1. Winter precipitation and snow water equivalent estimation and reconstruction for the Salt-Verde-Tonto River Basin for the Salt-Verde-Tonto River Basin based on remote-sensed and dendrochronological data. based on remote-sensed and dendrochronological data. Elzbieta Czyzowska – Wisniewski research conducted under guidance of Dr. K. Hirschboeck

2. Why do we have to study winter precipitation and snow cover in the Southwest USA - winter precipitation and snow cover supply up to 90% of annual precipitation; - winter precipitation and snow cover is very sensitive to global climate change; - estimation of snow water equivalent (SWE) based on - estimation of snow water equivalent (SWE) based on instrumental data – remote sensing data complement sparse ground data; - estimation of snow cover and SWE is needed for better - estimation of snow cover and SWE is needed for better understanding of spatial and temporal variation of these elements in the decadal and century time scales;

3. Robinson D.A., 2004, Blended snow cover, Snow Cover Conference Proceedings Seasonal and annual snow cover changes in the Northern Hemisphere 1966 - 2004

4. Monthly snow cover changes in the Northern Hemisphere Robinson D.A., 2004, Blended snow cover, Snow Cover Conference Proceedings

5. Salt River Project – main source of water supply for Phoenix SRP canals Roosevelt Lake (June 2002) Roosevelt Dam

6. Phoenix – a few words - population – about 2 000 000 - agriculture uses up to 90 - 95% of water - 2 nd fastest growing town in the USA - depletion of the ground water - 5 –10% other

7. Area of studies – the Salt River Basin Landsat ETM+ (June – July 2002) Tree-ring sites for streamflow reconstructions (Hirschboeck & Meko, 2004)

8. IIa. Comparison of fractional snow area distribution with snow maps produced by: MODIS NDSI;Landsat NDSI; NOAA; SNOTEL ; Ikonos Landsat ETM+ snow MODIS Terra & Aqua Aster snow DEM Fractional snow cover in mountain areas IIIc. SWE at 1 st April (1972 – 2007) snow IIb. Weekly and annual MODIS fractional snow cover distribution; 1972 1980 snow 2006 Models IIIa. Spatial and temporal changes of SWE based on MODIS fractional snow cover; Model 1972 1980 snow 2006 IV. Models: Tree – SWE ; Tree - Days with snow ANN & GIS V. Reconstruction of SWE and snow days (1400 – 2007); VI. Relations between snow cover and climatic forcing (1400 – 2007); VII. Spatial and temporal distribution of snow cover and SWE as a direct record of climate changes with possibilities for future predictions ; snow I. Neural network based fractional snow cover estimator Future plans: 2005 - 2008

9. Which trees can we use ? 1 ) annual growth; annual rings; 2) sensitive // complacent trees; 3) distinct rings; 4) strong common patterns of properties such as ring width;

10. Ikonos Landsat TM/ETM+ classification map snow MODIS Terra/Aqua snow 10 m DEM ModisFSC snow GIS snow ANN1ANN1 / 4 / 4 ANN2ANN2 A –LandsatFSC development B – ModisFSC development C – Verification Ikonos classification map 30 m DEM ANN1 training ANN2 training LandsatFSC snow MODIS, Landsat NDSI NOHRSC, SNOTEL - panchromatic - visible - near infrared - shortwave infrared - thermal Legend: ANN2 parameters ANN1 parameters Landsat and MODIS fractional snow cover

11. 30 m GroundSatellite Remote sensing – introduction 1 30 m

12. Ground Satellite Remote sensing – introduction 2

13. 30 m Ground Satellite Present day snow classification < snow > non snow snow non snow non snow 30 m Normalized Difference Snow Index Snow detection – current methods: NDSI

14. Snow cover in remotely sensed images Normalized Difference Snow Index

15. MOD10A1: Daily Tile Snow Map Hall D., 2004, MODIS, snow products, Hall D., 2004, MODIS, snow products, Snow Cover Conference Proceedings Snow in northern Italy - March 29, 2002 MOD09 bands 1,4,3 (Surface Reflectance Product) MOD10A1 (Snow Daily Tile Product)

16. MOD10A2: 8-day Composite Tile Snow Map Hall D., 2004, MODIS, snow products, Hall D., 2004, MODIS, snow products, Snow Cover Conference Proceedings Western North America - April 23, 2002 MOD09 bands 1,4,3 (Surface Reflectance Product) MOD10A2 (Snow 8-Day Tile Product)

17. 2003 December 18 th 2004 January 16 th 2000 February 1 st 2004 February 17 th 2004 March 4 th 2004 March 20 th 2004 April 5 th 2004 April 21 st Temporal changes of snow cover distribution using MODIS (250 m)

18. Snow cover detection – different spatial resolution - Landsat: SST, TM & ETM+; - spatial resolution: 30 m (15 m); - spatial resolution: 30 m (15 m); - temporal resolution: 16 days; - temporal resolution: 16 days; - MODIS (Aqua, Terra); - spatial resolution: 250 m (500 m); - temporal resolution: 1 day; - temporal resolution: 1 day; - AVHRR; - spatial resolution: 1000 m; - temporal resolution: 1 day; - temporal resolution: 1 day; - IKONOS (my wish); - spatial resolution: 4 (1) m; - temporal resolution: 1 day; - temporal resolution: 1 day; - ASTER; - spatial resolution: 15 m (30 m); - temporal resolution: 14 days; - temporal resolution: 14 days;

19. Ikonos as a source information for snow cover monitoring 30 m Landsat Ikonos 1 m 4 m snow how much of the pixel is covered by snow ?

20. Ikonos as a source information for snow cover monitoring 30 m Landsat Ikonos 1 m 1 Landsat pixel (30m) = 900 Ikonos pixels (1m); 1 MODIS pixel (500m) = 278 Landsat pixels = 250 000 Ikonos forest road water house fresh snow old snow metamorphosed snow % snow cover

21. Ikonos Landsat TM/ETM+ classification map snow MODIS Terra/Aqua snow 10 m DEM ModisFSC snow GIS snow ANN1ANN1 / 4 / 4 ANN2ANN2 A –LandsatFSC development B – ModisFSC development C – Verification Ikonos classification map 30 m DEM ANN1 training ANN2 training LandsatFSC snow MODIS, Landsat NDSI NOHRSC, SNOTEL - panchromatic - visible - near infrared - shortwave infrared - thermal Legend: ANN2 parameters ANN1 parameters Landsat and MODIS fractional snow cover

22. Fractional snow cover monitoring in complex forested-alpine environment Unsolved problems in snow cover monitoring: 1) Dense vegetation and snow cover; Vikhamar D., Solberg R., 2002, Subpixel mapping of snow cover in forest by optical remote sensing, Remote Sensing of Environment, 84, 1, p. 69 – 82.

23. Fractional snow cover monitoring in complex forested-alpine environment Unsolved problems in snow cover monitoring: 2) Forested-alpine environment: - elevation; - slope steepness; - exposition; - shadow effect; - solar illumination; - look geometry; - snow depth; - patchy snow cover;

24. Fractional snow cover monitoring in complex forested-alpine environment Unsolved problems in snow cover monitoring: 3) Snow cover age: - fresh snow cover; - old snow cover; - snow cover with metamorphosis; a)Partially developed snow cover; b)Fully developed snow cover; c)Snow cover with significant melt; d)Vegetation cover without snow cover;