Stochastic overland flows

A theoretical solution framework to the nonlinear stochastic partial differential equations (SPDE) of the kinematic wave and diffusion wave models of overland flows under stochastic inflows/outflows, stochastic surface roughness field and stochastic state of flows was obtained. This development was realized by means of an eigenfunction representation of the time-space overland flow depths, and by transforming the problem into the phase space. By using Van Kampen's lemma and the cumulant expansion theory of Kubo-Van Kampen-Fox, the deterministic partial differential equation (PDE) for the evolutionary probability density function (pdf) of overland flow depths was finally obtained. Once this deterministic PDE is solved for the time-varying pdf of overland flow depths, then the time-space varying pdf of overland flow depths can be obtained by a transformation given in the text. In this solution framework it is possible to incorporate the stochastic dynamic behavior of the parameters and of the forcing functions of the overland flow process. For example, not only the individual rainfall duration and fluctuating rain intensity characteristics but also the sequential behavior of rainfall patterns is incorporated into the evolutionary probability density function of overland flow depths.     

Current Problems of Stochastic Hydrology

An attempt is made to generalize the results of studies in stochastic hydrology and discuss the future lines of investigations in accordance with the development of science and practical needs.     

Stochastic Models of Quasigeostrophic Turbulence

Atmospheric and oceanic eddies are believed to be manifestations of quasigeostrophic turbulence — turbulence that occurs in rapidly rotating, vertically stratified fluid systems. The heat, momentum, and water transport by these eddies constitute a significant component of the climate balance, without which climate change cannot be understood. A major, unsolved problem is whether the turbulent eddy fluxes can be parameterized in terms of the large-scale, background flow. In the past, stochastic models have been used quite extensively to investigate quasigeostrophic turbulence in the case in which the eddy statistics are isotropic and homogeneous. Unfortunately, these models ignore the background shear which is absolutely essential to maintaining the eddies in the presence of dissipation. Recent attempts to extend stochastic models to shear flows have shown significant skill in predicting the structure of the eddy fluxes in arbitrary, three-dimensionally varying flows. This paper provides an accessible introduction to these models. The topics reviewed include quasigeostrophic turbulence and two-dimensional turbulence, non-modal andoptimal perturbations, mathematical theory of stochastic models, stochastic model simulations with realistic background states, and recent closure theories. A list of unsolved problems concludes this review.     

Stochastic analysis of estuarine hydraulics

A new methodology is presented for the solution of the stochastic hydraulic equations characterizing steady, one-dimensional estuarine flow. The methodology is predicated on quasi-linearization, perturbation methods, and the finite difference approximation of the stochastic differential operators. Assuming Manning's roughness coefficient is the principal source of uncertainty in the model, stochastic equations are presented for the water depths and flow rates in the estuarine system. Moment equations are developed for the mean and variance of the water depths. The moment equations are compared with the results of Monte Carlo simulation experiments. The results confirm that for any spatial location in the estuary that (1) as the uncertainty in the channel roughness increases, the uncertainty in mean depth increases, and (2) the predicted mean depth will decrease with increasing uncertainty in Manning'sn. The quasi-analytical approach requires significantly less computer time than Monte Carlo simulations and provides explicit     

Stochastic response of rigid foundations

While the study of kinematic interaction (i.e. the dynamic response of massless foundations to seismic loads) calls, in general, for advanced analytical and numerical techniques, an excellent approximation was proposed recently by Iguchi.^1,2 This approximation was used by the authors to analyse embedded foundations subjected to spatially random SH-wave fields, i.e. motions that exhibit some degree of incoherence. The wave fields considered ranged from perfectly coherent motions (resulting from seismic waves arriving from a single direction) to chaotic motions, resulting from waves arriving simultaneously from all directions. Additional parameters considered were the shape of the foundation (cylindrical or rectangular) and the degree of embedment. It was found that kinematic interaction usually reduces the severity of the motions transmitted to the structure, and that incoherent motions do not exhibit the frequency selectivity (i.e. narrow valleys in the foundation response spectra) that coherent motions do.     

Stochastic sketching of infiltration-advance isochrones

A technique has been developed for predicting the irregular advance pattern often observed as water spreads on the surface of the ground. The technique is a combination of stochastic sketching, potential theory, probability theory, and a mass balance equation in the form of an advance equation. The technique can be used on flat as well as sloping terrain and addresses any form of obstructions or constraints to the flow of the water. The stochastic sketching portion of the technique uses cellular automata with transition probability movement rules to sketch the dynamics of small volume water elements in the defined environment. Randomly selected small volume flow path segments are computed and plotted. The envelope of these segments defines the wetted area and the advance front. Several examples are presented showing the patterns produced for various situations.     

厄尔尼诺事件的随机特征

厄尔尼诺事件与强潮汐、日食、月食、火山、地震以及洋流冷暖循环相关．统计资料表明，厄尔尼诺事件的发生具有明显的随机特征，当各因素的最大值相互叠加时，就会发生强厄尔尼诺事件．厄尔尼诺与火山地震活动密切相关，具有区域性强，能量变化大，活动频繁，有规律但无严格周期等特点．厄尔尼诺事件是多种因素形成的。因而更具有随机特征．     

Stochastic analysis of flood series

A stochastic model based on the renewal process was developed and used to analyse the characteristics of floods: the volume exceedence, the duration of the flood and the maximum annual flow. The model contains a method for determination of total annual volume exceedence and total annual duration of floods, as well as a method for calculation of maximum annual exceedence, maximum flood duration and maximum flow. The subset of the flood occurrence number in a given time interval is common for all analysed phenomena (volume exceedence, flood duration, maximum flow). The subset of given exceedences is common for total annual volume exceedence, as well as for maximum annual volume exceedence. The same holds for durations of individual floods. The model was then applied to analyse the floods on the Drina River at the Paunci hydrological station and on the Danube River at the Bezdan station.     

随机不动点地震反演方法

地震反演可以通过地表观测数据来推测地下介质模型参数,因此地震反演技术自提出至今都是储层预测最常用的手段之一.不动点地震反演方法（FPSI）通过将求解目标泛函转换为寻找目标泛函不动点来获得准确与高分辨率的反演结果.由于FPSI对地震信号的振幅变化具有较高的敏感性,因此即使地震剖面存在部分弱反射区域,利用FPSI仍然可以获得较为准确的反演结果.然而FPSI存在抗噪能力不足以及多解性强等问题.为提高FPSI的抗噪能力及实用性,本研究将随机反演框架引入至不动点反演理论,提出随机不动点地震反演方法（SFPSI）.此外,利用纵波阻抗的初始模型计算得到初始反射系数,并将初始反射系数与初始模型约束引入反演框架及目标泛函,有效提高了反演方法的鲁棒性及计算效率.具体地,首先对以待反演参数初始模型为均值的高斯分布进行随机抽样并利用不动点理论判定抽样结果是否被接受.在此基础上对反演参数初始模型进行更新,并利用更新后的反演结果进行下一轮抽样与判定,直至残差小于阈值或迭代次数大于设定值.因此,SFPSI通过反复执行随机过程与不动点修正来使得初始模型逐渐逼近真实解,并最终收敛于目标泛函的不动点.由于本方法具有一定的随机性,因此需要进行多次反演并以多次反演结果的均值作为最终反演结果.最后,模型以及实际资料测试结果表明本方法有效的提高了FPSI抗噪能力,且本方法的反演结果较FPSI的反演结果具有更高的地层分辨率,并保持了良好的横向连续性.     

Stochastic models in hydrology

A stochastic approach to the analysis of hydrologic processes is defined along with a discussion of causes of tendency, periodicity and stochasticity in hydrologic series. Sources of temporal non-stationarity are described along with objectives and methods of analysis of processes and, in general, of information extraction from data. Transferred information as measured by correlation coefficients is compared with the transferable information as measured by entropy coefficients. Various multivariate approaches to hydrologic stochastic modeling are classified in light of complexities of spatial/temporal hydrologic processes. Alternatives of time series structural decomposition and modeling are compared. A special approach to modeling of space properties further contributes to approximate simulations of spatial/temporal processes over large areas. Several aspects of stochastic models in hydrology are concisely reviewed.     

Stochastic forecasting of mine water inrushes

An event-based stochastic forecasting approach is used to model water inrushes into underground works under karstic water hazard. The stochastic properties of inrushes are related to the statistical properties of fissures in the karstic rock. The probability distributions (DF) of five random variables of interest in design are estimated; namely, inrush yield q, number N of inrushes per unit area, distance L between inrushes, maximum q_max in N events and total yield Q. The phenomenological hypotheses of log normal DF of q and Poisson DF of N are reinforced by observation data. On the basis of these DF, a Monte Carlo simulation of a spatial Poisson process of inrushes is run to estimate the DF of q_max and Q. The derivation of Bayesian DF to account for parameter uncertainty is discussed. The stochastic model is used for design and operation of minewater control facilities in the Transdanubian karstic region of Hungary.     

Stochastic response of multistorey yielding frames

A model for the random vibration of hysteretic and degrading plane frames to Gaussian shot noise or filtered shot noise is presented. The model, which combines the discrete hinge concept previously used in deterministic frame analysis with Bouc's smooth system hysteresis, results in a non-linear set of differential equations which can be linearized in closed form without recourse to the Krylov-Bogoliubov assumptions. Solution is iterative in the stationary case and by numerical integration with stepwise updates in the non-stationary case. Numerical studies are presented of strong girder and strong column two storey, one bay frames. These studies indicate that the model can locate the sites of yielding and provide reasonable values of structural response as compared with simulation data. Moreover, the model can provide a joint by joint breakdown of the structure energy dissipation rate.     

Stochastic analysis of rigid foundation filtering

The effect of the space–time variation of earthquake ground motion on the translational response of structures supported on large rigid mat foundations is considered. A stochastic space–time ground motion model, based on the analysis of recordings from the SMART-1 seismograph array in Lotung, Taiwan, is used. Random vibration theory is utilized to obtain an expression for the reduction in the maximum structural response, for a specified probability level and strong motion duration. Numerical computations are performed to examine the sensitivity of the reduction in structural response to various ground motion and structural parameters. The results indicate that spatial correlation and travelling wave effects give rise to significant reductions in structural responses when the seismic waves have low apparent propagation velocities. Utilization of this result in the design of structures on large foundations should yield substantial cost savings.     

Stochastic generation of hourly rainstorm events

Occurrence of rainstorm events can be characterized by the number of events, storm duration, rainfall depth, inter-event time and temporal variation of rainfall within a rainstorm event. This paper presents a Monte-Carlo based stochastic hourly rainfall generation model considering correlated non-normal random rainstorm characteristics, as well as dependence of various rainstorm patterns on rainfall depth, duration, and season. The proposed model was verified by comparing the derived rainfall depth–duration–frequency relations from the simulated rainfall sequences with those from observed annual maximum rainfalls based on the hourly rainfall data at the Hong Kong Observatory over the period of 1884–1990. Through numerical experiments, the proposed model was found to be capable of capturing the essential statistical features of rainstorm characteristics and those of annual extreme rainstorm events according to the available data.     

Stochastic modelling of unresolved eddy fluxes

A subgrid-scale parameterization scheme motivated by statistical closure theory, but employing statistics obtained from high-resolution direct numerical simulations, is applied to large eddy simulations of two-level quasigeostrophic turbulence on the sphere. It is shown that these parameterizations are consistent with the phenomenology of quasigeostrophic turbulence. The parameterizations consist of 2 × 2 dissipation and stochastic forcing covariance matrices at each wavenumber, with the off-diagonal elements of the matrices representing vertical mixing. Two flow regimes, characterized by their deformation scales, are considered, namely atmospheric and oceanic. In the former, the deformation scale is fully resolved, and the truncation scale is within the enstrophy cascading interial range. In the latter, the deformation scale is not fully resolved, and the truncation scale is within the energy cascading inertial range. It is demonstrated through numerical experiments that both stochastic and deterministic variants of the scheme give comparable results for the energy spectra in the atmospheric regime. In the oceanic regime, the stochastic variant again gives excellent results, but the deterministic variant is found to be numerically unstable.     

Stochastic spatial analysis of earthquake sequence

In this paper, by means of the statistical analysis method of stochastic spatial point process, statistical analysis of spatial distribution of earthquakes in the large northern region of China is made. Emphasis is on the test and analysis of the complete spatial randomness, correlation of earthquake distribution in the different magnitude interval and random labeling. It is shown by the analysis that the spatial distribution of earthquakes in the large northern region is “clustered”, the distributions of earthquakes in different magnitude interval are positively correlated and can be modeled by a two-dimensional process. The results obtained in the paper can be used for the establishment of a reasonable spatial distribution model and have some application in the reasonable estimation of seismic hazard. The Chinese version of this paper appeared in the Chinese edition ofActa Seismologica Sinica,15, 129–135, 1993.     

Stochastic delineation of well capture zones

In this work, we describe a stochastic method for delineating well capture zones in randomly heterogeneous porous media. We use a moment equation (ME) approach to derive the time-dependent mean capture zones and their associated uncertainties. The mean capture zones are determined by reversely tracking the non-reactive particles released at a small circle around each pumping well. The uncertainty associated with the mean capture zones is calculated based on the particle displacement covariances for nonstationary flow fields. The flow statistics are obtained either by directly solving the flow moment equations derived with a first-order ME approach or from Monte Carlo simulations (MCS) of flow. The former constitutes a full ME approach, and the latter is a hybrid ME-MCS approach. This hybrid approach is invoked to examine the validity of the transport component of the stochastic method by ensuring that the ME and MC transport approaches have the same underlying flow statistics. We compared both the full ME and the hybrid ME-MCS results with those obtained with a full MCS approach. It has been found that the three approaches are in excellent agreement when the variability of hydrologic conductivity is small (_Y²=0.16). At a moderate variability (_Y²=0.5), the hybrid ME-MCS and the full MCS results are in excellent agreement whereas the results from the full ME approach deviate slightly from the full MCS results. This indicates that the (first-order) ME transport approach renders a good approximation at this level of variability and that the first-order ME flow approximation may not be sufficiently accurate at this variability in the case of divergent/convergent flow. The first-order ME flow approach may need to be corrected with higher-order terms even for moderate _Y² although the literature results reveal that the first-order ME flow approach is robust for uniform mean flow (i.e., giving accurate results even with _Y² as large as four).     

Stochastic linearization of multi-degree-of-freedom non-linear systems

An equivalent linearization technique to obtain the response of non-linear multi-degree-of-freedom dynamic systems to stationary gaussian excitations is developed. The non-linearities are assumed to be single-valued functions of accelerations, velocities and displacements. Using a property of gaussian vector processes, the closed forms of the coefficients of the equivalent linear system are obtained by the direct application of partial differentiation and expectation operators to the non-linear terms. It is shown that when the non-linearities possess potentials, the linear system has symmetric coefficient matrices. A geometrical interpretation of the linear coefficients, in connection with the original non-linearities, is presented. The accuracy is investigated by means of examples.