黑河流域水资源动态变化及其趋势的灰色Markov链预测

根据有关水文气象台站的观测数据,对黑河流域水资源时空分布特征及变化规律进行了分析,并基于GM(1,1)模型和Markov链原理,提出了一个用于黑河流域的水资源变化趋势预测的灰色离散随机过程模型。结果表明,从1944年有观测记录以来的近60a时间里,以黑河干流为代表的黑河流域天然径流经历了几个长度不等的丰水段与枯水段,而目前正处于1996年开始第5个丰水段的下降段的最低点或自2000年开始的第6个枯水段的起始点。未来几年里,黑河流域天然水资源的变化总体上将呈现一种偏枯或平水偏枯的状态,但径流偏枯的幅度将不会很大。     

新疆渭干河黑孜水库年径流随机数学模型的建立

黑孜水库是新疆最大的山区拦河水库，具有灌溉、防洪、发电等综合效益。为了使水库调节遇到各种丰、平枯水的组成情况，以便最佳调节运用以发挥其最大效益。本文应用黑孜水库水文站已有３６年年径流序列将其中折趋势、跳跃、周期、随机和纯随机５种成份分析出来，建立各自的数学模型，然后综合成总全数学模型，用以延长年径流序列。延长后的序列经过各种统计检验，保持了原有序列的统计特性，所以是可以实用的。     

灰色预测模型在径流长期预报中的应用

基于灰色系统的建模理论，利用河西地区有关河流的径流观测资料，建立了一个ＧＭ（１，１）残差序列周期修正径流预测模型，并用于春旱缺水期３～６月河流来水量的长期预报，经生产部门验证，准确率在８０％以上     

时间序列分析在乌鲁木齐河年径流预测中的应用

本文以时间序列分析为基础,对河流年径流过程的预测进行了分析与探讨。着重分析了径流过程的分解、各组成分量的特性及其模拟方法。并以乌鲁木齐河年径流过程为例进行了预测。     

新疆渭干河黑孜水库月径流随机数学模型的建立

通过应用相关解集模型，建立了渭干河黑孜水库的月径流机数学模型，通过长系列的月径流可进行水库的各种丰枯水序列组合情况下的调节运算，从而为水库的设计和管理服务，相关解集模型还可根据水库调节需要，进一步将月径流再分解为旬径流，其方法与原理与年径流分解为径流相同，最后，文章对建立的月径流散数学模型进行了分析和检验，结果令人满意。     

区间降水对电站入库径流的影响

正确估计区间面平均降水情况及产流时间,一直是困扰气象水文界的难题。河池境内及周边区域大型水电站密布,通过对区间面平均降雨量及径流入库时间等因素进行分析,对及时掌握河池境内库区暴雨的时空分布及致洪特点,对提高电站调峰作用,正确控制下泄流量,不单对电站调节用水有重大作用,对防御下游洪涝发生也有积极意义。分析表明,暴雨时空分布越集中,越有利于区间洪峰的形成。     

黑河莺落峡站年径流长期预报模型研究

通过分析黑河莺落峡站出山径流变化规律,并将年径流系列按照频率25%及75%为界,划分成3种情况:即多水年x_i > 55.0,中水年42.1≤x_i≤55.0,小水年x_i < 42.1。计算其状态转移概率得出:年径流过程从某一状态转移至其他状态的可能性都有,但其转移概率的最大值达81.8%。由此可知,年径流的变化过程不仅有随机性,而且有很强的相依性。通过多年对站年径流预报工作的实践,经过各种方法的比较检验,认为建立前期大气环流因子与年平均流量的预报模型和建立年平均流量的时间序列组合模型,其逐年预报的精度较高,经过误差评定分析,两个模型均为甲级方案,检验预报时性能较稳定,能对莺落峡站年平均流量进行有效预报,为黑河流域调水提供技术支撑。     

黄河万家寨水库流域降雨径流的分布式模拟及率定

应用SWAT (Soil and Water Assessment Tool)模型,研制了黄河万家寨水库流域分布式降雨径流模拟系统,并对万家寨水库流域进行了10年（2000-2009年）径流模拟。其中,以2000-2006年为系统参数的敏感性分析期和模型率定期,以2007-2009年为模型的验证期,模拟结果显示,万家寨站点上模拟径流与实测径流过程线的相关系数达到0.97以上。在此基础上还将数值预报模式与模拟系统相耦合,对万家寨水库入库径流进行了预报和检验分析,结果较好。因此,该模拟系统能够较好地反映万家寨水库入库径流量的变化过程,具有一定的适用性,为进一步进行降雨径流预测提供了技术支撑。     

金衢盆地洪水的马尔可夫链预测

根据马尔可夫链预测的原理和方法，分析了金衢盆地未来洪水的变化趋势。结果表明，该区１９９７～１９９８年出现的洪水属于中等年，而１９９９～２０００年出现的洪水则属于偏小年，近期出现偏大或特大洪水的可能性均比较小。     

塔里木盆地三源河年径流过程的非线形特征

This paper attempted to identify fractal and chaotic characteristics of the annual runoff processes in headwaters of the Tarim River. Methods of fractal analyses were used to explore several aspects of the temporal changes from 1957 to 2002. The main findings are as follows: (1) The annual runoff processes of the three headwaters of the Tarim River are complex nonlinear systems with fractal as well as chaotic dynamics. (2) The correlation dimensions of attractor derived from the time series of the annual runoff for the Hotan, Yarkand and Aksu rivers are all greater than 3.0 and non-integral, implying that all three rivers are chaotic dynamical systems that are sensitive to initial conditions, and the dynamic modeling of their annual runoff process requires at least four independent variables. (3) The time series of annual runoff in each river presents a long-term correlation characteristic. The Hurst exponent for the period of 1989 to 2002 suggests that we may expect to see an increasing trend in the annual runoff of the Aksu and Yarkand rivers in the years after 2002, but a decreasing tendency for the Hotan River in the same period. Foundation: Knowledge Innovation Project of CAS, No.KZCX2-XB2-03; Major Direction of Knowledge Innovation Project of CAS, No.KZCX2-YW-127; Shanghai Academic Discipline Project (Human Geography), No.B410 Author: Xu Jianhua (1965–), specialized in geographical quantitative methods and GIS.     

Bayesian Analysis of the Discovery Process Model Using Markov Chain Monte Carlo

Based upon the Bayesian framework for analyzing the discovery sequence in a play, a Markov chain Monte Carlo sampler—the Metropolis–Hastings algorithm, is employed to sample model parameters and pool sizes from their joint posterior distribution. The proposed sampling scheme ensures that the parameter space of changing dimension can be traversed in spite of the unknown number of pools. The equal sample weights make it easy to obtain the confidence intervals and assess the statistical error in the estimates, so that the statistical behaviors of the discovery process modeling can be well understood. Two application examples of the Halten play in Norwegian Sea and the Bashaw reef play in the Western Canada Basin show that, the computational advantage of this method to the simple Monte Carlo integration is considerable. In order to increase the convergence speed of the sample chains to the posterior distributions, several parallel simulations with different starting values are recommended.     

How to Choose Priors for Bayesian Estimation of the Discovery Process Model

The Bayesian version of the discovery process model provides an effective way to estimate the parameters of the superpopulation, the efficiency of the exploration effort, the number of pools and the undiscovered potential in a play. The posterior estimates are greatly influenced by the prior distribution of these parameters. Some empirical and statistical relationships for these parameters can be obtained from Monte Carlo simulations of the discovery model. For example, there is a linear relationship between the expectation of a pool size in logarithms and the order of its discovery, the slope of which is related to the discoverability factor. Some simple estimates for these unknown play parameters can be derived based upon these empirical and statistical conclusions and may serve as priors for the Bayesian approach. The priors and posteriors from this empirical Bayesian approach are compared with the estimates from Lee and Wang's modified maximum likelihood approach using the same data.