Mountain Hydrology, The Fourth Paradigm, and the Color of Snow<br />Jeff Dozier<br />(photo T. H. Painter)<br />
An “exaflood” of observational data requires a new generation of scientific computing tools<br />– Jim Gray<br />http://fo...
Along with The Fourth Paradigm, an emerging science of environmental applications<br />The Fourth Paradigm<br />Thousand y...
Arizona/New Mexico:<br />39%                              <br />140<br />6<br />Utah:<br />60%<br />120<br />5<br />Colora...
Snow-pillow data for Leavitt Lake, 2929 m, Walker R drainage, near Tuolumne & Stanislaus basins<br />
Automated measurement with snow pillow<br />
Manual measurement of SWE (snow water equivalent), started in the Sierra Nevada in 1910<br />
[Bales et al., 2006]<br />
Kings River below Pine Flat Reservoir, April-July unimpaired runoff (units are km3)<br />9<br />
(R. Rice, UC Merced)<br />
[Chapman & Davis, 2010]<br />
[D. Marks]<br />
Sierra Nevada, trends in 220 long-term snow courses (> 50 years, continuing to present)<br />
Peak snow is occurring earlier<br />[Kapnick & Hall, 2010]<br />
Snow redistribution and drifting<br />(D. Marks)<br />
Daily integrated solar radiation is more heterogeneous when Sun is lower<br />16<br />(40°N, 30° slope)<br />[Lundquist & ...
Orographic effect varies (Tuolumne-Merced River basins example)<br />
Snow is one of nature’s most colorful materials (e.g., Landsat snow & cloud)<br />Bands 3 2 1 (red, green, blue)<br />Band...
Spectra with 7 MODIS “land” bands (500m resolution, global daily coverage)<br />
Snow mapping a standard product from MODIS, available daily at 500 m resolution<br />20<br />[Hall et al., 2002]<br />
[Erbe et al., 2003]<br />
[Rosenthalet al.,2007]<br />
Snow spectral reflectance is sensitive to the absorption coefficient of ice<br />[Wiscombe  & Warren, 1980]<br />
The 1.03mm absorption feature is sensitive to grain size<br />[Nolin & Dozier, 2000]<br />
For clean snow, net solar radiation is greatest in the near-IR wavelengths<br />
Dust<br />algae<br />(T. H. Painter)<br />
Spectral reflectance of dirty snow and snow with red algae (Chlamydomonasnivalis)<br />[Painter et al., 2001]<br />
Seasonal solar radiation (Mammoth Mtn, 2005)<br />28<br />
Response of Colorado R to dust radiative forcing<br />Loss of Runoff (BCM)<br />Loss of Runoff (%)<br />Mexico’s annual al...
Fractional snow-covered area, Sierra Nevada (MODIS images available daily)<br />
31<br />
[Dozier et al., 2008]<br />
Downscaled NLDAS assimilated data<br />(K. Rittger)<br />Tuolumne<br />Merced<br />
(K. Rittger)<br />
Combine fractional snow cover with snowmelt model to reconstruct SWE<br />SCA, %<br />100<br />80<br />60<br />40<br />20<...
Reconstructed snow water equivalent<br />SWE, cm<br />450<br />250<br />190<br />130<br />60<br />04/10/05<br />1<br />36<...
Snow water equivalent anomalies<br />2001 – 2007 Average<br />2007<br />2005<br />2002<br />2004<br />SWE anomaly, %<br />...
interpolation, like Fassnacht et al., [2003]<br />energy balance reconstruction<br />38<br />
Reconstruction of heterogeneous snow in a grid cell<br />39<br />z<br />x<br />y<br />Daily potential melt<br />fSCA<br />...
Issues: Topography, vegetation<br />detail<br />Vegetation causes differences in view angle<br />40<br />
Information about water is more useful as we climb the value ladder<br />Forecasting<br />Reporting<br />Done poorly,but a...
The data cycle perspective, from creation to curation<br />The science information user:<br />I want reliable, timely, usa...
Finis<br />“the author of all books”– James Joyce, Finnegan’s Wake<br />http://www.slideshare.net/JeffDozier<br />43<br />
References<br />Bales, R. C., N. P. Molotch, T. H. Painter, M. D. Dettinger, R. Rice, and J. Dozier (2006), Mountain hydro...
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AGU Nye Lecture December 2010

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American Geophysical Union John Nye Lecture, 14 December 2010

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AGU Nye Lecture December 2010

  1. 1. Mountain Hydrology, The Fourth Paradigm, and the Color of Snow<br />Jeff Dozier<br />(photo T. H. Painter)<br />
  2. 2. An “exaflood” of observational data requires a new generation of scientific computing tools<br />– Jim Gray<br />http://fourthparadigm.org<br />
  3. 3. Along with The Fourth Paradigm, an emerging science of environmental applications<br />The Fourth Paradigm<br />Thousand years ago —experimental science<br />Description of natural phenomena<br />Last few hundred years —theoretical science<br />Newton’s Laws, Maxwell’s Equations . . .<br />Last few decades — computational science<br />Simulation of complex phenomena<br />Today — data-intensive science<br />Model/data integration<br />Data mining<br />Higher-order products, sharing<br />“We seek solutions. We don't seek—dare I say this?—just scientific papers anymore.”<br />Steven Chu<br />Nobel Laureate<br />U.S. Secretary of Energy<br />
  4. 4. Arizona/New Mexico:<br />39% <br />140<br />6<br />Utah:<br />60%<br />120<br />5<br />Colorado:<br />63%<br />SWE<br />100<br />4<br />Flow<br />3<br />80<br />Sierra Nevada:<br />67%<br />Average Monthly SWE(in)<br />60<br />2<br />Average Monthly Flow (1000AF)<br />40<br />1<br />20<br />0<br />-1<br />Oct<br />Nov<br />Dec<br />Jan<br />Feb<br />Mar<br />Apr<br />May<br />Jun<br />Jul<br />Aug<br />Sep<br />Month<br />Snow contributions to annual precipitation<br />Most runoff & recharge come from snowmelt<br />(Serrezeet al., 1999)<br />
  5. 5. Snow-pillow data for Leavitt Lake, 2929 m, Walker R drainage, near Tuolumne & Stanislaus basins<br />
  6. 6. Automated measurement with snow pillow<br />
  7. 7. Manual measurement of SWE (snow water equivalent), started in the Sierra Nevada in 1910<br />
  8. 8. [Bales et al., 2006]<br />
  9. 9. Kings River below Pine Flat Reservoir, April-July unimpaired runoff (units are km3)<br />9<br />
  10. 10. (R. Rice, UC Merced)<br />
  11. 11. [Chapman & Davis, 2010]<br />
  12. 12. [D. Marks]<br />
  13. 13. Sierra Nevada, trends in 220 long-term snow courses (> 50 years, continuing to present)<br />
  14. 14. Peak snow is occurring earlier<br />[Kapnick & Hall, 2010]<br />
  15. 15. Snow redistribution and drifting<br />(D. Marks)<br />
  16. 16. Daily integrated solar radiation is more heterogeneous when Sun is lower<br />16<br />(40°N, 30° slope)<br />[Lundquist & Flint, 2006]<br />
  17. 17. Orographic effect varies (Tuolumne-Merced River basins example)<br />
  18. 18. Snow is one of nature’s most colorful materials (e.g., Landsat snow & cloud)<br />Bands 3 2 1 (red, green, blue)<br />Bands 5 4 2 (swir, nir, green)<br />
  19. 19. Spectra with 7 MODIS “land” bands (500m resolution, global daily coverage)<br />
  20. 20. Snow mapping a standard product from MODIS, available daily at 500 m resolution<br />20<br />[Hall et al., 2002]<br />
  21. 21. [Erbe et al., 2003]<br />
  22. 22. [Rosenthalet al.,2007]<br />
  23. 23. Snow spectral reflectance is sensitive to the absorption coefficient of ice<br />[Wiscombe & Warren, 1980]<br />
  24. 24. The 1.03mm absorption feature is sensitive to grain size<br />[Nolin & Dozier, 2000]<br />
  25. 25. For clean snow, net solar radiation is greatest in the near-IR wavelengths<br />
  26. 26. Dust<br />algae<br />(T. H. Painter)<br />
  27. 27. Spectral reflectance of dirty snow and snow with red algae (Chlamydomonasnivalis)<br />[Painter et al., 2001]<br />
  28. 28. Seasonal solar radiation (Mammoth Mtn, 2005)<br />28<br />
  29. 29. Response of Colorado R to dust radiative forcing<br />Loss of Runoff (BCM)<br />Loss of Runoff (%)<br />Mexico’s annual allotment<br />Dust<br />Clean<br />Post-disturbance<br />------------------------1850AD<br />Pre-disturbance<br />Naturalized Runoff (BCM/day)<br />LA<br />LV<br />Present dusty conditions:<br />3 week earlier peak<br />Steeper rising limb<br />5% less annual runoff<br />5% is:<br />2x Las Vegas’ allocation<br />18 months of L.A.’s use<br />½ Mexico’s allocation<br />Neff et al 2008 Nature Geosciences<br />[Painter et al., 2010]<br />
  30. 30. Fractional snow-covered area, Sierra Nevada (MODIS images available daily)<br />
  31. 31. 31<br />
  32. 32. [Dozier et al., 2008]<br />
  33. 33. Downscaled NLDAS assimilated data<br />(K. Rittger)<br />Tuolumne<br />Merced<br />
  34. 34. (K. Rittger)<br />
  35. 35. Combine fractional snow cover with snowmelt model to reconstruct SWE<br />SCA, %<br />100<br />80<br />60<br />40<br />20<br />04/15/05<br />03/30/05<br />03/24/05<br />1<br />SWE, cm<br />450<br /><ul><li>Reconstructed snow water equivalent</li></ul>[N. Molotch, based on concept from Martinec & Rango, 1981]<br />250<br />190<br />130<br />60<br />04/10/05<br />1<br />
  36. 36. Reconstructed snow water equivalent<br />SWE, cm<br />450<br />250<br />190<br />130<br />60<br />04/10/05<br />1<br />36<br />
  37. 37. Snow water equivalent anomalies<br />2001 – 2007 Average<br />2007<br />2005<br />2002<br />2004<br />SWE anomaly, %<br />avg. SWE, cm<br />0 60 120 180<br />-100 -60 -10 10 60 100+<br />
  38. 38. interpolation, like Fassnacht et al., [2003]<br />energy balance reconstruction<br />38<br />
  39. 39. Reconstruction of heterogeneous snow in a grid cell<br />39<br />z<br />x<br />y<br />Daily potential melt<br />fSCA<br />Reconstructed SWE<br />A. Kahl<br />[Homan et al., 2010]<br />
  40. 40. Issues: Topography, vegetation<br />detail<br />Vegetation causes differences in view angle<br />40<br />
  41. 41. Information about water is more useful as we climb the value ladder<br />Forecasting<br />Reporting<br />Done poorly,but a few notablecounter-examples<br />Analysis<br />Integration<br />Data >>> Information >>> Insight<br />Distribution<br />>>> Increasing value >>><br />Done poorly to moderately,not easy to find<br />Aggregation<br />Quality assurance<br />Sometimes done well,generally discoverable and available,butcould be improved<br />Collation<br />Monitoring<br />(I. Zaslavsky & CSIRO, BOM, WMO)<br />
  42. 42. The data cycle perspective, from creation to curation<br />The science information user:<br />I want reliable, timely, usable science information products<br /><ul><li>Operational agencies:</li></ul>We want data from a network of authors<br />In a way that improves our decisions<br /><ul><li>The science information author:</li></ul>I want to help users (and build my citation index)<br />Data Acquisition & Modeling<br />Collaboration & Visualization<br />Disseminate & Share<br />Archiving & Preservation<br />Analysis & Data Mining<br />(J. Frew, T. Hey)<br />
  43. 43. Finis<br />“the author of all books”– James Joyce, Finnegan’s Wake<br />http://www.slideshare.net/JeffDozier<br />43<br />
  44. 44.
  45. 45. References<br />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.<br />Chapman, D. S., and M. G. Davis (2010), Climate change: Past, present, and future, Eos. Trans. AGU, 91, 325-326.<br />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.<br />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.<br />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.<br />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.<br />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.<br />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.<br />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.<br />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.<br />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.<br />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.<br />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.<br />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.<br />45<br />

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