SlideShare a Scribd company logo
141°0'0"E
141°0'0"E
38°0'0"N
38°0'0"N
Effectofthe 9.0MEa rthqua ke a nd T suna mionthe popula tion
ofS enda i,J a pa n
S ervice L a yerCredits:Esri,DeL orme,GEBCO,NOAANGDC,a nd othercontributors
S ource:Esri,Digita lGlobe,GeoEye,Ea rthsta rGeogra phics,CNES /AirbusDS ,US DA,
US GS ,AEX ,Getma pping,Aerogrid,IGN,IGP,swisstopo,a nd the GIS UserCommunity
S ources:Esri,DeL orme,US GS ,NPS
S ources:Esri,US GS ,NOAA
Esri,HER E,DeL orme,Ma pmyIndia ,© OpenS treetMa pcontributors,a nd the GIS user
Tectonic Situation
9.0 ML Earthquake
155°E
155°E
150°E
150°E
145°E
145°E
140°E
140°E
135°E
135°E
130°E
130°E
45°N
45°N
40°N
40°N
35°N
35°N
30°N
30°N
Magnitude
6-6.5
6.51-7
7.1-7.5
7.51-8
8.1-9
Depth Km
0-24
25-49
50-149
150-588
T ectonic pla te bounda ries
Q
143°E
143°E
142°E
142°E
141°E
141°E
140°E
140°E
139°E
139°E
138°E
138°E
40°N39°N38°N37°N
Magnitude
6-6.5
6.51-7
7.1-7.5
7.51-8
8.1-9
Depth
0-24
25-29
30-39
40-166
Peak Ground Acceleration
Value
High
L ow
Q9.0MEa rthqua ke
Tsunami Flood Zone
Water Depth (m)
1 - 3
4 - 5
6 - 8
The Tsunami
0 100 200 300 40050
Miles
0 6 12 18 243
Miles
0 25 50 75 10012.5
Miles
0 6 12 18 243
Miles
8m Tsunami
Population Affected
0 - 1000
1001 - 10000
10001 - 25000
25001 - 50000
50001 - 100000
Prefecture % Land area affected Population of prefecture Tsunami affected population
S enda i-shi,T a iha ku-ku 99.06 224,558 222,439
R ifu-cho 98.56 36,029 35,512
Ona ga wa -cho 90.56 7,512 6,802
S oma -shi 86.70 36,195 31,381
Ishinoma ki-shi 86.36 150,966 130,370
Ma tsushima -ma chi 84.74 15,062 12,765
S hinchi-ma chi 82.87 7,957 6,594
Na tori-shi 75.05 74,740 56,089
Y a ma moto-cho 69.49 13,234 9,195
S enda i-shi,Miya gino-ku 64.14 187,306 120,136
S hichiga ha ma -ma chi 60.61 19,728 11,963
Wa ta ri-cho 60.46 33,942 20,524
T a ga jo-shi 54.82 62,203 34,108
S enda i-shi,Wa ka ba ya shi-ku 54.12 130,107 70,406
Iwa numa -shi 53.63 43,788 23,485
Higa shima tsushima -shi 53.11 40,221 21,359
S hioga ma -shi 52.95 56,256 29,785
S hioga ma -shi 47.05 56,256 26,471
Higa shima tsushima -shi 46.90 40,221 18,862
Iwa numa -shi 46.37 43,788 20,303
S enda i-shi,Wa ka ba ya shi-ku 45.88 130,107 59,701
T a ga jo-shi 45.18 62,203 28,095
Wa ta ri-cho 39.54 33,942 13,418
S hichiga ha ma -ma chi 39.39 19,728 7,765
S enda i-shi,Miya gino-ku 35.86 187,306 67,170
Y a ma moto-cho 30.52 13,234 4,039
Na tori-shi 24.95 74,740 18,651
S hinchi-ma chi 17.13 7,957 1,363
Ma tsushima -ma chi 15.24 15,062 2,297
Ishinoma ki-shi 13.64 150,966 20,596
S oma -shi 13.30 36,195 4,814
Ona ga wa -cho 9.45 7,512 710
R ifu-cho 1.44 36,029 517
S enda i-shi,T a iha ku-ku 0.94 224,558 2,119
Total 2,279,608 1,139,804
Methodology
Aim
Results and Discussion
T hispostercollectsa nd presentsva rioussourcesofda ta a va ila ble toestima te numbersofthose directlya ffected bythe 9.0Mtsuna mi,a swell a s
provide some historica l contextforthe eventwhich occurred a t14:46:18J S T on11th ofMa rch,2011.Bycombining sepa ra telya va ila ble sources
ofda ta toproduce new a dditiona l da ta sets,with scope toestima te the numberofthe popula tiondirectlya ffected bythe tsuna miflood wa ters.T his
will be a chieved bymodelling a n8mtsuna miinunda tiononthe S enda icoa sta l regiona nd combining thiswith censusda ta byprefecture.
J a pa nla ysonthe westernedge ofthe pla te bounda rybetweenthe
Pa cific a nd North America npla tes.Atthisa ctive pla te ma rgin,the
Pa cific pla te isbeing subducted a ta ra te ofa pproxima tely83mm/yr
(S enoeta l.,1996),forming a deepregionbenea th the sea knowna s
the J a pa nT rench.Overthe pa st100yea rs,the inter-pla te bounda ry
tothe north-ea stofJ a pa nha shosted ma nysma llerea rthqua kesup
7.8M,with the regionha ving hosted 9eventsgrea tertha n7in
ma gnitude since 1973(Ha shimotoeta l.,2009).
T he la sttime a nea rthqua ke with compa ra ble ma gnitude a nd loca tion
occurred wa sthe J oga nEa rthqua ke,13th J uly,869.T histsuna mi
leftsedimentdepositskilometresinsca le onthe S enda ipla in
(Minoura eta l.,2001)with a nestima ted ma gnitude of8.2
(Fujiieta l.,2011)which ca used a round 1000dea thsa ccording to
historica l documents.T he 2011Ea rthqua ke,isa rela tivelyra re
disa sterwhich occurred ina na ctive pla te bounda ry,fa rsurpa ssing
otherea rthqua kesfromthe J a pa nT rench.
Ina tota l of44prefectureswith a tota l popula tionof1,778,052,
628,890(35.4%)were directlya ffected bythe tsuna mi.T he
prefecturesmosta ffected bythe inunda tionwere S enda iTa iha ku-
ku,Ishinoma ki-shi,S enda iMiya gino-kua nd S enda iWa ka ba ya shi-
ku.With Popula tionsa ll over130,000a nd over54%tsuna mi
a ffected la nd.Collectively,S enda ia nd itsdistricts(ku)ha sa
popula tionof317,413,with a tota l 137,576directlya ffected,
a pproxima tely43.3% (Miya gino-35.9%,Wa ka ba ya shi-54.1%).
T hese prefecturesha ve a high popula tiondensity,which despite
onlya round 40%ofthe la nd wa sa ffected,tensofthousa ndsof
people were displa ced a sa result.Incontra stOna ga wa ,which
despite ha ving 90.8% ofthe la nd covered bythe tsuna mi,710
people were a ffected due tothe low popula tionofpeople inthis
rura l region.Othersignifica ntprefecturesimpa cted a re a sfollows
with most%Popula tiona ffected first:S oma -36,195(87%),Wa ta ri
-33,942(60.4%),Iwa numa -43,788(53.6%),S hioga ma -56,256,
(52.9%),Na tori-18,051(24.2%),Ishinoma ki-20,596(13.6%)a nd
Ona gowa -710(9.5%).Otherprefecturesca nbe referenced from
the ta ble.
Whencompa red toresultsproduced byCenterforS a tellite Ba sed
CrisisInforma tion(DL R ),T he resultsa re compa ra ble.DL R 'sstudy
concluded 12% ofthe the 1,000,200were a ffected,where a smy
resultsconclude 35.4% of1,778,052were rea ched byflood wa ters.
DL R published theirresultsonthe 18th ofma rch,2011,using
la ndsa tda ta a cquired fromthe 12th ofma rch.T hisdifference in
a pproa ch could a ccountforthe differing results.Forexa mple,
ma nma de structuresnotseeninthe DEMha ve a neffect,which
ma ysheltered some a rea sofS enda ifrominunda tion,thuslessening
the numbersofthose a ffected.Da ta relea sed byT he Na tiona l
Police Agencysta te,there were 15,840dea ths,with 3,607missing.
92.5% ofthese dea thsa re a ttributed tothe tsuna miflood wa ters
(http://www.npa .go.jp/a rchive/keibi/biki/higa ijokyo_ e.pdf)a
fa ta lityra te of1.09%forthe a ffected popula tionof1,778,052.
T he limita tionsforthe method used inthisstudya re a sa resultof
the a ssumptionsma de.Asthe da ta freelya va ila ble wa spopula tion
perprefecture,thismea nta ssumptionsha d tobe ma de tha tthe
popula tionofea ch prefecture wa sevenlydistributed overthe la nd.
Inrea litypopula tionsa re notevenlydistributed a nd a re focused on
cities,townsa nd villa ges,which wa snota ble tobe included inthis
study.T he method could be improved ifpopula tiondensityda ta
wa sloca ted.
Ana dditiona l limitofthismethod isthe 8mtsuna miflood model.
T hisva lue wa schosena sthe a vera ge from(T sujieta l.,2010)
pa per.Ofcourse inrea litythe wa ve heighta tthe coa stwill va ry
depending onconditionssuch a sshore depth a nd slope a ngle.
Using the resultsofthisstudyitma ybe possible tofla g popula tions
forcoa sta l defence development,pa rticula rlythose ofthe S enda i
districtswhich were ba dlyda ma ged.S cope forfuture studywould
include,costperunitcoa sta l defence wa ll,orpossible sea bed
exca va tiontoreduce slope a ngle.
±
Abe T ,GotoK,S uga wa ra D(2012)R ela tionshipbetweenthe
ma ximum extentoftsuna misa nd a nd the inunda tionlimitofthe
2011T ohoku-okitsuna mionthe S enda iPla in,J a pa n.S ediment
Geol 282:142–150
Fujii,Y .,K.S a ta ke,S .S a ka i,M.S hinoha ra ,a nd T .Ka na za wa
(2011),T suna misource ofthe 2011offthe Pa cific coa stof
T ohoku,J a pa nea rthqua ke,Ea rth Pla netsS pa ce,63(7),815–820,
doi:10.5047/eps.2011.06.010.
Minoura ,K.,F.Ima mura ,D.S uga wa ra ,Y .Kono,a nd T .Iwa shita
(2001),T he 869J oga ntsuna mideposita nd recurrence interva l of
la rge-sca le tsuna mionthe Pa cific coa stofnorthea stJ a pa n,J .Na t.
Disa sterS ci.,23(2),83–88.
Ha shimoto,C.,A.Noda ,T .S a giya ,a nd M.Ma tsu’ura (2009),
Interpla te seismogenic zones a long the Kuril‐J a pa ntrench inferred
from GPS da ta inversion,Na t.Geosci.,2,141–144.
S eno,T .,T .S a kura i,a nd S .S tein(1996),Ca nthe Okhotstpla te be
discrimina ted from the North America npla te?,J .Geophys.R es.,
101,11,305–11,315.
T suji,Y .,T .T a ka ha shi,a nd K.Ima i(2010),Compa risonoftsuna mi
heightdistributionsofthe 1960a nd the 2010Chilea nea rthqua kes
onthe coa stsofthe J a pa nese Isla nds,Abstra ctG36A-0832
presented a t2010Fa ll Meeting,AGU,S a nFra ncisco,Ca lif.,13–17
Dec.
References

More Related Content

Similar to japanmap_2

Trends in deep learning in 2020 - International Journal of Artificial Intelli...
Trends in deep learning in 2020 - International Journal of Artificial Intelli...Trends in deep learning in 2020 - International Journal of Artificial Intelli...
Trends in deep learning in 2020 - International Journal of Artificial Intelli...
gerogepatton
 
Sea level rise impact modelling on small islands: case study gili raja island...
Sea level rise impact modelling on small islands: case study gili raja island...Sea level rise impact modelling on small islands: case study gili raja island...
Sea level rise impact modelling on small islands: case study gili raja island...
Luhur Moekti Prayogo
 
Monthly precipitation forecasting with Artificial Intelligence.
Monthly precipitation forecasting with Artificial Intelligence.Monthly precipitation forecasting with Artificial Intelligence.
Monthly precipitation forecasting with Artificial Intelligence.
bouachahcene
 
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...
Dr Ramesh Dikpal
 
Rectal cancer 2
Rectal cancer 2Rectal cancer 2
Rectal cancer 2
Prof. Shad Salim Akhtar
 
Radiometric survey of aluu landfill, in rivers state, nigeria
Radiometric survey of aluu landfill, in rivers state, nigeriaRadiometric survey of aluu landfill, in rivers state, nigeria
Radiometric survey of aluu landfill, in rivers state, nigeria
Alexander Decker
 
Sanogo paris2015-poster
Sanogo paris2015-posterSanogo paris2015-poster
Sanogo paris2015-poster
Souleymane Sanogo
 
Monitoring NDTI-River Temperature relationship along the river ganga in the s...
Monitoring NDTI-River Temperature relationship along the river ganga in the s...Monitoring NDTI-River Temperature relationship along the river ganga in the s...
Monitoring NDTI-River Temperature relationship along the river ganga in the s...
IRJET Journal
 
1 ijsrms 02516 (1)
1 ijsrms 02516 (1)1 ijsrms 02516 (1)
1 ijsrms 02516 (1)
Mohammed Badiuddin Parvez
 
Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010
Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010
Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010
All India Institute of Medical Sciences
 
The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...
The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...
The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...
IJMER
 
C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...
C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...
C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...
Blue Planet Symposium
 
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTA
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTAINVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTA
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTA
IAEME Publication
 
Midfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptx
Midfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptxMidfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptx
Midfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptx
aamirbashir1992
 
Factors Affecting the Long-term Results of Endodontic Treatment
Factors Affecting the Long-term Results of Endodontic TreatmentFactors Affecting the Long-term Results of Endodontic Treatment
Factors Affecting the Long-term Results of Endodontic Treatment
Cat Lunac
 
Multitemporal analysis Po river Prodelta
Multitemporal analysis Po river ProdeltaMultitemporal analysis Po river Prodelta
Multitemporal analysis Po river Prodelta
Ciro Manzo
 
超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況
超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況
超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況
Iugo Net
 
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...
Agriculture Journal IJOEAR
 
Computing net radiation from temperature variables: Improvising for under-res...
Computing net radiation from temperature variables: Improvising for under-res...Computing net radiation from temperature variables: Improvising for under-res...
Computing net radiation from temperature variables: Improvising for under-res...
IOSR Journals
 
36th publication todentj - 1st name
36th publication   todentj - 1st name36th publication   todentj - 1st name
36th publication todentj - 1st name
CLOVE Dental OMNI Hospitals Andhra Hospital
 

Similar to japanmap_2 (20)

Trends in deep learning in 2020 - International Journal of Artificial Intelli...
Trends in deep learning in 2020 - International Journal of Artificial Intelli...Trends in deep learning in 2020 - International Journal of Artificial Intelli...
Trends in deep learning in 2020 - International Journal of Artificial Intelli...
 
Sea level rise impact modelling on small islands: case study gili raja island...
Sea level rise impact modelling on small islands: case study gili raja island...Sea level rise impact modelling on small islands: case study gili raja island...
Sea level rise impact modelling on small islands: case study gili raja island...
 
Monthly precipitation forecasting with Artificial Intelligence.
Monthly precipitation forecasting with Artificial Intelligence.Monthly precipitation forecasting with Artificial Intelligence.
Monthly precipitation forecasting with Artificial Intelligence.
 
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...
 
Rectal cancer 2
Rectal cancer 2Rectal cancer 2
Rectal cancer 2
 
Radiometric survey of aluu landfill, in rivers state, nigeria
Radiometric survey of aluu landfill, in rivers state, nigeriaRadiometric survey of aluu landfill, in rivers state, nigeria
Radiometric survey of aluu landfill, in rivers state, nigeria
 
Sanogo paris2015-poster
Sanogo paris2015-posterSanogo paris2015-poster
Sanogo paris2015-poster
 
Monitoring NDTI-River Temperature relationship along the river ganga in the s...
Monitoring NDTI-River Temperature relationship along the river ganga in the s...Monitoring NDTI-River Temperature relationship along the river ganga in the s...
Monitoring NDTI-River Temperature relationship along the river ganga in the s...
 
1 ijsrms 02516 (1)
1 ijsrms 02516 (1)1 ijsrms 02516 (1)
1 ijsrms 02516 (1)
 
Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010
Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010
Dept of Neuro Surgery(JPNATC)-Head injuries audit 2010
 
The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...
The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...
The Efficiency of Meteorological Drought Indices for Drought Monitoring and E...
 
C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...
C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...
C3.04: Assessing the impact of observations on ocean forecasts and reanalyses...
 
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTA
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTAINVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTA
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTA
 
Midfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptx
Midfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptxMidfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptx
Midfacial Changes Through Anterior Maxillary Distraction Osteogenesis in.pptx
 
Factors Affecting the Long-term Results of Endodontic Treatment
Factors Affecting the Long-term Results of Endodontic TreatmentFactors Affecting the Long-term Results of Endodontic Treatment
Factors Affecting the Long-term Results of Endodontic Treatment
 
Multitemporal analysis Po river Prodelta
Multitemporal analysis Po river ProdeltaMultitemporal analysis Po river Prodelta
Multitemporal analysis Po river Prodelta
 
超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況
超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況
超高層大気長期変動の全球地上ネットワーク観測・研究(IUGONET) プロジェクトの進捗と超高層・太陽・気象データ登録状況
 
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...
 
Computing net radiation from temperature variables: Improvising for under-res...
Computing net radiation from temperature variables: Improvising for under-res...Computing net radiation from temperature variables: Improvising for under-res...
Computing net radiation from temperature variables: Improvising for under-res...
 
36th publication todentj - 1st name
36th publication   todentj - 1st name36th publication   todentj - 1st name
36th publication todentj - 1st name
 

japanmap_2

  • 1. 141°0'0"E 141°0'0"E 38°0'0"N 38°0'0"N Effectofthe 9.0MEa rthqua ke a nd T suna mionthe popula tion ofS enda i,J a pa n S ervice L a yerCredits:Esri,DeL orme,GEBCO,NOAANGDC,a nd othercontributors S ource:Esri,Digita lGlobe,GeoEye,Ea rthsta rGeogra phics,CNES /AirbusDS ,US DA, US GS ,AEX ,Getma pping,Aerogrid,IGN,IGP,swisstopo,a nd the GIS UserCommunity S ources:Esri,DeL orme,US GS ,NPS S ources:Esri,US GS ,NOAA Esri,HER E,DeL orme,Ma pmyIndia ,© OpenS treetMa pcontributors,a nd the GIS user Tectonic Situation 9.0 ML Earthquake 155°E 155°E 150°E 150°E 145°E 145°E 140°E 140°E 135°E 135°E 130°E 130°E 45°N 45°N 40°N 40°N 35°N 35°N 30°N 30°N Magnitude 6-6.5 6.51-7 7.1-7.5 7.51-8 8.1-9 Depth Km 0-24 25-49 50-149 150-588 T ectonic pla te bounda ries Q 143°E 143°E 142°E 142°E 141°E 141°E 140°E 140°E 139°E 139°E 138°E 138°E 40°N39°N38°N37°N Magnitude 6-6.5 6.51-7 7.1-7.5 7.51-8 8.1-9 Depth 0-24 25-29 30-39 40-166 Peak Ground Acceleration Value High L ow Q9.0MEa rthqua ke Tsunami Flood Zone Water Depth (m) 1 - 3 4 - 5 6 - 8 The Tsunami 0 100 200 300 40050 Miles 0 6 12 18 243 Miles 0 25 50 75 10012.5 Miles 0 6 12 18 243 Miles 8m Tsunami Population Affected 0 - 1000 1001 - 10000 10001 - 25000 25001 - 50000 50001 - 100000 Prefecture % Land area affected Population of prefecture Tsunami affected population S enda i-shi,T a iha ku-ku 99.06 224,558 222,439 R ifu-cho 98.56 36,029 35,512 Ona ga wa -cho 90.56 7,512 6,802 S oma -shi 86.70 36,195 31,381 Ishinoma ki-shi 86.36 150,966 130,370 Ma tsushima -ma chi 84.74 15,062 12,765 S hinchi-ma chi 82.87 7,957 6,594 Na tori-shi 75.05 74,740 56,089 Y a ma moto-cho 69.49 13,234 9,195 S enda i-shi,Miya gino-ku 64.14 187,306 120,136 S hichiga ha ma -ma chi 60.61 19,728 11,963 Wa ta ri-cho 60.46 33,942 20,524 T a ga jo-shi 54.82 62,203 34,108 S enda i-shi,Wa ka ba ya shi-ku 54.12 130,107 70,406 Iwa numa -shi 53.63 43,788 23,485 Higa shima tsushima -shi 53.11 40,221 21,359 S hioga ma -shi 52.95 56,256 29,785 S hioga ma -shi 47.05 56,256 26,471 Higa shima tsushima -shi 46.90 40,221 18,862 Iwa numa -shi 46.37 43,788 20,303 S enda i-shi,Wa ka ba ya shi-ku 45.88 130,107 59,701 T a ga jo-shi 45.18 62,203 28,095 Wa ta ri-cho 39.54 33,942 13,418 S hichiga ha ma -ma chi 39.39 19,728 7,765 S enda i-shi,Miya gino-ku 35.86 187,306 67,170 Y a ma moto-cho 30.52 13,234 4,039 Na tori-shi 24.95 74,740 18,651 S hinchi-ma chi 17.13 7,957 1,363 Ma tsushima -ma chi 15.24 15,062 2,297 Ishinoma ki-shi 13.64 150,966 20,596 S oma -shi 13.30 36,195 4,814 Ona ga wa -cho 9.45 7,512 710 R ifu-cho 1.44 36,029 517 S enda i-shi,T a iha ku-ku 0.94 224,558 2,119 Total 2,279,608 1,139,804 Methodology Aim Results and Discussion T hispostercollectsa nd presentsva rioussourcesofda ta a va ila ble toestima te numbersofthose directlya ffected bythe 9.0Mtsuna mi,a swell a s provide some historica l contextforthe eventwhich occurred a t14:46:18J S T on11th ofMa rch,2011.Bycombining sepa ra telya va ila ble sources ofda ta toproduce new a dditiona l da ta sets,with scope toestima te the numberofthe popula tiondirectlya ffected bythe tsuna miflood wa ters.T his will be a chieved bymodelling a n8mtsuna miinunda tiononthe S enda icoa sta l regiona nd combining thiswith censusda ta byprefecture. J a pa nla ysonthe westernedge ofthe pla te bounda rybetweenthe Pa cific a nd North America npla tes.Atthisa ctive pla te ma rgin,the Pa cific pla te isbeing subducted a ta ra te ofa pproxima tely83mm/yr (S enoeta l.,1996),forming a deepregionbenea th the sea knowna s the J a pa nT rench.Overthe pa st100yea rs,the inter-pla te bounda ry tothe north-ea stofJ a pa nha shosted ma nysma llerea rthqua kesup 7.8M,with the regionha ving hosted 9eventsgrea tertha n7in ma gnitude since 1973(Ha shimotoeta l.,2009). T he la sttime a nea rthqua ke with compa ra ble ma gnitude a nd loca tion occurred wa sthe J oga nEa rthqua ke,13th J uly,869.T histsuna mi leftsedimentdepositskilometresinsca le onthe S enda ipla in (Minoura eta l.,2001)with a nestima ted ma gnitude of8.2 (Fujiieta l.,2011)which ca used a round 1000dea thsa ccording to historica l documents.T he 2011Ea rthqua ke,isa rela tivelyra re disa sterwhich occurred ina na ctive pla te bounda ry,fa rsurpa ssing otherea rthqua kesfromthe J a pa nT rench. Ina tota l of44prefectureswith a tota l popula tionof1,778,052, 628,890(35.4%)were directlya ffected bythe tsuna mi.T he prefecturesmosta ffected bythe inunda tionwere S enda iTa iha ku- ku,Ishinoma ki-shi,S enda iMiya gino-kua nd S enda iWa ka ba ya shi- ku.With Popula tionsa ll over130,000a nd over54%tsuna mi a ffected la nd.Collectively,S enda ia nd itsdistricts(ku)ha sa popula tionof317,413,with a tota l 137,576directlya ffected, a pproxima tely43.3% (Miya gino-35.9%,Wa ka ba ya shi-54.1%). T hese prefecturesha ve a high popula tiondensity,which despite onlya round 40%ofthe la nd wa sa ffected,tensofthousa ndsof people were displa ced a sa result.Incontra stOna ga wa ,which despite ha ving 90.8% ofthe la nd covered bythe tsuna mi,710 people were a ffected due tothe low popula tionofpeople inthis rura l region.Othersignifica ntprefecturesimpa cted a re a sfollows with most%Popula tiona ffected first:S oma -36,195(87%),Wa ta ri -33,942(60.4%),Iwa numa -43,788(53.6%),S hioga ma -56,256, (52.9%),Na tori-18,051(24.2%),Ishinoma ki-20,596(13.6%)a nd Ona gowa -710(9.5%).Otherprefecturesca nbe referenced from the ta ble. Whencompa red toresultsproduced byCenterforS a tellite Ba sed CrisisInforma tion(DL R ),T he resultsa re compa ra ble.DL R 'sstudy concluded 12% ofthe the 1,000,200were a ffected,where a smy resultsconclude 35.4% of1,778,052were rea ched byflood wa ters. DL R published theirresultsonthe 18th ofma rch,2011,using la ndsa tda ta a cquired fromthe 12th ofma rch.T hisdifference in a pproa ch could a ccountforthe differing results.Forexa mple, ma nma de structuresnotseeninthe DEMha ve a neffect,which ma ysheltered some a rea sofS enda ifrominunda tion,thuslessening the numbersofthose a ffected.Da ta relea sed byT he Na tiona l Police Agencysta te,there were 15,840dea ths,with 3,607missing. 92.5% ofthese dea thsa re a ttributed tothe tsuna miflood wa ters (http://www.npa .go.jp/a rchive/keibi/biki/higa ijokyo_ e.pdf)a fa ta lityra te of1.09%forthe a ffected popula tionof1,778,052. T he limita tionsforthe method used inthisstudya re a sa resultof the a ssumptionsma de.Asthe da ta freelya va ila ble wa spopula tion perprefecture,thismea nta ssumptionsha d tobe ma de tha tthe popula tionofea ch prefecture wa sevenlydistributed overthe la nd. Inrea litypopula tionsa re notevenlydistributed a nd a re focused on cities,townsa nd villa ges,which wa snota ble tobe included inthis study.T he method could be improved ifpopula tiondensityda ta wa sloca ted. Ana dditiona l limitofthismethod isthe 8mtsuna miflood model. T hisva lue wa schosena sthe a vera ge from(T sujieta l.,2010) pa per.Ofcourse inrea litythe wa ve heighta tthe coa stwill va ry depending onconditionssuch a sshore depth a nd slope a ngle. Using the resultsofthisstudyitma ybe possible tofla g popula tions forcoa sta l defence development,pa rticula rlythose ofthe S enda i districtswhich were ba dlyda ma ged.S cope forfuture studywould include,costperunitcoa sta l defence wa ll,orpossible sea bed exca va tiontoreduce slope a ngle. ± Abe T ,GotoK,S uga wa ra D(2012)R ela tionshipbetweenthe ma ximum extentoftsuna misa nd a nd the inunda tionlimitofthe 2011T ohoku-okitsuna mionthe S enda iPla in,J a pa n.S ediment Geol 282:142–150 Fujii,Y .,K.S a ta ke,S .S a ka i,M.S hinoha ra ,a nd T .Ka na za wa (2011),T suna misource ofthe 2011offthe Pa cific coa stof T ohoku,J a pa nea rthqua ke,Ea rth Pla netsS pa ce,63(7),815–820, doi:10.5047/eps.2011.06.010. Minoura ,K.,F.Ima mura ,D.S uga wa ra ,Y .Kono,a nd T .Iwa shita (2001),T he 869J oga ntsuna mideposita nd recurrence interva l of la rge-sca le tsuna mionthe Pa cific coa stofnorthea stJ a pa n,J .Na t. Disa sterS ci.,23(2),83–88. Ha shimoto,C.,A.Noda ,T .S a giya ,a nd M.Ma tsu’ura (2009), Interpla te seismogenic zones a long the Kuril‐J a pa ntrench inferred from GPS da ta inversion,Na t.Geosci.,2,141–144. S eno,T .,T .S a kura i,a nd S .S tein(1996),Ca nthe Okhotstpla te be discrimina ted from the North America npla te?,J .Geophys.R es., 101,11,305–11,315. T suji,Y .,T .T a ka ha shi,a nd K.Ima i(2010),Compa risonoftsuna mi heightdistributionsofthe 1960a nd the 2010Chilea nea rthqua kes onthe coa stsofthe J a pa nese Isla nds,Abstra ctG36A-0832 presented a t2010Fa ll Meeting,AGU,S a nFra ncisco,Ca lif.,13–17 Dec. References