Journal of Hydrologic Engineering

Hydrologic drought is a type of drought which directly affects the water supply of a region. Long streamflow dry spells or streamflow under a specific threshold are usually considered as hydrologic drought. The annual extreme hydrologic dry spell length (AEHDSL) data of the Halilrud basin in the southeastern semiarid region of Iran were considered to estimate the return period of hydrologic drought and the associated risk in this region. The method of $L$ -moments was applied to check discordant stations and test the homogeneity of the region which consists of 15 gauging watersheds. One discordant station was found and the region was homogeneous according to the homogeneity measure after removing the discordant station. The three-parameter lognormal distribution was found to be representative of the regional distribution for the entire region based on the goodness-of-fit test. For prediction in ungauged basins, the AEHDSL regional regression was developed for the region. The regression model indicates that the vegetation cover and relief of watersheds play important roles in the hydrologic drought length of the Halilrud basin. These two variables control the infiltration and hydraulic slopes of a watershed, respectively.

Precipitation is a major input in hydrological models. Radar rainfall data compared with rain gauge measurements provide higher spatial and temporal resolutions. However, radar data obtained form reflectivity patterns are subject to various errors such as errors in reflectivity-rainfall $\left(Z\text{-}R\right)$ relationships, variation in vertical profile of reflectivity, and spatial and temporal sampling among others. Characterization of such uncertainties in radar data and their effects on hydrologic simulations is a challenging issue. The superposition of random error of different sources is one of the main factors in uncertainty of radar estimates. One way to express these uncertainties is to stochastically generate random error fields and impose them on radar measurements in order to obtain an ensemble of radar rainfall estimates. In the present study, radar uncertainty is included in the $Z\text{-}R$ relationship whereby radar estimates are perturbed with two error components: purely random error and an error component that is proportional to the magnitude of rainfall rates. Parameters of the model are estimated using the maximum likelihood method in order to account for heteroscedasticity in radar rainfall error estimates. An example implementation of this approached is presented to demonstrate the model performance. The results confirm that the model performs reasonably well in generating an ensemble of radar rainfall fields with similar stochastic characteristics and correlation structure to that of unperturbed radar estimates.

One of the main problems of neural networks is the lack of consensus on how to best implement them. This work targets the question of the transfer function selection—a vital part of neural network providing nonlinear mapping potential. Three nonlinear transfer functions bounded by −1 and 1 are selected for testing, based on a literature review: the Elliott sigmoid, the bipolar sigmoid, and the tangent sigmoid. They are used to design multilayer perceptron neural networks for multistep ahead streamflow forecasting over five diverse watersheds and lead times from 1 to 5 days. All multilayer perceptrons have shown a good performance on the account of the four selected criteria, which confirms that the selected multilayer perceptron implementation procedure was adequate, namely, the data set length, the Kohonen network clustering method to create the training and testing sets, and the Levenberg-Marquardt back-propagation training procedure with Bayesian regularization. Specifically, results endorsed the tangent sigmoid as the most pertinent transfer function for streamflow forecasting, over the bipolar (logistic) and Elliott sigmoids, but the latter requires less computing time and as such may be a valuable option for operational hydrology. Also, results averaged over five lead times confirmed the universal approximation theorem that a linear transfer function is suitable for the output layer—a nonlinear transfer function in the output layer failed to improve performance values.

Various parametric and nonparametric models have been suggested in literature for stochastic generation of seasonal streamflows. State-of-the-art nonparametric models are reviewed herein and their drawbacks identified. We developed a simple model that employs the k-nearest neighbor resampling algorithm with gamma kernel perturbation (denoted as KGK model), which enables generation of data that are not the same as the historical data. For preserving the annual variability two approaches are developed. The first one employs the aggregate variable concept (KGKA model), and the second one uses a pilot variable that leads the generation of the seasonal data (KGKP model). The pilot variable refers to the annual data that has been previously generated, but its role is not for disaggregation but rather for conditioning the state that guides the generation of seasonal flows. The proposed models have been compared with a currently available nonparametric model that considers the reproduction of the interannual variability. Monthly streamflows of the Colorado and Niger Rivers were utilized and the performance of the alternative models evaluated by comparing a number of statistics such as the mean, variance, and storage capacity determined from the generated data (monthly and annual) and the corresponding historical data. The extensive testing and comparison of the models led to the conclusion that overall, the suggested model structure KGK (particularly the KGKP model) performs quite well in preserving the monthly and annual historical statistics. The possibilities of extensions of the models are also discussed.

Protection zones established for stream drainage systems or water supply reservoirs serve as a major protection measure to reduce water contamination. In essence, a number of physical, chemical, and/or biological processes act together or separately in these areas to reduce a contaminant’s concentration or to convert it to a less threatening form. Yet, time is an important criterion for these processes to occur, as there is less potential for the concentration of a contaminant to be reduced when there is a shorter time of travel between the point where the contaminant enters the stream and the reservoir intake point. In this regard, this study employs the time component of flow among a set of spatial criteria that commonly target assessments of functionality and effectiveness for potential protection zones in a watershed. Such criteria could be practically evaluated in a multicriteria decision-making framework for a proper design of protection zones. The approach is expected to contribute to protection zone design with its increased practicality and similar use in common applications, which may not always be achieved from case-specific modeling exercises.

The Santa Fe River Watershed in Santa Fe County, New Mexico was identified as one of the top five high risk nonpoint source pollution areas out in the Rio Grande Basin. This watershed was selected to demonstrate the use of hydrologic modeling as a powerful tool for assessing the impacts of land management practices on erosion and sediment control at the watershed level. A method based on the Hydrologic Simulation Program-Fortran was used to address the local nonpoint sediment pollution concerns. The model was modified to reflect predicted future land uses related to expected urban expansion in the watershed. Six scenarios were created and the costs and benefits of each were weighed. The total estimated costs ranged from under $1 million to over $66 million. Total average annual sediment yields at the watershed outlet ranged from 3,441 to 4,111 tonnes/year, depending on management practices employed. These results indicate the magnitude of expected sediment reductions under various management strategies. Additionally, they provide an indication of the magnitude of expected sediment reductions in the Santa Fe Watershed and the estimated cost of each management practice.