HEAT RISK MAPPING ACROSS TIME SCALES

4D-EARTH

Potential supervisors

prof. dr. Maarten van Aalst, A second-supervisor: still to be identified, Julie Arrighi (advisor; Visiting Scientist at ITC / Red Cross Red Crescent Climate Centre)

Spatial Engineering

This topic is adaptable to Spatial Engineering and it covers the following core knowledge areas:
  • Spatial Planning for Governance (SPG)
  • Spatial Information Science (SIS)

Suggested Electives

Additional Remarks

Description

Heatwaves are frequently the most deadly disaster, and the risk is rising rapidly due to global warming. For instance, both in 2019 and 2020, the most deadly disaster in the world was a heatwave in Europe. In many developing countries, the mortality is not even known, given that no statistics are gathered.

However, there is good predictability of temperature extremes across timescales almost everywhere (see: Erin Coughlan de Perez et al. 2018,  Environ. Res. Lett. 13 054017). Actions can be taken during the heatwave based on a forecast, but also on longer timescales in response to trends in risk, for instance by adjusting housing design and urban development to reduce heat exposure and vulnerability of exposed populations.

Addressing these rising risks, and bridging the gap between science and practice from heat information to heat action, is a key priority for the International Red Cross Red Crescent. This research aims to develop and apply risk mapping approaches that combine heat hazard risk across timescales with information on exposure and vulnerability to inform interventions to reduce risk (and over time assess their effectiveness).

Objectives and Methodology

In this research the following questions could be addressed, to be further developed for a concrete context in consultation with stakeholders:
- Combine climate data, remote-sensed heat mapping with exposure and vulnerability information to assess urban heat risk across timescales.
- Where available: collect public health data and other impact information (across space and time) and correlate with heat statistics.
- Compare and contrast postive and negative trends across locations with similar climates.

Further reading