6.3.1 CLIMADA model demo

CLIMADA – an open-source and -access global
probabilistic risk modelling and
adaptation economics platform
NAP Expo: “Raising adaptation ambition by advancing National Adaptation Plans”
8 to 12 April 2019, Songdo, Republic of Korea
Evelyn Mühlhofer, Prof. Dr. David N. Bresch, Institute for Environmental Decisions, ETH Zurich/MeteoSwiss
www.wcr.ethz.ch, CLIMADA: https://github.com/davidnbresch/climada_python

Guyana: Flash flood
risk to a developing
urban area
Tanzan
ia
Tanzania: Drought
risk to health and
power generation
Samoa: Risk of sea level
rise to a small island
state
Florida: Hurricane risk to
public and private assets
Mali: Risk of climate
zone shift to agriculture
India: Drought risk to
agriculture
US Gulf Coast:
Hurricane risk to the
energy system
New York: Cyclones
and surge risk to a
metropolis
Hull, UK: Flood and
storm risk to urban
property
China: Drought risk
to agriculture
El Salvador: Flood
and landslide risk to
vulnerable people
Bangladesh: Flood risk
to a fast-developing city
Caribbean: Hurricane
risk to small islands
8x
THE WORLD BANK
>20 adaptation case studies worldwide

Overview
11 April 2019
CLIMADA risk assessment & adaptation economics platform,
Evelyn Mühlhofer, WCR Group, ETH Zürich
2

• Introduction
• Economics of Climate Adaptation
• Risk
• CLIMADA
• Outputs
• Core Components
• Input
• Hands-on demonstrations in Python: Example of Tropical Cyclone Risk in
Bangladesh
• Discussion
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Introduction
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“Provide decision makers on local to national scales with the facts and methods necessary to design and execute
an adaptation strategy to natural hazards in a changing climate, in a robust and replicable way”
Rigorous risk management approach to
(1) identify and …
(2) assess (quantify) risk today, additional risk due to socio-economic development and climate change
(3) Propose, appraise and prioritize a basket of adaptation measures to address weather and
climate risk on an economic basis (e.g. cost-benefit metric)
Supported by the open-source & -access CLIMADA simulation platform
https://github.com/CLIMADA-project/climada_python
Guidebook for Practitioners & Methodology:
www.kfw-entwicklungsbank.de/PDF/Download-Center/Materialien/2016_No6_Guidebook_Economics-of-Climate-Adaptation_EN.pdf
https://www.ethz.ch/content/dam/ethz/special-interest/usys/ied/wcr-dam/documents/Economics_of_Climate_Adaptation_ECA.pdf
Economics of Climate Adaptation (ECA)

ECA (2) - Risk Quantification
Risk
today
Total future
risk
Socio-
Economic
Development
Projections
Climate change
Scenarios
IncreasingRisk
April 8, 2019 Risk Analysis & Adaptation Option Appraisal. Evelyn Mühlhofer,
WCR Group, ETH Zürich
16

Climate change
ECA (2) - Risk Quantification
risk = hazard x
exposure x
vulnerability
(= probability x intensity)

General finding1:
40-60% of risk can be cost-effectively avoided up to 2030Total future
risk
Climate
adaptation
Residual
risk
• General finding: 40-60%
of risk can be cost-
effectively avoided up to
2030
• Further reduction only at
(excessively) high costs or
not at all
ECA (3) Economic Appraisal of Options
Reduced
Damage

ECA (3) - Economic Appraisal of Options
benefit/costratio(US$/US$)
Cumulated number of lives saved over timespan X
Climate
adaptation
Residual
risk
Reduced
Damage
Reduced
Damage
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1. Problem definition, Goal
7. Implementation
6. Decision (?)
NO
NO
Criteria
met?
Problem
defined
correctly?
2. Decision criteria
3. Risk analysis
4. Identify options
Simulation
platform
8. Monitoring
5. Options appraisal
see e.g : Souvignet, Wieneke, Müller & Bresch, 2016: Economics of Climate Adaptation (ECA) - Guidebook for Practitioners. Materials on Development Financing, UNU, KfW.
https://www.kfw-entwicklungsbank.de/PDF/Download-Center/Materialien/2016_No6_Guidebook_Economics-of-Climate-Adaptation_EN.pdf Figure based upon IPCC und UKCIP
Decision Cycle, ECA and CLIMADA

CLIMADA
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CLIMADA Outputs
0
5
10
15
5
3
4
12
Lossamount109
+ 152%
Annual Expected Loss (AEL)
2012 today's
expected loss
Incremental increase
from economic
gow th; no climate
change
Incremental increase
from climate change
2030, total
expected loss
USFL MiamiDadeBrow ardPalmBeach2012  TCNA hazard event set 2012
USFL MiamiDadeBrow ardPalmBeach2030  TCNA hazard event set 2012
USFL MiamiDadeBrow ardPalmBeach2030  climate change scenario based on TCNA hazard event set 2012
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Screen Shot 2019-03-28 at 14.50.27
impact
intensity
CLIMADA
probabilistic
event-based
simulation
vulnerability
exposure
hazard
resilience measures
outputs:
Risk analysis
Risk mapping
Early Warning
Economic appraisal of
measures
Quantification of
uncertainty
CLIMADA: Components

CLIMADA: Open Access & Open Source
https://github.com/CLIMADA-project/climada_python and
https://climada-python.readthedocs.io/en/latest/install.html
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Demo…
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Exposures
Disc
Rates
Impact
Funcs
Measures
Hazard
excel
mat
pkl
CLIMADA: Workflow
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CLIMADA: Hazard
Hazard Type Coverage Resolution
Tropical Cyclone global 10x10km and 1x1 km
European winter storms Europe 10x10km
Earthquake and Volcano global 10x10km and 1x1 km
Wildfire global 10x10km and 1x1 km
Flood (Rivers) global 1x1 km
Landslides global 1x1 km
Droughts global 10x10km and 1x1 km
Format: for each geo-coded point  frequency, intensity & time of event
Data sources: modular, default provides global open-source data
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Demo…
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2007 Super Clyclonic Storm SIDR
Damage: $1.7 billion (2007 USD)
Fatalities: 3,447–15,000
Highest 3-min sustained wind speed: 215 km/h

CLIMADA: Exposure
• LitPop
• night light intensities and gridded population count
• 1kmx1km
• BlackMarble
• night light intensities for a specific year
• 1kmx1km resolution
• Monetary asset values (non-financial wealth, GDP, ..)
• Population
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Demo…
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CLIMADA: Vulnerability
• Intensity-Impact relationship
• “Impact Functions”
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Demo…
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CLIMADA: Risk Quantification (I)
• “Risk” ~ “Impact” ~ “Expected Damage”
• Combination of all three elements – Exposure, Hazard, Vulnerability
• per year or per specified time frame, damage exceedance curve
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Demo…
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Expected Annual Damage today: (exemplary!) 6.887e+08 USD

CLIMADA: Risk Quantification (II)
• Total Future Risk =
• Risk today
• + Risk from socio-economic development
• + Risk from climate change
• From Today‘s to Future Risk
• Same procedure as before: Hazard, Exposure, Vulnerability
• Modifications:
• Change Exposure (population / asset values)
• Change Hazard Set (frequency / intensity)
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CLIMADA: Adaptation Measure
Parametrization
• Modifies Hazard
• Frequency
• Intensity
• Modifies Impact Function
• Shift
• Reduction
• Cut-off
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CLIMADA: Adaptation Measure
Parametrization - Example
• Building Code in Miami, FL:
• Houses must be built to withstand wind intensities up to 140 mph
(62.5 m/s)
• Targets Impact function: Cut-off
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Demo…
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CLIMADA: Adaptation Measure Appraisal
• Same procedure as before: Hazard, Exposure, Vulnerability
• Comparison of damage: total risk before & after modifications
• Weighting against implementation costs
 Cost-Benefit metrics
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Uncertainty, Development, Climate Change
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CLIMADA
vulnerability
exposure
weather
 hazard
development
scenarios
climate scenarios
Impact-response
relationships
CLIMADA: Uncertainty, Development, Climate
Change
resilience measures
parameters

Discussion
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• Questions?
• Utility?
• Expertise?
• Input & Output data?
• Alternatives?
• Experience?
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Back-up
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Risk assessment (2)
Cost-benefit analysis (3)
Roadmap
Aligning risk appetite with development priorities
Incorporating further criteria to cost-benefit ratio
(Re-)prioritizing risk reduction and transfer measures
Integrating into NAP and related planning documents
Develop a roadmap including priority initiatives
Using this roadmap and business case for funding discussions
Speed-up implementation with additional funding; strengthen resilience
The Business Case for Adaptation (2) + (3)
Risk
today
Socio-
Economic
Developm
ent
Total
future
risk
Climate
change

CLIMADA
Architecture
in Python
Note:
Illustrative
purpose; does
not correspond
to the most up-
to-date
architecture in
detail

Note on inputs – the user defines it all!
▪ Links to many databases, easiest interface via Excel
▪ Expects data to be geo-encoded
▪ Garbage in – garbage out, i.e. climada supports data cleaning, but does not do it on its own ;-)
 All documented in https://github.com/davidnbresch/climada/blob/master/docs/climada_manual.pdf
CLIMADA

CLIMADA
Architecture
in Python

What skills & data are required?
Level of skills needed by field
of expertise
From: Materials on
Development Financing:
Economics of Climate
Adaptation (ECA) -
Guidebook for Practitioners,
KfW Development Bank
April 8, 2019
Risk Analysis & Adaptation Option Appraisal. Evelyn Mühlhofer,
WCR Group, ETH Zürich
39

Development
Tools
• libraries (python 3.6):
• numpy
• scipy
• matplotlib, cartopy
• xarray
• pandas
• scikit-learn
• h5py, netcdf4
• pathos
• Pint
• ...
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Deployment
• package manager: pip, conda
• virtual environment: conda, virtualenv, pipenv
• packaging: setuptools
• version control:
• GitHub
• fork to GitLab at MeteoSwiss
• coding rules:
• pylint
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Tutorials / Manual
• In Jupyter Notebook currently available:
• Hazard (1x general / 1x Tropical Cylcones)
• Exposure (1x general / 1x LitPop /1x BlackMarble)
• Impact Functions
• Manual in written text form
• KfW ECA Guidebook
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Useful Data SourcesWe do mention key sources in the respective climada modules, but some data we came across is worth mentioning in more general terms.
• http://www.naturalearthdata.com and directly http://www.naturalearthdata.com/downloads: global shape files (see e.g. climada_shaperead and the
module country_risk). One will likely need the MATLAB mapping toolbox to ease working with these files. Could be used to either improve hazard sets
(e.g. using location of reefs in surge model…) or assets.
• http://download.geofabrik.de: highly detailed shape files for most countries (e.g. more than 1.5 mio shapes of building’s outlines in Switzerland).
Could be used in conjunction with climada module country_risk to further refine the asset base. See also http://www.openstreetmap.org/about,
https://mapzen.com and http://planet.openstreetmap.org
• Also www.diva-gis.org/gdata and www.diva-gis.org/Data for GIS data almost any country and also www.eea.europa.eu/data-and-maps/data/urban-
atlas
• GIS data for Seychelles https://www.webgis.gov.sc/map_default.phtml
• SRTM elevation data (3-arc seconds resolution ~90m) http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp and generally http://srtm.csi.cgiar.org
• Hydrosheds, based on SRTM, manipulated for river routing http://hydrosheds.cr.usgs.gov/index.php
• http://www.geoportal.org/web/guest/geo_home_stp
• https://www.drought.gov/drought/content/products-current-drought-and-monitoring-drought-indicators/palmer-drought-severity-index
• http://beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4/sets/browse population density, water bodies, admin center points with population
estimates (http://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates)
• https://search.earthdata.nasa.gov earth data search
• https://datahub.io/ open data sets of various kinds
• http://databank.worldbank.org/data/home.aspx macroeconomic data
• http://databank.worldbank.org/data/reports.aspx?source=subnational-population sub-national population (admin1)
• http://www.trademap.org/Index.aspx trade statistics
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• http://www.cger.nies.go.jp/gcp/population-and-gdp.html population and GDP
• http://data.footprintnetwork.org global country-level data on footprint etc.
• https://hazards.fema.gov/femaportal/wps/portal/NFHLWMSkmzdownload FEMA, US
• http://www.nws.noaa.gov/gis/shapepage.htm NOAA shape files (mainly US)
• http://www.mapcruzin.com/free-netherlands-arcgis-maps-shapefiles.htm NLD shape files, http://www.mapcruzin.com/do-it-
yourself-gis-maps-shapefiles/ mainly US and http://www.mapcruzin.com/free-world-country-arcgis-maps-shapefiles.htm global
• http://floodobservatory.colorado.edu floods
• https://freegisdata.rtwilson.com good global overview of free GIS data
• https://www.census.gov/geo/maps-data/data/cbf/cbf_msa.html US census
• https://data.oecd.org/gdp/real-gdp-forecast.htm OECD GDP past and future
• http://colorbrewer2.org good color scales to print well etc.
• http://www.reefbase.org global coral reef data, good preview via http://www.reefbase.org/gis_maps/default.aspx
• http://datadryad.org/resource/doi:10.5061/dryad.dk1j0 Gridded global datasets for GDP and Human Development Index over
1990-2015
• http://maps.elie.ucl.ac.be/CCI/viewer/ land cover map, resolution 10arcsec (300m)
• http://www.globallandcover.com/GLC30Download/index.aspx 30m land cover map
• See also e.g. http://data.geocomm.com/catalog
• https://pmm.nasa.gov/applications/global-landslide-model landslide model/map
• http://ghsl.jrc.ec.europa.eu/data.php global human settlement layer
• Models
• http://www.hrc-lab.org Hydrologic Research Center, (local) flood model
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Contact
Prof. Dr. David N. Bresch
Head of the Weather and Climate Risks Group
dbresch@ethz.ch
Dr. Grabiela Aznar Siguan
Senior Researcher, Implementation of CLIMADA Python
gabriela.aznar@usys.ethz.ch
Evelyn Mühlhofer
Research Associate
evelyn.muehlhofer@usys.ethz.ch
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References
Images
p. 18 Wikipedia
p. 20 Maps of Bangladesh Blogspot (http://maps-of-bangladesh.blogspot.com/ ) ; World-Map
(http://ontheworldmap.com/bangladesh/large-detailed-map-of-bangladesh-with-cities.html )
p. 22 Getty Images; K. Emanuel (2011) Global Warming Effects on U.S. Hurricane Damage.
p. 27 SEVENTH INTERNATIONAL WORKSHOP ON TROPICAL CYCLONES
(https://www.wmo.int/pages/prog/arep/wwrp/tmr/otherfileformats/documents/4_2.pdf )
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6.3.1 CLIMADA model demo

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6.3.1 CLIMADA model demo