Spatial Pattern Difference of Contribution between Short and Long-duration Heavy Rainfall to Total Heavy Rainfall in China from 1961 to 2015

Many regions are pounded with heavy rainfall, causing flood, casualties, property damage and severe destruction to ecosystem in multiple urban areas. Frequent occurrence of extremely heavy precipitation event under the background of global climate change has caused terrible harm on economic and social development, life security, ecosystem, etc.;brought profound impact on sustainable development of disaster area;become a key factor of global and regional disasters and environmental risk;and been widely concerned by academic circle and all sectors of the society. So severe disasters caused by extreme precipitation events have attracted more and more attention, while the relationship between heavy rainfall with different duration and total heavy rainfall has become the hottest scientific frontier issue. Contribution of heavy rainfall with different duration to the total heavy rainfall has significant spatial differences. Here we used daily rainfall data from 1961 to 2015 of 659 meteorological stations in China. When the rainfall is greater than 50 mm in 24 hours, that is a heavy rainfall event. Heavy rainfall only lasting one day is defined as short- duration heavy rainfall, while heavy rainfall lasting more than two days is defined as long-duration heavy rainfall. Results indicated that: on the basis of duration days defined long-duration heavy rainfall, on the spatial distribution, total rainfall, total heavy rainfall and short-duration heavy rainfall showed "increasing-decreasing-increasing" from the southeast coast to northwest inland in China from 1961 to 2015, and on the whole meteorological station with increasing trend predominant. In the meantime, long-duration heavy rainfall showed "increasing-decreasing" spatial pattern, and on the whole meteorological station with decreasing trend predominant. We detected that there was a belt of becoming drought from northeast to southwest. The contribution of total heavy rainfall to total rainfall as well as long-duration heavy rainfall to total heavy rainfall showed "high in southeast-low in northwest" spatial distribution pattern. On the contrary, the contribution of short-duration heavy rainfall to total heavy rainfall showed "low in southeast-high in northwest" spatial distribution pattern. The contribution trend of total heavy rainfall to total rainfall and short-duration heavy rainfall to total heavy rainfall showed "increasing-mosaic with increasing and decreasing-increasing" spatial distribution pattern from northeast to southwest, and on the whole meteorological station with increasing trend predominant. On the contrary, the contribution trend of long-duration heavy rainfall to total heavy rainfall showed mosaic with increasing and increasing in the northeast, slightly decreasing in the southwest, and on the whole meteorological station with decreasing trend predominant. There was a climate transition zone from northeast to southwest, which was essentially coincident with the arid zone. The results suggested that the precipitation in China was changing to extremely accompanied by short-duration storm increased significantly. Chinese heavy rainfall especially the increase of short-duration heavy rainfall suggests that human activity is likely to be triggered an increasing in extreme precipitation.

Spatiotemporal Characteristics of Rainfall over Different Terrain Features in the Middle Reaches of the Yangtze River Basin during the Warm Seasons of 2016–20

Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variations over the middle reaches of the Yangtze River basin.For all three types of terrain(i.e.,mountain,foothill,and plain),the amount of TR and SDHR both maximize in June/July,and the contribution of SDHR to TR(CST)peaks in August(amount:23%;frequency:1.74%).Foothill rainfall is characterized by a high TR amount and a high CST(in amount);mountain rainfall is characterized by a high TR frequency but a small CST(in amount);and plain rainfall shows a low TR amount and frequency,but a high CST(in amount).Overall,stations with high TR(amount and frequency)are mainly located over the mountains and in the foothills,while those with high SDHR(amount and frequency)are mainly concentrated in the foothills and plains close to mountainous areas.For all three types of terrain,the diurnal variations of both TR and SDHR exhibit a double peak(weak early morning and strong late afternoon)and a phase shift from the early-morning peak to the late-afternoon peak from May to August.Around the late-afternoon peak,the amount of TR and SDHR in the foothills is larger than over the mountains and plains.The TR intensity in the foothills increases significantly from midnight to afternoon,suggesting that thermal instability may play an important role in this process.

Statistical Characteristics of Environmental Parameters for Warm Season Short-Duration Heavy Rainfall over Central and Eastern China

Water vapor content, instability, and convergence conditions are the key to short-duration heavy rainfall forecasting. It is necessary to understand the large-scale atmospheric environment characteristics of short- duration heavy rainfall by investigating the distribution of physical parameters for different hourly rainfall intensities. The observed hourly rainfall data in China and the NCEP final analysis （FNL） data during 1 May and 30 September from 2002 to 2009 are used. NCEP FNL data are 6-hourly, resulting in sample sizes of 1573370, 355346, and 11401 for three categories of hourly rainfall （P） of no precipitation （P 〈 0.1 mm h-1）, ordinary precipitation （0.1≤ P 〈 20 mm h-1）, and short-duration heavy rainfall （P ≥ 20.0 mm h-1）, respectively, by adopting a temporal matching method. The results show that the total precipitable water （PWAT） is the best parameter indicating the hourly rainfall intensity. A PWAT of 28 mm is necessary for any short-duration heavy rainfall. The possibility of short-duration heavy rainfall occurrence increases with PWAT, and a PWAT of 59 mm is nearly sufficient. The specific humidity is a better indicator than relative humidity. Both 700- and 850-hPa relative humidity greater than 80% could be used to determine whether or not it is going to rain, but could not be used to estimate the rainfall intensity. Temperature and potential pseudo-equivalent temperature are also reasonable indicators of short-duration heavy rainfall. Among the atmospheric instability parameters, the best lifted index （BLI） performs best on the short- duration rainfall discrimination; the next best is the K index （KI）. The three rainfall categories are not well recognized by total totals （TT） or the temperature difference between 850 and 500 hPa （DT85）. Three- quarters of short-duration heavy rainfall occurred with BLI less than -0.9, while no short-duration heavy rainfall occurred when BLI was greater than 2.6. The minimum threshold of KI was 28.1 for short-duration heavy rainfall. The importance of dynamic conditions was well demonstrated by the 925- and 850-hPa divergence. The representativeness of 925-hPa divergence is stronger than that of 850 hPa. Three-quarters of short-duration heavy rainfall occurred under a negative divergence environment. However, both the best convective potential energy （BCAPE） and vertical wind shear were unable to discriminate the hourly rainfall intensities.

Distribution and Diurnal Variation of Warm-Season Short-Duration Heavy Rainfall in Relation to the MCSs in China

Short-duration heavy rainfall（SDHR） is a type of severe convective weather that often leads to substantial losses of property and life. We derive the spatiotemporal distribution and diurnal variation of SDHR over China during the warm season（April–September） from quality-controlled hourly raingauge data taken at 876 stations for 19 yr（1991–2009）, in comparison with the diurnal features of the mesoscale convective systems（MCSs） derived from satellite data. The results are as follows. 1） Spatial distributions of the frequency of SDHR events with hourly rainfall greater than 10–40 mm are very similar to the distribution of heavy rainfall（daily rainfall 50 mm） over China's Mainland. 2） SDHR occurs most frequently in South China such as southern Yunnan, Guizhou, and Jiangxi provinces, the Sichuan basin, and the lower reaches of the Yangtze River, among others. Some SDHR events with hourly rainfall 50 mm also occur in northern China, e.g., the western Xinjiang and central-eastern Inner Mongolia. The heaviest hourly rainfall is observed over the Hainan Island with the amount reaching over 180 mm. 3） The frequency of the SDHR events is the highest in July, followed by August. Analysis of pentad variations in SDHR reveals that SDHR events are intermittent, with the fourth pentad of July the most active. The frequency of SDHR over China's Mainland increases slowly with the advent of the East Asian summer monsoon, but decreases rapidly with its withdrawal. 4） The diurnal peak of the SDHR activity occurs in the later afternoon（1600–1700 Beijing Time（BT））, and the secondary peak occurs after midnight（0100–0200 BT） and in the early morning（0700–0800 BT）; whereas the diurnal minimum occurs around late morning till noon（1000–1300 BT）. 5） The diurnal variation of SDHR exhibits generally consistent features with that of the MCSs in China, but the active periods and propagation of SDHR and MCSs difer in diferent regions. The number and duration of local maxima in the diurnal cycles of SDHR and MCSs also vary by region, with single, double, and even multiple peaks in some cases. These variations may be associated with the diferences in large-scale atmospheric circulation, surface conditions, and land-sea distribution.

基于机器学习的黑龙江省强降水致灾预估方法研究

采用黑龙江省1984—2019年各县强降水灾情资料和逐日降水资料,以逻辑回归和长短时记忆网络模型为基础,建立了黑龙江全省、大兴安岭、小兴安岭、松嫩平原、三江平原和东南半山区的强降水致灾与否二分类预估模型。通过机器学习,得到黑龙江省以及5个地区判断强降水致灾与否的最佳观测天数在4~6 d、最佳的日降水量阈值为16~20 mm。比较全连接逻辑回归模型、优先考虑日期的部分连接逻辑回归模型D、优先考虑站点的部分连接逻辑回归模型S和长短时记忆网络LSTM模型等四个模型的表现,前三种逻辑回归模型表现差距不大,相对表现最好的全连接模型,其在大部地区所表现的准确率、精确率、召回率和F1分数均在0.7以上,而LSTM模型只在大兴安岭表现更好一些。

京津冀暖季短时强降水环境特征对比分析

利用2013—2021年暖季(6—9月)京津冀地区逐时降水资料和ERA5再分析资料,根据天气形势对短时强降水进行天气学分型,统计短时强降水发生的时空特征,对比动力、水汽和热力不稳定条件等环境要素特征。结果表明:短时强降水以冷涡型和副高型为主,占到55%,且主要发生在7月中下旬到8月上中旬,强降水主要集中在下午到前半夜。对比发现,不同类型短时强降水空间分布特征区别明显,分型合理;各类型大多表现为低层辐合和高层辐散,西南涡型动力强度表现最强,且随降水临近是增加的,弱天气强迫型在降水前各个时段动力表现最弱;副高型、台风型和西南涡型水汽最为充沛,副高型925 hPa比湿中位数达到19.14 g·kg~(-1),西南涡型在高低层相对湿度均较大,低层平均相对湿度中位数达到87%,弱天气强迫型相对较差,为74%,各类型整层大气可降水量在降水前随时间基本是增加的;弱天气强迫型的热力不稳定性最为突出,850 hPa和500 hPa温度差中位数最大为25.74℃,西南涡型最小,除弱天气强迫型外,各类型热力条件随时间是减弱的。

近20年季风爆发前后珠江三角洲前汛期短时强降水的时空演变特征与成因

相较于暴雨这种日尺度强降水,短时强降水(≥20 mm h^(−1))是造成山洪滑坡与城市内涝等灾害更为直接的因素。本文利用地面气象观测站和ERA5再分析数据,重点研究南海季风爆发前后珠江三角洲地区(简称珠三角)短时强降水的时空演变特征,并探索了短时强降水在季风爆发前后特征差异的可能成因。研究表明:(1)相较于季风爆发前,珠三角地区季风爆发后的降水明显增多,其中短时强降水贡献的比例显著增加。对短时强降水本身而言,区域平均强度以及极端性在季风爆发前后差异总体较小,但短时强降水频率在季风爆发后增加70%。(2)短时强降水高发区主要集中在珠三角东北部和珠江口西侧沿海,季风爆发后上述两个地区的频次增多最明显。短时强降水频率由季风爆发前的单峰型(下午)转为季风爆发后的双峰型(早晨与下午)。(3)短时强降水具有明显的区域性变化特征,短时强降水在季风爆发后的平均雨强和极端性在珠江口西侧沿海较内陆地区明显增强,其频次峰值时间在沿海地区从季风爆发前的午后转为季风爆发后的早晨,内陆地区在季风爆发前后均集中在下午。(4)季风爆发后,短时强降水期间的低层环境水汽超过同期气候态水平的16%。充沛的水汽在夜间在季风加速作用下被输送至沿海,并与陆风作用增强了辐合,这解释了沿海短时强降水的在季风爆发前后频次峰值时间转换现象。(5)相较于季风爆发前,季风爆发后珠三角短时强降水频率与低层水汽通量的相关性明显升高。珠三角沿海地区夜间—早晨短时强降水的增多与中低层风场结构改变造成的动力强迫有关。内陆地区季风爆发前后短时强降水与环境热力和不稳定条件关系更大。这些结果有助于我们更好地了解珠三角地区在季风爆发前后短时强降水的时空分布特征和理解其产生机制。

川西高原甘孜州汛期短时强降水的时空分布特征

利用2012—2021年4—10月甘孜州18个基准(基本)气象站和489个区域气象站逐小时降水观测资料,分析川西高原甘孜州汛期短时强降水的时空分布特征。结果表明:(1)2012—2021年甘孜州汛期短时强降水总频次为1906站次,平均每年190.6站次,且频次随量级增大呈指数衰减,15~25 mm/h短时强降水占84%以上。(2)短时强降水易发生在午后到上半夜,其日变化特征为双峰型,峰值出现在20—22时(北京时),峰值最高和次高分别为22时和20时;月变化呈单峰型,7月最多发(占30.16%),6月和8月次之;年际变化不均,2012年短时强降水发生频次最少,仅38站次,而2020年短时强降水发生频次最多,达336站次。(3)短时强降水空间分布不均,呈东多西少、南多北少的特征。发生频次最高地区位于泸定县南部,次高地区为九龙县南部。(4)受复杂地形与区域环流的共同影响,甘孜州短时强降水与海拔高度有密切关系,主要出现在1100~<3900 m海拔高度,尤其在1100~<1900 m海拔高度最易发生,其雨强最大峰值(63.3 mm/h)出现在1100~<1300 m高度范围内。

中国短时强降雨对暴雨的贡献特征

中国暖季暴雨有显著的对流性特征,但尚不明确对流导致的短时强降雨对不同等级暴雨的贡献程度。利用1951—2019年的逐时降水资料,统计分析了中国两种强度的短时强降雨(小时雨量≥20 mm和小时雨量≥50 mm,分别简称为HR20和HR50)和不同等级暴雨之间的关系,得到了两类短时强降雨对不同等级暴雨的贡献特征。结果显示,短时强降雨发生频率高的暴雨分布区域与暴雨日数多的区域并不一致,在华北南部到黄淮地区和西南地区到华南地区短时强降雨对暴雨的影响最为显著,其超过50%的暴雨中伴随HR20,华北南部和华南中部地区暴雨中发生短时强降雨的占比超过了70%;随着暴雨等级的提升,伴随短时强降雨的比例逐渐增大,尤其HR50的占比增加显著,超60%的特大暴雨中伴有HR50,表明暴雨越强,其对流性越强。在华北南部到黄淮地区、西南地区东部和华南地区,短时强降雨雨量对暴雨雨量的贡献也最大,且随着暴雨等级的提升,这些地区短时强降雨雨量在暴雨总雨量中的占比呈显著增长的趋势,HR50的贡献增幅超过100%;而江淮、江南等地区短时强降雨雨量的贡献较小,随暴雨强度等级的增强其增大程度也相对不明显。此外,伴随有HR20的暴雨、大暴雨平均日雨量较无短时强降雨的暴雨平均日雨量分别多20%和40%以上,进一步印证了中国暴雨对流性强的特点。

基于CMA-BJ的北京地区短时强降水预报试验

基于2019—2021年4—9月北京快速更新数值预报系统(CMA-BJ)产品以及北京地区地面气象站逐时降水实况,从表征水汽条件、热力和能量条件以及动力条件的多个物理量中筛选出在有无降水、是否强降水情形中有显著差异的物理量作为因子,采用配料法和模糊逻辑算法构建北京地区0~12 h时效逐小时短时强降水概率预报模型。以2019—2021年4—9月最优TS评分和偏差评分的概率值和组合反射率因子为确定性预报的概率阈值和消空处理阈值,运用该预报模型对2022年4—9月每日4次0~12 h预报时效北京地区短时强降水产品进行预报和检验。结果表明:北京地区短时强降水TS评分和偏差评分分别为0.104和1.341,预报效果明显优于CMA-BJ预报产品。概率预报模型能够有效提升强降水高发地区,即山前及平原地区的短时强降水预报技巧,获得较为平衡的命中率和空报率,但对山区预报技巧的提升有限。

基于小时降水资料的贵州降水精细特征分析

利用2010—2019年贵州地区2 683个自动气象站逐时降水观测资料,结合地形特征分析了贵州降水的时空特征,并从平均雨强和降水概率方面进一步研究了贵州各季日内降水的精细化特征。结果表明:(1)贵州年降水时长和年降水量空间分布有所差异,位于大娄山脉北侧的遵义赤水和习水地区仅为降水时长的大值区;(2)四季降水时长分布受当季主要降水系统位置的影响,平均降水强度兼受地理条件的影响,偏向山脉的南麓;(3)在日内降水分布上,贵州四季都存在明显的夜雨现象,其中下半夜到凌晨(00—06时)降水发生概率更大且强度更强,其中心有明显自西向东传播的特征;(4)短时强降水主要出现在春夏季且向东传播明显,主要在夜间到早晨。

阜康地区夏季短时强降水时空分布特征

利用阜康地区气象自动站2013—2021年夏季逐小时降水量资料,分析了研究区夏季短时强降水过程(一天中有一个或以上的小时降水量≥10 mm)的时空分布特征。结果表明:阜康夏季短时强降水总体呈现随海拔高度升高,发生频次增多的特征,在水平和垂直空间上的分布呈现明显的不均衡性;受阜康特殊地势的影响,山区短时强降水空间分布特征与夏季洪水发生规律基本一致、呈现西多东少的特点,平原地区呈现南多北少的特点;时间分布上,8月中旬是研究区短时强降水最为活跃的时期,其中山区降水日变化呈双峰型分布,18:00—次日03:00(北京时,下同)为降水活跃期,主峰和次峰分别在02:00和21:00,与天山北坡累计降水量高值时段出现在夜间,且自西向东降水量的单峰形态有向双峰结构演变的趋势结论一致;平原短时强降水主要时段在19:00—24:00,为单峰型,峰值出现在22:00;20 mm以上的极端短时强降水过程集中出现在6月下旬和8月中旬。

基于FY-4A卫星的川藏铁路成雅段短时强降水TBB特征

利用FY-4A卫星资料和区域自动站逐时降水数据,研究了2019—2021年川藏铁路成雅段短时强降水时空分布及其云顶亮温(TBB)的变化特征。结果表明:川藏铁路成雅段短时强降水出现频次随着雨强的增大而减少,且呈“北少南多”的空间分布特征,4—10月均有发生且主要集中在7—9月,夜间为高发时段,尤其在01—07时和22—23时频发。该区域短时强降水的降水量与TBB呈显著负相关,与TBB梯度呈负相关,与亮温差(水汽通道与红外通道的亮温差值)呈正相关,短时强降水易出现在TBB偏低、梯度偏小且亮温差偏大的对流云团处。短时强降水的出现伴随着TBB降到最低值,随着TBB逐渐上升,短时强降水结束。如果已出现短时强降水,TBB仍在继续下降,则短时强降水将持续,其持续时间越长,则对流云团越强。短时强降水出现时TBB和最小TBB主要在190~230 K之间,其中以190~200 K居多。

2022年9月16日石林、罗平、江城局地短时强降雨分析

9月16日09:00~11:00石林、罗平、江城经历了一次暴发速度快、降水量多、局地边界明显、破坏性强的降水过程。通过分析雷达和地形,得出了“速度值越大,降水越强;坡度越大,降水越强”的结论,指出了预报此类降水的手段是“看速度的变化,注意云团移动方向前方是否有陡峭的山脉”,提出了快速发布的建议,即让智能程序自动检测采用“自下而上”的预警流程;并提出了规避风险的做法,即推广半永久性钢架大棚,使作物生长周期避开灾害多发时间。

中国短历时和长历时暴雨对总暴雨贡献的空间差异性研究(1961-2015)

采用1961-2015年659站日值降水数据,以持续1 d和持续2 d及以上暴雨作为短和长历时暴雨标准,分析不同历时暴雨变化趋势,结果表明,中国总降雨、总暴雨和短历时暴雨从东南沿海向西北内陆依次呈"增-减-增"的分布特征,且整体以增加趋势的站点占主导,而长历时暴雨则呈现出"增-减"的分布特征,且整体以减少趋势的站点占主导,并且检测出中国自东北向西南存在一条变干带。同时中国总暴雨对总降雨、长历时暴雨对总暴雨的贡献呈现出"东南高-西北低"的分布特征,而短历时暴雨对总暴雨的贡献呈现出"东南低-西北高"的分布特征。中国总暴雨对总降雨、短历时暴雨对总暴雨贡献的变化趋势呈现出"增-增减镶嵌-增"的分布特征,且以增加趋势的站点占主导,而长历时暴雨在东部沿海地区呈现出增减镶嵌的趋势,而西北内陆地区呈略微减少趋势,且以减少趋势的站点占主导,也检测出自东北向西南存在一条气候过渡带并与上述变干带基本重合。

中国长短历时暴雨时空变化格局及其对总暴雨贡献的研究(1951—2010)

采用中国1951—2010年659个站点的日值降水数据,以暴雨持续长短为标准对短和长历时暴雨计算,结果表明:在空间上,中国短历时暴雨量从1951到2010年呈现出由东南沿海向西北内陆梯次减少的现象,而长历时暴雨量则主要集中在广东、广西、海南等东南沿海地区。在时间上,中国年际和年代际短和长历时暴雨均呈现增加趋势。在降水贡献率占比上,1951—2010年中国总暴雨量占总降雨量以及总暴雨日占总降雨日的比例分别为6.1%—27.7%和0.6%—2.5%;同期中国短历时暴雨量在总暴雨量和短历时暴雨日在总暴雨日中的比例分别为75.9%—89.4%和75.6%—89.2%,短历时暴雨占主导地位;而长历时暴雨量在总暴雨量和长历时暴雨日在总暴雨日中的比例分别只占10.6%—24.1%和10.8%—24.4%。在降水贡献率变化趋势上,在1951—2010年间,中国总暴雨对总降雨的贡献率呈增加趋势,其雨量和雨日的贡献率趋势分别为2.1%/10a和0.2%/10a;短历时暴雨对总暴雨的贡献率也呈增加趋势,其雨量和雨日的贡献率趋势分别为0.5%/10a和0.4%/10a;而长历时暴雨对总暴雨的贡献率呈减少趋势,其雨量和雨日的贡献率趋势分别为-0.5%/10a和-0.4%/10a。以上结果表明中国降水在朝着极端化方向变化,短历时暴雨增多显著。

中国地区降水持续性的季节变化特征

本文利用我国588个气象站1969—2008年逐12小时的降水资料,分析了中国地区降水持续性的空间分布特征及其季节演变规律。分析结果表明,35°N以南,西部和东部年平均的降水平均持续时间较长,中部略短;35°N以北,西北和内蒙西部最短,东北地区北部略长。将降水事件按持续时间分类自南向北,东南地区、江淮和黄淮地区、东北和华北北部地区短时降水(持续一个时次,12小时)的降水量和降水频率占全年总降水的比例逐渐增加,持续性降水(持续3个时次及以上)的比例减少。降水平均持续时间随季节的变化基本能反映出江南春雨、江淮梅雨、东北和华北夏季雨季、关中盆地和汉水谷地的秋雨以及青藏高原地区和西南地区夏季雨季。同时,东南地区秋冬季节、江淮和黄淮地区10月上旬和西南地区10月下旬存在降水平均持续时间的峰值,与降水量的变化不一致,是由持续性降水频率的增加和短时降水频率的减少造成的。此外,东部三个区域降水平均持续时间的夏季季节内变化对应了季风雨带的"北跳和南撤"过程。

短时强降水诊断物理量敏感性的点对面检验

对诊断物理量的准确认识可以帮助提高短时强降水的预报准确率,并帮助理解产生短时强降水的可能机制。考虑我国降水观测网的布设特点,结合NCEP最终分析资料的物理量场,以大气水汽总量和最优抬升指数为例,通过点对面检验分析了多个用于表征短时强降水环境特征的诊断物理量的敏感性。结果表明:常规的点对点检验是点对面检验的特殊情况。大气水汽总量和最优抬升指数对短时强降水的指示均存在最佳阈值,且140 km范围内的大气状况才对某点3 h内能否出现短时强降水有直接影响。对于水平分辨率为1°×1°的NCEP资料,建议点对面检验的搜索半径和记录数阈值分别为140 km和2个记录。对多个诊断物理量对比分析显示,短时强降水对水汽相关量最为敏感,其次是表征热力条件的物理量,而表征动力条件和垂直风切变的量的指示意义不够显著。

CMORPH卫星-地面自动站融合降水数据在中国南方短时强降水分析中的应用

对比分析了国家级气象观测站逐时地面降水资料和CMORPH卫星-地面自动站融合降水数据在反映中国南方地区2008—2013年4—10月短时强降水时空分布特征上的差异,并在此基础上利用融合降水数据分析了短时强降水与暴雨的关系,结果表明:(1)融合降水数据所反映的短时强降水的大尺度特征与站点资料一致,并能更好地描述地形的影响;(2)短时强降水的季节变化与东亚夏季风进程和雨带的季节性位移密切相关;(3)短时强降水与暴雨日的空间分布特征和季节变化趋势相似,4月下半月—10月上半月,超过60%的短时强降水发生在暴雨日,同时短时强降水也是暴雨形成的重要因素,短时强降水暴雨日数占总暴雨日数的比例(68.6%)普遍高于非短时强降水暴雨日(31.4%),但是短时强降水暴雨日的发生具有显著的季节和区域差异。

降雨入渗作用下土质边坡稳定性分析

为探讨降雨入渗条件下土质边坡稳定性的变化规律,研究了降雨强度、降雨历时、前期降雨、土体渗透特性以及降雨类型等因素对边坡稳定性的影响。研究结果表明,边坡的安全性随着降雨强度和降雨历时增加而不断降低;前期降雨过程通过改变后期降雨的初始含水量来间接影响边坡的稳定性,并随着降雨时间间隔的增长呈现出先降低后升高的趋势;土体渗透特性对边坡稳定性起着关键作用,土体渗透系数越大,边坡安全性能也随之增强;与短时强降雨相比,长时弱降雨对边坡的危害程度更大。