Challenges of Integrated Modeling Across Space and Time Scales

Abstract:

New data collection methods combined with rapid advances in processing technologies enabled by increases in data processing and storage capabilities are causing an significant shift in our modeling capabilities. Freely available global data sets allow us to build more quickly models for bigger areas. By linking the right data, models, and tools we gain significant insight at scales that hadn't considered possible a few decades ago. However, by increasing the spatial extent of our models, we risk missing regionally important critical elements by limitations of model resolution, processes selected, or blind spots in our big data world. At the same time we are pushing the time scales of our models from events and seasonal scale out to decades, centuries, or millennia to simulate the dynamics of the earth surface under varying external conditions. Also here we simplify and ignore to gain performance to resolve bigger time and space domains; are we including all the relevant elements in our models? These elements are often easy to spot from the right perspective. However, what is that perspective when you try to comprehend the results of baffling integrated global models and the amount of data is overwhelming? At the same time we want to know results with an ever increasing accuracy and detail: Will my house flood? Can we reduce flood risk, increase shipping capacity here, and at the same time reduce the maintenance costs by optimizing our dredging strategy? Can we build a number of interoperable cyberinfrastructures that when combined address all these questions? This presentation gives an overview of our work in this field at Deltares, and the main challenges that we foresee.