Journal of Hydrologic Engineering

Highway infrastructure is a major non-point source of storm water runoff and associated pollutants. Forty-four simulated rainfall tests were conducted over a turf-covered plot-scale test bed that represented typical highway slopes of 25, 33, and 50%. The two soil-turf combinations were Argentine Bahia over AASHTO A-3 soil (fine sand) and Pensacola Bahia over AASHTO A-2-4 soil (silty fine sand). All the tests were conducted in central Florida, where the weather is subtropical. This paper presents the analysis of curve numbers (CN) obtained from simulated rainfall-runoff data. The CN values for the 24 tests conducted on the Argentine Bahia A-3 soil combination varied from 70.4 to 93.0 with a mean value of 79.6. The CN values for the 20 tests conducted on the Pensacola Bahia A-2-4 soil combination varied from 89.0 to 97.2 with a mean of 94.0. The scatter of the CN values was more for fine sand (standard deviation of 8.31) than for silty fine sand (standard deviation of 2.48). Single factor ANOVA suggested that the CN values are significantly lower ($\alpha =0.05$) for Argentine/A-3 compared with Pensacola/A-2-4. However, no significant difference ($\alpha =0.05$) was shown between the CN values calculated based on experimentally determined initial abstraction ratio ($\lambda$) and the CN values calculated based on the Natural Resources Conservation Service (NRCS) suggested $\lambda$ of 0.2. The CN values, in general, increased with slope and rainfall intensity. The CN values determined in this study compare reasonably well with the 70–100 range of CN values reported in the literature for different types of turf-covered soil slopes.

Determination of groundwater contaminant source characteristics such as release histories of unknown groundwater pollutant sources from concentration observation data is an inverse problem. Often solution to this inverse problem is nonunique, and it is an ill-posed problem. A linked simulation-optimization approach can be used to solve this problem efficiently. However, this approach is computationally intensive, and the results obtained tend to be highly susceptible to errors in the measured data and estimated hydrogeological parameters. Apart from this, accuracy of the solutions is highly dependent on the choice of monitoring locations. An adaptive simulated annealing (ASA)-based solution algorithm is shown to be computationally efficient for optimal identification of the source characteristics in terms of execution time and accuracy. This computational efficiency appears to prevail even with moderate levels of errors in estimated parameters and concentration measurement errors. Also, the contaminant concentration monitoring locations are shown to be critical in the efficient characterization of the unknown contaminant sources. Optimal identification results for different monitoring networks are presented to demonstrate the relevance of a network suitable for efficient source identification.

A physically based mathematical model is developed in this study for the hydrologic process taking place in a bioretention filter. The hydrologic storage equation is adopted to determine the variation of water depth in the ponding area. The Green and Ampt equations are used to model the movement of water in the underlying storage zone, or soil filter, taking into account the finite depth of the filter. The perforated drain pipes placed at the bottom of a soil filter are represented by atmospheric pressure at this location. The ponding area and the soil filter equations are coupled through the infiltration term. The governing equations are written in terms of a set of dimensionless parameters and are solved numerically by using an explicit finite-difference scheme. The solutions to the dimensionless governing equations are also in terms of dimensionless parameters, and they can be generalized on the basis of the principle of hydrologic similarity. A series of solutions is obtained for numerous and systematically chosen combinations of filter size and hydrologic and soil conditions and is presented in chart form. These charts can be used as a design aid for quick preliminary sizing of new bioretention filters and quick evaluation of existing filters.

Accurate and current rainfall characterization is an important tool for water-related system design and management. Updated rainfall intensity-duration-frequency (IDF) relationships in peninsular India were developed; impacts on runoff and groundwater recharge attributable to changes in rainfall characteristics are discussed. Two data sets were used from gage in Hyderabad city, the capital of Andhra Pradesh: hourly rainfall data for the 19 years from 1993–2011 and daily rainfall data for the 30 years from 1982–2011. Hourly data were used to develop updated rainfall IDF relationships; daily data were used for trend analysis of threshold-based rainfall events. IDF curves were developed for return periods of 2, 5, 10, 15, 25, 50, 75, and 100 years for 1-, 2-, 4-, 8-, and 24-h durations. The updated IDF relationships showed a significant change in rainfall characteristics compared with older relationships for the region surrounding Hyderabad, India; they showed greater rainfall intensities across all durations and return periods. Greater intensity storms may reduce groundwater recharge and increase runoff, making the surface storage of runoff increasingly important to enhance recharge and reduce flooding risks.

Assuming time-averaged velocity as a random variable, a two-dimensional (2D) velocity distribution in open channels was derived by maximizing the Tsallis entropy, subject to mass conservation. The derived distribution was tested using field data and was also compared with other two-dimensional velocity distributions. The Tsallis entropy–based velocity distribution was found to predict the velocity near the boundary well.

This paper focuses on calibration and validation of a hydrologic model of the Rio Conchos Basin, a main Mexican tributary of the binational Rio Grande Basin. The Rio Conchos provides approximately 55% of the water deliveries to the United States under the 1944 water treaty signed between both countries. The hydrologic modeling has been developed using the one-dimensional, two-layer soil moisture accounting scheme embedded in the Water Evaluation and Planning (WEAP) model. A 10-year period was used to calibrate the model, which was achieved by a trial-and-error method for the adjustment of the model parameters. The results show a Nash-Sutcliffe coefficient of 0.84 at Ojinaga station (mouth of the basin) and 0.81 at La Boquilla, indicating good model performance. In general the model predicts well the monthly, annual, and maximum flows; but there are significant differences between the model values and undeveloped (naturalized) flows for low flow periods, especially at La Boquilla station. For model validation, a special period of 10 years was used, corresponding to drought conditions in the basin. Results show a Nash-Sutcliffe coefficient of 0.88 at Ojinaga station, with error in annual volume less than 1% on average.

In this paper, a hybrid harmony search (HS) algorithm is proposed for the parameter estimation of the nonlinear Muskingum model. The BFGS algorithm is used as local search algorithm with a low probability for accelerating the HS algorithm. In the proposed technique, an indirect penalty function approach is imposed on the model to prevent negativity of outflows and storages. The proposed algorithm finds the global or near-global minimum regardless of the initial parameter values with fast convergence. The proposed algorithm found the best solution among 12 different methods. The results demonstrate that the proposed algorithm can be applied confidently to estimate optimal parameter values of the nonlinear Muskingum model. Moreover, this hybrid methodology may be applicable to any continuous engineering optimization problems.