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Christopher C Pain - One of the best experts on this subject based on the ideXlab platform.
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unstructured mesh adaptivity for Urban Flooding modelling
Journal of Hydrology, 2018Co-Authors: F Fang, Pablo Salinas, Christopher C PainAbstract:Abstract Over the past few decades, Urban floods have been gaining more attention due to their increase in frequency. To provide reliable Flooding predictions in Urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this paper, a 2D control-volume and finite-element flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the Flooding process. For example, the high-resolution meshes around the buildings and steep regions are placed when the Flooding water reaches these regions. In this work a Flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh Flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost.
J.a.e. Ten Veldhuis - One of the best experts on this subject based on the ideXlab platform.
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quantitative fault tree analysis for Urban water infrastructure Flooding
Structure and Infrastructure Engineering, 2011Co-Authors: J.a.e. Ten Veldhuis, F H L R Clemens, P.h.a.j.m. Van GelderAbstract:Flooding in Urban areas can be caused by heavy rainfall, improper planning or component failures. Few studies have addressed quantitative contributions of different causes to Urban flood probability. In this article, we apply probabilistic fault tree analysis for the first time to assess the probability of Urban Flooding as a result of a range of causes. We rank the causes according to their relative contributions. To quantify the occurrence of flood incidents for individual causes we use data from municipal call centres complemented with rainfall data and hydrodynamic model simulations. Results show that component failures and human errors contribute more to flood probability than sewer overloading by heavy rainfall. This applies not only to Flooding in public areas but also to Flooding in buildings. Fault tree analysis has proved useful in identifying relative contributions of failure mechanisms and providing quantitative data for risk management.
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Automatic classification of municipal call data to support quantitative risk analysis of Urban drainage systems
Structure and Infrastructure Engineering, 2010Co-Authors: J.a.e. Ten Veldhuis, Robin Harder, Marco LoogAbstract:Quantitative analyses of Urban flood risks are often limited by lack of data on flood incidents. Call data are a valuable source of information about Urban flood incidents, yet the unstructured nature of call information results in large time investments to prepare the data for application in quantitative analyses. Consequently, the existing call databases are not used for this purpose. If automatic classification routines can be applied to transfer unstructured call data into a quantitative data source, large stores of currently unused data can be made available for quantitative risk analysis of Urban infrastructure systems. This article aims to assess whether automatic classification of calls from municipal call centres can reach sufficient accuracy to allow for use of the results in quantitative risk analysis. This is illustrated by the application of automatic classification results in quantitative fault tree analysis for Urban Flooding, for two cases with datasets of approximately 6000 calls. The res...
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Quantitative risk analysis of Urban Flooding in lowland areas
2010Co-Authors: J.a.e. Ten VeldhuisAbstract:Urban flood risk analyses suffer from a lack of quantitative historical data on Flooding incidents. Data collection takes place on an ad hoc basis and is usually restricted to severe events. The resulting data deficiency renders quantitative assessment of Urban flood risks uncertain. The study reported in this thesis reviews existing approaches to quantitative flood risk analysis and evaluation of Urban Flooding guidelines. It proceeds to explore historical data on Flooding incidents from municipal call centres in two cities in the Netherlands with the final aim to quantitatively assess Urban flood risk. The data from municipal call centres consist of texts describing citizens’ observations of Urban drainage problems. The texts provide information about causes, locations and consequences of Flooding incidents. Call information on Flooding causes is used to identify causes of Urban Flooding through application of probabilistic fault tree analysis. Urban Flooding probabilities are quantified as well as contributions of a range of causes to the overall flood probability. Call information on Flooding consequences is used to draw risk curves for a range of consequence classes: separate risk curves are drawn for consequences associated with human health, damage to private property and damage related to traffic distUrbance. The curves depict a combination of flood consequences of increasing severity and associated probabilities of occurrence. Health risk associated with Urban Flooding is evaluated additionally in a screening-level quantitative microbial risk assessment. The assessment is based on analyses of samples from Flooding incidents and from combined sewers. Risk values from call data analysis are translated into monetary values and into numbers of people affected by Flooding in order to obtain risk outcomes that can be weighed against investments to reduce flood risk. It is discussed how outcomes in monetary terms differ from those based on numbers of affected people affected. The effectiveness of Urban flood reduction strategies is assessed based on a comparison of flood risk values associated with three main failure mechanisms causing Urban Flooding. The effectiveness of existing strategies for flood risk control is discussed and potential improvements are indicated. Finally the acceptability of flood risk is discussed in view of the quantitative flood risk outcomes of this thesis. It is shown how quantitative risk values based on call data provide a starting point for the development of risk-based standards for Urban Flooding.
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fault tree analysis for Urban Flooding
Water Science and Technology, 2009Co-Authors: J.a.e. Ten Veldhuis, F H L R Clemens, P H A J M Van GelderAbstract:Traditional methods to evaluate flood risk generally focus on heavy storm events as the principal cause of Flooding. Conversely, fault tree analysis is a technique that aims at modelling all potential causes of Flooding. It quantifies both overall flood probability and relative contributions of individual causes of Flooding. This paper presents a fault model for Urban Flooding and an application to the case of Haarlem, a city of 147,000 inhabitants. Data from a complaint register, rainfall gauges and hydrodynamic model calculations are used to quantify probabilities of basic events in the fault tree. This results in a flood probability of 0.78/week for Haarlem. It is shown that gully pot blockages contribute to 79% of flood incidents, whereas storm events contribute only 5%. This implies that for this case more efficient gully pot cleaning is a more effective strategy to reduce flood probability than enlarging drainage system capacity. Whether this is also the most cost-effective strategy can only be decided after risk assessment has been complemented with a quantification of consequences of both types of events. To do this will be the next step in this study.
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Fault tree analysis for Urban Flooding
2008Co-Authors: J.a.e. Ten Veldhuis, François Clemens, P.h.a.j.m. Van GelderAbstract:Traditional methods to evaluate flood risk mostly focus on storm events as the main cause of Flooding. Fault tree analysis is a technique that is able to model all potential causes of Flooding and to quantify both the overall probability of Flooding and the contributions of all causes of Flooding to the overall flood probability. This paper gives the results of a fault tree analysis for Urban Flooding for the case of Haarlem, a city of 105.000 inhabitants. Data from a complaint register, rainfall data and hydrodynamic model calculations are used to quantify the probabilities of the basic events in the fault tree. The flood probability that is calculated for Haarlem is 0.78/week. Gully pot blockages make the main contribution to flood probability: 79%, storm events contribute only 5%. This implies that in this case an increased efficiency of gully pot cleaning is a more effective strategy to reduce flood probability than to increase the drainage system capacity. Whether this is also the most cost-effective measure can only be decided if the risk calculation is completed with a quantification of the consequences of both types of events. To do this will be the next step in this study.
Faith Ks S Chan - One of the best experts on this subject based on the ideXlab platform.
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social capital and community preparation for Urban Flooding in china
Applied Geography, 2015Co-Authors: Faith Ks S ChanAbstract:Abstract Social capital can enhance community resilience to environmental change. Productive and trusted relations among social actors and effectual social norms can help local residents share resources, information and risks. The main objective of our study is to understand the ways in which social attributes and risk considerations influence adoption of resilient economic measures by individuals for reducing potential losses due to catastrophic rainstorm and Flooding. This article provides evidence from China on how social capital contributes to anticipatory adaptation to environmental change. The inquiry is based on structured interviews with local residents of Tianjin, a flood-prone port city in China, and a standard regression analysis. Findings show that the intention to make preparation increases with the levels of social expectation, social relationship, and institutional trust. Perceived risk and damage experience, however, have no significant impacts. This suggests that building social capacity and trust will be more effective in enhancing community resilience than merely increasing awareness of hazard risks. We call for greater efforts on strengthening the capacity of formal and informal communal institutions. The structural changes required, however, are challenging.
F Fang - One of the best experts on this subject based on the ideXlab platform.
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unstructured mesh adaptivity for Urban Flooding modelling
Journal of Hydrology, 2018Co-Authors: F Fang, Pablo Salinas, Christopher C PainAbstract:Abstract Over the past few decades, Urban floods have been gaining more attention due to their increase in frequency. To provide reliable Flooding predictions in Urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this paper, a 2D control-volume and finite-element flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the Flooding process. For example, the high-resolution meshes around the buildings and steep regions are placed when the Flooding water reaches these regions. In this work a Flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh Flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost.
Ting Zhang - One of the best experts on this subject based on the ideXlab platform.
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application of a three dimensional unstructured mesh finite element Flooding model and comparison with two dimensional approaches
Water Resources Management, 2016Co-Authors: Ping Feng, Ting Zhang, Cedo Maksimovic, Paul D BatesAbstract:Urban flood modelling plays a key role in assessment of flood risk in Urban areas by providing detailed information of the Flooding process (e.g. location, depth and velocity of Flooding). Accurate modelling results are the basis of reliable flood risk evaluation. In this paper, modelling of a flood event in a densely Urbanized area within the city of Glasgow is presented. Modelling is performed using a new three-dimensional (3D) Flooding model, which is an unstructured mesh, finite element model that solves the Navier-Stokes equations, and developed based on Fluidity. The terrain data considered comes from a 2 m Light Detection and Ranging (LiDAR) Digital Terrain Model (DTM) and aerial imagery. The model is validated with flood inundation area and flow features, and sensitivity analyses are conducted to identify the mesh resolution required for accuracy purposes and the effect of the uncertainty in the inflow discharge. Good agreement has been achieved when comparing the results with those published in other 2D shallow water models in ponded areas. However, larger vertical velocity (>0.2 m/s) and larger differences between the 3D and 2D models can be observed in areas with greater topographic gradients (>3 %). Finally, performance of the proposed 3D Flooding model has been analysed. Through the modelling of a real Flooding event this paper helps illustrate the case that 3D modelling techniques are promising to improve accuracy and obtain more detailed information related to Urban Flooding dynamics, which is useful in Urban flood control planning and risk management. To the best of our knowledge, this is the first paper to apply a 3D unstructured mesh finite-element model (FEM model) to a real Urban Flooding event. It highlights some of the differences between the 3D and 2D Urban flood modelling results. Copyright Springer Science+Business Media Dordrecht 2016
