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  • 1
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    Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates (2021)
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    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
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  • 2
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    Quantifying Flood Vulnerability Reduction via Private Precaution (2021)
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    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
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  • 3
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    Seamless Estimation of Hydrometeorological Risk Across Spatial Scales (2021)
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    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
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  • 4
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    Comprehensive evaluation of an improved large‐scale multi‐site weather generator for Germany (2021)
    John Wiley & Sons, Ltd. | Chichester, UK
    Details
    Publication Date: 2021-06-29
    Description: In this work, we present a comprehensive evaluation of a stochastic multi‐site, multi‐variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km2. The regional weather generator, which is based on a first‐order multi‐variate auto‐regressive model, is setup using 53‐year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi‐day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma‐Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for non‐precipitation variables. The weather generator can be used to generate synthetic event footprints for large‐scale trans‐basin flood risk assessment.
    Description: The regional weather generator is greatly capable of capturing most of the crucial statistical properties of weather events. Hence, it can be used to generate synthetic event footprints for large‐scale trans‐basin flood risk assessment. However, due to its deficiency in capturing spatial precipitation correlation structure leading to an overestimation of areal precipitation extremes, further improvement is envisaged for future research.
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: 551.6 ; correlation ; extreme ; flood ; large‐scale ; multi‐variate ; weather generator
    Type: article
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  • 5
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    Projected Changes in Compound Flood Hazard From Riverine and Coastal Floods in Northwestern Europe (2021)
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    Publication Date: 2021-06-27
    Description: Compound flooding in coastal regions, that is, the simultaneous or successive occurrence of high sea levels and high river flows, is expected to increase in a warmer world. To date, however, there is no robust evidence on projected changes in compound flooding for northwestern Europe. We combine projected storm surges and river floods with probabilistic, localized relative sea‐level rise (SLR) scenarios to assess the future compound flood hazard over northwestern coastal Europe in the high (RCP8.5) emission scenario. We use high‐resolution, dynamically downscaled regional climate models (RCM) to drive a storm surge model and a hydrological model, and analyze the joint occurrence of high coastal water levels and associated river peaks in a multivariate copula‐based approach. The RCM‐forced multimodel mean reasonably represents the observed spatial pattern of the dependence strength between annual maxima surge and peak river discharge, although substantial discrepancies exist between observed and simulated dependence strength. All models overestimate the dependence strength, possibly due to limitations in model parameterizations. This bias affects compound flood hazard estimates and requires further investigation. While our results suggest decreasing compound flood hazard over the majority of sites by 2050s (2040–2069) compared to the reference period (1985–2005), an increase in projected compound flood hazard is limited to around 34% of the sites. Further, we show the substantial role of SLR, a driver of compound floods, which has frequently been neglected. Our findings highlight the need to be aware of the limitations of the current generation of Earth system models in simulating coastal compound floods.
    Description: Key Points: We combine high‐resolution projected storm surges with localized sea level rise and projected river floods to assess compound flood hazard in the RCP8.5 scenario We find decreasing compound flood hazard for 66% of the locations across northwestern Europe. Models reproduce upper tail dependence between storm surge and river floods, yet discrepancies exist in capturing dependence strength.
    Description: Alexander von Humboldt‐Stiftung (Humboldt Foundation) http://dx.doi.org/10.13039/100005156
    Keywords: 551.489 ; compound flood ; storm surge ; river floods ; sea level rise ; climate change ; Europe
    Type: article
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  • 6
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    Joint Trends in Flood Magnitudes and Spatial Extents Across Europe (2021)
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    Publication Date: 2021-09-27
    Description: The magnitudes of river floods in Europe have been observed to change, but their alignment with changes in the spatial coverage or extent of individual floods has not been clear. We analyze flood magnitudes and extents for 3,872 hydrometric stations across Europe over the past five decades and classify each flood based on antecedent weather conditions. We find positive correlations between flood magnitudes and extents for 95% of the stations. In central Europe and the British Isles, the association of increasing trends in magnitudes and extents is due to a magnitude-extent correlation of precipitation and soil moisture along with a shift in the flood generating processes. The alignment of trends in flood magnitudes and extents highlights the increasing importance of transnational flood risk management.
    Keywords: 551.48 ; flood ; synchrony ; magnitude ; climate change ; classification ; spatial statistics
    Language: English
    Type: map
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  • 7
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    Impact Forecasting to Support Emergency Management of Natural Hazards (2021)
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    Publication Date: 2021-10-27
    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 twofold 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 multihazard 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.
    Keywords: 550 ; impact forecasting ; natural hazards ; early warning
    Language: English
    Type: map
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