Journal of Hydrologic Engineering

In order to reduce future flood damage in the Red River of the North Basin, the U.S. Army Corps of Engineers, St. Paul District, is developing a Corps Water Management System (CWMS) to assist in real-time operation of reservoirs to regulate reservoir outflows. This paper presents an application of the distributed snow process model (DSPM) and the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) in gridded snowmelt and rainfall-runoff modeling for reservoir operational forecasting, which is an essential component of the CWMS model. The model setup, calibration, and verification are described. The paper particularly focuses on information requirements of DSPM and HEC-HMS for model setup/calibration and continuing forecast operations and shows how these requirements could be met in a practical and operational setting. Elements of the modeling process that are important for operational forecasting are discussed and are expected to benefit similar CWMS operational forecasting in other watersheds both in cold and warm regions.

The genetic algorithm (GA)–based simulation optimization approach is used for optimal identification of unknown groundwater pollution sources. Simple as well as complex scenarios of multiple unknown groundwater pollution sources are considered. A flow and transport simulation model is externally linked to the GA-based optimization model to simulate the physical processes involved. The simulation model uses potential pollution source characteristics that are evolved by the GA and simulates the resulting concentration measurement values at observation locations. These simulated spatial and temporal pollutant concentration measurement values are used to evaluate the fitness function value of the GA. The main advantage of the proposed methodology is the external linking of the numerical simulation model with the optimization model. This approach makes it feasible to solve the source-identification problems for complex aquifer study areas with multiple unknown pollution sources. The performance of the developed methodology is evaluated for combinations of source characteristics (locations, magnitudes, and release periods), data availability conditions, and concentration measurement error levels.

In this paper a regional flood frequency analysis based on the two-component extreme value (TCEV) distribution is developed using flood data recorded in Sicily. The hierarchical approach, characterized by three investigation levels for estimating the parameters of the theoretical distribution, is discussed first. The highest level of homogeneity hypothesis with regard to the skewness coefficient was verified by using a Monte Carlo technique and taking account of the separation effect proposed by Matalas et al. in 1975. This analysis also showed (1) the inability of the generalized extreme value model to reproduce the empirical cumulative distribution function (CDF) of the skewness coefficients, and (2) the convenience of using annual maximum peak flood data instead of annual maximum mean daily discharge records to estimate the parameters of the TCEV model. At the second level of regionalization, three different methods for identifying hydrologically homogeneous subregions are then performed: the first is based on the $R$ -statistic test discussed by Wiltshire in 1986; the second is based on the hypothesis of statistical self-similarity of each selected area considering sampling statistical moments, discussed by Gupta et al. in 1994; and the third is founded on the same hypothesis of statistical self-similarity by using the sampling probability weighted moments discussed by Kumar et al. in 1994. The analysis showed that a further division into three subregions proposed by Cannarozzo et al. in 1995 is not necessary, and that a single theoretical growth curve is able to reproduce the empirical CDF of the standardized variable $x^{\prime }$ . Finally, the third level of investigation showed that two different empirical relationships proposed for estimating the index flood $\overline{x}$ could be used for establishing the $Q\text{-}T$ relationship anywhere in the region.

The KINEROS2 rainfall-runoff model was used in this case study to estimate plane and channel infiltration within the Upper Split Wash watershed on Yucca Mountain, Nev. Averaged over the watershed area, estimated mean annual runoff, channel infiltration, and bedrock infiltration were 3.6, 0.6, and $8.7\mathrm{mm}∕\text{year}$ . Most overland flow resulted from saturation of thin soils during low-intensity winter storms. The greatest excess infiltration (the difference between actual infiltration and precipitation) occurs on lower portions of hillslopes having deep soils receiving runoff from upslope areas. The greatest bedrock infiltration occurs on hillslope areas with thin soils and high values of bedrock hydraulic conductivity $\left(K_S\right)$ . A sensitivity analysis suggests that estimated runoff and infiltration rates are most sensitive to soil depth and are less sensitive to bedrock $K_S$ , the soil hydraulic parameter defining field capacity and wilting point, the soil capillary drive parameter, and the antecedent precipitation index.

A drought is usually represented by duration and severity, and may last several months or years. Multidimensional characteristics of a drought make univariate analysis unable to reveal the significant relationship among drought properties. Furthermore, historical records tend to be too short to fully evaluate drought characteristics. A practical method was proposed in this study to estimate the bivariate return period of droughts based on the use of synthetic data to overcome the above considerations. The bivariate return period of droughts is dependent on the drought interarrival time and the joint distribution of drought properties. A nonparametric method was employed in this study to estimate the joint distribution of drought properties. The historical droughts in the Conchos River Basin, Mexico were evaluated based on their return period estimated by the proposed method. The proposed method allowed a better understanding of the joint probabilistic behavior of droughts beyond the limitation of the univariate/parametric frequency analysis.

This study presents a downstream boundary condition for the nonlinear Boussinesq equation (NBE) in an unconfined rectangular aquifer using experimental results, which is often caused by drought flow in a recession period. It is possible to obtain a semianalytical solution for NBE using a series expansion method. The semianalytical solutions are compared with the numerical and experimental results. Their agreement between the semianalytical and numerical solutions and the semianalytical and experimental ones is satisfactory.

Using the copula method, bivariate distributions of flood peak and volume, and flood volume and duration were derived. A major advantage of this method is that marginal distributions of individual variables (i.e., flood peak, volume, and duration) can be of any form and the variables can be correlated. The copula method was applied to obtain the conditional return periods that are needed for hydrologic design. The derived distributions were tested using flood data from Amite River at Denham Springs, La., and the Ashuapmushuan River at Saguenay, Quebec, Canada. The derived distributions were also compared with the Gumbel mixed and the bivariate Box–Cox transformed normal distributions. The copula-based distributions were found to be in better agreement with plotting position-based frequency estimates than were other distributions.

A reservoir is one of the most efficient measures for integrated water resources development and management. The reservoir storage curve is a vital parameter for multipurpose reservoir operation and its precision is a key issue for water balance and strategic risk management. Compared with the traditional approach, the method based on remote sensing (RS) data provides better information, which can be helpful in reservoir operation and management. Fengman Reservoir was chosen as a case study to obtain the new storage curve that is based on LandSat data. The inflows of the reservoir were calculated in dry seasons (December, January, and February) from 1958 to 1986 on the basis of the designed storage curve and the new estimated curve, respectively. Compared with the observation data, the average relative error of inflow using the new estimated curve is much less than one using the designed curve. The results showed that reservoir storage curve estimation based on RS data is reasonable and has relatively high precision.

Essential to the application of the kinematic wave theory to open channel flow is the kinematic wave parameters which relate the discharge to the flow area in the channel. Earlier works have developed the kinematic wave parameters for a variety of channel shapes, but none for the trapezoidal channel. This paper unveils the kinematic wave parameters for four channel types: (1) trapezoidal channel with unequal side slopes; (2) trapezoidal channel with equal side slopes; (3) trapezoidal channel with one side vertical; and (4) rectangular channel. However, in the application of these parameters, all of them require the determination of flow area which is quite troublesome as the expression is not explicit. For the purpose of developing explicit analytical solutions, for channel types (2) and (3), it is shown that they can be approximated by a wide and a deep trapezoidal channels. For channel type (4), it can be approximated by a wide rectangular, a square and a deep rectangular channel. Further, for cases where the above approximations are not appropriate, alternative kinematic wave parameters have been established by mathematical fitting. The limits of applicability for all these approximations, and the maximum differences in discharge between the approximations and the “true” values have also been determined.

The linear reservoir is a widely used conceptual model of the rainfall–runoff process. The linear reservoir model transforms rainfall excess to direct surface runoff. The shape of a physical analog of the linear reservoir has been developed mathematically and a laboratory-scale physical model constructed and tested experimentally under steady and unsteady flow conditions. The series of steady flow experiments was conducted to verify the accuracy of a simplifying assumption underlying the development of the physical analog of the linear reservoir. The unsteady series of experiments simulates the rising and recession limbs of the discharge hydrograph from a catchment. Good agreement has been found between the computed and experimental data. The physical analog developed may be useful as a laboratory demonstration apparatus for explaining to hydrology students the concept of the linear reservoir. In addition, in the context of stormwater management in small urban catchments, the linear reservoir physical analog may be a useful flood attenuation device where a linear relationship between surcharge storage and outflow is required.

Artificial neural networks (ANNs) are flexible tools from neuroinformatics that have performed well in a number of hydrologic applications so far. They tend to be particularly useful when applied to complex processes, the details of which are not well understood. The dissolved oxygen regime in constructed wetland ponds is, in turn, such a complex process, governed by a considerable number of hydrologic, hydrodynamic, and ecological controls which operate at a wide range of spatiotemporal scales. This paper reports on the results from a study conducted to test the adequacy of artificial neural networks in modeling near-bottom concentrations of dissolved oxygen in the Finnish free water surface wetland at Hovi. Various different networks of the multilayer perceptron (MLP) type of ANN were developed. The application proved successful, and in particular it was observed that MLPs were able to “learn” the mechanism of convective oxygen transport quite well. The ANN was also used to determine the relative influence of flow rate and wind shear on near bottom oxygen saturation.