Background

Floods are among the most devastating natural disasters worldwide, causing significant human, economic, and environmental impacts. Southeast Asia, and in particular the Peninsular Malaysia region, is highly vulnerable to recurrent flood events, which are often intensified by seasonal weather patterns and climate change.

In this context, early warning systems are critical tools to support preparedness and anticipatory action. By leveraging advanced forecast models and geospatial data, these systems help mitigate the impacts of floods on affected populations and infrastructure.

How to Use this Scenario

The Flood Early Warning System scenario provides an interactive map to visualize flood risk based on forecasted river discharge in Peninsular Malaysia.

You can explore the scenario by:

• Flood Hazard (River Discharge): select and view the forecasted flood hazard based on river discharge levels.
• Date Selection: choose a date within the next 30 days to observe how flood risk evolves over time.
• Population Exposure: visualize population density in relation to hazard zones.
• Relative Wealth Index: visualize the Relative Wealth Index of the region, to identify areas with lower wealth and therefore higher vulnerability to floods.

Use the layer selection menu and date picker to interactively explore different flood scenarios and assess potential risk dynamics across the region.

About the Scenario

This scenario demonstrates the potential of EU Space data and related services to power a Flood Early Warning System in Peninsular Malaysia. It integrates forecast data from the Copernicus Emergency Management Service – Global Flood Awareness System (GloFAS).

The platform ingests GloFAS forecast data for the upcoming three days and applies a categorization based on established thresholds from scientific literature, enabling clear identification of areas at risk. In addition, population exposure is assessed using data from the Copernicus Human Settlement Layer, helping users understand the potential human impact of forecasted floods.

Through this integrated approach, the scenario provides valuable insights for humanitarian organizations, decision-makers, and emergency responders, supporting anticipatory action and improved preparedness.

Last, the Relative Wealth Index predicts the relative standard of living of each country based on privacy-protected connectivity data, satellite imagery, and other novel data sources. It can be used to identify areas with lower wealth and therefore higher vulnerability to drought. It is assessed using the dataset from The Humanitarian Data Exchange.

Objective

The objective of this scenario is to showcase how forecast-based early warning for floods can be built using open EO data and services from the EU Space Programme. By combining GloFAS hydrological forecasts with human settlement data, the scenario illustrates a practical workflow to identify at-risk populations and inform early response actions.

Ultimately, this scenario highlights how anticipatory humanitarian action can be enhanced through the integration of EU Space data into operational flood early warning systems.

Credits

All of the displayed basemaps are provided by © MapTiler © OpenStreetMap contributors