Material for Professor Sassa, March 3, 2014

Purpose of my presentation
Towards building up an adaptation strategy
against the climate change
Eiichi Nakakita
Disaster Prevention Research Institute (DPRI)
Kyoto University, Japan
nakakita@hmd.dpri.kyoto‐u.ac.jp

A) It should be emphasized that building up
a new philosophy of adaptation for theclimate change is very important.
B) It is also important to really re-recognizethat improvement of understanding andreal-time-forecasting accuracy of extreme
weather should be one of the majorcountermeasure as adaptation.
21

Contents of my presentation

Contents of my presentation

1. What are important aspects in building up
adaptation strategy for coming climatechange2. A trial aiming to realize a next-generation
operational observation network withdifferent types of sensors for earlier
detection and/or prediction of generatingstorm from the stage of air plume and/or
cloud.

1. What are important aspects in building up
adaptation strategy for coming climatechange.
2. A trial aiming to realize a next-generation
operational observation network withdifferent types of sensors for earlier
detection and/or prediction of generatingstorm from the stage of air plume and/orcloud.

34

Disaster Prevention Research InstituteKyoto University

Prediction and evaluation of disaster environment in JapanDPRI / Kyoto-Univ.
Slope

Outputfrom GCMand RCM
Interpreta‐tion ofoutput

Mountains

Coastal Area

Regional climate model （RCM_5km, RCM_2km, RCM_1km)
Stochastictyphoon model

Surface hydrological model

Probability density function of extreme value（depending on spacio‐temporal scales）
Stochastic precipitation model (time series depending on spacio‐temporal scales)

Sedimentation andtransportation of soil

Rainfall runoff

River channel flowStorm surge

Soil runoff

Building damage by strong wind

Reservoir operation

Inundation including
underground shopping
moleEvaluation

Habitable Area

（25‐years time series (20km) and ensemble predictions (60km) for current, near future and century end）

Soil production

VariousModels（with long‐term run）

River

Hourly precipitation, temperature, water vapor, wind velocity, radiation and air pressure

Decreasing of safety against landslide, debris flow, flood, draught, storm surge and strong wind .
Assessment of current protection system and proposal of alternatives

History of CCIA supported by MEXT

• Kyousei(共生)Program:2002-2006– 20kmRCM (daily rainfall)• Kakushin(革新)Program:2007-2011– 20kmGCM, 5,2,1kmRCM (hourly rainfall)– Natural Disaster (Inc. water resources)• Sousei(創生)Program:2012-2016– Impact assessment and producing
adaptation methodologies (First priority)– for Natural Disaster, Water resources,
Ecosystem and Eco service (KyotoUniversity will lead the nation wideassessment team)8

Projected typhoon by GCM20

It is the typhoon resolving hourly output fromGCM20 that has realized the impact assessment
on Japanese river regime

910

Design value

Fut.－Pres.

Risk of shallow land slide%

Increase by 10〜20％ in whole area

Range for disaster Mitigation

95%

River discharge
Storm surge

Sgn.

Risk of deep land slide

Design value(Return period)%

Increase by 10〜20％ in mid and western Japan

Range for disaster Prevention

95%
Sgn.
Oku and Nakakita (2012)11

100-years return values of Storm surge(deviation from the average year value)

River discharge
Draught flow change(Q355 discharge)
10yrs return period

Flood flow change(Q1: Annual Maximum discharge)
100yrs return period

End of century

Current1413

Mase, Mori and Yasuda G. (2011)

Uncertainty inherent to GCM projection

Heading to adaptation

1. Uncertainty in cumulus parameterization
Projected
2. We
only usevalue
single 25-years time-series of output forst century
present, near future and endof the 21
RandomUncertainty––Is this enough number of years as statistical sample? ＝＞ReModel’s uncertainty
sampling or ensemble projection will be needed.
Quite extreme event may not occur within
25-yearsuncertainty
time series
CO2 single
Scenario's＝＞Worst scenario experiment is required!

3. Uncertainty in CO2 release scenarios–We are using most likelihood A1B scenario
Time

There is high uncertainty
in projected design value
Designvalue by
returnvalue

Range for disastermitigation (includinglarge scale disaster)
Projected design value
It is almost certain that
average of design value
would increase.

Range for disasterprevention

Virtual Shifting of typhoon’s initial position- for making a worst scenario Virtual Shifting of typhoons
initial position by keeping
potential vorticity same(a vorgas method)River discharge（ton/sec)

NHM-5kmRiver Discharge by the virtual shifting of typhoon
which was projected by GCM

AGCM20

Dynamic downscaleby RCM

Track of a typhoon
projected by GCM20

Possibility of peak discharge of almost
double of current design discharge

Current designvalue(crresponding to200 return
period)Time

Worst case impact assessmentonTrack of virtuallyshifted typhoon• Land： extreme wind and rainfall• Ocean： storm surge and wave height

Ishikawa et al (2009)

Oku et al (2009)

Heading to adaptation
Worst case
Survivability Critical,a Edgeof scenario
Survivability

Range for disastermitigation (includinglarge scale disaster)

There is high uncertainty
in projected design value
Designvalue byreturnvalue

Projected design value
It is almost certain that
average of design value
would increase.

Range for disasterprevention

Launching of Sousei Program• Kyousei(共生)Program:2002-2006– 20kmRCM (daily rainfall)• Kakushin(革新)Program:2007-2011– 20kmGCM, 5,2,1kmRCM (hourly rainfall)– Natural Disaster (Inc. water resources)• Sousei(創生)Program:2012-2016– Impact assessment and producing
adaptation methodologies (First priority)– for Natural Disaster, Water resources,
Ecosystem and Eco service (DPRI,Kyoto
University is leading the nation wideassessment team)Groups in SOUSEI Program(Program for Risk Information on
Climate Change)• A: Prediction and diagnosis of imminent global
climate change (Univ. of Tokyo)• B: Climate change projection contributing tostabilization target setting (JAMSTEC)• C: Development of Basic Technology for Risk
Information on Climate Change (MRI)• D: Precise impact assessments on climatechange (Kyoto Univ.)

Sousei (創生) Program (2012-2016)
Proposing adaptation philosophy
consistent with mitigation
philosophy

Ultimate Goal

Developing decision making
methodology under highuncertainty of risk
Developing decision making
methodology under noinformation on probability of aworst case

Generating PDF of extreme
values with higher accuracy

Post Sousei

Generating of PDF using a lotof 60km ensemble
Converting extreme values in
60km-scale into values inregional-scale using RCM5
and RCM2 dynamicallydownscaled from GSM20.

Sousei (2012-2016)
Kakushin(2007-2011)
Topics:Natural hazard, Water Resources
and Ecosystem and Eco-service

Worst case senario
Worst case typhoon
Multi-hazard
Social Senario

For adaptation decision making
Deterministic, Probabilistic and Beyond

Precise impact assessments on climate change(PI: E. Nakakita, KU (Kyoto University))•i. Climate change impacts on natural hazards (E. Nakakita, KU)
i‐a Risk assessment of meteorological disasters under climate change (T. Takemi, KU)
i‐b Risk assessment of water‐related disasters under climate change (Y. Tachikawa, KU)
i‐c Risk assessment of coastal disasters under climate change (N.Mori, KU)
i‐d Measuring socio‐economic impacts of climate change and effectiveness of adaptation strategies(H. Tatano, KU)– i‐e Development of risk assessment and adaptation strategies for water‐related disaster in Asia (N.
Yasuda, ICHARM, PWRI)––––•

ii. Climate change impacts on water resources (K. Tanaka, KU)– ii‐a Assessment of socio‐economic impacts on water resources and their uncertainties underchanging climate (K. Tanaka, KU)– ii‐b Assessment of climate change impacts on the social‐ecological systems of water resources andhydrological cycles (T. Oki, UT)•iii. Climate change impacts on ecosystem and biodiversity (T. Nakashizuka, TU)– iii‐a Assessment of climatic impacts on ecosystem and biodiversity (T. Nakashizuka, TU)– iii‐b Economic evaluation of ecosystem service (S. Managi, TU)– iii‐c Eco‐climate system in Northeastern Eurasia and Southeast Asian
tropics: impacts of global climate change (T. Kumagai, NU)– iii‐d Assessment of multiple effects of climate change on coastal marine ecosystem (Y. Yamanaka,HU)

Contents of my presentation• What are important aspects in building up
adaptation strategy for coming climatechange.
• A trial aiming to realize a next-generation
operational observation network withdifferent types of sensors for earlier
detection and/or prediction of generatingstorm from the stage of air plume and/orcloud.
24

Urban Flash Flood
About 50 people
were washed awayby the flash flood.
andfive were dead.
14: 22

14: 36

14: 40

14: 44(Ministry of Land, Infrastructure , Transportation, and Tourism,MLIT)
Japanese Government decided to equip X-band (compact type)dual-polarization Doppler radar around the main cities.

25

C and X band operational network by MLIT
C‐band Radar
Obs.: every 5 min
Distrib.: 5‐10 min

26

X-RAIN (Network by 35 X-band pol. Doppler radars)(by MLIT)(operation)

X‐band Radar
Obs.: every 1 min
Distrib.: 1‐2 min(2012)(2013)

26 C-band Conventional(+ 20 JMA C-band Doppler)
2 C-band full Polarimetricsince April 2009
4 C-band Doppler full Polarimetricwithin 1 year

XRAIN

11 X-band Doppler full Polarimetricsince April 201017 X-band Doppler full Polarimetricsince April 2011

1,000 km
Copy: MLIT (Ministry of Land, Infrastructure, Transport and Tourism)

8

X-band Doppler full Polarimetricsince Sept. 2013

XRAIN（X-MPレーダネットワーク）
28

New operational network by X-RAIN andNew Osaka City radar

60 km range
30 kmrange

Kobe

Kyoto

Osaka

Radar images by XRAIN

Conventional（Cband non Pol. ）XRAIN

Real-time calliblation by rain gauge

Advantage of XRAIN

地点：板橋（⾃治体）⇒ QPE for 10 min rainfall has
been very improved by KDP.
20
⇒ Discrimination and10movement of individual storm
cell has been made clearer.
0
17:00 17:40 18:20 19:00 19:40 20:20 21:00 21:40 22:20 23:00⇒Nowcast has been improved.
2010年7⽉5⽇
40
30

10分間⾬量•Higher sensitivity by :X band radar•Higher spatial resolution by :X band radar (250~500 m)Dense network•Free from attenuation by :Polarimetric function (KDP)
Dense network together with Cband•Higher accuracy by :Polarimetric function (ZDR, KDP)
•Shorter scan interval with lowelevation :1 minutes•Shorter transmission time :2 minute•Earlier detection of baby cell:
Volume scan (3D image) 29

地上⾬量(mm)Xバンドレーダ⾬量(mm)30Advances in nowcasting： 2D Cell Tracking■Advantages• Advances in nowcasting within 30 – 60 munities prediction (within a life time of single cell)• Growth and decay of individual cell can be easier identified andpredicted because higher spatio-temporal observation
resolutions

Early detection of risky
raincellObservations（X-MP）

Copyright 2014 by Nakakita

31
Copyright 2011 by Nakakita

京都大学・日本気象協会・日水コン 共同研究チーム

Predictions

Masuda and Yamaji (2012)

33

Rain-based urban flood forecasting method
Concept of urban flood alert criteria nomograph• WLi = f ( Pi, Dit )• f(Po, Dit ) ≥ WLi

Flood
forecasting

C.T

200150P• The urban flood alert criteria nomograph is assembledand comprised by the relationship with the flood
discharge and water level from the various rainfall
scenarios (rainfall intensity and duration time) based on
rainfall information.

WL6
WL5
WLo≥WL1
WL4Flood
WL3
Occurrence
WL2
SafetyWL1
Wloo<WLWL<WL1110050i : reference flood levels, Pi : rainfall intensity at i
Dit : rainfall duration at i, WLi : iso-waterlevel at i
Po : rainfall intensity at real-time
Dot: rainfall duration at real-time00

10203040

Rainfall Duration (min)

Dt

5060

• We can forecast the flood using the location that isdetermined by rainfall intensity and duration. If the location
is exceeded some flood level, we can guess there would besome possibility of the flood risk.

Kyoto University

Yoon and Nakakita (2013)

Localized heavyrainfall over northernpart of Kyoto city
(2012/７/15)--Baby cells were
continually generated overwestern part of Rokomountains--Same as the heavy rainfall
over Uji city on 8/14--Same as the heavy rainfall
over Uji city in 1986--Baby cell aloft of the Togariver disaster was also
generated over same place

Kyoto
Kobe
Osaka

Disaster Prevention Research InstituteMulti-sensor observation of storm genesis
over urban area

Chains of clouds along Rokko mountains
Milli wave （Ka‐band, cloud）radar (next to Kobe Int. Univ.)

Vide sonde observationr
Nagoya Univ.
(Pol. X-band)
Our own dense GPS network for
estimating spatial distribution of water
vapor over Osaka Bay
Detection of generating hydrometers by Pol. X-bandradarsRISH(Ka Band)
Detection of generating cloud by a cloud radar

北北西向きに撮影

Detection of updraft (air plume) without cloud by
a Doppler lider
NICT(Lidar)

Ka‐band (cloud) radar(RISH）

Further technology – almost at onece observation byphased array radar

Doppler Lidar and X‐band Pol. Doppler radar(on the roof of Kobe International Univ.)
西向きに撮影Doppler Lidar（ＮＩＣＴ）

Phased Array Radar

Ｘ‐band Pol. Dop,. Radar（Naogya Univ.）

Mechanical antenna operation

National Severe Storms Laboratory (2007)

38

Synchronized
Observation

LIDAR Obserabtions
B

0'

A
B

ohgimiCOBRA

0'RHI scan in the direction of the video
sonde

onna

A
Mi. Rokko

0'
27˚30'

128˚00'128˚30

Sonde release &tracking Radar
operation

Cold air slides down from B to A along
mountain slope when concoctive system
passed over the mountain.

Yamaguchi et al. (2013)40

Video-Sonde and Hydrometeor types
Hydrometeor Classification (mixture)
Ice crystal

7000

16 mm

GraupelSnow flake

Video-sonde is the radiosonde with a video camera.
The video-sonde is launched with the balloon, and it can directly observehydrometeors in the cloud.
Real particle images, Electric charge,
Air temperature, Atmospheric pressure, Humidity

An idealized experimental observation network
over Kobe, Osaka, Kyoto area.

XRAIN
HYVIS

X-PARGPS

GPSX-PARXRAIN
Compact X
Ka X-MPOsaka

Copyright 2013 by Nakakita

Snow Flake

1000
00.0001

0.001

0.01

0.1

11033）
Mass Density(g/m)
MassDensity（g/mCopyright 2009 by Nakakita

XRAIN

We wish this combined observation to be operational!!
within five years

Graupel +Snow Flake

雨2000

Thank you for yourkind attention!

XRAIN

Graupel +Ice Crystal

Rain

3000

Kyoto
Compact X
Small X

Ice
Crystal4000

Compact X

Compact X

Kobe

Ice Crystal
Snow Flake

VideosondeVideosonde

Lidar

Lidar

（）
Heightm
Height(m)Takahashi at el.(2001)

Graupel

5000
13ｍｍ

Rain

6000

Middle and ending ceremonies of the“Okinawa Campaign Observation”
with young researchers and students
from various universities.
(since 2007)

Rain
Graupel
Snow
Flake
G+IC
G+SF
IC+SF

Raindrop42