Zoran Vojinovic

Coupled 1D and Noninertia 2D Flood Inundation Model for Simulation of Urban Flooding

Pluvial flooding in urban areas drained by storm sewer networks is characterized by surcharge-induced inundation. Urban inundation models need to reproduce the complex interaction between the sewer flow and the surcharge-induced inundation to make reasonable predictions of the likely flood damage in urban areas. In the framework of the present work, the storm sewer model SWMM5 and a newly developed two-dimensional (2D) noninertia overland-flow model have been coupled to simulate the interaction between the sewer system and the urban floodplain. The solution of the 2D model is on the basis of an alternating direction implicit scheme that solves the 2D noninertia free-surface shallow-water equations. For accuracy reasons, the time step is limited and controlled by the use of iteration to home-in on an accurate solution at each sweep. The dynamic interaction between the two models is bidirectional, and the interacting discharges are calculated according to the water level differences between the flows in the...

Effects of model schematisation, geometry and parameter values on urban flood modelling

One-dimensional (1D) hydrodynamic models have been used as a standard industry practice for urban flood modelling work for many years. More recently, however, model formulations have included a 1D representation of the main channels and a 2D representation of the floodplains. Since the physical process of describing exchanges of flows with the floodplains can be represented in different ways, the predictive capability of different modelling approaches can also vary. The present paper explores effects of some of the issues that concern urban flood modelling work. Impacts from applying different model schematisation, geometry and parameter values were investigated. The study has mainly focussed on exploring how different Digital Terrain Model (DTM) resolution, presence of different features on DTM such as roads and building structures and different friction coefficients affect the simulation results. Practical implications of these issues are analysed and illustrated in a case study from St Maarten, N.A. The results from this study aim to provide users of numerical models with information that can be used in the analyses of flooding processes in urban areas.

A Methodology for Processing Raw LiDAR Data to Support Urban Flood Modelling Framework: Case Study—Kuala Lumpur Malaysia

High quality representation of the topographic and the correct representation of significant urban features would be a fundamental foundation to a better urban flood model. Without such a representation, simulation of flood behaviours would be less successful as the flow patterns were completely dependent on ground levels and the shape of the features. Typically, such data can be obtained via Light Detection and Ranging (LiDAR) surveys. The process of turning raw LiDAR data into a useful Digital Terrain Model (DTM) involves careful processing and application of thinning, filtering and interpolation algorithms. Filtering is a process of automatic detection and interpretation of bare earth and objects from the point cloud of LiDAR data, which results in the generation of a DTM. To date, many filtering algorithms have been developed, and in a more general sense, many of them have become standard industry practice. However, when it comes to the use of a DTM for urban flood modelling applications, these algorithms cannot be always considered suitable. Depending on the terrain characteristics, they can even lead to misleading results and degrade the predictive capability of the modelling technique. This is largely due to the fact that urban environments often contain a variety of features (or objects) such as buildings, elevated roads, bridges, curbs and others which have the ability to store or divert flows during flood events. As these objects dominate urban surfaces, appropriate filtering methods need to be applied in order to identify such objects and to represent them correctly within a DTM so that the DTM can be used more safely in modelling applications. The work described in this chapter concerns improvements of a LiDAR filtering algorithm. The key characteristics of this improved algorithm are: ability to recover curbs and the use of appropriated roughness coefficient of Manning’s value to represent close-to-earth vegetation (e.g. grass and small bush). The results of the improved algorithm were demonstrated using Kuala Lumpur (Malaysia) as a case study. Improvement, in terms of a difference in flood depths and flood flows were observed between the hydraulics models built from several available filtering algorithms and the improved algorithm (MPMA). The overall results suggest that the improvement made in MPMA can lead to some difference in model results, which may in some cases be significant with a tendency towards incorrect flood flow by those models in which such features are not properly represented.

Flood Impacts on Road Transportation Using Microscopic Traffic Modelling Techniques

This paper proposes a novel methodology for modelling the impacts of floods on traffic. Often, flooding is a complex combination of various causes (coastal, fluvial and pluvial). Further, transportation systems are very sensitive to external disturbances. The interactions between these two complex and dynamic systems have not been studied in detail so far. To address this issue, this paper proposes a methodology for a dynamic integration of a flood model (MIKE FLOOD) and a microscopic traffic simulation model (SUMO). The flood modelling results indicate which roads are inundated for a period of time. The traffic on these links will be halted or delayed according to the flood characteristics—extent, propagation and depth. As a consequence, some of the trips need to be cancelled; some need to be rerouted to unfavourable routes; and some are indirectly affected. A comparison between the baseline and a flood scenario yields the impacts of that flood on traffic, estimated in terms of lost business hours, additional fuel consumption and additional CO2 emissions. The proposed methodology will be further developed as a workable tool to evaluate the flooding impact on transportation network at city scale automatically.

Capturing the multifaceted phenomena of socioeconomic vulnerability

Vulnerability and disaster risk assessment has been evaluated from different perspectives with focus on global or national scale. There is a lack of methodologies on city scale, which are able to capture inner-city disparities with regard to socioeconomic aspects. Therefore, the main objective was to develop a transparent and comprehensive indicator-based approach which is flexible in terms of data availability and is not tied to a specific case study side. This research proposes two flexible methodological approaches on how to perform socioeconomic vulnerability assessment. Susceptibility, Coping and Adaptation are the main elements of a modular hierarchical structure to capture the societal sphere of vulnerability. The first method is completely based on official census data at block scale. The second method is an expansion and includes data derived from a field survey to add components of risk perception. The proposed methodologies were developed and applied in the city of Genoa (Italy). The results are displayed spatially explicit on maps. Furthermore statistical analysis, to reveal the driving forces which influence vulnerability, was performed. The census-based approach revealed that vulnerability is forced along the river by the inherent susceptibility, as well as the lack of adaptation. The two approaches can be used effectively in gaining different insights. The flexibility of the framework proved to be suitable to the objective of the research. However, the values computed in this research do not claim completeness, and the aim was to provide useful information for stakeholders in decision making process to reduce vulnerability and risk.

Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

ABSTRACT Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM) is used to derive shallow water bathymetry data along Sint Maarten Island and Ameland Inlet, The Netherlands, by combining echo-sounding measurements and the reflectance of blue, green, or red bands of Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Landsat 8 Operational Land Imager (OLI) imagery with 30 m spatial resolution. In the analysis, 80% of data points of the echo-sounding measurements are used for training and the remaining 20% data points are used for testing. The model utilizes the radial basis kernel function (nonlinear) and the other training factors such as the smoothing parameter, penalty parameter C, and insensitivity zone ε are selected and tuned based on the learning (i.e. training) process. The overall errors during test phases for Sint Maarten Island (1–15 m) and Ameland Inlet (1.00–3.50 m) are 8.26% and 14.43%, respectively, reflecting that the model produces significant estimations for the shallow depths ranges, considered in this study. The results obtained are also compared statistically with those estimated from the widely used linear transform model and ratio transform model, which establish a linear relationship between the water depth and band reflectances. Based on the results, it is evident that SVM provides a comparable or better performance for shallow depth ranges and can be used effectively for deriving accurate and updated medium resolution bathymetric maps.

Multi-objective Evaluation of Urban Drainage Networks Using a 1D/2D Flood Inundation Model

The present paper describes a framework for the evaluation of a drainage system’s capacity in order to get a better understanding of the interactions between three rehabilitation measures: the Upgrading of Pipes (UP), Distributed Storage (DS) and the combination of both (UP+DS). It is posed as a multi-objective optimisation problem with the aim of minimising rehabilitation costs and flood damage. The approach of Expected Annual Damage Cost (EADC) was also introduced as the probabilistic cost caused by floods for a number of probable flood events (i.e. the accumulation of damage during a timeframe). The study combines computational tools such as a 1D/2D flood inundation model and an optimisation engine in the loop to compute potential damages for different rainfall events and to optimise combinations of rehabilitation measures. The advantages of this approach are demonstrated on a real-life case study in Dhaka City, Bangladesh. The optimal solutions confirm the usefulness and effectiveness of the proposed approach where both rehabilitation and damage costs are reduced by the optimal implementation of the UP and DS measures. In addition, the results of the proposed EADC approach indicate a damage cost reduction of at least 56% by implementing UP and of 27% by implementing DS, and both measures have lower rehabilitation costs. The proposed approach can be found appealing to water/wastewater utilities who are often challenged to achieve optimal design and rehabilitation of urban drainage systems.

Using multidimensional views of photographs for flood modelling

Using physically based computational models coupled with remote sensing technologies, photogrammetry techniques, and GIS applications are important tools for flood hazard mapping and flood disaster prevention. Also, information processing of massive input data with refined accuracy allows us to develop and to improve urban-flood-modeling at a detailed level. The topographical information from digital surface model (DSM) or digital terrain model (DTM) is essential for flood managers who actually require this high accuracy and resolution of input data to set up their practical applications. Light detecting and ranging (LiDAR) techniques are mainly used, but these costly techniques can be appraised by equipments, maintenance, and operations which include aircraft. Recent advances in photogrammetry and computer vision technologies like structure form motion (SfM) technique are widely used and offer cost-effective approaches to reconstruct 3D-topographical information from simple 2D photos, so-called 3D reconstruction. In terms of input data for flood modeling, the SfM technique can be comparable to other acquisition-techniques. In this paper, there are one experimental and two case studies. Firstly, a result of the experiment showed a similarity between flood maps by applying the SfM process form the 3D-reconstruction and using benchmark information. These 3D-reconstruction processes started from 2D photos, which were taken from virtual scenes by using multidimensional-view approach. These photos can be used to generate 3D information which is later used to create the DSM from multidimensional fusion of views (MFV-DSM). Then, the DSM was used as input data to set up 2D flood modeling. Thereafter, when using the DSMs as topographical input data, comparison between a benchmark DSM and MFV-DSM shows similarity flood-map results in both flood depths and flood extends. Secondary, the two cases from real world scenes also showed possibilities of using the SfM technique as an alternative acquisition tool, providing 3D information. This information can be used as input data for setting up modeling and can possibly be comparable or even outcompete with other acquisition techniques, such as LiDAR. As a result, using the SfM technique can be extended to become promising methods in practicable applications for modeling real flood events in real world scenes.

Multi-criteria Approach for Selection of Green and Grey Infrastructure to Reduce Flood Risk and Increase CO-benefits

Continuous changes in climate conditions combined with urban population growth pose cities as one of the most vulnerable areas to increasing flood risk. In such an atmosphere of growing uncertainty, a more effective flood risk management is becoming crucial. Nevertheless, decision-making and selection of adequate systems is a difficult task due to complex interactions between natural, social and built environments. The combination of green (or sustainable) and grey (or traditional) options has been proposed as a way forward to ensure resilience in advance of extreme events, and at the same time to obtain co-benefits for society and the environment. The present paper describes a novel method for selection of urban flood measures, based on a multi-criteria analysis that includes flood risk reduction, cost minimization and enhancement of co-benefits. The aim of this method is to assist decision makers in selecting and planning measures, which afterwards can be part of either high level scoping analysis or more complex studies, such as model based assessment. The proposed method is implemented within a tool which operates as a standalone application. Through this tool, the method has been applied in three study cases. The findings obtained indicate promising potential of the method here introduced.