snow hydrology modeling
DESCRIPTION
Snow Hydrology Modeling. Gayle Dana, Ph.D. Division of Hydrologic Sciences Desert Research Institute, Reno NV [email protected]. Talk Outline. Background Approaches Spatial Distribution Assumptions Uncertainties. Snow Hydrology in a Nutshell. Snow Terms. SWE - Snow Water Equivalent - PowerPoint PPT PresentationTRANSCRIPT
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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]
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Talk OutlineTalk Outline
• Background
• Approaches
• Spatial Distribution
• Assumptions
• Uncertainties
• Background
• Approaches
• Spatial Distribution
• Assumptions
• Uncertainties
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Snow Hydrology in a NutshellSnow Hydrology in a Nutshell
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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
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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
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OutlineOutline
• Background
• Approaches• Spatial Distribution• Assumptions• Uncertainties
• Background
• Approaches• Spatial Distribution• Assumptions• Uncertainties
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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
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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)
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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.
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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
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(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
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Modeled ProcessesModeled Processes
From Melloh, 1999
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Meteorological RequirementsMeteorological Requirements
From Melloh, 1999
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Talk OutlineTalk Outline
• Background• Approaches
• Spatial Distribution• Assumptions• Uncertainties
• Background• Approaches
• Spatial Distribution• Assumptions• Uncertainties
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Spatial Distribution of Snow ModelsSpatial Distribution of Snow Models
• Lumped
• Polygon Discretization
• Gridded
• Lumped
• Polygon Discretization
• Gridded
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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
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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
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Gridded (Fully Distributed)Gridded (Fully Distributed)
Parameters assigned to each cell in grid adapted from Cline et al 1998
Emerald Lake Basin, CA
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Regression TreesRegression Trees
Parameters assigned to grid cells based on physical characteristicsderived from DEM
Winstral et al, 2002
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Incorporating Remote SensingIncorporating Remote Sensing
…..and Depletion Curves…..and Depletion Curvesfrom Cline et al, 1998
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Calibration / ValidationCalibration / Validation
Winstral et al, 2002
Snow depth Snow depth sampled at sampled at 504 sites!504 sites!
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Calibration / ValidationCalibration / Validation
SNOTEL data SNOTEL data often used for often used for calibration & calibration & validationvalidation
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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
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Talk OutlineTalk Outline
• Background• Approaches• Spatial Distribution
• Assumptions• Uncertainties
• Background• Approaches• Spatial Distribution
• Assumptions• Uncertainties
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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)
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Talk OutlineTalk Outline
• Background• Approaches• Spatial Distribution• Assumptions
• Uncertainties
• Background• Approaches• Spatial Distribution• Assumptions
• Uncertainties
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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
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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
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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.
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Any Questions?Any Questions?