Remember the movie “Moneyball”? The Oakland A’s are struggling, financially and on the baseball field. Then they introduce an innovative system for figuring out which players will improve team performance. Moving away from observations by scouts, the A’s begin to use advanced statistics to value players. With their new insights, the A’s acquire high-impact players for relatively little money. Within a season, they’re at the top of the game and so successful that within a few years the rest of the league has reorganized how they value players, too.
“Moneyball” highlights the power of innovative knowledge systems: creative new sets of tools and practices for collecting, analyzing and applying data to solving problems. All organizations depend on knowledge systems, but it’s not uncommon, over time, for the knowledge they generate to become stale and poorly adapted to changing contexts.
As researchers on resilience and sustainability of cities, we’ve found that unfortunately that has become the case for a number of cities. This is already causing problems: Outdated knowledge systems have exacerbated recent disasters and contributed to growing financial losses from extreme weather, which have exceeded US$110 billion in the U.S. this year alone.
Discussions around improving resilience and adaptation to extreme events often focus on upgrading infrastructure or building new infrastructure, such as bigger levees or flood walls. But cities also need new ways of knowing, evaluating and anticipating risk by updating their information systems.
Consider the use of 100-year or 500-year flood levels to guide urban planning and development. Using this framework, cities hope to prevent small floods while limiting the occurrence of catastrophic flooding.
Yet, the data behind this strategy are rapidly becoming obsolete. Weather statistics are now changing in many places. As a result, cities are experiencing repeat 500-year floods, sometimes multiple times, in a few decades or less. Yet cities continue to rely almost exclusively on historical data for projecting future risks.
The city of Houston, Texas, for example, has experienced a 167 percent increase in the intensity of heavy downpours between 2005-2014 as compared to 1950-1959. The 2017 Hurricane Harvey flood in Houston represented the third 500-year flood to occur in the past three years. Prior to Harvey, Harris County flood control managers downplayed the need to change their knowledge systems, arguing that the two prior flooding events were isolated events.
New possible futures
Cities need to better anticipate what would happen in the case of these types of unprecedented extreme weather events. The past few years have seen a growing number of record-breaking storms, droughts and other weather events.
The National Weather Service labeled Hurricane Harvey “unprecedented,” both for the rapidity of its intensification and the record levels of rainfall it dumped on Houston. Hurricane María hit San Juan as the third-strongest storm to make landfall in the U.S., based on air pressure measurements. Its rapid intensification surprised forecasters and presents yet another challenge to climate and weather models.
AP Photo/Ramon Espinosa
Record-breaking events like these cannot be made sense of using statistics grounded on the past frequency of occurrence. Not recognizing the growing risks from extreme weather is dangerous and costly if cities continue to create more buildings that are more expensive in increasingly vulnerable locations.
What’s needed are new and more creative ways to explore possible futures and their potential implications. One approach is to use climate or other predictive models. Such models are never perfect but can add important elements to discussions that can’t be gotten from historical data.
For instance, cities can look at projected sea level rise or storm surges and decide whether it makes economic sense to rebuild homes after damaging storms, or whether it’s better to compensate homeowners to move outside the flood zone.