Climatic Change ‘related’ modeling
Food Security package:
Utilize satellite-derived products
Atsushi Higuchi (CEReS, Chiba University, Japan)
Collaborated with: Kenji Tanaka (DPRI, Kyoto University/CEReS CU)，
Hideaki Takenaka (JAXA EORC/CEReS CU), Shunji Kotsuki (AICS, Riken),Taro Shinoda, Masaya Kato (ISEE, Nagoya University)，
Koichi Hasegawa (CTI Inc.)，Koki Honma (Tohoku University),
Misako Kachi, Takuji Kuboda (JAXA/EORC），Yasushi Kajiwara (CEReS, CU）
GEOSS-AP Symposium: WG5 Agriculture and Food Security, Jan 12 Tokyo, Japan

CEReS released Himawari-8 gridded
product with free of charge!

Himawari-8 Standard data (FD)CEReS H-8 gridded product40 sec. all data can process
for precise geo-correction

Release note: http://www.cr.chiba-u.jp/databases/GEO/H8_9/FD/

ALL BAND (16 Bands) equivalent to same spatial resolution (0.02, 0.01, 0.005 deg.)

Background Rapid growth of world population→ major increase in food and water demand.

 Key word: Irrigation

 Good : Producing much food (about 2.5 times) Bad : Requiring much water

Ratio compared with 1960Food Production of The World (FAOSTAT)
Area of IrrigatedcroplandArea ofcropland

World Water Use (Shiklomanov, 2000)
Agriculture

FoodProduction

irrigation

DomesticIndustry

Why we use numerical modeling?

Slide by Dr. Nagai ＠JAMSTEC, Japan

Land surface model
wind
Canopy energy fluxes
Sensible Latent

precipitation

Radiative Fluxes

Interception
by canopy

Top ofcanopy

Shortwave Longwave

Energy budget Transpiration
Radiation budget

Bare soil energy fluxes
Sensible Latent
Groundheat flux
Heat exchange

Throughfall
Infiltration

Surface runoff

Water budget

Diffusion/drainageBaseflow

Land surface model (SiBUC)
Grid box is divided into
three landuse categories
1. Green Area
2. Urban Area
3. Water Body

１．Broadleaf-evergreen trees
２．Broadleaf-deciduous trees
３．Broadleaf and needle leaf trees
４．Needle leaf-evergreen trees
５．Needle leaf-deciduous trees
６．Short vegetation/C4 grassland
７．Broadleaf shrubs with bare soil
８．Dwarf trees and shrubs
９．Farmland (non-irrigated)
10. Paddy field (non-irrigated)
11. Paddy field (irrigated)
12. Spring wheat (irrigated)
13. Winter wheat (irrigated)
14. Corn (irrigated)
15. Cotton (irrigated)
16. Other crops (irrigated)Irrigation scheme
Water control in farmland

Soil moistureDays• Basic concept is to maintain water depth / soil moisture
within appropriate ranges for optimal crop growth• New water layer is added to treat paddy field moreaccurately• Application to wheat, corn, soy bean and rice (paddyfield) etc…

Irrigation scheme
Water control in paddy field• Basic concept is to maintain water depth / soil moisture withinappropriate ranges for optimal crop growth• New water layer is added to treat paddy field more accurately• Application to wheat, corn, soy bean and rice (paddy field)
etc…

Integrated water resources model
Crop Dynamics

Yield

Crop Growth

Water Cycle
LAI

Crop calendar

Human Effects

Land Surface

SIMRIW is now coupling

Irrigation

Soil moisture

Intake & drainage

SiBUC (Tanaka,2004)

RunoffStream flow

SiBUC (Tanaka,2004)

Dam operation
Outflow demand

Discharge

Kinematic wave

Hanasaki et al, (2006)

Outflow & Inflow

Food Security Package: Utilizing High Frequency Satellite Products with Integrated Land ProcessModels (SiBUC & SIMRIW) & Short-time Prediction with Cloud Resolving Model (CReSS)High Time Frequency Forcing Data by RS

Integrated Land Process ModelingHydrology [SiBUC]+ Crop Growth [SIMRIW]

Precipitation: GSMaP

http://sharaku.eorc.jaxa.jp/GSMaP/

Solar Radiation: EXAM

“Real” Forcing
to ModelMore realistic initial condition estimation
http://amaterass.org/

RS Products for Model Input and/or Validation

Short-term Prediction by CReSS+SiBUC(CReSiBUC) (1-5 days forecast）
Biomass by Optical Sensor Soil Moisture by AMSR2Integrated ResearchDemonstration: Satellite
Observation & Numerical
Modeling
Test Short-term Alertsystem for crop yield
Real-time system
operation as a test of“infra-structure” for foodsecurity.

Atmospheric radiation budget product (from 2007 July 7)
Downward SW flux at the surface

Diffuse SW

Direct SW

Ground validation of SW flux

Cloud optical
thickness

Upward SW
flux at the TOA

Downward PAR
flux at the sfc

Downward UVA
flux at the sfc

Downward UVB
flux at the sfc

EXAM system estimates SW radiation by HIMAWARI-8by Dr. H. Takenaka (JAXA EORC /CEReS CU)

Food Security Package: Utilizing High Frequency Satellite Products with Integrated Land Process
Models (SiBUC & SIMRIW) & Short-time Prediction with Cloud Resolving Model (CReSS)High Time Frequency Forcing Data by RS

Integrated Land Process ModelingHydrology [SiBUC]+ Crop Growth [SIMRIW]

Precipitation: GSMaP

http://sharaku.eorc.jaxa.jp/GSMaP/

Solar Radiation: EXAM

“Real” Forcing
to ModelMore realistic initial condition estimation
http://amaterass.org/

RS Products for Model Input and/or Validation

Short-term Prediction by CReSS+SiBUC(CReSiBUC) (1-5 days forecast）
Biomass by Optical Sensor Soil Moisture by AMSR2Integrated Research
Demonstration: SatelliteObservation & Numerical
Modeling
Test Short-term Alertsystem for crop yield
Real-time system
operation as a test of“infra-structure” for foodsecurity.

Effect of EXAM（Shortwave Radiation forcing）

SMF: Seto

Kotsuki et al (2015): HRL-----: Exp.1 GPV/MSM, -----: Exp.2 JRA55, ――: Exp.3 JRA55+EXAM

Effect of EXAM (with in-situ JapanFlux observations)Represented by
Kotsuki et al (2015): HRL
Sites / Function

FHK

SMF

TKCTKY

Experiment 1SWnRLWnExperiment 2

SHF

LHF

0.413

0.790

SWn

−

RMSE

correlation coefficient [R] &Root Mean Squire Error [RMSE]
LWn

Experiment 3

SHF

LHF

0.478

0.779

SWn

−
39.4

37.9

LWn

SHF

LHF

0.689

0.767

28.0

24.5

−
32.3

27.1

R

0.720

0.483

0.233

0.528

0.899

0.837

0.434

0.653

0.953

0.879

0.566

0.699

RMSE

52.9

28.6

36.6

42.3

32.9

21.6

31.7

37.2

22.8

19.5

29.4

34.7

R

0.740

0.386

0.833

0.724

0.922

0.783

RMSE

58.0

35.0

46.2

23.9

33.921.0R

0.676

0.396

0.486

0.762

0.856

0.726

0.532

0.828

0.945

0.781

0.663

0.893

RMSE

65.0

40.6

30.5

36.9

46.5

30.6

27.2

25.8

28.4

28.4

23.2

22.7

−−−

SWn: Net-SW Radiation, LWn: Net-LW Radiation, SHF: Sensible Heat Flux, LHF: Latent Heat Flux

Best

Worst

-----: Exp.1 GPV/MSM, -----: Exp.2 JRA55, ――: Exp.3 JRA55+EXAM

Real-time Land Surface Monitoring @ Chiba-U.
• Re-Analysis (few-days delay)– EXAM, JRA55, GSMaP/Gauge• Near Real-Time (NRT) Analysis (few hours delay)– EXAM, GPV.MSM, GSMaP (GSMaP/Now < NRT < Gauge)• Forecast– Numerical forecast with an atmospherics model
Collaboration withRIKEN, Kyoto-U.
Nagoya-U., Tohoku-U.
& JAXA/EORC

Now GSMaP_NOW!

By Prof. Kenji Tanaka, DPRI KU

Surface SMLE

RootZone SMSnowGrowing Stage of RiceAGB of Rice(yield)

LAI

EXAM + GSMaP drive
SIBUC+SIMRIWForecast (CReSiBUC-SIMRIW @ ISEE Nagoya-U.)• Re-Analysis (few-days delay)– EXAM, JRA55, GSMaP/Gauge• Near Real-Time (NRT) Analysis (few hours delay)– EXAM, GPV.MSM, GSMaP (GSMaP/Now < NRT < Gauge)• Forecast– Numerical forecast with an atmospherics model
Collaboration withRIKEN, Kyoto-U.
Nagoya-U., Tohoku-U.
& JAXA/EORC

Forecast examples : Estimated yield of rice
1day forecast

7days forecast

Improved surface process can improve rainfall? （鬼怒川豪雨事例）
CReSiBUC-SIMRIW

CReSS・ No significant difference, but changed
・It seems better in CReSS only …

Climate change representation
Snow Water Equivalent (Snow Depth)
2015/Jan/15 (Normal Winter)2016/Jan/15 (Warmer Winter)Snow depth(SWE increase afterfrontal-low passed)

2016/Jan/17

2016/Jan/20

• FD Package:

Concluding Remarks– We developed ‘seamless’ package: By utilize H-8 derived
products (forcing: Solar radiation [EXAM], precipitation[GSMaP]), Re-analysis (few days delay), Near-RT (few hoursdelay) and forecast system with SiBUC-SIMRIW and CReSiBCSIMRIW.
– Now we try to validate our package outputs by several
satellite sensors retrieved outputs.

• Future Perspectives:– Expansion to “globe”: Use five GEO satellites, EXAM outputs
will use for global scale. In addition, major crops yield
estimation is next our target under collaboration with NARO
crop modeling team.
– More dynamic use of 3rd GEO: A kind of assimilation canimprove monitor crop-stage adjustment by dynamic use of H8 type GEO (3rd Generation GEO).

Proto-type EXAM global merged
product by five-GEOs

Downward Shortwave flux at the surface✓(Upper) no significant inter-platform difference by merging
Success in inter-calibration of each GEO’s radiometerNatural Color RGB (R:R, G:G, B:B)

RGB composition change (R:R, G:NIR,B:1micron） by H-8

East-Japan (start to irrigation in May)

West-Japan (Winter Wheat – Rice)