...

Easy Heading Macro
navigationExpandOption collapse-all-but-headings-1-2

Acronyms

Expand
title List of acronyms
Acronym Description C3S Copernicus Climate Change Service CDS Climate Data Store EQC Evaluation and Quality Control STATDOWN Statistical downscaled storm footprints NUTS Nomenclature of Territorial Units for Statistics

Introduction

Executive Summary

The Tier 3 indicators, described as such because they describe socio-economic impacts of the storms, are risk and loss indicators. These indicators have been built up using open-source information, with OpenStreetMap building level data as a basis. Each building is associated with a land cover type, a building type and related to these, a reconstruction type. Fragility curves, also from the public domain, are then applied to link the building type to the impact of the storm severity. This information is mapped to the NUTS3 statistical regions associated with the buildings and presented in these NUTS3 categories. Losses are tabulated per storm. As a Tier 3 indicator, the Loss indicator complements the other C3S Windstorm products as it describes the socio-economic impacts of windstorms within Europe. These loss indicators combine original and modelled climate data with additional geospatial and socio-economic information in a way that allows their use by the insurance sector. The loss indicator is complementary to the C3S windstorm risk data which provides an estimation of the potential losses that would occur within a particular location within a typical year. To aid comparison both the loss and risk data is presented in the same format.

Scope of Documentation

This document describes the C3S Tier 3 loss indicators using the standard C3S format for product descriptions, i.e., in terms of product target requirements, product overview, input data and method. It is based on the earlier C3S proof of concept contract (WISC) documents, particularly C3S_D426LOT2.KNMI.2.3.6_201911_loss_indicator_ description_V1.0 produced by Toon Haer on 28- 11-2019. The approach is further described in peer-reviewed publication (Koks & Haer, 2020).

Version History

Preceding the operational stage of the Windstorm Service for the Insurance sector, the pre- operational stage WISC¹ successfully demonstrated the estimation of economic losses for winter storm events over Europe, based on state-of-the-art numerical weather prediction models and economic loss models. The service applies a chain of models, from models to generate Tier 1 windstorm footprints to an economic model for estimating Tier 3 economic losses, where the latter uses the windstorm footprints as input. In the pre-operational stage the Tier 1 footprints were dynamically downscaled by the UK Met Office (UKMO) Unified mesoscale model based on the ERA- Interim and ERA-20C reanalysis datasets. As this model is not freely available therefore a different approach to develop Tier 1 windstorms was developed by KNMI. This new approach uses the new ERA5 wind fields, instead of the older ERA-interim wind fields, and applies a statistical downscaling using multiple linear regression (STATDOWN) approach described in van den Brink & Whan (2018). In the remainder of the user guide we will refer to the new footprints as STATDOWN footprints, and to the footprints from the pre-operational stage as WISC footprints.

Info
icon false
1https://wisc.climate.copernicus.eu/wisc/#/

Product Description

Product Target Requirements

Extreme wind events are among the costliest natural disasters in Europe, causing severe damages every year. For damage estimates, the community mostly relies on post-disaster data, which is often not publicly available. Few approaches offer more generic tools, but again these are often based on non-disclosed data. To offer a generic, high-resolution, reproducible, and publicly accessible tool, this dataset presents an estimate from a wind damage model that is built around publicly available hazard, exposure, and vulnerability data. The model is used to provide the current dataset that assesses building damages related to extratropical storms in Europe, but the methodology is applicable globally, given data availability, and to other hazards for which similar risk frameworks can be applied. The model is distributed as an open-source model to offer a transparent and useable windstorm damage model to a broad audience, and the dataset is provided through the Climate Data Store (CDS).

Product Overview

Data Description

Anchor
table1 table1

Table 1: Overview of key characteristics of the Tier 3 windstorm loss indicators

Variable Description

Anchor
table2 table2

Table 2: Overview and description of variables.

Input Data

Anchor
table3 table3

Table 3: Overview of climate model data for input to Tier 3 windstorm loss indicators, summarizing the model properties and available scenario simulations.

OpenStreetMaps (OSM)

All building footprint data are extracted from OSM, which has proven to be the most extensive dataset of publicly available building footprints for Europe. OpenStreetMap is a free, editable map of the whole world that is being built by volunteers largely from scratch and released with an open- content license. The OpenStreetMap License allows free (or almost free) access to our map images and all of our underlying map data. As OSM is user driven, it continuously evolves and improves, improving the building footprint coverage across Europe.

CORINE

CORINE is developed by the European Environmental Agency and distinguishes between 45 different land use classes, with a known percentage of residential, commercial, industrial, and other land use- classes. This CORINE dataset is used to classify OSM buildings into different sectoral landuse classes; agriculture, residential, commercial/industrial, and transport.

PAGER

PAGER, a US Geological Survey project, stands for the Prompt Assessment of Global Earthquakes for Response. Overall it is an automated system that takes in seismic data from remote sensors to estimate earthquake shaking and the scope and impact of earthquakes around the world. As part of this, PAGER links with the World Housing Encyclopedia to produce a global database of building stocks. While this is designed to assess vulnerability to earthquake shaking, the construction type information also provides characteristics that can be used directly to assess windstorm vulnerability.

Storm footprints

The storm footprints are based on statistical downscaling (STATDOWN) of the ERA5 dataset, using the main high resolution ERA5 field in each case. The operational footprints are derived from the new ERA5 storm tracks produced for the operational service.

Method

Background

The building level loss estimates are calculated using a conventional risk modelling framework (Fig. 1), where we define risk as a function of hazard – the probability and strength of an event with potential to cause harm; exposure – the value of assets subject to the hazard; and vulnerability – the susceptibility of the asset to hazards of a given severity. An overview of the modelling approach is show in Figure 1 and further described in 2.4.2.

...

Figure 1: Overview of the various steps for the loss estimations (Koks, Tiggeloven, et al., 2017)

Model / Algorithm

Hazard – To enable loss estimates, the storm footprints generated as netcdf format in WISC and STATDOWN are translated to geotiffs and reprojected to the ETR89 coordinate system (EPSG:3035), a similar coordinate system as the Corine Land Cover dataset (CORINE) (EEA, 2014). This provides geotiffs with wind speed information per grid cell, which can be overlaid with the exposure maps. Technical details of the STATDOWN (van den Brink and Whan, 2018).

...

To be able to distinguish between construction types for different countries, we use the PAGER database. The PAGER database defines 106 different building types for each country, which are aggregated to the 6 different building types considering in Feuerstein et al. (2011). Most of the European buildings fall into the latter two categories. Using the PAGER database, we obtain the share of each of the building types within a country (for example, 5% weak outbuildings, 30% strong brick structure, and 65% concrete building). The hazard, exposure, and vulnerability data is overlaid to obtain loss estimates. Loss estimates are made for each building footprints, for each vulnerability curve for different building construction type and multiplied by its relative share within the country. The loss estimates are aggregated for each NUTS3 region, for each country, and for each sector.

Validation

A full description of the sensitivity analysis (SA) is described in Koks & Haer (2020). We provide a summary here for the SA. The SA is performed in a Monte Carlo modelling framework following Crosetto et al. (2000) and Helton (1993). to investigate uncertainty and sensitivity related to the parameters. The following steps are performed: (1) assigning distributions to input parameters, (2) generating samples of different combinations of input parameters, (3) evaluating the model using the generated combinations of input parameters, and (4) analysing the results for uncertainty and sensitivity. Using SAlib, a publicly available Python library (Hermann, 2017), we perform a Delta Moment-Independent Measure (DMIM) analysis, as developed by Borgonovo (2007) and Plischke et al. (2013). This type of sensitivity analysis can be interpreted as a global sensitivity indicator which looks at the influence of input uncertainty on the entire output distribution without reference to a specific moment of the output (moment independence) and which can be defined also in the presence of correlations among the parameters. In total, we set up a set of 5000 different combinations of parameter values, focusing on the fragility curves. The sensitivity analysis shows that for each country/storm combination, the fragility curves are the most important driver of the results.

...

Figure 3: Loss estimates from the proof-of-concept phase (WISC), the current product using STATDOWN, in comparison with the vendor models and the observed losses.

Concluding Remarks

This dataset is build using a high-resolution damage model to estimate the damages to buildings due to extratropical windstorms in Europe. The approach provides flexibility in the derivation by developing the vulnerability curves from building level upwards. The approach is particularly valuable to support insurers' and academic assessments for post-disaster quick-scans and estimates of potential wind damage towards the future, allowing them to use an open-source and transparent approach. While we demonstrate the methodology on a continental scale, it is not bound by a geographic region, and thus can be applied globally provided that data is available.

References

Borgonovo, E. (2007) A new uncertainty importance measure. Reliab. Eng. Syst. Saf. 92, 771–784.

...

Info
icon false
This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

Related articles

Content by Label
showLabels false max 5 spaces CKB showSpace false sort modified reverse true type page cql label in ("wind","sis") and type = "page" and space = "CKB" labels era-interim