Retrospect on the tsunami simulation efforts for the German-Indonesian Tsunami Early Warning System
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1 Retrospect on the tsunami simulation efforts for the German-Indonesian Tsunami Early Warning System Natalja Rakowsky, Alexey Androsov, Annika Fuchs, Sven Harig, Antonia Immerz, Jörn Behrens, Wolfgang Hiller, Sergey Danilov, Jens Schröter Alfred Wegener Institute, Bremerhaven KlimaCampus, University of Hamburg, Germany EGU General Assembly April - 2 May 2014 Vienna, Austria Tsunami modeling group GITEWS Retrospect EGU / 18
2 Tsunami modeling group GITEWS Retrospect EGU / 18
3 Outline GITEWS overview Evolution of TsunAWI and the scenario repository Focus: dataproducts Focus: scenario selection Focus: inundation sensitivity study Tsunami modeling group GITEWS Retrospect EGU / 18
4 GITEWS Timeline German-Indonesian Tsunami Early Warning System GITEWS project funded by BMBF Nov Inauguration of the tsunami early warning system in Jakarta Sep Evaluation by international experts March 2011 Transfer of Ownership to Indonesia PROTECTS PROject for Training, Education and Consulting for Tsunami early warning Systems, BMBF Tsunami modeling group GITEWS Retrospect EGU / 18
5 GITEWS System Overview Tsunami modeling group GITEWS Retrospect EGU / 18
6 TsunAWI In a nutshell Non-linear SWE (sibling of full ocean model FESOM), p h Unstructured P1 P1NC finite element grid, 4x min ct gh, cg h Initial conditions: Okada parameters, source model, land slide model Leap-frog time stepping Modules for tides, non-hydrostatic pressure Fortran90, OpenMP, netcdf Visualization with Matlab, OpenDX, GIS Scripts for batch and post processing, shapefile output Tsunami modeling group GITEWS Retrospect EGU / 18
7 TsunAWI scenario repository Scenarios model physics linear shallow water source model by GFZ: RuptGen 1.0, 1900 sources 336 epicenters, Mw=7.5, 7.7, 8.0, 8.2, 8.5, 8.7, 9.0 bathymetry GEBCO 1, accurate datasets for coastal regions Tsunami modeling group GITEWS Retrospect EGU / 18
8 TsunAWI scenario repository Scenarios since 2011 model physics linear shallow water nonlin. advection added, Smagorinsky viscosity, improved inundation scheme source model by GFZ: RuptGen 1.0, 1900 sources 336 epicenters, Mw=7.5, 7.7, 8.0, 8.2, 8.5, 8.7, 9.0 RuptGen 2.1, 3470 sources 528 epicenters, Mw=7.2, 7.4, 7.6,..., 8.8, 9.0 bathymetry GEBCO 1, accurate datasets for coastal regions GEBCO 30 instead of GEBCO 1 technical improvements faster calculation, reduced scenario file size Tsunami modeling group GITEWS Retrospect EGU / 18
9 TsunAWI scenario repository Model domain for scenarios 2011 Tsunami modeling group GITEWS Retrospect EGU / 18
10 TsunAWI scenario repository Model domain for scenarios 2011 and extension 2013 Tsunami modeling group GITEWS Retrospect EGU / 18
11 TsunAWI scenario repository Scenarios 2011 Extension 2013 #scenarios 3450 New, East: 1100 Replace: 1100 magnitudes 7.2, 7.4,..., 8.8, 9.0 #grid nodes 2.3 Mio 15 Mio reduced 1.1 Mio 7.5 Mio resolution 50m - 150m - 15km model time 3 h 12 h compute time 0:45 h 15 h 2 8 Core 1 8 Core Xeon Nehalem Xeon Westmere file size 1.1GB 22GB 500MB without timesteps Tsunami modeling group GITEWS Retrospect EGU / 18
12 Scenario data products ETA isochrones and maximum amplitude Example: Magnitude 9.0 in the Eastern Sunda Arc Tsunami modeling group GITEWS Retrospect EGU / 18
13 Scenario data products Coastal forecast points Example: Magnitude 9.0 in the Eastern Sunda Arc, zoom to Lembar, Eastern Lombok Maximum SSH and ETA at coastal forecast points Time series at tide gauge locations Tsunami modeling group GITEWS Retrospect EGU / 18
14 Scenario selection algorithm Uncertainty reduction with multiple sensors Regard each sensor type with its characteristics in mind! Epicenter and magnitude are derived from multiple sensor data by approved SeisComP3. Reliable GPS data comes fast, too. But little experience so far, limited number of stations. Tide gauges hard to use for early warning in a fully automated algorithm. Tsunami modeling group GITEWS Retrospect EGU / 18
15 Scenario selection algorithm Uncertainty reduction with multiple sensors Regard each sensor type with its characteristics in mind! Epicenter and magnitude are derived from multiple sensor data by approved SeisComP3. Use epicenter and magnitude to pre-select scenarios. Reliable GPS data comes fast, too. But little experience so far, limited number of stations. Refine scenario selection by comparing GPS measurement and scenario data. Tide gauges hard to use for early warning in a fully automated algorithm. Very valuable for all-clear and hind-casts. Tsunami modeling group GITEWS Retrospect EGU / 18
![Scenario selection algorithm 1. Step: Seismic pre-selection Magnitude uncertainty: [M 0.5; M + 0.3], M w + 0.](/docs-images/78/78231716/images/16-1.jpg "2 for momentum tensor Magnitude Epicenter uncertainty: Ellipse parallel to the trench r L = 10 0.5[M+0.3] 1.8 km, r W = 1 2 r L.")
16 Scenario selection algorithm 1. Step: Seismic pre-selection Magnitude uncertainty: [M 0.5; M + 0.3], M w for momentum tensor Magnitude Epicenter uncertainty: Ellipse parallel to the trench r L = [M+0.3] 1.8 km, r W = 1 2 r L. Tsunami modeling group GITEWS Retrospect EGU / 18
17 Scenario selection algorithm 2. Step: Refine selection with GPS data e.g., Benkgulu Sept USGS Finite Fault: Tsunami source NW of the epicenter. Measured GPS-dislocations strong in the NW, but not SE. GPS matching would reject all scenarios in the SE, and some very strong scenarios in the NW. Tsunami modeling group GITEWS Retrospect EGU / 18
18 Inundation simulation Sensitivity study on topography data Three groups AIFDR, ITB, AWI, Three models ANUGA, TUNAMI-N3, TsunAWI, Three regions Padang (Sumatra), Maumere (Flores), Palu (Sulawesi) One conclusion High quality topography data is crucial! Free SRTM data (90m horizontal resolution, 16m vertical accuracy) only for rough estimates, Intermap (5m; 0.7m) and LiDar (1m; 0.15m) comparable for shallow water models, Results more sensitive to varying data sets than to varying resolution. Tsunami modeling group GITEWS Retrospect EGU / 18
19 Inundation simulation Sensitivity study on topography data Example: synthetic scenario for Maumere, Flores Tsunami modeling group GITEWS Retrospect EGU / 18
20 Outline GITEWS overview Evolution of TsunAWI and the scenario repository Focus: dataproducts Focus: scenario selection Focus: inundation sensitivity study Thank You, Terima Kasih! Poster: B238 Tsunami modeling group GITEWS Retrospect EGU / 18
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