INSTANTANEOUS PEAK FLOW ESTIMATION PROCEDURES FOR NEWFOUNDLAND STREAMS1

ABSTRACT: A procedure of estimating instantaneous flood flows for various return periods on the Island of Newfoundland is presented. The procedure is based on annual maximum instantaneous flows rather than annual maximum daily-mean flows, as the latter requires the conversion of estimated daily-mean flows into instantaneous flows. Regression equations were developed for each of three homogeneous regions for the desired return periods. The flood flow estimation capability of the presented procedure is demonstrated to be better than any other currently available procedure on the Island.     

POLLUTANT LOADS IN URBAN STORMWATER: REVIEW OF METHODS FOR PLANNING-LEVEL ESTIMATES1

ABSTRACT: A review of methods for planning-level estimates of pollutant loads in urban stormwater focuses on transfer of charac. teristic runoff quality data to unmonitored sites, runoff monitoring, and simulation models. Load estimation by transfer of runoff quality data is the least expensive, but the accuracy of estimates is unknown. Runoff monitoring methods provide best estimates of existing loads, but cannot be used to predict load changes resulting from runoff controls, or other changes of the urban system. Simulation models require extensive calibration for reliable application. Models with optional formulations of pollutant build up, washoff, and transport can be better calibrated and the selection of options should be based on a statistical analysis of calibration data. Calibrated simulation models can be used for evaluation of control alternatives.     

PARAMETRIC ESTIMATION OF COVARIANCES OF REGIONALIZED VARIABLES1

ABSTRACT: The usefulness of stochastic models in describing the spatial variability of hydrogeologic quantities, such as permeability, storativity, piezometric head, seepage velocity, and solute concentrations is now widely recognized. In practice, these quantities are represented as the sum of a well-structured component, or drift, and a more erratic fluctuation component which is described statistically through its covariance function. This paper reviews some of the most recent and most promising methods for the estimation of parameters of these covariances from existing data. They are maximum likelihood, restricted maximum likelihood, minimum-variance unbiased quadratic estimation, and minimum-norm (weighted least squares) estimation. The applicability of such methods to conditional and unconditional probability problems is discussed.     

RUNOFF PREDICTION USING REMOTE SENSING IMAGERY1

ABSTRACT: Percent imperviousness is an important parameter in modeling the urban rainfall-runoff process and is usually determined using manual methods such as random sampling or conventional accounting methods. In this study two computerized methods are used for estimating the percent imperviousness of urban watersheds using high altitude remote sensing imagery. These methods include the Laser Image Processing Scanner and the Video-Tape Camera system. Imperviousness is directly estimated in the former method while in the latter it is estimated as a function of the statistics of the responses on emulsions of the imagery. The percent imperviousness computed by utilizing remote sensing imagery was used with the conceptual models of rainfall-runoff models. The models were applied to four urban watersheds and the runoff prediction results indicate that imperviousness determined by using remote sensing imagery was as accurate as that obtained by the manual methods, and that the use of remote sensing imagery requires significantly less time and money.     

STATISTICAL METHODS AND SAMPLING DESIGN FOR ESTIMATING STEP TRENDS IN SURFACE-WATER QUALITY1

ABSTRACT: This paper addresses two components of the problem of estimating the magnitude of step trends in surface water quality. The first is finding a robust estimator appropriate to the data characteristics expected in water-quality time series. The Hodges-Lehmann class of estimators is found to be robust in comparison to other nonparametric and moment-based estimators. A seasonal Hodges-Lehmann estimator is developed and shown to have desirable properties. Second, the effectiveness of various sampling strategies are examined using Monte Carlo simulation coupled with application of this estimator. The simulation is based on a large set of total phosphorus data from the Potomac River. To assure that the simulated records have realistic properties, the data are modeled in a multiplicative fashion incorporating flow, hysteresis, seasonal, and noise components. The results demonstrate the importance of balancing the length of the two sampling periods and balancing the number of data values between the two periods. The inefficiency of sampling at frequencies much in excess of 12 samples per year is demonstrated. Rotational sampling designs are discussed, and efficient designs, at least for this river and constituent, are shown to involve more than one year of active sampling at frequencies of about 12 per year.     

KRIGED ESTIMATES OF TRANSMISSIVITY IN THE MESILLA BOLSON,NEW MEXICO1

ABSTRACT: Kriging utilizes a statistically based procedure of spatial interpolation that incorporates the spatial correlation structure of the phenomenon, and provides an error estimate. Kriging was applied to a total of 141 transmissivity values in an attempt to quantify the transmissivity distribution of the Santa Fe aquifer in Mesilla Bolson. New Mexico. The analysis produced contour maps of estimated transmissivity values and associated estimation variances. Through variogram analysis and fitting of an exponential variogrsm to 141 natural log of transmissivity (InT) values, the range was determined to be 3 miles, the average variance 2.74 (σ_InT= 1.65) with a mean of 8.65. Kriged estimates were generally lower when compared to estimates based on available transmissivity maps.     

A dynamic model for intertemporal allocation of old-growth forests in the Pacific Northwest

Across the globe, continued policy debates regarding the management of old-growth forests center around the difficult task of balancing economic and ecological considerations. Though the forests of the Pacific Northwest United States are among the most studied old-growth ecosystems, ecological and economic analyses have yielded public land management directives that remain controversial. Specifically, the recently adopted Northwest Forest Plan lacks explicit goals for maintaining intergenerational equity for the use of forest resources and the diversity of old-growth ecosystems. Unlike previous studies which rely on monetary quantification of costs and benefits, this study develops and applies a conceptual framework for evaluating socially optimal Pacific Northwest old-growth forest utilization strategies. Conditions for the optimal management of old-growth forests are derived using dynamic programming. The objective function synthesizes relevant biological and economic attributes of the old-growth allocation problem. Results in the form of extraction paths are compared given social pressure for consumptive and non-consumptive benefits, as well as different planning horizons, rates of social time preference, and environmental variance. Lengthening the planning horizon results in a vast divergence of optimal policies in the absence of discounting. Extraction rates appear to approach zero as the planning horizon approaches infinity. While higher rates of social time preference increase the rate of extraction, forest stocks remaining at the terminal time period equal levels remaining with a lower discount rate. Increasing environmental variance results in a higher level of stock remaining at the terminal time period. This analysis, while specific to the old-growth controversy of the Pacific Northwest, does provide general guidelines for addressing similar problems of multiple uses of natural areas, particularly where such uses are mutually incompatible, or where one use may be irreversibly destructive to another.     

关于铁路行车事故预测的应用研究

本文论述了事故预测对铁路行车安全的重要性,并应用灰色预测模型和概率回归估计法模型研制了预测软件,进行铁路行车事故的宏观及微观预测,为采取有针对性的防范措施提供一个可靠信息。     

DETERMINING SIGNIFICANCE AND PRECISION OF ESTIMATED PARAMETERS FOR RUNOFF FROM SEMIARID WATERSHEDS1

ABSTRACT Significant parameters for predicting thunderstorm runoff from small semiarid watersheds are determined using data from the Walnut Gulch watershed in southern Arizona. Based on these data, thunderstorm rainfall is dominant over watershed parameters for predicting runoff from multiple linear regression equations. In some cases antecedent moisture added significantly to the models. A technique is developed for estimating precision of predicted values from multiple linear regression equations. The technique involves matrix methods in estimating the variance of mean predicted values from a regression equation. The estimated variance of the mean predicted value is then used to estimate the variance of an individual predicted value. A computer program is developed to implement these matrix methods and to form confidence limits on predicted values based on both a normality assumption and the Chebyshev inequality.     

Evaluation of Material Biodegradability in Real Conditions–Development of a Burial Test and an Analysis Methodology Based on Numerical Vision

This work validated a burial protocol for in situ testing and presents a robust, repeatable and time-saving technique to measure degraded areas in the sample, i.e. an image analysis method. 1440 specimens of degraded samples have been compiled in a data base. To this end, twenty samples presenting different levels of biodegradability (i.e. PHBV/HV, PLA, PCL, PCL-Starch, paper, PE, PE-Starch) were buried at 4 different locations and then disinterred at 4, 6, 9, 12, 18, and 24-month intervals. The biodegradation levels of these samples were determined by computing weight and area loss. Weight loss was measured after careful cleaning, whereas area loss was quantified using image analysis. Image analysis gives reliable information on visual pollution while only requiring a rudimentary and thus quicker cleaning of the samples.