Journal of Hydrologic Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Using a semiquantitative analytic hierarchy process (AHP), watershed vulnerability was assessed for Bernalillo County, New Mexico, an area that is characterized by intermittent precipitation events and limited water availability. The model was designed using a multicriteria decision support (MCDS) methodology implemented in a geographic information system (GIS). A vulnerability map was produced by means of a weighted overlay analysis that combined soil erosion and infiltration maps derived from the AHP. Model results were categorized into five vulnerability classes: not vulnerable (N), slightly vulnerable (SV), moderately vulnerable (MV), highly vulnerable (HV), and extremely vulnerable (EV). The results indicate that approximately 83% of the study area is SV to MV, and less than 4% of the area is HV or EV. Shrub land-use classes were identified to experience the most vulnerability. Model output compared similarly with the results predicted by the revised universal soil loss equation (RUSLE) model with the exception of the N class. The eastern portion of the county was identified as most vulnerable because of its high slope and high precipitation. In this large area scenario, structural stormwater control measures (SCMs) may be viable for managing runoff and sediment transport offsite. This MCDS/GIS approach can provide useful information to guide local governments and decision makers in selection of suitable structural and nonstructural SCMs for the arid Southwest.

The Fokker-Planck equation (FPE) methodology was applied to the transient state confined groundwater flow equation under the initial and boundary conditions leading to the Theis problem. The differential Theis equation was transformed to the FPE form, which describes the evolution of the probability density function of the state variable for this system. Numerical solutions of the FPE were obtained with a hybrid numerical scheme that uses the explicit Lax-Wendroff scheme with a flux limiter for the advective terms and the implicit Euler scheme for the dispersive terms. The results of the FPE methodology were compared with the Monte-Carlo solutions of the analytical Theis solution. The comparison shows a good match in terms of the ensemble mean, variance, and probability density functions in general. The computation times also were improved in the FPE methodology in comparison with the method of Monte-Carlo simulations.

Flood frequency analysis (FFA) provides an important basis for determining the construction size of hydraulic facilities. The selection of a suitable distribution and parameter estimation method (D/E procedure) is of vital importance in FFA. In this study, the method of Halphen (HP) distribution, coupled with the principle of maximum entropy (ME), was proposed for FFA. First, the relations among Lagrange multipliers, constraints, and parameters were derived firstly. Annual maximum flood data series at 12 gauging stations and Monte Carlo simulation were used to evaluate the descriptive and predictive abilities of the proposed HP/ME procedure. The proposed HP/ME procedure was compared with the commonly used procedures. It is shown that the HP/ME procedure has the smallest root-mean-square error (RMSE) values for 10 of the 12 sites and the smallest Akaike information criterion (AIC) values for 7 of the 12 sites. The descriptive ability of the HP/ME procedure is good because of its flexible shapes and excellent tail properties. The Monte Carlo simulation results demonstrate that the HP/ME procedure performs well, which gives a narrower 90% confidence interval and produces higher efficiency than other compared procedures in several simulated cases. The proposed HP/ME procedure performs well to estimate design floods at two selected sites and might be suggested as an alternative candidate for hydrologic frequency analysis.

It is imperative for cities to develop sustainable water management and planning strategies in order to best serve urban communities that are currently facing increasing population and water demand. Water resources managers are often chastened by experiencing failures attributed to natural extreme droughts and floods. However, recent changes in water management systems have been responding to these uncertain conditions. Water managers have become thoughtful about the adverse effects of uncertain extreme events on the performance of water supply systems. Natural hydrologic variability and inherent uncertainties associated with the future climate variations make the simulation and management of water supplies a greater challenge. The hydrologic simulation process is one of the main components in integrated water resources management. Hydrologic simulations incorporate uncertain input values, model parameters, and a model structure. Therefore, stochastic streamflow simulation and prediction, and consideration of uncertainty propagation on performance of water supply systems (WSSs) are essential phases for efficient management of these systems. The proposed integrated framework in this study models a WSS by taking into account the dynamic nature of the system and utilizing a Markov chain Monte Carlo (MCMC) algorithm to capture the uncertainties associated with hydrologic simulation. Hydrologic responses from the results of a rainfall-runoff model for three watersheds of Karaj, Latyan, and Lar in Tehran, Iran, as the case study are used as inputs to the reservoirs. Results confirm that uncertainties associated with the hydrologic model’s parameters propagate through the simulation and lead to a wide variation in reservoir storage and WSS performance metrics such as vulnerability and reliability. For example, water storage simulation in the Karaj Reservoir can vary up to 70% compared with the observed values. This causes contradiction and conflict in the management of reservoirs and water systems and decision making. The results emphasize the importance of analyzing WSS performance under uncertain conditions to improve the simulation of natural processes and support water managers for a more efficient decision-making process.

Boundary shear stress distribution of a compound channel is generally influenced by the geometric, roughness, and hydraulic parameters. Experiments are performed on both homogeneous and nonhomogeneous compound channels to study the dependency of variables on the boundary shear distribution. This study proposes an artificial neural network (ANN) model for the prediction of boundary shear stress distribution in straight compound channels. The most influential parameters such as width ratio, relative flow depth, aspect ratio, Reynolds number, and Froude number are considered as input parameters. A large number of experimental data sets comprising wide ranges of width ratio, relative flow depth, roughness ratio, Reynolds number, Froude number, bed slope, and aspect ratio with the present experimental data series are used for both training and validation of the model. Previous models can provide good results only for specific ranges of independent parameters, whereas back-propagation neural network (BPNN) models are capable of performing well for global ranges of independent parameters. This is because BPNN is able to perform nonlinear mapping between the dependent and independent variables during the training. The efficacy of the models is verified with the standard statistical error analysis using the global data sets.

Increasing nonstationarity increases the uncertainty of hydrological processes. Consequently, intrinsic randomness increases in magnitude and occurrence. In order to generate a reliable and robust predictive model, time series need to be better understood. This study addresses this challenge through the internal causality of annual runoff series. According to new methodological tendencies for hydrological research, complex temporal dependences within time series have been adequately captured through causal reasoning implemented by Bayes’ theorem. Those dependences permit quantifying the relative percentage of annual runoff change attributable to causality. This was later useful for calculating the temporally conditioned/nonconditioned runoff (TCR/TNCR) fractions. Results satisfactorily show the high and low temporally conditioned behavior of Porma-Esla and Adaja subbasin runoff, respectively. This study also provides a new stochastic approach for the return period (RP) assessment. Using TCR and TNCR fractions, the RP for each fraction was calculated and called the temporally conditioned RP (TCRP) and temporally nonconditioned RP (TNCRP), respectively. Results show coherent behavior, and, consequently, the highest RP corresponds to the largest runoff and vice versa.

Hydrological modeling has been used worldwide as an important tool to evaluate the consequences of land cover and land use change on hydrological processes. However, the lack of spatial-temporal rainfall and runoff data have compromised the reliability of the results in several regions of Brazil. In this study, the authors investigated the use of uncertainty analysis and Bayesian model averaging (BMA) as a tool for improving streamflow estimates in the Ribeirão da Onça Basin (ROB), located in southeastern Brazil. They used a set of two precipitation data sources (ground and remote sensing data) and different spatial interpolation schemes as input data for the Soil and Water Assessment Tool (SWAT) model, resulting in five model configurations. These models were submitted to automatic calibration and uncertainty analysis through the sequential uncertainty fitting ver-2 (SUFI-2) method. Then, the BMA method was used to merge those different model configuration results into a single probabilistic prediction, thereafter compared among themselves. An analysis of the accuracy and precision of all simulations produced by the precipitation ensemble members against the BMA simulation supports the use of the latter as a suitable framework for streamflow simulations at the ROB. Furthermore, the approaches evaluated in this study may be used to improve streamflow predictions in ungauged or data-scarce basins.

Sedimentation is one of the most important factors affecting stream channel stability. A proposed model was developed to estimate the sediment load of an artificial channel by the integration of the revised universal soil loss equation (RUSLE) and Watershed Assessment of River Stability and Sediment Supply (WARSSS). The developed model was tested in the channelized portion of the Hocking River near Athens, Ohio. It was estimated that the gross erosion from the watershed was $7.29\times {10}^{10}\mathrm{kg}/\mathrm{year}$, of which 96.64% resulted from surface erosion and 3.36% from bank erosion. A field measurement of total sediment yield in the channel, assumed to be the sum of suspended sediment and bedload, was conducted. The total annual sediment yield was estimated as $8.09\times {10}^9\mathrm{kg}$, of which 98.29% was suspended sediments and 1.71% bedload sediments. It was concluded that the sediment delivery ratio of the studied watershed was estimated to be 11.11%, which is consistent with those of the watersheds having similar sizes in the region. Based on these results, the authors believe that the proposed model can reasonably well estimate the sediment load in the studied portion of the Hocking River.