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Flood damage and risk assessment for urban area in Malaysia
In recent years, flood risk map has been widely accepted as a tool for flood mitigation. The risk of flooding is normally illustrated in terms of its hazard (flood inundation maps), while vulnerability emphasizes the consequences of flooding. In developing countries, published studies on flood vulnerability assessment are limited, especially on flood damage. This paper attempts to establish a flood damage and risk assessment framework for Segamat town in Johor, Malaysia. A combination of flood hazard (flood characteristics), exposure (value of exposed elements), and vulnerability (flood damage function curve) were used for estimating the flood damage. The flood depth and areal extent were obtained from flood modeling and mapping using HEC-HMS/RAS and Arc GIS, respectively. Expected annual damage (EAD) for residential areas (50,112 units) and commercial areas (9,318 premises) were RM12.59 million and RM2.96 million, respectively. The flood hazard map shows that Bandar Seberang area (46,184 properties) was the most affected by the 2011 flood. The flood damage map illustrates similar patterns, with Bandar Seberang suffering the highest damage. The damage distribution maps are useful for reducing future flood damage by identifying properties with high flood risk.
Journal Article
Testing empirical and synthetic flood damage models: the case of Italy
Flood risk management generally relies on economic assessments performed by
using flood loss models of different complexity, ranging from simple
univariable models to more complex multivariable models. The latter account for a
large number of hazard, exposure and vulnerability factors, being
potentially more robust when extensive input information is available. We
collected a comprehensive data set related to three recent major flood events
in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including
flood hazard features (depth, velocity and duration), building
characteristics (size, type, quality, economic value) and reported losses.
The objective of this study is to compare the performances of expert-based
and empirical (both uni- and multivariable) damage models for estimating the
potential economic costs of flood events to residential buildings. The
performances of four literature flood damage models of different natures and
complexities are compared with those of univariable, bivariable and
multivariable models trained and tested by using empirical records from
Italy. The uni- and bivariable models are developed by using linear,
logarithmic and square root regression, whereas multivariable models are
based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the
damage modelling approach for operational disaster risk management. Our
findings suggest that multivariable models have better potential for
producing reliable damage estimates when extensive ancillary data for flood
event characterisation are available, while univariable models can be
adequate if data are scarce. The analysis also highlights that expert-based
synthetic models are likely better suited for transferability to other areas
compared to empirically based flood damage models.
Journal Article
A Review of Flood Loss Models as Basis for Harmonization and Benchmarking
Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss-or flood vulnerability-relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.
Journal Article
Framework for dynamic modelling of urban floods at different topographical resolutions
Urban flood risks and their impacts are expected to increase as urban development in flood prone areas continues and rain intensity increases as a result of climate change while aging drainage infrastructures limit the drainage capacity in existing urban areas. The research presented in this thesis addresses the problem of capturing small-scale features in coarse resolution urban flood models with the aim of improving flood forecasts in geometrically complex urban environments.
Book
Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction
A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.
Journal Article
Using risk analysis for flood protection assessment
This book explores the benefits of using risk analysis techniques in the evaluation of flood protection structures, and examines the results of the environmental impact assessment for selected planned flood protection projects. The objective of the book is to propose a methodology for environmental impact assessment in water management. In more detail, flood mitigation measures are investigated with the aim of selecting the best option for the approval process. This methodology is intended to streamline the process of environmental impact assessment for structures in the field of the water management. The book?s environmental impact assessment system for water management structures analyzes the respective risks for different options. The results are intended to support the selection of future projects that pose minimum risks to the environment. Comparison of alternatives and designation of the optimal variant are implemented on the basis of selected criteria that objectively describe the characteristics of the planned alternatives and their respective impacts on the environment. The proposed Guideline for environmental impact assessment of flood protection objects employs multi-parametric risk analysis, a method intended to not only enhance the transparency and sensitivity of the evaluation process, but also successfully addresses the requirements of environmental impact assessment systems in the European Union. These modifications are intended to improve the outcomes of the environmental impact assessment, but may also be applied to other infrastructure projects. The case study proves that the primary aim? to improve transparency and minimize subjectivity in the environmental impact assessment process specific to flood protection structure projects? is met for the planned project in Kruézlov, Slovakia.
Book
A probabilistic approach to estimating residential losses from different flood types
Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.
Journal Article