Posthurricane damage assessment using satellite imagery and geolocation features

Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed-data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare-earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The data set curated in this work is made openly available (DOI: 10.17603/ds2-3cca-f398).

Stormwater and flood simulation of sponge city and LID mitigation benefit assessment

In the context of global climate change and the influence of human activities, the concept of "sponge city" is put forward to realize the purification, collection, and reuse of rainwater. The effective evaluation of LID facilities in sponge cities is of great guiding significance for the promotion and construction of sponge cities. IFMS (Integrated Flood Modeling System) Urban was selected to construct the rainstorm simulation. LID parameters were added to simulate the improvement of urban waterlogging after the construction of sponge city. A reasonable disaster loss assessment method was used to calculate the disaster mitigation benefit brought by the construction of sponge city. Through the comparison of the inundation situation before and after LID facilities' construction, it can be concluded that the mitigation effect of LID facilities on the overall inundation area of the city decreases with the increase of rainfall recurrence period, with the maximum reduction rate reaching 13.63% in the 5-year recurrence period and the minimum reduction rate of 11.06% in the 50-year recurrence period. LID facilities have a better disaster reduction effect for rainfall events with a small recurrence period than for rainfall events with a large recurrence period.

Publicly Available Data-Based Flood Risk Assessment Methodology: A Case Study for a Floodplain in Poland

Flood risk assessment is used to estimate the expected consequences and probability of a flood. It leads to the strengthening of resilience through appropriate preparation for an event of a specific scale. The methodology described in this paper was developed by the authors for the purposes of flood risk assessment in Poland, introduced to and applied on an actual example. It is based on simple calculations and a comparison of the results with a template. All of the data required for calculation came from freely available sources. Therefore, it is essential to evaluate the effectiveness of the flood risk assessment methodology in improving construction safety and identifying the factors that influence its implementation. The approach presented in this article is based on implementation of the parameters of floods, describing the characteristics of the exposed area and human vulnerability, among other factors, to the national risk assessment methodology, and then using it to determine the directions of activities aimed at reducing the risk of flooding. Simultaneously, assessment of these parameters might not be related directly to flood threats, but rather to the broader approach to risk assessment, including other threats. As a result of the application of the described methodology, it was estimated that the flood risk in the studied area is catastrophic, which requires immediate decisions of people responsible for safety.

Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk

This study provides a literature-based comparative assessment of uncertainties and biases in global to world-regional scale assessments of current and future coastal flood risks, considering mean and extreme sea-level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea-level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio-economic development (factors 2.3-5.8), digital elevation data (factors 1.2-3.8), ice sheet models (factor 1.6-3.8) and greenhouse gas emissions (factors 1.6-2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth-damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea-levels, storm surges, tides and waves. Advancing the state-of-the-art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth-damage functions would be best reduced through open community-based efforts, in which many scholars work together in collecting and validating these data.

Flood Risk Analysis and Assessment, Applications and Uncertainties: A Bibliometric Review

Studies looking at flood risk analysis and assessment (FRA) reviews are not customary, and they usually approach to methodological and spatial scale issues, uncertainty, mapping or economic damage topics. However, most of these reviews provide a snapshot of the scientific state of the art of FRA that shows only a partial view, focused on a limited number of selected methods and approaches. In this paper, we apply a bibliometric analysis using the Web of Science (WoS) database to assess the historic evolution and future prospects (emerging fields of application) of FRA. The scientific production of FRA has increased considerably in the past decade. At the beginning, US researchers dominated the field, but now they have been overtaken by the Chinese. The Netherlands and Germany may be highlighted for their more complete analyses and assessments (e.g., including an uncertainty analysis of FRA results), and this can be related to the presence of competitive research groups focused on FRA. Regarding FRA fields of application, resilience analysis shows some growth in recent years while land planning, risk perception and risk warning show a slight decrease in the number of papers published. Global warming appears to dominate part of future FRA production, which affects both fluvial and coastal floods. This, together with the improvement of economic evaluation and psycho-social analysis, appear to be the main trends for the future evolution of FRA. Finally, we cannot ignore the increase in analysis using big data analysis, machine learning techniques, and remote sensing data (particularly in the case of UAV sensors data).

A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system-wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.

Evaluating the Benefits of Adaptation of Critical Infrastructures to Hydrometeorological Risks

Infrastructure adaptation measures provide a practical way to reduce the risk from extreme hydrometeorological hazards, such as floods and windstorms. The benefit of adapting infrastructure assets is evaluated as the reduction in risk relative to the "do nothing" case. However, evaluating the full benefits of risk reduction is challenging because of the complexity of the systems, the scarcity of data, and the uncertainty of future climatic changes. We address this challenge by integrating methods from the study of climate adaptation, infrastructure systems, and complex networks. In doing so, we outline an infrastructure risk assessment that incorporates interdependence, user demands, and potential failure-related economic losses. Individual infrastructure assets are intersected with probabilistic hazard maps to calculate expected annual damages. Protection measure costs are integrated to calculate risk reduction and associated discounted benefits, which are used to explore the business case for investment in adaptation. A demonstration of the methodology is provided for flood protection of major electricity substations in England and Wales. We conclude that the ongoing adaptation program for major electricity assets is highly cost beneficial.

A Framework for Flood Risk Analysis and Benefit Assessment of Flood Control Measures in Urban Areas

Flood risk analysis is more complex in urban areas than that in rural areas because of their closely packed buildings, different kinds of land uses, and large number of flood control works and drainage systems. The purpose of this paper is to propose a practical framework for flood risk analysis and benefit assessment of flood control measures in urban areas. Based on the concept of disaster risk triangle (hazard, vulnerability and exposure), a comprehensive analysis method and a general procedure were proposed for urban flood risk analysis. Urban Flood Simulation Model (UFSM) and Urban Flood Damage Assessment Model (UFDAM) were integrated to estimate the flood risk in the Pudong flood protection area (Shanghai, China). S-shaped functions were adopted to represent flood return period and damage (R-D) curves. The study results show that flood control works could significantly reduce the flood risk within the 66-year flood return period and the flood risk was reduced by 15.59%. However, the flood risk was only reduced by 7.06% when the flood return period exceeded 66-years. Hence, it is difficult to meet the increasing demands for flood control solely relying on structural measures. The R-D function is suitable to describe the changes of flood control capacity. This frame work can assess the flood risk reduction due to flood control measures, and provide crucial information for strategy development and planning adaptation.

Flood risk assessments at different spatial scales

Managing flood risk, i.e. both the hazard and the potential consequences, is an important aspect of adapting to global change and has gained much traction in recent decades. As a result, a priori flood risk assessments have become an important part of flood management practices. Many methodologies have been set up, ranging from global risk assessments for the world as a whole, to local assessments for a particular stretch of a river/coast or small town. Most assessment frameworks generally follow a similar approach, but there are also notable differences between assessments at different spatial scales. This review article examines these differences, for instance those related to the methodology, use of assessments and uncertainties. From this review, future research needs are identified in order to improve flood risk assessments at different scales. At global/continental scale, there is a clear need for harmonised information on flood defences to improve assessments. Furthermore, inclusions of indirect economic effects at the macro-/meso-scale would give a better indication of the total effects of catastrophic flooding. At the meso-/micro-scale, there is an urgent need to improve our understanding of the effects of flooding on critical infrastructures, given their importance to society, the economy, emergency management and reconstruction. An overarching theme at all scales is the validation of flood risk assessments, which is often limited. More detailed post-disaster information would allow for improved calibration, validation and thus performance of flood risk models. Lastly, the link between spatial scales also deserves attention, for instance up- or downscaling methodologies.

Modeling flood induced interdependencies among hydroelectricity generating infrastructures

This paper presents a new kind of integrated modeling method for simulating the vulnerability of a critical infrastructure for a hazard and the subsequent interdependencies among the interconnected infrastructures. The developed method has been applied to a case study of a network of hydroelectricity generating infrastructures, e.g., water storage concrete gravity dam, penstock, power plant and transformer substation. The modeling approach is based on the fragility curves development with Monte Carlo simulation based structural-hydraulic modeling, flood frequency analysis, stochastic Petri net (SPN) modeling, and Markov Chain analysis. A certain flood level probability can be predicted from flood frequency analysis, and the most probable damage condition for this hazard can be simulated from the developed fragility curves of the dam. Consequently, the resulting interactions among the adjacent infrastructures can be quantified with SPN analysis; corresponding Markov Chain analysis simulates the long term probability matrix of infrastructure failures. The obtained results are quite convincing to prove the novel contribution of this research to the field of infrastructure interdependency analysis which might serve as a decision making tool for flood related emergency response and management.

Impacts of climate change on coastal flood risk in England and Wales: 2030-2100

Coastal flood risk is a function of the probability of coastal flooding and the consequential damage. Scenarios of potential changes in coastal flood risk due to changes in climate, society and the economy over the twenty-first century have been analysed using a national-scale quantified flood risk analysis methodology. If it is assumed that there will be no adaptation to increasing coastal flood risk, the expected annual damage in England and Wales due to coastal flooding is predicted to increase from the current 0.5 billion pounds to between 1.0 pound and 13.5 billion pounds, depending on the scenario of climate and socio-economic change. The proportion of national flood risk that is attributable to coastal flooding is projected to increase from roughly 50% to between 60 and 70%. Scenarios of adaptation to increasing risk, by construction of coastal dikes or retreat from coastal floodplains, are analysed. These adaptations are shown to be able to reduce coastal flood risk to between 0.2 pounds and 0.8 billion pounds. The capital cost of the associated coastal engineering works is estimated to be between 12 pounds and 40 billion pounds. Non-structural measures to reduce risk can make a major contribution to reducing the cost and environmental impact of engineering measures.