Diagrammatic solutions for hydraulic head moments in 1-D and 2-D bounded domains

We present a diagrammatic method for solving stochastic 1-D and 2-D steady-state flow equations in bounded domains. The diagrammatic method results in explicit solutions for the moments of the hydraulic head. This avoids certain numerical constraints encountered in realization-based methods. The diagrammatic technique also allows for the consideration of finite domains or large fluctuations, and is not restricted by distributional assumptions. The results of the method for 1-D and 2-D finite domains are compared with those obtained through a realization-based approach. Mean and variance of head are well reproduced for all log-conductivity variances inputted, including those larger than one. The diagrammatic results also compare favorably to hydraulic head moments derived by standard analytic methods requiring a linearized form of the flow equation.     

单位权方差的岭估计和压缩估计

本文提出了求单位权方差的有偏估计方法─岭估计和压缩估计，证明了在一定条件下，岭估计和压缩估计的均方误差要小于最小二乘估计的约方误差，提出了求岭参数和压缩系数的方法。     

Kalman filter estimation for periodic autoregressive-moving average models

An exact maximum likelihood procedure is presented for estimating the parameters of a periodic autogressive-moving average (PARMA) model. To develop an estimator which is both statistically and computationally efficient, the PARMA class of models is written using a state-space representation and a Kalman filtering algorithm is used to estimate the parameters. In order to demonstrate how to fit PARMA models in practice, the most appropriate types of PARMA models are identified for fitting to two average monthly riverflow time series and the new estimator is employed for estimating the model parameters.     

核旋仪探头高度的探讨

本文根据试验观测结果,探讨了核旋仪探头高度改变对野外流动地磁场总强度观测质量的影响。认为适当提高探头高度,可以有效地消除地表物质不均匀磁性所带来的随机干扰。具体的探头高度可由不同工作区域的试验工作加以确定。     

The development of stochastic space transformation and diagrammatic perturbation techniques in subsurface hydrology

This paper develops concepts and methods to study stochastic hydrologic models. Problems regarding the application of the existing stochastic approaches in the study of groundwater flow are acknowledged, and an attempt is made to develop efficient means for their solution. These problems include: the spatial multi-dimensionality of the differential equation models governing transport-type phenomena; physically unrealistic assumptions and approximations and the inadequacy of the ordinary perturbation techniques. Multi-dimensionality creates serious mathematical and technical difficulties in the stochastic analysis of groundwater flow, due to the need for large mesh sizes and the poorly conditioned matrices arising from numerical approximations. An alternative to the purely computational approach is to simplify the complex partial differential equations analytically. This can be achieved efficiently by means of a space transformation approach, which transforms the original multi-dimensional problem to a much simpler unidimensional space. The space transformation method is applied to stochastic partial differential equations whose coefficients are random functions of space and/or time. Such equations constitute an integral part of groundwater flow and solute transport. Ordinary perturbation methods for studying stochastic flow equations are in many cases physically inadequate and may lead to questionable approximations of the actual flow. To address these problems, a perturbation analysis based on Feynman-diagram expansions is proposed in this paper. This approach incorporates important information on spatial variability and fulfills essential physical requirements, both important advantages over ordinary hydrologic perturbation techniques. Moreover, the diagram-expansion approach reduces the original stochastic flow problem to a closed set of equations for the mean and the covariance function.     

随机效应线性模型中随机回归系数的泛岭估计类

本文提出了随机效应线性模型中随机回归系数的一个线性有偏估计类（称为泛岭估计类）；通过偏参数的不同选择，构造了许多很有意义的线性有偏估计；并讨论了泛岭估计的优良性和可容许性，获得了若干深入的结果。     

Parameter identification in tidal models with uncertain boundary conditions

In this paper a parameter estimation algorithm is developed to estimate uncertain parameters in two dimensional shallow water flow models. Since in practice the open boundary conditions of these models are usually not known accurately, the uncertainty of these boundary conditions has to be taken into account to prevent that boundary errors are interpreted by the estimation procedure as parameter fluctuations. Therefore the open boundary conditions are embedded into a stochastic environment and a constant gain extended Kalman filter is employed to identify the state of the system. Defining a error functional that measures the differences between the filtered state of the system and the measurements, a quasi Newton method is employed to determine the minimum of this functional. To reduce the computational burden, the gradient of the criterium that is required using the quasi Newton method is determined by solving the adjoint system.     

A model for the generation of simultaneous daily discharges of two rivers at their point of confluence

A geomorphological study at the confluence of the Danube and the Isar in Bavaria required long series of daily discharges in both rivers. A model that generates simultaneous correlated streamflows in both rivers was developed and tested. The model is a modified shot noise model, first developed by Treiber (1975) for a single river, that was adapted to two rivers. It generates correlated pulses of events that produce flow for each river, and these pulses are then convoluted with a river specific systems function. The model, after being calibrated for the two rivers on the basis of 85 years of records, yields artificial series of discharges, in which the statistical properties of the historical records are reproduced. The performance of the model was tested with 20 generated series each 100 years long.     

Probabilistic modeling of aerated lagoons: A comparison of methodologies

Two stochastic models are developed to describe the BOD output (i.e. effluent) variation of facultative aerated lagoons in series. One of the models uses the uncertainty analysis (UA) technique and the other is based on the moment equation solution methodology of stochastic differential equations (SDE's). The former considers a second-order approximation of the expectation (SOAE) and a first-order approximation of the variance (FOAV). The SDE model considers that output variability is accounted for by random variations in the rate coefficient. Comparisons are provided. Calibration and verification of the two models are aciieved by using field observations from two different lagoon systems in series. The predictive performances of the two models are compared with each other and with another SDE model, presented in a previous paper, that considers input randomness. The three methods show similar predictive performances and provide good predictions of the mean and standard deviation of the lagoon effluent BOD concentrations and thus are considered as appropriate methodologies.     

AR and ARMA spectral estimation

Autoregressive (AR) and Autoregressive-moving average (ARMA) methods of spectral analysis have been developed and are being increasingly used as alternatives to traditional methods of spectral analysis. Two of these methods developed by Marple and Friedlander are tested in this study by using generated data from models with known spectra. The Blackman-Tukey spectral estimates are also compared to the Marple and Friedlander estimates. The variability of the Marple and Friedlander estimates with sample sizes is investigated. Although both Marple's and Friedlander's methods are satisfactory, Friedlander's method is preferred because of its ability to handle a wider class of models.