S. Rocky Durrans

Urban Wastewater Management in the United States: Past, Present, and Future

as either centralized, where all the wastewater is collected and conveyed to a central location for treatment or disposal, or decentralized, where the wastewater is primarily treated or disposed of on-site or near the source. Historically, municipalities, consulting engineers, and individuals have had the option of centralized or decentralized wastewater management and could have chosen from a variety of collection and disposal technologies to implement the management strategy. Although these options were available, the majority of engineers, public health officials, policy makers, and members of the public typically preferred one management strategy and one technology to the others. The reasons for a particular preference were based on a combination of cost, urban development patterns, accepted scientific theories, tradition, religious attitudes, prevailing public opinion on sanitation, the contemporary political environment, and many other factors. The development of urban wastewater management strategies and technologies from the early nineteenth century to the present

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and diAerent geologists may provide diAerent interpretations. In this paper, we present a lowcost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into diAerent forms representing diAerent perspectives of observation of lithofacies. Each form of input is processed by a diAerent adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorical data, and the third processes fuzzyset data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an errorbackpropagation neural network, 57.3%. 7 2000 Published by Elsevier Science Ltd. All rights reserved.

Distributions of fractional order statistics in hydrology

A critical issue in parametric methods of frequency analysis, regardless of the phenomenon being modeled, is that of selection of a form of probability distribution to be applied. When one is interested in continuous distributions there exists little theoretical guidance, other than perhaps that provided by the central limit theorem or the (asymptotic) results of extreme value theory, upon which one may base a choice. This paper, in a very general way, introduces a whole new class of probability models which are referred to as distributions of fractional order statistics. The potential efficacies of various member distributions within the class for hydrologic data analysis are also rationalized in a very intuitive way. Considered in some detail is an application of the theory of fractional order statistics to generalize the Gaussian distribution. Monte Carlo results comparing the performance of the generalized distribution with other common hydrologic models are also set forth.

Low‐flow analysis with a conditional Weibull Tail Model

Estimates of low-flow quantiles, such as the 7-day, 10-year low flow, which are usually obtained by statistical modeling of observed data series, are widely used in water quality management. This paper presents a conditional modeling approach to low-flow analysis that employs only those data values which are less than or equal to a ceiling value. Modeling in this fashion has been motivated by the observation that annual low flows may derive from mixed processes and by the subjective nature of graphical methods, such as those employed by the U.S. Geological Survey, which are often employed in such cases. Results of Monte Carlo experiments demonstrate that the conditional modeling approach yields a low-flow quantile estimator whose bias and RMSE are comparable to more conventional modeling approaches of fitting a classical textbook probability distribution on the basis of all observed data values, even when the underlying population is of a “well-behaved” form. Since the complex forms of mixed low-flow data distributions are not capable of being represented by classical textbook distributions and since the conditional modeling approach performs comparably to those models even when the data derive from well-behaved probability distributions, these results imply that the conditional modeling approach is worthy of consideration for use by hydrologists. The conditional modeling approach also leads rather naturally to a scheme, much like that used in index flood methods, whereby a regional low-flow estimator might be devised. An application of the conditional modeling approach to 48 low-flow data series in Alabama is presented.

Quantitatively directed sampling for main channel and hyporheic zone water-quality modeling

Abstract Management of water quality in streams and rivers, including determinations and allocations of acceptable total maximum daily loads (TMDLs), requires use of calibrated and reliable water quality models for prediction of pollutant concentrations under alternative management or load allocation scenarios. However, the accuracy and reliability (precision) of a model can be no greater than those of the input parameters upon which its predictions are based. Thus, questions facing model users concern which of the potentially many model parameters should be estimated based on field sampling, and where those parameters should be sampled along the length of a stream. Quantitatively directed exploration (QDE), a quantitative method for directing field sampling efforts, is presented herein as a mean to both identify parameters to sample, and where to sample them. The QDE approach is demonstrated for a stream where the transient storage effects of the hyporheic zone are important considerations for water quality modeling. The fundamental idea underlying QDE is that the reliability (i.e. variance) of a model-predicted result is a function of the variances of the model inputs. By examining individual contributions to the total variances of model predictions, it provides a means for identification of those parameters and their sampling locations that have the greatest influences. In doing so, it yields an objective and quantifiable way to reduce the model output variance, and hence to improve the reliability of the model results. In this paper, model predictions are taken as pollutant concentrations in a stream channel. In the interest of making a clear exposition of the QDE approach, while not cluttering the presentation with unnecessary complications, the water quality model used in this paper is a simple feed-forward system of continuously stirred tank reactors (CSTRs).

Parameter estimation for the Pearson type 3 distribution using order statistics

Abstract The Pearson type 3 distribution and its relatives, the log Pearson type 3 and gamma family of distributions, are among the most widely applied in the field of hydrology. Parameter estimation for these distributions has been accomplished using the method of moments, the methods of mixed moments and generalized moments, and the methods of maximum likelihood and maximum entropy. This study evaluates yet another estimation approach, which is based on the use of the properties of an extreme-order statistic. Based on the hypothesis that the population is distributed as Pearson type 3, this estimation approach yields both parameter and 100-year quantile estimators that have lower biases and variances than those of the method of moments approach as recommended by the US Water Resources Council.

STORMWATER QUALITY DESCRIPTIONS USING THE THREE PARAMETER LOGNORMAL DISTRIBUTION.

The cumulative probability distribution used to describe the variability of stormwater pollutant concentrations has been a matter of interest in recent years. …

Factors Affecting Scour of Previously Captured Sediment from Stormwater Catchbasin Sumps

The sediment-retaining performance in conventional catchbasin sumps has been reported to be in the wide range between 14 and 99% (Metcalf & Eddy 1977); obvious…

Short Time-Interval Rainfall Disaggregation for Continuous Hydrologic Simulation

Traditionally design storms have been used to design and analyze urban drainage systems and hydraulic structures. Design storms can be developed with the desi…

Particulate Transport in Grass Swales

The Department of Civil and Environmental Engineering at the University of Alabama conducted research on the effectiveness of grass swales for stormwater sediment transport to quantify swale hydraulic and sediment transport under relatively small flows. This research was supported by the Water Environment Research Foundation (Johnson et al.,2003) and the University Transportation Center of Alabama (Nara and Pitt, 2005). Grass swales are vegetated open channels that collect and transport stormwater runoff that can be used as beneficial alternatives to concrete gutters for stormwater management due to their advantages of infiltration and filtration of stormwater. Introduction The objective of this research was to understand the effectiveness of grass swales in sediment transport and the associated effects of different swale and hydraulic variables and to develop a predictive model. To achieve these objectives, experimental grass swales were constructed and tested in an indoor greenhouse facility, and a full-scale grass swale was also tested to verify the observations from the controlled tests. The variables tested in the experiments were slope, grass type, depth of flow, sampling time, and length of swales. A water-sediment mixture with a known sediment concentration of sieved sands and fine particles of silica was used to analyze the variables. During the preliminary set of controlled experiments, 108 samples were collected and analyzed for turbidity, total solids, and particle size distributions to investigate the effects of the experimental variables. After completing the initial tests, a second set of controlled experiments was conducted. During this second set of tests, an additional 108 samples were collected and analyzed for turbidity, total solids, total suspended solids, total dissolved solids, and particle size distribution. To examine how the results obtained from the indoor swale experiments could be applied to full-scale swales, sediment samples were collected during storm events