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Snow Hydrology Modeling Gayle Dana, Ph.D. Division of Hydrologic Sciences Desert Research Institute, Reno NV [email protected]
Snow Hydrology ModelingSnow Hydrology Modeling
Gayle Dana, Ph.D.Division of Hydrologic Sciences
Desert Research Institute, Reno [email protected]
Gayle Dana, Ph.D.Division of Hydrologic Sciences
Desert Research Institute, Reno [email protected]
Talk OutlineTalk Outline
• Background
• Approaches
• Spatial Distribution
• Assumptions
• Uncertainties
• Background
• Approaches
• Spatial Distribution
• Assumptions
• Uncertainties
Snow Hydrology in a NutshellSnow Hydrology in a Nutshell
Snow TermsSnow 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
• 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: 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
– 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
OutlineOutline
• Background
• Approaches• Spatial Distribution• Assumptions• Uncertainties
• Background
• Approaches• Spatial Distribution• Assumptions• Uncertainties
Two Basic ApproachesTwo Basic Approaches
• Empirical– Temperature Index
Models– Regression Models
• Physically based– Energy Balance
Models
• Empirical– Temperature Index
Models– Regression Models
• Physically based– Energy Balance
Models
Empirical: Temperature IndexEmpirical: 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)
• 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?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.
• During melting, the snow surface temperature near 0 C, and energy inputs (radiation, turbulent) are approximately linear functions of air temperature.
Empirical: RegressionEmpirical: Regression
Y = a + b1BF + b2FP + b3WP + b4S + b5SP
Y = predicted runoff volumeBF = base flow indexFP = fall precipitation indexWP = winter precipitation indexS = snow water equivalent indexSP = spring precipitation indexa = streamflow intercept
bi = regression coefficients
Y = a + b1BF + b2FP + b3WP + b4S + b5SP
Y = predicted runoff volumeBF = base flow indexFP = fall precipitation indexWP = winter precipitation indexS = snow water equivalent indexSP = spring precipitation indexa = streamflow intercept
bi = regression coefficients
(K-K) + (L - L ) + Qe + Qh + Qg + Qp = Q
Physical: Energy Balance
Albedo
Humidity
ENERGY
MASS
MELTING
REFREEZING
Snow
Rain
Vapor
Solar
ReflectedSolar
Incident/Emitted
Longwave
Wind
ConductionMelt Flow
CanopyShortwaveReduction
CanopyLongwaveEmissions
CanopyWind
Reduction
Thermally Active Soil Layer
Snow
TurbulentExchange
Solar
Temperature
Atmosphere
K
K
L
Qe Qh
Qg
Qp
L
Q
Modeled ProcessesModeled Processes
From Melloh, 1999
Meteorological RequirementsMeteorological Requirements
From Melloh, 1999
Talk OutlineTalk Outline
• Background• Approaches
• Spatial Distribution• Assumptions• Uncertainties
• Background• Approaches
• Spatial Distribution• Assumptions• Uncertainties
Spatial Distribution of Snow ModelsSpatial Distribution of Snow Models
• Lumped
• Polygon Discretization
• Gridded
• Lumped
• Polygon Discretization
• Gridded
LumpedLumped
Parameters assigned to sub basins
Upper-Upper Basin
Lower-Upper Basin
Mid BasinLower Basin
Exit toLake Tahoe
Incline Creek Watershed, Lake TahoeIncline Creek Watershed, Lake Tahoe
Polygon DiscretizationPolygon Discretization
Parameters assigned to land classes based on physical characteristics
TaylorTaylor Valley, Antarctica Valley, Antarctica
46 land classes based on slope, aspect, surface type
Gridded (Fully Distributed)Gridded (Fully Distributed)
Parameters assigned to each cell in grid adapted from Cline et al 1998
Emerald Lake Basin, CA
Regression TreesRegression Trees
Parameters assigned to grid cells based on physical characteristicsderived from DEM
Winstral et al, 2002
Incorporating Remote SensingIncorporating Remote Sensing
…..and Depletion Curves…..and Depletion Curvesfrom Cline et al, 1998
Calibration / ValidationCalibration / Validation
Winstral et al, 2002
Snow depth Snow depth sampled at sampled at 504 sites!504 sites!
Calibration / ValidationCalibration / Validation
SNOTEL data SNOTEL data often used for often used for calibration & calibration & validationvalidation
Which Approach?Which Approach?
Empirical Physical
DataNeeds
Modest Large
Use Runoff & Operational
Snow, Watershed Processes
Avalanche Fore.
Scales WatershedDaily, monthly, seasonal
Micro to watershedHours, Days
Accuracy Good at larger scales Depends on formulation
Talk OutlineTalk Outline
• Background• Approaches• Spatial Distribution
• Assumptions• Uncertainties
• Background• Approaches• Spatial Distribution
• Assumptions• Uncertainties
AssumptionsAssumptions
• 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)
• 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 OutlineTalk Outline
• Background• Approaches• Spatial Distribution• Assumptions
• Uncertainties
• Background• Approaches• Spatial Distribution• Assumptions
• Uncertainties
Uncertainties Leading to Model Error
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
• 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 ResourcesWeb Resources• SNOW MODELERS INTERNET
PLATFORMwww.geo.utexas.edu/climate/Research/SNOWMIP/
• Snow Models Intercomparison Project (SnowMIP)
www.geo.utexas.edu/climate/Research/SNOWMIP/
• 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/
• SNOW MODELERS INTERNET PLATFORMwww.geo.utexas.edu/climate/Research/SNOWMIP/
• Snow Models Intercomparison Project (SnowMIP)
www.geo.utexas.edu/climate/Research/SNOWMIP/
• 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
ReferencesReferences• 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 terrain-based parameters. J. Hydrometeorology (3):524-538.
• 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 terrain-based parameters. J. Hydrometeorology (3):524-538.
Any Questions?Any Questions?
