Spatial patterns and temporal trends of precipitation in Iran

Spatial patterns of monthly, seasonal and annual precipitation over Iran and the corresponding long-term trends for the period 1951–2009 are investigated using the Global Precipitation Climatology Centre gridded dataset. Results suggest that the spatial patterns of annual, winter and spring precipitation and the associated coefficients of variation reflect the role of orography and latitudinal extent between central-southern arid and semi-arid regions and northern and western mountainous areas. It is also shown that precipitation occurrence is almost regularly distributed within the year in northern areas while it is more concentrated in a few months in southern Iran. The spatial distribution of Mann–Kendal trend test (Z statistics) for annual precipitation showed downward trend in north-western and south-eastern Iran, whereas western, central and north-eastern exhibited upward trend, though not statistically significant in most regions. Results for winter and autumn revealed upward trend in most parts of the country, with the exception of north-western and south-eastern where a downward trend is observed; in spring and summer, a downward trend seems to prevail in most of Iran. However, for all seasons the areas where the detected trend is statistically significant are limited to a few spot regions. The overall results suggest that the precipitation is decreasing in spring and summer and increasing in autumn and winter in most of Iran, i.e. less precipitation during the warm season with a consequent intensification of seasonality and dryness of the country. However, since the detected trends are often not statistically significant, any stringent conclusion cannot be done on the future tendencies.     

Homogeneity analysis of precipitation series in Iran

Assessment of the reliability and quality of historical precipitation data is required in the modeling of hydrology and water resource processes and for climate change studies. The homogeneity of the annual and monthly precipitation data sets throughout Iran was tested using the Bayesian, Cumulative Deviations, and von Neumann tests at a significance level of 0.05. The precipitation records from 41 meteorological stations covering the years between 1966 and 2005 were considered. The annual series of Iranian precipitation were found to be homogeneous by applying the Bayesian and Cumulative Deviations tests, while the von Neumann test detected inhomogeneities at seven stations. Almost all the monthly precipitation data sets are homogeneous and considered as “useful.” The outputs of the statistical tests for the homogeneity analysis of the precipitation time series had discrepancies in some cases which are related to different sensitivities of the tests to break in the time series. It was found that the von Neumann test is more sensitive than the Bayesian and Cumulative Deviations tests in the determination of inhomogeneity in the precipitation series.     

Spatial patterns and temporal trends of daily precipitation indices in Iran

Spatial patterns of daily precipitation indices and their temporal trends over Iran are investigated using the APHRODITE gridded daily precipitation dataset for the period 1961–2004. The performance and limitations of the gridded dataset are checked against observations at ten rain-gauge stations that are representative of different climates in Iran. Results suggest that the spatial patterns of the indices reflect the role of orography and sea neighborhoods in differentiating central-southern arid and semi-arid regions from northern and western mountainous humid areas. It is also found that western Iran is impacted by the most extreme daily precipitation events occurring in the country, though the number of rainy days has its maximum in the Caspian Sea region. The time series of precipitation indices is checked for long-term trends using the least squares method and Mann-Kendall test. The maximum daily precipitation per year shows upward trends in most of Iran, though being statistically significant only in western regions. In the same regions, upward trends are also observed in the number of wet days and in the accumulated precipitation and intensity during wet days. Conversely, the contribution of precipitation events below the 75th percentile to the annual total precipitation is decreasing with time, suggesting that extreme events are responsible for the upward trend observed in the total annual precipitation and in the other indices. This tendency towards more severe/extreme precipitation events, if confirmed by other datasets and further analyses with longer records, would require the implementation of adequate water resources management plans in western Iran aimed at mitigating the increasing risk of intense precipitation and associated flash floods and soil erosion.     

Spatial and temporal analysis of observed trends in extreme precipitation events in different climatic zones of Nigeria

Theoretical and Applied Climatology - The purpose of this study is to assess spatial and temporal changes in extreme precipitation events from 1983 to 2017 over different climatic regions of...     

Spatial analysis of monthly precipitation in Turkey

Summary  The principal objectives of this paper are to develop and validate an optimum interpolation method for the spatial analysis of monthly precipitation in Turkey. A two-dimensional optimum interpolation objective analysis scheme has been developed for the spatial analysis of precipitation. The model is developed for generating statistically optimum interpolation based on the irregular distribution of meteorological stations. One question that affects the optimum interpolation method and, indeed, all such techniques, is how many observations should be allowed to influence a given grid point? The method developed in this paper addresses this question. For the implementation of the method, 52 stations are considered for Turkey, with 30 years of monthly data at each point. It is observed that each monthly average spatial correlation function shows a monotonically decreasing pattern based on 15 km interval averages. The method provides high estimation accuracy in dense station locations such as in northwestern Turkey. Precipitation contour maps obtained by the optimum interpolation method indicate two spatial trends over Turkey which have not been identified in any previous study. Received June 24, 1999/Revised April 26, 2000     

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。     

江西省极端降水分类及特征分析

利用多种观测及再分析资料对江西省极端降水过程进行统计分类和天气学分析。采用百分位法、广义极值法对比计算江西省极端降水强度阈值标准,分析江西省极端降水事件的空间分布特征,筛选出2010—2019年江西省极端降水的典型过程,并进行天气学成因分析。结果表明：1） 江西省各站极端降水强度阈值空间分布差异较大,由西南向东北逐步增加,具有非常明显的地域性特征;2） 江西省典型极端降水过程发生在夏季（6—8月）,主要分为梅雨锋、强台风、西北气流类;3） 江西省梅雨锋类极端降水占典型极端降水过程的70%,该类极端降水具有稳定的大尺度环流形势,主要表现为南亚高压北跳至25°N以北,阻塞高压异常偏强,东北冷涡发展,副热带高压与大陆高压相结合,配合天气尺度稳定的切变线以及异常的西南暖湿气流,形成持续的水汽输送及异常的垂直上升运动中心。     

宜昌一次致灾极端短时强降水成因分析

利用常规气象观测资料、地面加密自动站资料、NCEP 1°×1°再分析资料、卫星及风廓线雷达和多普勒雷达资料,对2016年7月7日夜间湖北宜昌地区一次致灾极端短时强降水过程,从大尺度环流背景、中尺度特征以及地形等方面进行分析。结果表明:这次局地强降水产生于副热带高压边缘的西南暖湿气流中,表现出中低层中尺度动力抬升强、降水效率高、地形作用明显等特点。峡谷入口处地面中尺度涡旋与强垂直风切变相互作用造成强上升运动为强降水提供了充足的动力条件,较弱的引导气流和山体阻挡作用使得局地降水维持时间长,共同造成了此次极端短时强降水的发生。回波的低质心结构提高了降水效率,降水过程中单体的后向传播也使局地累计雨量增大。     

A relation between extreme daily precipitation and extreme short term precipitation

The Royal Netherlands Meteorological Institute (KNMI) has published the KNMI’06 climate scenarios in 2006. These scenarios give the possible states of the climate in The Netherlands for the next century. Projections of changes in precipitation were made for a time scale of 1 day. The urban drainage sector is, however, more interested in projections on shorter time scales. Specifically, time scales of 1 h or less. The aim of this research is to provide projections of precipitation at these shorter time scales based on the available daily scenarios. This involves an analysis of climate variables and their relations to precipitation at different time scales. On the basis of this analysis, one can determine a numeric factor to translate daily projections into shorter time scale projections.     

基于降水极端预报指数的福建台风极端降水预报研究

应用1961—2017年中国气象局热带气旋最佳路径数据集、国家地面气象观测站日降水观测资料和2015年8月—2017年12月欧洲中期天气预报中心(ECMWF)集合预报系统降水极端预报指数(EFI)数据,根据百分位法定义台风影响期间福建省各站点的台风极端降水阈值,采用最小阈值法剔除台风极端降水时EFI箱线图中的异常值,保...     

Climatology and trend analysis of extreme precipitation in subregions of Northeast Brazil

The present paper aims of computing climatology and trend analysis of occurrence and intensity of extreme events of precipitation in subregions of Northeast Brazil (NEB). We used daily rainfall data of 148 rain gauges collected from the hydrometeorological network managed by the National Water Agency during 1972 to 2002 and used quantiles technique in order to select rainfall events. Defining heavy rainfall events as those when at least one rain gauge recorded rainfall above the 95th percentile, normal rainfall was between the 45th and 55th percentiles, and weak rainfall events were under the 5th percentile. The Mann-Kendall nonparametric test was used to calculate the linear trend of the quantity and intensity of rainfall events. The NEB was divided in five subregions using the cluster analysis based on Euclidean distance and Ward’s method: Northern coast, Northern semiarid, Northwest, Southern semiarid, and Southern coast. The results suggest that the subregions are less influenced by El Niño and La Niña, and dry areas have higher variability, with the greatest number of intense events.     

赣江流域近50a来极端降水时空变化特征

基于赣江流域1964—2013年13个水文站的日降水资料,采用AM抽样和POT抽样相结合的方法,对极端降水序列,选定日最大降水量(RX1)、极端降水量(R95)、极端降水天数(RD95)和极端降水强度(RI95)四个指标,利用Mann-Kendall趋势分析方法、Pettitt变点检验法分别对赣江流域极端降水进行时间变化趋势和突变的分析,并利用普通克里金插值,对各指标进行空间分布的分析。研究结果表明,时间变化上,赣江流域RX1、R95和RD95均表现出一定的增加趋势,但RI95变化不大,各指标在过去50 a没有发生显著突变;空间分布上,RX1、R95和RI95沿着赣江流向从西南向东北增加,而RD95的空间分布没有明显的变化规律,存在多个极大极小值中心。     

Spatial analysis of variations in precipitation intensity in the USA

In this study, we used various spatial analytical methods to examine variations and trends in precipitation intensity in the conterminous USA. We found that three different measures of precipitation intensity were highly correlated; intensity increased in a spatially coherent fashion in the northeastern quarter of the USA and generally decreased in the center portion of the western USA. Evidence is presented that spatial and temporal patterns in the trends of precipitation intensity are related to the Atlantic multidecadal oscillation. Our results are generally in agreement with others who are reporting an upward trend in precipitation intensity during a period when the planet appears to have warmed.     

中国大陆地区小时极端降水阈值的计算与分析

利用广义极值分布和百分位两种阈值确定方法,对我国465个气象站点不同极端程度的小时降水强度阈值进行了分析。广义极值分布结果表明,重现期为2、5、10、50a的降水强度阈值具有一致的空间分布特征：华南沿海阈值最高,长江中下游地区北部、四川盆地西部、华北地区东部次之,云南中西部、华北西部和东北西部阈值相对更低,最小值出现在我国西部地区。百分位法得到的阈值空间分布呈现出与广义极值分布结果较为一致的东南大、西北小的整体特征。考察465站中位数发现,第99．9百分位的强度阈值与二年一遇降水的阈值接近。具体分析各站第99．9百分位降水阈值对应的重现期发现,长江流域及其以南地区重现期大多低于2a;35°N一带重现期长于4a;我国北方和西北部分地区重现期长于8a。     

Spatial and temporal trends of extreme temperature and precipitation in the Daqing River Basin,North China

Theoretical and Applied Climatology - According to the daily maximum and minimum temperature and the precipitation at 40 meteorological stations in the Daqing River Basin of China during...     

A precipitation regionalization and regime for Iran based on multivariate analysis

Theoretical and Applied Climatology - Monthly precipitation time series of 155 synoptic stations distributed over Iran, covering 1990–2014 time period, were used to identify areas with...     

Secular change of extreme monthly precipitation in Europe

Summary ¶Temporal changes in the occurrence of extreme events in time series of observed precipitation are investigated. The analysis is based on a European gridded data set and a German station-based data set of recent monthly totals (1896/1899–1995/1998). Two approaches are used. First, values above certain defined thresholds are counted for the first and second halves of the observation period. In the second step time series components, such as trends, are removed to obtain a deeper insight into the causes of the observed changes. As an example, this technique is applied to the time series of the German station Eppenrod. It arises that most of the events concern extreme wet months whose frequency has significantly increased in winter. Whereas on the European scale the other seasons also show this increase, especially in autumn, in Germany an insignificant decrease in the summer and autumn seasons is found. Moreover it is demonstrated that the increase of extreme wet months is reflected in a systematic increase in the variance and the Weibull probability density function parameters, respectively.Received July 18, 2002; revised January 24, 2003; accepted February 1, 2003 Published online September 10, 2003