扎赉诺尔煤田水文地球化学特征及成因分析

扎赉诺尔煤田地处山前,有利的地理环境条件形成了独特的水文地质特征—地下水的垂直分带和地下水顺层径流。其特有的水文地球化学环境导致了地下水成分的差异性。本文从水文地球化学的角度,对煤田地下水的水文地球化学特征及成因进行了论述分析。     

基于DEM的分布式水文模型构建方法

基于 DEM的分布式水文模型是现代水文学同高科技 (如计算机技术、 3S技术等 )相结合的产物 ,是研究变化环境下水文循环与水资源演化规律的理想工具 ,代表了水文模型的最新发展方向。从 DEM的特性出发 ,本文探讨并总结了分布式水文模型的特点、建模思路和模型基本结构框图。在流域离散化方面 ,重点介绍了分布式水文模型常用的三种单元划分方法 ;最后 ,针对分布式水文模型构建问题 ,从“输入模块”、“单元水文模型”、“河网汇流模型”三方面 ,阐述了分布式水文模型微结构构建方法。     

安徽铜陵地区蚀变流体填图方法的探讨

蚀变流体填图是一项区域流体地质调查研究工作 ,也是一项具有战略意义的、综合性的基础地质研究工作。区域大面积的流体填图工作的理论依据、思路方法、填图要素、技术要求和规范等还没有一套成熟的方法体系。在实施中国地质调查局和国土资源部科学技术司的“铜陵地区蚀变流体填图”项目中 ,探讨了流体填图的理论依据、思路方法、研究内容和填图要素 ,对填图单位制定了 3级填图单元等级体制 ,即流体系统流体子系统流体单元。在铜陵地区划分出 4个流体系统 ,7个流体子系统和 18个流体单元。     

QN—模型及其在覆盖区资源靶区预测中的应用

本文把数量化理论Ⅲ与Ｑ－型非线性映射在机地结合在一起，定义了一种新的数学模型－ＱＮ－模型。详细地介绍了该模型的基本原理和计算步骤，给出了模型在覆盖区资源靶区以预测中应用的实例。     

不整合界限地层单位确立及划分的重要意义──兼论华北地层区不整合界限地层单位的划分和命名

在进行岩石地层划分时，有必要进行不整合界限地层单位的划分。不整合界限地层单位是顶底以不整合界面为限，而由有关联的若干个基本岩石地层单位──组（群）组成的组合。和岩石地层单位一样，是自然界存在的客观实体。初步建议华北地区划分为８个不整合界限地层单位：桑干河序列、五台山序列、滹沱河序列、秦幽序列、冀鲁序列、三晋序列、燕辽序列、河海序列。不整合界限地层单位的划分、确立，是进行地层格架分析、进行层序地层学研究的前提，有利于以岩石地层单位（即以不整合界限地层单位）编制小比例尺地质图。     

应力波与可滑移岩石界面间的相互作用研究

研究了应力波与用节理刚度系数描述的线性滑移岩石界面间的相互作用，给出了其透、反射波幅的一般表达式。通过应力波垂直人射界面时的具体分析，反映出岩石界面的节理刚度对应力波传播的重要影响，并揭示了岩石界面对应力波的高频滤波作用。     

SEDIMENT TRANSPORT MODEL OF A WATERSHED

ChinaisoneOfthecountrieswhichsufferfromthemostserioussoilerosionintheworld,andthehillyandgullyareasof~PlateauinthemiddlereachesOftheYellowRiverarethemostseriouserosionareainChina.Inthisarea,themainerosivefactorsarerainfallandrunoff.Sometimes,thesedimentyieldinasinglefloodeventcanbe40%oftheannualsedimentload.Therefore,itisveryimPOrtanttostudytheyieldandgraphOfSedimentOfsinglefindevent.Forthispn~,thispaperiswritten.AInongsedimentmodelsforsinglefloodevent,moreandmoreattentionwaspaidtOunitse…     

Regionalization of unit hydrograph parameters: 1. Comparison of regression analysis techniques

Hydrologic regionalization is a useful tool that allows for the transfer of hydrological information from gaged sites to ungaged sites. This study developed regional regression equations that relate the two parameters in Nash's IUH model to the basin characteristics for 42 major watersheds in Taiwan. In the process of developing the regional equations, different regression procedures including the conventional univariate regression, multivariate regression, and seemingly unrelated regression were used. Multivariate regression and seeming unrelated regression were applied because there exists a rather strong correlation between the Nash's IUH parameters. Furthermore, a validation study was conducted to examine the predictability of regional equations derived by different regression procedures. The study indicates that hydrologic regionalization involving several dependent variables should consider their correlations in the process of establishing the regional equations. The consideration of such correlation will enhance the predictability of resulting regional equations as compared with the ones from the conventional univariate regression procedure.     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.