Journal of Hydro-environment Research最新文献

Accurate streamflow (Q_t) prediction can provide critical information for urban hydrological management strategies such as flood mitigation, long-term water resources management, land use planning and agricultural and irrigation operations. Since the mid-20th century, Artificial Intelligence (AI) models have been used in a wide range of engineering and scientific fields, and their application has increased in the last few years. In this study, the predictive capabilities of the reduced error pruning tree (REPT) model, used both as a standalone model and within five ensemble-approaches, were evaluated to predict streamflow in the Kurkursar basin in Iran. The ensemble-approaches combined the REPT model with the bootstrap aggregation (BA), random committee (RC), random subspace (RS), additive regression (AR) and disjoint aggregating (DA) (i.e. BA-REPT, RC-REPT, RS-REPT, AR-REPT and DA-REPT). The models were developed using 15 years of daily rainfall and streamflow data for the period 23 September 1997 to 22 September 2012. A set of eight different input scenarios was constructed using different combinations of the input variables to find the most effective scenario based on the linear correlation coefficient. A comprehensive suite of graphical (time-variation graph, scatter-plot, violin plot and Taylor diagram) and quantitative metrics (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliff efficiency (NSE), Percent of BIAS (PBIAS) and the ratio of RMSE to the standard deviation of observation (RSR)) was applied to evaluate the prediction accuracy of the six models developed. The outcomes indicated that all models performed well but the AR-REPT outperformed all the other models by rendering lower errors and higher precision across a number of statistical measures. The use of the BA, RC, RS, AR and DA models enhanced the performance of the standalone REPT model by about 26.82%, 18.91%, 7.69%, 28.99% and 28.05% respectively.

Seepage well is an emerging Low Impact Development (LID) technology that can effectively control the storm runoff. However, its rainwater infiltration rate and storage capacity still require further enhancement. By setting a horizontal infiltration structure at the bottom of conventional rainwater seepage well (CSW), an enhanced seepage well (ESW) was proposed in this study, and its infiltration performances compared with the permeable pavement (PP) and the CSW were systemically investigated using static infiltration experiment and HYDRUS-2D simulation. The results showed that the infiltration efficiency of ESW was significantly higher than that of PP and CSW, and the process of water infiltrated through soil mainly controlled the macroscopic infiltration rate. The Nash-Sutcliff Efficient (NSE) index was used to evaluate the accuracy and reliability of the HYDRUS-2D model, and the results of NSE values greater than 0.75 (varied between 0.75 and 0.91) confirmed the applicability of HYDRUS-2D to describe correctly the hydraulic behavior of the ESW system. Simulation infiltration tests showed that the ESW performed a higher average infiltration rate and fewer total runoff volume than the CSW, indicating the effectively enhancement of the infiltration and water retention capacity of ESW, especially under heavy rainfall intensities. Additionally, the ESW system exhibited an excellent runoff-control and rainwater retention capacity in an actual rainfall scenario.

The drinking water supply system in many high rise buildings in densely populated cities consists of a complex labyrinth of copper pipes and brass fixtures (valves, meters, couplings). Lead contamination in these non-lead pipe systems can occur due to the presence of lead-soldered connections, and lead containing brass fixtures. The prediction of lead concentration characteristics of random daytime (RDT) samples in these high rise buildings has hitherto not been studied. The stochastic variation of lead concentration of RDT samples is studied by a coupling of 3-D and 1-D models and the Monte-Carlo Method. A 3-dimensional CFD model based on an equilibrium concentration ($E_0$) approach is used to simulate the leaching process from different lead sources. With the calibrated $E_0$ for different materials obtained from leaching experiments, the lead source strengths of leaded components in a water supply chain can be predicted by the 3D model as a function of stagnation time. Using the predicted distributed lead sources, the transport and mixing of lead in the turbulent pipe flow can be accurately simulated by a 1D advection–diffusion model. Using the Monte-Carlo method, a large number of simulations of consumer tap water Pb concentrations is performed using randomly sampled stagnation time, inter-use time, and flushing time. The computations are performed for two representative prototypes: (i) a full scale lead contaminated water supply chain; and (ii) a chain with only clean pipes and brass fixtures. The effect of stagnation time and flushing time before water use on tap Pb levels are investigated. The predicted range and distribution of RDT sample concentrations are validated by a three-year field data set (2017–2020) of the Hong Kong Water Supplies Department.

This paper describes application of airborne LiDAR bathymetry (ALB) with near-infrared and green pulsed lasers for gathering distributed vegetation conditions and topo-bathymetric data for rivers. For the lower Asahi River of Okayama Prefecture in Japan, the ALB data validity was verified using field observation data. This study also examined the applicability of ALB data for numerical simulations of the lower Asahi River flooding in early July 2018 in Japan, comparing simulated and observed data. Results demonstrated that the methodology for this study works well for parameterization of distributed vegetation on a reach scale. This study also applied numerical tests to investigate the effects of vegetation establishment on flood control plans for the lower Asahi River using parameters validated for flood flow simulations. Results demonstrate that the predicted water level markedly exceeds the high water level because of thick vegetation presently established along few-kilometer-long upstream sections of the targeted river reach. Therefore, we conclude that the present findings can support cost-effective management tasks for vegetated rivers.

Hydraulic structures disrupt fish migration thereby contributing to declines in fish populations around the world. Methods for piping fish upstream over dams can offer much steeper lift than conventional fishways. We describe the lifting mechanism of a tube fishway, demonstrated using numerical modelling, verified by a physical model. Efficacy is demonstrated by safely lifting two species of Australian native fish over 8 m up an embankment. Significant volumes of water can be transported from a chamber at the foot of a dam over its crest using simple conduits and two valves. Unsteady flow contributes entirely or significantly to the volume of water lifted. We explore how this piped system could be scaled up, while controlling turbulence impacts on fish. We propose new methods of characterising hydraulic efficiency for fishways that recognise the energy used and the value of the water discharged.

Conflict between water demand and environmental flow requirements is a challenging aspect in the reservoir management. Hence, optimizing environmental flow regime is one of the most important tasks at downstream of the large dams. The present study proposes a coupled simulation–optimization method based on the wetted perimeter method as an assessment method of the environmental flow and optimization of the reservoir operation to minimize difference between habitat loss and water demand loss using different metaheuristic algorithms. Moreover, the fuzzy TOPSIS as the decision-making system was applied for ranking the optimization algorithms. Indices including reliability index, vulnerability index, root mean square error and mean absolute error were utilized as criteria to measure the system performance and to select the best algorithm. Based on the results, gravity search algorithm (GSA) was the best method to optimize environmental flow regime at downstream of the reservoir in the case study. The proposed method is able to optimize environmental flow to minimize conflicts between human’s needs and aquatic’s needs considering storage constraints in the reservoir management. The proposed method might minimize negotiations between environmental managers and stakeholders. Furthermore, it should be noted that original wetted perimeter method is not able to provide optimal environmental flow regime based on a balance between users and constraints in the reservoir management such as storage constraints. The proposed method converts wetted perimeter method from an assessment method to a simulation–optimization method for optimizing environmental flow at downstream of the reservoirs.

This study develops two different approaches to perform temporal and spatial measurements of surface wave profile for experimental studies in transparent wave flumes. Both are based on image acquisition and processing with an Internet of Things (IoT) system consisting of three sets of GoPro camera cum Raspberry Pi connected wirelessly together in a local LAN. The first approach uses advanced edge algorithms with perspective transformation of the multiple cameras for the detection, while the second approach adopts Convolutional Neural Network (CNN) algorithms instead with training of the processed image data using information from additional discrete probes installed. Their accuracy is assessed under a range of experimental conditions of regular and irregular waves with different wave heights and periods, based on metrics that consist of the average errors of the predicted water surface profile as well as position errors for wave crests and troughs. The effects on the measurement accuracy due to the image acquisition frequency, camera resolution and camera location are also investigated. The results show that higher wave steepnesses generally lead to larger detection errors, and measurements for irregular waves are also more challenging. In addition, positioning the cameras closer to the wave flume sidewalls yields better detection results as expected, particularly in resolving wave crests and troughs, although the field of view narrows at the same time. However, higher video frequencies and camera resolutions might not necessarily improve the accuracy contrary to common expectation due to jaggedness in the image processing. Overall, both approaches are shown to be viable for the measurement of wave profile in the laboratory. The first approach is more straight forward in terms of implementation, and it performs well for regular wave conditions. The second approach requires more complex training of the neural networks, but its accuracy is significantly higher particularly for irregular waves.

Urban water sources are susceptible to various contamination events as a result of natural, accidental, and human-induced occurrences. An early warning monitoring system provides timely information on changes in urban water quality. In this study, an analysis was made with CANARY event detection software (EDS) to monitor water quality parameters in river water and to identify the onset of anomalous water quality periods. Water quality signals including pH, conductivity, and turbidity from the Milwaukee River over specified periods during the summer season of 2018–2020 were employed as inputs to event detection algorithms in CANARY. The data analysis results show that CANARY can be useful as an early warning system for monitoring contamination in urban water sources and help to identify abnormal conditions quickly. The sensibility of the model relies on optimizing the configuration parameters, which involves selecting the ideal set of parameters for the event detection algorithm and adjusting the BED parameters to increase or decrease the probability of generating an alarm. The number of events reported between the Linear Prediction Correction Filter (LPCF) and Multivariate Nearest Neighbor (MVNN) algorithms varied as a result of different residual calculation mechanisms. Climate factors that contributed to the abnormal water quality events in the river were examined. The analysis of rainfall on water quality was carried out using a statistical method by determining whether there is a significant difference (p-value) between the seasonal mean water quality data and the mean value of water parameters during the sampling duration. Regression analysis was also performed to estimate the best model that describes the relationship between each of the water quality parameters and temperature.

Simulated storm events were applied to four large bioretention columns to approximate 1.6 years of equivalent volume in Edmonton, Alberta’s typical climate. Summer, winter, and spring runoff were simulated in temperature-controlled laboratories with a range of −20 °C to +20 °C. During summer less porous bioretention media (i.e. loam soil) effectively weakened peak flows by >83% for 1:2 year events while more porous bioretention media (i.e. sandy loam soil) maintained hydraulic conductivities >9.1 cm/h. Winter operation consisted of all columns being subjected to −20 °C and then 1 °C repeatedly. Events were applied at an air temperature of 1 °C and, although frozen initially, more porous media experienced faster water breakthrough and ponding disappearance in winter indicating that hydraulic performance during intermittent warming periods in winter may be achievable. All columns’ hydraulic performance rebounded quickly in the subsequent summer. All columns successfully managed 1:2 year events in terms of infiltration rate, ponding depth and duration. Preliminary results also showed that both media have the potential to manage less frequent (1:5 and 1:10 year) events.

Bioretention is one of low-impact development measures, which widely used not only because it can reduce stormwater runoff total volume, decrease peak flow rate and delay peak flow time, but also can remove the runoff pollutants. Infiltration is an important hydrological process for bioretention to evaluate its runoff total volume reduction and pollutants removal. So, it is important to find an optimal infiltration model that can well describe the infiltration performance of bioretention. The Horton, Philip and Kostiakov infiltration models were selected to compare their accuracy when using for describe the infiltration characteristics of bioretention, and the errors between the different models simulate results and experiment results were assessed via the maximum absolute error (MAE), bias and coefficient of determination (R²). The experimental results showed that Horton model is fitting well and flexible under different experiment conditions, especially when the hydraulic head was 10 cm, with MAE of 0.50–0.81 cm/h, bias of 0.1–0.23 cm/h and R² of 0.98–0.99. R² of the Philip and Kostiakov models were all over than 0.87 at the initial infiltration period, but the model fitting accuracy decreased significantly with infiltration time elapse. Furthermore, the total runoff volume capture ratio and emptying time were advanced used to evaluate the flexibility of Horton model, and the Nash-Sutcliffe efficiency coefficients of them were over than 0.61 and 0.58, respectively. Therefore, the Horton model can be optimal selected to describe the infiltration process of bioretention and for its hydrological evaluation.