GLORIA

GEOMAR Library Ocean Research Information Access

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Interpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental US (2023)
    Details
    Publication Date: 2023-11-16
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Floods cause average annual losses of more than US$30 billion in the US and are estimated to significantly increase due to global change. Flood resilience, which currently differs strongly between socio‐economic groups, needs to be substantially improved by proactive adaptive measures, such as timely purchase of flood insurance. Yet, knowledge about the state and uptake of private adaptation and its drivers is so far scarce and fragmented. Based on interpretable machine learning and large insurance and socio‐economic open data sets covering the whole continental US we reveal that flood insurance purchase is characterized by reactive behavior after severe flood events. However, we observe that the Community Rating System helps overcome this behavior by effectively fostering proactive insurance purchase, irrespective of socio‐economic backgrounds in the communities. Thus, we recommend developing additional targeted measures to help overcome existing inequalities, for example, by providing special incentives to the most vulnerable and exposed communities.〈/p〉
    Description: Plain Language Summary: Flood resilience of individuals and communities can be improved by bottom‐up strategies, such as insurance purchase, or top‐down measures like the US National Flood Insurance Program's Community Rating System (CRS). Our interpretable machine learning approach shows that flood insurances are mostly purchased reactively, after the occurrence of a flood event. Yet, reactive behaviors are ill‐suited as more extreme events are expected under future climate, also in areas that were not previously flooded. The CRS counteracts this behavior by fostering proactive adaptation across a widespread range of socio‐economic backgrounds. Future risk management including the CRS should support and motivate individuals' proactive adaptation with a particular focus on highly vulnerable social groups to overcome existing inequalities in flood risk.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Flood insurance purchase in the US is dominated by reactive behavior after severe floods〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The Community Rating System (CRS) fosters proactive insurance adoption irrespective of socio‐economic background〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The CRS should further balance existing inequalities by targeting specific population segments〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: https://api.census.gov/data/2018/acs/
    Description: https://www.fema.gov/about/openfema/data-sets#nfip
    Description: https://www.fema.gov/fact-sheet/community-rating-system-overview-and-participation
    Description: https://msc.fema.gov/portal/home
    Description: https://www.fema.gov/case-study/information-about-community-rating-system
    Description: https://doi.org/10.5281/zenodo.8067448
    Keywords: ddc:363.34 ; FEMA ; machine learning ; flood insurance ; human behavior ; flood resilience
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 2
    facet.materialart.
    Unknown
    Bias in Flood Hazard Grid Aggregation (2023)
    Details
    Publication Date: 2023-12-12
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Reducing flood risk through disaster planning and risk management requires accurate estimates of exposure, damage, casualties, and environmental impacts. Models can provide such information; however, computational or data constraints often lead to the construction of such models by aggregating high‐resolution flood hazard grids to a coarser resolution, the effect of which is poorly understood. Through the application of a novel spatial classification framework, we derive closed‐form solutions for the location (e.g., flood margins) and direction of bias from flood grid aggregation independent of any study region. These solutions show bias of some key metric will always be present in regions with marginal inundation; for example, inundation area will be positively biased when water depth grids are aggregated and volume will be negatively biased when water surface elevation grids are aggregated through averaging. In a separate computational analysis, we employ the same framework to a 2018 flood and successfully reproduce the findings of our study‐region‐independent derivation. Extending the investigation to the exposure of buildings, we find regions with marginal inundation are an order of magnitude more sensitive to aggregation errors, highlighting the importance of understanding such artifacts for flood risk modelers. Of the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. This work provides insight into, and recommendations for, aggregating grids used by flood risk models.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Through a novel framework, we show analytically that hazard grid aggregation leads to bias of key metrics independent of any study region〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉This aggregation is shown to always positively bias inundation area when water depth grids are aggregated〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉For example, aggregating from 1 to 512 m resolution resulted in a doubling of the inundated area for a 2018 flood in Canada〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: Deutsche Forschungsgemeinschaft
    Description: https://doi.org/10.5281/zenodo.8271996
    Description: https://doi.org/10.5281/zenodo.8271965
    Description: http://geonb.snb.ca/li/index.html
    Description: http://www.snb.ca/geonb1/e/DC/floodraahf.asp
    Keywords: ddc:551.48 ; flood risk ; model scaling ; data aggregation ; flood hazard ; error ; resampling
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 3
    facet.materialart.
    Unknown
    A probabilistic approach to estimating residential losses from different flood types (2020)
    Paprotny, Dominik ; Kreibich, Heidi ; Morales-Nápoles, Oswaldo ; [et al.]
    Springer Netherlands
    Details
    Publication Date: 2023-06-19
    Description: 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.
    Description: EIT Climate-KIC http://dx.doi.org/10.13039/100013283
    Description: Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661
    Description: Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZ (4217)
    Keywords: ddc:551.48 ; Fluvial floods ; Coastal floods ; Pluvial floods ; Bayesian networks ; Flood damage surveys
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 4
    facet.materialart.
    Unknown
    Toward an adequate level of detail in flood risk assessments (2023)
    Blackwell Publishing Ltd | Oxford, UK
    Details
    Publication Date: 2024-01-24
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Flood risk assessments require different disciplines to understand and model the underlying components hazard, exposure, and vulnerability. Many methods and data sets have been refined considerably to cover more details of spatial, temporal, or process information. We compile case studies indicating that refined methods and data have a considerable effect on the overall assessment of flood risk. But are these improvements worth the effort? The adequate level of detail is typically unknown and prioritization of improvements in a specific component is hampered by the lack of an overarching view on flood risk. Consequently, creating the dilemma of potentially being too greedy or too wasteful with the resources available for a risk assessment. A “sweet spot” between those two would use methods and data sets that cover all relevant known processes without using resources inefficiently. We provide three key questions as a qualitative guidance toward this “sweet spot.” For quantitative decision support, more overarching case studies in various contexts are needed to reveal the sensitivity of the overall flood risk to individual components. This could also support the anticipation of unforeseen events like the flood event in Germany and Belgium in 2021 and increase the reliability of flood risk assessments.〈/p〉
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: BMBF http://dx.doi.org/10.13039/501100002347
    Description: Federal Environment Agency http://dx.doi.org/10.13039/501100010809
    Description: http://howas21.gfz-potsdam.de/howas21/
    Description: https://www.umwelt.niedersachsen.de/startseite/themen/wasser/hochwasser_amp_kustenschutz/hochwasserrisikomanagement_richtlinie/hochwassergefahren_und_hochwasserrisikokarten/hochwasserkarten-121920.html
    Description: https://download.geofabrik.de/europe/germany.html
    Description: https://emergency.copernicus.eu/mapping/list-of-components/EMSN024
    Description: https://data.jrc.ec.europa.eu/collection/id-0054
    Description: https://oasishub.co/dataset/surface-water-flooding-footprinthurricane-harvey-august-2017-jba
    Description: https://www.wasser.sachsen.de/hochwassergefahrenkarte-11915.html
    Keywords: ddc:551.48 ; decision support ; extreme events ; integrated flood risk management ; risk assessment
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 5
    facet.materialart.
    Unknown
    Impact Forecasting to Support Emergency Management of Natural Hazards (2020)
    AGU (American Geophysical Union) | Wiley
    Details
    Publication Date: 2023-02-08
    Description: Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain language summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organising evacuations. We review and compare the state‐of‐the‐art of impact forcasting across a wide range of natural hazards, and outline opportunities and key challenges for research and development of impact forecasting.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 6
    facet.materialart.
    Unknown
    Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates (2021)
    Details
    Publication Date: 2021-04-22
    Description: 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.
    Description: Key Points Recent severe pluvial flood events highlight the need to integrate pluvial flooding in urban flood risk assessment Probabilistic models provide reliable estimation of pluvial flood loss across spatial scales Beta distribution model reduces the 90% prediction interval for Hurricane Harvey building loss by U.S.$3.8 billion or 78%
    Description: Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347
    Description: NSF GRFP
    Description: Fulbright Doctoral Program
    Keywords: 551.5 ; pluvial flooding ; loss modeling ; urban flooding ; probabilistic ; Hurricane Harvey ; climate change adaptation
    Type: article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 7
    facet.materialart.
    Unknown
    Quantifying Flood Vulnerability Reduction via Private Precaution (2021)
    Details
    Publication Date: 2021-06-27
    Description: Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.
    Description: Plain Language Summary: Private precautionary measures such as adapted building use, sealing basements and purchasing flood barriers reduce flood damage to residential buildings. Using an empirical dataset consisting of 948 flooded households in Germany, we estimate that the average loss reducing effect of implementing private precautionary measures is 11‐15 thousand EUR per household. This is approximately equal to 27% of the average building loss suffered by the flooded households (48000 EUR). Despite this significant risk mitigation effect, these precautionary measures are hardly considered in flood risk assessment modelling. This results in biased flood loss predictions being used for evaluating risk management strategies. Hence, we compare state‐of‐the‐art flood loss models in respect to their ability to account for building loss reduction due to private precaution. From all tested flood loss models, the expert Bayesian Network based model BN‐FLEMOps and the rule‐based loss model FLEMOps are best able to capture the damage reducing effect of private precaution. These models can be valuable for evaluating adaptable flood risk management strategies.
    Description: Key Points: Private precaution significantly reduces the flood vulnerability of private households as shown by robust empirical matching methods State‐of‐the‐art flood damage models differ strongly based on their ability to capture differences in vulnerability of private households Methodology applied and validated using an extensive object‐level flood damage data set from Germany
    Description: European Union http://dx.doi.org/10.13039/100011102
    Keywords: 333.91 ; flood loss ; average treatment effect ; matching methods ; loss models ; risk analysis ; adaptation
    Type: article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 8
    facet.materialart.
    Unknown
    Seamless Estimation of Hydrometeorological Risk Across Spatial Scales (2021)
    Details
    Publication Date: 2021-07-24
    Description: Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe. We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.
    Keywords: 551.489 ; spatial scales ; risk assessment ; hydro-meteorological hazards ; object-based damage modeling ; uncertainty ; probabilistic approaches
    Language: English
    Type: article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 9
    facet.materialart.
    Unknown
    The behavioral turn in flood risk management, its assumptions and potential implications (2021)
    John Wiley & Sons, Inc. | Hoboken, USA
    Details
    Publication Date: 2021-06-27
    Description: Recent policy changes highlight the need for citizens to take adaptive actions to reduce flood‐related impacts. Here, we argue that these changes represent a wider behavioral turn in flood risk management (FRM). The behavioral turn is based on three fundamental assumptions: first, that the motivations of citizens to take adaptive actions can be well understood so that these motivations can be targeted in the practice of FRM; second, that private adaptive measures and actions are effective in reducing flood risk; and third, that individuals have the capacities to implement such measures. We assess the extent to which the assumptions can be supported by empirical evidence. We do this by engaging with three intellectual catchments. We turn to research by psychologists and other behavioral scientists which focus on the sociopsychological factors which influence individual motivations (Assumption 1). We engage with economists, engineers, and quantitative risk analysts who explore the extent to which individuals can reduce flood related impacts by quantifying the effectiveness and efficiency of household‐level adaptive measures (Assumption 2). We converse with human geographers and sociologists who explore the types of capacities households require to adapt to and cope with threatening events (Assumption 3). We believe that an investigation of the behavioral turn is important because if the outlined assumptions do not hold, there is a risk of creating and strengthening inequalities in FRM. Therefore, we outline the current intellectual and empirical knowledge as well as future research needs. Generally, we argue that more collaboration across intellectual catchments is needed, that future research should be more theoretically grounded and become methodologically more rigorous and at the same time focus more explicitly on the normative underpinnings of the behavioral turn. This article is categorized under: Engineering Water 〉 Planning Water Human Water 〉 Water Governance Science of Water 〉 Water Extremes
    Description: The work carried out by Sebastian Seebauer was supported by the Austrian Climate and Energy Fund and was carried out within the Austrian Climate Research Program;
    Description: Austrian Climate and Energy Fund
    Keywords: 333.91 ; capacities ; effectiveness ; motivation ; resources ; risk governance ; vulnerability
    Type: article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
  • 10
    facet.materialart.
    Unknown
    Process‐Based Flood Risk Assessment for Germany (2021)
    Details
    Publication Date: 2022-03-25
    Description: Large‐scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process‐based Regional Flood Model (RFM) to simulate a 5000‐year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D–2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector‐wise exposure data and empirical multi‐variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5% confidence for Germany to be €0.529 bn and €8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. The overall EAD is comparable to other large‐scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large‐scale assessments with homogeneous return periods. Thus, the process‐based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German‐wide flood risk assessments.
    Description: Plain Language Summary: We provide spatially consistent flood risk estimates for the residential, commercial and agricultural sectors of Germany. The Regional Flood Model (RFM) simulates a 5000‐year flood event catalogue from which the flood risk curves are derived based on the losses per economic sector. The RFM is a process‐based model chain, that couples the weather generator providing spatially consistent precipitation fields with the hydrological and hydrodynamic models considering processes such as dike overtopping and hinterland storage. The coherent heterogeneous return period flows result in flood maps consisting of inundation depth and duration. These are intersected with sector specific assets at high spatial resolution. Detailed flood loss models are used to estimate losses. From the risk curves, we estimate the Expected Annual Damage and losses corresponding to a 200‐year return period for Germany to be €0.529 bn and €8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. Owing to the process‐based, spatially consistent approach implemented, our risk estimates for extreme events are more realistic compared to other large‐scale assessments.
    Description: Key Points: Regional Flood Model provides spatially consistent flood risk estimates for residential, commercial and agriculture sectors for Germany. Flood risk is derived using a 5000‐year event catalog, yielding a realistic representation of risk along with uncertainty quantification. The median Expected Annual Damage and Value At Risk at 99.5% confidence for Germany is estimated to be €0.53 bn and €8.87 bn, respectively.
    Description: Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347
    Description: Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:551.489
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    CITATION GEO-LEO
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...