大别杂岩中混合岩的矿物空间分布研究

介绍了矿物空间分布研究的基本原理及两种统计方法(接触频数法和线切法)。作者对混合岩矿物空间分布的研究表明:(1)前人提出的统计方法存在方法上的缺陷和应用上的局限性,作者推导出矿物接触类型的概率公式;(2)部分浅色体中矿物显示聚集分布的特征,而绝大部分的浅色体中矿物具有分散分布的特点。结合质量平衡和地球化学研究认为:大别杂岩中主体混合岩成因机制是重熔和交代作用。     

近45 a来中国西北年极端高、低温的变化及对区域性增暖的响应

利用中国西北五省(区)1960-2004年129个台站逐日最低、最高温度资料,从中统计出年极端高温发生频次、年极端低温发生频次、年极端高温强度、年极端低温强度以及年极端高温和低温开始和结束日期,分析了它们近45 a来的变化情况。结果表明:近45 a来中国西北年极端高温发生频次的增加趋势是明显的,而年极端低温发生频次的减少趋势更显著;中国西北近45 a来年极端高温的强度在不断增强,而极端低温的强度在不断减弱;近45 a来中国西北年极端高温开始日期逐渐提前,结束日期逐渐推迟,而年极端低温的开始日期在逐渐推迟,结束日期在逐渐提前;年极端低温发生频次的减少对于西北区域增暖的响应比年极端高温发生频次增加更显著,而年极端低温强度的减弱要比年极端高温强度的增强对西北区域性增暖的响应偏弱,年极端高温发生频次的增加同年极端高温强度的增强对西北区域性增暖的响应程度基本相当。     

近几十年我国极端气温变化特征分区方法探讨

采用聚类统计检验分析和旋转主分量分析相结合确定中心站的方法,利用我国多年极端气温资料,对我国最高和最低气温年际变化型态进行区划。结果表明,这两种方法结合可以互相补充,使分区结果更具客观性。中国极端高温和极端低温年际变化分别可划为12和11个不同类型的区域,分别计算了各区域第一主成分的方差贡献率以及各区域之间的两两相关系数,检验证明分区是合理的。     

东北漫岗黑土区春季冻融期浅沟侵蚀

浅沟侵蚀是东北漫岗黑土区农耕地上常见的水蚀类型,往往对坡耕地造成严重的破坏。2005年春季,通过对两个小流域浅沟侵蚀的调查测量,发现该区浅沟侵蚀相当严重,两流域分别形成浅沟14条、16条,浅沟总长度分别达3 269 m、2 146 m,浅沟密度分别为908 m/km2、766 m/km2,侵蚀深度分别为0.17 mm、0.16 mm,侵蚀模数分别达181.8 t/km2、173.6 t/km2。2005年春季两流域浅沟侵蚀期的径流深分别是6.8 mm、7.7 mm。分析表明,研究区在春季表层土壤解冻、地表裸露和存在季节性冻土层的条件下,春季融雪及强降水易造成强烈的浅沟侵蚀。在分布上,浅沟一般位于坡面的中下部,而且多发育在瓦背状坡面的集流水路上。另外,耕作措施对浅沟的形成和发展也有重要影响。     

“96·8”河北特大暴雨成因初探

运用常规气象资料和卫星云图，分析了１９９６年８月３—５日河北特大暴雨的成因。认为：它是出现在东亚特定的强经向环流形势下，由９６０８号台风低压与副热带高压两侧之间形成的强偏南风低空急流，将低纬度地区高温高湿水汽源源不断地向华北输送并与近地面层弱冷空气相互作用，诱发两个中尺度云团的形成和发展直接造成的     

从安徽气候变化看2003年洪涝和高温的必然性

利用近 50年温度和降水资料研究了安徽夏季气候变化特征 ,解释了 2 0 0 3年夏季洪涝、高温等极端气候事件出现的必然性。研究结果表明 :(1 )近 50年来安徽夏季温度呈下降趋势 ,降水则呈增加趋势 ,两者变化是相协调的。目前夏季温度处于较低的气候基本态 ,降水处于高基本态。 (2 )无论是温度还是降水 ,其变率都在 2 0世纪80年代中后期开始上升 ,目前均处于高气候变率时期。降水的“两高”(高基本态和高气候变率 )结合决定了 2 0 0 3年夏季洪涝出现的必然性 ;温度的较低基本态决定了“凉夏”背景 ,但由于基本态的回升和变率的加大 ,仍会出现像 2 0 0 3年夏季的若干高温天气。 (3)最大熵谱估计表明 ,安徽夏季降水变化的主周期为 2 5年 ,反映了降水的准两年振荡特征     

Meteorological Office, Pune-411 005

Natural disasters like floods, tornadoes, tropicalcyclones, heat and cold wavewreak havoc and cause tremendous loss ofproperty all over the world. Most ofthe natural disasters are either dueto weather or are triggered due toweather related processes.Extreme weather events claimed thousands oflives and caused damage on vastscale. Recent super cyclone which affectedOrissa in 1999, Bangladesh cyclone of1970 and Hurricane Andrew in 1992 areexamples of some of the more damagingtropical cyclones which affected developingas well as the developed world. Heatand cold waves are also extreme events,which cause enormous losses in terms oflives lost and human discomfort and ailmentsarising out of them. The heat waveof 1995 and 1998 are still fresh in the mindof the Indian public. The estimated lossof human lives due to heat wave in 1998 was morethan 15,000. Economic losses asa result of these disasters and in particular inassociation with tropical cyclones haveincreased enormously over the last three decades.During 1961–1991, total loss oflives from drought alone was 1,333,728 overthe whole world. In terms of economiclosses, there is 8–10 fold increase from thebase figure of 1960. The socio-economicimpact of natural disaster is complex dependingupon the vulnerability of the placeand mitigation strategies that are put in place.Meteorology plays a crucial role in forewarningpeople about the severe/extremeweather systems and a constant endeavour by themeteorological services worldover has gone a long way towards minimizing thelosses caused by natural disasters.The paper summarises the natural disasterstatistics over south Asia and the possibleprediction strategies for combating theirsocio-economic impacts.     

Geostatistical simulation of random fields with bivariate isofactorial distributions by adding mosaic models

This work deals with the geostatistical simulation of a family of stationary random field models with bivariate isofactorial distributions. Such models are defined as the sum of independent random fields with mosaic-type bivariate distributions and infinitely divisible univariate distributions. For practical applications, dead leaf tessellations are used since they provide a wide range of models and allow conditioning the realizations to a set of data via an iterative procedure (simulated annealing). The model parameters can be determined by comparing the data variogram and madogram, and enable to control the spatial connectivity of the extreme values in the realizations. An illustration to a forest dataset is presented, for which a negative binomial model is used to characterize the distribution of coniferous trees over a wooded area.     

Statistical identification of orographic effects in the regional analysis of extreme rainfall

Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.     

A nonstationary extreme value distribution for analysing the cessation of karst spring discharge

The effects of climate change and population growth in recent decades are leading us to consider their combined and potentially extreme consequences, particularly regarding hydrological processes, which can be modeled using a generalized extreme value (GEV) distribution. Most of the GEV models were based on a stationary assumption for hydrological processes, in contrast to the nonstationary reality due to climate change and human activities. In this paper, we present the nonstationary generalized extreme value (NSGEV) distribution and use it to investigate the risk of Niangziguan Springs discharge decreasing to zero. Rather than assuming the location, scale, and shape parameters to be constant as one might do for a stationary GEV distribution analysis, the NSGEV approach can reflect the dynamic processes by defining the GEV parameters as functions of time. Because most of the GEV model is designed to evaluate maxima (e.g. flooding, represented by positive numbers), and spring discharge cessation is a ?minima’, we deduced an NSGEV model for minima by applying opposite numbers, i.e. negative instead of positive numbers. The results of the model application to Niangziguan Springs showed that the probability of zero discharge at Niangziguan Springs will be 1/80 in 2025, and 1/10 in 2030. After 2025, the rate of decrease in spring discharge will accelerate, and the probability that Niangziguan Springs will cease flowing will dramatically increase. The NSGEV model is a robust method for analysing karst spring discharge. Copyright © 2013 John Wiley & Sons, Ltd.