Journal of Hydrologic Engineering

With the recent and drastic decline of hydrological in situ networks, flood estimation from ungauged basins is an imperative research area in scientific hydrology. In this regard, geomorphologic instantaneous unit hydrograph (GIUH) based on models by Clark in 1945 and Nash in 1957 are developed and applied to the Ajay River Basin at Jamtara in northern India. The geomorphologic parameters of the basin were estimated using ERDAS Imagine 8.5 image processing and geographic information system (GIS) software. The direct surface runoff (DSRO) hydrographs derived by the GIUH-based models without using historical runoff data, the conventional Clark IUH model option of the HEC-1 package, and the Nash IUH model, were compared with observed DSRO hydrographs employing four performance criteria. The DSRO hydrographs are computed with reasonable accuracy by the GIUH-based Clark and Nash models, when compared to the Clark IUH model option of the HEC-1 package and the Nash IUH model. It is observed that the GIS supported GIUH-based models hold great potential of estimating floods from ungauged basins, or basins with scant data availability. Further, the effect of the basin map scale on the performance of the GIUH-based models was evaluated for the same basin. The DSRO hydrographs computed using the GIUH-based Clark and Nash models, were estimated with reasonable accuracy at the two map scales (viz. 1:50,000 and 1:250,000).

A simple three-point grid test shows that some common and mathematically convenient intergrid hydraulic conductivity means violate the min–max principle for elliptic boundary value problems (steady-state flow). In some cases in models of Richard's equation for three types of homogenous rock matrix and fracture in Yucca Mountain tuff, these violations lead to nonphysical oscillations, eliminating these means as generally valid approaches in such models. These models are used to show that Darcian means and the upstream means have similar RMS errors in water content and flow in convergence to the fine-grid solution. But in the presence of vertical space step discretization error, the Darcian means produce sharper wetting fronts.

An analytical solution of the nonlinear kinematic wave model of overland flow on a sloping plane for time-varying excess rainfalls of sinusoidal functions is presented by the method of characteristics. To obtain the closed form of the analytical solution, discharge per unit width in the expression of Manning’s formula is approximated by a parabolic curve. The analytical solution is also compared with the numerical one and their agreement is found to be good. The analytical solution is applied to rainfall and runoff processes with given hyetograph using Fourier series analysis. The computational results of the analytical solution are compared with the observed data. It explains the observation on an impermeable slope very well. It is found that the analytical solution is suitable for estimating design flood from excess rainfall in a simple watershed.

Atmospheric scientists are continuously improving the capability of climate models to predict accurately the frequency of heavy precipitation events in a changing climate. A brief review of recent studies related to the accuracy of precipitation outputs from climate models indicated that most of them concentrate on spatially aggregated precipitation patterns rather than point rainfall frequency characteristics. To fill this gap, this verification study focused on point rainfall frequency characteristics and used the most recent climate model results (that is, results from a regional climate model known as CMM5, by Liang et al.). Both the conventional moving-window analyses and an alternative meteorological event-based approach were used in this study to examine more completely the capability of CMM5 in reproducing rainfall events as observed at individual points. It was found that CMM5 reproduced the major characteristics of point rainfall frequency distributions reasonably well. The meteorological event-based analyses proposed in this study also assisted in the identification of a minor but perhaps systematic bias on the part of CMM5 in overestimating days with small amounts of precipitation. The overall performance of the current generation of climate models as demonstrated in this study encourages more and wider uses of their results in hydrologic engineering.

This paper applies the Soil Water Assessment Tool (SWAT) to model the hydrology in the Pocono Creek watershed located in Monroe County, Pa. The calibrated model will be used in a subsequent study to examine the impact of population growth and rapid urbanization in the watershed on the base flow and peak runoff. Of particular interest in this paper is the exploration of potential use of Next Generation Weather Radar (NEXRAD) technology as an alternative source of precipitation data to the conventional surface rain gauges. NEXRAD estimated areal average precipitations are shown to compare well with the gauge measured ones at two climate stations in the study area. Investigation of the spatially distributed NEXRAD precipitation estimates revealed that average annual precipitation can vary spatially as much as 12% in the Pocono Creek watershed. The SWAT model is calibrated and validated for monthly stream flow, base flow, and surface runoff. Hydrographs generated from both gauge and NEXRAD driven model simulations compared well with observed flow hydrographs. Although little effort is spent on daily calibration, model simulations and observed flows were in good agreement at the daily scale as well. Almost similar model efficiency statistics, i.e., mass balance error (MBE), coefficient of determination $\left(R^2\right)$ , and Nash-Sutcliffe efficiency $\left(E_{\mathit{NS}}\right)$ , were obtained during the calibration period in the gauge and NEXRAD driven simulations. In the validation period, NEXRAD simulations generated higher model efficiencies at the monthly scale. On the other hand, simulations with gauge precipitations resulted in slightly better model efficiencies at the daily time scale. The spatial representation of precipitation did not contribute much to model performance when stream flow at the watershed outlet was the required output. However, the use of NEXRAD technology appears to offer a promising source of precipitation data in addition to currently existing surface gauge measurements. Discussions on new directions in radar-rainfall technology are provided.

Few case studies are available on stream water quality monitoring during road construction, especially in highly protected watersheds. Recent reconstruction of two tunnels and approach roads in the Great Smoky Mountains National Park potentially could have impacted the adjacent stream due to sediment and acid-generating geology. To determine whether impact occurred, grab and passive sampling were conducted at stream sites above and below each construction area before, during, and after construction. Performance of individual structural best management practices (BMPs) was also monitored. Due to the small disturbance area, the results showed that tunnel reconstruction had no statistical impact on stream water quality based on comparison of upstream and downstream pH and suspended solids. Surprisingly, median suspended solids concentrations for the stream sampling locations were lowest during construction, ostensibly because construction occurred in winter when the ground was frozen. Differences in stream pH and water quality between construction and pre- and postconstruction phases were attributed to normal seasonal variation. BMP performance was mixed; the efficiency of check dams was dependent upon their ability to pond water.

This study reviewed various combining methods that have been commonly used in economic forecasting, and examined their applicability in hydrologic forecasting. The following combining methods were investigated: The simple average, constant coefficient regression, switching regression, sum of squared error, and artificial neural network combining methods. Each method combines ensemble streamflow prediction (ESP) scenarios of the existing rainfall-runoff model, TANK, those of the new rainfall-runoff model that has been developed using an ensemble neural network for forecasting the monthly inflow to the Daecheong multipurpose dam in Korea. In addition to the combining, the ESP scenarios were adjusted using correction methods, such as optimal linear and artificial neural network correction methods. Among the tested combining methods, sum of squared error (SSE), a combining method using time-varying weights, performed best with respect to the root mean square error. When SSE was coupled with optimal linear correction (OLC), denoted SSE/OLC, its bias became sufficiently close to zero. SSE/OLC also considerably improved the probabilistic forecasting accuracy of the existing ESP system.

Rapid urbanization and its implications for both water quality issues and floods have increased the need for modeling of urban drainage systems. Many operational models are based on deterministic solutions of hydraulic equations. Improving such models by adding a “black-box” component to deal with any systematic structure in the residuals is proposed. In this study, a conventional deterministic stormwater drainage network model is first developed for a rapidly developing catchment using the HYDROWORKS (now called Infoworks) package, from Wallingford Software in the United Kingdom. However, despite the generally satisfactory results, the HYDROWORKS model tended to underestimate the flow volume. In this paper, a black-box or “systems” model is fitted to the hydraulic urban drainage model in order to improve its overall efficiency. A study was conducted of suitable black-box models, which included the nonlinear artificial neural network model (ANN), and the linear time series models of Box and Jenkins in 1976. They were added to either the output (in simulation mode) or, in updating mode, to the residuals (i.e., difference between modeled and measured output) of the deterministic hydraulic model. The updating procedure provided a considerable improvement in the overall model efficiency for different lead-time forecasting. In simulation mode, however, only the nonlinear ANN model gave better performance in calibration, and a slight improvement in validation.

The Nash–Sutcliffe efficiency index $\left(E_f\right)$ is a widely used and potentially reliable statistic for assessing the goodness of fit of hydrologic models; however, a method for estimating the statistical significance of sample values has not been documented. Also, factors that contribute to poor sample values are not well understood. This research focuses on the interpretation of sample values of $E_f$ . Specifically, the objectives were to present an approximation of the sampling distribution of the index; provide a method for conducting hypothesis tests and computing confidence intervals for sample values; and identify the effects of factors that influence sample values of $E_f$ including the sample size, outliers, bias in magnitude, time-offset bias of hydrograph models, and the sampling interval of hydrologic data. Actual hydrologic data and hypothetical analyses were used to show these effects. The analyses show that outliers can significantly influence sample values of $E_f$ . Time-offset bias and bias in magnitude can have an adverse effect on $E_f$ . The time step at which the data are recorded appears to be an insignificant factor unless the sample size is small. The Nash–Sutcliffe index can be a reliable goodness-of-fit statistic if it is properly interpreted.

Erosion by lateral, subsurface flow is known to erode streambank sediment in numerous geographical locations; however, the role of seepage erosion on mass failure of streambanks is not well understood. In the absence of an established sediment transport model for seepage erosion, the objectives of this research were to investigate the mechanisms of erosion due to concentrated, lateral subsurface flow and develop an empirical sediment transport model for seepage erosion of noncohesive sediment on near-vertical streambanks. Laboratory experiments were performed using a two-dimensional soil lysimeter of a reconstructed streambank profile packed with three different soil layers to mimic seepage erosion occurring at Little Topashaw Creek (LTC) in northern Mississippi. Soil samples from LTC streambanks indicated considerable hydraulic conductivity contrast between an overlying silt loam layer (SiL), highly permeable loamy sand, and confining clay loam layer. Lysimeter experiments were conducted with various upstream water table heads, overburden heights, and lysimeter slopes. Bank failure occurred prior to the total release of negative pore-water pressures in the SiL layer suggesting that such a mechanism was not critical for bank collapse due to seepage erosion. A seepage erosion transport model for conductive, noncohesive soil layers was derived based on a dimensionless sediment discharge and dimensionless seepage flow shear stress. The advantage of this sediment transport model is that it relates sediment flux to seepage discharge from the streambank.

Optimization techniques for deriving a unit hydrograph (UH) have received considerable attention. But the UHs derived in some studies are not physically justifiable as the recession limb has a wavy shape. This study extends an existing optimization model by introducing additional constraints to ensure a monotonic decline in the recession limb of the UH. Although there is a marginal deterioration of the objective function, the shape of the developed UH is physically correct. Moreover, the match between the observed and computed direct surface runoff hydrographs is close.

Adequate knowledge of the hydraulic characteristics of production wells is indispensable for the proper development and management of wells and for the selection of suitable pumps. In this study, the characteristic hydraulic parameters of production wells were determined by the widely used graphical analysis of step-drawdown pumping test data as well as the two traditional gradient-based nonlinear optimization techniques (viz., Levenberg–Marquardt and Gauss–Newton) and the nontraditional optimization technique, genetic algorithm. Three stand-alone and interactive computer programs were developed to optimize the hydraulic parameters of production wells by these numerical techniques. The efficacy and robustness of the developed computer codes were examined using eleven sets of step-drawdown data from diverse hydrogeologic conditions. The results of this study revealed that all the three numerical techniques yielded superior well parameters with lower values of root-mean-square errors (RMSE) for all the eleven data sets compared to the parameters obtained by the graphical method. The values of the exponent $\left(P\right)$ ranged from 1.2 to 6.0, which gave minimum RMSE values. Furthermore, it was found that both the Gauss–Newton and Levenberg–Marquardt techniques are sensitive to the initial guess values of well parameters. It is concluded that both the traditional and nontraditional numerical techniques offer an efficient and reliable tool for the determination of well parameters with a greater accuracy and better insight.

Incorporating the storm duration and a nonlinear relation for initial abstraction $\left(I_a\right)$ , this paper presents an enhanced version of the Soil Conservation Service curve number-based Mishra–Singh model. The proposed version is compared with existing formulations using a large set of storm rainfall-runoff events derived from the water database of the United States. The results of the paired $t$ -test of related samples and standard error of estimate $\left(S_e\right)$ statistic comparison of split sampling-based calibration and validation and simulation on full data set show that the proposed version performs far better than all other existing versions.