇓⇓⇓⇓-37]，目前多是在全年或雨季(4—9月)进行降水特征研究，江淮流域梅雨监测指标和梅雨期降水时空变化特征的研究还比较少。本文使用1961—2020年江淮流域梅雨监测区内239个气象站的逐日降水和气温数据、美国国家环境预报中心和国家大气研究中心(NCEP/NCAR)的500 hPa位势高度场数据，按照国标识别3个子区域的梅雨过程，分析入梅日、出梅日、梅雨期长度、梅雨雨强和梅雨强度指数等梅雨监测指标的时空变化特征，将梅雨期降水分为小雨、中雨、大雨和暴雨4个降水量级，研究不同降水量级的雨日数、降水发生率、降水量和降水贡献率的时空变化特征，最后定量计算城市化对梅雨期强降水的贡献，以期深入认识江淮流域梅雨期降水时空特征，为区域水文预报、防汛调度及水资源调度等工作提供参考。

1 资料与方法

1.1 研究区域与数据介绍

江淮流域梅雨监测区位于28°~34°N，110°~123°E，涉及浙江省、江苏省、安徽省、江西省、湖南省、湖北省和上海市，国标将江淮流域分为江南区(Ⅰ区)、长江中下游区(Ⅱ区)和江淮区(Ⅲ区)等3个子区域[1,4]。本文选用1961—2020年239个气象站点逐日降水和逐日平均气温资料，数据来源于中国气象数据网(https://data.cma.cn/)，站点选取原则如下： ① 只在国标规定的梅雨监测代表站中选取；② 梅雨最早开始于5月，最晚结束于8月，若5—8月缺测资料超过5 d则该年记为缺测；③ 把缺测资料天数控制在总资料天数的5%左右，对个别缺测数据采用气候平均值补充[1,38]。最终有分布均匀的239个代表站满足条件，Ⅰ区、Ⅱ区和Ⅲ区分别有53、138和48个站点(图1)。

图1

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图1   江淮流域梅雨监测站点位置

Fig.1   Spatial distribution of Meiyu monitoring stations over the Yangtze-Huai River Basin


选用NCEP/NCAR逐日再分析资料集中的500 hPa位势高度场和纬向风场确定副高脊线位置[1,8,25,39]，其水平分辨率为2.5°×2.5°。下载网址为：ftp://ftp.cdc.noaa.gov/pub/Datasets。选用1980、1990、2000、2010和2020年江淮流域土地利用/土地覆被变化(LUCC)资料动态判别城市站点与农村站点[40-41]，数据空间分辨率为1°×1°，来源于中国科学院资源环境科学与数据中心(https://www.resdc.cn/)。

1.2 梅雨过程识别及梅雨强度计算

1.2.1 梅雨过程识别

梅雨过程识别步骤简述如下(详细过程请参见国标[1])：

(1) 识别子区域的梅雨期，首先根据子区域日降水量确定区域雨日、雨期开始日、雨期结束日和雨期，然后用500 hPa位势高度场和纬向风场确定副高脊线位置，最后结合日平均气温得到入梅日和出梅日，入梅日至出梅日前一天的累计日数即为该区域的梅雨期长度。

(2) 确定江淮流域的梅雨季节，将3个区域梅雨期的最早入梅日作为梅雨季节的入梅日，将最晚出梅日作为梅雨季节的出梅日，江淮流域入梅日至出梅日前一天的累计日数即为梅雨季长度。

1.2.2 梅雨强度计算

子区域梅雨雨强P为区域内所有梅雨监测站在梅雨期总降水量的均值，其计算公式为：

P=1N∑j=1NPj

式中：Pj为该区域内第j站梅雨期总降水量(后文均称梅雨量)，N为该区域内总站数。

子区域梅雨强度指数M通过衡量梅雨期长度和梅雨量等因素，综合评价梅雨强度，该指标越大越容易发生暴力梅，其计算公式为：

M=LL-+12R/LR-/L-+RR--2.5

式中：L为当年梅雨期长度，L-为历年梅雨期长度均值，R为当年梅雨期各站点总降水量，R-为历年梅雨期各站点总降水量均值，梅雨强度指数等级划分见表1。

表1   区域梅雨强度指数的等级划分

Tab.1  Classification of regional Meiyu intensity index

等级弱偏弱正常偏强强
M界值M≤-1.25-1.25<M≤-0.375-0.375<M≤0.3750.375<M≤1.25M>1.25

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江淮流域梅雨雨强为以上3个区域梅雨雨强的平均值，江淮流域梅雨强度指数为以上3个区域梅雨强度指数的平均值。

1.3 研究方法

用一元线性回归模型[42]分析梅雨指标的变化趋势，用变差系数CV[42]分析梅雨指标的离散程度，CV值越大，数据越分散，其计算表达式为：

Cv=1x-1n∑i=1n(xi-x-)2

式中：xi为梅雨指标序列，x-为样本均值，n为样本容量。

依据中国降水量级划分，将站点的雨日[43](日降水量≥ 0.1 mm)降水分为小雨(0.1~10 mm)、中雨 (10~25 mm)、大雨(25~50 mm)和暴雨(≥50 mm)4个量级[7]，分析梅雨期各站点不同降水量级的雨日数、降水发生率、降水量和降水贡献率的时空特征。

某降水量级的降水发生率α、降水贡献率β计算公式如下[44]：

α=l0l×100%
β=p0p×100%

式中：l0为该站点梅雨期内某降水量级的雨日数，l为梅雨期长度；p0为该站点梅雨期内某降水量级的降水量，p为该站点梅雨期的降水量。

本文选取强降水量R95P(日降水量大于梅雨期内第95%分位值的总降水量)和极端强降水量R99P(日降水量大于梅雨期内第99%分位值的总降水量)2个极端降水指标定量分析城市化对梅雨期强降水的影响[34,43,45]。在1980、1990、2000、2010和2020年共5期土地利用数据的基础上，以站点为圆心半径2 km范围内的人造地表面积若超过1/3，则认为该站点为城市站点，否则为农村站点[41]。由一元线性回归方程可得城市站点和农村站点强降水指标的变化速率ku和kr，城市化贡献率C计算公式为[33,40-41]：

C=ku-krku×100%

式中：当ku>kr时，C>0，说明城市化加剧强降水指标上升；当ku=kr时，C=0，说明城市化对强降水指标没有影响；当ku<kr时，C<0，说明城市化抑制强降水指标上升，当C≥100%或C≤-100%时，说明存在未知的当地人为或自然因素影响城市或农村站的降水。

2 结果与分析

2.1 江淮流域及其各子区域入梅日、出梅日和梅雨季/期长度时空特征

由图2可知，江淮流域1961—2020年平均入梅日为6月8日，南部的Ⅰ区于6月9日入梅，Ⅱ区、Ⅲ区依次为6月15日和6月21日入梅。入梅日年际波动较大，在不考虑空梅的情况下，江淮流域最早入梅日比最晚入梅日早32 d，Ⅰ区最早入梅日比最晚入梅日早33 d，Ⅱ区最早入梅日比最晚入梅日早44 d，Ⅲ区最早入梅日比最晚入梅日早41 d。

图2

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图2   基于梅雨监测指标的江淮流域及其各区1961—2020年入梅日、出梅日和梅雨季/期长度

注：图中*、#分别表示空梅、二度梅。图3同。

Fig.2   Meiyu onset and ending dates and length of the Meiyu period over the Yangtze-Huai River Basin and its subregions from 1961 to 2020 based on the latest national standard of Meiyu monitoring indices


江淮流域1961—2020年平均出梅日为7月18日，Ⅰ区、Ⅱ区和Ⅲ区平均出梅日分别为7月9日、7月14日和7月15日。出梅日年际波动较大，在不考虑空梅的情况下，江淮流域最早出梅日比最晚出梅日早42 d，Ⅰ区最早出梅日比最晚出梅日早48 d，Ⅱ区最早出梅日比最晚出梅日早54 d，Ⅲ区最早出梅日比最晚出梅日早45 d。

江淮流域1961—2020年平均梅雨季长度为40 d，Ⅰ区、Ⅱ区和Ⅲ区平均梅雨期长度分别为30 d、30 d和24 d。梅雨季/期长度的年际变化剧烈，在不考虑空梅的情况下，江淮流域梅雨季最短11 d(1981年)比最长62 d(2020年)少51 d，Ⅰ区梅雨期最短6 d(2003年)比最长60 d(2015年)少54 d，Ⅱ区梅雨期最短8 d(1961年、1987年)比最长62 d(2015年)少54 d，Ⅲ区梅雨期最短6 d(1985年)比最长53 d(2020年)少47 d。江淮流域梅雨季长度的变差系数为0.42，Ⅰ区、Ⅱ区和Ⅲ区梅雨期长度的变差系数分别为0.46、0.53和0.66，说明梅雨季/期长度年际波动较大，且3个子区域从南到北波动程度越来越强。

Ⅰ区、Ⅱ区和Ⅲ区分别有2、3和3 a出现二度梅，分别有1、1和8 a为空梅年，Ⅲ区空梅年明显多于其他区域，说明江淮流域梅雨具有南北空间上的异质性。由表2可知，江淮流域及各子区域入梅日、出梅日及梅雨季/期长度均有所变化，但以上变化趋势都未通过95%置信度下的显著性检验，趋势性不明显。

表2   江淮流域及其各区1961—2020年入梅日、出梅日和梅雨季/期长度变化速率

Tab.2  Linear variation trend of Meiyu onset and ending dates and length of the Meiyu period over the Yangtze-Huai River Basin and its subregions during 1961-2020 (d/10 a)

指标Ⅰ区Ⅱ区Ⅲ区江淮流域
入梅日-0.11-0.190.82-0.20
出梅日1.091.430.110.64
梅雨季/期长度1.201.620.710.84

注：均未通过95%置信度下的显著性检验。

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2.2 江淮流域及其各子区域梅雨雨强和梅雨强度指数时空特征

由图3可知，江淮流域及其子区Ⅰ区、Ⅱ区和Ⅲ区1961—2020年梅雨雨强多年平均值分别为306.5、367.6、298.4和253.5 mm。梅雨雨强的年际波动较大，江淮流域最大梅雨雨强比最小梅雨雨强多593.4 mm，Ⅰ区最大梅雨雨强比最小梅雨雨强多804.1 mm，Ⅱ区最大梅雨雨强比最小梅雨雨强多724.5 mm，Ⅲ区最大梅雨雨强比最小梅雨雨强多765.4 mm。江淮流域及其子区Ⅰ区、Ⅱ区和Ⅲ区梅雨雨强的变差系数分别为0.42、0.46、0.53、0.66，可见梅雨雨强年际变化较为剧烈。

图3

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图3   基于梅雨监测指标的江淮流域及各分区1961—2020年梅雨雨强和梅雨强度指数

Fig.3   Meiyu intensity and Meiyu intensity index of the Yangtze-Huai River Basin and its subregions from 1961 to 2020 based on the latest national standard of Meiyu monitoring indices


江淮流域及其子区Ⅰ区、Ⅱ区和Ⅲ区1961—2020年梅雨强度指数多年平均值分别为0.20、0.23、0.31和0.07。梅雨强度指数的年际波动较大，江淮流域最大梅雨强度指数为2.3，最小为-1.3，Ⅰ区最大梅雨强度指数为2.3，最小为-1.6，Ⅱ区最大梅雨强度指数为2.6，最小为-1.5，Ⅲ区最大梅雨强度指数为2.9，最小为-2.1。从不同等级的梅雨强度指数出现频次来看，Ⅱ区梅雨最强，其次是Ⅲ区、Ⅰ区和江淮流域。江淮流域及其子区Ⅰ区、Ⅱ区和Ⅲ区梅雨强度指数的变差系数分别为3.99、3.72、3.23、18.15，梅雨强度指数的年际变化较大，特别是Ⅲ区变化非常剧烈。

由表3可知，只有Ⅱ区梅雨雨强呈显著增加趋势，通过了95%置信度下的显著性检验，变化速率为23.43 mm/10 a。江淮流域及其各子区域梅雨强度指数变化幅度均未通过95%置信度下的显著性检验，趋势性不明显。

表3   江淮流域及各分区1961—2020年梅雨雨强和梅雨强度指数变化速率

Tab.3  Linear change trend of Meiyu intensity and Meiyu intensity index over the Yangtze-Huai River Basin and its subregions during 1961-2020

指标Ⅰ区Ⅱ区Ⅲ区江淮流域
梅雨雨强(mm/10 a)20.6923.43*3.7715.97
梅雨强度指数(1/10 a)0.02-0.01-0.04-0.01

注：*为通过95%置信度下的显著性检验。

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2.3 梅雨量、雨日数、降水发生率及梅雨期日降水强度时空特征

梅雨期各站点的梅雨量多年平均值为211.9~489.1 mm(图4a)，占年降水量的16.8%~28.2%(图4b)，占夏季降水量的44.4%~61.3%(图4c)，梅雨量在Ⅰ区和Ⅱ区的114°~120°E范围内较大，且南部高于北部，占年降水量的比值在Ⅰ区和Ⅱ区的东南沿海和西部边界线一带较低，占夏季降水量的比例则是明显的东高西低。梅雨量的变化速率为-11.4~44.5 mm/10 a(图4d)，有121个站点的变化速率超过20.0 mm/10 a，其中55个站点(占23.0%)呈显著增加趋势，主要分布在Ⅰ区和Ⅱ区的东部。有15个站点变化速率为负值，多位于Ⅲ区，均没有通过95%置信度下的显著性检验。

图4

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图4   1961—2020年各站点梅雨量、雨日数、降水发生率等指标的时空特征

注：图d、f、h和j中大圆圈表示该站点变化趋势通过了95%置信度下的显著性检验，小圆圈表示没有通过。

Fig.4   Spatiotemporal variation characteristics of Meiyu intensity indicators over the subregions during 1961-2020


梅雨期各站点雨日数多年平均值为12.9~22.1 d(图4e)，Ⅰ区雨日数最多，Ⅱ区雨日数大概以武汉和长沙的连线为界呈东高西低，Ⅲ区雨日数明显较少。梅雨期雨日数的变化速率为-1.1~1.1 d/10 a(图4f)，各站点变化幅度均未通过显著性检验，没有明显趋势性变化。梅雨期降水发生率为48.9%~74.7%(图4g)，梅雨期降水发生率超过50%的站点有237个，Ⅰ区所有站点的降水发生率都大于60%，Ⅱ区降水发生率则大概以武汉和长沙的连线为界呈东高西低，Ⅲ区降水发生率在60%左右。降水发生率变化速率为-3.3%/10 a~1.3%/10 a(图4h)，其中37个站点呈显著减少趋势，主要分布在Ⅱ区的东部(江苏省东南部及上海市)。

日降水强度为梅雨量与梅雨期雨日数的比值，可以反映梅雨期的降水强度。由图4i、4j可知，日降水强度为62.6~73.9 mm/d，达到暴雨量级，没有明显的空间分布差异。日降水强度的变化速率为-8.7~27.8 mm/10 a，有40个站点呈显著增加趋势。

2.4 梅雨期不同降水量级雨日数及其降水发生率时空特征

梅雨期小雨日数为7.1~12.5 d(图5a1)，Ⅰ区和Ⅱ区的东部小雨日数较多，Ⅲ区明显少于Ⅰ区和Ⅱ区。小雨日数的变化速率为-0.9~0.7 d/10 a(图5a2)，绝大部分站点小雨日数变化趋势不明显，只有Ⅲ区的2个站点呈显著减少趋势。小雨发生率为29.1%~40.9%(图5a3)，小雨发生率表现为东高西低，其变化速率为-3.4%/10 a~1.0%/10 a(图5a4)，其中66个站点小雨发生率呈显著减少趋势，主要分布在Ⅰ区和Ⅱ区，江苏省东南部与上海市最为集中。

图5

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图5   各站点1961—2020年梅雨期内不同降水量级雨日数及其降水发生率时空变化特征

注：图a2、a4、b2、b4、c2、c4、d2和d4中大圆圈表示该站点变化趋势通过了95%置信度下的显著性检验，小圆圈表示没有通过。

Fig.5   Spatiotemporal characteristics of rainy days and their incidence rates for different precipitation levels during the Meiyu period from 1961 to 2020 for each meteorological station in the Yangtze-Huai River Basin and its subregions


由图5b1、b2可知，中雨日数比小雨日数少，为2.7~5.4 d，中雨日数呈东部高于西部、南部高于北部的特征；中雨日数变化速率为-0.3~0.4 d/10 a，各站点中雨日数变化幅度均未通过显著性检验，趋势性不明显。中雨发生率比小雨少，为9.5%~18.2%(图5b3)，中雨发生率大致以武汉和长沙的连线为界呈东高西低的特征；中雨发生率变化速率为-1.7%/10 a~1.3%/10 a(图5b4)，有21个站点呈显著性下降趋势，主要分布在Ⅱ区。

如图5c1所示，大雨日数为2.0~3.9 d，Ⅰ区大雨日数较多，Ⅲ区较少；大雨日数的变化速率为-0.2~0.4 d/10 a(图5c2)，其中24个站点呈显著增长趋势，主要分布在Ⅱ区东部(上海市)和中部；大雨发生率为6.2%~13.7%(图5c3)，Ⅰ区和Ⅲ区部分站点大雨发生率较高；大雨发生率的变化速率为-1.3%/10 a~1.5%/10 a(图5c4)，8个站点呈显著下降趋势，主要分布在Ⅱ区，Ⅲ区有4个站点呈显著上升趋势。

暴雨日数为1.5~3.4 d(图5d1)，暴雨日数低值区分布在Ⅰ区和Ⅱ区的东部和西部。暴雨日数的变化速率为-0.3~0.4 d/10 a(图5d2)，其中29个站点呈显著增加趋势，主要分布在Ⅱ区东部。梅雨期暴雨发生率为5.2%~13%(图5d3)，Ⅰ区和Ⅱ区的东部和西部较低。暴雨发生率的变化趋势为-1.4%/10 a~2.0%/10 a(图5d4)，绝大部分站点暴雨发生率变化幅度未通过显著性检验，趋势性不明显。

2.5 梅雨期不同降水量级降水量及其降水贡献率时空特征

梅雨期小雨降水量为19.8~36.8 mm(图6a1)，小雨降水量在Ⅰ区和Ⅱ区的长沙和武汉连线以东较大，Ⅲ区较小；小雨降水量的变化速率为-1.6~2.8 mm/10 a(图6a2)，其中3个站点呈显著增加趋势；小雨贡献率为7.3%~20.4%(图6a3)，小雨贡献率在Ⅰ区和Ⅱ区的113°~120°E范围内较小，小雨贡献率的变化速率为-3.1%/10 a~3.1%/10 a(图6a4)，有17个站点呈显著降低趋势，大多分布在Ⅱ区的中东部。

图6

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图6   各站点1961—2020年梅雨期内不同降水量级降水量及其降水贡献率的时空变化特征

注：图a2、a4、b2、b4、c2、c4、d2和d4中大圆圈表示该站点变化趋势通过了95%置信度下的显著性检验，小圆圈表示没有通过。

Fig.6   Spatiotemporal characteristics of precipitation amount and precipitation contribution rate for different precipitation levels at each station in the Yangtze-Huai River Basin and its subregions during the Meiyu period during 1961-2020


中雨降水量为45.7~88.7 mm(图6b1)，空间分布与小雨降水量相似。中雨降水量的变化速率为-5.7~7.6 mm/10 a(图6b2)，其中6个站点呈显著增加趋势。中雨贡献率为17.2%~34.3%(图6b3)，空间分布与小雨贡献率相似。中雨贡献率的变化速率为-3.7%/10 a~1.8%/10 a(图6b4)，其中17个站点呈显著降低趋势，大多分布在Ⅰ区和Ⅱ区的中东部。

大雨降水量为67.2~135.4 mm(图6c1)，空间分布与小雨降水量相似。大雨降水量变化速率为-8.2~15.9 mm/10 a(图6c2)，28个站点呈显著增加趋势，大多分布在Ⅱ区的中东部。大雨贡献率为25.8%~43.1%(图6c3)，空间分布与小雨贡献率相似。大雨贡献率的变化速率为-4.7%/10 a~5.3%/10 a(图6c4)，有12个站点呈显著减少趋势，分散在流域各处，空间分布差异不明显。

暴雨降水量为101.0~304.3 mm(图6d1)，暴雨降水量在Ⅰ区和Ⅱ区的113°~120°E范围内较大。暴雨降水量变化速率为-20.7~41.1 mm/10 a(图6d2)，其中24个站点呈显著增加趋势，分布在Ⅰ区和Ⅱ区东部。暴雨贡献率为30.3%~58.5%(图6d3)，暴雨贡献率低值区位于Ⅰ区和Ⅱ区东南部，有22个站点低于40%，125个站点超过50%。暴雨贡献率的变化速率为-3.8%/10 a~4.9%/10 a(图6d4)，有7个站点呈显著增加趋势，2个站点呈显著降低趋势，其空间分布差异不明显。

2.6 城市化对梅雨期强降水的影响

依据1980、1990、2000、2010和2020年的土地利用数据识别出相应年份土地利用资料的城市站点数，分别为33、57、90、142和182个，可知33个站点一直为城市站点，149个经历了从农村站点转变为城市站点的过程，此外，有57个站点一直为农村站点。

图7为33个城市站点、149个农村→城市站点和57个农村站点的R95P和R99P的变化速率时空分布，3类站点的2个强降水指标绝大部分站点变化速率都为正值。如图7a所示，城市站点R95P变化速率为-2.12~57.16 mm/10 a，有21个站点(占城市站点总数的63.6%)呈显著增加趋势；农村→城市站点变化速率为-2.89~62.66 mm/10 a(图7b)，有82个站点(占农村→城市站点总数的55.0%)呈显著增加趋势；农村站点变化速率为-4.02~61.67 mm/10 a，有30个站点(占农村站点总数的52.6%)呈显著增加趋势。可见，R95P在城市站点中显著上升的站点比例最高，其次是农村→城市站点和农村站点。城市站点的R95P呈显著增加的站点主要分布在Ⅰ区和Ⅱ区的116°E以东，农村→城市站点和农村站点中呈显著增加的站点主要分布在Ⅰ区和Ⅱ区西部。

图7

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图7   江淮流域城市站点、农村—城市站点和农村站点的1961—2020年梅雨期R95P和R99P时空变化特征

注：图中大圆圈表示该站点变化趋势通过了95%置信度下的显著性检验，小圆圈表示没有通过。

Fig.7   Spatiotemporal characteristics of R95P and R99P at urban stations, rural-urban stations and rural stations during the Meiyu period in 1961 to 2020 over the Yangtze-Huai River Basin and its subregions


城市站点R99P的变化速率为-1.66~40.92 mm/10 a(图7d)，有12个站点(占城市站点总数的36.4%)呈显著增加趋势；农村→城市站点的变化速率为-3.98~43.55 mm/10 a(图7e)，有49个站点(占农村→城市站点总数的32.9%)呈显著增加趋势；农村站点的变化速率为-2.47~40.81 mm/10 a(图7f)，有18个站点(占农村站点总数的31.6%)呈显著增加趋势。可见，R99P在城市站点中呈显著上升的站点比例最高，其次是农村→城市站点和农村站点。R99P的变化速率较R95P小，空间分布特征和R95P相似。

以各期土地利用资料年份为节点，分别统计所有城市站点和农村站点的2个强降水指标逐年均值。由图8a可知，城市站点R95P比农村站点大，特别是在1975年之后，两类站点的R95P都明显增高且差异增大，近60 a城市站点变化速率(18.3 mm/10 a)比农村站点(16.4 mm/10 a)大，城市化贡献率为10.59%。图8b城市站点R99P大于农村站点，2类站点的R99P在1975年之后明显增大且差异增大，城市站点R99P变化速率(10.6 mm/10 a)较农村站点(8.7 mm/10 a)大，城市化贡献率为17.39%。

图8

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图8   城市站点和农村站点梅雨期R95P和R99P变化趋势及城市化贡献率

Fig.8   Multi-year trends and urbanization contribution for R95P and R99P at urban and rural stations during the Meiyu period over the Yangtze-Huai River Basin


3 讨论

3.1 梅雨过程识别技巧

周平等[8]根据安徽江淮地区6个气象站资料识别梅雨过程时发现，当研究区小于子区域时，以最后一个雨期的次日定义的出梅日与根据环流和雨日条件确定的出梅日并不等效，应根据环流和雨日条件确定出梅日。本文在梅雨识别过程中也有部分年份不能同时满足国标“最后一个雨期结束日的次日即为出梅日，此时西太平洋副热带高压第二次北跳，副高脊线位置超出北界范围”的规定，但满足国标“当副高脊线5 d滑动位置有1候超过北界位置2个纬度，且没有继续出现雨日，监测区域内出现高温干热天气，该区域梅雨期结束”的规定，故在满足上述条件时本文也判定为出梅。

国标在统一国家和地方梅雨监测业务的同时给出了277个梅雨监测代表站[1]。有学者用比国标规定的子区域更小范围内的站点成功识别出区域梅雨过程[8,25,27⇓-29]。本文使用江淮流域239个站点数据识别的梅雨期长度和梅雨雨强与国家气候中心的基本一致[4]，说明使用国标识别梅雨过程并没有对站点的严格限制。这是因为国标在使用站点数据的时候是通过发生降水的站点比例和降水均值判断区域雨日的，而不是站点的绝对数量，但梅雨过程识别结果与站点数量和站点空间分布情况有关，所以在使用子区域部分站点识别局部梅雨过程时，关于国标的适用性和使用技巧在后续研究中需进一步探索。

3.2 梅雨期降水影响因素分析

全球气候变暖造成地表温度升高、水汽增加、水汽辐合和大气不稳定性显著增加，这些因素为梅雨期强降水的形成提供了热力和动力条件，提高了长江三角洲地区极端降水的频率和强度[13,34-35]。本文发现城市化增加了梅雨期的强降水量(图7~8)，城市化主要通过城市热岛效应和城市下垫面变化影响降水的强度和时间[2,31]。城市热岛效应是指城市气温明显高于外围郊区及乡村的现象，城市热岛扰动大气边界层，破坏大气层结构稳定性，加之在水汽充足且凝结核丰富或者其他有利的天气形势下，易形成对流云和对流性降水，或对暴雨产生诱导、强化作用，导致城市下风处的降水受此影响较大[18,31⇓-33]。城市下垫面变化一方面通过改变地表能量平衡，使局地蒸发减少、感热通量增加和边界层水汽混合更均匀，从而对局地降水产生影响[18,31,35]；另一方面由于城市冠层结构的存在，增大空气动力学粗糙度，扰动边界层水汽和能量过程，阻滞降水系统停留时间，降低近地面风速，迫使降水时间延长，导致降水量增加[2,30-31,35]。本文中梅雨量、梅雨期降水发生率和日降水强度的变化趋势在以上海为代表的城市化高速发展地区最为显著，这可能和城市化有关。也有研究表明，当城市化发展到一定阶段后，因为潜热通量变小导致局地大气水分供应减少，最终使城市上空降水减少[31,34,40,46]，这可能是2000年后梅雨期R95P和R99P减少的原因(图8)。人为排放的大气污染物在吸收水汽凝结核后形成的气溶胶也是导致降水变化的原因之一。有研究发现，气溶胶通过影响深层对流云导致暴雨随着气溶胶负荷的增加而增加，气溶胶浓度的增加在一定程度上可以解释中国东部小雨频率和数量下降的现象[2,30-31,47]。

4 结论

本文探讨了江淮流域及其子区域1961—2020年入梅日、出梅日、梅雨季/期长度、梅雨雨强和梅雨强度指数等梅雨监测指标的时空变化特征，分析梅雨期不同降水量级的雨日数、降水发生率、降水量和降水贡献率的时空变化特征，并定量分析了城市化对梅雨期强降水的影响。得到以下主要结论：

(1) 江淮流域及其子区Ⅰ区、Ⅱ区和Ⅲ区多年平均入梅日分别为6月8日、6月9日、6月15日和6月21日，多年平均出梅日分别为7月18日、7月9日、7月14日和7月15日，梅雨季/期长度多年平均值分别为40、30、30和24 d，入梅日、出梅日和梅雨季/期长度年际变化较大，但趋势性均不显著。

(2) 江淮流域及其子区Ⅰ区、Ⅱ区和Ⅲ区梅雨雨强多年平均值分别为306.5、367.6、298.4和253.5 mm，梅雨强度指数多年平均值分别为0.20、0.23、0.31和0.07，梅雨雨强和梅雨强度指数的年际变化较大，只有Ⅱ区的梅雨雨强呈显著增加趋势，梅雨强度指数趋势性不明显。

(3) 各站点梅雨量多年平均值为211.9~489.1 mm，占年降水量的16.8%~28.2%，占夏季降水量的44.4%~61.3%，梅雨量在Ⅰ区和Ⅱ区的114°E~120°E范围内较大，且南部高于北部；梅雨量变化速率为-11.4~44.5 mm/10 a，Ⅰ区和Ⅱ区的东部的55个站点呈显著增加趋势；梅雨期雨日数为12.9~22.1 d，Ⅰ区雨日数最多，Ⅱ区次之，Ⅲ区雨日数最少，雨日数没有明显趋势变化。梅雨期降水发生率为48.9%~74.7%，Ⅱ区东部(江苏省东南部及上海市)的37个站点降水发生率呈显著减少趋势。

(4) 梅雨期雨日以小雨为主，其次是中雨、大雨和暴雨，绝大多数站点小雨日数、中雨日数趋势性不明显，Ⅱ区东部(上海)和中部有24个站点大雨日数、29个站点暴雨日数呈显著增加趋势。小雨、中雨、大雨和暴雨的降水发生率依次减小，Ⅰ区和Ⅱ区较多站点小雨发生率呈显著减少趋势，江苏东南部与上海市最为集中。21个站点中雨发生率呈显著下降趋势，主要分布在Ⅱ区。绝大部分站点的大雨、暴雨发生率趋势性不明显。

(5) 暴雨降水量占梅雨期降水量比重最大，其次是大雨、中雨和小雨，大雨量有28个站点呈显著增加趋势，大多分布在Ⅱ区的中东部。暴雨量有24个站点呈显著增加趋势，分布在Ⅰ区和Ⅱ区东部。暴雨的降水贡献率最大，其次是大雨、中雨和小雨，小雨贡献率有17个站点呈显著降低趋势，大多分布在Ⅱ区的中东部。中雨贡献率有17个站点呈显著降低趋势，大多分布在Ⅰ、Ⅱ区的中东部。绝大部分站点的大雨、暴雨贡献率趋势性不明显。

(6) 梅雨期3类站点R95P和R99P以增加趋势为主，城市站点、农村→城市站点和农村站点R95P变化速率的均值分别为20.42、18.76和15.24 mm/10 a，R99P变化速率的均值分别为12.63、11.14和7.66 mm/10 a。城市站点R95P和R99P的变化速率大于农村站点，R95P和R99P的城市化贡献率分别为10.59%和17.39%。

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基于中国1951-2014年728个气象站点日降水数据,利用POT抽样、变异点分析、趋势分析、分段回归等方法全面分析中国极端降水事件（量级、频率与发生时间）非平稳性特征及其对夏季温度响应。研究结果发现中国极端降水量级有明显变异特征,但无显著趋势变化,中国极端降水频率则相反。并且中国极端降水次数在全国大部分区域有显著增加趋势。另外,研究还表明变异点对中国极端降水量级和频率趋势特征有明显的改变,对极端降水发生时间趋势特征改变较弱;中国极端降水发生时间在中南部呈显著上升趋势,其他区域趋势性不显著。中国大部分区域夏季温度呈上升或显著上升趋势,且变异特征显著。在转折点前,中国大部分区域如西部干旱区东南部、东部干旱区西南部、华北区、华中区和西南区北部夏季温度呈下降或显著下降趋势;在转折点后,上述区域夏季温度转为上升或显著上升趋势。

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Daily precipitation and temperature data at 728 stations covering a period of 1951 to 2014 across China were analyzed to investigate extreme precipitation regimes in terms of spatiotemporal patterns, precipitation magnitude, precipitation frequency, occurrence timing, non-stationarity of extreme precipitation regimes and also their responses to summer temperature changes. The methods adopted in this study are Peak over Threshold (POT) sampling technique, change point analysis, trend analysis and piecewise regression method. Results of this study indicated that magnitude of extreme precipitation events was subject to evident alterations but free of significant trends. However, occurrence rates of extreme precipitation were subject to adverse changing properties when compared to changes in magnitude of extreme precipitation. The entire China was dominated by apparent increase of occurrence rates of extreme precipitation. Besides, change points within magnitude and occurrence rates of extreme precipitation tended to modify the changing tendency therein. However, occurrence timing of extreme precipitation events seemed to be free of impacts from change points. In most regions of China, occurrence time of extreme precipitation events was delayed and no changes in occurrence time of extreme precipitation could be observed in other regions of China.

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气候异常变化导致洪涝灾害、干旱等极端水文事件的发生及其增加的水灾害风险正成为人类生存所面临的重大挑战。近年来,开展气候变化背景下流域极端水文事件的变化趋势、发生机理及其对气候变化的响应与预测研究正逐渐受到国内外众多学者的关注。本文从极端水文事件的定义、研究方法、水文极值分布函数、观测事实及模拟研究等方面分析评述了极端水文事件问题的研究现状和研究成果。在此基础上,讨论了研究现状存在的问题并对该领域未来研究方向进行了展望。

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Extreme hydrologic events such as floods and droughts caused by abnormal climate change and increased risks of water disasters are becoming the major challenges to the survival of the people survival. In recent years, the research on the change trend, mechanisms, response and prediction of the climate change of the extreme hydrologic events in basins under climate change is drawing more and more attention from the scholars at home and abroad. This paper begins with the definitions of extreme hydrologic events, and then analyzes and reviews the research methods, and hydrologic extreme value distribution, as well as the observation and simulation of extreme hydrologic events. On this basis, this paper makes a discussion on the problems in the present research. The directions of further research are also provided.

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利用北京地区20个气象站点35年（1980—2014年）的逐日降水、气温资料和社会经济、人口、土地覆被以及夜间灯光遥感的栅格数据,通过聚类分析和层次分析相结合的方法将站点分类,对北京市城市化进程中的极端气候变化趋势和城市化影响的响应进行了分析。结果表明：北京市整体的极端冷事件呈减少趋势,极端暖事件呈增加趋势,极端降水事件没有明显变化趋势;城市化对极端温度事件有着显著的影响,相对于乡村站点来说,城市化使得极端冷事件减少的更快,极端暖事件增加的更快,造成城市的气温日较差明显变小;城市化对极端降水的影响虽不显著,但在乡村站点年降水量总量和降水日数减小的趋势下,城市化造成城市降水总量和降水日数均多于乡村,减缓了连续无降水日数的增长趋势,同时城市的极端降水有向更加极端的情景发展。

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It had been proved that the process of urbanization had an effect on extreme weather events under the global warming. This study analyzed the urbanization effects on extreme climate events for the period 1980-2014 by classifying 20 meteorological stations into urban, suburban, and rural categories, based on the daily precipitation, temperature data and raster data of socio-economic, population, land cover and the night-time light images combined with cluster analysis and analytic hierarchy methods. The results show that the number of extreme cold events in Beijing showed decreasing trends, while that of the extreme warm events exhibited increasing trends, and the extreme precipitation events showed no obvious change trend. Compared with rural sites, urbanization has a significant impact on extreme temperature events. In urban areas, the number of extreme cold events was reduced faster; that of extreme warm events increased faster; and the daily-temperature-range significantly became smaller than that in urban areas. Compared with the decreasing trends of annual precipitation and annual rainfall days at rural sites, urbanization increased total precipitation amount. Besides, the urbanization in Beijing slowed down the growth trend of consecutive dry days, and at the same time, it intensified the extreme heavy precipitation events. However, the impact of urbanization on extreme precipitation events is not significant.

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