This challenge addresses the increasing occurrence of floods and landslides in Peru, particularly in Piura region and Catacaos city, caused by extreme weather events such as massive storms which are exacerbated by climate change or by "El Niño" and "La Niña" phenomena.
The goal is to simulate flood scenarios using a combination of rainfall data, satellite imagery, and digital elevation models (DEMs). This developed model aims to predict how water will advance across the landscape, which areas are likely to be affected, and to estimate river level rises.
The relevant decision-makers and stakeholders include the following:
- Local and regional disaster risk management authorities.
- Municipal planning offices.
- Emergency response units and civil protection agencies.
- NGOs working on climate resilience.
- Community leaders in flood-prone areas.
These decision-makers require real-time and predictive information on:
- Areas likely to be inundated.
- Timing and extent of floodwater advancement.
- Population density in affected areas.
- Infrastructure at risk (roads, hospitals, schools, etc.).
Has this problem been acknowledged in the past?
Yes, by AMERIGEO and others
Can this challenge be solved using space technologies and data?
Yes, using data collected on rainfall, digital elevation models, and satellite images. Precipitation data can be downloaded from the SENAMHI platform. For digital elevation models, the SRTM Data platform can be used.
Expected timeframe to develop solution
3 days
Potential consequences if no action happens
No action by the authorities
What are additional physical requirements for a solution?
Computers and software. I suggest the use of the Hydrologic Engineering Center River Analysis System (HEC-RAS) Tool. Currently, I do not have access to the HEC-RAS Tool.
Location and Repetition of Flooding:
Flooding events are recurrent in Catacaos-Piura-Peru, particularly in the January, February and March months. Historical data indicates repeated flooding, causing displacement, damage to infrastructure, and loss of livelihoods.