A HORIZONTAL 2-D HYDRAULIC NUMERICAL MODEL AND IT’S APPLICATIONS TO FLOOD FORECAST

1 INTRODUCTION There were many flood disasters in China in recent years. When the water level in a river is very high, weak parts of its dike may be destroyed resulted in the submersion of the protected land and properties. It is of significance for decision-makers to exactly predict the processes of flood propagation during flood control. There are many modes of dike bursting, such as seepage destroying by overflow on top of dike caused by dike body sinking induced by piping and soil fl…     

复杂介质结构中折射界面的哈格多恩原理波前成像

在城市活断层探测中 ,浅层结构常常表现为强烈的非均匀性 ,界面横向强烈起伏 ,层内速度变化较大 ,传统的基于平界面均匀层模型的折射资料处理方法不能适用。研究开发能应用于复杂介质结构中折射资料处理的方法就显得十分必要。文中基于惠更斯原理 ,用波前扩张法对波场作正演计算 ,根据哈格多恩折射波前成像原理 ,在lecomte算法和Hole有限差分计算程序的基础上 ,开发出 1种复杂介质结构中折射资料的处理方法与软件 ,并用此方法处理了福州城市活断层折射探测试验中在义序完成的 2条折射剖面资料。结果表明 :探测区浅层为 3层结构 ,分别为盖层、强风化层和基岩。基岩顶界面的埋深约为 5 8～ 5 2m ,盖层P波速度变化较大     

水资源复杂适应配置系统的理论与模型

在对水资源配置系统的复杂性及其复杂适应机理分析的基础上，应用复杂适应系统理论的基本原理和方法，构架出了全新的水资源配置系统分析模型，同时对系统的动力机制、主体行为特性和系统状态的评价方法进行了描述，形成了一个较为完整的研究体系，用于分析水资源配置系统的演化规律，并应用这种分析方法对南水北调工程对受水区水资源配置的影响进行了简要的研究。     

面向对象的GIS数据模型与实现

介绍了面向对象数据模型的基本概念，重点讨论了面向对象数据模型在地理信息系统中的应用及具体实现途径。     

大别山南坡蕲春等地榴辉岩的发现及相关问题

20世纪90年代早、中期，一些研究者根据榴辉岩的出露情况，将大别山腹地的大别杂岩出露区划分为“北大别地体”、“UHP地体”和“宿松地体”3个不同性质的大地构造单元，其中“北大别地体”和“宿松地体”2个地体被视为不含榴辉岩的构造单元。然而，自90年代后期以来，在“北大别地体”中陆续发现了大量的榴辉岩露头。近期笔者在“宿松地体”中也首次发现了榴辉岩露头。上述事实表明前人仅仅根据榴辉岩的出露将大别杂岩划分为3个构造单元的认识是不妥的，大别杂岩应该为一个具有一定成因联系的构造-岩石单元，属于同一个大地构造单元。     

西秦岭中川花岗岩岩浆活动特征及地质效应

西秦岭中川花岗岩为3期5次侵入的复式岩体,其主侵入体为印支—燕山期黑云母同碰撞S型二长花岗岩。该岩浆活动在沉积建造中形成热变质作用带和岩浆侵位构造,并产生成矿地质效应。     

辽宁地区的幔枝构造

将辽宁地区划分为4个幔枝构造:山海关幔枝、辽东南幔枝、建平-阜新幔枝、西丰幔枝,文章分述了它们各自组成与构造特点,并举例说明了辽宁幔枝构造通过其三个次级构造单元对成矿的控制规律.     

System identification of linear structures based on Hilbert–Huang spectral analysis. Part 2: Complex modes

A method, based on the Hilbert–Huang spectral analysis, has been proposed by the authors to identify linear structures in which normal modes exist (i.e., real eigenvalues and eigenvectors). Frequently, all the eigenvalues and eigenvectors of linear structures are complex. In this paper, the method is extended further to identify general linear structures with complex modes using the free vibration response data polluted by noise. Measured response signals are first decomposed into modal responses using the method of Empirical Mode Decomposition with intermittency criteria. Each modal response contains the contribution of a complex conjugate pair of modes with a unique frequency and a damping ratio. Then, each modal response is decomposed in the frequency–time domain to yield instantaneous phase angle and amplitude using the Hilbert transform. Based on a single measurement of the impulse response time history at one appropriate location, the complex eigenvalues of the linear structure can be identified using a simple analysis procedure. When the response time histories are measured at all locations, the proposed methodology is capable of identifying the complex mode shapes as well as the mass, damping and stiffness matrices of the structure. The effectiveness and accuracy of the method presented are illustrated through numerical simulations. It is demonstrated that dynamic characteristics of linear structures with complex modes can be identified effectively using the proposed method. Copyright © 2003 John Wiley & Sons, Ltd.     

Testing a model for predicting the timing and location of shallow landslide initiation in soil‐mantled landscapes

The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever‐expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km² catchment a near‐annual aerial photographic coverage records just three single storm events over a 45 year period that produced multiple landslides. Such data enable us to test model performance by running the entire rainfall time series and determine whether just those three storms are correctly detected. To do this, we link a dynamic and spatially distributed shallow subsurface runoff model (similar to TOPMODEL) to an in?nite slope model to predict the spatial distribution of shallow landsliding. The spatial distribution of soil depth, a strong control on local landsliding, is predicted from a process‐based model. Because of its common availability, daily rainfall data were used to drive the model. Topographic data were derived from digitized 1 : 24 000 US Geological Survey contour maps. Analysis of the landslides shows that 97 occurred in 1955, 37 in 1982 and ?ve in 1998, although the heaviest rainfall was in 1982. Furthermore, intensity–duration analysis of available daily and hourly rainfall from the closest raingauges does not discriminate those three storms from others that did not generate failures. We explore the question of whether a mechanistic modelling approach is better able to identify landslide‐producing storms. Landslide and soil production parameters were ?xed from studies elsewhere. Four hydrologic parameters characterizing the saturated hydraulic conductivity of the soil and underlying bedrock and its decline with depth were ?rst calibrated on the 1955 landslide record. Success was characterized as the most number of actual landslides predicted with the least amount of total area predicted to be unstable. Because landslide area was consistently overpredicted, a threshold catchment area of predicted slope instability was used to de?ne whether a rainstorm was a signi?cant landslide producer. Many combinations of the four hydrological parameters performed equally well for the 1955 event, but only one combination successfully identi?ed the 1982 storm as the only landslide‐producing storm during the period 1980–86. Application of this parameter combination to the entire 45 year record successfully identi?ed the three events, but also predicted that two other landslide‐producing events should have occurred. This performance is signi?cantly better than the empirical intensity–duration threshold approach, but requires considerable calibration effort. Overprediction of instability, both for storms that produced landslides and for non‐producing storms, appears to arise from at least four causes: (1) coarse rainfall data time scale and inability to document short rainfall bursts and predict pressure wave response; (2) absence of local rainfall data; (3) legacy effect of previous landslides; and (4) inaccurate topographic and soil property data. Greater resolution of spatial and rainfall data, as well as topographic data, coupled with systematic documentation of landslides to create time series to test models, should lead to signi?cant improvements in shallow landslides forecasting. Copyright © 2003 John Wiley & Sons, Ltd.