Transport of suspended sediments under the influence of bank macro‐roughness

River restoration works often include measures to promote morphological diversity and enhance habitat suitability. One of these measures is the creation of macro‐roughness elements, such as lateral cavities and embayments, in the banks of channelized rivers. However, in flows that are heavily charged with fine sediments in suspension, such as glacier‐fed streams and very low‐gradient reaches of large catchment rivers, these lateral cavities may trap these sediments. Consequently, the morphological changes may be affected, and the functionality of the restoration interventions may be compromised. Herein, we analyse the influence of these macro‐roughness elements on the transport of fine sediments in the main channel. Laboratory tests with uniform flow charged with sediments in a channel with banks equipped with large‐scale rectangular roughness elements were carried out. The laboratory experiments covered a wide range of rectangular cavity geometrical configurations and shallowness ratios. The influence of key parameters such as flow shallowness, geometric ratios of the cavities and initial sediment concentration was tested. Surface particle image velocimetry, sediment samples and temporal turbidity records were collected during the experiments. The amount of sediments captured by the cavities, the temporal evolution of the concentration of sediments in suspension and the flow hydrodynamics are cross‐analysed and discussed. It is shown that the trapping efficiency of the macro‐roughness elements is a clear function of the channel geometry and the shallowness of the flow. Copyright © 2017 John Wiley & Sons, Ltd.     

Determination of the Optimal Location of the Air Hole in Venturi Aerators

Dissolved oxygen is one of the most important water quality parameters. Aeration improves the water quality by maintaining good dissolved oxygen levels in water. Dissolved oxygen enters water by entrainment of air bubbles. A method of aeration that has become popular in recent years is the venturi aeration. In the present paper, experimental studies were conducted to investigate the effect of the location of the air hole in venturi tubes upon air injection. It was observed from the results that the location of the air hole playes a significant role for the air injection. The optimal air hole location that maximized the air injection in venturi aerators was determined.     

On the Use of Venturi Tubes in Aeration

The ecological quality of water depends largely on the amount of oxygen that the water can hold. The higher the level of dissolved oxygen, the better the quality of a water system. By measuring dissolved oxygen, scientists determine the quality of water and health of an ecosystem. Oxygen enters water by entrainment of air bubbles. Many industrial and environmental processes involve the aeration of a liquid by such entrainment of air bubbles. Venturi aeration is a method of aeration that has become popular in recent years. When a minimal amount of differential pressure exists between the inlet and outlet sides of a venturi tube, a vacuum (air suction) occurs at the suction holes of the venturi tube. The present paper describes the effect of Reynolds Number, air inlet hole diameter, inlet diameter, pipe length, and angle of pipe downstream of the venturi tube, on the air injection rate. It is observed from the results that venturi tubes have high air injection efficiencies. Therefore, venturi tubes can be used as highly effective aerators in ponds, lakes, fish hatcheries, water treatment plants, etc.     

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R² parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R²), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models.     

Prediction of coarse particle movement with adaptive neuro‐fuzzy inference systems

The use of a neuro‐fuzzy approach is proposed to model the dynamics of entrainment of a coarse particle by rolling. It is hypothesized that near‐bed turbulent flow structures of different magnitude and duration or frequency and energy content are responsible for the particle displacement. A number of Adaptive Neuro‐Fuzzy Inference System (ANFIS) architectures are proposed and developed to link the hydrodynamic forcing exerted on a solid particle to its response, and model the underlying nonlinear dynamics of the system. ANFIS combines the advantages of fuzzy inference (If‐Then) rules with the power of learning and adaptation of the neural networks. The model components and forecasting procedure are discussed in detail. To demonstrate the model's applicability for near‐threshold flow conditions an example is provided, where flow velocity and particle displacement data from flume experiments are used as input and output for the training and testing of the ANFIS models. In particular, a Laser Doppler velocimeter (LDV) is employed to obtain long records of local streamwise velocity components upstream of a mobile exposed particle. These measurements are acquired synchronously with the time history of the particle's position detected by a setup including a He‐Ne laser and a photodetector. The representation of the input signal in the time and frequency domain is implemented and the best performing models are found capable of reproducing the complex dynamics of particle response. Following a trial and error approach the different models are compared in terms of their efficiency and forecast accuracy using a number of performance indices. Copyright © 2011 John Wiley & Sons, Ltd.     

Back‐calculation of bedload transport in steep channels with a numerical model

In August 2005 severe flood events occurred in the Alps. A sediment routing model for steep torrent channel networks called SETRAC has been applied to six well‐documented case study streams with substantial sediment transport in Austria and Switzerland. For these streams information on the sediment budget along the main channel is available. Flood hydrographs were reconstructed based on precipitation data and stream gauges in neighbouring catchments. Different scenarios are modelled and discussed regarding sediment availability and the effect of armouring and macro‐roughness on sediment transport calculations. The simulation results show the importance of considering increased flow resistance for small relative flow depth when modelling bedload transport during high‐intensity flood events in torrents and mountain rivers. Without any correction of increased flow resistance using a reduced energy slope, the predicted bedload volumes are about a factor of 10 higher on average than the observed values. Simulation results were also used for a back‐calculation of macro‐roughness effects from bedload transport data, and compared with an independent estimate of flow resistance partitioning based on flow resistance data. Copyright © 2010 John Wiley & Sons, Ltd.     

Effect of bed topography on soil aggregates transport by rill flow

After its formation, a rill may remain in the field for months, often receiving lower flow rates than the formative discharge. The objective of this work was to evaluate the rill flow transport capacity of soil aggregates at discharges unable to erode the rill, and to analyse the influence of the rill macro‐roughness on this transport process. A non‐erodible rill was built in which roughness was reproduced in detail. In order to assess only the rill macro‐roughness, a flat channel with a similar micro‐roughness to that in the rill replica was built. Rill and channel experiments were carried out at a slope of 8 and at six discharges (8·3 × 10^?5 to 5·2 × 10^?4 m³ s^?1) in the rill, and eight discharges (1·6 × 10^?5 to 5·2 × 10^?4 m³ s^?1) in the channel. Non‐erodible aggregates of three sizes (1–2, 3–5 and 5–10 mm) were released at the inlet of the rill/channel. The number of aggregates received at the outlet was registered. The number and position of the remaining aggregates along the rill/channel were also determined. The rill flow was a major sediment transport mechanism only during the formation of the rill, as during that period the power of the flow was great enough to overcome the influence of the macro‐roughness of the rill bed. At lower discharges the transport capacity in the previously formed rill was significantly less than that in the flat channel under similar slope and discharge. This was determined to be due to local slowing of flow velocities at the exit of rill pools. Copyright © 2006 John Wiley & Sons, Ltd.     

The strategy of building a flood forecast model by neuro‐fuzzy network

A methodology is proposed for constructing a flood forecast model using the adaptive neuro‐fuzzy inference system (ANFIS). This is based on a self‐organizing rule‐base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall‐runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self‐constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back‐propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model. Copyright © 2005 John Wiley & Sons, Ltd.     

Changes in the saltation flux following a step‐change in macro‐roughness

The effect of a step change in macro‐roughness on the saltation process under sediment supply limited conditions was examined in the atmospheric boundary layer. For an array of roughness elements of roughness density λ = 0.045 (λ = total element frontal area/total surface area of the array) the horizontal saltation flux was reduced by 90% (±7%) at a distance of ≈150 roughness element heights into the array. This matches the value predicted using an empirical design model and provides confidence that it can be effectively used to engineer roughness arrays to meet sand flux reduction targets. Measurements of the saltation flux characteristics in the vertical dimension, including: saltation layer decay (e‐folding) height and particle size, revealed that with increasing distance into the array, the rate of mass flux change with increasing height decreased notably, and (geometric) mean particle diameter decreased. The distribution of the saltation mass flux in the vertical remains exponential in form with increasing distance into the roughness array, and the e‐folding height increases as well as increasing at a greater rate as particle diameter diminishes. The increase in e‐folding height suggests the height of saltating particles is increasing along with their mean speed. This apparent increase in mean speed is likely due to the preferential removal, or sequestration, of the slower moving particles across the size spectrum, as they travel through the roughness array. Copyright © 2018 John Wiley & Sons, Ltd.     

Evolution of overland flow connectivity in bare agricultural plots

Soil surface roughness not only delays overland flow generation but also strongly affects the spatial distribution and concentration of overland flow. Previous studies generally aimed at predicting the delay in overland flow generation by means of a single parameter characterizing soil roughness. However, little work has been done to find a link between soil roughness and overland flow dynamics. This is made difficult because soil roughness and hence overland flow characteristics evolve differently depending on whether diffuse or concentrated erosion dominates. The present study examined whether the concept of connectivity can be used to link roughness characteristics to overland flow dynamics. For this purpose, soil roughness of three 30‐m² tilled plots exposed to natural rainfall was monitored for two years. Soil micro‐topography was characterized by means of photogrammetry on a monthly basis. Soil roughness was characterized by the variogram, the surface stream network was characterized by network‐based indices and overland flow connectivity was characterized by Relative Surface Connection function (RSCf) functional connectivity indicator. Overland flow hydrographs were generated by means of a physically‐based overland flow model based on 1‐cm resolution digital elevation models. The development of eroded flow paths at the soil surface not only reduced the delay in overland flow generation but also resulted in a higher continuity of high flow velocity paths, an increase in erosive energy and a higher rate of increase of the overland flow hydrograph. Overland flow dynamics were found to be highly correlated to the RSCf characteristic points. By providing information regarding overland flow dynamics, the RSCf may thus serve as a quantitative link between soil roughness and overland flow generation in order to improve the overland flow hydrograph prediction. Copyright © 2016 John Wiley & Sons, Ltd.     

Hydrological time‐series modelling using an adaptive neuro‐fuzzy inference system

Accurate forecasting of hydrological time‐series is a quite important issue for a wise and sustainable use of water resources. In this study, an adaptive neuro‐fuzzy inference system (ANFIS) approach is used to construct a time‐series forecasting system. In particular, the applicability of an ANFIS to the forecasting of the time‐series is investigated. To illustrate the applicability and capability of an ANFIS, the River Great Menderes, located in western Turkey, is chosen as a case study area. The advantage of this method is that it uses the input–output data sets. A total of 5844 daily data sets collected from 1985 to 2000 are used for the time‐series forecasting. Models having various input structures were constructed and the best structure was investigated. In addition, four various training/testing data sets were built by cross‐validation methods and the best data set was obtained. The performance of the ANFIS models in training and testing sets was compared with observations and also evaluated. In order to get an accurate and reliable comparison, the best‐fit model structure was also trained and tested by artificial neural networks and traditional time‐series analysis techniques and the results compared. The results indicate that the ANFIS can be applied successfully and provide high accuracy and reliability for time‐series modelling. Copyright © 2007 John Wiley & Sons, Ltd.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

Simulating short‐circuiting flow in a constructed wetland: the implications of bathymetry and vegetation effects

Short‐circuiting flow, commonly experienced in many constructed wetlands, reduces hydraulic retention times in unit wetland cells and decreases the treatment efficiency. A two‐dimensional (2‐D), physically based, distributed modelling approach was used to systematically address the effects of bathymetry and vegetation on short‐circuiting flow, which previously have been neglected or lumped in one‐dimensional wetland flow models. In this study, a 2‐D transient hydrodynamics with advection‐dispersion model was developed using MIKE 21 and calibrated with bromide tracer data collected at the Orlando Easterly Wetland Cell 7. The estimated topographic difference between short‐circuiting flow zone and adjacent area ranged from 0·3 to 0·8 m. A range of the Manning roughness coefficient at the short‐circuiting flow zone was estimated (0·022–0·045 s m^?1/3). Sensitivity analysis of topographical and vegetative heterogeneity deduced during model calibration shows that relic ditches or other ditch‐shaped landforms and the associated sparse vegetation along the main flow direction intensify the short‐circuiting pattern, considerably affecting 2‐D solute transport simulation. In terms of hydraulic efficiency, this study indicates that the bathymetry effect on short‐circuiting flow is more important than the vegetation effect. Copyright © 2009 John Wiley & Sons, Ltd.     

Analysis of Manning coefficient for small‐depth flows on vegetated beds

The limitations of Manning's formula are analysed when it is in extreme conditions, and more specifically with small‐depth flows on natural‐vegetation beds. A thorough analysis is made of research carried out on macro‐rough beds, placing particular emphasis on vegetated beds. Research carried out to date on the roughness of vegetated beds and macro‐roughness is commented on, including that carried out at the Laboratory of Hydraulics and Hydrology of the Forestry Engineering Faculty (Polytechnic University of Madrid). The work was done in two phases, the first in a laboratory channel with artificial vegetation and the second in natural beds. The results of the experimental research allow the development of a new approximate method of determining Manning coefficient according to the Froude number. This method may be applied in extreme conditions, both in small depths and steep slopes. It was proved that the Manning coefficient depends not only on roughness height, but also on depth and slope values; thus, it is advisable to choose the appropriate method for its calculation. Copyright © 2005 John Wiley & Sons, Ltd.     

Performance of A Combination Process of UV/H2O2/Micro‐Aeration for Oxidation of Dichloroacetic Acid in Drinking Water

The decomposition of dichloroacetic acid (DCAA) in water using a UV/H₂O₂/micro‐aeration process was investigated in this paper. DCAA cannot be removed by UV radiation, H₂O₂ oxidation or micro‐aeration alone, while UV/H₂O₂/micro‐aeration combination processes have proved effective and can degrade this compound completely. With initial concentrations of about 110 μg/L, more than 95.1% of DCAA can be removed in 180 min under UV intensity of 1048.7 μW/cm², H₂O₂ dosage of 30 mg/L and micro‐aeration flow rate of 2 L/min. However, more than 30 μg/L of DCAA was left after 180 min by UV/H₂O₂ combination process without micro‐aeration with the same UV intensity and H₂O₂ dosage. The effects of applied UV radiation intensity, H₂O₂ dose, initial DCAA concentration and pH on the degradation of DCAA have been examined in this study. Degradation mechanisms of DCAA with hydroxyl radical oxidation have been discussed. The removal rate of DCAA was sensitive to operational parameters. There was a linear relationship between rate constant k and UV intensity and initial H₂O₂ concentration, which indicated that a higher removal capacity can be achieved by improvement of both factors. A newly found nitrogenous disinfection by‐product (N‐DBP)‐DCAcAm, which has the potential to form DCAA, was easier to remove than DCAA by UV/H₂O₂ and UV/H₂O₂/micro‐aeration processes. Finally, a preliminary cost comparison revealed that the UV/H₂O₂/micro‐aeration process was more cost‐effective than the UV/H₂O₂ process in the removal of DCAA from drinking water.     

Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

Abstract

The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS.     

Using CFD in a GLUE framework to model the flow and dispersion characteristics of a natural fluvial dead zone

Monte Carlo simulations of a two‐dimensional depth‐averaged distributed bed‐roughness flow model, TELEMAC‐2D, are used to model a detailed tracer dispersion test in a complex reach of the River Severn in the Generalized Likelihood Uncertainty Estimation (GLUE) framework. A time efficient, zero equation, spatially distributed eddy viscosity model is derived from physical reasoning and used to close the flow equations. It is shown to have the property of low numerical diffusion, avoiding recourse to a globally large value of the eddy viscosity. For models of complex river flows, there are typically so many degrees of freedom in the specification of distributed parameters owing to the limitations of field data collection, that the identification of a unique model structure is unlikely. The data used here to constrain the model structure come from a continuous tracer injection experiment, comprising six spatially distributed time series of concentration measurements. Several hundred Monte‐Carlo simulations of different model structures were investigated and it was found that multiple model structures produced feasible simulations of the tracer mixing, giving rise to the phenomenon of equifinality. Rather than optimizing the model structure on the basis of the constraining data, we derive relative possibility measures that express our relative degree of belief in each model structure. These measures can then be used as weights for assessing predictive uncertainty when using a range of model structures, to estimate the flow distribution under varying stages, or for providing maps indicating fully distributed confidence limits in the risk assessments process. Such an approach is used here, and helps to identify the circumstances under which two‐dimensional modelling can be useful. The framework is not limited to the model structures that are developed herein, and more advanced process representation techniques can be included as computational efficiency increases. Copyright © 2001 John Wiley & Sons, Ltd.     

Measuring Flow Rate in Crystalline Bedrock Wells Using the Dissolved Oxygen Alteration Method

Determination of vertical flow rates in a fractured bedrock well can aid in planning and implementing hydraulic tests, water quality sampling, and improving interpretations of water quality data. Although flowmeters are highly accurate in flow rate measurement, the high cost and logistics may be limiting. In this study the dissolved oxygen alteration method (DOAM) is expanded upon as a low‐cost alternative to determine vertical flow rates in crystalline bedrock wells. The method entails altering the dissolved oxygen content in the wellbore through bubbler aeration, and monitoring the vertical advective movement of the dissolved oxygen over time. Measurements were taken for upward and downward flows, and under ambient and pumping conditions. Vertical flow rates from 0.06 to 2.30 Lpm were measured. To validate the method, flow rates determined with the DOAM were compared to pump discharge rates and found to be in agreement within 2.5%.     

Modelling the effect of time‐dependent river dune evolution on bed roughness and stage

This paper presents an approach to incorporate time‐dependent dune evolution in the determination of bed roughness coefficients applied in hydraulic models. Dune roughness is calculated by using the process‐based dune evolution model of Paarlberg et al. ( 2009 ) and the empirical dune roughness predictor of Van Rijn ( 1984 ). The approach is illustrated by applying it to a river of simple geometry in the 1‐D hydraulic model SOBEK for two different flood wave shapes. Calculated dune heights clearly show a dependency on rate of change in discharge with time: dunes grow to larger heights for a flood wave with a smaller rate of change. Bed roughness coefficients computed using the new approach can be up to 10% higher than roughness coefficients based on calibration, with the largest differences at low flows. As a result of this larger bed roughness, computed water depths can be up to 15% larger at low flow. The new approach helps to reduce uncertainties in bed roughness coefficients of flow models, especially for river systems with strong variations in discharge with time. Copyright © 2010 John Wiley & Sons, Ltd.