Flood resilience planning in rural Bangladesh
- Anwendungsfälle
In northern Bangladesh, where the mighty Brahmaputra River winds its way through low-lying rural landscapes, flooding is a constant threat. Infrastructure is sparse, urban sprawl is largely unmanaged, and reliable elevation data is almost nonexistent.
This was the starting point for a climate adaptation project led by Benjamin Holm from the International Water and Climate Resilience Department at Ramboll. He used Scalgo Live to support the development of flood resilience strategies for the cities of Islampur and Gaibandha.
"Scalgo Live is very useful for checking elevation data and doing initial screening of proposed solutions. It’s extremely time efficient and user-friendly."
Benjamin Holm, Ramboll
"We created our own elevation data using drone surveys," says Benjamin. "It was the only way to get data detailed enough for flood modelling. But we also needed a way to validate and assess the quality of the data before diving into complex dynamic flood modelling. That’s where Scalgo Live came in."
"In the profile view, I could see it didn’t make sense. It turned out to be a "stitching" error when we put the elevation model together."
Benjamin Holm, Ramboll
In countries where local detailed elevation data is not yet available, you can upload your own data to run detailed Scalgo Live analyses.
The team collaborated with local surveyors to produce drone-based elevation data across the cities. The plan was to build dynamic flood models based on this data and use them to develop flood resilience strategies. But building complex models on poor data can be time-consuming and misleading. To avoid this, they began by uploading the terrain data into Scalgo Live.
"Scalgo Live was used to validate the data. We asked simple questions: Do we see blue spots where we expect to? Do flow paths follow natural streams? Do railroads disrupt flow paths as they should? These checks helped us determine whether the data was reliable enough."
For example in the Islampur data, Benjamin spotted a suspiciously straight flow path and an unexpected elevation change in an open field. "In the profile view, I could see it didn’t make sense. It turned out to be a "stitching" error when we put the elevation model together."
The profile tool showed a sudden change in the data which was an error and showed that the data quality in the area was not good enough for dynamic flood modelling.
In other cases, expected flood patterns were absent—pointing to interpolation issues in the survey data. These findings showed that for some areas, the data wasn’t good enough to support complex modelling.
"In these areas, we chose to focus on field observations and building simplified network models instead. It was about putting our time where it made most sense."
In Gaibandha, the drone data was of better quality. Here, Scalgo Live was again used to support initial assessments. "There was already a list of proposed solutions, but it would have taken too much time to model the entire city in a dynamic model. We used Scalgo Live to identify which solutions needed further investigation."
"We checked both options in Scalgo Live and could instantly see if they seemed to be realistic and if more detailed assessments were needed."
Benjamin Holm, Ramboll
The area with a red circle and is always flooded during monsoon season. Two different solutions were tested in Scalgo Live.
One such solution involved a low-lying area that floods during monsoon season. Local engineers proposed building a new canal to connect with an existing one, or a drain along a major road. "We checked both options in Scalgo Live and could instantly see if they seemed to be realistic and if more detailed assessments were needed."
They also tested the feasibility of the canal by editing the terrain model directly in Scalgo Live. "We could see the catchments and flow paths—it helped us understand if it was worth building a dynamic model. In this case, it was."
Reflecting on the project, Benjamin says, "Scalgo Live is very useful for checking elevation data and doing initial screening of proposed solutions. It’s extremely time efficient and user-friendly."
