PRELIMINARY STUDY OF BAROTROPIC STOCHASTIC DIFFERENCE MODEL APPLIED TO WEATHER PREDICTION

In order to consider both the deterministic and the stochastic property of atmospheric motion simul-taneously,in this paper,the weather prediction is proposed as the problem of the evolution of meteorologicalfield.The historical viewpoint of atmospheric motion is emphasized here.Based on time series analysis te-chnique,a stochastic-dynamical model with multiple initial fields is derived.Thus,weather forecasting is sum-meal up as a problem of solving a set of stochastic difference equations.For the barotropic atmosphere,thenumerical solutions of the equations are obtained by using the method of empirical orthogonal functions(EOF),and examples of medium-range weather prediction are given here.Meanwhile,selecting the order oftime series,i.e.,determining the number of initial fields properly,is also discussed.     

河西内陆河春季流量变化特征的分析和预测

利用昌马、莺落峡、九条岭３个水文站四十余年的月平均流量资料,分析河西地区３条主要内陆河春季流量的长期变化特征,并用均生函数模型对流量进行了预报,为河西地区水资源的研究和利用提供参考依据     

疏勒河流域极端水文事件对极端气候的响应

为了明确疏勒河流域极端水文事件对极端气候事件的响应关系,选取疏勒河流域内及其周边的托勒、敦煌、瓜州、玉门、酒泉、马鬃山等气象站点的气温、降水和蒸发的日值数据,昌马堡水文站的日径流数据,通过趋势分析、滑动平均、主成分分析等方法,分析疏勒河流域极端气候指数、极端水文事件的年际变化规律以及影响极端水文事件的因素,并明确该流域极端洪水年内分布特征。结果表明:疏勒河流域年际气温升高趋势明显,降水量呈波动变化,增加趋势不明显,而蒸发量呈下降的变化趋势。表征高温的极端气温指数呈显著上升趋势,表征低温的极端气温指数呈显著下降趋势,说明疏勒河流域气温增幅明显。极端降水指数呈显著的增加趋势。该流域极端洪水事件和频次呈上升趋势,而极端枯水事件和频次呈下降趋势。极端洪水事件主要受控于极端降水事件,特别是极端降水总量,极端高温事件对极端洪水总量的增加也有影响,而极端枯水事件主要受控于极端低温事件。此外,2000-2016年年最大洪峰流量出现的时间有由8月向7月转变的趋势。     

基于连续消光特征分析的岩石薄片分析系统

连续消光是指在正交偏光显微镜下,岩石薄片随载物台旋转而发生消光强弱连续变化的现象.作为人工鉴定岩石薄片的重要依据,提取后的连续消光特征可以用于实现岩石薄片的自动分析.结合数字图像处理技术与聚类划分算法,开发出一套能够分割出孔隙、颗粒轮廓,实现颗粒类别划分,并对结果做定量分析的岩石薄片分析系统.从矿物的消光特征本质出发,...     

Methodology to set trigger levels in an urban drainage flood warning system – an application to Jhonghe,Taiwan

A wireless water-level monitoring system for an urban drainage flood warning is developed, and stations equipped with pressure sensors are installed to monitor water levels. The water levels for flood warning are investigated. Two stages of warning water level for “larger” conduits are set based on the rate of rising water levels. In a similar way, only one stage of the warning water level is set for “smaller” conduits. The average rates of rising water levels for different scenarios are estimated using the US EPA Storm Water Management Model (SWMM). When evaluating the impact of flooding, the outflows from manholes simulated by the SWMM are used as sources for a two-dimensional overland flow simulation. The integrated system is successfully executed in Jhonghe, New Taipei City, Taiwan, which has experienced urban drainage floods. Therefore, this system can provide urban drainage flood warnings to the authorities to take disaster reduction measures.     

A direct analysis of flood interval probability using approximately 100-year streamflow datasets

ABSTRACT

Series of observed flood intervals, defined as the time intervals between successive flood peaks over a threshold, were extracted directly from 11 approximately 100-year streamflow datasets from Queensland, Australia. A range of discharge thresholds were analysed that correspond to return periods of approximately 3.7 months to 6.3 years. Flood interval histograms at South East Queensland gauges were consistently unimodal whereas those of the North and Central Queensland sites were often multimodal. The exponential probability distribution (pd) is often used to describe interval exceedence probabilities, but fitting utilizing the Anderson-Darling statistic found little evidence that it is the most suitable. The fatigue life pd dominated sub-year return periods (<1 year), often transitioning to a log Pearson 3 pd at above-year return periods. Fatigue life pd is used in analysis of the lifetime to structural failure when a threshold is exceeded, and this paper demonstrates its relevance also to the elapsed time between above-threshold floods. At most sites, the interval medians were substantially less than the means for sub-year return periods. Statistically the median is a better measure of the central tendency of skewed distributions but the mean is generally used in practice to describe the classical concept of flood return period.

Editor Z.W. Kundzewicz; Associate editor I. Nalbantis     

Using regional regression within index flood procedures and an empirical Bayesian estimator

Studies have illustrated the performance of at-site and regional flood quantile estimators. For realistic generalized extreme value (GEV) distributions and short records, a simple index-flood quantile estimator performs better than two-parameter (2P) GEV quantile estimators with probability weighted moment (PWM) estimation using a regional shape parameter and at-site mean and L-coefficient of variation (L-CV), and full three-parameter at-site GEV/PWM quantile estimators. However, as regional heterogeneity or record lengths increase, the 2P-estimator quickly dominates. This paper generalizes the index flood procedure by employing regression with physiographic information to refine a normalized T-year flood estimator. A linear empirical Bayes estimator uses the normalized quantile regression estimator to define a prior distribution which is employed with the normalized 2P-quantile estimator. Monte Carlo simulations indicate that this empirical Bayes estimator does essentially as well as or better than the simpler normalized quantile regression estimator at sites with short records, and performs as well as or better than the 2P-estimator at sites with longer records or smaller L-CV.     

长江巨洪前期物理因子的配置

提出了长江巨洪前期物理因子配置的概念，在每一次巨洪发生之前，影响巨洪的主要物理因子之间一般都会表现出相似的特征，即出现一些固有的配置，这些配置正是长江发生巨洪的强信号，对长江巨洪的预测研究具有重要的指示作用。     

FluBiDi – a model to estimate flood based on runoff: validation using extreme and natural basin conditions

FluBiDi is a two-dimensional model created to simulate real events that can take days and months, as well as short events (minutes or hours) and inclusive laboratory tests. To verify the robustness of FluBiDi, it was tested using a previous study with both designed and real digital elevation models. The results highlight good agreement between the models (i.e. Mike Flood, SOBEK, ISIS 2D, and others) tested and FluBiDi (around 90% for a specific instant and 95% for the complete time simulation). In the simulated hydrographs, the discharge peak value, time to peak, and water level results were accurate, reproducing them with an error of less than 5%. The velocity differences observed in a couple of tests in FluBiDi were associated with very short periods of time (seconds). However, FluBiDi is highly accurate for simulating floods under real topographical conditions with differences of around 2 cm when water depth is around 150 cm. The average water depth and velocities are precise, and the model describes with high accuracy the pattern and extent of floods. FluBiDi has the capability to be adjusted to different types of events and only requires limited input data.     

基于进化神经网络混凝土大坝变形预测

根据丰满大坝多年变形观测数据,建立了基于进化神经网络混凝土大坝变形预测方法。经典的BP神经网络的缺陷在于收敛速度慢和泛化能力弱等特性。与普通的多元回归方法和传统的BP神经网络相比,采用遗传算法训练的人工神经网络预测模型预报大坝的变形具有精度高和全局收敛的特点。在丰满大坝工程实际应用表明,所建立的基于进化神经网络混凝土大坝变形预报方法与广泛采用的统计方法相比,可以显著提高大坝变形预报精度。