American Geophysical Union John Nye Lecture, 14 December 2010

1. Mountain Hydrology, The Fourth Paradigm, and the Color of Snow Jeff Dozier (photo T. H. Painter)

2. An “exaflood” of observational data requires a new generation of scientific computing tools – Jim Gray http://fourthparadigm.org

3. Along with The Fourth Paradigm, an emerging science of environmental applications The Fourth Paradigm 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 “We seek solutions. We don't seek—dare I say this?—just scientific papers anymore.” Steven Chu Nobel Laureate U.S. Secretary of Energy

4. Arizona/New Mexico: 39% 140 6 Utah: 60% 120 5 Colorado: 63% SWE 100 4 Flow 3 80 Sierra Nevada: 67% Average Monthly SWE(in) 60 2 Average Monthly Flow (1000AF) 40 1 20 0 -1 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Month Snow contributions to annual precipitation Most runoff & recharge come from snowmelt (Serrezeet al., 1999)

5. Snow-pillow data for Leavitt Lake, 2929 m, Walker R drainage, near Tuolumne & Stanislaus basins

6. Automated measurement with snow pillow

7. Manual measurement of SWE (snow water equivalent), started in the Sierra Nevada in 1910

8. [Bales et al., 2006]

9. Kings River below Pine Flat Reservoir, April-July unimpaired runoff (units are km3) 9

10. (R. Rice, UC Merced)

11. [Chapman & Davis, 2010]

12. [D. Marks]

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

14. Peak snow is occurring earlier [Kapnick & Hall, 2010]

15. Snow redistribution and drifting (D. Marks)

16. Daily integrated solar radiation is more heterogeneous when Sun is lower 16 (40°N, 30° slope) [Lundquist & Flint, 2006]

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

18. Snow is one of nature’s most colorful materials (e.g., Landsat snow & cloud) Bands 3 2 1 (red, green, blue) Bands 5 4 2 (swir, nir, green)

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

20. Snow mapping a standard product from MODIS, available daily at 500 m resolution 20 [Hall et al., 2002]

21. [Erbe et al., 2003]

22. [Rosenthalet al.,2007]

23. Snow spectral reflectance is sensitive to the absorption coefficient of ice [Wiscombe & Warren, 1980]

24. The 1.03mm absorption feature is sensitive to grain size [Nolin & Dozier, 2000]

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

26. Dust algae (T. H. Painter)

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

28. Seasonal solar radiation (Mammoth Mtn, 2005) 28

29. Response of Colorado R to dust radiative forcing Loss of Runoff (BCM) Loss of Runoff (%) Mexico’s annual allotment Dust Clean Post-disturbance ------------------------1850AD Pre-disturbance Naturalized Runoff (BCM/day) LA LV Present dusty conditions: 3 week earlier peak Steeper rising limb 5% less annual runoff 5% is: 2x Las Vegas’ allocation 18 months of L.A.’s use ½ Mexico’s allocation Neff et al 2008 Nature Geosciences [Painter et al., 2010]

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

31. 31

32. [Dozier et al., 2008]

33. Downscaled NLDAS assimilated data (K. Rittger) Tuolumne Merced

34. (K. Rittger)

36. Reconstructed snow water equivalent SWE, cm 450 250 190 130 60 04/10/05 1 36

37. Snow water equivalent anomalies 2001 – 2007 Average 2007 2005 2002 2004 SWE anomaly, % avg. SWE, cm 0 60 120 180 -100 -60 -10 10 60 100+

38. interpolation, like Fassnacht et al., [2003] energy balance reconstruction 38

39. Reconstruction of heterogeneous snow in a grid cell 39 z x y Daily potential melt fSCA Reconstructed SWE A. Kahl [Homan et al., 2010]

40. Issues: Topography, vegetation detail Vegetation causes differences in view angle 40

41. 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 Distribution >>> Increasing value >>> 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)

43. Finis “the author of all books”– James Joyce, Finnegan’s Wake http://www.slideshare.net/JeffDozier 43

45. 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. 45