Supplement of Scenario-based numerical modelling and the palaeo-historic record of tsunamis in Wallis and Futuna, Southwest Pacific

Similar documents
INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 4, 2011

EMERGENCY PLANNING IN NORTHERN ALGERIA BASED ON REMOTE SENSING DATA IN RESPECT TO TSUNAMI HAZARD PREPAREDNESS

Bathymetry Data and Models: Best Practices

Digital Elevation Models. Using elevation data in raster format in a GIS

Lidar-derived Hydrography as a Source for the National Hydrography Dataset

European Geosciences Union General Assembly Vienna, Austria 27 April - 02 May 2014

Changes in bottom morphology of Long Island Sound near Mount Misery Shoal as observed through Repeated Multibeam Surveys

MAPPING POTENTIAL LAND DEGRADATION IN BHUTAN

Joint Hydrographic Center, National Oceanic and Atmospheric Administration, Durham, NH 03824, USA

Popular Mechanics, 1954

Riskscape module Documentation: Inundation Modelling in Bay of Plenty. X. Wang C. Mueller

Submitted to. Prepared by

VALIDATION OF TSUNAMI INUNDATION MODELING FOR THE 2004 SUMATRA-ANDAMAN EARTHQUAKE FOR MAKING HAZARD MAPS IN PENANG AND LANGKAWI, MALAYSIA

Predicting tsunami waves and currents on the West Coast of Canada: A case study for Ucluelet, BC

Image Services Providing Access to Scientific Data at NOAA/NCEI

Future Ocean Floor Mapping: Ocean Stewardship & Initial Industry Contributions. U.S Hydro Galveston, TX March 23, 2017 David Millar - Fugro

Updating the GEBCO Grid

MARINE GEOLOGY & GEOGRAPHY

COMPARISON OF MODELS AND VOLUMETRIC DETERMINATION FOR CATUSA LAKE, GALATI

Digital Elevation Model (DEM) of Sable Island Bank and adjacent areas

Topography and Bathymetry

Digital Elevation Models (DEM)

2 nd Tidal and Water Level Working Group Meeting

multibeam bathymetric data is often based on the local depth datum, which is the local lowest normal low sea level, see the Figure 1. Multibeam Bathym

ENGRG Introduction to GIS

Digital Elevation Models (DEM) / DTM

Topographic Maps and Landforms Geology Lab

EMODnet High Resolution Seabed Mapping - further developing a high resolution digital bathymetry for European seas

ENGRG Introduction to GIS

EMODnet High Resolution Seabed Mapping - further developing and providing a high resolution digital bathymetry for European seas

NUMERICAL SIMULATIONS FOR TSUNAMI FORECASTING AT PADANG CITY USING OFFSHORE TSUNAMI SENSORS

2) re-positioning of the SSS data, 3) individuation of geomorphological features and morphometrical parameters correlated to instability phenomena.

What is a map? A Map is a two or three-dimensional model or representation of the Earth s surface. 2-Dimensional map

EROSIONAL RATES IN THE POINT AUX CHENES BAY AREA, MISSISSIPPI: Kathleen P. Wacker G. Alan Criss INTRODUCTION

Technical Drafting, Geographic Information Systems and Computer- Based Cartography

GRAPEVINE LAKE MODELING & WATERSHED CHARACTERISTICS

LiDAR APPLICATIONS REMS6090. Assignment 2 HYDROLOGICAL APPLICATIONS of LiDAR DATA Due Date April 8, Venessa Bennett W

Modeling Coastal Change Using GIS Technology

DATA APPLIANCE FOR ARCGIS

1. Name at least one place that the mid-atlantic Ridge is exposed above sea level.

4 th Joint Project Team Meeting for Sentinel Asia 2011

Digital Elevation Models (DEM) / DTM

Existing NWS Flash Flood Guidance

Town of Chino Valley. Survey Control Network Report. mgfneerhg mc N. Willow Creek Road Prescott AZ

Tsunami Response and the Enhance PTWC Alerts

Indian Ocean Tsunami Warning System: Example from the 12 th September 2007 Tsunami

Notes and Summary pages:

The Bottom of the Ocean

NUMERICAL SIMULATION AS GUIDANCE IN MAKING TSUNAMI HAZARD MAP FOR LABUAN ISLAND

Comparison of Intermap 5 m DTM with SRTM 1 second DEM. Jenet Austin and John Gallant. May Report to the Murray Darling Basin Authority

The route towards a new GEBCO grid

Mapping Undersea Feature Names in S-100. UFNPT at SCUFN 31 Wellington, New Zealand October, 2018

GSA DATA REPOSITORY

Draft exercise for share fair at Bozeman workshop only. This exercise is not ready for distribution. Please send helpful suggestions to

Effect of the Emperor seamounts on trans-oceanic propagation of the 2006 Kuril Island earthquake tsunami

Map shows 3 main features of ocean floor

Determination of flood risks in the yeniçiftlik stream basin by using remote sensing and GIS techniques

Changes in Geomorphology and Backscatter Patterns in Mount Misery Shoal, Long Island Sound as Revealed through Multiple Multibeam Surveys

FLOOD HAZARD AND RISK ASSESSMENT IN MID- EASTERN PART OF DHAKA, BANGLADESH

Digital Elevation Model of Tutuila, American Samoa: Procedures, Data Sources, and Analysis

Differentiating earthquake tsunamis from other sources; how do we tell the difference?

12/26/2012. Geographic Information Systems * * * * GIS (... yrezaei

PROANA A USEFUL SOFTWARE FOR TERRAIN ANALYSIS AND GEOENVIRONMENTAL APPLICATIONS STUDY CASE ON THE GEODYNAMIC EVOLUTION OF ARGOLIS PENINSULA, GREECE.

Papua New Guinea LiDAR Factsheet. Pacific-Australia Climate Change Science and Adaptation Planning. Vanimo. Bismark Sea. Wewak

MID-TERM CONFERENCE CREST

Display data in a map-like format so that geographic patterns and interrelationships are visible

Lab # - Ocean Bottom Topography. Background Information:

Bathymetry Measures the vertical distance from the ocean surface to mountains, valleys, plains, and other sea floor features

A SIMPLE GIS METHOD FOR OBTAINING FLOODED AREAS

Understanding elevation and derivative layers

Applications of GIS in assessing Coastal Change Rachel Hehre November 30, 2004 NRS 509 OVERVIEW

THE REVISION OF 1:50000 TOPOGRAPHIC MAP OF ONITSHA METROPOLIS, ANAMBRA STATE, NIGERIA USING NIGERIASAT-1 IMAGERY

Using Morphometric models and Open Source Software to locate Flood prone areas A guide to pilot Implementation

Abstracts - Technical Presentations 1 st Joint LiDAR Workshop 29 June 2016

International Journal of Advance Engineering and Research Development

P1.3 DIURNAL VARIABILITY OF THE CLOUD FIELD OVER THE VOCALS DOMAIN FROM GOES IMAGERY. CIMMS/University of Oklahoma, Norman, OK 73069

Chapter Overview. Bathymetry. Measuring Bathymetry. Measuring Bathymetry

Topographic Maps. More than a Road Map

C M E M S O c e a n C o l o u r S a t e l l i t e P r o d u c t s

ACCURACY ASSESSMENT OF ASTER GLOBAL DEM OVER TURKEY

DATA BASE DEVELOPMENT OF ETA (ESTIMATED TIME OF ARRIVAL) FOR TSUNAMI DISASTER MITIGATION AT SOUTHWESTERN CITIES OF ACEH, INDONESIA

HKND Group Ltd Grand Canal Project

6 - STORM SURGES IN PUERTO RICO_Power Plants-Aguirre. Aguirre

HIGH RESOLUTION DATA PROCESSING AND TSUNAMI ASSESSMENT AND APPLICATIONS TO PORTS IN THE SEA OF MARMARA

By Paul M. (Mitch) Harris 1 and James Ellis 2. Search and Discovery Article #50080 Posted June 5, Abstract

Automatic Change Detection from Remote Sensing Stereo Image for Large Surface Coal Mining Area

Remote Sensing and GIS Contribution to. Tsunami Risk Sites Detection. of Coastal Areas in the Mediterranean

Detailed mapping of seabed topography,

DATA SOURCES AND INPUT IN GIS. By Prof. A. Balasubramanian Centre for Advanced Studies in Earth Science, University of Mysore, Mysore

Earth Science Lesson Plan Quarter 2, Week 10, Day 1

Coastal Zone Mapping and Imaging Lidar (CZMIL)

NUMERICAL SIMULATION OF TSUNAMI PROPAGATION AND INUNDATION ALONG THE RAKHINE COAST AREAS IN MYANMAR

Varying Bathymetric Data Collection Methods and their Impact on Impoundment Volume and Sediment Load Calculations I.A. Kiraly 1, T.

MARINE GEOLOGY & GEOGRAPHY

GEBCO 2013 TSCOM. EMODNET Hydrography status report

Extreme Phenomena in Dobrogea - Floods and Droughts

and applications Litto 3 D - GEBCO 09/29/09 Vincent DONATO

Baltic Sea Research Institute

Regional and Nearshore Bathymetry of American Samoa: Implications for Tsunami Run-Up and Public Awareness

Evaluating Flood Hazard Potential in Danang City, Vietnam Using FOSS4G

Transcription:

Supplement of Nat. Hazards Earth Syst. Sci., 15, 1763 1784, 2015 http://www.nat-hazards-earth-syst-sci.net/15/1763/2015/ doi:10.5194/nhess-15-1763-2015-supplement Author(s) 2015. CC Attribution 3.0 License. Supplement of Scenario-based numerical modelling and the palaeo-historic record of tsunamis in Wallis and Futuna, Southwest Pacific G. Lamarche et al. Correspondence to: G. Lamarche (geoffroy.lamarche@niwa.co.nz) The copyright of individual parts of the supplement might differ from the CC-BY 3.0 licence.

Generation of land topography and lagoon bathymetry integrated Digital Terrain Model (DTM) While regional seafloor topography is acceptable in deep oceanic environments, best possible accuracy is required in the coastal zone for numerical modelling of tsunami propagation. For this study, particular attention was devoted to generate two distinct DTMs for Wallis and for Futuna-Alofi. For Wallis the DTM combines a variety of existing data and new data specifically collected and processed for this project. Below we provide details on the methodology and data used to generate both DTMs. 1 Onshore Data Onshore we used 1:25,000 topographic maps for Wallis (IGN, 2007b) and Futuna-Alofi (IGN, 2007a). These include the 5 m elevation and subsequent 10 m contour lines. 1.1 Wallis land elevation Land elevation measurements for Wallis were collected using a Real-Time Kinematic GPS (RTK GPS) over a period of 4 days in September 2011. The instrumentation consisted of a stationary base GPS and a roving unit mounted on a car. The position of the base station was calibrated to existing survey marks at different locations around the island. The resulting accuracy was better than 5 cm in horizontal and vertical directions. A total of nearly 10,000 points was collected along roads and other easily accessible areas on the north, east and SE sides of the island. Measurements were limited to 15 m above sea level. No GPS measurements were made along the west shore due to the lack of both roads close to the shore and survey marks. Most survey marks in Wallis are referenced to the GRS1980 ellipsoid and are not referenced to sea level heights (orthometric). Since elevations relative to sea level are required for tsunami

modelling, survey data were converted into orthometric heights. There are only five reference marks with known orthometric heights in Wallis, which required us to calibrate each survey with 2 or 3 survey marks, using their ellipsoidal heights. The extra mark located on Nukutapu has an orthometric height of lower precision, but was introduced into the data set to extend the offset model to the south and capture the surveyed data south of Halalo (Fig. 3). The Mata-Utu B station was shifted east by ca. 1km to allow for extending the offset model to cover the area of the northern beach road of Mata-Utu. The offset surface was interpolated from the difference between orthometric height and ellipsoidal height at each survey mark, using the Kriging method with exponential semivariogram, to a 30m raster. The resulting surface was used to derive orthometric heights for each of the surveyed data points by applying the offset to the ellipsoidal height. For the creation of a coastal DEM, the 0 m and 15 m contour lines were digitized from the 1:200 000 IGN topographic map and combined with the RTK GPS data to derive a detailed DEM of the zone between 0 and 15 m around the entire island. The interpolation was done using the Topo-to-Raster routine in ArcGIS to create a hydrologically correct DEM, preserving streams and ridges. 1.2 Futuna land elevation We were unable to retrieve GPS data in Futuna and Alofi in a similar fashion as in Wallis. Instead, we used the digitised the 0, 5 and 10 m contour lines from the 1:25,000 topographic maps (IGN, 2007a). 2 Offshore data Away from the coast of Wallis and Futuna, we used the bathymetry of the Pacific Basin from the publically available GEBCO dataset. In the vicinity of the islands, we used the SHOM bathymetric charts for Wallis (SHOM, 2008) and Futuna-Alofi (SHOM, 2010), and the multibeam bathymetry datasets acquired during the ALAUFI survey, March 2001, (Pelletier et al., 2001; Pelletier et al., 2000a) around Futuna and Alofi and the WALFUT survey, June 2011, (Pelletier et al., 2011) around Wallis. Bathymetric data in the Wallis lagoon are sparse, with only one existing chart covering the south and SE shores at a scale of 1:20,000 (SHOM, 2008). The deepest part of the lagoon was chartered during the WALFUT project using multibeam echosounder technology and was integrated in this study. The SE, NE and NW part of the lagoon have a very limited depth

information. For the uncharted part, we developed a remote sensing approach to estimate water depth in the lagoon, whereby images from the 13 November 2004 Quickbird satellite survey were used to predict depths based on ocean colour. The Quickbird satellite captures images in 4 spectral bands: red, green, blue and near-infrared. Ratios of different bands were tested to best match bathymetric contour lines in the mapped area of the lagoon (Fig. 1). Best matches between the satellite images and chart depths were achieved by first normalizing both, the blue and green channels with the help of the red channel and subsequently calculating their ratio ( (blue/red) / (green/red) ). The result was reclassified into 255 classes and the threshold classes were identified which best corresponded to the charted water depths of 5 and 10 m (class 170 and 185 respectively). Depth contour lines were then interpolated based on the classified image. Depths greater than 10 m could not be resolved using this method. In the NW lagoon of Wallis where no data is available (no SHOM charts) we used data from Andréfouët (2006). By using the coastline as an additional depth contour of 0 m, an acceptable bathymetry for the entire lagoon could be generated. The reef in Futuna was digitised from Google Earth extracts (Fig. 2) and IGN topo maps. In a final step, all data sets, land-based DEM and bathymetry were merged into two distinct DTMs for Wallis and for Futuna-Alofi.

Figure 1 - Remote sensing-derived depth contours along the SW shore of Wallis Island (from Lamarche et al., 2013)