Journal of Hydrologic Engineering

A geographic information system (GIS) based approach is presented for the development of a terrain model based on stream channel representation of the U. S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic model. As input, the approach requires a completed HEC-RAS simulation, a digital elevation model (DEM), and a GIS representation of the stream thalweg. The process begins with export of the channel data from HEC-RAS to GIS, followed by data conversion from hydraulic model coordinates to geographic coordinates. A digital terrain model is subsequently synthesized by merging HEC-RAS data for the stream channel with comparatively lower-resolution DEM data for the floodplain. The resulting surface model provides a good representation of the general landscape and contains additional detail within the stream channel. The accuracy of the terrain model is comparable to high-resolution terrain data acquired through aerial photogrammetry and can be used in conjunction with the new generation of river hydraulic models capable of accepting GIS terrain data. An example application to Waller Creek in Austin, Texas, is presented.

Hillslope subsurface drainage is one of the key elements in the hydrology of upland watersheds. This explains or simulates the simple rainfall-runoff response to precipitation and the hydrology under drought conditions. The purpose of this study is the derivation of the solution of the nonlinear partial differential equation to given drought conditions and its comparison with the experimental results. To analyze drought flows, subsurface drainage from a hillslope above a sloping impermeable layer is theoretically and experimentally studied. The resultant governing equation is a nonlinear diffusion equation, which is difficult to solve analytically. The approximate solution obtained by the method of variation is in good agreement with the experimental results. The linearized solution of the nonlinear diffusion equation is also compared with the nonlinear solution. Finally, the effect of the slope angle of an impermeable layer and the applicability of Darcy’s law to hillslope subsurface flow are discussed.

This paper presents a practical inverse approach for forecasting nonlinear hydrological time series. The proposed approach involves: (1) calibrating the delay time, embedding dimension and number of nearest neighbors simultaneously using a single definite criterion, namely, optimum prediction accuracy; (2) verifying that the optimal parameters have wider applicability outside the scope of calibration; and (3) demonstrating that chaotic behavior is present when optimal parameters are used in conjunction with existing system characterization tools. The proposed approach was shown to be better than the standard approach for a theoretical chaotic time series (Mackey-Glass) and two real runoff time series (Tryggevaelde catchment in Denmark and Altamaha river at Doctortown, Ga.).

The main objective of this study is to examine if hydrologic monthly series are stationary. A segmentation algorithm is applied so that nonstationary series are identified and partitioned into stationary segments. Four sets of hydrologic data are analyzed, including monthly streamflow, temperature, precipitation, and Palmer’s drought severity index series. The first-order differenced standardized monthly series are also analyzed, in addition to the standardized monthly series. The results indicate that more series of monthly streamflow and Palmer’s drought severity index are identified as nonstationary than stationary, while more series of monthly temperature and precipitation are stationary. The change points are commonly observed during two periods, one between 1960 and 1970, and the other between 1930 and 1940. In general, standardized hydrologic monthly series, either differenced or not, are nonstationary.

Estimates of low streamflow statistics are required for a variety of water resource applications. At gauged river sites, the estimation of low streamflow statistics requires estimation of annual n-day minimum streamflows, selection of a probability distribution to describe annual minimums, and estimation of the distribution’s parameters. Using L-moment diagrams, the ability of various probability distributions to describe low streamflow series was examined at 1,505 gauged river sites in the United States. A weighted distance statistic was developed to compare the goodness-of-fit of different probability distributions for describing low streamflow series. Compared to perennial streamflow sites, a shift of L-moments ratios was observed at intermittent river sites where discharge is sometimes reported as zero. An analytical experiment compared the observed shifts in L-moment ratios at intermittent sites with theoretical L-moment ratio shifts for a number of real- and log-spaced probability distributions. Results of these experiments indicate that Pearson Type III and the 3-parameter lognormal distributions should be the recommended distributions for describing low streamflow statistics in the United States at intermittent and nonintermittent (perennial) sites, respectively.

The shape of a flood hydrograph is a random event. This study develops a method for describing statistical properties of the shape of a flood hydrograph. Two shape variables, namely, shape mean $\left(S_m\right)$ and shape variance $\left(S_v\right)\text{,}$ are defined to express the randomness of a flood hydrograph. The two-parameter beta probability density function (pdf) is adopted to represent the shape of a flood hydrograph. The parameters are estimated using the shape variables. On the basis of this approach, one can derive the design-flood hydrograph shape corresponding to various combinations of flood peak, volume, and duration. The applicability of the proposed method is demonstrated by using observed flood data from the Ashuapmushuan basin in the province of Quebec, Canada. It is concluded that the method is appropriate for representing the statistical properties of the shape of a flood hydrograph.

A multitude of natural processes occurring in environmental and water resources are diffusive processes, and their observations exhibit a natural lower bound of zero and practically no upper bound. Such processes can be modeled using a dye diffusion equation whose solution yields a concentration distribution. This function, when normalized, leads to a two-parameter probability distribution that is seen to be a superposition of two normal distributions. Parameters of this distribution were estimated by the methods of moments and maximum likelihood for Monte Carlo-generated processes.

It is fairly well accepted that contaminant transport in aquifers can be modelled accurately only if dispersion coefficients are allowed to increase with distance downstream. However, this also increases the difficulty of obtaining exact solutions of the contaminant transport equation. Therefore, singular perturbation methods are used to obtain a relatively simple approximate solution for scale-dependent dispersion from a pit located in a region of uniform flow. Comparison of exact and approximate perturbation solutions for two simpler but related problems suggests that the perturbation approach used here is likely to give sufficient accuracy for many, if not most, applications.

The present study examines the estimation of hourly precipitation over the Folsom Lake watershed in mountainous California. The objectives are to (1) quantify the uncertainty associated with the estimation of precipitation by the existing operational real-time network of gauges, and (2) identify possible sites for the placement of additional gauges in order to reduce the precipitation interpolation error. To reach these goals, historical hourly data are used from a dense network operated during the period 1980–1986. The approach consists of developing a series of surrogate operational networks from the dense network using an increasing number of gauges and quantifying the reduction of estimation error in each case. Kriging is used to interpolate the point measurements to the nodes of a rectangular 3.7 km grid superimposed over the catchment. The variance of the kriging estimates is considered in the analysis. The recommended additional gauge sites are mainly located in the upper parts of the forks of the American River, which drain the Folsom Lake watershed. The estimated reduction in local variance due to the placement of the additional gauges is up to 70%, with substantial reduction of the estimation bias, in the headwaters of the Middle and North Forks of the American River, with a smaller reduction for the South Fork. The proposed gauge network is most appropriate for short-term flood forecasting applications.

A three-equation model for subsurface response is presented. The model is based on storm events for various sized watersheds within the Susquehanna River Basin. The equations represent the peak flow, time to peak, and time base of the subsurface response. The model was developed using multiple variable regression and numerical optimization. The variables used in the regression analysis are related to the physical processes causing subsurface response and were obtained from streamflow and precipitation gauges, a digital elevation model, and land use and soils data. The measured peak subsurface discharge, time to peak, and time base required for the regression analysis were obtained by separating storm hydrographs into direct runoff and subsurface responses. A linear separation from the initial rise of the hydrograph to the inflection point on the recession limb was utilized. The model was developed from a dataset of 50 watersheds by 20 storms (1,000 watershed-events), with the watersheds ranging in size from 14 km² to 67,000 km². The results indicate that the measured and predicted subsurface response variables agree well, considering the great variation in the observed responses and the time and space scales of the available data.