Capturing regional differences in flood vulnerability improves flood loss estimation

Flood vulnerability is quantified by loss models which are developed using either empirical or synthetic approaches. In reality, processes influencing flood risk are stochastic and loss predictions bear significant uncertainty, especially due to differences in vulnerability across exposed objects and regions. However, many state-of-the-art flood loss models are deterministic, i.e., they do not account for data and model uncertainty. The Bayesian Data-Driven Synthetic (BDDS) model was one of the first approaches that used empirical data to reduce the prediction errors at object-level and enhance the reliability of synthetic flood loss models. However, the BDDS model does not account for regional differences in vulnerability which may result in over-/under-estimation of losses in some regions. In order to overcome this limitation, this study introduces a hierarchical parameterization of the BDDS model which enhances synthetic flood loss model predictions by quantifying regional differences in vulnerability. The hierarchical parameterization makes optimal use of the process information contained in the overall data set for the various regional applications, so that it is particularly suitable for cases in which only a small amount of empirical data is available. The implementation and performance of the hierarchical parametrization is demonstrated with the Multi-Colored Manual (MCM) loss functions and empirical damage dataset from the UK consisting of residential buildings from the regions Appleby, Carlisle, Kendal and Cockermouth that suffered losses during the 2015 flood event. The developed model improves prediction accuracy of flood loss compared to MCM by reducing the absolute error and bias by at least 23 and 90%, respectively. The model reliability in terms of hit rate (i.e., the probability that the observed value lies in the 90% high density interval of predictions) is 88% for residential buildings from the same regions used for calibration and 73% for residential buildings from new regions. The approach is of high practical relevance for all regions where only limited amounts of empirical flood loss data is available

Optimized Effective Potential Method in Current-Spin Density Functional Theory

Current-spin density functional theory (CSDFT) provides a framework to describe interacting many-electron systems in a magnetic field which couples to both spin- and orbital-degrees of freedom. Unlike in usual (spin-) density functional theory, approximations to the exchange-correlation energy based on the model of the uniform electron gas face problems in practical applications. In this work, explicitly orbital-dependent functionals are used and a generalization of the Optimized Effective Potential (OEP) method to the CSDFT framework is presented. A simplifying approximation to the resulting integral equations for the exchange-correlation potentials is suggested. A detailed analysis of these equations is carried out for the case of open-shell atoms and numerical results are given using the exact-exchange energy functional. For zero external magnetic field, a small systematic lowering of the total energy for current-carrying states is observed due to the inclusion of the current in the Kohn-Sham scheme. For states without current, CSDFT results coincide with those of spin density functional theory.Comment: 11 pages, 3 figure

Flood loss models and risk analysis for private households in can Tho City, Vietnam

Vietnam has a long history and experience with floods. Flood risk is expected to increase further due to climatic, land use and other global changes. Can Tho City, the cultural and economic center of the Mekong delta in Vietnam, is at high risk of flooding. To improve flood risk analyses for Vietnam, this study presents novel multi-variable flood loss models for residential buildings and contents and demonstrates their application in a flood risk assessment for the inner city of Can Tho. Cross-validation reveals that decision tree based loss models using the three input variables water depth, flood duration and floor space of building are more appropriate for estimating building and contents loss in comparison with depth-damage functions. The flood risk assessment reveals a median expected annual flood damage to private households of US$3340 thousand for the inner city of Can Tho. This is approximately 2.5%of the total annual income of households in the study area. For damage reduction improved flood risk management is required for the Mekong Delta, based on reliable damage and risk analyses

Needed: a systems approach to improve flood risk mitigation through private precautionary measures

Private precautionary measures play an increasingly important role in flood risk management. The degree to which private precautionary measures mitigate flood risk depends mainly on the type of measure (and how effective it is) and how frequently and successfully it is implemented. These aspects are influenced by a complex interaction of physical and socio-economic processes, which makes the assessment and the prediction of the mitigation of flood risk via private precautionary measures a challenge. This paper provides an overview of factors and processes that influence the implementation and effectiveness of private precaution in mitigating flood risk, underpinning it with highlights from international examples. We recommend private precautionary measures for further use to improve flood risk mitigation, but stress that they need to be considered and implemented through a holistic systems approach to maximize their effectiveness

Multi-variate analyses of flood loss in Can Tho city, Mekong delta

Floods in the Mekong delta are recurring events and cause substantial losses to the economy. Sea level rise and increasing precipitation during the wet season result in more frequent floods. For effective flood risk management, reliable losses and risk analyses are necessary. However, knowledge about damaging processes and robust assessments of flood losses in the Mekong delta are scarce. In order to fill this gap, we identify and quantify the effects of the most important variables determining flood losses in Can Tho city through multi-variate statistical analyses. Our analysis is limited to the losses of residential buildings and contents. Results reveal that under the specific flooding characteristics in the Mekong delta with relatively well-adapted households, long inundation durations and shallow water depths, inundation duration is more important than water depth for the resulting loss. However, also building and content values, floor space of buildings and building quality are important loss-determining variables. Human activities like undertaking precautionary measures also influence flood losses. The results are important for improving flood loss modelling and, consequently, flood risk assessments in the Mekong delta

Bayesian Data-Driven approach enhances synthetic flood loss models

Flood loss estimation models are developed using synthetic or empirical approaches. The synthetic approach consists of what-if scenarios developed by experts. The empirical models are based on statistical analysis of empirical loss data. In this study, we propose a novel Bayesian Data-Driven approach to enhance established synthetic models using available empirical data from recorded events. For five case studies in Western Europe, the resulting Bayesian Data-Driven Synthetic (BDDS) model enhances synthetic model predictions by reducing the prediction errors and quantifying the uncertainty and reliability of loss predictions for post-event scenarios and future events. The performance of the BDDS model for a potential future event is improved by integration of empirical data once a new flood event affects the region. The BDDS model, therefore, has high potential for combining established synthetic models with local empirical loss data to provide accurate and reliable flood loss predictions for quantifying future risk

Interpolated wave functions for nonadiabatic simulations with the fixed-node quantum Monte Carlo method

Simulating nonadiabatic effects with many-body wave function approaches is an open field with many challenges. Recent interest has been driven by new algorithmic developments and improved theoretical understanding of properties unique to electron-ion wave functions. Fixed-node diffusion Monte Caro is one technique that has shown promising results for simulating electron-ion systems. In particular, we focus on the CH molecule for which previous results suggested a relatively significant contribution to the energy from nonadiabatic effects. We propose a new wave function ansatz for diatomic systems which involves interpolating the determinant coefficients calculated from configuration interaction methods. We find this to be an improvement beyond previous wave function forms that have been considered. The calculated nonadiabatic contribution to the energy in the CH molecule is reduced compared to our previous results, but still remains the largest among the molecules under consideration.Comment: 7 pages, 3 figure

Evolutionary leap in large-scale flood risk assessment needed

Current approaches for assessing large-scale flood risks contravene the fundamental principles of the flood risk system functioning because they largely ignore basic interactions and feedbacks between atmosphere, catchments, river-floodplain systems and socio-economic processes. As a consequence, risk analyses are uncertain and might be biased. However, reliable risk estimates are required for prioritizing national investments in flood risk mitigation or for appraisal and management of insurance portfolios. We review several examples of process interactions and highlight their importance in shaping spatio-temporal risk patterns. We call for a fundamental redesign of the approaches used for large-scale flood risk assessment. They need to be capable to form a basis for large-scale flood risk management and insurance policies worldwide facing the challenge of increasing risks due to climate and global change. In particular, implementation of the European Flood Directive needs to be adjusted for the next round of flood risk mapping and development of flood risk management plans focussing on methods accounting for more process interactions in flood risk systems