Journal of Hydrologic Engineering

In this paper we are presenting a numerical model to simulate transient flow and solute transport through a variably saturated zone. The mixed form of the Richards equation is used for the solution of the flow component as it is mass conserving, and the advection dispersion equation (ADE) is used for the solute transport. The Richards equation is solved numerically by finite difference and modified Picards iteration is used to deal with its inherent nonlinearity, and the resulting matrix is solved by a strongly implicit procedure. The numerical solution of ADE poses oscillations when there is a sharp front due to discontinuity in the solution. In this paper, we are presenting a numerical scheme to address the problems faced by the advective dominant front. The ADE is solved by the operator split method in which advection is solved by explicit finite volume and dispersion by the fully implicit finite difference method. A new numerical scheme is presented for the solution of the advection part of the equation. The advection flux is solved by using a locally one-dimensional approach, with the predictor-corrector method for the time step. Each step is made total variation diminishing to avoid oscillations. Performance of the model is tested for a few cases, like flow and transport into very dry soils, transient unconfined recharge and drainage through seepage face, and transient infiltration into randomly heterogeneous soils. The model is also used to study the effect of water table boundary and seepage face on the movement of solute.

Distributed watershed modeling has gained popularity in recent years. Prevalent models in the United States include CASC2D and WEPP, among others. Although the existing models have been successful for some applications, a number of limitations exist. This paper presents a new watershed runoff and erosion model, SRH-W, with the aim to improve the numerical methods used by the existing models. The new model builds upon process features of the existing models such as CASC2D. However, a new representation of the watershed geometry with the zonal hybrid mesh is proposed, and a new solution technique with the conservative finite-volume formulation is implemented. The modeling concept and the mathematical and numerical formulations are first described. An illustrative case is then simulated to show some new features of the model. A number of verification and validation cases are also presented to demonstrate the model performance, accuracy, and efficiency. The new model is flexible, overcomes some shortcomings of the existing models, and performs well in the selected verification cases.

We developed a new, coupled, hydrologic-erosion model and a targeted field data-collection program to quantify hillslope soil erosion rates and sediment yield in semiarid regions. While the methodology has a wide range of actual and potential applications, we use military training activities as an example. The methodology is applied at the Yakima Training Center (YTC) in south central Washington, USA where most erosion and transport occurs during extreme events of short duration, often associated with rapid rain-induced snowmelt on frozen soil. The distributed hydrologic and soil erosion modeling system is used to simulate continuous water balance and soil thermal state throughout all seasons of the year. We estimated surface runoff and sediment yield for relatively undisturbed areas as well as from roads, firebreaks, and vehicle tracks associated with training activities at the study site. Field data were collected on over 100 hillslope profile transects across the YTC to parameterize, test, and evaluate the linked modeling system. We successfully validated the modeling system against several databases, upland sediment delivery to stream networks, and measured sediment yield from 12 sedimentation ponds within the YTC. The simulation results are superior to those from a currently used model, which help to illustrate applicability of the presented erosion prediction technology.

For many locations, exponential probability distributions fit well the frequency distributions of rainfall event volume, duration, and interevent time. Based on these exponential distributions and a simplified scheme for rainfall-streamflow transformation, closed-form analytical expressions were derived for the determination of the flow–duration relationships of surface runoff dominated small urban streams. The main advantage of the derived analytical relationships is that they relate the flow–duration characteristics of a stream directly to its upstream catchment’s and local rainfall characteristics. Using the derived relationships, flow–duration curves that are needed for stream restoration design can be directly constructed for small urban streams without observed streamflow data. Verification studies demonstrated that simplifying assumptions invoked in the derivation process are generally acceptable for small urban streams where baseflow variations are insignificant or streamflows are generated predominantly by surface runoff.

Holistic approaches are needed for understanding and addressing a wide range of environmental issues that require multidisciplinary studies of complex and interlocking systems. The writers’ vision of a cyberinfrastructure for end-to-end environmental exploration (C4E4) that combines data and modeling tools in an integrated environment across different spatial and temporal scales is presented. The overall goal behind C4E4 is to enable a broad environmental research and remediation community to address the challenges of environmental data management and integration in real-world settings. The St. Joseph Watershed in northern Indiana is chosen as a test bed in this effort. The C4E4 framework will allow researchers to combine heterogeneous data resources with state-of-the-art modeling and visualization tools through a user-friendly web portal. By engaging TeraGrid resources, C4E4 will have the computational resources to store, manipulate, and query large data sets, thereby facilitating new science. C4E4 will serve as a prototype, and provide valuable experience for scaling up to larger observatories at the national level. This paper presents the writers’ vision and goals, initial efforts, and briefly describes how C4E4 can benefit the environmental community.

Total-load sampling has been a perpetual problem in sediment monitoring. Usually a combination of bed-load sampling devices, suspended load suction samplers, and some kind of flume, for total flow rate, is used. Total-load, sediment-sampler-design concepts that can perform all three of these functions are proposed. The resulting designs would require installation at sites that can provide a step-overfall height about equal to the maximum channel flow depth. The concepts are simple, but appear to have been overlooked or ignored for the past many decades, and are based on a moving conveyor belt that is long and wide, with many slots, all of the same size, onto which the stream to be sampled discharges. All flow drops through the slots, and with equal sized slots each must catch a similar proportion of the total flow. Hence, only one slot needs to be collected. As a practical extension it is proposed to replace the conveyor belt with a rack having several slots that represent a short section of the total conveyor belt that is large enough so that the flow does not notice the missing belt parts. This rack is then traversed back and forth on a track through the falling nappe. Laboratory tests of this proposed sampling-assembly rack indicated that the number of the required slots is related to the channel depth and the sum of the slot openings. When the rack is composed of sufficient slots so that the slot-width sum is more than half the channel overfall depth, the system undersampled from 0 to 2% but when there are insufficient slots whose sum represents less than one-third of the overfall depth, the system undersampled by over 8%. The concepts are extended to the condition with a stopped belt where several sampling-slot groups are equally spaced beneath the overfall. A “test of concept” sampler assembly of the stopped-belt idea was built and tested. The sample catch across the stream was within about 4% of expected, offering a total load sampling system where motorized equipment is difficult to install, or electric power is not available. Design and construction suggestions are presented. The catch rate can be small enough to facilitate convenient flow measurement of the catch, which can be converted to total streamflow without the need for separate channel flow measurements.

River flow predictions are needed in many water resource management activities. Hydrologists have relied on individual techniques such as time series, conceptual, or artificial neural networks (ANNs) to model the complex rainfall-runoff process in the past. These techniques, when used individually, provide reasonable accuracy in modeling and forecasting river flow. This paper presents an integrated approach for river flow prediction in an attempt to achieve better forecast accuracy. Specifically, three different models are presented for daily river flow prediction: a time series model of autoregressive type, a nonlinear conceptual model, and an integrated model. The conceptual model uses the Green-Ampt method to model infiltration, time area method to translate rainfall input in time, and a nonlinear reservoir for flood routing. The integrated model uses conceptual, ANN, genetic algorithm, data-decomposition, and model-fusion techniques. The data derived from the Kentucky River basin were employed to calibrate and validate all the models. A wide variety of standard performance indices was used to evaluate model performance. The performance of the time series model was found to be the worst and the conceptual model was found to perform reasonably well. The integrated model performed the best, demonstrating a need for developing innovative hybrid models capable of exploiting advantages of individual techniques in order to achieve improved accuracies in short-term river flow forecasting.

In this study, a method has been suggested for quantification of unsteady flow into a multiaquifer shaft during passage of a flood wave in a nearby stream. The shaft penetrates two aquifers, which are separated by an aquiclude. During flood events, the water level in the stream rises, and consequently, the water level in the upper unconfined aquifer rises; as a result, the lower aquifer gets recharged through the shaft. By applying image-well theory, the finite aquifer and well system is transformed into an infinite aquifer and well system. A solution for the multiaquifer shaft and stream system for a time variant boundary condition has been obtained by applying the discrete kernel approach, which is based on Duhamel’s superposition principle. The distance of the shaft from the stream, the extent of its penetration into the aquifer to be recharged, and the radius of the shaft govern the volume of recharge for a particular hydrogeologic site.

This study evaluates the performance of different estimation techniques for the infilling of missing observations in extreme daily hydrologic series. Generalized regression neural networks (GRNNs) are proposed for the estimation of missing observations with their input configuration determined through an optimization approach of genetic algorithm (GA). The efficacy of the GRNN-GA technique was obtained through comparative performance analyses of the proposed technique to existing techniques. Based on the results of such comparative analyses, especially in the case of the English River (Canada), the GRNN-GA technique was found to be a highly competitive method when compared to the existing artificial neural networks techniques. In addition, based on the criteria of mean squared and absolute errors, a detailed comparative analysis involving the GRNN-GA, $k$ -nearest neighbors, and multiple imputation for the infilling of missing records of the Saugeen River (Canada), also found the GRNN-GA technique to be superior when evaluated against other competing techniques.

A reliable prediction of the total runoff hydrograph is necessary for water resources management. This study investigates two approaches to generate total runoff hydrograph by adding baseflow to direct runoff hydrographs. The first approach uses a method, derived from a digital filter algorithm for hydrograph separation, to generate baseflow hydrographs from direct runoff hydrographs. The method appears to perform well in producing the overall shape of the total runoff hydrographs and the acceptable mass balance errors for a year of water cycle. For application, the recession baseflow constant needs to be estimated reliably and the initial baseflow could be approximated to the long-term mean dry weather flow. The second approach assumes a constant baseflow rate. Although this approach is still capable of producing the overall hydrograph shape, it yields high mass balance errors in the total runoff hydrographs for both monthly and long-term periods. Further analysis shows that two-third of the mass balance errors are contributed from periods with direct runoff, implying that the constant baseflow assumption could introduce significant errors into the computations of total runoff hydrograph.