THE EFFECTS OF A MOVING RAINSTORM ON DIRECT RUNOFF PROPERTIES1

ABSTRACT: The effects of a moving rainstorm on flood runoff characteristics were investigated. A flood hydrograph simulation model called “FH-Model” and a natural watershed were used. A hypothetical rainstorm of 50 years recurrence interval, 75 mm depth, and 4 hours duration was used to show the effects of velocity and direction of the moving rainstorm on the runoff characteristics. Compared with an equivalent stationary rainstorm (ESRS), the peak flow caused by a rainstorm moving in a downstream direction with a speed equal to channel velocity, V, was 27.5 percent higher and the peak flow caused by the same rainstorm moving in an upstream direction was 21.7 percent smaller. These percentages reduced to 10.5 percent and 8.6 percent for storms moving downstream and upstream, respectively, at three times the channel velocity, 3V. There were negligible differences in the time of peak, T_p between runoff caused by storms moving downstream and runoff produced by ESRS. However, T_p for a storm moving upstream at V velocity was 82 percent higher than that produced by ESRS, but was reduced to 27 percent higher when the storm velocity was 3V.     

天津市城区暴雨沥涝仿真模拟系统的研究

天津市位于黄河水系尾闾，是我国北方重要的工业和港口城市。该市历史上一直遭受洪涝灾害的威胁，加之近几年市区范围不断扩大，不透水面积增加，排水能力远远不能适应城市的发展要求，城市沥涝问题日趋严重。为了掌握天津市暴雨沥涝的规律，减轻洪涝灾害对该市的影响，作者利用二维不恒定流水动力学模型和计算机信息管理及图形技术，采用Power Station Fortran 4.0和Visual Basic5.0编程语言，在Widows98环境下开发了天津市暴雨沥涝仿真模拟系统。该系统首次实现了从城市暴雨预报、监测到城市暴雨沥涝仿真模拟的研究， 不仅能够处理实时的不均匀的降雨信息，还能处理数值预报模型的预报降雨信息。系统的信息前后处理模块用图形方式管理仿真模型的各类信息，方便用户显示、查询和修改，使系统更加完整、实用。     

两种定性天气预报模型的对比分析

以南京１９６５～１９９４ 年４ 月平均气温作为预报量, 选取前期５００ ｈ Ｐａ 月平均高度场相关因子, 分别建立了事件概率回归预报模型和神经网络预报模型。通过对比分析发现, 在同等条件下,由于神经网络方法能更好地反映预报量与预报因子间的非线性关系, 并能有效避免采用事件概率回归方法预报建模时, 对预报因子分级造成信息损失的缺点。因此, 其拟合和预报效果明显优于传统的概率回归预报方法     

我国废钢铁资源的可持续利用政策

本文概述了我国废钢铁资源回收利用的现状以及钢铁工业对废钢铁资源的需求现状，用三种不同的模型预测了未来钢铁工业的发展对废钢铁资源的需求，提出了废钢铁资源可持续利用存在的问题和政策建议。     

区域洪涝灾害风险的模糊综合评价与预测

以广西1992~2006年洪涝灾情数据为基础,选取适当的评价指标,用基于遗传算法、特征值法和优化算法的投影寻踪法和层次分析法确定各评价指标的分类权重和排序权重,用优化算法进行综合得到组合权重,经过加权求和得出洪涝灾害的模糊综合评价,进而对15个评价对象进行了分类排序,其分类结果很好地反映了洪涝灾情的实际情况。在以上模糊综合评价结果的基础上,利用距离贴近度进一步计算和分析各个评价指标之间的贴近度,并由择近原则确定各个评价指标之间在洪涝灾害评价中的相关程度。同时,建立了基于改进的灰色GM(1,1)的时间响应式的灾变年份预测模型。     

基于优势隐含周期分析的气象灾害预报

建立了优势因子和优势隐含周期的概念,并提出了多因子逐步回归周期分析的新方法。该方法既能增长预报期,又能充分利用物理因子的重要信息,同时在计算过程中还能挑选出起显著作用的优势因子和优势隐含周期,对预报气象要素的变化趋势效果较好,有一定的实用价值,是气象灾害预报的一条比较有效的途径。     

时变参数模型用于滑坡位移量预测的研究

滑坡的位移量发展受地质条件、气候环境以及人类活动等多种因素的影响,变化复杂,通常难以用固定参数的数学模型准确表达。时变参数模型的模型参数随时间变化,能够描述更为复杂的函数关系。将时变参数模型应用于滑坡位移量预测,通过对比发现,时变参数模型有望提高滑坡位移量的预测精度。     

基于模糊聚类的马氏链模型在交通事故预测中的应用

运用模糊聚类分析方法,借助于MATLAB软件,对中国31个地区交通事故的危害程度进行动态分类和综合评价。根据中国1999—2005年31个地区的交通事故4个指标和5个社会经济因素数据,把31个地区分为3类,即轻灾区、较重灾区、重灾区;其中浙江、福建与宁夏的交通事故危害严重,上海、北京与天津的交通状况正在好转,这与客观实际符合。根据模糊聚类的结果,求得交通事故状态转移概率矩阵,利用马氏链模型可预测未来31个地区的交通事故发展趋势。     

灾变预测在企业安全事故预测中的应用

简述了灰色理论中灾变预测的特点及其建模过程,并将其应用于企业安全事故预测之中.经检验,模型的精度等级为好,模型的预测结果可靠,对于企业安全事故的预测有一定帮助.     

China's Present Situation of Coal Consumption and Future Coal Demand Forecast

This article analyzes China's coal consumption changes since 1991 and proportion change of coal consumption to total energy consumption. It is argued that power, iron and steel, construction material, and chemical industries are the four major coal consumption industries, which account for 85% of total coal consumption in 2005. Considering energy consumption composition characteristics of these four industries, major coal demand determinants, potentials of future energy efficiency improvement, and structural changes, etc., this article makes a forecast of 2010s and 2020s domestic coal demand in these four industries. In addition, considering such relevant factors as our country's future economic growth rate and energy saving target, it forecasts future energy demands, using per unit GDP energy consumption method and energy elasticity coefficient method as well. Then it uses other institution's results about future primary energy demand, excluding primary coal demand, for reference, and forecasts coal demands in 2010 and 2020 indirectly. After results comparison between these two methods, it is believed that coal demands in 2010 might be 2620-2850 million tons and in 2020 might be 3090-3490 million tons, in which, coal used in power generation is still the driven force of coal demand growth.