Flood Hazard Information Research Articles

Understanding flood risk in urban environments: spatial analysis of building vulnerability and hazard areas in the Lisbon metropolitan area

AbstractClimate change has brought about new risks while exacerbating existing ones, with floods now accounting for about 45% of global disasters. This trend indicates that the exposure to floods and resulting damages will continue to rise. This paper aims to contribute to the global efforts to enhance flood resilience in urban areas by introducing a physical vulnerability index for buildings in flood-prone urban areas and exploring its connection with flood hazard, as defined by the European Union Directive 2007/60/EC. An index-based methodology is proposed to assess the physical vulnerability of buildings to flooding, utilising the Portuguese Census and Georeferenced Buildings Database, collected on a nationwide scale. The physical vulnerability of buildings is evaluated in the context of the Lisbon metropolitan area (LMA) in Portugal, and the results are compared across different scales, contrasting flood hazard information for the entire LMA with the areas identified under the Flood Directive as having significant potential flood risk. Using cluster analysis, spatial patterns of flood risk are identified, highlighting areas where high flood depth and flood velocity overlap the high vulnerability of buildings. Lastly, potential adaptation paths are discussed, considering the diverse nature of the flood hazard and the lessons learnt from other flood events.

Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa

Introduction: Cities located in lower income countries are global flood risk hotspots. Assessment and management of these risks forms a key part of global climate adaptation efforts. City scale flood risk assessments necessitate flood hazard information, which is challenging to obtain in these localities because of data quality/scarcity issues, and the complex multi-source nature of urban flood dynamics. A growing array of global datasets provide an attractive means of closing these data gaps, but their suitability for this context remains relatively unknown.Methods: Here, we test the use of relevant global terrain, rainfall, and flood hazard data products in a flood hazard and exposure assessment framework covering Addis Ababa, Ethiopia. To conduct the tests, we first developed a city scale rain-on-grid hydrodynamic flood model based on local data and used the model results to identify buildings exposed to flooding. We then observed how the results of this flood exposure assessment changed when each of the global datasets are used in turn to drive the hydrodynamic model in place of its local counterpart.Results and discussion: Results are evaluated in terms of both the total number of exposed buildings, and the spatial distribution of exposure across Addis Ababa. Our results show that of the datasets tested, the FABDEM global terrain and the PXR global rainfall data products provide the most promise for use at the city scale in lower income countries.

Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach

Flooding is a global threat and predicting flood risk accurately is vital for effective mitigation and increasing society's awareness of the negative impacts of floods. Over the years, researchers have worked on physical and data-driven models to predict flood damage, striving to improve accuracy and understanding. However, the challenge lies in the scarcity and limitedness of comprehensive datasets needed to develop these models. This study aims to enhance the National Flood Insurance Program (NFIP) claims dataset from Hurricane Katrina in coastal Alabama to make it adequate for multi-variable flood damage assessment. The NFIP claims dataset was combined with the Alabama property dataset, simulated flood hazard information, and property location characteristics. Oversampling techniques are employed to address data imbalance in the datasets. Subsequently, several ensemble machine learning approaches, including random forest, extra tree, extreme gradient boosting, and categorical boosting, are utilized to develop multi-variable flood damage models. The validation of these models demonstrates that extreme gradient boosting performs best, achieving satisfactory results in identifying damaged properties with precision (0.89), recall (0.90), and F1-score (0.90), as well as determining relative damage with R-squared (0.59), root mean squared error (0.21), and Spearman correlation (0.70). Utilizing data oversampling techniques improves the model performance of imbalanced flood damage datasets. Despite the dataset's limitations and data augmentation techniques employed, the model's output explanation based on SHapley Additive exPlanations (SHAP) is constructive as it aligns with the study's expectations regarding the interaction of different features to produce the final results.

Empowering local leaders in flood inundation mapping in Bagelen, Purworejo, Central Java.

This aricle discusses the reliability of flood inundation information that is obtained from participatory mapping. The commonly applied method to map flood inundation requires both direct and interpretive measurement data based on remote sensing images. Such assessments have limited availability of data; as a result, participatory mapping has become the solution. A number of studies have conducted participatory mapping to obtain flood hazard information in areas with limited sources of data, however, there has been little discussion about its reliability. This research conducted participatory flood inundation mapping by involving local leaders as respondents. The mental map drawn by the local leaders was digitised to obtain a shapefile format map. The information obtained from the semistructured interview was then included in the geographic information system (GIS) data as attributes. The obtained information was compared with the field data to determine its quality. A literature study was then conducted to discuss how the participatory mapping could support managing a disaster. Information obtained through participatory mapping can be effectively applied to disaster management because of its precise location information, lower cost and less time-consuming nature. The reliability of the information has weak accuracy of quantitative data; however, it has advantages in terms of qualitative data, especially in the detailed descriptions of flood information. In the future, participatory mapping should rely on integrating the perspectives of cross-disciplinary researchers, a comprehensive study of multidisciplinary knowledge and level of understanding of the stakeholders.

Commercial Real-Estate at Risk: An Examination of Commercial Building and Economic Impacts in the United States Using a High-Precision Flood Risk Assessment Tool

Environmental changes are predicted to exacerbate changes in flood events, resulting in consequences for exposed systems. While the availability and quality of flood risk analyses are generally increasing, very little attention has been paid to flood impacts related to the commercial market. This is notable given that the commercial market is often made up of the most valuable physical structures in communities, employs much of the local labor force, and generally plays a key role in the sustainability of economies. This study provides the first national spatial model of flood risk for commercial and multi-unit residential buildings at a property level resolution within the United States. This is achieved through the use of high-resolution inputs (hazard and property data), flood hazard information for the four major flood types, multi-return period hazard information, component-based depth-damage functions, GDP and economic multipliers information, and future facing projections. This study estimates that over the next 30 years, the absolute count of commercial and multi-unit buildings with risk will increase 8%, structural damage costs will increase 25.4%, downtime days will increase 29.1%, and economic impacts will increase 26.5%. Additionally, these impacts are concentrated in certain spatial locations. A high resolution model capturing flood risk as related to these commercial buildings is important for a comprehensive understanding of overall flood risk within the United States.Classification CodesJEL C30, E00, G17, M20, R10, R30

Community Flood Impacts and Infrastructure: Examining National Flood Impacts Using a High Precision Assessment Tool in the United States

Changing environmental conditions are driving worsening flood events, with consequences for counties, cities, towns, and local communities. To understand individual flood risk within this changing climate, local community resiliency and infrastructure impacts must also be considered. Past research has attempted to capture this but has faced several limitations. This study provides a nation-wide model of community flooding impacts within the United States currently and in 30 years through the use of high-resolution input data (parcel-level), multi-source flood hazard information (four major flood types), multi-return period hazard information (six return periods), operational threshold integration, and future-facing projections. Impacts are quantified here as the level of flooding relative to operational thresholds. This study finds that over the next 30 years, millions of additional properties will be impacted, as aspects of risk are expected to increase for residential properties by 10%, roads by 3%, commercial properties by 7%, critical infrastructure facilities by 6%, and social infrastructure facilities by 9%. Additionally, certain counties and cities persistently display impact patterns. A high-resolution model capturing aspects of flood risk as related to community infrastructure is important for an understanding of overall community risk.

Community Flood Risk and Infrastructure: Examining National Flood Impacts Using a High Precision Risk Assessment Tool

Changing environmental conditions are driving worsening flood events, with consequences for counties, cities, towns, and local communities. To understand individual flood risk within this changing climate, local community resiliency and infrastructure impacts must also be considered. Past research has attempted to capture this but has faced several limitations. This study provides a nation-wide model of community flooding risk currently and in 30 years through the use of high resolution input data (parcel-level), multi-source flood hazard information (four major flood types), multi-return period hazard information (six return periods), operational threshold integration, and future facing risk. Risk is quantified here as the level of flooding relative to operational thresholds. This study finds that over the next 30 years, millions of additional properties will be at risk, as risk is expected to increase for residential properties by 10%, roads by 3%, commercial properties by 7%, critical infrastructure facilities by 6%, and social infrastructure facilities by 9%. Additionally, certain counties and cities persistently display risk patterns. A high-resolution model of community infrastructure flood risk is important for an understanding of community infrastructure risk and to serve as a comprehensive input for advancing future and related research.

Evaluating implications of flood vulnerability factors with respect to income levels for building long-term disaster resilience of low-income communities

Communities residing in hazard-prone zones are more frequently affected by natural disasters and generally tend to bear higher losses than other communities. General factors such as sensitivity and adaptive capacity to a natural event determine the vulnerability of the communities. Several studies in this research area have claimed that both economic and socio-demographic factors are significant indicators of vulnerability and adaptive capacity. However, the possibility of a correlation between these two has not been thoroughly explored. In this study, we aimed to establish a method to identify the correlation of various vulnerability factors to the economic factor in a particular geographical location based on census data and historical flood hazard information. All factors of vulnerability do not affect all communities equally, and therefore it is necessary to devise a method that can be used to identify those factors of vulnerability that are specific to a community. This study was conducted for the low-income community as income has already been established as a significant indicator of adaptive capacity, responsible for reducing the resiliency of a community while responding to a natural disaster. Through the method proposed in the study, not only the factors specific to an area that renders the lower-income communities more vulnerable can be identified, but also the vulnerability scores of the communities can be analyzed to identify the most dominant factors. Thus, this method is expected to help in providing a direction to planning for risk management and development towards building long-term disaster resilience.

Capturing transformation of flood hazard over a large River Basin under changing climate using a top-down approach

Existing flood modeling studies over coastal catchments involving different combinations of model chain setup imparting complex information fails to entail the needs of policy or decision-makers. Thus, a comprehensive framework that pertains to the requirements of practitioners and provides more perspicuous flood hazard information is required. In this paper, a novel approach translating complex flood hazard information in the form of decision priority maps derived using a rational combination of models (physical and statistical) is elucidated at the finest administrative scale. The proposed methodology is illustrated over a highly flood-prone deltaic region in Mahanadi River Basin, India, to characterize impacts of climate change for a 1:100 years return period flood event under future conditions (2026–2055). The modeled flood events are further analyzed to capture the transformation dynamics of flood hazard classes (FHCs) in near-future, for prioritizing areas with greater hazard potential. Interestingly, the results capture a high transformation characteristic from low to high FHCs in agriculture-dominated areas, which are significantly greater than the areas experiencing flood hazard reduction. The results show a significant increase of 12.5% and 27.35% in areas with high FHCs under RCP4.5 and RCP8.5 scenarios, respectively. Moreover, a notable climate change response is indicated under both climate change scenarios, with approximately 22% (RCP4.5) and 25% (RCP8.5) in villages showing a drastic increment in flood hazard magnitude. The results thus highlight the importance of identifying and prioritizing the areas for flood adaptation where a relative change in flood hazard potential is higher due to climate change. Therefore, we conclude that this study can provide an insight into the implication of new approaches for effective communication of flood information by bridging the gaps between scientific communities and decision-makers in appraisal for better flood adaptation measures.

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

AbstractExisting needs to manage flood risk in the United States are underserved by available flood hazard information. This contributes to an alarming escalation of flood impacts amounting to hundreds of billions of dollars per year and countless disrupted lives and affected communities. Making information about flood hazards useful for the range of decisions that dictate the consequences of flooding poses many challenges. Here, we describe collaborative flood modeling, whereby researchers and end‐users at two coastal sites co‐develop fine‐resolution flood hazard models and maps responsive to decision‐making needs. We find, first of all, that resident perception and awareness of flooding are enhanced more by fine‐resolution depth contour maps than Federal Emergency Management Agency (FEMA) flood hazard classification maps and that viewing fine‐resolution depth contour maps helps to minimize differences in flood perception across subgroups within the community, generating a shared understanding. We also find that collaborative flood modeling supports the engagement of a wide range of end‐users in contemplating the risks of flooding and provides strong evidence that the co‐produced knowledge can be readily adopted and applied for Flood Risk Management (FRM). Overall, collaborative flood modeling advances FRM by providing multiple points of entry for diverse groups of end‐users to contemplate the spatial extent, intensity, timing, chance, and consequences of flooding, thus enabling the web of decision‐making related to flooding to be better informed with the best available science. This transdisciplinary approach emphasizes vulnerability reduction and is complementary to FEMA Flood Insurance Rate Maps used for flood insurance administration.

Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates

Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall‐triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small‐scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero‐inflated beta models reduce the 90% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78% (totalling U.S.$3.8 billion) compared to commonly used models.

Flood Hazard Information Map Using Geographical Information System (GIS) For Residential Community Resilience

The purpose of this research is to develop the residential flood hazard information map using Geographical Information System (GIS) for residential community resilience. The review approach is based on related literature contributing to identifying themes such as “flood hazard information map” and “geographical information system”. Integration of data will be sourced from Department of Irrigation and Drainage (DID), National Property Information Centre (NAPIC) and Jabatan Ukur dan Pemetaan Malaysia (JUPEM) using Unique Parcel Identifier (UPI) for respective residential land parcels. An extensive review of previous studies of flood hazard information map of residential community resilience for different flood disaster studies is considered to be the main restrictive factor resulting in a lack of empirical studies in this field. The development of residential property needs to be imposed with the elements for building specifications and materials that have flood resistance and resilience to protect the community life and property.Keywords: Community resilience, Floods, Geographical Information System (GIS), Residential propertyeISSN: 2398-4287 © 2019. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v4i10.1632

Effects of flood hazard visualization format on house purchasing decisions

ABSTRACTWe investigated how decision-making is affected by the visual presentation of flood hazard information. We exposed participants to different formats of flood hazard information while they simulated selecting a property to purchase. We compared three flood hazard formats: (i) maps currently used by the UK Environment Agency, (ii) tables that present flood level and frequency information and (iii) graphical representations depicting the level-frequency combination using a cartoon house image as a physical referent. In the experiment participants were presented, via computer screen, side-by-side information about two houses in a series of trials. Participants made a forced choice preference judgement between 108 different pairs of houses to indicate which they would purchase. Our findings indicate that when hazard information is presented in map format, individuals are less accurate in selecting lower-hazard houses, compared to when the same information is presented as a graphic representation of a house or as a table.

OPEN SOURCE WEB-BASED SOLUTIONS FOR DISSEMINATING AND ANALYZING FLOOD HAZARD INFORMATION AT THE COMMUNITY LEVEL

Abstract. We discuss in this paper the development, including the features and functionalities, of an open source web-based flood hazard information dissemination and analytical system called “Flood EViDEns”. Flood EViDEns is short for “Flood Event Visualization and Damage Estimations”, an application that was developed by the Caraga State University to address the needs of local disaster managers in the Caraga Region in Mindanao, Philippines in accessing timely and relevant flood hazard information before, during and after the occurrence of flood disasters at the community (i.e., barangay and household) level. The web application made use of various free/open source web mapping and visualization technologies (GeoServer, GeoDjango, OpenLayers, Bootstrap), various geospatial datasets including LiDAR-derived elevation and information products, hydro-meteorological data, and flood simulation models to visualize various scenarios of flooding and its associated damages to infrastructures. The Flood EViDEns application facilitates the release and utilization of this flood-related information through a user-friendly front end interface consisting of web map and tables. A public version of the application can be accessed at http://121.97.192.11:8082/. The application is currently expanded to cover additional sites in Mindanao, Philippines through the “Geo-informatics for the Systematic Assessment of Flood Effects and Risks for a Resilient Mindanao” or the “Geo-SAFER Mindanao” Program.

Probabilistic Flood Maps to support decision‐making: Mapping the Value of Information

AbstractFloods are one of the most frequent and disruptive natural hazards that affect man. Annually, significant flood damage is documented worldwide. Flood mapping is a common preimpact flood hazard mitigation measure, for which advanced methods and tools (such as flood inundation models) are used to estimate potential flood extent maps that are used in spatial planning. However, these tools are affected, largely to an unknown degree, by both epistemic and aleatory uncertainty. Over the past few years, advances in uncertainty analysis with respect to flood inundation modeling show that it is appropriate to adopt Probabilistic Flood Maps (PFM) to account for uncertainty. However, the following question arises; how can probabilistic flood hazard information be incorporated into spatial planning? Thus, a consistent framework to incorporate PFMs into the decision‐making is required. In this paper, a novel methodology based on Decision‐Making under Uncertainty theories, in particular Value of Information (VOI) is proposed. Specifically, the methodology entails the use of a PFM to generate a VOI map, which highlights floodplain locations where additional information is valuable with respect to available floodplain management actions and their potential consequences. The methodology is illustrated with a simplified example and also applied to a real case study in the South of France, where a VOI map is analyzed on the basis of historical land use change decisions over a period of 26 years. Results show that uncertain flood hazard information encapsulated in PFMs can aid decision‐making in floodplain planning.

Understanding Flood Risks for Better Planning and Resilience: Novel Stochastic Models and Methods for South-East Asia

Throughout history, human beings have been attracted to waterfront living. Today, most residents live in cities, most of which, in turn, are built on flood plains and in coastal areas – areas often threatened by floods. Physical changes to the environment have changed the response of catchments and rivers to heavy rainfall. Despite attempts to control the size of floods, economic growth – especially as experienced in Asia – has led to an explosion in exposure to floods. The most integrated, cost-effective method for disaster reduction and prevention requires that risk be assessed purposefully and adequately. Disaster risk is captured in two major components: occurrence probability and event intensity and reach, and its consequences. Understanding the risks associated with floods in Asia has been hindered by the complexity of flood dynamics in large river basins and in existing or unreliable datasets. With calculation power increasingly available, the development of flexible modeling systems and the appearance of new datasets, so-called probabilistic flood models can now be developed for large areas to quantify risks. A flexible modeling framework has been developed at DHI to better characterize flood plains and complex hydraulic systems in datapoor and highly exposed areas in Asia. The model relies on automated processes merging freely available datasets such as HydroSHEDS, WorldPop, crowd-sourced data available in OpenStreet Map and Landsat 7 and 8 satellite imagery. The combination of spatial data sources provides opportunities to optimize the hydrodynamic model domain and to improve the lowresolution digital elevation model. Such methods enhance flood hazard information conventionally derived from deterministic models by taking a full probabilistic approach considering source loading conditions, e.g., weather events and sea level rise, and the performance of existing and planned mitigation measures and failures of control structures such as dykes. With risks better quantified, new opportunities arise for cost-effective mitigation and resilience measures and for the development of novel risk transfer schemes through the use of insurance and capital markets.

Development of a Data Warehouse for Riverine and Coastal Flood Risk Management

Abstract. In New Brunswick flooding occurs typically during the spring freshet, though, in recent years, midwinter thaws have led to flooding in January or February. Municipalities are therefore facing a pressing need to perform risk assessments in order to identify communities at risk of flooding. In addition to the identification of communities at risk, quantitative measures of potential structural damage and societal losses are necessary for these identified communities. Furthermore, tools which allow for analysis and processing of possible mitigation plans are needed. Natural Resources Canada is in the process of adapting Hazus-MH to respond to the need for risk management. This requires extensive data from a variety of municipal, provincial, and national agencies in order to provide valid estimates. The aim is to establish a data warehouse to store relevant flood prediction data which may be accessed thru Hazus. Additionally, this data warehouse will contain tools for On-Line Analytical Processing (OLAP) and knowledge discovery to quantitatively determine areas at risk and discover unexpected dependencies between datasets. The third application of the data warehouse is to provide data for online visualization capabilities: web-based thematic maps of Hazus results, historical flood visualizations, and mitigation tools; thus making flood hazard information and tools more accessible to emergency responders, planners, and residents. This paper represents the first step of the process: locating and collecting the appropriate datasets.

Municipal flood hazard mapping: the case of British Columbia, Canada

Historical responses to flood hazards have stimulated development in hazardous areas. Scholars recommend an alternative approach to reducing flood losses that combines flood hazard mapping with land use planning to identify and direct development away from flood-prone areas. Creating flood hazard maps to inform municipal land use planning is an expensive and complex process that can require resources not always available at the municipal government level. Senior levels of government in some countries have addressed deficiencies in municipal capacity by assuming an active role in producing municipal flood hazard maps. In other countries, however, senior governments do not contribute to municipal flood hazard mapping. Despite a large body of research on the importance of municipal land use planning for addressing flood hazards, little is known about the extent of flood hazard information that is available to municipalities that do not receive outside assistance from senior governments for flood hazard mapping. We assess the status of flood hazard maps in British Columbia, where municipalities do not receive outside assistance in creating the maps. Our analysis shows that these maps are generally outdated and/or lacking a variety of features that are critical for supporting effective land use planning. We recommend that senior levels of government play an active role in providing municipalities with (1) detailed and current information regarding flood hazards in their jurisdiction and (2) compelling incentives to utilize this information.

How can residents know their flood risk? A review of online flood information availability in Australia

Widespread flooding across eastern Australia between November 2010 and February 2011 has again highlighted the country's susceptibility to natural disasters. The floods have also triggered discussion about flood awareness, risk information, and the role and responsibility of governments, insurance providers and residents. This paper examines the availability of flood hazard information on local government websites in Australia; it was completed in mid-2010 before the 2010–2011 flooding. The quality and accessibility of information is discussed, as are variations based on state and population size of different Local Government Areas (councils). Particular attention is given to those areas that experienced recent flooding, as well as to known areas of concentrated risk. There are large gaps in information availability, with less than 50% of councils provided flood risk information. A higher percentage of councils in the more populous states, some of which also have higher flood risk, provide information than those in less-populous states. Of those councils with a recognised flood risk, 67% provided flood information online.

Perception of flood hazard in countries of the North Sea region of Europe

Floods are natural phenomena and flood hazards cannot be eliminated. Thus it is necessary to learn living with this hazard. Floods represent a threat only with respect to human society, giving humans a central role: through location and through perception. Adequate perception of flood hazard is one of the premises to lower the vulnerability of the society and is an important element of non-structural measures for reducing flood risk. Public perception of flood hazard is essential for decision-making at all steps of flood risk assessment. The results of an investigation of the perception of flood hazard in five countries of the North Sea region of Europe are presented. 4000 Europeans living in flood-prone areas have been involved in the study. Stratified polls and focus groups were used as investigation tools. The study revealed many similarities in the perception of flood hazard among Europeans, such as limited interest in flood hazard, passiveness and reluctant attitude towards moving, while, for example, reckoning on flooding of their own houses differed between countries. The collected information offered a necessary background to streamline flood hazard information to laymen as well as for the second phase of the study, namely a search for consensus between public and authorities about what level of risk is tolerable.