PROBLEMS IN FORECASTING WATER REQUIREMENTS1

ABSTRACT. Methodological problems associated with forecasting water requirements by use of regression analysis are examined. Problems occurring when long-range forecasts are based on linear and nonlinear extrapolation of time series models include possible changes in socioeconomic conditions, water allocation system structure, and limits to growth. Problems arising in forecasting based on multiple regression models are likely to involve serially dependent errors, multicollinear explanatory variables, and difficulties inherent to the presence of explanatory variables that must themselves be predicted.     

A REVIEW ON DECISION MODELS IN ECONOMICS OF REGIONAL WATER QUALITY MANAGEMENT1

ABSTRACT The literature on decision models in economics of regional water quality management is reviewed and classified.     

SPNM,A MODEL FOR PREDICTING SEDIMENT,PHOSPHORUS, AND NITROGEN YIELDS FROM AGRICULTURAL BASINS1

ABSTRACT: A model called SPNM from the words “sediment-phosphorus-nitrogen model” was developed for simulating agricultural contributions to water pollution. SPNM is designed to predict sediment, P, and N yields for individual storms on small basins and to route these yields through streams and valleys of large basins. Users need no computer programming experience because the model is a problem-oriented computer language. SPNM is useful in planning water resources projects and in research. Tests of the model on a watershed provided realistic results.     

MATHEMATICAL SIMULATIONS OF THE TOCCOA FALLS,GEORGIA, DAM-BREAK FLOOD1

ABSTRACT: Four dam-break models were selected for testing with an observed data set from the November 6, 1977, disaster at Toccoa Falls, Georgia. The Kelly Barnes Dam failure occurred with a 35-ft head of water and produced a peak discharge of 23,000 ft³/s. The selected models included: (1) Modified Puls (MP), (2) U. S. Army Corps of Engineers Gradually Varied Unsteady Flow Profiles (USTFLO), (3) National Weather Service's Dam-Break Flood Forecast (DBFF), and (4) U. S. Geological Survey's method of characteristics (MOC) coupled with a general purpose streamflow simulation (J879DB). Achieving a successful simulation was easiest with the MP model. The DBFF model required a moderate effort while the MOC-J879DB models required some data alterations and considerable effort. The USTFLO model failed to simulate this test case. In the stream segment near the dam, the computed peak stages were generally within 5 feet of the observed high water marks. Elsewhere, the peak stage results were much better, generally within 2 feet. The peak discharges computed by the models were generally within 20 percent of discharges estimated by slope area and contracted opening measurements, except near the dam where the MOC-J879DB model's results was 80 percent too high.     

DETERMINISTIC MODELS FOR PREDICTING RESIDENTIAL WATER CONSUMPTION1

ABSTRACT: Forecasts of future urban water demand traditionally have been made by the projection of historic trends in per capita consumption and population. This paper outlines the use of two deterministic models to forecast the residential component of urban water demand. The models incorporate specific representation of the activities which result in water consumption at each residence. Predictions of water use can then be made by modeling the changes expected in the number of these activities and the consumption for each such activity.     

PRELIMINARY DESIGN AND COST ESTIMATING FOR RESERVOIR PROJECTS1

ABSTRACT: In estimating the costs of a reservoir project, it is difficult to produce accurate costs without performing a detailed design and quantity takeoff. The computerized procedure presented in this article provides a method for determining these planning level costs quickly and easily, while maintaining reasonable accuracy.     

STATE VARIABLE MODEL FOR URBAN RAINFALL-RUNOFF PROCESS1

ABSTRACT: A nonlinear hydrologic system model has been developed for analyzing the urban rainfall-runoff process. The model is formulated as a state variable model consisting of several parameters. A search technique is employed to find the set of parameters for which the model's response best fits observed data. The model could be used in either a simulation or forecasting mode. The model is applied to observed data for the Waller Creek Watershed in Austin, Texas, to develop the model parameters for various levels of urbanization of the watershed. The trend of each parameter with respect to levels of urbanization is examined.     

WATER FOR ENERGY: AN APPROACH TO COMPREHENSIVE IMPACT ASSESSMENT1

ABSTRACT: The effects of energy development on the water resources of the Colorado River and Great Basin regions is expected to be substantial. Complex physical, economic and institutional interactions may be expected. Most research on these impacts appears single purpose, fragmented, uncoordinated, and often inaccessible to potential users - particularly those with responsibility for energy/water policy and program decisions. A comprehensive, integrative framework for assessing alternative water allocation decisions is outlined, taking a heuristic decision making model for evaluating impacts on maximization of gross (or net) regional product, and regional social welfare, and for assessing the region's contribution to national objectives. The suggested model provides a structure for application and integration of data of various kinds to a range of situations arising from possible impacts from energy proposals. The focus is on water and energy relationships but the model may provide a framework for comprehensive analysis of a variety of environmental actions and resulting system perturbations and effects.     

Assessment of environmental impacts part two: Data collection

Intervention analysis is a relatively new branch of time series analysis. The power of this technique, which gives the probability that changes in mean level can be distinguished from natural data variability, is quite sensitive to the way the data are collected. The principal independent variables influenced by the data collection design are overall sample size, sampling frequency, and the relative length of record before the occurrence of the event (intervention) that is postulated to have caused a change in mean process level.For three of the four models investigated, data should be collected so that the post-intervention record is substantially longer than the pre-intervention record. This is in conflict with the intuitive approach, which would be to collect equal amounts of data before and after the intervention. The threshold (minimum) level of change that can be detected is quite high unless sample sizes of at least 50 and preferably 100 are available; this minimum level is dependent on the complexity of the model required to describe the response of the process mean to the intervention. More complex models tend to require larger sample sizes for the same threshold detectable change level.Uniformity of sampling frequency is a key consideration. Environmental data collection programs have not historically been oriented toward data analysis using time series techniques, thus eliminating a potentially powerful tool from use in many environmental assessment applications.     

OPTIMAL MANAGEMENT AND DESIGN OF HYDRAULIC SYSTEMS1

ABSTRACT: A possible methodology is developed to deal with the problem of designing complex pipeline systems, when they are subject to different rates of demand, and when a hypothesis of the flow distribution in different branches is not allowed. The mathematical algorithm used in linear programming. The problem, which is not linear, is dealt with by means of an iterative method; that is, by starting with a possible solution and inserting at each iteration the solution found in the preceding iteration. By taking as variables of the problem the piezometric heads of the ends for each branch of the network, the piezometric gradients and flows, and by thus considering the diameter as a derived variable, it is possible to isolate the nonlinearity in the cost function of the network. The latter is linearized each time close to the solution found in the preceding iteration.