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)
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
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
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)
41. Finis “the author of all books”– James Joyce, Finnegan’s Wake http://www.slideshare.net/JeffDozier
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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.
Hinweis der Redaktion
Fundamental properties of measuring snow on the ground start with the optical properties of ice (and water)