modelling and mapping of tsunami along the cuddalore coast
TRANSCRIPT
Geospatial Data Cyber Infrastructure and Real-time Services with special emphasis
on Disaster Management
November 25-27, 2009
Hyderabad, India
M. V. Ramana Murthy, Tune Usha and N.T.Reddy
Scientist-F
Ministry of Earth Sciences
Modelling and Mapping of Tsunami along the Cuddalore Coast
Modelling of Tsunami and Storm
Surges
• Data needs ( Marine and Land) for Inundation model
• Assimilation of data into computational model
• Computational Model ( 2 D)
• Generation of end products for disaster management
Dynamic Model ( Near Real Time / Real Time)
Low risk – Carnicobar Eq (8.1Mw)
High risk – Sumatra Eq (9.3Mw)
Overview : Large scale tsunami vulnerability map for coastal villagesof Cuddalore have been prepared for the followingscenarios
Data Collation Numerical Model Vulnerability Mapping
BathymetryGEBCO
C-MAP
Echo sounder
TopographyALTM
SRTM
RTKGPS
Data Assessment(Datum / Projection)
Grid Computation
Grid Assessment
Generation
(Mansinha & Smylie Theory)
Propagation
(Linear Shallow Water Equations)
Run-up / Inundation
(Non-linear Shallow Water Equations)
Preparation of
Thematic Maps
Landuse map
Administrative Boundary
Infrastructure
CRZ
Converting Model
Output to GIS ThemesConverting Model output
to GIS Themes
Waterlevel Maps
Final Inundation / Threat Level
MapValidation of Model with
December 2004 Tsunami
Methodology
HIGH RESOLUTION BATHYMETRY DATA
(4 M – 20 M CONTOUR)
•CMAP
•GEBCO
DATA SOURCES - Bathymetry
Methodology adopted for preparation of data
Step1 Extraction of GEBCO data for region of interest
Step2 Identification of area of local bathy with sources(CMAP)
Step3 Collation of Land topography (ALTM/SOI)
Step4 Merging of Land topo data with nearshore bathy(Spheroid,projection, datum)
Step5 Preparation of gridded dataLocal(step4) and GEBCO(step1)
Step6 Merge Local and GEBCO data and quality check
Step7 Check for gradients in the data and smoothening
ALTM DATA
Data gaps
Extensive field check required before Interpolation
Conversion of Ellipsoidal height to MSL.
DATA GAPS – NO DATA AREA UNDER WATER WAYS,
THICK CANOPY ETC
SRTM
CMAP
GEBCO
Computational Domains
B
A
C
B
D
C
Region of Computation
Rupture Location
Lower leftLong/Lat
(Deg)
Upper rightLong/Lat
(Deg)
Grid number (Horizontal)
Grid number (Vertical)
Grid spacing (m)
GRID-A(Linear)
65.6815-7.26553
103.7515526.0794
1693 1483 2502
GRID-B(Linear)
79.650510.5012
82.501513.5103
401 401 834
GRID-C(Non-linear)
79.650111.2511
80.500512.2510
341 401 278
GRID-D(Non-linear)
79.7248911.4653
79.912311.8578
226 472 93
Numerical Model
2525252525Focal Depth (km)
9090909090Rake Angle (Deg)
1212121212Dip Angle (Deg)
10356338340330Strike Angle (Deg)
0606061515Slip Amount (m)
9595120130130Fault Width (km)
350150390150220Fault Length (km)
10.509.105.804.332.50Latitude
92.0092.1093.4193.9095.10Longitude
Block-5Block-4Block-3Block-2Block-1Parameters
B1
B2
B3
B4
B5
Fault parameters used for simulation
Deformation of ocean bottom
Using Mansinha and Smylie’s equations (1971)
Generation
uDhuM )( vDhvN )(
0y
x
t
NM
0D D
MN
y
D
M
x
t
22
7/3
22
NMMgn
xgD
M
0D D
N
y
D
MN
x
t
22
7/3
22
NMNgn
ygD
N
M, N : Discharge fluxes in x&y directions
n : Manning’s roughness coefficient
21
22
37
2
NMMD
gnx
21
22
37
2
NMND
gny
Propagation
Shallow Water Equations
Selection of data resolution and type of model
Continental Shelf Effect
GADILAM RIVER
Cuddalore
old Town
Cuddalore New Town
Bay of Bengal
Inundation
3-4 m
2-3 m
1-2 m
0-1 m
DTM &
INUNDATION AT
CUDDALORE
Validation of Model Results with Field Results
Name Longitude Latitude
Observed
Inundation
Predicted
Inundation
Aryagoshti 79.7689 11.5201 664 600
Villanallur 79.7619 11.5367 416 517
Silambimangalam 79.7590 11.5497 366 283
Periyapattu 79.7577 11.5642 391 528
Andarmullipalam 79.7572 11.5783 514 553
Kayalpattu 79.7575 11.5941 573 529
Trichopuram 79.7594 11.6127 343 354
Thiyagavalli 79.7613 11.6291 729 557
Kudikadu 79.7710 11.6758 906 1641
Pachaiyankuppam 79.7763 11.6974 930 944
Parameters1881
Car Nicobar
1941
AndamanWorst-case
Source Car Nicobar North Andaman Car Nicobar
Longitude 92.430 92.50 E 92.430
Latitude 8.520 12.10 N 8.520
Magnitude 7.9 Mw 7.7 Mw 9.3 Mw
Slip 5 m 5 m 15 m
Fault Length 200 km 200 km 500 km
Fault Width 80 km 80 km 150 km
Strike Angle 3500 200 3450
Dip Angle 250 200 150
Rake Angle 900 900 900
Focal Depth 15 km 30 km 20 km
Hypothetical Source
Historical Source1941 Andaman
Historical Source1881 Carnicobar
Hypothetical Source
Historical Source1941 Andaman
Historical Source1881 Carnicobar
Run-up Heights along Cuddalore Coast
Vulnerability Mapping
I. Vulnerability classification
Low risk – Carnicobar Eq (81.Mw)
High risk – Sumatra Eq (9.3Mw)
Maximum risk – Hypo. Carnicobar eq (9.3 Mw)
From Satellite Imagery (entire Village)
•Landuse
From Aerial Photographs (upto 2Km from coast)
•Elevation Contours
•Infrastructure details
•Trees
•Roads
•Railways
•Buildings
Secondary data
•Cadastral boundaries and Survey Nos
•Administrative boundaries
II. Inundation Depth (sea water level due to
Sumatra 2004)
Information available in the map
III. Others details
Rectification of satellite data
GCP Points
II. Administrative boundary and Land use
ARC PAD GS 5+ used for feature identification Water level map for (Model output overlaid)
High Risk (Sumatra 2004) :
Maximum risk (Hypo) :
Maximum
Inundation Water level
1.2 km 3 – 4m
3.0 Km 6 – 8m
Tsunami Vulnerability Map of Cuddalore,
Tamil Nadu
Scale 1:25000
High Tide Line
CRZ buffer (200m, 500m, 1km)
Roads and Rails
Elevation contours – 1m
Infrastructure details from DC images
Scale 1:10000
Tsunami Vulnerability Map
of Cuddalore, Tamil Nadu
Cuddalore Old Town
Scale 1:5000
Vulnerability maps are useful for
Planning infrastructure before the disaster
Determining evacuation strategies.
The high beach ridges and the bio shielding effect of the green belt in
southern Cuddalore, found be the reason for the high run-up height and
the low inundation in this region, which was accurately captured by the
model.
Coastal geomorphology and land elevation are major controlling
parameters for inundation. Beach ridges and sand dunes restrict the
inundation processes while swales, creek and other inlets permit free flow of
water inland.
Conclusions
Thanks for kind attention