1. Snow and Snowmelt Runoff
Accounts for 50-80% of the annual runoff in the Western US – critical for water resources
Drives periodic flooding in the Western US
Need to understand and quantify changes due to
Climate change
Vegetation and land use change
It may be obvious to this audience, but just to set the stage, in considering snow and runoff some of the potential effects of climate change are: (read from slide). These speak to the need to examine trends and understand the interactions involved.

2. Learning Objectives
To be able to quantify the amount of snow in terms of depth, density and snow water equivalent
To be able to describe how snow is measured
To be able to calculate the energy required to melt a snowpack and how quickly it may melt

3. Snow Pack Characteristics

4. Snow Pack Characteristics
What is a Snow Pack?
Porous Medium
ice + air (+ liquid water)
Generally composed of layers of different types of snow
more or less homogeneous within one layer
Ice is in form of crystals and grains that are usually bonded together
forms a texture with some degree of strength

5. Snow Pack Characteristics
Snow Water Equivalent (SWE)
The height of water if a snow cover is completely melted, on a corresponding horizontal surface area.
Snow Depth x (Snow Density/Water Density)

6. Density of Snow Cover Snow Depth for One Inch Water Snow Type
Density (kg/m3)
Wild Snow
10 to 30
98” to 33”
Ordinary new snow immediately
after falling in still air
50 to 65
20” to 15”
Settling Snow
70 to 90
14” to 11”
Average wind-toughened snow
280
3.5”
Hard wind slab
350
2.8”
New firn snow
400 to 550
2.5” to 1.8”
Advanced firn snow
550 to 650
1.8” to 1.5”
Thawing firn snow
600 to 700
1.6” to 1.4”

7. Quantifying Snow Water Equivalent
Field Snow Surveys (courses, pits, tubes)
SNOTEL system
Stereo photo interpretation
Gamma radiation
LIDAR
Gravity
Microwave
Remote sensing
Model + data
assimilation
This reviews some of the methods for quantifying snow water equivalent. Ground based methods include field snow surveys and the SNOTEL system based on snow pillows. These have the disadvantage of being point measurements that are hard to extend over a large watershed. Remote sensing offers the only real opportunity to obtain spatial snow water equivalent and here are some of the methods. (Discuss advantages and limitations). I think that to make real progress in quantifying SWE we really need to integrate models with measurements, an assimilation approach. NOHRSC has made quite a bit of progress in this area, but more is needed.

8. SNOTEL System for Measuring Snow and Supporting Spring Runoff and Water Supply Forecasts

11. Snow Water Equivalent at Tony Grove Lake

12. Multiple Years of SWE
2011
2012

13. SNOTEL Stations Near Logan

14. Streamflow in the Logan River
From

15. Predicting Streamflow
Data from

16. March 25 Quick 2011 Peak Runoff Prediction 1622 +- 540 cfs (95% confidence interval)
1710 cfs
X=51.8 in
Y=1622 cfs
Var of Y cfs2
MSE = (1-R2)Var
RMSE = 270 (1 std dev error)
95% CI = 2 RMSE
=+-540 cfs
2450 cfs in 1907
2000 cfs in 1916
1980 cfs in 1899
1980 cfs in 1984
1970 cfs in 1986

17. April 18 Quick Peak Runoff Prediction
X=62.3 in
Y=1798 cfs
Var of Y cfs2
MSE = (1-R2)Var
RMSE = 257 (1 std dev error)
95% CI = 2 RMSE
=+-515 cfs
1710 cfs
1984
1986
2006
1997
1982
2450 cfs in 1907
2000 cfs in 1916
1980 cfs in 1899
1980 cfs in 1984
1970 cfs in 1986
1740 cfs in 1997
1530 cfs in 2006
1330 cfs in 1982

18. Challenges in the prediction of snowmelt and streamflow
Climate Change
Advance in snowmelt timing
Smaller snow pack area
Uncertainty in regional variability
Land Use Change
Transitions to Agriculture or Urban
Conifer/Deciduous
Bark Beetles
Both
Diminished statistical validity of past observations for current conditions

19. Quantifying snowmelt runoff requires knowledge of
the quantity of water held in snow packs
energy exchanges
the magnitude and rate of water lost to the atmosphere by sublimation
interception and the role of vegetation
the timing, rate, and magnitude of snow melt
the fate of melt water
If we are to quantify snowmelt runoff we need to know the following four things: (read from slide). These involve combining observation and modeling, so a theme that I will promote is learning through integration of observation and modeling.

20. Fluxes dependent on Canopy temperature
Utah Energy Balance Snowmelt Model To predict accumulation and melt of snow, and surface water inputs in open and forested environments
Above Canopy Inputs
Qsi ea Ta Precip Wind
Qhc(Ta,Tc) Qec(ea,Tc) Qlec(Tc)
Fluxes dependent on Canopy temperature
Qsib Qsid Qli , Qp,
Canopy Variable
Water Equivalent Wc
Leaf Area Index LAI
Direct radiance transmittance τb
Diffuse radiance transmittance τd
Fluxes dependent on
surface temperature
Qhs(Ta,Ts) Qes(ea,Ts) Qles(Ts)
Qlec (Tc)
τbQsib τdQsid τd Qli Qps
Energy Content Us
Water Equivalent Ws Q
Snow
Thermally active ground layer De
Soil Qg Qm
David Tarboton:

21. MFR
EQG
SWE at CSSL (1986)

22. Spatial Variability

23. Conclusions
Snow, as a driver of streamflow presents challenges for hydrologists needing to make predictions and inform watershed management
Spatial heterogeneity
Process physics
Observation sparsity
It is important to get it right!

24. Logan River at 6th West in Logan, June 2011

25. Show and Tell
Images from