Journal of Hydrologic Engineering

Similarity methods are used to obtain approximate solutions for the dispersion of a contaminant in a vertical plume. The solutions assume that the contaminant is released either from a line or point source on an impermeable boundary and can be used to model either the downward movement of a relatively dense contaminant or the upward movement of a buoyant contaminant. The solutions are valid at distances from the contaminant release point that are large compared with both the lateral dispersivity and the dimensions of the contaminant release region. The solutions also become invalid at extremely large distances from the release point, where molecular dispersion can no longer be ignored.

This paper presents a time-series methodology by which a significant trend in climatological variables can be identified and notes how this trend may be related to other climatological variables. The evapotranspiration (ET) record at a monitoring site near Davis, California, shows an upward trend in both the ET and wind speed series. A review of the site history indicates that the upward trend in the wind series is due to relocating the site from the middle of an agricultural field to a more open area. Further analysis of the series indicates that this increase in wind speed has some effect on the yearly ET totals, but could not be used to completely explain the increasing trend in ET.

There are now several kinds of probabilistic meteorology outlooks available to the water resource engineer or hydrologist. These outlooks are defined over different time periods at different lag times, and they forecast either event probabilities or only most-probable events. An existing operational hydrology approach (for making hydrology outlooks) builds a set of hydrological possibilities from past meteorology to match forecast event probabilities, but it does not consider most-probable event forecasts. This approach is extended to mix both types of probabilistic meteorology outlooks in determining weights to apply to the set of hydrological possibilities to make hydrological outlooks. Boundary condition equations for the weights are constructed corresponding to forecast event probabilities, and boundary condition inequalities are constructed corresponding to forecast most-probable events. The inequalities are converted to equivalent equations through the introduction of additional variables. The resulting set of all boundary condition equations is solved for physically relevant values. The solution is an optimization problem for the general case, similar to earlier consideration of only forecast event probabilities. An example illustrates the concepts and methods.

Regional frequency analysis is used to estimate flood quantiles in a region. It is also used to identify parent distributions by analyzing the data from a number of stations within a region. The basic idea behind regional frequency analysis is to make use of similarities in the characteristics of floods at different sites in a region. Consequently, regional homogeneity is essential for such an analysis. L-moments are used in this study to investigate the feasibility of regional frequency analysis in the Wabash River basin. The Wabash River basin is shown to be hydrologically heterogeneous. It is divided into smaller subregions by using L-moment ratio diagrams. The generalized extreme value distribution is recommended to be the regional parent distribution. Both the Pearson-III and the three-parameter lognormal distributions are acceptable at the at-site level. The goodness of fit at both the at-site and regional levels is compared for a homogeneous and for a heterogeneous region.

An analytical solution is developed for biochemical oxygen demand (BOD) and dissolved oxygen concentration (DO) in a river for the case when BOD loading concentration and DO loading concentration each vary sinusoidally. The analytical solution is for the quasi-steady-state (periodic) case as the initial condition is ignored, but the boundary conditions are satisfied. Thus, the analytical solution is applicable from the BOD and DO loading locations to the concentration front. This distance is calculated by making use of the memory length concept. The solution is developed by breaking the BOD and DO equations into three subproblems, which are solved separately, then combined by applying the superposition principle. The solution is tested against published solutions from the literature, which include an integral equation, a perturbation solution, and a numerical solution. The analytical solution is easy to apply and extends the cases with which engineers can compare their numerical models. The results suggest the importance of considering time variable wastewater loadings to a river when designing water quality monitoring networks.

Given a set of realizations of error data (i.e., the difference between model runoff estimates and stream gauge data) from rainfall-runoff hydrologic models, it is possible to generate a set of error transfer function realizations that, when convoluted with a suitable kernel function such as the hydrologic model output, equate to the original error data. In turn, these error transfer function realizations may be used to generate synthetic error data that is convolved from a separate design storm modeled runoff and the generated error transfer function realizations. The synthetic error data set is then added to the design storm modeled runoff to produce a set of equally likely outcomes for the model prediction. The set of equally likely outcomes is statistically analyzed to provide, for instance, a confidence interval for the possible outcomes of the design storm model. A four-section algorithm is presented that performs each of these tasks.

Budyko's hydroclimatological model of a coupled land surface–atmosphere system is used to assess changes in climate and water resources that may be related to changes in land use and surface albedo. Changes in land use may lead to changes in surface albedo. In turn, changes in surface albedo are linked to climatic changes at the various atmospheric spatial scales. Climatic changes affect water resources, i.e., rainfall amounts, rainfall patterns, runoff amounts, and runoff coefficients. Mean annual precipitation is separated across the climatic spectrum, from hyperarid to hyperhumid regions, into (1) runoff; (2) nonrecyclable evaporation; and (3) recyclable evaporation. A set of water balance coefficients is developed to assess the hydroclimatological impact of human activities.

The snowpack in the Alaskan Arctic can be described as light with the average annual snowpack varying between 5 cm and 20 cm of water in nonmountainous areas, and exposed to considerable wind redistribution during the winter. Melting of this snowpack occurs over a relatively short period of time (7 to 10 days) from early May to early June. Ablation has been monitored since 1985 in a small headwater drainage of the Kuparuk River. The results of the following three different models to predict snowmelt are presented: energy balance, degree day, and combined degree day-radiation. Performance of all of the models was very good, with the energy balance model predicting ablation the best. The models, particularly the temperature and temperature/radiation index models, had some difficulty in predicting what happens when cold spells interrupt the melt. There is also some discrepancy at the end of the snowmelt period when large areas become snow-free.

The Continuous Simulation Method (CSM) has been used to predict average annual total nitrogen storm-water loads, under wet-weather conditions, for various sites in Tampa, Florida. Predicted loads are compared to those predicted by other existing “spreadsheet” models, namely the Storm Water Management Model (SWMM) Level I—Preliminary Screening Procedure, the Simple Method, and the U.S. Geological Survey (USGS) Regression Model. Comparisons showed that the USGS Regression Model predictions were vastly different from those of the other models. The predictions by SWMM Level I, the Simple Method, and the CSM were closer, although there were substantial differences under certain conditions. Of the four models, the CSM provides the greatest flexibility to a model user; it has the capability to (1) model different soil types within a watershed; (2) model loading variations due to geographical (location) and demographic (land use and cover) differences between watersheds; and (3) simulate confidence intervals around the predicted average annual loads.