Journal of Hydrologic Engineering

In this study, the principle of maximum entropy concept is used to estimate the soil moisture profile to a depth of near 50 cm. The procedure involves the use of the surface observation as the principle constraint in physical space, and only requires that a water balance be kept at the surface so that the mean water content of the soil can be computed at each time step. Comparison to experimental data for the wet and dry phases of the infiltration cycle showed that the theoretical profile gave reasonable simulations as measured by the Nash Sutcliffe Efficiency Statistic. It was also demonstrated that the entropy principle, combined with the kinematic wave approximation, is capable of simulating the dynamic phase of the infiltration process if the unsaturated hydraulic conductivity is treated as a calibration parameter of the model.

From 1977 to 1995, a series of regression relations were developed to predict the peak discharge from breached embankment dams. In 2008, a study was conducted to review previous investigations, expand the working database of dam failures, and evaluate the appropriateness of the existing relationships. Forty-four case studies were collected and added to the existing database, resulting in a composite database of 87 case studies. The composite database was evaluated and a statistical analysis was performed using linear, curvilinear, and multiple-regression techniques. Enhanced simple- and multiple-regression expressions were developed to predict peak-breach discharge as a function of the height and/or volume of water behind the dam. A comparison of selected historical and newly derived expressions indicates that three previously developed relations remain valid for conservative peak outflow predictions. Newly developed expressions relating peak outflow as a function of the height $\left(H\right)$ and/or volume $\left(V\right)$ of water behind the dam at failure provide encouragement for practical applications where a best estimate of the peak-breach discharge is desired.

The optimal design of a groundwater remediation system using the pump-and-treat method is a complex task involving modeling physical phenomena such as groundwater flow and contaminant transport and optimizing several goals of concern, while satisfying bounds on certain parameters. Simulation tools such as the finite-element method (FEM) coupled with optimization tools such as the genetic algorithm (GA) has been found to be an efficient and easy to use methodology to solve such arduous problems. In this study, a simulation model using the FEM for groundwater flow and contaminant transport has been developed and coupled with a multiobjective optimization model based on the nondominated sorting genetic algorithm II (NSGA II). The model is used to minimize the cost optimization function as well as the time period for the remediation of the aquifer subject to bounds on pumping rates, groundwater heads, and concentration levels of the contaminant. The coupled FEM-NSGA II model has been applied for the remediation of a real field aquifer near Vadodara, India for a combination of flushing and pumping to demonstrate the effectiveness of the proposed multiobjective technique for achieving the optimal pumping policy. Total dissolved solids are considered to be the main pollutant. Three alternative remediation design scenarios with different pumping well locations have been compared and the best remediation design scenario has been identified to be the one that gives the best Pareto-optimal front. The FEM-NSGA II in general evolves a set of well-spread and consistent Pareto-optimal solutions, which represent the best designs that identify the trade-off between the expected cost and the time period of remediation for all the remediation design scenarios while satisfying the constraints, thus, giving the decision maker a wide set of choices.

The natural variability of floods cannot be represented appropriately by single design floods. Different hydrological scenarios are needed for sustainable design of flood protection structures such as flood control reservoirs and polders. In this paper a method to estimate the probability of generated hydrological scenarios for the risk analysis of a flood control system is presented. Bivariate probability analyses of different flood variables using copulas are applied to overcome the problem that univariate probability analysis may lead to an over- or underestimation of the hydrological risk. The mesoscale Unstrut river basin in Germany, which consists of two reservoirs located downstream of the main tributaries and flood polders, serves as test case. The spatial distribution of flood events within the river basin is analyzed by the joint probability of the inflow peaks at the two reservoirs. Furthermore, in a second application copulas are used for risk analysis of the individual flood detention structures. This study corroborated that consideration of the flood volume in addition to the flood peak is important for risk-based planning and design.

Regional frequency analysis based on L moments has been used to analyze low flow in the karst area of southwest China. The 7-day low flow was regarded as the statistical quantity. Data from 12 stream flow gauging sites of the Wujiang water system in Guizhou Province are screened using the discordancy measure, and homogeneity of the region is then tested employing the L-moments-based heterogeneity measure. For computing heterogeneity measure $H$ , 500 simulations are performed using the kappa distribution. Based on this test, it is observed that these 12 gauging sites constitute a homogeneous region. Based on the L-moments ratio diagram, $\left|Z^{\text{DIST}}\right|$ and $t_4$ statistic criteria, generalized logistic (GLO) distribution is identified as the robust distribution for the study area. Regional low flow frequency relationships are developed for estimation of low flow of various return periods for 12 gauged catchments using the GLO distribution and index flood method. Examination of the observed and computed low flow shows a good agreement in general, and the results justify practical application.