延河沉积物的石油污染调查与分析

对延河干流和两条支流受石油污染河段的沉积物的取样分析结果表明 ,研究河段内的沉积物都不同程度的受到了石油污染。干流河段沉积物的石油污染负荷一般为 40～ 80mg/kg ,上游河段和污染支流汇入段负荷较高 ,平均为390mg/kg,最高可达 784mg/kg ;支流上的坪桥川和杏子河河段沉积物污染负荷为 6 0～ 2 5 0mg/kg。干流中下游及杏子河支流河段的污染沉积物主要来源于雨期流域表层污染土壤径流入河后的沿程沉积 ,而非雨期岸边油井的直接排污则是造成干流上游和坪桥川支流沉积物石油污染的主要原因     

BUILDUP,WASHOFF, AND EVENT MEAN CONCENTRATIONS1

ABSTRACT: Most watershed water quality simulation models require the user to specify pollutant buildup and washoff rate parameters for pollutants, by land use. Buildup and washoff rates are difficult to measure directly, and only limited guidance and few observed data are available from the literature. Many studies, however, report storm event mean concentrations (EMCs). These EMCs must arise as a result of the buildup and washoff processes, but typically represent the net contribution from a variety of pervious and impervious surfaces. This paper explores the relationship between EMCs and buildup/washoff parameters. An assumption of the mathematical form of the buildup/washoff relationship gives an algebraic expression for the EMC consistent with model assumptions. This yields techniques to separate observed EMCs into contributions from different land uses and from pervious and impervious surfaces. Given this relationship, numerical optimization may be used to estimate site specific values of buildup and washoff parameters from observed storm EMCs for use in modeling. Use of this approach helps ensure that model parameters are consistent with observed data, providing a rational starting point for final model calibration. Several site examples demonstrate use of the method.     

水库中长期径流预报系统的开发研究

水库中长期径流预报系统采用PowerBuilder作为系统主要开发工具，集水情信息查询、检索、监视、预报与管理等功能于一体，实现了中长期水文预报和信息管理的各种功能要求。该系统具有功能全面，技术基础坚实，通用性和开放性强，自动化程度高，界面友好，全菜单操作等特点。     

NATURAL EROSION RATES AND THEIR PREDICTION IN THE IDAHO BATHOLITH1

ABSTRACT: Natural rates of surface erosion on forested granitic soils in central Idaho were measured in 40 m² bordered erosion plots over a period of four years. In addition, we measured a variety of site variables, soil properties, and summer rainstorm intensities in order to relate erosion rates to site attributes. Median winter erosion rates are approximately twice summer period rates, however mean summer rates are nearly twice winter rates because of infrequent high erosion caused by summer rainstorms. Regression equation models and regression tree models were constructed to explore relationships between erosion and factors that control erosion rates. Ground cover is the single factor that has the greatest influence on erosion rates during both summer and winter periods. Rainstorm intensity (erosivity index) strongly influences summer erosion rates, even on soils with high ground cover percentages. Few summer storms were of sufficient duration and intensity to cause rilling on the plots, and the data set was too small to elucidate differences in rill vs. interrill erosion. The regression tree models are relatively less biased than the regression equations developed, and explained 70 and 84 percent of the variability in summer and winter erosion rates, respectively.     

三峡库区降雨径流水体中铜、锰、镍、锌、铅含量及分布特征

本文按时段和随机采集了三峡库区降雨径流水体样品，测定了水体中Ｃｕ、Ｍｎ、Ｎｉ、Ｚｎ、Ｐｂ含量，统计结果表明：Ｍｎ、Ｚｎ、Ｐｂ在径流水体中的含量趋于平均，变异系数较小。随产流时间的变化，水体中５种元素的变化规律不明显     

渭河径流特性研究

由于工农业的快速发展,渭河径流逐年减少,污染加重,河道淤积不断发展,洪涝灾害时有发生,对中下游人民生产生活造成严重影响.为了减少洪害,减轻污染,必须保证渭河常年径流稳定.     

黄河三角洲风暴潮灾害及其对滨海湿地的影响

黄河三角洲是中国重要的滨海湿地分布区，也是中国风暴潮易发区。该区的风暴潮有温带气旋型与热带台风型两种，以温带风暴潮为主。近年来，重大风暴潮的发生有频率增加、危害加重的趋势。风暴潮对滨海湿地的影响是广泛而深远的，主要表现为淹没滨海低地、加速海岸蚀退、破坏地表结构、毁坏湿地植被、加剧海水入侵等，使滨海湿地不断损失退化，生态环境恶化。     

EFFECTS OF WATERSHED REPRESENTATION ON RUNOFF AND SEDIMENT YIELD MODELING1

ABSTRACT: This paper evaluates the effects of watershed geometric representation (i.e., plane and channel representation) on runoff and sediment yield simulations in a semiarid rangeland watershed. A process based, spatially distributed runoff erosion model (KINEROS2) was used to explore four spatial representations of a 4.4 ha experimental watershed. The most complex representation included all 96 channel elements identifiable in the field. The least complex representation contained only five channel elements. It was concluded that oversimplified watershed representations greatly influence runoff and sediment yield simulations by inducing excessive infiltration on hillslopes and distorting runoff patterns and sediment fluxes. Runoff and sediment yield decrease systematically with decreasing complexity in watershed representation. However, less complex representations had less impact on runoff and sediment‐yield simulations for small rainfall events. This study concludes that the selection of the appropriate level of watershed representation can have important theoretical and practical implications on runoff and sediment yield modeling in semiarid environments.     

Discriminate Analysis for Dust Storm Prediction in the Gobi and Steppe Regions in Mongolia

A discriminate analysis method for probability forecast of dust storms in Mongolia has been developed. The prediction method uses data recorded at 23 meteorological stations in the Gobi and steppe regions of Mongolia, including surface air pressure and geo-potential height at the 500-hPa level on grid points, and weather maps from 1975 to 1990. Weather elements such as air temperature, pressure, geo-potential height etc, which influence the formation of dust storms, are prepared as predictors. To select the most informative/important predictors (variables), we used a mean correlation matrix of variables together with the Mahalonobis distance, and correlation coefficients between dust storms and predictors with an orthogonalization for removing correlated predictors. The most informative predictors for dust storm prediction are intensities of surface cyclones and migratory anticyclones, passage of cold fronts, the horizontal gradients of the surface air pressure in the cold frontal zone, cyclonic circulations from the ground surface up to the 500-hPa level, the geo-potential height at 500-hPa level and its temporal changes. Selected predictors are used in discriminate analysis for formulating dust storm prediction equations. Sandstorm data have been classified into three classes, viz., strong, moderate and weak dust storms, depending on their intensities, durations and areas covered. Predictions of the probabilities of dust storm occurrence use the prediction equations for each class. The prediction is made from 12 hours to 36 hours. Verification of the probability forecasts of dust storms is also shown. The accuracy of forecasts is 72.2–79.9% with the data used for developing equations (dependent variables), in contrast to 67.1–72.0% with unrelated data for deriving equations (independent variables).