EGU21-521
https://doi.org/10.5194/egusphere-egu21-521
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Optimizing forecast-based actions for extreme rainfall in Peru

Jonathan Lala1, Juan Bazo2,3, Vaibhav Anand4, and Paul Block5
Jonathan Lala et al.
  • 1Department of Civil and Environmental Engineering, University of Wisconsin - Madison, Madison, United States of America (jonalala@hotmail.com)
  • 2Red Cross Red Crescent Climate Centre, Lima, Peru
  • 3Universidad Tecnológica del Peru, Lima, Peru
  • 4Department of Risk and Insurance, University of Wisconsin - Madison, Madison, United States of America
  • 5Department of Civil and Environmental Engineering, University of Wisconsin - Madison, Madison, United States of America

Natural disaster management has recently seen a major innovation through the advent of standardized forecast-based action and financing protocols. Given a forecast with adequate skill and lead time, relief actions can be taken before, rather than after, a disaster, saving lives and property while also transferring some ex-post risk to ex-ante risk for the relief agency. Multi-stage actions, in which forecasts with longer leads allow for preparation while short-term forecasts trigger direct actions, may be particularly effective at reducing risk. Multi-stage protocols, however, have not been explicitly optimized, either through trigger mechanisms or forecast tailoring. This study considers a multi-stage early action protocol developed by the Peruvian Red Cross for El Niño-induced extreme rainfall in coastal Peru. A sensitivity analysis of trigger thresholds, forecast methods, and levels of risk aversion is conducted to recommend optimal actions. Results demonstrate the relative importance of benefit-cost ratios at different lead times; forecast technology and risk aversion play a lesser but still valuable role. Moreover, the optimization framework can be utilized without post-disaster monitoring and evaluation, enabling the proliferation of effective plans in other disaster-prone regions.

How to cite: Lala, J., Bazo, J., Anand, V., and Block, P.: Optimizing forecast-based actions for extreme rainfall in Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-521, https://doi.org/10.5194/egusphere-egu21-521, 2021.

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