Journal of Hydrologic Engineering

Urban floods have adverse impacts on the performance of urban infrastructures and the life of residents. The floods cause heavy damages and perturbation in the serviceability of urban infrastructures as well as transportation. Therefore, different factors affecting the urban water flood characteristics should be considered in urban development planning, especially in metropolitan areas. In recent years, climate change and its consequences have affected the total components of the water cycle as well as floods. These effects are intensified in urban areas because of the anthropogenic effects they have on the water cycle, such as reducing the infiltration capacity of basins, construction regardless of the channel’s right of way, and disposal of sediment and solid wastes into channels that will decrease the channels’ safe carrying capacity. In this way, incorporating climate change impact on urban water studies could help to achieve more reliable results to be applied in real-time planning of urban areas through selection of best management practices (BMPs). In this study, an algorithm for selecting the BMPs to improve the system performance and reliability in dealing with urban flash floods is proposed that considers the anthropogenic and climate change effects. The suggested algorithm is applied to the Tehran metropolitan area drainage system as a case study. First, the future rainfall pattern of the study area under climate change impact is simulated. Then, the effectiveness of present and future development projects for improvement of drainage system performance is evaluated under different scenarios. Also, the effect of solid wastes and sediments carried with surface runoff in system performance is considered. Finally, feasibility of suggested BMPs and their effectiveness in urban flood management as well as their related costs and benefits are considered. The results of the study show the significance of using analytical and management tools in assessing and improving the urban drainage system.

Groundwater flow model MODFLOW 2000 and contaminant transport model MT3DMS 5.0 have been directly linked with nondominated sorting genetic algorithm (NSGA) II using a novel technique to solve a multiobjective pump-and-treat groundwater remediation problem. In this technique, the system simulators in their compiled forms are directly coupled with the optimization algorithm. The performance of the model has been evaluated on a multiobjective groundwater remediation problem based on a hypothetical confined multilayered aquifer under steady-state flow condition. Two conflicting objectives, viz, minimization of remediation cost and minimization of remediation time subject to upper and lower bounds on extraction rates, upper bounds on hydraulic heads, and contaminant concentration levels, are incorporated into the optimization formulation. Incorporation of discrete cost components into the objective cost function renders it to be discontinuous and nondifferentiable, and the resulting Pareto front exhibits disjoints at a few locations. The disjointed feature of the Pareto front is used to study the impact of remediation time on remediation cost. The main contribution of this study is the finding of the significance of remediation time on optimal groundwater remediation design problems. The results suggest that remediation time plays a crucial role in the optimal aquifer remediation design. Unless compulsory, incorporation of remediation time as a decision variable in the optimization model may yield better results in minimizing the remediation cost. Another contribution is the linking of computer programs written in different languages. The linking technique used in this study has the potential of coupling any two or more codes written in different languages without writing interface programs.

An entropy theory, comprising the Shannon entropy, the principle of maximum entropy, and the concentration theorem, has been applied in recent years to a wide range of problems in hydrology. From a hydrologic point of view, the applications can be organized into three classes: (1) physical, (2) statistical, and (3) mixed. This study focuses on the physical applications of the entropy theory, wherein the theory is coupled with the laws of mathematical physics and solutions are derived either in a time or space domain rather than the frequency domain. It is shown that a general framework can be developed to derive solutions to a wide range of seemingly disparate problems. The theory seems to have much potential that remains yet to be fully exploited.

The primary characteristics that influence the potential of defining a synthetic design hydrograph (SDH), are the hydrograph shape, peak discharge ($Q_p$), volume ($V$), and duration ($D$). This paper studies the advantages and shortcomings of using simple distribution functions with finite support (namely, beta and generalized standard two-sided power distributions) to represent and synthesize direct runoff hydrographs. The relationships among $Q_p$, $V$, $D$, and distribution parameters are explored on a few flood events selected by a recursive digital filter algorithm and an overthreshold approach. The results obtained indicate that the adopted procedure provides a good compromise between simplicity and accuracy for building SDHs with two assigned flood characteristics (e.g., $Q_p$ and $V$) and a defined shape.

Regionalization, a process of transferring hydrological information [i.e., parameters of a conceptual rainfall-runoff model, namely, the McMaster University-Hydrologiska Byråns Vattenbalansavdelning (MAC-HBV)] from gauged to ungauged basins, was applied to estimate continuous flows in ungauged basins across Ontario, Canada. To identify appropriate regionalization methods, different regionalization methods were applied, including the spatial proximity [i.e., kriging, inverse distance weighted (IDW), and mean parameters], physical similarity, and regression-based approaches. Furthermore, an approach coupling the spatial-proximity (IDW) method and the physical similarity approach is proposed. The analysis results show that the coupled regionalization approach as well as the IDW and kriging produce better model performances than the remaining three. Further investigations show that the coupled-regionalization approach provides slightly better performances than the other two spatial proximity methods. In addition, a modified Monte Carlo simulation method is used to assess the estimated flow confidence intervals. The prediction confidence intervals provide additional information on the range of variability of the simulated continuous streamflow in the ungauged basins, and this can be particularly useful for decision making, such as environmental flow determination in ungauged basins.

Watershed modeling requires reliable climate input data that provide a reasonable representation of spatial variability. In many cases, limited access, complex terrain, and remoteness make it difficult to acquire good data. This work characterizes the variability of precipitation in a small, mountainous watershed and quantifies the uncertainty of precipitation estimates caused by sparse precipitation gauging stations. Spatial precipitation variability was found to be of particular concern during the summer months. When one gauge within the watershed is recording precipitation, integration times of more than 8 days are necessary for all gauges to record. In the study catchment, the absolute error in daily mean catchment precipitation exponentially decreased with the increased number of precipitation gauges compared with the best available estimate. The use of 4 or more gauges implicitly allowed a close approximation of the best available daily mean catchment precipitation estimates. Fuzzy multiple linear regression was applied to estimate the average basin precipitation. With this method, the differences in average basin precipitation between the dense experimental gauging network and typically available sparse gauging setups were quantified. Depending on the integration time, the median relative error for the most suitable sparse setup was between 0.08 and 0.40, equivalent to a median absolute error between 0.6 and $1.8\mathrm{mm}{\mathrm{d}}^{-1}$.

A simple spreadsheet cell-oriented methodology is proposed for solving a groundwater flow inverse problem. Initially, the numerical simulation of the groundwater flow process is carried out by using an embedded optimization approach and spreadsheet solver. The result of the simulation is compared with the result of a GMS MODFLOW simulation. The comparison shows that the result of the spreadsheet simulation is similar to the result of the GMS MODFLOW simulation. Two groundwater inverse problems are then solved using the proposed methodology. The first problem estimates the unknown pumping rates of an aquifer, and the second problem estimates the aquifer transmissivity. Using the spreadsheet method, it is easy to define boundary conditions and to initialize starting values as well as their visualization. The methodology is simple in concept and can be used for solving graduate- and undergraduate-level groundwater simulation and inverse problems. The methodology may also be used by the practicing engineer for solving small-scale groundwater problems.

Employing the Shannon entropy, this note derives the well-known power law and the Prandt–von Kármán universal (or logarithmic) velocity distribution equations for open channel flow. The Shannon entropy yields probability distributions underlying these velocity equations. With the use of this entropy, one obtains an expression for the power law exponent in physically measurable quantities (surface flow velocity and average logarithmic velocity) and an expression for shear flow depth in flow depth, surface flow velocity and shear velocity, thus obviating the need for fitting.

This paper presents a general expression that may be used to determine the minimum number of locations in a river basin at which cross-correlated point precipitation time series must be stochastically generated to adequately capture the temporal variance of precipitation over large areas, based on the spatial correlation structure of the precipitation field. Using fewer precipitation time series than necessary leads to an overestimate of the variance of the areal precipitation. Application of the derived expression is demonstrated by using the Anseba River basin in Eritrea, East Africa, as an example. This test case is validated using a series of stochastic precipitation fields that are generated at different spatial scales. The expression was derived by expanding the method of moments equation for the variance at the field scale.