Characterization of Coastal Flood Damage States for Residential Buildings

Abstract. Considering the likely increase of coastal flooding in Small Island Developing States (SIDS), coastal managers at the local and global level have been developing initiatives aimed at implementing Disaster Risk Reduction (DRR) measures and adapting to climate change. Developing science-based adaptation policies requires accurate coastal flood risk (CFR) assessments, which are often subject to the scarcity of sufficiently accurate input data for insular states. We analysed the impact of uncertain inputs on coastal flood damage estimates, considering: (i) significant wave height, (ii) storm surge level and (iii) sea level rise (SLR) contributions to extreme sea levels, as well as the error-driven uncertainty in (iv) bathymetric and (v) topographic datasets, (vi) damage models and (vii) socioeconomic changes. The methodology was tested through a sensitivity analysis using an ensemble of hydrodynamic models (XBeach and SFINCS) coupled with an impact model (Delft-FIAT) for a case study at the islands of São Tomé and Príncipe. Model results indicate that for the current time horizon, depth damage functions (DDF) and digital elevation model (DEM) dominate the overall damage estimation uncertainty. We find that, when introducing climate and socioeconomic uncertainties to the analysis, SLR projections become the most relevant input for the year 2100 (followed by DEM and DDF). In general, the scarcity of reliable input data leads to considerable predictive error in CFR assessments in SIDS. The findings of this research can help to prioritise the allocation of limited resources towards the acquisitions of the most relevant input data for reliable impact estimation.

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Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

Energies ◽

10.3390/en12112055 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2055 ◽

Cited By ~ 14

Author(s):

Francesco Mancini ◽

Gianluigi Lo Basso ◽

Livio De Santoli

Keyword(s):

Data Analysis ◽

Energy Use ◽

Residential Buildings ◽

Automation System ◽

Research Activity ◽

Flexible Loads ◽

On Line ◽

House Floor ◽

Statistical Sample

This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.

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Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

10.5194/nhess-2017-342 ◽

2017 ◽

Author(s):

Francesca Carisi ◽

Kai Schröter ◽

Alessio Domeneghetti ◽

Heidi Kreibich ◽

Attilio Castellarin

Keyword(s):

Residential Buildings ◽

Economic Value ◽

Flood Damage ◽

Data Sets ◽

Local Data ◽

Data Set ◽

Damage Models ◽

Flood Loss ◽

Loss Data ◽

Loss Models

Abstract. Simplified flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data sets which is often rooted in a lack of protocols and reference procedures for compiling loss data sets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 52 km2 in Northern Italy. For this event we compiled a comprehensive flood loss data set of affected private households including buildings footprint, economic value, damages to contents, etc. based on information collected by local authorities after the event. By analysing this data set we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multi-variable loss models for residential buildings and contents. The accuracy of the proposed models is compared with those of several flood-damage models reported in the literature, providing additional insights on the transferability of the models between different contexts. Our results show that (1) even simple uni-variable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multi-variable models that consider several explanatory variables outperform uni-variable models which use only water depth. However, multi-variable models can only be effectively developed and applied if sufficient and detailed information is available.

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Future flood risk exacerbated by the dynamic impacts of sea level rise

10.21203/rs.3.rs-63173/v1 ◽

2020 ◽

Author(s):

Matthew Bilskie ◽

Diana Del Angel ◽

David Yoskowitz ◽

Scott Hagen

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Flood Risk ◽

Residential Buildings ◽

Flood Damage ◽

Coastal Communities ◽

Modeling Framework ◽

Sea Levels ◽

Coastal Landscapes ◽

Block Level

Abstract A growing concern of coastal communities is an increase in flood risk and non-monetary consequences as a result of climate-induced impacts such as sea level rise (SLR). While previous studies have outlined the importance of quantifying future flood risk, most have focused on broad aggregations of monetary loss using bathtub SLR-type models. Here we quantify, for the first time at the multi-state scale, actual impacts to coastal communities at the census block level using a dynamic, high-resolution, biogeophysical modeling framework that accounts for future sea-levels and coastal landscapes. We demonstrate that future SLR can increase the number of damaged residential buildings by 600%, the population of displaced people by 500% and the need for shelter assistance of up to 460% from present-day conditions. An exponential increase in flood damage associated with increasing sea level deems it essential for stakeholders to plan for plausible future conditions rather than the current reality.

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Relationships between empirical damage and direct/indirect costs for the assessment of seismic loss scenarios

Bulletin of Earthquake Engineering ◽

10.1007/s10518-021-01235-5 ◽

2021 ◽

Author(s):

Marco Di Ludovico ◽

Giuseppina De Martino ◽

Andrea Prota ◽

Gaetano Manfredi ◽

Mauro Dolce

Keyword(s):

Empirical Data ◽

Residential Buildings ◽

Fragility Curves ◽

Unit Cost ◽

Indirect Costs ◽

Mitigation Strategies ◽

Damage State ◽

Reconstruction Process ◽

2009 L’Aquila Earthquake ◽

Damage States

AbstractThe definition of relationships between damage and losses is a crucial aspect for the prediction of seismic effects and the development of reliable models to define risk maps, loss scenarios and mitigation strategies. The paper focuses on the analysis of post-earthquake empirical data to define relationships between buildings’ damage expressed as usability rating or as global damage state and the associated costs for repair (i.e. direct costs) or for population assistance (i.e. a part of total indirect costs). The analysis refers to the data collected on residential buildings damaged by 2009 L'Aquila earthquake. For different usability rating or damage states, the paper presents the costs expressed in terms of percentage with respect to the reference unit cost of a new building (%Cr and %Ca for repair and population assistance costs, respectively). In particular, the costs analysis refers to undamaged, lightly or severely damaged buildings classified according to usability rating (i.e. A, B-C or E according to Italian classification) or to five different global Damage States (DSs). DSs comply with European Macroseismic Scale (EMS-98) and derive from literature available matrices properly defined to convert empirical damage to structural and non-structural components into building global damage. The %Cr probability density functions and relevant statistics derive from the analysis of actual data of post-earthquake reconstruction process, while, to determine those related to %Ca, a deep analysis of population assistance types, person/month assistance cost for each assistance form, and a methodology to associate such costs to each building are herein presented and discussed. Finally, the paper presents a relationship calibrated on empirical data to directly correlate repair costs on a building with assistance costs to their occupants. The relationships between empirical damage and direct and indirect costs herein presented are of paramount importance because they allow reliable loss scenarios to be defined by simply using literature fragility curves (defined according to empirical or mechanical approaches) aimed at evaluating the probability of exceeding different usability rating or damage states of existing buildings.

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Characterization of Volatile Organic Compounds in New Residential Buildings Before Moving-in

Journal of Environmental Science International ◽

10.5322/jes.2012.21.1.11 ◽

2012 ◽

Vol 21 (1) ◽

pp. 11-21

Author(s):

Seung-Ho Shin ◽

Wan-Kuen Jo

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Residential Buildings ◽

Volatile Organic

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Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology

Energies ◽

10.3390/en14175556 ◽

2021 ◽

Vol 14 (17) ◽

pp. 5556

Author(s):

Benedetto Grillone ◽

Gerard Mor ◽

Stoyan Danov ◽

Jordi Cipriano ◽

Florencia Lazzari ◽

...

Keyword(s):

Linear Regression ◽

Energy Use ◽

Residential Buildings ◽

Data Driven ◽

Multilevel Regression ◽

Measurement And Verification ◽

Bayesian Linear Regression ◽

Bayesian Methodologies ◽

Posterior Estimation

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

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