Journal of Hydrologic Engineering

A detailed hydrologic/hydraulic model for an urbanized 381-acre watershed in Blacksburg, Virginia, is used to evaluate downstream results from simulation of seven stormwater management (SWM) strategies currently practiced within the United States. Each strategy has unique design criteria for specified design storms and is intended to achieve predevelopment peak runoff rates at the site outfall or a baseline peak runoff rate at a watershed point of interest (POI). Model simulations are run that represent each strategy implemented throughout the watershed within areas currently subjected to SWM. Runoff hydrographs at each site outfall and the watershed POI are evaluated. Evaluation of model results at the site outfalls and a downstream watershed POI lead to a number of observations about the performance of the various strategies and their respective design criteria. Results demonstrate that strategies met the predevelopment peak runoff rate targets for design storms specified in their design criteria at the site outfall. Most strategies did not meet the targets outside of the range of design storms specified in design criteria with the exception of the most frequent storm evaluated when an initial runoff capture and drawdown is required. None of the strategies are found to achieve the baseline peak runoff rates at the watershed POI for a full range of design storms, with almost all strategies failing to meet the baseline target for the more frequent, 1- and 2-year storm events. Strategies that require a watershed analysis to define site-scale design criteria did not perform better, indicating the efforts of a watershed analysis to develop a SWM strategy are not warranted. Results also indicate that as the capture on an initial volume and duration of drawdown increases, additional benefit does not translate downstream.

Railway formation (embankments and cuttings) in Queensland are experiencing increased maintenance costs due to rainfall induced erosion and sedimentation problems. The erosion problems can be reduced by establishing grass cover on the steep slopes (batters). The objective of the research reported in this paper was to develop a continuous rainfall-runoff-soil erosion model to investigate grass cover effects on runoff and soil loss on railway formation steep slopes. It utilizes the Saint Venant continuity and momentum equations for overland flow, and a modified Green-Ampt model for infiltration on steep slopes. The erosion component of the model consists of two different methods, based on (1) the modified universal soil loss equation, and (2) steep slope erosion dynamics. The efficiency of the model was evaluated by the percentage error and the Nash-Sutcliffe efficiency values. Field trials data gathered from 10-m wide sections with different grass cover percentages were used to calibrate the one-dimensional distributed rainfall-runoff model. Rainfall and runoff were monitored at 1-min intervals while the soil loss data were collected at different sampling intervals. The model has successfully predicted runoff and soil loss from the plots for the majority of the cases, with Nash-Sutcliffe efficiency values varying between 0.43 and 0.99 for 0% grass cover plots, 0.06 and 0.97 for 50% grass cover plots, and $-0.42$ to 0.94 for 100% grass cover plots. The percentage error values for soil loss vary from 5 to 58% for 0% grass cover plots for the steep slope erosion dynamics method and 4–59% for the modified universal soil loss equation-based method. For 50% grass cover plots, the percentage error values vary from 2 to 160 and 18 to 86%, for the steep slope erosion dynamics and modified universal soil loss equation-based methods, respectively. With the aid of an antecedent moisture parameter which varies with grass cover percentage, continuous simulation of runoff and erosion can be carried out, using either long records of observed fine timescale rainfall or that derived from a stochastic rainfall model.

This study selected a typical waterfront lake, the Inner Lake, as the research area. Based on field measurements and laboratory experiments, the basic properties of sediment particles were analyzed, including size, bulk and dry bulk density, and hydrostatic and hydrodynamic settling velocity. With the finite-volume method, the flux vector splitting (FVS) scheme was employed to establish a two-dimensional unsteady flow and sediment mathematical model. The underwater terrain shaping processes in the short term (2017), middle term (2020), and long term (2025) were predicted. The results showed that sediment deposition thickness will significantly increase in the Inner Lake, reaching 13.5 cm in the short term (January 2017), 28.6 cm in the middle term (January 2020), and 55.2 cm in the long term (January 2025). Owing to the strong hydrodynamic condition of the Leading Channel, sediment deposition there is the lowest, with the lake bed elevating rate at $3.92\mathrm{cm}·{\mathrm{a}}^{-1}$ on average. The elevating rate at the northern bottomland and the southern bottomland is 54.3% higher than at the Leading Channel because of the reduction of sediment carrying capacity, and it is at a middle level at Jiaonan Gate because of the backwater effect of the tide. The average elevating rate of the whole lake is $4.50\mathrm{cm}·{\mathrm{a}}^{-1}$ in the short term, $5.03\mathrm{cm}·{\mathrm{a}}^{-1}$ in the middle term, and $5.32\mathrm{cm}·{\mathrm{a}}^{-1}$ in the long term. The increase in average elevating rate indicates that sediment deposition will further affect hydrodynamic conditions and thereby accelerate the lake bed elevating process.

Wet and dry spells are among the most significant natural phenomena because they affect the water resources, crop yield, and food demand, causing surpluses and deficits within a region. Wet- and dry-induced natural disasters have increased due to climate change effects with their temporal and spatial coverage during the last two decades because of anthropogenic and naturally unexpected developments such as the global warming implying greenhouse effect and precipitation deficiency. Many countries have set up national drought and flood preparedness plans in the expectation of reducing societal vulnerability to such effects by the adaptation of preventive anticipatory policies and programs. Although wet- or dry-prone countries may have different causal effects, the drought and flood evaluation methodologies are common irrespective of geographical locations. It is therefore necessary to elaborate on different wet and dry hazard assessment methodologies on a scientific basis so as to be useful to all concerned parties whether hydrologist, agriculturalist, meteorologist, researcher, or administrator. This paper provides regional probabilistic wet and dry period areal coverage modeling, which is useful for temporal and spatial wet or dry risk predictions and parameter assessments. The basis of the methodology is mutually exclusive and has independent subareal (site) dry and wet spell heterogeneous probabilities. Derived expressions are general and reduce to a simple Bernoulli (homogeneous) trial case as available in the literature. The application of the proposed methodology is presented for six precipitation sites in the northwestern part of Turkey.

Infiltration is an essential process in watershed hydrology. The curve number (CN) method has been widely used to calculate watershed infiltration for a given rainfall input but does not consider steady infiltration. This study develops a modified CN (MCN) method that included a term for the steady infiltration amount ($F_c$). Observed rainfall–runoff data for 14 rainfall events from a typical small watershed on the Loess Plateau of China were used to derive the watershed final infiltration rate ($f_c$). Watershed infiltration after runoff initiation was then calculated by both the MCN and CN methods using initial abstraction values that were either observed ($I_{a-obs}$) or calculated ($I_{a-0.2S}$) based on the calibrated $f_c$. Three criteria [relative error ($E_r$), model efficiency coefficient ($E$), and root mean square error (RMSE)] and visual assessments of graphed results were used to evaluate the methods’ simulation performances. The MCN method generally outperformed the CN method when using either of the initial abstractions for infiltration calculations. Moreover, infiltration calculated by the MCN method was more accurate when using $I_{a-\mathrm{obs}}$ ($E=0.9$; $\mathrm{RMSE}=24.4\%$) than when using $I_{a-0.2S}$ ($E=0.8$; $\mathrm{RMSE}=30.8\%$). The CN method using either initial abstraction tended to underestimate infiltration, especially higher values, which were always lower than the MCN method estimates. In addition, watershed runoff was calculated by both methods using only $I_{a-\mathrm{obs}}$. The MCN method ($E=0.4$ and $\mathrm{RMSE}=78.1\%$; excluding 3 outliers) outperformed the CN method ($E=-23.8$ and $\mathrm{RMSE}=506.8\%$; excluding 3 outliers) but not to the same extent as when calculating infiltration. The theoretical analyses and practical application results indicated that the MCN method is more appropriate than the CN method for predicting infiltration in small watersheds on the Loess Plateau.

In this study the fractional governing equations for diffusion wave and kinematic wave approximations to unsteady open-channel flow in prismatic channels in fractional time-space were developed. The governing fractional equations were developed from the mass and motion conservation equations in order to provide a physical basis to these equations. A fractional form of the resistance formula for open-channel flow was also developed. Detailed dimensional analyses of the derived equations were then performed in order to ensure dimensional consistency of the derivations. It is shown that these fractional equations of unsteady open-channel flow are fundamentally nonlocal in terms of nonlocal fluxes. The derived fractional governing equations of diffusion wave and kinematic wave open-channel flow can accommodate both the long-memory nonlocal behavior of open-channel flow as well as its local, finite memory behavior, as is numerically demonstrated in the accompanying paper by the authors.

In this study, finite difference numerical methods, first order accurate in time and second order accurate in space, are proposed to solve the governing equations of the one-dimensional unsteady kinematic and diffusion wave open-channel flow processes in fractional time and fractional space, which were derived in the accompanying paper. Advantages of modeling open-channel flow in a fractional time-space framework over integer time-space framework are threefold. First, the nonlocal phenomena in the open-channel flow process in either space or time can be considered by taking the global correlations into consideration. Second, the proposed governing equations of the open-channel flow process in the fractional order differentiation framework are generalization of the governing equations in the integer order differentiation framework. Third, the physics of the observed heavy tailed distributions of particle displacements in transport processes, as reported in the literature, may be explained by a flow field that is governed by the nonlocal (or long-range dependence) phenomena. Numerical examples in this study demonstrate that the proposed finite difference methods are capable of solving the governing equations of the one-dimensional unsteady kinematic and diffusion wave open-channel flow processes in fractional time and fractional space. The numerical examples also show that the proposed governing equations, which were derived in the accompanying paper for the one-dimensional unsteady kinematic and diffusion wave open-channel flow processes in fractional time and fractional space, may provide additional flexibility and understanding to model open-channel flow processes.

Calibration of a distributed hydrologic model (WetSpa) for modeling river flows is performed using automatic parameter optimization. The main purpose of this research is to provide more confidence in the uncertainty analysis of the model parameters and predictions. A Box-Cox transformation and an autoregressive integrated moving average (ARIMA) time series model are used to transform the correlated and nonstationary model residuals to white noise disturbances, which can be minimized by ordinary least squares optimization. The WetSpa model is applied to the Illinois River basin, with a spatial resolution of 30 m and 1 h time step for a 10-year simulation period (1996–2006). The model is calibrated using river flow records (1996–2002) and validated using the remaining flow data (2002–2006). The results show that simple calibration of the model is inaccurate, as the residuals exhibit heteroscedasticity, which results in inaccurate estimates of the model parameters and large prediction uncertainty. The model calibration is improved when the calibration is combined with a Box-Cox transformation of the discharge and ARIMA modeling of the residuals, which considerably enhances the confidence of the model parameter estimates and of the model predictions.

Many hydraulic structures are designed based on intensity-duration-frequency (IDF) curves. A design based on an inaccurate design storm can cause problems, such as malfunction of the infrastructure, excessive cost, or loss of life. In previous studies the authors developed IDF curves under future climate scenarios for Alabama using six different North American Regional Climate Change Assessment Program (NARCCAP)-based projections. Results demonstrated that these models do not project identical results, and there is uncertainty regarding future rainfall intensities projected by these six climate models. Understanding the uncertainties associated with climate model outputs can help decision makers to explain the impacts of climate change with more confidence. Therefore, the objective of this study was to develop probability-based IDF curves incorporating climate projections from six different climate models using a kernel density estimator. IDF curves were previously created using two different temporal disaggregation methods: a stochastic method and an artificial neural network (ANN) model. A kernel density estimator was applied to the resulting estimated rainfall intensities from both methods and probability-based IDFs were developed. In addition to the probability-based IDFs, typical IDF curves as a resultant of incorporating all models were also developed. A comparison of the results with the current (historical) IDF curves for the city of Auburn, Alabama, indicated that, when the stochastic method was used for rainfall disaggregation, future rainfall intensities are expected to decrease by 29 to 39% for durations less than 6 to 8 h, and to increase by 14 to 19% for longer durations. Analysis of the results of the ANN model for durations of less than 2 h indicates that the precipitation pattern for Alabama veers toward less intense rainfalls for the investigated durations and for all the return periods. This decrease is expected to be between 48 and 52% for the city of Auburn.

In order to investigate the behavior of flow through granular materials, a packed column test was employed. The laboratory model consisted of a permeameter having 0.2 m diameter and 1 m length. Two kinds of crushed and rounded gravel particles with six different sizes were used as porous materials. By using flow velocity–hydraulic gradient relationships, it was observed that the relationships between velocity and hydraulic gradient are nonlinear and why it is called non-Darcy flow. Evaluating the results of this research for both kinds of aggregates by means of statistical indicators and comparison of different relationships showed the best empirical equation. The relation between Reynolds number and diameter of materials indicated that the friction factor for higher Reynolds numbers decreased and reached to a constant value for both kinds of materials.

In many parts of the world, old sewer systems have been designed without consideration for change in climate, so probabilities and risks of sewer surcharge and flooding are elevated due to increase in extreme rainfall events as a consequence of global warming. The current paper is aiming to assess how the climate change on interannual to multidecadal timescale (2020s; 2050s; 2080s) will affect design standards of wastewater networks due to the presumed increase in rainfall intensity and frequency in the Northwest of England area (selected site). Design storms have been analyzed for future rainfall obtained from the UK Climate Projection version 2009 (UKCP09) weather generator (WG), which was applied to the existing urban drainage system to check the level of service in winter and summer seasons. Two emission scenarios (SRES) have been adopted to simulate the greenhouse gas concentration; high scenario (A1FI) and low scenario (B1). Results indicate that the impact of increase in the design storm of the system in winter led to a potential of increase flood volume from manholes and intern basements at risk of flooding with the worst condition associated with 24 h storm in 2080s. Moreover, when this design storm depth increased by only 15%, the corresponding flood volume increase by 40%, this indicates that the relation between the cause of flooding and its consequences is nonlinear. Summer season has an opposite picture, and flood volume is projected to decrease with the increase in the storm duration causing low risk. Considering climate change in this study caused most of urban drainage models runs to be very slow with some interruption in the simulation due to the inflation in some parameters, so caution should be taken.

Reliable forecasts of reservoir water levels are important for reservoir operations and water resources management. A reservoir water level forecasting model was developed by integrating the Xinanjiang model and a reservoir routing model. The integrated model, of which the hydrologic parameters are calibrated based on the observed water level directly, is used to forecast the reservoir water levels, while that of the conventional method calibrates the hydrologic model using the estimated reservoir inflows from water balance. Through an application to the China’s Shuibuya Reservoir, the integrated model shows a much higher accuracy than the conventional method, with an average RMS error of 0.10 m, whereas that of the conventional method is 5.13 m. The presented model provides a reliable tool for real-time forecasts of reservoir water levels.

This work describes the reasons and circumstances concerning why researchers came up short at the Dishna riverbank filtration (RBF) pilot site in Egypt, which—1.5 years after construction—has yet to deliver at least 40% of the bank filtrate. Limited site investigation, intermittent well operation, low pumping rates, and Egyptian drinking water well design regulations have been identified as main reasons for less than 10% bank filtrate. The authors draw on experience and lessons learned to develop recommendations for RBF site investigations and implementation on a limited budget. The investigations require a minimum investment of 20,000 Egyptian pounds and at least 1.5 years’ time per site. A generalized RBF well design is proposed for sites with scarce data or areas where groundwater is of low quality and unsuitable for potable supply.

The complex shapes of estuaries need to be considered when developing spatial interpolation methods for water quality analyses. In this study, a statistical interpolation method (kriging) is used to interpolate water quality data in Chesapeake Bay, and the issue of shape is addressed by incorporating “water distance” into the method (i.e., the shortest path over water between any two points). Results show that water-distance-based kriging performed just as well as, and in most cases better than, a kriging method based on Euclidean distance. Benefits of the water-distance-based method with kriging include improved estimates in regions with complex geometry and lower uncertainty in the kriging predictions.

A flood season segmentation-based reservoir optimal operation framework was proposed to maximize the use of the flood control storage in flood seasons. The proposed framework consists of an ensemble-method approach to segment the flood season for improved reservoir operation, a stochastic simulation model to generate abundant inflow hydrographs, and a Monte Carlo technique to calculate the failure probabilities of flood regulation under different given flood-limiting water levels (FLWLs). The benefits of the proposed framework were illustrated through the operation of Chengbihe Reservoir in China. Results indicate that an ensemble-method approach of a fuzzy clustering method, a probability change-point analysis, and a statistical graphical technique provide more reasonable segmentations than that of an individual method alone. The flood season of the Chengbihe Reservoir is segmented into an early flood season (April 1–May 31), a main flood season (June 1–August 20), and a late flood season (August 21–October 31). The FLWL in the main flood season remains to be the value currently in use (185.00 m), whereas the FLWL in the late flood season can be lifted maximally up to 188.50 m, which is equivalent to an increase of the active storage by $1.4\times {10}^8{\mathrm{m}}^3$ (a shrinkage of 66.7% of the original designed flood control storage volume) without lowering the designed flood control standard.

An approach to modeling daily flows using artificial neural networks (ANNs) is presented. In addition to previous streamflow values and mean areal rainfall sequences, a new runoff index was used and tested as ANN input. This runoff index was generated as a combination of two output variables of the weather research and forecasting (WRF) mesoscale model, which contains an integrated land surface model. Inclusion of the new index improved ANN model performance and increased simulation skill. A case study was conducted for the northeast Guadalquivir catchment in southeastern Spain. Accurate one-day-ahead streamflow forecasts were achieved in terms of overall fit and timing of peaks. Model performance was satisfactory, with a persistence index (PI) equal to 0.81 and a Nash–Sutcliffe efficiency $R^2$ equal to 0.95 for an independent data set. These favorable results prove that WRF outputs contain useful information on the hydrologic state of a basin and can therefore be used as valuable ANN inputs.

A rain garden is a typical stormwater infiltrating facility designed for on-site water-quality enhancement. A rain garden consists of a surface storage basin and subsurface infiltrating media. The storage basin is designed to be shallow, flat, and wide with an overtopping weir to pass excess flows, while the subsurface infiltrating system is composed of two layers of filtering media. The upper layer is filled with sand mix, and the lower layer is formed with gravels. In this study, the storage basin is sized to capture the water quality capture volume (WQCV) that is determined using the lognormal distribution to describe the population of rainfall event depths. The subsurface filtering layers are sized to achieve the targeted flow release and drain time. A surface–subsurface hydrologic model is developed in this study to simulate the water-loading process in the storage basin and also the infiltrating process through the sand-mix layer. Based on field tests and data, this hydrologic model is further calibrated to determine the best-fitted values for soil initial moisture content and hydraulic conductivity involved in the flow simulations. This calibrated model is a useful tool to understand the clogging situation in the rain garden and also helps the engineer make operational decisions on drain time and flow releases from the rain garden.