What's in the download?
This starter data kit collects extracts from global, open datasets
relating to climate hazards and infrastructure systems. Many of them
are visualised on an interactive map in the
GRI Risk Viewer.
These extracts are derived from global datasets which have been
clipped to the national scale (or subnational, in cases where national
boundaries have been split, generally to separate outlying islands or
non-contiguous regions), using Natural Earth (2023) boundaries, and is
not meant to express an opinion about borders, territory or
sovereignty.
Human-induced climate change is increasing the frequency and severity
of climate and weather extremes. This is causing widespread, adverse
impacts to societies, economies and infrastructures. Climate risk
analysis is essential to inform policy decisions aimed at reducing
risk. Yet, access to data is often a barrier, particularly in low and
middle-income countries. Data are often scattered, hard to find, in
formats that are difficult to use or requiring considerable technical
expertise. Nevertheless, there are global, open datasets which provide
some information about climate hazards, society, infrastructure and
the economy. This "data starter kit" aims to kickstart the process and
act as a starting point for further model development and scenario
analysis.
Hazards:
- coastal and river flooding (Ward et al, 2020)
-
extreme heat and drought (Russell et al 2023, derived from Lange et
al, 2020)
-
tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal
et al 2020 and Bloemendaal et al 2022)
Exposure:
- population (Schiavina et al, 2023)
- built-up area (Pesaresi et al, 2023)
- roads (OpenStreetMap, 2023)
- railways (OpenStreetMap, 2023)
- power plants (Global Energy Observatory et al, 2018)
- power transmission lines (Arderne et al, 2020)
The spatial intersection of hazard and exposure datasets is a first
step to analyse vulnerability and risk to infrastructure and people.
To learn more about related concepts, there is a free short course
available through the Open University on
Infrastructure and Climate Resilience
. This
overview of the course
has more details.
These Python libraries may be a useful place to start analysis of the
data in the packages produced by this workflow:
-
snkit
helps clean network data
-
nismod-snail
is designed to help implement infrastructure exposure, damage and
risk calculations
The
open-gira
repository contains a larger workflow for global-scale open-data
infrastructure risk and resilience analysis.
For a more developed example, some of these datasets were key inputs
to a regional climate risk assessment of current and future flooding
risks to transport networks in East Africa, which has a related online
visualisation tool at
https://east-africa.infrastructureresilience.org/
and is described in detail in Hickford et al (2023).
References
-
Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks,
Elco E. (2020). Data from: Predictive mapping of the global power
system using open data [Dataset]. In Nature Scientific Data (1.1.1,
Vol. 7, Number Article 19). Zenodo. DOI:
10.5281/zenodo.3628142
-
Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan);
Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed
return periods. 4TU.ResearchData. [Dataset]. DOI:
10.4121/12705164.v3
-
Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J.
(Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022):
STORM climate change tropical cyclone wind speed return periods.
4TU.ResearchData. [Dataset]. DOI:
10.4121/14510817.v3
-
Global Energy Observatory, Google, KTH Royal Institute of Technology
in Stockholm, Enipedia, World Resources Institute. (2018) Global
Power Plant Database. Published on Resource Watch and Google Earth
Engine;
resourcewatch.org/
-
Hickford et al (2023) Decision support systems for resilient
strategic transport networks in low-income countries – Final Report.
Available online:
https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
-
Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp,
T., et al. (2020). Projecting exposure to extreme climate impact
events across six event categories and three spatial scales. Earth's
Future, 8, e2020EF001616. DOI:
10.1029/2020EF001616
-
Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available
online:
www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
-
OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg
contributors (2023) Road and Rail networks derived from
OpenStreetMap. [Dataset] Available at
global.infrastructureresilience.org
-
Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up
surface grid, derived from Sentinel2 composite and Landsat,
multitemporal (1975-2030) European Commission, Joint Research Centre
(JRC) PID:
data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
-
Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual
probability of extreme heat and drought events, derived from Lange
et al 2020 (Version 2) [Dataset]. Zenodo. DOI:
10.5281/zenodo.8147088
-
Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP
R2023A - GHS population grid multitemporal (1975-2030). European
Commission, Joint Research Centre (JRC) PID:
data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
-
Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman,
H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods
Methodology. Technical Note. Washington, D.C.: World Resources
Institute. Available online at:
www.wri.org/publication/aqueduct-floods-methodology.