Asset exposure data for global physical risk assessment

Eberenz, Samuel; Stocker, Dario; Röösli, Thomas; Bresch, David N.

doi:10.5194/essd-12-817-2020

Cited by 49 publications

(63 citation statements)

References 27 publications

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“…Here, we use gridded asset exposure value at a resolution of 10 km x 10 km. The dataset is based on the disaggregation of national estimates of total asset value (TAV , Table A3) proportional to the product of nightlight intensity and population count (Eberenz et al, 2020). Following the approach in GAR 2013 (De Bono and Mora, 2014), the 135 TAV per country is represented by produced capital stock of 2014 from the World Bank Wealth Accounting (World Bank, 2019a).…”

Section: Asset Exposurementioning

confidence: 99%

“…Out of the 62 countries used for calibration, 32 come with produced capital estimates. For the remaining 30, an estimate of non-financial wealth is used as a fall back (Eberenz et al, 2020), based on GDP of 2014 from the World Bank Open Data portal (World Bank, 2019b) combined with an GDP-to-wealth factor from the Global Wealth Report (Credit Suisse Research Institute, 2017). The asset exposure dataset utilized here and a detailed overview over limitations and data 140 availability per country is documented in Eberenz et al (2020).…”

Section: Asset Exposurementioning

confidence: 99%

“…The exposure component can be represented by the spatial distribution of people, assets or economic values potentially affected by TCs (Geiger et al, 2018;Ward et al, 2020). For the modelling of direct economic damage, exposure is usually derived from building inventories for local risk assessments (Sealy and Strobl, 2017), or estimated by spatially disaggregating national asset value estimates (De Bono and Mora, 2014;Eberenz et al, 2020;Gettelman et al, 2017).…”

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confidence: 99%

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Regional tropical cyclone impact functions for globally consistent risk assessments

Eberenz¹,

Lüthi²,

Bresch³

2020

Preprint

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Abstract. Assessing the adverse impacts caused by tropical cyclones has become increasingly important, as both climate change and human coastal development increase the damage potential. In order to assess tropical cyclone risk, direct economic damage is frequently modelled based on hazard intensity, asset exposure and vulnerability, the latter represented by impact functions. In this study, we show that assessing tropical cyclone risk on a global level with one single impact function calibrated for the USA – which is a typical approach in many recent studies – is problematic, biasing the simulated damages by as much as a factor of 36 in the North West Pacific. Thus, tropical cyclone risk assessments should always consider regional differences in vulnerability, too. This study proposes a calibrated model to adequately assess tropical cyclone risk in different regions by fitting regional impact functions based on reported damage data. Applying regional calibrated impact functions within the risk modelling framework CLIMADA at a resolution of 10 km worldwide, we find global annual average direct damage caused by tropical cyclones to range from 51 up to 121 billion USD (current value of 2014, 1980–2017), with the largest uncertainties in the West Pacific basin, where the calibration results are the least robust. To better understand the challenges in the West Pacific and to complement the global perspective of this study, we explore uncertainties and limitations entailed in the modelling setup for the case of the Philippines. While using wind as a proxy for tropical cyclone hazard proves to be a valid approach in general, the case of the Philippines reveals limitations of the model and calibration due to the lack of an explicit representation of sub-perils such as storm surge, torrential rainfall, and landslides. The globally consistent methodology and calibrated regional impact functions are available online as a Python package, ready for application in practical contexts like physical risk disclosure and providing more credible information for climate adaptation studies.

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Section: Asset Exposurementioning

confidence: 99%

Section: Asset Exposurementioning

confidence: 99%

mentioning

confidence: 99%

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Regional tropical cyclone impact functions for globally consistent risk assessments

Eberenz¹,

Lüthi²,

Bresch³

2020

Preprint

Self Cite

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“…Exposure data for the impact assessment of wildfires was taken from LitPop (Eberenz et al, 2020b). This data set combines night light intensity and population density to spatially distribute macroeconomic indicators (such as GDP, produced capital or total asset value) onto grid cells at resolutions as fine as 1 km globally.…”

Section: Asset Exposurementioning

confidence: 99%

“…We combine historical fire hazards from satellite data (Giglio et al, 2016) with CLIMADA's exposure model Lit-Pop (Eberenz et al, 2020b). We then assess economic impacts with a vulnerability component calibrated using impact data of past events from the disaster risk database EM-DAT (Guha-Sapir, 2021) (Sect.…”

Section: Introductionmentioning

confidence: 99%

Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

Lüthi

Aznar–Siguán

Fairless³

et al. 2021

Geosci. Model Dev.

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Abstract. In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organizations, governmental authorities, and investors and portfolio owners are increasingly required to account for climate-related physical risks. In response to these societal challenges, we present an extension to the open-source and open-access risk modelling platform CLIMADA (CLImate ADAptation) for modelling economic impacts of wildfires in a globally consistent and spatially explicit approach. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 km using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for tropical cyclones. To complement the global perspective of this study, we conducted two case studies on the recent megafires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment, near-real-time impact estimates or physical climate risk disclosure.

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Vulnerability in a Tropical Cyclone Risk Model: Philippines Case Study

Baldwin

Lee

Walsh³

et al. 2022

Preprint

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The authors describe a tropical cyclone risk model for the Philippines, using methods that are open-source and can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally forced tropical cyclone hazard model (using environmental forcing from the recent historical period) are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and nightlights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values. Use of different vulnerability parameters for the region around Manila yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Even then, however, the model predicts no losses for a substantial number of historical storms which did in fact produce them, a difference the authors hypothesize is at least in part due to the use of wind speed as the sole metric of TC hazard, omitting explicit representation of flooding due to storm surge and/or rainfall.

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Asset exposure data for global physical risk assessment

Cited by 49 publications

References 27 publications

Regional tropical cyclone impact functions for globally consistent risk assessments

Regional tropical cyclone impact functions for globally consistent risk assessments

Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

Vulnerability in a Tropical Cyclone Risk Model: Philippines Case Study

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