Model study of the relationship between sediment yield and river basin area

The SHETRAN physically based, spatially distributed model is used to investigate the scaling relationship linking specific sediment yield to river basin area, for two contrasting topographies of upland and more homogeneous terrain and as a function of sediment source, land use and rainfall distribution. Modelling enables the effects of the controls to be examined on a systematic basis, while avoiding the difficulties associated with the use of field data (which include limited data, lack of measurements for nested basins and inability to isolate the effects of individual controls). Conventionally sediment yield is held to decrease as basin area increases, as the river network becomes more remote from the headwater sediment sources (an inverse relationship). However, recent studies have reported the opposite variation, depending on the river basin characteristics. The simulation results are consistent with these studies. If the sediment is supplied solely from hillslope erosion (no channel bank erosion) then, with uniform land use, sediment yield either decreases or is constant as area increases. The downstream decrease is accentuated if rainfall (and thence erosion) is higher in the headwaters than at lower elevations. Introducing a non‐uniform land use (e.g. forest at higher elevations, wheat at lower elevations) can reverse the trend, so that sediment yield increases downstream. If the sediment is supplied solely from bank erosion (no hillslope erosion), the sediment yield increases downstream for all conditions. The sediment yield/basin area relationship can thus be inverse or direct, depending on basin characteristics. There still remains, therefore, considerable scope for defining a universal scaling law for sediment yield. Copyright © 2006 John Wiley & Sons, Ltd.     

Meteorological monitoring of an active volcano: Implications for eruption prediction

Rainfall data collected on and around the Soufriere Hills Volcano, Montserrat between 1998 and 2003 were analysed to assess the impact on primary volcanic activity, defined here as pyroclastic flows, dome collapses, and explosions. Fifteen such rainfall-triggered events were identified. If greater than 20 mm of rain fell on a particular day, the probability of a dome collapse occurring on that day increased by a factor of 6.3% to 9.2%, compared to a randomly chosen day. Similarly, the probability of observing pyroclastic flows and explosions on a day with > 20 mm of rainfall increased by factors of 2.6 and 5.4, respectively. These statistically significant links increased as the rainfall threshold was increased. Seventy percent of these rainfall-induced dome collapse episodes occurred on the same calendar day (most within a few hours) as the onset of intense rainfall, but an extra 3 occurred one or two calendar days later. The state of the volcano was important, with the rainfall–volcanic activity link being strongest during periods of unstable dome growth and weakest during periods of no dome growth or after a recent major collapse.Over 50% of the heavy rain days were associated with large-scale weather systems that can potentially be forecast up to a few days ahead. However, the remaining heavy rain days were associated with small-scale, essentially unpredictable weather systems. There was significant variability in the amount of rainfall recorded by different rain gauges, reflecting topographic variations around the volcano but also the inherent small-scale variability within an individual weather system. Hence, any monitoring/warning program is recommended to use a network, rather than just a single gauge. The seasonal cycle in rainfall was pronounced, with nearly all the heavy rain days occurring in the May–December wet season. Hence, the dome was at its most vulnerable at the beginning of the wet season after a period of uninterrupted growth. Interannual variability in rainfall was related to tropical Pacific and Atlantic sea surface temperature anomalies, and holds out the prospect of some limited skill in volcanic hazard forecasts at even longer lead times.     

地下水在引发边坡病害中的作用

人们普遍认为由于地下水的浸泡降低滑带土的抗剪强度值是导致边坡失稳的内部机制。文章以云南元磨公路边坡为实例，对其进行了探究，发现病害边坡主要滑带土的天然含水量与其剪切试验所得的内聚力和内摩擦角之间并没有明显的线性关系。于是，文章采用极限平衡方程从理论上论述了地下水在边坡失稳过程中的力学作用，并对比边坡开挖前后入渗系数值的变化幅度，揭示出工程活动与降雨共同对边坡破坏的作用机制。     

单站GPS遥感水汽含量范围的确定及结果分析

随着GPS技术的日益成熟和研究的不断深入,GPS的定位精度得到了大幅度的提高,为其在测绘、交通、林业资源、石油勘探、精细农业等行业的应用提供了更广阔的空间。GPS气象学(GPS/Meteorology简称GPS/MET)的诞生改善了原有大气监测技术的局限性,极大的提高了气象监测的时空分辨率,但是在将这项技术由试验阶段向推广普及应用的过程中,单站地基GPS监测的有效范围以及水汽解的精度一直为广大气象学家们所忽略,本文在对以往GPS气象数据收集和整理的基础上,根据理论计算和实际验算,得出了一些有益的结论。     

副高边缘暴雨的多普勒雷达回波特征

利用常规观测资料和多普勒雷达探测资料，对2003-2004年湖南省4次副高边缘暴雨的天气形势和雷达回波特征进行了分析研究。结果表明：4次暴雨过程的副高位置适当，高空有低槽，中低层有低空急流和低涡切变线，地面上有冷锋。在基本反射率图上，低槽暴雨有S-N向的窄带回波特征、冷式切变线暴雨有准W—E向的积层回波特征、暖式切变线有NE—SW向的积层回波特征，但每一次暴雨过程不尽相同。在多普勒速度图上常出现低空急流、冷暖平流、冷锋、逆风区以及高层大风核等特征，并常是几种特征同时出现，有利于强降水产生。     

一次大暴雨过程低空急流脉动与强降水关系分析

应用香港天文台提供的时空分辨率都非常高的风廓线雷达资料对深圳2005年8月19—20日大暴雨过程强降雨时段进行了详细分析。结果表明，风廓线雷达资料每小时风场揭示每次强降水的发生都对应一次西南急流的迅速加强和向下扩展。低空急流指数I可以说明低空急流的脉动向地面扩展程度与中小尺度降水的密切关系，对强降水的出现及雨强大小有一定的预示性。     

焦作市4—9月短期降水精细化预报系统

在降水时空变化研究的基础上，完成辖区降水区划；用计算机识别T213风场中槽线、切变线等影响系统，并按其相对于预报站方位，确定对其影响强度指数，组合预报因子，建立判别方程，制作分县0～12小时、12～24小时降水分级预报。经检验，系统对降水落区有较强预报能力。     

基于“配料”的梅雨锋强降水预报方法

在梅雨锋暴雨预报中应用“配料法”，通过降水量的基本公式解释了强降水形成的基本配料，给出了建立配料的基本步骤，同时，通过一次暴雨个例分析，具体阐述了“配料法”在梅雨锋暴雨预报中的应用。结果表明，基于暴雨等强对流天气形成物理机理认识的基础上，所选取的可降水量等动力、热力因子与暴雨有着较好的对应关系，通过分析以上关键物理因子建立的过程，可以给出暴雨潜势预报。     

区域降水数值预报产品人工神经网络释用预报研究

利用T213、日本细网格降水预报等数值预报产品,采用人工神经网络方法进行预报释用。通过聚类分析方法对广西自治区测站进行分类,简化预报对象,对数量众多的T213数值预报产品采用自然正交分解(EOF)方法,浓缩大量因子的有效信息,并结合日本降水预报因子建立广西5~6月区域降水量级的逐日人工神经网络预报模型。运用与实际业务预报相同的方法进行逐日预报试验。结果表明,用这种数值预报产品释用方法建立广西3个预报区域的B-P人工神经网络预报模型对中雨以上降水量级预报的TS评分分别为0.55、0.5和0.26,比目前业务预报中参考使用的T213和日本数值预报产品降水预报具有更好的预报效果。     

利用雷达回波与GIS技术反演面雨量研究

自动气象站可以直接测量单点或较小范围的降水量,测量精度较高,但自动气象站的分布密度不够,往往漏掉强降水、暴雨中心。雷达能实时探测云和降水结构及系统发生、发展演变情况,能迅速提供一定区域的实时降水情况,但雷达测量误差较大,测定局地降水量精度不高,因此,将自动气象站与雷达进行点面结合,采用一定的数学方法和GIS技术,可以得到能够代表某特定区域平均降水情况的面雨量。以深圳市行政区域面雨量反演为例,建立区域内所有测站的6 min累积降雨资料与雷达回波数据之间的回归关系,借助GIS技术和包括对经度、纬度、海拔高度3个因子的地理订正,构建雷达图上特定行政区域的面雨量计算模型,并对格点拟合雨量进行空间分辨率的精细化反演,得到雷达图上的特定行政区域的面雨量图。