Journal of Hydrologic Engineering

A combined stream function is derived for investigating horizontal dispersion of pollutants in a moderate-sized circular rotating basin when internal wave and gyre flow field coexist in the upper mixing layer. The combined flow field is analogous to wave-current flow in which the steady part is the depthwise velocity profile of circumferential gyre that is simulated using a depthwise cosine function. The periodic components of the Kelvin-type internal wave (assuming a small Burger number) are derived up to the second order in which the variation in the wave amplitude in the radial direction is governed by the modified Bessel function of the first kind. The combined vertical diffusivity is estimated by adding its wave-induced and current-induced components. The resultant closed-form solution of the wave-current longitudinal dispersion coefficient (WCLDC) shows additional wave-induced dispersion over the dispersion due to codirectional gyres. The celerity for the combined flow shows a consistently higher magnitude than does the wave-only celerity. As the lack of data on combined gyre-internal wave flow did permit validation of the model, the analysis was applied to a few lakes using the data available for Kelvin waves superimposed on hypothetical gyre flow. For higher wave amplitudes, the combined flow structure shows enhanced wave influence over flow depth that result in higher WCLDC.

The application of partial least-squares regression (PLSR) in seasonal streamflow forecasting was investigated using snow water equivalent, precipitation, temperature from automatic Snow Telemetry sites, and previous flow conditions as input variables. The forecast performance of PLSR models was compared to principal components regression (PCR) models as well as to the Natural Resources Conservation Service (NRCS) official forecasts in three Rio Grande watersheds including the Rio Grande Headwater Basin, Conejos River Basin in Colorado, and Rio Grande Basin above Elephant Butte Reservoir, New Mexico. The results indicated that using a correlation-weighted precipitation index is a relatively effective method in both improving forecast accuracy and developing relatively parsimonious regression models. In comparison of PLSR and PCR, similar forecast accuracies were obtained for both methods in jackknife cross validation and the test period (2003–2007) although PLSR has higher calibration coefficient of determination $\left(R^2\right)$ and can reach its minimal prediction error with a smaller number of components than PCR. The comparison with NRCS official forecasts showed that the application of PLSR in seasonal streamflow forecasting is promising. This approach could be combined into NRCS’s operational forecasting environment for possible forecast improvement.

In this paper, a groundwater level forecasting model is proposed by combining the theory of self-organizing map (SOM) and radial basis function network (RBFN). The proposed model is referred to as SOM-RBFN model. Recently, RBFN has been applied in time series forecasting. Traditionally, the number of hidden units and the positioning of the radial basis centers are crucial problems for establishing RBFN. The proposed model can decide the number of RBFN’s hidden units with using the two-dimensional feature map which is constructed by SOM, and then it can determine the positioning of the radial basis centers easily. The proposed model is applied to actual groundwater level data in southern Taiwan from 1997 to 2003. It is found that the multisite model can predict the 1 month ahead groundwater level more precisely than the single-site model. Moreover, it is also found that the four-site model is more competent in predicting groundwater level as compared to the single-site model and six-site model. Therefore, too much information cannot improve the generalization ability of the model. For groundwater level prediction, the SOM-RBFN multisite model is recommended as an alternative to the other methods because it has a clear principle and a simple structure. In addition, it can produce more reasonable forecasts.

This work solves an optimal capacity-expansion planning problem with land subsidence constraints using a hybrid algorithm that combines the genetic algorithm (GA) and constrained differential dynamic programming (CDDP). The main structure of the hybrid algorithm is the GA, in which each chromosome represents a possible network design and its expansion schedule. The present fixed cost of each chromosome is computed easily using the GA, and CDDP is then used to solve the optimal pumping rates and compute the optimal present operating costs associated with the chromosome. Simulation results indicate that the well network has lower total present cost with the capacity-expansion system instead of installing the full system capacity at the beginning. However, the well network designed by the capacity-expansion model without considering land subsidence may induce more local land subsidence than that determined by the conventional model demonstrating the necessity of considering land subsidence constraints in the system design. This work also investigates other important issues related to the optimal design of the system-installing schedule. The proposed model is highly promising for facilitating a cost-efficient well system design for regional groundwater supply and environmental conservation.

An application of the Hydrologic Simulation Program–FORTRAN (HSPF) model was developed for the entire Illinois River basin. During the simulation period from 1985 to 1995, simulated monthly and annual flows correlated well with observed data. However, its performance for daily flows in the lower portion of the basin was not satisfactory because of the complicated hydraulic condition of the Illinois River. In order to compensate the weakness of HSPF for flow routing, we have coupled a calibrated hydraulic model, one-dimensional unsteady flow through a full network of open channels (UNET), to improve flow routing accuracy and the overall performance of the HSPF model. In this study we intended to test how HSPF modeling accuracy can be improved from modifying function tables (FTABLES). We proposed four alternative sets of FTABLES: three (Sets B, C, and D) were based on Manning’s equation and one (Set E) used the flow-stage relationship. Simulations from these four alternatives were compared to the default FTABLES (Set A) and the coupled HSPF-UNET model. A comparison showed that improving the estimation on hydraulic gradients from flood frequency analysis (Set B) or improving the computation of reach geometry from surveyed cross sections (Set C) could only improve model accuracy. However, much better modeling accuracy can be obtained by improving both reach geometry and hydraulic gradients (Set D). Using the stage-discharge relationships simulated from a hydraulic model (Set E) also provided better results. The proposed methods have been successfully applied to watershed modeling with HSPF in the Illinois River basin. This study demonstrated that better HSPF modeling accuracy can be achieved with minimum effort from representative channel geometries and hydraulic gradients.

This study presents a hydrologic analysis of a flood event that took place over a small urbanizing watershed in Austin, Texas. The physically based, distributed-parameter gridded surface subsurface hydrologic analysis (GSSHA) hydrologic model was used to simulate the watershed response to a very high-intensity rain event. The hydrologic model was forced by both gauge-observed and multisensor precipitation estimator (MPE) rainfall input. Observed discharge was compared to GSSHA-generated hydrograph under various degrees of representation of the watershed physiography. In addition, simulation hydrographs by GSSHA using five different model grid sizes were compared in order to evaluate the effect of grid size on model predictions. The simulation hydrograph for the model using a 30-m grid cell generally compared well to the observed flow data once the effects of storm water detention were simulated. The comparison of simulation results from models using 30, 60, 90, 120, and 150 m grid size highlighted the loss of accuracy as the model grid size is increased. Driving the hydrologic model by data from the two rain gauges existing on the watershed resulted in significant overestimation of the runoff.

In this study, two-parameter Nakagami- $m$ distribution has been introduced along with two-parameter gamma (GM), three-parameter generalized logistic (GLG), three-parameter Pearson type 3 (PT3), and three-parameter generalized extreme value distributions for the derivation of flood hydrographs for gauged catchments. Parameters of these distributions have been optimized using genetic algorithm (GA) in MATLAB v. 7.1 GA toolbox. Observed storm events of three catchments have been used to evaluate the efficiency of the hydrographs generated by these distributions. A validation test has been performed on the different storm event data set of one of the catchments and results were found satisfactory. Statistical estimators (viz., coefficient of efficiency, root-mean squared error, and mean absolute percent error), which indicate the efficiency of the optimization technique in deriving flood hydrographs have been used to find out the rank of these distributions for deriving the hydrographs for the particular catchments.