Bangladesh is one of the most vulnerable countries to natural hazards and disasters. Among the natural hazards and disasters, flooding is one of the most frequent and severe due to the country's geographical location, dense river networks, and monsoon climate. Flood events displace populations and communities, damage infrastructure, and disrupt livelihoods. This challenge focuses on assessing social vulnerability to flooding by considering factors such as population density, poverty levels, access to resources, and adaptive capacity. By understanding vulnerability at a granular level, targeted interventions can be designed to enhance resilience.
Has this problem been acknowledged in the past?
Yes, various government and non-government organizations have recognized the issue:
- Government of Bangladesh: Bangladesh Delta Plan 2100, National Adaptation Plan, Flood Action Plan.
- NGOs & International Agencies: World Bank, UNDP, BRAC, and Red Crescent Society have conducted flood resilience projects.
- Academic & Research Institutions: Several studies have highlighted the increasing risk due to climate change.
Despite these efforts, the integration of socio-economic factors with flood risk assessment remains a gap that this challenge aims to address. Also, having a subnational flood risk assessment for the country of Bangladesh is a great resource for data-driven decision making.
Can this challenge be solved using space technologies and data?
Yes, space technologies will play a crucial role in solving this challenge. Most of the dataset will be used coming from freely available Earth Observations (EO). Key data sources include:
- Satellite Remote Sensing (Optical & SAR) – Sentinel-1/2, Landsat, MODIS for flood mapping and monitoring. I might also use some other high-resolution dataset such as Planet Labs data for validation purposes.
- Elevation and Hydrological Data – SRTM, Copernicus DEM for terrain and flood modeling.
- Socio-Economic Data Integration – Nighttime light data (VIIRS), population density maps (WorldPop), and land use classification for vulnerability assessment.
Expected timeframe to develop a solution
- Short-term (1-6 months): Data collection, preprocessing, and model development.
- Medium-term (7-12 months): Model validation, stakeholder engagement, and implementation.
- Long-term (1+ years): Scaling the solution to national and regional levels, integrating with policy frameworks.
Potential consequences if no action happens
Consequences may include:
- Increased displacement and loss of lives due to unplanned flood response.
- Economic losses, particularly in agriculture, fisheries, and infrastructure.
- Rising inequalities as vulnerable populations bear the brunt of climate-induced disasters.
- Long-term environmental degradation and loss of livelihoods.
What are additional physical requirements for a solution?
The following things are needed to accomplish this solution.
- Access to cloud-based geospatial processing platforms (e.g. Google Earth Engine, AWS).
- If possible, field data collection for ground validation using GPS or similar mobile devices.
- The development of GIS-based web applications for data sharing and decision-making.
- Engaging the vulnerable community by initiating training programs and workshops for local capacity building.
1. Which regions and communities are at risk?
2. What socio-economic factors contribute to flood vulnerability?
3. How can we use remote sensing data to improve flood risk mitigation strategies?
The goal is to develop a data-driven framework for prioritizing interventions, emergency response planning, and long-term resilience-building efforts.
1. Develop a social vulnerability index integrating flood exposure, sensitivity, and adaptive capacity.
2. Provide data-driven insights for policymakers, NGOs, and disaster response teams.
3. Enhance early warning systems and adaptive strategies based on socio-economic risk factors.
1. High-resolution vulnerability maps covering the entire country.
2. Policy recommendations based on the findings of the study.
