Snow Hydrology Modeling
Gayle Dana, Ph.D.
Division of Hydrologic Sciences
Desert Research Institute, Reno NV
Gayle.Dana@dri.edu
Talk Outline
•
•
•
•
•
Background
Approaches
Spatial Distribution
Assumptions
Uncertainties
Snow Hydrology in a Nutshell
Snow Terms
• SWE - Snow Water Equivalent
– The height of water if a snow cover is
completely melted, on a corresponding
horizontal surface area
• Snow Depth x (Snow Density/Water Density)
• SNOTEL
– Network of automated sites collecting
precipitation and SWE data
Snow models can be found in:
– General Circulation Models (GCM)
– Regional Climate Models
– Weather Prediction Models
– Snow Process/Hydrology Models
– Watershed Models
– Operational Runoff Forecasting
– Frozen Soils Studies
– Avalanche Forecasting
– Erosion Control
Outline
• Background
• Approaches
• Spatial Distribution
• Assumptions
• Uncertainties
Two Basic Approaches
• Empirical
– Temperature Index
Models
– Regression Models
• Physically based
– Energy Balance
Models
Empirical: Temperature Index
• Estimates snowmelt, M (cm d-1), as linear
function of near-surface air temperature:
M = a Td
Td , daily average temperature (ºC)
A, melt factor (cm d-1 deg ºC -1)
(situation specific)
Why does the Temperature Index
Method Work?
• During melting, the
snow surface
temperature near 0 C,
and energy inputs
(radiation, turbulent)
are approximately
linear functions of air
temperature.
Empirical: Regression
Y = a + b1BF + b2FP + b3WP + b4S + b5SP
Y = predicted runoff volume
BF = base flow index
FP = fall precipitation index
WP = winter precipitation index
S = snow water equivalent index
SP = spring precipitation index
a = streamflow intercept
bi = regression coefficients
Physical: Energy Balance
(K-K) + (L  - L ) + Qe + Qh + Qg + Qp = Q
A tm osphere
Solar
K
Solar
Incident/
Em itted
Longw ave
L
ENERGY
L
MASS
Snow
W ind
R eflected
Solar
Qp
Canopy
Shortw ave
Reduction
C anopy
W ind
R eduction
C anopy
Longw ave
Em issions
K
R ain
Tem perature
Vapor
Hum idity
Qe Qh
Turbulent
Exchange
Albedo
Snow
M ELTIN G
M elt Flow
Therm ally A ctive Soil Layer
Q
R EFR E E ZIN G
Conduction
Qg
Modeled Processes
From Melloh, 1999
Meteorological Requirements
From Melloh, 1999
Talk Outline
• Background
• Approaches
• Spatial Distribution
• Assumptions
• Uncertainties
Spatial Distribution of Snow Models
• Lumped
• Polygon Discretization
• Gridded
Lumped
Incline Creek Watershed, Lake Tahoe
Upper-Upper Basin
Lower-Upper Basin
Lower Basin
Mid Basin
Exit to
Lake Tahoe
Parameters assigned to sub basins
Polygon Discretization
Taylor Valley, Antarctica
46 land classes based on slope, aspect, surface type
Parameters assigned to land classes
based on physical characteristics
Gridded (Fully Distributed)
Emerald Lake
Basin, CA
Parameters assigned to each cell in grid
adapted from Cline et al 1998
Regression Trees
Parameters
assigned to grid
cells based on
physical
characteristics
derived from DEM
Winstral et al, 2002
Incorporating Remote Sensing
from Cline et al, 1998
…..and Depletion Curves
Calibration / Validation
Snow depth
sampled at
504 sites!
Winstral et al, 2002
Calibration / Validation
SNOTEL data
often used for
calibration &
validation
Which Approach?
Data
Needs
Use
Scales
Empirical
Physical
Modest
Large
Runoff &
Operational
Snow, Watershed
Processes
Avalanche Fore.
Micro to watershed
Hours, Days
Daily, monthly, seasonal
Watershed
Accuracy Good at larger scales
Depends on
formulation
Talk Outline
• Background
• Approaches
• Spatial Distribution
• Assumptions
• Uncertainties
Assumptions
• Assumed values for snow
properties difficult to measure
• Spatial interpolation of point
data (e.g., meteorological) is
valid for entire modeled area
• Heat conduction from soil
negligible (some models)
• Uniform density and
compaction (simple models)
Talk Outline
•
•
•
•
Background
Approaches
Spatial Distribution
Assumptions
• Uncertainties
Uncertainties Leading
to Model Error
• Data availability
• Data consistency
• Data quality, especially
wind effects on:
– Snow precipitation
– Redistribution of snow
on the ground
• Extrapolating point data
• Poor understanding of
physical processes
Web Resources
•
SNOW MODELERS INTERNET
PLATFORM
www.geo.utexas.edu/climate/Research/SNOW
MIP/
•
Snow Models Intercomparison
Project (SnowMIP)
www.geo.utexas.edu/climate/Research/SNO
WMIP/
•
National Snow and Ice Data
Center (NSIDC)
nsidc.org/
•
Snow Data Assimilation System
(SNODAS)
nsidc.org/data/g02158.html
•
SNOTEL (Natural Resources
Conservation Service)
http://www.wcc.nrcs.usda.gov/snotel/
SnowMIP results for Sleeper River
References
•
Cline, D., R. C. Bales, and J. Dozier. 1998. Estimating the spatial distribution of snow in mountain basins using
remote sensing and energy balance modeling. Water Resources Research, 34(5):1275–1285.
•
Luce, C.H. and D. G. Tarboton. 2004. The application of depletion curves for parameterization of subgrid
variability of snow. Hydrol. Process. 18, 1409–1422.
•
Martinec, J., and A. Rango. 1981. Areal distribution of snow water equivalent evaluated by snow cover
monitoring, Water Resour. Res., 17(5), 1480–1488.
•
Melloh, R. 1999. A synopsis and comparison of selected snowmelt algorithms. CRREL Report 99-8-17.
Online: www.crrel.usace.army.mil/techpub/CRREL_Reports/reports/CR99_08.pdf
•
Seidel, K. and J. Martinec, 2004. Remote Sensing in Snow Hydrology-Runoff Modeling, Effect of Climate
Change. Springer.
•
Singh, P. and V. P. Singh, 2001. Snow and Glacier Hydrology, Kluwer Academic Publishers, 742p
•
U.S. Army Corps of Engineers. Runoff from Snowmelt. 1998. Engineer Manual 1110-2-1406. Online:
www.usace.army.mil/inet/usace-docs/eng-manuals/em1110-2-1406/entire.pdf
•
Winstral, A., K. Elder, and R. E. Davis, 2002. Spatial snow modeling of wind-redistributed snow using terrainbased parameters. J. Hydrometeorology (3):524-538.
Any Questions?