Physical climate risk calculation engine.
An OS-Climate project, physrisk is a library for assessing the physical effects of climate change and thereby the potential benefit of measures to improve resilience.
An introduction and methodology is available here.
Physrisk is primarily designed to run 'bottom-up' calculations that model the impact of climate hazards on large numbers of individual assets (including natural) and operations. These calculations can be used to assess financial risks or socio-economic impacts. To do this physrisk collects:
- hazard indicators and
- models of vulnerability of assets/operations to hazards.
Hazard indicators are on-boarded from public resources or inferred from climate projections, e.g. from CMIP or CORDEX data sets. Indicators are created from code in the hazard repo to make calculations as transparent as possible.
Physrisk is also designed to be a hosted, e.g. to provide on-demand calculations. physrisk-api and physrisk-ui provide an example API and user interface. A development version of the UI is hosted by OS-Climate.
The library can be run locally, although access to the hazard indicator data is needed. The library is installed via:
pip install physrisk-lib
Hazard indicator data is freely available. Members of the project are able to access OS-Climate S3 buckets. Credentials are available here. Information about the project is available via the community-hub. Non-members are able to download or copy hazard indicator data.
Hazard indicator data can be downloaded or copied from the 'os-climate-public-data' bucket. A list of the keys to copy is available from https://os-climate-public-data.s3.amazonaws.com/hazard/keys.txt
An inventory of the hazard data is maintained here (this is used by the physrisk library itself). The UI hazard viewer is a convenient way to browse data sets.
Access to hazard event data requires setting of environment variables specifying the S3 Bucket, for example:
OSC_S3_BUCKET=physrisk-hazard-indicators
OSC_S3_ACCESS_KEY=**********
OSC_S3_SECRET_KEY=**********
For use in a Jupyter environment, it is recommended to put the environment variables in a credentials.env file and do, for example:
from dotenv import load_dotenv
load_dotenv(dotenv_path=dotenv_path, override=True)