agu nye lecture december 2010
DESCRIPTION
American Geophysical Union John Nye Lecture, 14 December 2010TRANSCRIPT
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Mountain Hydrology, The Fourth Paradigm, and the Color of Snow
Jeff Dozier
(photo T. H. Painter)
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• An “exaflood” of observational data requires a new generation of scientific computing tools
– Jim Grayhttp://fourthparadigm.org
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Along with The Fourth Paradigm, an emerging science of environmental applications
“We seek solutions. We don't seek—dare I say this?—just scientific papers anymore.”
Steven ChuNobel Laureate
U.S. Secretary of Energy
1. Thousand years ago —experimental science
– Description of natural phenomena
2. Last few hundred years —theoretical science
– Newton’s Laws, Maxwell’s Equations . . .
3. Last few decades — computational science
– Simulation of complex phenomena
4. Today — data-intensive science
– Model/data integration– Data mining– Higher-order products,
sharing
The Fourth Paradigm
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(Serreze et al., 1999)
Most runoff & recharge come from snowmelt
Sierra Nevada: 67%
Colorado: 63%
Utah: 60%
Arizona/New Mexico:
39%
20
40
60
80
100
120
140
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
Month
Avera
ge M
on
thly
Flo
w (
100
0A
F)
-1
0
1
2
3
4
5
6
Avera
ge M
on
thly
SW
E(i
n)
SWEFlow
Snow contributions to annual precipitation
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Snow-pillow data for Leavitt Lake, 2929 m, Walker R drainage, near Tuolumne & Stanislaus basins
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Automated measurement with snow pillow
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Manual measurement of SWE (snow water equivalent), started in the Sierra Nevada in 1910
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[Bales et al., 2006]
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• Kings River below Pine Flat Reservoir, April-July unimpaired runoff (units are km3)
9
50-yr mean Max Min
Prediction on
May 1
% of avg
80% prob rang
e
1.510 3.840 0.338
1.122 74%1.00
–1.27
2009
1.813120%
1.69 –
1.962010
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(R. Rice, UC Merced)
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[Chapman & Davis, 2010]
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[D. Marks]
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Sierra Nevada, trends in 220 long-term snow courses (> 50 years, continuing to present)
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Peak snow is occurring earlier
[Kapnick & Hall, 2010]
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Snow redistribution and drifting
(D. Marks)
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Daily integrated solar radiation is more heterogeneous when Sun is lower
(40°N, 30° slope)
[Lundquist & Flint, 2006]
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Orographic effect varies (Tuolumne-Merced River basins example)
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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)
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Spectra with 7 MODIS “land” bands (500m resolution, global daily coverage)
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Snow mapping a standard product from MODIS, available daily at 500 m resolution
[Hall et al., 2002]
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[Erbe et al., 2003]
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[Rosenthal et al.,2007]
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Snow spectral reflectance is sensitive to the absorption coefficient of ice
[Wiscombe & Warren, 1980]
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The 1.03mm absorption feature is sensitive to grain size
[Nolin & Dozier, 2000]
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For clean snow, net solar radiation is greatest in the near-IR wavelengths
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Dust
(T. H. Painter)
algae
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Spectral reflectance of dirty snow and snow with red algae (Chlamydomonas nivalis)
[Painter et al., 2001]
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Seasonal solar radiation (Mammoth Mtn, 2005)
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Response of Colorado R to dust radiative forcing
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
Dust
Clean
Natu
raliz
ed R
unoff
(B
CM
/day)
Loss
of
Runoff
(B
CM
)
Loss o
f Runoff
(%)
Mexico’s annual allotment
LV
LA
Post
-dis
turb
ance
----
----
----
----
----
----
18
50
AD
Pre
-dis
turb
ance
Neff et al 2008 Nature Geosciences
[Painter et al., 2010]
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Fractional snow-covered area, Sierra Nevada (MODIS images available daily)
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[Dozier et al., 2008]
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Downscaled NLDAS assimilated data (K. Rittger)
Tuolumne
Merced
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(K. Rittger)
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Combine fractional snow cover with snowmelt model to reconstruct SWE
SCA, %
103/24/0
504/15/0
503/30/0
5
20
40
60
80
100
1
60
130
190
250
450
SWE, cm
04/10/05
• Reconstructed snow water equivalent
[N. Molotch, based on concept from Martinec & Rango, 1981]
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Reconstructed snow water equivalent
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1
60
130
190
250
450
SWE, cm
04/10/05
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Snow water equivalent anomalies
2002 2004 2005 20072001 – 2007
Average
avg. SWE, cm
0 60 120 180
SWE anomaly, %
-100 -60 -10 10 60 100+
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interpolation, like Fassnacht et al., [2003]energy balance
reconstruction
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Reconstruction of heterogeneous snow in a grid cell
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Daily potential melt
z
fSCA
xy
Reconstructed SWE
A. Kahl
[Homan et al., 2010]
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Issues: Topography, vegetation
detail
Vegetation causes
differences in view angle
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Information about water is more useful as we climb the value ladder
Monitoring
Collation
Quality assurance
Aggregation
Analysis
Reporting
Forecasting
Distribution
Done poorly,but a few notablecounter-examples
Done poorly to moderately,not easy to find
Sometimes done well,generally discoverable and available,
but could be improved
>>> Incr
easing v
alue >
>>Integration
Data >
>> Info
rmatio
n >>> In
sight
(I. Zaslavsky & CSIRO, BOM, WMO)
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The data cycle perspective, from creation to curation
• The science information user:
– I want reliable, timely, usable science information products
• Operational agencies:
– We want data from a network of authors
– In a way that improves our decisions
• The science information author:– I want to help users (and
build my citation index)
Data Acquisitio
n & Modeling
Analysis & Data
Mining
Colla
bora
tion
&
V
isu
aliz
atio
nDis
sem
inate
&
Sh
are Archiving
& Preservatio
n
(J. Frew, T. Hey)
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Finis“the author of all books”
– James Joyce, Finnegan’s Wake
http://www.slideshare.net/JeffDozier
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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.
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