DETECTABILITY OF STEP TRENDS IN THE RATE OF ATMOSPHERIC DEPOSITION OF SULFATE1

A method is presented to assist policy makers in determining the combination of number of sampling stations and number of years of sampling necessary to state with a given probability that a step reduction in atmospheric deposition rates of a given magnitude has occurred at a pre-specified time. This pre-specified time would typically be the time at which a sulfate emission control program took effect, and the given magnitude of reduction is some percentage change in deposition rate one might expect to occur as a result of the emission control. In order to determine this probability of detection, a stochastic model of sulfate deposition rates is developed, based on New York State bulk collection network data. The model considers the effect of variation in precipitation, seasonal variations, serial correlation, and site-to-site (cross) correlation. A nonparametric statistical test which is well suited to detection of step changes in such multi-site data sets is developed. It is related to the Mann-Whitney Rank-Sum test. The test is used in Monte Carlo simulations along with the stochastic model to derive statistical power functions. These power functions describe the probability of detecting (α=0.05) a step trend in deposition rate as a function of the size of the step-trend, record length before and after the step-trend, and the number of stations sampled. The results show that, for an area the size of New York State, very little power is gained by increasing the number of stations beyond about eight. The results allow policy makers to determine the tradeoff between the cost of monitoring and time required to detect a step-trend of a given magnitude with a given probability.     

GENERATED MONTHLY STREAMFLOWS FOR THE MIDWESTERN AND SOUTHWESTERN UNITED STATES1

ABSTRACT A critical examination of single gage site, monthly streamflow statistical characteristics for two southern Illinois rivers, an Oklahoma river and a Texas river was made using a digital computer at Northwestern University. High flow variability for the rivers was evident in that, for the rivers tested, 8 to 11 months had coefficients of variation in excess of unity. The gamma distribution was not as efficient as the normal distribution for fitting power or logarithmic transforms of the historical monthly flow data (i.e., F^1-0, F^0-5, F^0-25, Fa¹²⁵, F^0.085, and log F). No single transform to a normal distribution was adequate for all twelve monthly flows, since definite seasonal grouping patterns were found for the four rivers examined. The highly variable flow in the low-flow season(s) indicated much more skewness than was typical of the remainder of the year. For the low-flow seasons, the higher-root (smaller exponent) transforms were particularly useful. Flows were generated from a linear regression model of lag one utilizing two or more transforms for the twelve periods. The definite seasonal patterns found historically were reproduced quite well in the generated streamflows. The effect of a change in data transform from one season to the next was insignificant after one month. Thus the use of different transforms within the year did not bias the results from the linear regression model appreciably, but did help in reproducing the seasonal distribution pattern. The technique seems especially well suited for rivers with highly variable flows.     

FLOOD PLAIN MODELING1

Computer simulation of river basin hydrology has rapidly progressed from an interesting academic exercise to a practical engineering procedure of increasing utility. Mathematical models of the many interrelated processes which occur in a basin have been developed along with efficient numerical procedures for their solution. The present paper is concerned with a particular model which has been used to describe the transformation of a temporally and spatially varying rainfall into a time history of stage and discharge on a flood plain. Although developed principally as a model of the physical processes involved, it is envisioned that the model can serve as one component of an information system for flood plain planning and management. The model consists of the following elements: (i) a rainfall simulation which generates stochastic inputs to the model according to specified rainfall statistics, (ii) a catchment-runoff model which converts the rainfall to surface runoff, (iii) a flood stage model which converts the surface runoff to time histories of flood stage and discharge. The model has been used to investigate the effect of various structural flood control measures in a basin and, in particular, to establish frequency-stage information for each of these. Of particular interest in development of the model have been recurring and partially unanswered questions relative to the proper balance among availability of data for use in the model, data requirements of the model and the objectives of the outputs produced by the model.     

THE PROBABILITY OF CONSECUTIVE RAINLESS DAYS1

ABSTRACT: A major objective of this work was to develop a method applicable to the Intermountain region for estimating the probability of n consecutive dry days, where n ≤ 30 days. One result was a computationally simple method of producing the desired estimates directly from the rainfall record. For a consecutive dry-day period of n days, these estimates are equivalent to those obtained from a Markov model of order n-1. The efficacy of a simple Markov model was also evaluated. In this climatic region, the simple Markov model produces probability estimates of consecutive dry days that are too conservative, especially at long dry-day periods. In this data set, it was found that the longer the dry-day sequence, the more conservative the Markov estimate. The source of these conservative estimates is the strong dry-day persistence, which is characteristic of summer weather in the Intermountain region. In this region, the best estimates of the probability of consecutive dry days are probably those obtained directly from a representative rainfall record and smoothed by the partial sums of a fourier Series.     

DEVELOPMENT OF DESIGN STORM HYETOGRAPHS FOR CINCINNATI,OHIO1

ABSTRACT A synthetic storm rainfall hyetograph for a one-year design frequency is derived from the one-year intensity-duration curve developed for Cincinnati, Ohio. Detailed rainfall data for a three-year period were collected from three raingages triangulating the Bloody Run Sewer Watershed, an urban drainage areas of 2380 acres'in Cincinnati, Ohio. The advancement of the synthetic storm pattern is obtained from an analysis of the antecedent precipitation immediately preceding the maximum period of three selected durations. Rains which produced excessive runoff at least for some duration were considered only. The same approach can be used for other design frequencies. The purpose of this study is to provide synthetic storm hyetographs to be used as input in deterministic mathematical models simulating urban storm water runoff for the design, analysis and possible surcharge prediction of sewer systems.     

ECONOMIC FORECASTING FOR VIRGINIA'S WATER RESOURCE PROGRAMS1

ABSTRACT: Water resource and water quality management planning depend, to a large degree, on forecasts of industrial activity and population projections. A flexible economic data base is especially important where planning follows varying formats of geographical and industrial detail. Records of employment and payroll are collected in the administration of Unemployment Insurance (U.I.) programs and are available from State Employment Agencies. These statistics have been collected over a long period of record (thirty-five years). Many years of record are available on punched-cards or magnetic tape and may be arrayed and manipulated by computer. This basic approach has been followed in Virginia. Historical U.I. payroll and employment records for the period 1956 through 1970 were procured on magnetic tape. This data was arrayed by major hydrologic area and by regional planning district. Projections of manufacturing activity were then generated by fitting several exponential equations to annual payroll data in two-digit Standard Industrial Classifications. These exponentials were then extrapolated to provide a range of industrial projections. Other parameters of manufacturing activity were then correlated to the payroll data to generate projections of indexes such as employment, value-added, and gross manufacturing output. U.I. payroll data is now being correlated to parameters in non-manufacturing categories. Projections for industries such as trade and services will link extrapolated payroll data with benchmark correlations of payroll and sales receipts.     

震害经济损失动态预测模型研究

提出了一种考虑时间因素的地震灾害经济损失预测的数学模型。在建立区域震害动态预测数据库的基础上，利用本模型可预测未来不同年份发生地震时所造成的经济损失，为政府部门制定抗震防灾规划提供科学依据     

FACTORS AFFECTING HERBICIDE YIELDS IN THE CHESAPEAKE BAY WATERSHED,JUNE 19941

ABSTRACT: Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 1990–94 herbicide use. Annually averaged herbicide-use data are too general to use as a predictor for short-term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more-current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.     

IMPROVING REGIONAL-MODEL ESTIMATES OF URBAN-RUNOFF QUALITY USING LOCAL DATA1

ABSTRACT: Urban water-quality managers need load estimates of storm-runoff pollutants to design effective remedial programs. Estimates are commonly made using published models calibrated to large regions of the country. This paper presents statistical methods, termed model-adjustment procedures (MAPs), which use a combination of local data and published regional models to improve estimates of urban-runoff quality. Each MAP is a form of regression analysis that uses a local data base as a calibration data set to adjust the regional model, in effect increasing the size of the local data base without additional, expensive data collection. The adjusted regional model can then be used to estimate storm-runoff quality at unmonitored sites and storms in the locality. The four MAPs presented in this study are (1) single-factor regression against the regional model prediction, P_u; (2) least-squares regression against P_u; (3) least-squares regression against P_u and additional local variables; and (4) weighted combination of P_u and a local-regression prediction. Identification of the statistically most valid method among these four depends upon characteristics of the local data base. A MAP-selection scheme based on statistical analysis of the calibration data set is presented and tested.     

中国省域建筑安全生产水平的动态综合评价及策略分析

为从宏观层面上把握我国建筑安全生产水平的差异,因地制宜地采取措施,优化资源配置,在分析我国建筑业安全生产状况的基础上,利用安全事故统计分析原理,从绝对指标和相对指标两个方面构建了区域建筑安全生产水平评价体系;利用熵权法确定各指标权重,引入时间加权平均算子"TOWA"进行二次加权,对2009-2016年我国30个省域的建筑安全生产水平进行了相对优劣排序。结果表明,中部地区建筑业安全生产水平总体高于东部、东北部及西部地区。根据综合评价值将30个省域划分为建筑安全生产水平良好、不足、低下3个层次,进而提出了技术优先策略、管理优先策略及政策优先策略。