Collaborative Research: Advancing Statistical Surrogates for Linking Multiple Computer Models with Disparate Data for Quantifying Uncertain Hazards

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The Oso, Washingon landslide of 2014, which resulted in 43 fatalities, and the ash plumes from the Eyjafjallajökull (Iceland) eruption of 2010, which shut down air travel in Europe, are examples of rare and catastrophic geophysical events. Their rare nature makes such events nearly impossible to forecast, if forecasts are based only on previous observations. To capture rare events, researchers must rely on complex physical and mathematical models that often require significant computational resources to exercise. Furthermore, events like these may be best described by a series of different models of different phenomena at different scales. For example, a researcher may need to combine a model of rainfall, a model of slope failure, and a model of sliding debris to create on overall model for a landslide event. The main objective of this research is the development of efficient statistical and computational strategies to combine such models, thus advancing the state of the art in hazard forecasting.Direct simulation-based hazard assessment would require thousands to tens of thousand of linked, space-time simulations. Furthermore, to be of most use in hazard assessment, these simulations should be informed and validated by observational data sets, which themselves can range from sparse data (rare events) to massive data (e.g. satellite data), and explored for emerging scenarios. To complicate the matter, a number of features of the problems of interest are either poorly characterized or unpredictable, and one would like to run the simulation programs at a range of values of each of them; this quickly leads to a perceived need to run a simulation program (which may take hours to complete) for hundreds of thousands or millions of different combinations of parameter values and conditions. There simply is not enough time or enough computing power for such a brute force approach to succeed. To tackle the situation just described, the PIs will continue to develop parallel partial emulators for massive space-time simulator data allowing emulator construction on the adaptive space-time grids commonly used in geophysical simulations, creating smoothers for their output, and enabling the use of reduced input spaces. The PIs will begin the investigation of a strategy for linking multiple simulators via multiple emulators. A particularly powerful semi-analytic way of linking emulators will be pursued, with a variety of research questions arising centering around the accuracy of the method, as well as the possibility of its implementation in the huge data scenario envisaged for the parallel partial emulator. The PIs will also begin to investigate techniques to extract (nearly) optimal basis sets, data reduction methods, and algorithmic approaches to accelerate the construction of emulators, all of which contribute to a more robust handling of large datasets. These new methodologies will provide tools to rapidly construct probability-based hazard forecast maps for cascading geophysical events. Rapid forecast maps allow end users to perform hazard analysis under a wide variety of aleatoric scenarios. Furthermore this new methodology will enable fast assessment of epistemic uncertainties. This approach constitutes a dramatic improvement in scientifically-based decision support.