In the world of urban planning and flood risk management, data is king. Or so we thought. A recent study published in the *Journal of Flood Risk Management* (translated as *Journal of Flood Risk Management*) challenges this notion, revealing that even incomplete sewer network data can yield surprisingly accurate flood models. This research, led by C. Montalvo of the Water and Environmental Engineering Group at the Center for Technological Innovation in Construction and Civil Engineering (CITEEC) at Universidade da Coruña in Spain, could revolutionize how cities approach flood modeling, particularly in the energy sector where infrastructure investments are at stake.
Montalvo and his team used the 2D/1D dual drainage model Iber-SWMM to study urban pluvial flooding in two case study areas: Differdange, Luxembourg, and Osuna, Spain. They simulated six different return period storms, creating various scenarios of data completeness by simplifying the original sewer networks based on conduit segments’ Strahler Order number and length. The results were eye-opening. “We found that the lower the degree of data completeness, the higher the overestimation of the maximum flood extent,” Montalvo explained. “For 80% completeness, the False Alarm Ratio is less than 0.05, but it can increase exponentially to over 0.30 when network completeness drops to 20%.”
However, the story doesn’t end there. The researchers discovered that if the available information includes the most important conduits, such as the main collectors, errors are minimal. Moreover, if the data on surface elements (inlets) is also complete, the accuracy of flood modeling is maintained compared to the complete data scenario. This is a game-changer for large urban areas where complete sewer network data sets are often unavailable, and information preprocessing can be complex and time-consuming.
The implications for the energy sector are significant. Urban pluvial floods can disrupt power supply, damage infrastructure, and lead to costly repairs and downtime. Accurate flood modeling is crucial for energy companies to assess risks, plan investments, and design resilient infrastructure. This research suggests that even with incomplete data, energy companies can still make informed decisions, saving time and resources.
Montalvo’s research also highlights the potential for simplifying flood model setup. “Our results can contribute to the simplification of flood model setup in large urban areas,” he said. This could mean faster, more efficient modeling processes, allowing cities and energy companies to respond more quickly to potential flood risks.
As cities grow and climate change intensifies, the demand for accurate, efficient flood modeling will only increase. This research offers a promising path forward, demonstrating that we don’t always need perfect data to make informed decisions. It’s a reminder that in the world of urban planning and flood risk management, data is indeed important, but it’s how we use it that truly matters.
In the words of Montalvo, “It’s not about having all the data, but about having the right data.” And that’s a lesson we can all take to heart.