滑坡灾害监测与预测时序分析

本文提出了一种边坡滑坡时序建模预测分析方法,以AR模型为例,探讨了时序建模及其预报的基本思想,最后用AR模型对清江电站进口边坡406阻滑键监测数据建模及预报,取得了一定的成果.     

PROBLEMS IN FORECASTING WATER REQUIREMENTS1

ABSTRACT. Methodological problems associated with forecasting water requirements by use of regression analysis are examined. Problems occurring when long-range forecasts are based on linear and nonlinear extrapolation of time series models include possible changes in socioeconomic conditions, water allocation system structure, and limits to growth. Problems arising in forecasting based on multiple regression models are likely to involve serially dependent errors, multicollinear explanatory variables, and difficulties inherent to the presence of explanatory variables that must themselves be predicted.     

CASING AND LEAK DEPTHS,AND SOLUTE TRAVEL TIMES TO WELLS1

ABSTRACT: A mathematical solution based on porous media flow is developed for solute travel time to a well as affected by a leak around the upper part of the casing. Consider a well of radius 0.2 meters (m) penetrating, fully, a semiconfined aquifer of thickness 6 m with impermeable casing length of 4.5 m, and screened casing length 1.5 m. Around the upper 1.5 m of the impermeable casing length, there is a highly permeable region (a leak). The radius of influence of the well is 10 m. The porous flow medium has a hydraulic conductivity of 10 m/day and a porosity of 0.25. Between the water table and the water level in the well, there is a steady state pumped down head difference of 0.3 m. Solute travel time from a point at the bottom of the leak to the well is 2.33 days. If the leak is sealed (grouted), the travel time is 6.24 days. Examples of six different geometries are given. Laboratory studies verify the theory. The computations should be useful in the design and protection of water wells from solutes, such as from agriculture, industry, strip mines, or sanitary landfills.     

ESTIMATION OF SURFACE WATER LAG TIME FROM THE KINEMATIC WAVE EQUATIONS1

ABSTRACT Numerous concepts of surface water lag time have been developed and applied in the past. In this report, hydraulic solutions of a lag time derived by Overton [1970] are presented for several idealistic surfaces using the kinematic wave equations. These surfaces are: (1) a uniform plane; (2) hillslope as a cascade of planes; (3) V-shaped watershed; (4) V-shaped watershed with hill-slopes; (5) converging surface; (6) concave surface. The lag times are shown to be related to roughness, length and catchment slope, and the input rate. These relations may be used immediately in predicting lag time as the parameter in a unit response function. A lag relation has been developed for a nonuniform catchment in terms of the lag of a uniform plane and a convergence factor. A numerical procedure is shown whereby the convergence factor can be evaluated for any nonuniform catchment from observed input and output data.     

楼房火灾的安全灭火时限分析与施救对策

在楼房建筑物火灾灭火时 ,常由于火燃造成建筑材料强度失效导致整体坍塌 ,给施救工作带来很大的威胁。强调了在灭火过程的始终 ,应不断进行高温状态下建筑材料强度的失效判定 ,评估出安全的灭火时限和调整施救对策 ,有利于从根本上预防楼房坍塌意外事故的发生。定义了火灾高温状态下建筑材料危险应力点上的可靠度 ,提出依据火灾现场因素和数据 ,用数值模拟方法分析建筑材料强度失效随时间的变化过程 ,确定安全的灭火时限。给出了以火灾施救为主线的并适时考虑构件强度分析和安全时限判定交叉平行作业的多阶段施救对策框架 ,建立了楼房火灾施救安全支持系统结构。     

湖南省历史旱灾时空分布规律

本文综合考虑气候条件、下垫面条件、史料记载的均一性等因素，将湖南省划分为１０个研究区；根据１４５０～１９４９年湖南省气候史料记载１），采用区内平均等级，描绘了全省１０个区旱灾的时间序列，并据此探讨了全省近５００ａ来旱灾的时空分布规律。     

ESTIMATING ANNUAL FLOOD PROBABILITIES USING FOURIER SERIES METHOD1

ABSTRACT: The Fourier series method is proposed as a feasible non-parametric approach for the estimation of the density and distribution functions of annual floods. Clearly, the goodness of fit to empirical data improves as higher Fourier terms are incorporated, and the choice of a higher term depends on whether the inclusion of this term will reduce the fitting error to within a specified tolerance level. This method was applied to the flood data from eight rivers, and to data simulated from known distributions. The results are clearly better than other parametric methods, just like other non-parametric techniques currently used to estimate annual flood probabilities.     

FOURIER INFERENCE: SOME METHODS FOR THE ANALYSIS OF ARRAY AND NONGAUSSIAN SERIES DATA1

Fourier inference is a collection of analytic techniques and philosophic attitudes, for the analysis of data, wherein essential use is made of empirical Fourier transforms. This paper sets down some basic results concerning the finite Fourier transforms of stationary process data and then, to illustrate the approach, uses those results to develop procedures for: 1) estimating cloud and storm motion, 2) passive sonar and 3) fitting finite parameter models to nonGaussian time series via bispectral fitting. This last procedure is illustrated by an analysis of a stretch of Mississippi River runoff data. Examples 1), 2) refer to data having the form Y(x_j, y_j, t) for j = 1, …, J and t = 0, …, T-l say, and view that data as part of a realization of a spatial-temporal process. Such data has become common in geophysics generally and in hydrology particularly. The goal of this paper is to present some new statistical procedures pertinent to problems in the water sciences, equally it is to illustrate the genesis of those procedures and how their properties may be approximated.     

PARAMETRIC/NONPARAMETRIC MIXTURE DENSITY ESTIMATION WITH APPLICATION TO FLOOD-FREQUENCY ANALYSIS1

Much attention has been invested in the model choice problem for peak annual flows, in the context of flood frequency analysis. The authors would sidestep this dilemma through non-parametric density estimation methodology, but recognize that the standard nonparametric estimators preclude the use of prior information and related data, and furthermore have virtually no tail at all. Here we offer a remedy for these inadequacies by introducing an estimator which mixes parametric and nonparametric density estimates. We prove that our mixture rule is consistent. By this procedure, we do allow incorporation of prior information, experience, and regional data information, but nevertheless provide a safeguard against incorrect model choice.     

PAST AND FUTURE OF ANALYSIS OF WATER RESOURCES TIME SERIES1

ABSTRACT: water resources supply and demand time series consist of several or all of the four basic characteristics: tendency, intermittency, periodicity and stochasticity. Their importance changes from one type of variables to another. Historic developments of analysis of time series in hydrology have varied significantly over the past, from the stress on search for periodicities and persistence in annual series to the emphasis on the series stochastic properties. Supply and demand series are often highly interrelated, which fact is most often neglected in planning water resources systems in general, and water storage capacities in particular. The future of series analysis in water resources will likely be by a joint use of physically-based structural analysis and the use of advanced methods of treating data by stochastic processes, statistical estimation and inference techniques. The most intriguing challenge of the future of this analysis may be the treatment of nonnormal, nonlinear and in general nonstationary hydrologic and water use time series. The proper treatment of complex multivariate processes will also challenge the specialists, especially for the purposes of transfer of information between data on variables at given points, or between data at several points of a given variable, or both.