Winter precipitation and snow water equivalent estimation and reconstruction for the Salt-Verde-Tonto River Basin for the Salt-Verde-Tonto River Basin.

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Presentation transcript:

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

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;

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

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

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

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

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

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; snow 2006 Models IIIa. Spatial and temporal changes of SWE based on MODIS fractional snow cover; Model 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:

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;

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

30 m GroundSatellite Remote sensing – introduction 1 30 m

Ground Satellite Remote sensing – introduction 2

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

Snow cover in remotely sensed images Normalized Difference Snow Index

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)

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)

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)

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;

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 ?

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 = Ikonos forest road water house fresh snow old snow metamorphosed snow % snow cover

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

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.

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;

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;