nov. 28, 2006 vancouver –result of climatic change?
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Research contributions: as a self-introduction. at the A1/B1 joint meeting on Apr. 25, 2007 YES, JAMSTEC by Kaoru Tachiiri ( 立入 郁 ) [email protected]. Nov. 28, 2006 Vancouver –result of climatic change?. Brief career. 0. Born in June, 1970, Ibaraki City, Osaka (0-18) - PowerPoint PPT PresentationTRANSCRIPT
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 )[email protected]
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