Mountain Hydrology, The Fourth Paradigm, and the Color of Snow

Jeff Dozier

(photo T. H. Painter)

• An “exaflood” of observational data requires a new generation of scientific computing tools

– Jim Grayhttp://fourthparadigm.org

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

(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

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

Automated measurement with snow pillow

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

[Bales et al., 2006]

• 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

(R. Rice, UC Merced)

[Chapman & Davis, 2010]

[D. Marks]

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

Peak snow is occurring earlier

[Kapnick & Hall, 2010]

Snow redistribution and drifting

(D. Marks)

16

Daily integrated solar radiation is more heterogeneous when Sun is lower

(40°N, 30° slope)

[Lundquist & Flint, 2006]

Orographic effect varies (Tuolumne-Merced River basins example)

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)

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

[Hall et al., 2002]

[Erbe et al., 2003]

[Rosenthal et al.,2007]

Snow spectral reflectance is sensitive to the absorption coefficient of ice

[Wiscombe & Warren, 1980]

The 1.03mm absorption feature is sensitive to grain size

[Nolin & Dozier, 2000]

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

Dust

(T. H. Painter)

algae

Spectral reflectance of dirty snow and snow with red algae (Chlamydomonas nivalis)

[Painter et al., 2001]

28

Seasonal solar radiation (Mammoth Mtn, 2005)

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]

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

31

[Dozier et al., 2008]

Downscaled NLDAS assimilated data (K. Rittger)

Tuolumne

Merced

(K. Rittger)

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]

Reconstructed snow water equivalent

36

1

60

130

190

250

450

SWE, cm

04/10/05

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+

38

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

reconstruction

Reconstruction of heterogeneous snow in a grid cell

39

Daily potential melt

z

fSCA

xy

Reconstructed SWE

A. Kahl

[Homan et al., 2010]

40

Issues: Topography, vegetation

detail

Vegetation causes

differences in view angle

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)

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)

43

Finis“the author of all books”

– James Joyce, Finnegan’s Wake

http://www.slideshare.net/JeffDozier

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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