基于Monte Carlo方法的由GPS观测计算地应变率的误差分析

在利用GPS观测计算地应变率中,应变率的误差分布在地球动力学研究中非常重要. 为解决这一问题,文中提出基于Monte Carlo技术,利用GPS观测的速度计算地应变率进行误差分析的方法;以青藏高原地区为例,利用带有误差的GPS观测数据进行了大量、独立的应变率重复计算,然后运用统计方法给出了应变率分量及其误差的分布. 结果显示青藏高原的喜马拉雅及中东部地区地应变率的东西向、南北向及剪应变率分量的误差相对较大,但整个青藏高原及附近地区应变率结果稳定可靠;研究结果进一步表明,由GPS观测计算应变率的误差主要决定于GPS观测速度的误差, 在变形小的区域（如中国东部地区）,目前GPS的观测精度还不能完全满足地应变率计算的要求.     

重大地震灾害链的时空有序性及其预测研究

重大地震与地震灾害链预测是当今世界地学前缘课题.本文通过国内外具体实例,重点介绍了重大地震灾害链的时空有序性、地震灾害链网络结构及其预测方法.     

河南区域经济增长俱乐部趋同研究

1990~2004年,河南区域经济增长形成了高收入、中高收入、中低收入和低收入 4个趋同俱乐部。高收入和低收入趋同俱乐部的稳定性最大且连片扩张,区域经济增长水平和空间结构的两极分化日趋增强。总体上,一个区域的"邻居区域环境"对其经济增长及其向何种趋同俱乐部转移有规律性的影响。低收入区域的经济增长及其向中低收入趋同俱乐部转移的概率与其邻居区域的经济增长水平正相关。中低收入区域更倾向于向低收入趋同俱乐部转移。对于中高收入区域,与经济增长水平越高的区域为邻,其向高收入趋同俱乐部转移的概率越大;反之,与经济增长水平越低的区域为邻,其向中低收入趋同俱乐部转移概率也越大。高收入区域的经济增长及稳定性受邻居区域的影响比较小。     

Consolidation in spatially random unsaturated soils based on coupled flow‐deformation simulation

This paper integrates random field simulation of soil spatial variability with numerical modeling of coupled flow and deformation to investigate consolidation in spatially random unsaturated soil. The spatial variability of soil properties is simulated using the covariance matrix decomposition method. The random soil properties are imported into an interactive multiphysics software COMSOL to solve the governing partial differential equations. The effects of the spatial variability of Young's modulus and saturated permeability together with unsaturated hydraulic parameters on the dissipation of excess pore water pressure and settlement are investigated using an example of consolidation in a saturated‐unsaturated soil column because of loading. It is found that the surface settlement and the pore water pressure profile during the process of consolidation are significantly affected by the spatially varying Young's modulus. The mean value of the settlement of the spatially random soil is more than 100% greater than that of the deterministic case, and the surface settlement is subject to large uncertainty, which implies that consolidation settlement is difficult to predict accurately based on the conventional deterministic approach. The uncertainty of the settlement increases with the scale of fluctuation because of the averaging effect of spatial variability. The effects of spatial variability of saturated permeability k_sat and air entry parameters are much less significant than that of elastic modulus. The spatial variability of air entry value parameters affects the uncertainties of settlement and excess pore pressure mostly in the unsaturated zone. Copyright © 2016 John Wiley & Sons, Ltd.     

Stage level,volume and time‐frequency information content of Lake Tana using stochastic and wavelet analysis methods

Lake Tana is the largest fresh water body situated in the north‐western highlands of Ethiopia. In addition to its ecological services, it serves for local transport, electric power generation, fishing, recreational purposes, and source of dry season irrigation water supply. Evidence shows that the lake has dried at least once at about 15,000–17,000 before present owing to a combination of high evaporation and low precipitation events. Past attempts to understand and simulate historical fluctuation of Lake Tana based on simplistic water balance approach of inflow, outflow, and storage have failed to capture well‐known events of drawdown and rise of the lake that have happened in the last 44 years. This study tested different stochastic methods of lake level and volume simulation for supporting Lake Tana operational planning decision support. Three stochastic methods (perturbations approach, Monte Carlo methods, and wavelet analysis) were employed for lake level and volume simulation, and the results were compared with the stage level measurements. Forty‐four years of daily, monthly, and mean annual lake level data have shown a Gaussian variation with goodness of fit at 0.01 significant levels of the Kolmogorov–Smirnov test. The stochastic simulations predicted the lake stage level of the 1972, 1984, and 2002/2003 historical droughts 99% of the time. The information content (frequency) of fluctuation of Lake Tana for various periods was resolved using Wigner's Time‐Frequency Decomposition method. The wavelet analysis agreed with the perturbations and Monte Carlo simulations resolving the time (1970s, 1980s, and 2000s) in which low frequency and high spectral power fluctuation has occurred. The Monte Carlo method has shown its superiority for risk analysis over perturbation and deterministic method whereas wavelet analysis reconstructed historical record of lake stage level at daily and monthly time scales. Copyright © 2012 John Wiley & Sons, Ltd.     

Pulse generation models for daily intermittent streamflows / Modèles de génération d’impulsions pour des débits journaliers intermittents

Abstract

Abstract Generating pulses and then converting them into flow are two main steps of daily streamflow generation. Three pulse generation models have been proposed on the basis of Markov chains for the purpose of generating daily intermittent streamflow time series in this study. The first one is based on two two-state Markov chains, whereas the second uses a three-state Markov chain. The third model uses harmonic analysis and fits Fourier series to the three-state Markov chain. Results for a daily intermittent streamflow data series show a good performance of the proposed models.     

Assessing uncertainties in a conceptual water balance model using Bayesian methodology / Estimation bayésienne des incertitudes au sein d’une modélisation conceptuelle de bilan hydrologique

Abstract

Abstract The aim of this study was to estimate the uncertainties in the streamflow simulated by a rainfall–runoff model. Two sources of uncertainties in hydrological modelling were considered: the uncertainties in model parameters and those in model structure. The uncertainties were calculated by Bayesian statistics, and the Metropolis-Hastings algorithm was used to simulate the posterior parameter distribution. The parameter uncertainty calculated by the Metropolis-Hastings algorithm was compared to maximum likelihood estimates which assume that both the parameters and model residuals are normally distributed. The study was performed using the model WASMOD on 25 basins in central Sweden. Confidence intervals in the simulated discharge due to the parameter uncertainty and the total uncertainty were calculated. The results indicate that (a) the Metropolis-Hastings algorithm and the maximum likelihood method give almost identical estimates concerning the parameter uncertainty, and (b) the uncertainties in the simulated streamflow due to the parameter uncertainty are less important than uncertainties originating from other sources for this simple model with fewer parameters.     

Daily rainfall projections from general circulation models with a downscaling nonhomogeneous hidden Markov model (NHMM) for south‐eastern Australia

An ensemble of stochastic daily rainfall projections has been generated for 30 stations across south‐eastern Australia using the downscaling nonhomogeneous hidden Markov model, which was driven by atmospheric predictors from four climate models for three IPCC emissions scenarios (A1B, A2, and B1) and for two periods (2046–2065 and 2081–2100). The results indicate that the annual rainfall is projected to decrease for both periods for all scenarios and climate models, with the exception of a few scenarios of no statistically significant changes. However, there is a seasonal difference: two downscaled GCMs consistently project a decline of summer rainfall, and two an increase. In contrast, all four downscaled GCMs show a decrease of winter rainfall. Because winter rainfall accounts for two‐thirds of the annual rainfall and produces the majority of streamflow for this region, this decrease in winter rainfall would cause additional water availability concerns in the southern Murray–Darling basin, given that water shortage is already a critical problem in the region. In addition, the annual maximum daily rainfall is projected to intensify in the future, particularly by the end of the 21st century; the maximum length of consecutive dry days is projected to increase, and correspondingly, the maximum length of consecutive wet days is projected to decrease. These changes in daily sequencing, combined with fewer events of reduced amount, could lead to drier catchment soil profiles and further reduce runoff potential and, hence, also have streamflow and water availability implications. Copyright © 2012 John Wiley & Sons, Ltd.     

Generation of correlated properties in heterogeneous porous media

The spatial distribution of rock properties in porous media, such as permeability and porosity, often is strongly variable. Therefore, these properties usefully may be considered as a random field. However, this variability is correlated frequently on length scales comparable to geological lengths (for example, scales of sand bodies or facies). To solve various engineering problems (for example, in the oil recovery process) numerical models of a porous medium often are used. A need exists then to understand correlated random fields and to generate them over discretized numerical grids. The paper describes the general mathematical methods required to do this, with one particular method (the nearest neighbor model) described in detail. How parameters of the mathematical model may be related to rock property statistics for the nearest neighbor model is shown. The method is described in detail in one, two, and three dimensions. Examples are given of how model parameters may be determined from real data.     

A temporal model for landslide risk based on historical precipitation

Of the recognized nonsteady-state factors that influence slope stability, probably most critical in many field situations is the character of precipitation and infiltration activity. A groundwater response model used in conjunction with precipitation records can provide a historical catalog of estimated maximum groundwater levels in a particular study area. An extreme-value statistical analysis of this catalog is linked with geotechnical slope stability analyses to provide a landslide hazard model for estimating the probability of slope failure within a given time. This modeling approach can provide meaningful input to risk assessments for landslide mitigation programs and to decision analyses and cost-benefit studies important for land-use planning and resource management.This paper was presented at Emerging Concepts, MGUS 87 Conference, Redwood City, California, 13–15 April 1987.