Urban Flood Modelling Research Articles

Risk Simulation of Urban Rainstorm Flood Disasters Considering Crowd Activities

Social activities have a significant impact on the rainstorm flood disaster risk. It is crucial to explore the dynamic changes of urban rainstorm flood disaster risk caused by crowd activities. In this study, a risk simulation method of urban rainstorm flood disasters is proposed, composed of an urban rainstorm flood model based on SWMM and LISFLOOD-FP and a crowd activities model based on ABM. Taking the Futian District of Shenzhen as an example, the temporal and spatial changes in rainstorm flood disaster risk for buildings and roads are analyzed under three scenarios: midnight, morning peak, and evening peak. The results show that: (1) Although the overall risk of urban rainstorm flood disasters increases as the inundation area expands, the average risks of roads and buildings increase rapidly and then stabilize during the morning peak due to commuting activities, while the average risk of roads remains high level during the evening peak due to commuting activities, while; (2) The risk of urban rainstorm flood varies significantly at different time periods. The average risk of buildings is the largest during the morning peak, about twice that during the evening peak. The number of high-risk roads during the evening peak is much higher than in the morning peak, and both buildings and roads have the least risk during midnight; (3) The spatial distribution of urban rainstorm flood disaster risk changes with the crowd activities, shifting from residential areas to industrial areas, schools, shopping malls, etc., during the morning peak, while the evening peak shows the reverse.

A hybrid shallow water approach with unstructured triangular grids for urban flood modeling

Classic shallow water equations (SWEs) and their various simplified approaches provide the theoretical basis for urban flood modeling. Aiming at the limitations of the local inertial approach (LInA), this paper proposes a hybrid approach that combines the LInA and classic SWEs to eliminate the errors caused by LInA. Results show that the hybrid approach can reduce the error from 7% to 1.5%, with slightly reduced computation efficiency in the dam break case. In the urban flood case, the hybrid approach can reduce the relative error on flood depth from 50% to 20% in a few grids, while the relative error on flood velocity is improved from 60% to below 30%. The hybrid approach proposed here can provide a practical strategy to eliminate the errors caused by the inherent defects of the simplified approach and still maintain the computation efficiency.

Integration of physically-based numerical tools and socio-hydrological variables in urban flood modeling

Floods are considered the most destructive natural catastrophes on the planet; therefore, studying their dynamics in the context of each region is a fundamental pillar of society. A very popular tool for studying the degree of threat is physics-based modeling using 2D models. It is imperative to include physical, hydrological, and anthropogenic variables in flood modeling approaches. The reviewed literature did not develop a methodology or strategy to facilitate this process. Based on this reason, it was considered for Riohacha, a coastal city in northern Colombia. A survey was conducted to determine social, economic, and flood depth variables during extreme events. Twenty socio-hydrological variables were obtained from the survey analysis using Kruskal–Wallis and multiple correspondence analysis (MCA) tests. Flood depth was the response variable used to select the socio-hydrological variables, among which the following stood out: age of the house, internal slope, toilet overflow, proximity to a wetland, paved road, raft area, and slope of the soil in front of the house. An iterative process supported by a Support Vector Machine (SVM) dummy was used to determine the appropriate combination of parameters and optimize the calibration process of the TELEMAC-2D hydrodynamic model. The curve number (CN) and Manning friction coefficient were used as calibration parameters. The optimization process was performed by entering the dummy SVM into the socio-hydrological variables CN and Manning's n to test up to 20,000 possible combinations of the parameters and evaluate the mean absolute error (MAE), mean error (ME), absolute relative error (RAE), root mean square error (RMSE), and inertia root mean squared error (IRMSE). The best combination of parameters to simulate an extreme flood event and determine the metrics with the observed and simulated flood depths was entered into TELEMAC-2D. This paper presents a methodological proposal for optimizing the calibration process of physics-based flood models (traditional method), considering socio-hydrological variables, to articulate it as a new step in the modeling protocol. In the traditional simulation, an RMSE of 0.48 m, MAE of 0.37 m, and IRMSE of 1.37 m are obtained, which decrease to 0.33 m, 0.27 m, and 0.92 m, respectively, when the socio-hydrological variables are included. Contrary to the previous metrics, the ME improves slightly from 0.15 m with the traditional method to 0.17 m when the socio-hydrological variables are included. These results can be improved using a digital terrain model (DTM) that more realistically represents the complexity of the urban fabric. With only 7 iterative processes, the model calibration was optimized using only seven iterative processes. Therefore, the proposed methodology was established as a tool for optimizing the flood modeling processes.

Urban flood risk assessment characterizing the relationship among hazard, exposure, and vulnerability.

Risk assessment is an effective means to alleviate urban flood disasters and has attracted the attention of many studies. However, most previous studies about urban flood risk assessment often focused more on urban inundation area and depth, less on the inter-relationship of the components of risk. In this study, an urban flood risk assessment approach that characterizes the relationship among the three components of risk "hazard-exposure-vulnerability" (H-E-V) is developed. Firstly, eleven flood risk indicators are selected based on the flood simulation results of urban flood model and statistical data to establish the urban flood risk assessment index system. Then, the combination of analytic hierarchy process (AHP) and entropy weight method is employed to determine the weight of each indicator and the comprehensive urban flood risk is assessed. Most importantly, the coupling coordination degree model (CCDM) is used to reveal the relationship among H-E-V. After applying this method to Haikou city, China, the results show that the comprehensive effect and the coupling coordination degrees among H-E-V have a multidimensional impact on urban flood risk. For example, some sub-catchments, although at high risk of flooding, may experience a potential waste of resources. Urban flood assessment can be made more detailed and three-dimensional by comparing hazard, exposure, and vulnerability horizontally. Understanding and grasping the internal relationships among these three risk components can help implement flood prevention measures, optimize the allocation of flood prevention resources, and effectively reduce urban flood risks.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

A hydrodynamic-machine learning coupled (HMC) model of real-time urban flood in a seasonal river basin using mechanism-assisted temporal cross-correlation (MTC) for space decoupling

The intricate nature of urban waterlogging models arises from the compounding effects of human activities and dynamic alterations in the natural environment. Based on extensive data correlation and hydrodynamic process analysis, this study offers an a priori index mechanism-assisted temporal cross-correlation (MTC) for model space decoupling, which helps to reduce the computational complexity of the urban flood model. Furthermore, a hydrodynamic-machine learning (ML) coupled (HMC) model is proposed for predicting the river and drainage pipe water levels in urban seasonal river basins. The observations of the rainfalls, water levels of the river and manholes during 10 rainstorm events are gathered, with 8 of which serving as training datasets and 2 as prediction datasets. The simulation results show that MTC can provide a thorough evaluation of the impact of input parameters on predicting objects. It can also demonstrate the causality of hydrological processes behind monitoring data correlation. In addition, in comparison to ML and hydrodynamic models, the simulation stability of the HMC model is observed to be superior. Meanwhile, the NSE of the river channel and manhole is observed to be higher than 0.95 and 0.90, respectively, and the simulation uncertainty is found to be reduced by 42.2 %. The HMC model requires an average of 25 s to simulate an 8-hour rainfall event. This approach is found to be effective for space decoupling and rapid urban flood simulation.

GIS-based hydrodynamic modeling for urban flood mitigation in fast-growing regions: a case study of Erbil, Kurdistan Region of Iraq

Floods threaten urban infrastructure, especially in residential neighborhoods and fast-growing regions. Flood hydrodynamic modeling helps identify flood-prone locations and improve mitigation plans' resilience. Urban floods pose special issues due to changing land cover and a lack of raw data. Using a GIS-based modeling interface, input files for the hydrodynamic model were developed. The physical basin's properties were identified using soil map data, Land Use Land Cover (LULC) maps, and a Digital Elevation Model (DEM). So, the HEC-RAS 2-D hydrodynamic model was developed to estimate flood susceptibility and vulnerability in Erbil, Iraq. The case study examines the quality of flood modeling results using different DEM precisions. Faced with the difficulty, this study examines two building representation techniques: Building Block (BB) and Building Resistance (BR). The work presented here reveals that it is possible to apply the BR technique within the HEC-RAS 2-D to create urban flood models for regions that have a lack of data or poor data quality. Indeed, the findings confirmed that the inundated areas or areas where water accumulated in past rainfall events in Erbil are the same as those identified in the numerical simulations. The study's results indicate that the Erbil city is susceptible to flood hazards, especially in areas with low-lying topography and substantial precipitation. The study's conclusions can be utilized to plan and develop flood control structures, since it identified flood-prone areas of the city.

Approximation method for the sewer drainage effect for urban flood modeling in areas without drainage-pipe data

Introduction: The underground drainage-pipe network is one of the vital components of a modern city, and it plays an important role in preventing or mitigating urban flooding. Thus, pipe network data are necessary for simulation of the entire urban rainfall–runoff process. However, pipe network data are sparse or unavailable in most urban areas.Methods: To solve this problem, we developed a novel approximation method that can be used calculate the drainage capacity of the pipe system. This method is named the road-drainage method, and it works under the assumption that the pipe network functions by subtracting the corresponding mass from the water conservation equation only from areas of road. The mass is determined from weir flow formulas together with the road properties and correction for this mass is applied to the rainfall source term.Results: Two test cases were used to compare the performance of the new method with an existing method, under which mass is subtracted from the entire area during the rainfall–runoff process. The results showed that the new method considerably improves the accuracy of simulated peak volume, with an improvement of 2.62%–58.75% compared with the existing method across various scenarios. Moreover, the proposed new method reduces the time shift of the rise and peak of surface inundation by 10–45 min in various scenarios, which reflects a more realistic model of the rainfall–runoff process.Discussion: These results demonstrate that the proposed new method can represent the drainage capability more accurately and is more consistent with reality. The road-drainage method has promising potential for application in urban flood simulation in areas without drainage system data and for the support of large-scale urban hydrologic modeling.

Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China.

The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, using an urban stormwater model as necessary. Most studies have used urban hydrological models to assess flood risk; however, due to limited flow pipeline data, the calibration and the validation of the models are difficult. This study applied the MIKE URBAN model to build a drainage system model in the Beijing Future Science City of China, where the discharge of pipelines was absent. Three methods, of empirical calibration, formula validation, and validation based on field investigation, were used to calibrate and validate the parameters of the model. After the empirical calibration, the relative error range between the simulated value and the measured value was verified by the formula as within 25%. The simulated runoff depth was consistent with a field survey verified by the method of validation based on field investigation, showing the model has good applicability in the study area. Then, the rainfall scenarios of different return periods were designed and simulated. Simulation results showed that, for the 10-year return period, there are overflow pipe sections in northern and southern regions, and the number of overflow pipe sections in the northern region is more than that in the southern region. For the 20-year return period and 50-year return period, the number of overflow pipe sections and nodes in the northern region increased, while for the 100-year return period, the number of overflow nodes both increased. With the increase in the rainfall return period, the pipe network load increased, the points and sections prone to accumulation and waterlogging increased, and the regional waterlogging risk increased. The southern region is prone to waterlogging because the pipeline network density is higher than that in the northern region and the terrain is low-lying. This study provides a reference for the establishment of rainwater drainage models in regions with similar database limitations and provides a technical reference for the calibration and validation of stormwater models that lack rainfall runoff data.

Urban Flood Modeling: Uncertainty Quantification and Physics‐Informed Gaussian Processes Regression Forecasting

AbstractEstimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models. We consider the effects of the model's complexity and uncertainty in key input parameters. The effect of rainfall intensity on the uncertainty in water depth predictions is also studied. As a test study, we choose the Interconnected Channel and Pond Routing (ICPR) model of a part of the city of Minneapolis. The uncertainty in the ICPR model's predictions of the floodwater depth is quantified in terms of the ensemble variance using the multilevel Monte Carlo (MC) simulation method. Our results show that uncertainties in the studied domain are highly localized. Model simplifications, such as disregarding the groundwater flow, lead to overly confident predictions, that is, predictions that are both less accurate and uncertain than those of the more complex model. We find that for the same number of uncertain parameters, increasing the model resolution reduces uncertainty in the model predictions (and increases the MC method's computational cost). We employ the multilevel MC method to reduce the cost of estimating uncertainty in a high‐resolution ICPR model. Finally, we use the ensemble estimates of the mean and covariance of the flood depth for real‐time flood depth forecasting using the physics‐informed Gaussian process regression method. We show that even with few measurements, the proposed framework results in a more accurate forecast than that provided by the mean prediction of the ICPR model.

Applications of Advanced Technologies in the Development of Urban Flood Models

Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system.

Development of a 1D/2D Urban Flood Model Using the Open-Source Models SWMM and MOHID Land

Urban pluvial floods are the outcome of the incapacity of drainage systems to convey the runoff generated by intense rainfall events. Cities have been struggling to control such hazards due to several pressures, such as urbanization increase, more frequent experiences of extreme rainfall events, and increases in tide levels. Such pressures demand the study of adaptation strategies, which conventional one-dimensional drainage models fall short of simulating. Thus, 1D/2D models have been emerging with the aim of allowing better integration of key processes for flood modeling, namely, runoff interception by stormwater inlet devices and manhole overflows. The current paper presents a 1D/2D urban flood model based on an offline coupling procedure between the 1D model SWMM and the 2D model MOHID Land. The SWMM/Land model is applied to a synthetic street case study and to a real case study in downtown Albufeira, Portugal. The results obtained for the real case study are coherent with local observations of past flooding events, and the model shows potential for better decision-making regarding urban flood risk management.

Developing a framework for urban flood modeling in Data-poor regions

Forecasting floods is an effective non-engineering measure for flood control. However, for urbanized regions, the lack of drainage data and complex underlying surface conditions make flood forecasting extremely difficult. This paper proposes a framework for urban flood modeling in data-poor regions to address these issues. The drainage capacity of the pipe system was represented by the constant infiltration rate, which was calibrated by coupling satellite soil moisture product and measured flood data to better reflect real drainage conditions. Subsequently, given the complex underlying surface conditions, this framework divides the land use/cover into several dominant urban land use types, which were assigned unique runoff generation and flow confluence parameters in urbanized regions. The framework also takes the impact of dense buildings on water flow into consideration. A building treatment method for structured grids was proposed, which can consider the anisotropy of buildings and scale of the grid. The results showed that it is feasible to estimate drainage capacity using the constant infiltration approach, and the process of soil infiltration is crucial for urban flood forecasting. Compared with the highest resolution publicly available land use/cover dataset, the urban land use types extracted from high-resolution remote sensing images can reveal the runoff generation and flow confluence mechanism more accurately in urbanized regions. This study verified the necessity of considering the impact of buildings in urban flood modeling. The building treatment method of this framework can identify synergistic effects of local terrain and building boundaries on water flow. The results provide a reference for effective flood forecasting in urban regions with poor data.

Hydrodynamic Modelling and Flood Risk Analysis of Urban Catchments under Multiple Scenarios: A Case Study of Dongfeng Canal District, Zhengzhou.

In recent years, urban flooding has become an increasingly serious problem, posing a serious threat to socio-economic development and personal safety. In this paper, we consider the Dongfeng Canal area in Zhengzhou City as an example and build a 1D/2D coupled urban flood model using the InfoWorks ICM. This study area uses six scenarios with rainfall return periods of 5 a, 20 a, and 50 a, corresponding to rainfall ephemeris of 1 h and 2 h to assess the flood risk. The results of the study show that (1) The flood depth, inundation duration, and extent of inundation in the study area vary with the return period and rainfall history. Generally, most of the water accumulation is concentrated in the low-lying areas adjacent to the river and near the roadbed. (2) As the rainfall recurrence period and rainfall duration increase, the proportion of overflow at the nodes becomes more pronounced and the overload from the pipe network flows mainly to the overload. (3) The high-risk areas under the different scenarios are mainly distributed on both sides of the river, and most of the low-risk areas transform into medium- and high-risk areas as the rainfall recurrence period and rainfall duration increase. This study analyses the flood risk situation under different scenarios, as well as the elements and areas that should be monitored in case of flooding, with the aim of providing a reference for flood prevention and control in the study area and formulating corresponding countermeasures. It also serves as a reference for flood risk analysis in other areas with similar situations.

City Flood Disaster Scenario Simulation Based on 1D–2D Coupled Rain–Flood Model

In order to realize the reproduction and simulation of urban rainstorm and waterlogging scenarios with complex underlying surfaces, based on the 1D–2D coupled models, we constructed an urban storm–flood coupling model considering one-dimensional river channels, two-dimensional ground and underground pipe networks. Luoyang City, located in the western part of Henan Province, China was used as a pilot to realize the construction of a one-dimensional and two-dimensional coupled urban flood model and flood simulation. The coupled model was calibrated and verified by the submerged water depths of 16 survey points in two historical storms flood events. The average relative error of the calibration simulated water depth was 22.65%, and the average absolute error was 13.93 cm; the average relative error of the verified simulated water depth was 15.27%, the average absolute error was 7.54 cm, and the simulation result was good. Finally, 28 rains with different return periods and different durations were designed to simulate and analyze the rainstorm inundation in the downtown area of Luoyang. The result shows that the R2 of rainfall and urban rainstorm inundation is 0.8776, and the R2 of rainfall duration and urban rainstorm inundation is 0.8141. The study results have important practical significance for urban flood prevention, disaster reduction and traffic emergency management.

A Coupled River Basin‐Urban Hydrological Model (DRIVE‐Urban) for Real‐Time Urban Flood Modeling

AbstractReliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling.

Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

Based on station precipitation observations, radar quantitative precipitation estimates (QPE), and radar fusion data during Typhoon Fitow (2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China, as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows: (1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%. (2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details. (3) One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result—all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

Application of Particle Swarm Optimization for Auto-Tuning of the Urban Flood Model

Reliable time-efficient prediction of urban floods is one of the essential tasks for planning of disaster prevention and mitigation measures. A key challenge of urban flood models is to obtain reliable input data. While geometric data can be directly measured, some other data, such as roughness and head loss of each flow system, are not easy to measure. This study proposes a novel approach for the auto-tuning of these unmeasurable data based on Particle Swarm Optimization (PSO). In this paper, we first performed a sensitivity analysis of the present urban flood model to find important parameters, which dominantly determine the predictive skills of the present urban flood model. We then developed a PSO-based auto-tuning system for estimation of these parameters. The entire computation domain was evenly split into square segments, and optimum values of these parameters were determined in each segment. The capability of this method was confirmed by comparisons of Nash–Sutcliffe efficiency, normalized root-mean square error, Kling–Gupta efficiency, and Akaike Information Criteria. As a result, it was found that important parameters for the present urban flood model were Manning’s roughness of the pipeline and a coefficient for determination of the discharge from the ground surface to sewer pipelines. It was also found that the present PSO-based auto-tuning system showed reasonably good performance in tuning these parameters, which clearly improve the predictive skills of the present urban flood model.

Grid edge classification method to enhance levee resolution in dual-grid flood inundation models

Dual-grid models address the computational bottlenecks of large-scale (>103 km2) urban flood modeling with solution updates on a coarse grid that are informed by topographic data on a fine grid. However, dual-grid models may poorly resolve levees, leading to loss of accuracy. Here we present a grid edge classification method whereby specific edges of the coarse grid are flagged to gather nearby topographic data from the fine grid and create a contiguous physical barrier. The method relies on levee location data in a polyline format, and does not require levee height data since that information is stored on the fine grid. Using a 6804 km2 model of the Los Angeles Metropolitan Region with 3 m topographic data and 987 km of levees, the proposed method is implemented and evaluated. Simulations using coarse grids of 15, 30 and 60 m capture flood extent consistent with fine-grid models based on a critical success index (CSI) of 90, 87 and 82%, respectively. Edge classification improves CSI up to 7 percentage points over a model with unclassified coarse grid edges, and reduces the false alarm ratio up to 10 percentage points. Differences in model performance across the study area are noted, including lower accuracy on urbanized alluvial fans. With compute costs that scale with the coarse grid, dual-grid models can efficiently realize more accurate large-scale models of urban flood hazards.

Modelling urban sewer flooding and quantitative microbial risk assessment: A critical review

AbstractModelling urban inundation and its associated health implications is numerous in its many applications. Flood modelling research contains a broad wealth of material, and microbial risk assessment has gained more popularity over the last decade. However, there is still a relative lack of understanding of how the microbial risk can be quantified from urban sewer flooding. This article intends to review the literature encompassing contemporary urban flood modelling approaches. Hydrodynamic and microbial models that can be applied for quantitative microbial risk assessment will be discussed. Consequently, urban sewer flooding will be the focus. This review found that the literature contains a variety of different hazards posed by urban flooding. Yet, far fewer examples encompass microbial risk from sewer system exceedance. To date, there is no evidence of a perfect model or technique, to carry out a quantitative microbial risk assessment from hydrodynamic simulations. The literature details many different methods. We intend to detail the advantages and limitations of each method. Along similar lines, hydraulic data constitutes a large part of the uncertainty which is inherent to this research field. Many studies in the literature detail data paucity and uncertainty in input data. As such, any advancement in this discipline will very likely aid future research.