This document discusses snowpack in the Sierra Nevada mountains and its importance as a water source. It covers topics like measuring snow water equivalent using snow pillows, reconstructing snowpack levels from remote sensing data, and modeling snowmelt and runoff. The document emphasizes that integrating data from surface measurements, remote sensing, and models is key to understanding snowpack and forecasting water supply, especially as the climate changes.

1. Water from the mountains, The Fourth Paradigm, and the color of snow Jeff Dozier University of California, Santa Barbara

2. 1/5th of Earth’s population gets water from snow and ice 1978 SierraNevada 2002 Changes in the QoriKalis Glacier, Quelccaya Ice Cap, Peru (Barnett et al., 2005) (L. Thompson)

3. Thousand years ago —experimental science Description of natural phenomena Last few hundred years —theoretical science Newton’s Laws, Maxwell’s Equations . . . Last few decades — computational science Simulation of complex phenomena Today — data-intensive science Model/data integration Data mining Higher-order products, sharing Jim Gray, 1944-2007 http://fourthparadigm.org

4. Snow is one of nature’s most colorful materials (Landsat Thematic Mapper snow & cloud) Bands 3 2 1 RGB(0.66, 0.57, 0.48 μm) Bands 5 4 2(1.65, 0.83, 0.57 μm)

5. Automated measurement with snow pillow Measures the snow water equivalent (SWE) amount of water that would result if the snow melted snow  depth = kg m–2 (mass/area) (snow  depth)/water = depth of water equivalent 1 kg m–2= 1 mm depth (R. Julander)

6. Snow-pillow data for Leavitt Lake, 2929 m, Sierra Nevada

7. Manual measurement started in the Sierra Nevada in 1910

8. April-July 2011 forecast, Tuolumne River

9. Distribution of errors in the April-July forecast (R. Rice, UC Merced)

10. [Chapman & Davis,2010] Historical record during a period of climate change

11. [D. Marks]

12. Snow course RBV, elev 1707m, 38.9°N 120.4°W (American R)

13. Orographic effect varies (Tuolumne-Merced River basins example)

14. Sierra Nevada, trends in 220 long-term snow courses(> 50 years, continuing to present)

15. Daily integrated solar radiation is more heterogeneous when Sun is lower (40°N, 30° slope) [Lundquist & Flint, 2006]

16. Optical properties of ice and water 𝐼𝑠𝐼0=exp−4𝜋𝑘𝑠𝜆

17. [Erbe et al., 2003] “Snowflakes are hieroglyphs sent from the sky” UkichiroNakaya

18. Snow spectral reflectivity (albedo) is sensitive to the absorption coefficient of ice [Wiscombe & Warren, 1980]

19. Dust algae (M. Skiles)

20. Spectral reflectivity of dirty snow and snow with red algae (Chlamydomonasnivalis) [Painter et al., 2001]

21. For clean snow, net solar radiation is greatest in the near-IR wavelengths

22. Seasonal solar radiation (Mammoth Mtn, 2005)

23. Terra satellite 705 km altitudeorbit 98 minutes MODIS instrument sees all of Earth’s surface in 2 days (almost all in 1 day) Path of Satellite

24. (moderate resolution imaging spectroradiometer)MODIS spectral bands

25. Spectra with 7 MODIS “land” bands(500 m resolution, daily coverage)

26. MODIS image

27. Fractional snow-covered area, Sierra Nevada (MODIS images available daily)

28. Not just snow cover, but also its reflectivity

29. Spatially distributed snow water equivalent SWE, mm 4500 2500 1900 1300 600 (N. Molotch) 10 04/10/05

30. Interpolation statistical 3D interpolation from snow pillows and snow courses, constrained by remotely sensed snow-covered area SNODAS – the U.S. “national snow model” assimilate numerical weather & snowmelt models with surface data & remote sensing Reconstruction (after the snow is gone) from remotely sensed snow cover, estimate rate of snowmelt from energy input, and back-calculate how much snow there was. Three independent ways

31. Snow redistribution and drifting (D. Marks)

32. Reconstruction of heterogeneous snow in a grid cell z x y Daily potential melt fSCA Reconstructed SWE A. Kahl [Homan et al., 2010]

33. Solar radiation at 1 hr time steps – details Cosgrove et al., 2003; Pinker et al., 2003; Mitchell et al., 2004 Erbs et al., 1982; Olyphant et al., 1984 Dozier and Frew, 1990 Dubayah and Loechel., 1997 Link and Marks, 1999; Garren and Marks, 2005 Painter et al., 2009; Dozier et al., 2008

34. Comparison of modeled and observed SWE, April 1, 2006

35. “All models are wrong, but some are useful” – G. Box Interpolation SNODAS Reconstruction 2006 April May June July August

36. km3 mm Interpolation SNODAS Reconstruction

37. Persistent, high-elevation snowpack not measured by surface stations

38. Data Acquisition & Modeling Archiving & Preservation (J. Frew, T. Hey) Collaboration & Visualization Dissemination & Sharing http://fourthparadigm.org Analysis & Data Mining

39. Information about water is more useful as we climb the value ladder Forecasting Reporting Done poorly,but a few notablecounter-examples Analysis Integration Data >>> Information >>> Insight >>> Increasing value >>> Distribution Done poorly to moderately,not easy to find Aggregation Quality assurance Sometimes done well,generally discoverable and available,butcould be improved Collation Monitoring (I. Zaslavsky & CSIRO, BOM, WMO)

40. (MOD for Terra/MYD for Aqua)

41. Finis “the author of all books”– James Joyce, Finnegan’s Wake http://www.slideshare.net/JeffDozier

43. References Bales, R. C., N. P. Molotch, T. H. Painter, M. D. Dettinger, R. Rice, and J. Dozier (2006), Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, doi: 10.1029/2005WR004387. Chapman, D. S., and M. G. Davis (2010), Climate change: Past, present, and future, Eos. Trans. AGU, 91, 325-326. Hall, D. K., G. A. Riggs, V. V. Salomonson, N. E. DiGirolamo, and K. J. Bayr (2002), MODIS snow-cover products, Remote Sens. Environ., 83, 181-194, doi: 10.1016/S0034-4257(02)00095-0. Dozier, J., T. H. Painter, K. Rittger, and J. E. Frew (2008), Time-space continuity of daily maps of fractional snow cover and albedo from MODIS, Adv. Water Resour., 31, 1515-1526, doi: 10.1016/j.advwatres.2008.08.011. Homan, J. W., C. H. Luce, J. P. McNamara, and N. F. Glenn (2010), Improvement of distributed snowmelt energy balance modeling with MODIS-based NDSI-derived fractional snow-covered area data, Hydrol. Proc., doi: 10.1002/hyp.7857. Kapnick, S., and A. Hall (2010), Observed climate-snowpack relationships in California and their implications for the future, J. Climate, 23, 3446-3456, doi: 10.1175/2010JCLI2903.1. Lundquist, J. D., and A. L. Flint (2006), Onset of snowmelt and streamflow in 2004 in the western United States: How shading may affect spring streamflow timing in a warmer world, J. Hydrometeorol., 7, 1199-1217, doi: 10.1175/JHM539.1. Martinec, J., and A. Rango (1981), Areal distribution of snow water equivalent evaluated by snow cover monitoring, Water Resour. Res., 17, 1480-1488, doi: 10.1029/WR017i005p01480. Nolin, A. W., and J. Dozier (2000), A hyperspectral method for remotely sensing the grain size of snow, Remote Sens. Environ., 74, 207-216, doi: 10.1016/S0034-4257(00)00111-5. Painter, T. H., K. Rittger, C. McKenzie, R. E. Davis, and J. Dozier (2009), Retrieval of subpixel snow-covered area, grain size, and albedo from MODIS, Remote Sens. Environ., 113, 868–879, doi: 10.1016/j.rse.2009.01.001. Painter, T. H., J. S. Deems, J. Belnap, A. F. Hamlet, C. C. Landry, and B. Udall (2010), Response of Colorado River runoff to dust radiative forcing in snow, Proc. Natl. Acad. Sci. U. S. A.,doi: 10.1073/pnas.0913139107. Rosenthal, W., J. Saleta, and J. Dozier (2007), Scanning electron microscopy of impurity structures in snow, Cold Regions. Sci. Technol., 47, 80-89, doi: 10.1016/j.cold.regions.2006.08.006. Serreze, M. C., M. P. Clark, R. L. Armstrong, D. A. McGinnis, and R. S. Pulwarty (1999), Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145-2160, doi: 10.1029/1999WR900090. Wiscombe, W. J., and S. G. Warren (1980), A model for the spectral albedo of snow, I, Pure snow, J. Atmos. Sci., 37, 2712-2733.