T57 Tsunamis: Monitoring, Notification, Geology, Modeling, Education and Outreach; The State of the Art (Posters)

ASSESSMENT OF THE SENSITIVITY OF TSUNAMI INUNDATION MODELING TO GRIDDING METHODOLOGIES USED IN BUILDING HIGH-RESOLUTION DIGITAL ELEVATION MODELS

Paper number: T57-5
Program: Technical Sessions
Time: 6:00 PM – 8:00 PM

Authors

Abstract

The need to provide coastal communities with a reliable tsunami warning system necessitates the development of accurate tsunami models to simulate tsunami source, propagation and inundation. High resolution bathymetric-topographic digital elevation models (DEMs) are the main input to tsunami inundation models and play a critical role in the accuracy and reliability of flooding forecasts used in tsunami preparedness. These DEMs need to provide the best possible representation of input data and a reasonable interpretation in areas where no data is available. There are many types of error associated with developing DEMs, including those inherent in the input data, gridding algorithms, grid resolution and datum conversions. This study will provide a deeper understanding of the sensitivity of inundation modeling to DEM variability resulting from different gridding methodologies. King Cove, Alaska was chosen for this study, because of its rugged topography, lack of high resolution data, and potential risk of tsunamis.

Our research project will consist of two parts. First, we will generate evaluation DEMs using various gridding methodologies. The statistical analysis and the comparison with a control DEM will be performed for each of the DEMs. The control DEM was developed by the NGDC using spline interpolation, and was extensively tested to ensure high quality. The second part involves the simulation of a tsunami event that occurred in 1946, by using the University of Alaska Fairbanks (UAF) model. The same evaluation DEMs will be used for the tsunami modeling and the impact of each DEM on the inundation will be assessed. This analysis will develop better quality control procedures of the DEMs, and deeper understanding of the impact of gridding methodologies on the quality of DEMs.