Environmental Scientist Specializing in Disaster Prevention and Remote Sensing Studies

1. Research contributions: as a self-introduction at the A1/B1 joint meeting on Apr. 25, 2007 YES, JAMSTEC by Kaoru Tachiiri (立入　郁) tachiiri@jamstec.go.jp Nov. 28, 2006Vancouver –result of climatic change?

2. Brief career 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))

3. Photos of Vancouver, BC Downtown, from the Kits Beach The Burrard Bridge The Queen Elizabeth Park The Stanley Park Van Dusen Park

4. UBC, a beautiful university The Rose Garden Totem pole The Nitobe Garden On-campus trail A squirrel at my apartment

5. Research contributions Continental scale studies using existing datasets Regional scale studies ・Remote sensing ・GIS ・Model(l)ing Disaster prevention/mitigation (Desertification, Drought, Infectious disease)

6. 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 InnerMongolia, 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

7. Continental scale studies 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 ° • Reclassification (to 10 classes) • Correspondence Analysis • Cluster Analysis • Classification map

8. First axis: Precipitation • Second axis: Temperature and Precipitation • Distribution of each land type is understandable by a latitudinal structure and a concentric one.

9. Land type vs Land degradation Count Non-degraded, Low, Medium, High, Very high as 0,1,2,3,4 and then averaged. • Overlay the resultant map with the Global Assessment of Human Induced Soil Degradation (GLASOD) Intensity of soil degradation Land type • Humid tropics (9,10) have the most serious water erosion. • Semi-arid area (2-5) have not only wind but water erosion.

10. 2. Drought frequency estimation of North Africa IGBP land cover data NCAR rainfall data Drought is defined as: (1) Back-to-back dry years (<300mm rainfall), for cultivated area (2) Two year average is < (AVG – STD), for grazed area Interpolated by TIN (triangulated irregular network) Estimated drought frequency for cultivated area Estimated drought frequency for grazed area

11. Regional scale environmental monitoring/modeling Low terrace - Meadow soil Low terrace - Sandy soil Low terrace - Salinized soil Large sand dune - Sandy soil Small sand dune - Sandy soil Flood plain - Meadow soil Flood plain - Sandy soil 3. Desertification in InnerMongolia, China • Landsat/MSS (TSAVI) • By land type • NOAA/AVHRR (Seasonal change) • CORONA (1960s)

12. Ground water table survey

13. Development of desertification model Bareland area derived from Landsat/MSS in1975-1994 By year For 20 yrs MR MR Interestingly, there was a strong negative correlation between the spring’s rainfall and the summer’s vegetation. Natural condition module (Uncontrollable)・Rainfall・Land condition Social condition module (Controllable)・Livestock density・Other human factors Integration MR: Multiple Regression prediction

14. 4. Atmospheric correction of NOAA/AVHRR (Kenya) From Google Map

15. Atmospheric correction using 6S • Linear extension of 6S (existing code for atmospheric correction) for NOAA/AVHRR images (by decoupling absorption and scattering) (y: Observed radiation, acr: Actual reflectance or radiation)

16. 5. Drought/Dzud monitoring in Mongolia Average livestock loss rate in 2000-2002

17. Tree based model (regression tree) More robust method is needed Result of linear regression The dependent variable: livestock mortality Independent: NDVI, SWE, population, previous mortality

18. 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 [All parameters are previous year’s] Start NDVI in Early-Aug<0.99*AVG? N Y Livestock loss ≥6.0%? N Y SWE in Dec≥0.37*AVG? N Y NDVI in Late-Aug<0.94*AVG? N Y Serious Dzud

19. 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

20. Mosquito biology model (Process model) • 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 Adultmosquitoes • Data for validation: BCCDC*’s mosquito trap data *: British Columbia Centre for Disease Control