Snow Hydrology Modeling

Snow Hydrology ModelingSnow Hydrology Modeling

Gayle Dana, Ph.D.Division of Hydrologic Sciences

Desert Research Institute, Reno NVGayle.Dana@dri.edu

Gayle Dana, Ph.D.Division of Hydrologic Sciences

Desert Research Institute, Reno NVGayle.Dana@dri.edu

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?