AbstractManholes in combined sewers may become surcharged during storm events, resulting in complex mixing conditions. Although manhole hydrodynamics are reasonably well understood, predicting mixing ...across a surcharged manhole remains a challenge. An analytical compartmental mixing model for manholes, based on jet theory, has been further developed and applied to generate cumulative residence time distributions (CRTDs), which describe mixing. The modeled CRTDs were compared with the experimentally derived CRTDs of over 850 manhole configurations to evaluate how well the new compartmental model represents physical processes. The model underpredicts short-circuiting in manholes with manhole diameter to pipe diameter ratios greater than 4.4 and consequently overestimates mixing. Otherwise, the modeled CRTDs show good agreement with the experimental CRTDs. The new compartmental model represents key manhole hydrodynamics that are not represented in current software modeling packages, which assume manholes are instantaneously well-mixed. The compartmental model provides good predictions of the experimental downstream concentration profiles, although with reduced peak concentrations in those manhole configurations where short-circuiting is not well-predicted. Despite this, the compartmental model still predicts concentrations downstream of a manhole in closer agreement with the recorded data than the complete instantaneously well-mixed assumption. As an analytical model requiring no inputs other than manhole geometry, the new compartmental model applies to a wide range of manhole configurations, is robust, and is useful for predicting manhole mixing in practical applications.
Recent studies have demonstrated the effectiveness of low impact development (LID) in preventing urban flooding in urban catchments. Majority of the past research focuses on the overall effects of ...LID on urban flood reduction in various configurations. However, how urban drainage system (UDS) performance changes at spatial scale under LID effectiveness within urban catchment is rarely explored. This study evaluates performance of UDS under different spatial placement strategies of LID to understand how urban flood dynamics of drainage system changes at catchment and local-scales. A practical UDS in China was chosen as a case study and divided into three sections (upstream, center, and downstream), with a combination of four LID practices installed on one of these sections or the entire catchment under six different rainfall scenarios and five different setting scales. An evaluation of individual LID practices demonstrated bioretention cell takes first place, followed by rain garden and green roof, and permeable pavement ranked at last place based on their overall performances. Results also confirmed the significant impact of the placement location of LID on UDS performance. Uniform placement strategy proves to be the best among four strategies because of the maximum potential for flood mitigation and improvement of UDS performance. Other investigated spatial placement strategies have approximately similar performances but are relatively poorer compared to the uniform strategy. Furthermore, the placement of LID facilities nearer to the flooded locations maximizes the benefits in terms of flood reduction and also reduces probability of transferring hydraulic load to other parts of UDS.
All urban drainage networks are designed to manage a maximum rainfall. This situation implies an accepted flood risk for any greater rainfall event. This risk is often underestimated as factors such ...as city growth and climate change are ignored. But even major structural changes cannot guarantee that urban drainage networks would cope with all future rain events. Thus, being able to forecast urban flooding in real time is one of the main issues of integrated flood risk management. Runoff and hydraulic models can be essential elements of flood forecast systems, as an active part of the system or as studying tools. This paper gives an overview of current available options for pluvial flood modelling in urban areas, from basic estimations with a one-dimensional urban drainage model to detailed flood process representation with one dimensional-two dimensional hydrodynamic coupled models. Each type of modelling solution is described with pros and cons regarding urban flood analysis. The paper then elaborates on real-time flood forecast systems and the influence of their main components. A classification of real-time urban flood systems is given based on the use of urban models, i.e. empirical scenarios, pre-simulated scenarios and real-time simulations. A review of existing operational systems is done using this classification.
AbstractThere has been a recent increase of interest in sewer network water quality, both for pollutants and wastewater epidemiology. Of particular interest is the ability to perform cost-effective ...small-scale monitoring to understand the sewer network and perform source localization (the process of identifying the sources of materials of interest within the network), enabling prioritization of combined sewer overflow (CSO) interventions and targeted response to the detection of infectious diseases. Rhodamine WT fluorescent dye tracing was carried out in the combined sewer networks of four UK cities, for which network geometries were available. Over 100 dye concentration profiles were recorded, from which discharge, travel time (velocity), and dispersion were quantified. A simplified hydraulic and water quality (conservative solute transport) modeling approach was used to investigate dispersion further. A theoretical method for calculating dispersion over a reach with nonuniform properties was derived and used with the models and recorded data to develop a method for estimating the dispersion coefficient in sewers. Novel simultaneous injections into multiple manholes within one sewer network were conducted. Modeling of these injections validated the modeling approach and explained the measured concentration profiles, demonstrating both the potential of hydraulic and solute transport modeling and the new dispersion coefficient predictor for source localization. Such modeling can be used to develop sewer network “fingerprints” and source location probability plots based on residence time distribution (RTD) theory to maximize information from limited water quality monitoring. This will aid managers and operators in identifying potential intermittent sources of material within the network.
Sinks configure the surface networks for overland flow processes representations during 1D hydrodynamic modelling. The excessive number of sinks detected from high-resolution DEMs can boost 1D ...computational costs significantly. To pursue optimal sink numbers and their optimal spatial distribution, a Volume Ratio Sink Screening (VRSS) method was developed to screen for computationally important sinks, while compensating for volume losses from removed (unimportant) sinks, such that 1D hydrodynamic modelling yields faster computing times without significant loss of accuracy. In comparison with an existing geometry-based sink screening method, we validated this method by conducting sensitivity analyses for the proposed screening criteria in three Danish case areas of distinct topographies. Two iterative procedures were programmed to assess and compare their sink screening performances in terms of sink number reductions and volume loss reductions, and a volume loss solver was developed to quantify catchment-wide volume losses in the 1D surface network. Compared to a geometry-based sink screening method, the VRSS method performs more robustly and produces more efficient reductions in the number of sinks, as well as efficient reductions in volume losses.
Abstract
An improved understanding of bioretention cell (BC) design configuration at both the unit scale and catchment scale is necessary for critical insight into dynamical behaviors of design ...parameters, which resultantly guides and improves the effectiveness and efficiency of a BC. A comprehensive sensitivity analysis (SA) of BC design parameters was conducted in this study by using the Stormwater Management Model (SWMM) which is globally used for BC's modeling. The preliminary screening of various design parameters is conducted by the one-factor-at-a-time (OAT) SA method and the key influential parameters (i.e., conductivity, berm height, vegetation volume, suction head, porosity, wilting point, and soil thickness) are selected for further SA. To this end, 1,000 random uniformly distributed samples of each sensitive design parameter are simulated by a Python wrapper of SWMM (PySWMM) under different design storms at the unit scale and catchment scale, respectively. Unit-scale SA results found unique characteristics of each design parameter under different storm scenarios, and their behaviors toward different model responses dynamically change within their factor spaces. Catchment-scale SA results conclude vegetation and soil layers design parameters have significant impacts on controlling stormwater at the catchment scale, and optimal selection of design parameters of vegetation (type, density, and height) and soil (type, layer thickness, and void ratio) is necessary for significantly improving the effectiveness of the BC at the catchment scale.
Wastewater‐based epidemiology (WBE) has been proven to be a useful tool in monitoring public health‐related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, ...wastewater‐detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under‐represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically‐based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS‐CoV‐2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time‐resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back‐tracing of human‐excreted biomarkers based on only sampling at the outlet or other key points, but would require high‐frequency, contaminant‐specific sensor systems that are not available currently.
Plain Language Summary
Viral outbreaks such as COVID‐19 are commonly monitored using traditional surveillance methods (e.g., nose swabs). However, individuals may not follow such testing schemes for different reasons, such as if they do not have symptoms. When people are infected by COVID‐19, they excrete SARS‐CoV‐2, the virus that causes the disease. Hence, analyzing sewage using wastewater‐based epidemiology (WBE) methods could help monitor the virus outbreak. Recent literature has identified a gap in combining machine learning and hydraulic models with WBE to detect viral outbreaks. In this study, we combine a hydraulic model that simulates virus transport with a machine learning model that tracks hotspot areas within a sewer network. The methodology developed in this paper was applied to a hypothetical sewer network to detect COVID‐19 disease hotspots rapidly. Results showed that the machine learning model has the potential to recognize hotspots but requires high time‐resolution data and is dependent on the sewer system's characteristics, such as flow velocity and virus sampling procedure. The developed methodology opens new possibilities for WBE to potentially trace human‐excreted viral genetic material based on only sampling at particular points. However, it would require high‐frequency sampling sensor systems that are not available currently.
Key Points
Using a numerical model of wastewater network and machine learning has the potential for the early detection of viral outbreaks
The ability to recognize disease hotspots depends on sampling frequency and method
The use of sewer models can improve the usefulness of wastewater‐based epidemiology data
Accurate model-based forecasts (discharge and water level) are considered significant for efficient planning and management of urban drainage systems. These model-based predictions can be improved by ...assimilating system measurements in physically based, distributed, 1D hydrodynamic urban drainage models. In the present research, a combined filtering and error forecast method was applied for the data assimilation to update the states of the urban drainage model. The developed data assimilation set-up in combination with the 1D hydrodynamic model was applied at the Damhusåen Catchment, Copenhagen. Discharge assimilation represented significant potential to update model forecast, and maximum volume error was reduced by 22% and 6% at two verification locations. The assimilation of water levels had a minor impact on the update of the system states. The updated forecast skill using error forecast models was enhanced up to 1-2 hours and 6-7 hours lead time at upstream assimilation and downstream verification locations, respectively.
Abstract
Effectiveness of a bioretention cell (BC) in mitigating stormwater peak flow load to an urban drainage system is highly dependent on the design configuration and retrofitting scale. The ...selection of appropriate BC design parameters has always been critical to achieving its maximum benefits. The BC modeling in the stormwater management model (SWMM) relies on numerous design parameters (in total of 18) with wide selection ranges, making parameter selection challenging. This study investigated the effect of hydrologic dynamics of BC design parameters under different rainfall conditions (i.e., intensity, duration, time-to-peak) on surface infiltration, surface outflow, and storage. Firstly, the seven influential BC design parameters (i.e., conductivity, berm height, vegetation volume, suction head, porosity, wilting point, and soil thickness) were selected based on one-factor-at-a-time (OAT) method, and 1000 random samples of each parameter were generated across the factor space by following respective uniform distributions. The simulations were performed with a python wrapper of SWMM (PySWMM) that used random samples for one parameter while leaving the others constant each time. In general, the statistical results of each parameter represent a significant variation in the volume distribution under various design rainfall conditions. Comparatively, the parameters of conductivity, berm height, and vegetation volume are categorized as the most influential compared to other BC design parameters for surface infiltration, storage, and surface outflow, respectively.
The accuracy of two-dimensional hydrodynamic models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) are used. However, the entailed high spatial discretisation results in ...excessive computational expenses, thus prohibiting their implementation in real-time forecasting especially at a large scale. This paper presents a sub-model approach that adapts 1D static models to tailor high-resolution 2D model grids relevant to specified targets, such that the tailor-made 2D hydrodynamic sub-models yield fast processing without significant loss of accuracy via a GIS-based multi-scale simulation framework. To validate the proposed approach, model experiments were first designed to separately test the impact of two outcomes (i.e., the reduced computational domains and the optimised boundary conditions) towards final 2D prediction results. Then, the robustness of the sub-model approach was evaluated by selecting four focus areas with distinct catchment terrain morphologies as well as distinct rainfall return periods of 1–100 years. The sub-model approach resulted in a 45–553 times faster processing with a 99% reduction in the number of computational cells for all four cases; the goodness of fit regarding predicted flood extents was above 0.88 of F2, flood depths yield Root Mean Square Errors (RMSE) below 1.5 cm and the discrepancies of u- and v-directional velocities at selected points were less than 0.015 ms−1. As such, this approach reduces the 2D models’ computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting.
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