Quantifying Numerical Model Accuracy and Variability

Abstract:

The 2011 Tohoku tsunami event has changed the logic on how to evaluate tsunami hazard on coastal communities. Numerical models are a key component for methodologies used to estimate tsunami risk. Model predictions are essential for the development of Tsunami Hazard Assessments (THA). By better understanding model bias and uncertainties and if possible minimizing them, a more accurate and reliable THA will result. In this study we compare runup height, inundation lines and flow velocity field measurements between GeoClaw and the Method Of Splitting Tsunami (MOST) predictions in the Sendai plain. Runup elevation and average inundation distance was in general overpredicted by the models. However, both models agree relatively well with each other when predicting maximum sea surface elevation and maximum flow velocities. Furthermore, to explore the variability and uncertainties in numerical models, MOST is used to compare predictions from 4 different grid resolutions (30m, 20m, 15m and 12m). Our work shows that predictions of particular products (runup and inundation lines) do not require the use of high resolution (less than 30m) Digital Elevation Maps (DEMs). When predicting runup heights and inundation lines, numerical convergence was achieved using the 30m resolution grid. On the contrary, poor convergence was found in the flow velocity predictions, particularly the 1 meter depth maximum flow velocities. Also, runup height measurements and elevations from the DEM were used to estimate model bias. The results provided in this presentation will help understand the uncertainties in model predictions and locate possible sources of errors within a model.