The continuous simulation approach has proven to be relatively effective under assumptions of stationary climate, meaning that historical data is representative of the future. However, with climate change altering the spatial and temporal distribution of rainfall, more advanced methods are required in order to develop a better understanding of how system performance, and the occurrence of flooding, will change over time. To do this, we turn to climate models.
Climate models are our most advanced tools for simulating the global climate system response to increasing greenhouse gas emissions. They are also important tools for understanding past patterns in precipitation and projecting future changes. By leveraging advancements in climate modeling, we can build capacity for modifying historical precipitation data to reflect projected changes; these capabilities are key elements in advancing our understanding of flood risk in a dynamic future. Now, there are inherent limitations with the direct application of coarse resolution (i.e. large grid spacing and large time-steps) climate model output data to local flood impact assessments. However, when it comes to the future planning and management of drainage system infrastructure, they provide the best means for understanding how system performance may change over time.
A secondary approach for improving flood predictions relies on a network of remote weather stations and continuous flow monitoring data. With recent advances in machine learning methods – a branch of artificial intelligence that utilizes concepts like pattern recognition – algorithms can process big data sets to make powerful predictions by mapping the relationships between system inflows and system response. In developing a predictive relationship, decision-makers will be able to predict flood events in real time to allow for adaptation measures in response to a predicted flood event. In the future, the acquired real-time data has the added benefit of complimenting the future planning analysis by providing valuable insight into the spatial distribution of precipitation at the catchment scale and by providing an indication of how changes in climate and land use are impacting flood events.
Although applications of machine learning for flood prediction are still in the initial stages of development, the idea is exciting to consider. It’s very possible that this is just the tip of the iceberg (although they are shrinking due to climate change!) of integrating climate modeling, big data, and machine learning into the future of flood risk management.