ISIMIP Extreme Heat and Drought Occurrence and Exposure

This repository contains a data processing pipeline to produce maps of the annual probability of drought (soil moisture below a baseline threshold) or extreme heat (temperature and humidity-based indicators over a threshold) events.

• Spatial resolution: 0.5° grid
• Model variables:
  • 8 hydrological models
  • 4 GCMs
  • baseline, RCP 2.6 and RCP 6.0 emission scenarios
  • current (baseline) and future maps for 2030, 2050 and 2080

Lange et al (2020) provide a timeseries of extreme events, which has been processed into an annual probability of occurrence by the authors of this repository.

Event definitions are given in Lange et al, Table 1:

• Land area is exposed to drought if monthly soil moisture falls below the 2.5th percentile of the preindustrial baseline distribution for at least seven consecutive months.
• Land area is exposed to extreme heat if both a relative indicator based on temperature (Russo et al 2015, 2017) and an absolute indicator based on temperature and relative humidity (Masterton & Richardson, 1979) exceed their respective threshold values.

This is a draft dataset, used for visualisation in https://global.infrastructureresilience.org/ but not otherwise reviewed or published.

If you use this, please cite:

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

Data citation:

Stefan Lange, Jan Volkholz, Tobias Geiger, Fang Zhao, Iliusi Vega del Valle, Ted Veldkamp, Christopher Reyer, Lila Warszawski, Veronika Huber, Jonas Jägermeyr, Jacob Schewe, David N. Bresch, Matthias Büchner, Jinfeng Chang, Philippe Ciais, Marie Dury, Kerry Emanuel, Christian Folberth, Dieter Gerten, Simon N. Gosling, Manolis Grillakis, Naota Hanasaki, Alexandra‐Jane Henrot, Thomas Hickler, Yasushi Honda, Akihiko Ito, Nikolay Khabarov, Aristeidis Koutroulis, Wenfeng Liu, Christoph Müller, Kazuya Nishina, Sebastian Ostberg, Hannes Müller Schmied, Sonia I. Seneviratne, Tobias Stacke, Jörg Steinkamp, Wim Thiery, Yoshihide Wada, Sven Willner, Hong Yang, Minoru Yoshikawa, Chao Yue, Katja Frieler (2020): Land area fractions and population fractions exposed to extreme climate impact events derived from ISIMIP2b output data (v1.0). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.924045

This is shared under a CC0 1.0 Universal Public Domain Dedication (CC0 1.0)

When using ISIMIP data for your research, please appropriately credit the data providers, e.g. either by citing the DOI for the dataset, or by appropriate acknowledgment.

The ISIMIP2b climate input data and impact model output data analyzed in this study are available in the ISIMIP data repository at ESGF, see https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input and https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=output, respectively. More information about the GHM, GGCM, and GVM output data is provided by Gosling et al. (2020), Arneth et al. (2020), and Reyer et al. (2019), respectively.

Population exposure is calculated as annual expected population directly exposed to the occurrence of extreme heat or drought events, assuming any population directly within the footprint of an event is exposed, but not otherwise taking any other risk-mitigating or -propagating factors into account.

Population is held constant at 2020 levels, using the JRC GHSL GHS-POP R2023A release, which can be cited as:

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: http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE

Concept and methodology:

Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016) Development of new open and free multi-temporal global population grids at 250m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE), AGILE 2016

Workflow

The data processing pipeline is defined in the Snakefile, which uses Python, snakemake and other dependencies which are listed in environment.yml.

For example, using the micromamba python package manager (which is a smaller, faster version of the perhaps more familiar conda or mamba):

# Install the python environment
micromamba install -f environment.yml
# Activate the python environment
micromamba activate isimip-exposure
# Run the workflow
snakemake --verbose -c32

License

This code is released as open source under the MIT License, (c) 2023 Tom Russell and contributors.

Acknowledgments

This research received funding from the FCDO Climate Compatible Growth Programme. The views expressed here do not necessarily reflect the UK government's official policies.