Nov. 28, 2006 　 Vancouver

–result of climatic change?

Research　contributions:　as　a　self-introduction

at　the　A1/B1　joint　meetingon　Apr.　25,　2007YES,　JAMSTECby　Kaoru　Tachiiri　(立入　郁 )tachiiri@jamstec.go.jp

0.　Born　in　June,　1970,　Ibaraki　City,　Osaka　(0-18)1.　Kyoto　(B　Sci,　M.　Eng.　Kyoto　U.,　18-25)　　Yoshida　Dormitory,　Alpine　Club　　Modeling　(chaotic)　vegetation　activity　using　NN.2.　Tokyo　(Ph.D　(Agr),　U.　of　Tokyo,　25-28)　“Monitoring　and　modeling　desertification　using　environmental　

information　in　drylands　of　Northeast　China”　(Supervisor:　Prof.　Takeuchi)

3.　Tsukuba　(JSPS　Research　Fellow,　U.　of　Tsukuba,　28-31)　　Received　2　Encouragement　Awards　4.　Nagasaki　(R.　Associate,　Nagasaki　U.,　31-33)　　(Got　married)5.　Vancouver　(Postdoc,　UBC,　33-36)　　JICA　Project　(training　of　the　meteorological　agency,　Mongolia)6.　Yokohama　(Scientist,　36-?)　　Kakushin　P.Field:　Inner　Mongolia　(China),　Kenya,　Burkina　Faso,　　BC　(Canada),　

Mongolia

Some　(non-JAMSTEC)　friends　involved　in　the　Kakushin　P(NEID,　DPRI　(Kyoto-U))

Brief　career

Photos　of　Vancouver,　BCDowntown,　from　the　Kits　BeachThe　Burrard　

Bridge

The　Queen　Elizabeth　ParkThe　Stanley　

ParkVan　Dusen　Park

UBC,　a　beautiful　universityThe　Rose　GardenTotem　pole

The　Nitobe　Garden

On-campus　trail

A　squirrel　at　my　apartment　

Research　contributions

Continental　scale　studies　using　existing　datasets

Regional　scale　studies・ Remote　sensing・ GIS・Model(l)ing

Disaster　prevention/mitigation(Desertification,　Drought,　Infectious　disease)

Studies presented

Continental scale land assessment:

1. Land classification of East Asia

2. Drought frequency estimation of North Africa

Regional scale environmental monitoring/modeling:

3. Desertification in Inner Mongolia, China

4. Atmospheric correction of NOAA/AVHRR (Kenya)

5. Drought/Dzud* monitoring in Mongolia

6. WNV monitoring/modeling in BC, Canada

* Climatic disaster (ex. snow, wind) causing significant livestock mortality in winter

1. Land classification of East Asia

Input: • Elevation (ETOPO5)• Temperature (NCAR)• Precipitation (NCAR)• Soil type (FAO, qualitative)• Vegetation type (Matthews,

qualitative)

Extent• E60 ° -160 °, S20 ° -N60 °

Resolution• 1° X 1 °

Correspondence Analysis

Cluster Analysis

Classification map

Reclassification (to 10 classes)

Continental　scale　studies

First axis: Precipitation Second axis: Temperature and Precipitation

Distribution of each land type is understandable by a latitudinal structure and a concentric one.

Land type vs Land degradation

Overlay the resultant map with the Global Assessment of Human Induced Soil Degradation (GLASOD)

0.0

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6 7 8 9 10

Water erosion

Wind erosion

Land　type

Intensity　of　soil　degradation

Humid tropics (9,10) have the most serious water erosion.

Semi-arid area (2-5) have not only wind but water erosion.

Count Non-degraded, Low, Medium, High, Very high as 0,1,2,3,4 and then averaged.

Interpolated　by　TIN　(triangulated　irregular　network)

2.　Drought　frequency　estimation　of　North　Africa

IGBP land cover dataNCAR rainfall dataDrought is defined as:(1) Back-to-back dry years (<300mm rainfall), for cultivated area(2) Two year average is < (AVG – STD), for grazed area

Estimated　drought　frequency　for　cultivated　areaEstimated　drought　frequency　for　grazed　area

3. Desertification in Inner Mongolia, China

Landsat/MSS (TSAVI) By land type NOAA/AVHRR (Seasonal change) CORONA (1960s)

Low　terrace　-　Meadow　soil

Low　terrace　-　Sandy　soilLow　terrace　-　Salinized　

soilLarge　sand　dune　-　Sandy　

soilSmall　sand　dune　-　Sandy　

soilFlood　plain　-　Meadow　

soilFlood　plain　-　Sandy　soil

Regional　scale　environmental　monitoring/modeling

Ground　water　table　survey

Development of desertification model

Bareland area derived from Landsat/MSS in1975-1994MR

Integration

prediction

Natural condition module(Uncontrollable)・ Rainfall・ Land condition

MR

MR: Multiple Regression

Social condition module(Controllable)・ Livestock density・ Other human factors

By year For 20 yrsInterestingly,　there　was　a　strong　negative　correlation　between　the　spring’s　rainfall　and　the　summer’s　vegetation.

4. Atmospheric correction of NOAA/AVHRR (Kenya)

From　Google　Map

Atmospheric correction using 6S

s

svsARvsgvsvsTOA S

TTT

1

)()(),(),,(

Linear extension of 6S (existing code for atmospheric correction) for NOAA/AVHRR images

(by decoupling absorption and scattering)

s

svsARvsgvsvsTOA S

TTT

1

)()(),(),,(

(y: Observed radiation, acr: Actual reflectance or radiation)

5. Drought/Dzud monitoring in Mongolia

Average　livestock　loss　rate　in　2000-2002

Tree　based　model　(regression　tree)

Result　of　linear　regression

More　robust　method　is　needed

The　dependent　variable:　livestock　mortalityIndependent:　NDVI,　SWE,　population,　previous　mortality

Input: Anomalies in NDVI and Snow

Water Equivalent (RS based) Previous year’s livestock number

and mortality

Output: predicted livestock mortality

Emergency is predicted by: Low NDVI in August*

High mortality *

High SWE in December *

*: Variable in the previous year

Start

NDVI in Early-Aug<0.99*AVG?

Livestock loss ≥6.0%?

SWE in Dec≥0.37*AVG?

NDVI in Late-Aug<0.94*AVG?

Y

N

Y

Y

Y

Serious Dzud

N

N

N

[All parameters are previous year’s]

6. WNV monitoring/modeling in BC, Canada

Identified 1937 in Uganda As BC is the forefront,

BCCDC* needs to assess the risk.

Birds and mosquitoes are important in infection cycle.

20% symptomatic (fever, headache, body aches etc).

Death rate: approximately 1%

*: British Columbia Centre for Disease Control

Calculation of deaths using temperature-dependent mortality rate (Table 1)

Next day

No

Timing of adult emergence? **

Yes

End

Start (from adults’ host-seeking)

No

Yes

No

Timing of oviposition? †

Yes Replace the no. of mosquitoes with no. of eggs laid †† (with no. of active adults handed over)

Move to the next stage

Move to the egg stage of the next generation

No

Stage cleared? *

Calculation of growth using a lower threshold (base) and

degree-days (Table 1)

No

Yes

Numbers of active adults for each day

Calculation of active adults using daily temperature-dependent

mortality (Table 1)

Warm enough to terminate diapause?

Input daily mean temperature

End of the year?

Output abundance of newly emerged adults

as active adults for the day

Mosquito　biology　model　(Process　model)

*:　British　Columbia　Centre　for　Disease　Control

Input: temperature

←T(x)=m(x)+ε1(x)+ε2(x) m(x): regressed by elevation,

cos(latitude) and distance to the sea

ε1(x) and ε2(x): spatially dependent and independent residuals

Important parameters: 　　 growth rate (dd, cf. gdd5 in

SEIB-DGVM) and mortality Time step: 1 day Output: No. of Adult 　

mosquitoes Data for validation: BCCDC*’s

mosquito trap data