Snow Hydrology: A Primer Martyn P. Clark NIWA, Christchurch, NZ Andrew G. Slater CIRES, Boulder CO,...
Transcript of Snow Hydrology: A Primer Martyn P. Clark NIWA, Christchurch, NZ Andrew G. Slater CIRES, Boulder CO,...
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Snow Hydrology:A Primer
Martyn P. Clark NIWA, Christchurch, NZ
Andrew G. Slater CIRES, Boulder CO, USA
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Outline
• Snow measurement
• Hydrological predictability available from knowledge of snow
• Snow modelling methods• Energy balance models• Temperature index models
• Snow data assimilation• Potential role of remotely sensed snow products
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Measurement Methods
• Snow Water Equivalent
• Snow Depth
• Precipitation
• Meteorology etc.
December 8th, 2007
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Measurement Methods
Photos: A. Slater
SNOTEL and Precipitation Gauges Snow Board
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Measurement Methods
Photos: A. Slater
Sonic Snow Depth Sensor
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Measurement MethodsAlter
Wyoming
DFIR
Nipher
Photos: NCAR
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Measurement Methods
Photos: A. Slater
Pyranometer and Stevenson Screen
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Other Data Sources
CAIC TowerBerthoud Pass
Photos: A. Slater
Snow courses & weather networks
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Field campaigns
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MODIS in the West
• Yampa Basin, Colorado
MissingCloudSnowSnow-Free
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MODIS in the West
MissingCloudSnowSnow-Free
• Yampa Basin, Colorado
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MODIS in the West
• Important period often cloud contaminated
• No mass information included (?)
• Calibration potential
• SWE inversion?
MissingCloudSnowSnow-Free
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AMSR-E – Microwave Miracles?
• Radiances vs. Products• Products tend to be “global”• Statistical vs. Physical inversion• Same old questions:
Validation Error estimate
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AMSR-E
• Some information exists – can we exploit it?
• Global algorithm (Chang) is not ideal
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Outline
• Snow measurement
• Hydrological predictability available from knowledge of snow
• Snow modelling methods• Energy balance models• Temperature index models
• Snow data assimilation• Potential role of remotely sensed snow products
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Historical Simulation
Q
SWESM
Historical Data
Past Future
SNOW-17 / SAC
Sources of Predictability
1. Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;
Model solutions to the streamflow forecasting problem…
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Historical Simulation
Q
SWESM
Historical Data Forecasts
Past Future
SNOW-17 / SAC SNOW-17 / SAC
1. Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;
2. Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.
Sources of PredictabilityModel solutions to the streamflow forecasting problem…
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Historical Simulation
Q
SWESM
Historical Data Forecasts
Past Future
SNOW-17 / SAC SNOW-17 / SAC
Sources of PredictabilityModel solutions to the streamflow forecasting problem…
Meteorological predictability• Derived from accurate weather forecasts
Hydrological predictability• Derived from knowledge of basin initial conditions
BETTER INITIAL CONDITIONS = BETTER FORECASTS
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Outline
• Snow measurement
• Hydrological predictability available from knowledge of snow
• Snow modelling methods• Energy balance models• Temperature index models
• Snow data assimilation• Potential role of remotely sensed snow products
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Snow Modelling
1) Detailed physically-based conceptualizationof snow processes
2) The real world
The art of modelling is to define the complexity of the model that is justified in light of
• the data that we have available
• the problem we are trying to solve
• the environment in which the model is applied
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Energy balance approaches
Accurate at the point scaleif there is good data available
Data Requirements:PrecipitationTemperatureHumidityIncoming shortwave radiationDownwelling longwave radiationWind speedPressure
In operational models data must be interpolated across large distances, and the complexity of energy balance models cannot be justified by the
limited data available
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Temperature-index method
sss mpdt
dS−= state equation (conservation of mass)
accm
accms TT
TTpp
>≤
=0 assume precipitation either rain or snow
assume melt depends on temperature alone( ) meltmelt
melts TTTT
TTm
>−
≤=κ
0
The melt factor can be parameterized to• Vary seasonally• Decrease immediately after snowfall events• Increase during rain-on-snow events
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Sub-grid variability in SWE
• Important to accurately model the timing of streamflow Shallow areas of snow melt first,
and only contribute melt for a limited period of time; deep areas of snow contribute melt late into summer
Early-season melt controlled by available energy; late-season melt controlled by snow covered area
• Sub-grid model (after Liston, 2004):
CV Parameter = 1.0CV Parameter = 0.1
Example simulations where sub-grid SWE parameterized with probability distributions
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Example snow simulations (parameter sensitivity)
South Island, New Zealand
Columns:Temperature threshold for snow accumulation
Rows:Mean and seasonal amplitude of the melt factor
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Outline
• Snow measurement
• Hydrological predictability available from knowledge of snow
• Snow modelling methods• Energy balance models• Temperature index models
• Snow data assimilation• Potential role of remotely sensed snow products
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Data Assimilation: The Basics
• Improve knowledge of Initial conditions• Assimilate observations at time t • Model “relocated” to new position
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Example: Direct Insertion & Nudging
• Small basin with SNOTEL type station
• Objective : determine basin SWE
• Observation is SWE, as is model state
• Direct Insertion: Assumes observation is perfect
• Newtonian Nudging: Nudges model as suggested by observation
xSNOTEL
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1. Xt
- = AXt-1 + Bft
2. Kt = P(P + R)-1
3. Xt
+ = Xt
- + Kt(zt – Xt
- )
• Predict model states (X)
• Get relative weights (K) of model and observations
• Update model state as a combination of its own projected state and that of the observations (z)
• P = model error• R = observation error
Optimized Assimilation: General Case
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Optimized Assimilation: Scalar Example
Our Model predicts : X- = 6
Model error variance : P = 2x = 2
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Optimized Assimilation: Scalar Example
Our Observations say : Z = 4
Obs. error variance : R = 2z= 1
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Optimized Assimilation: Scalar Example
222
111
zxa +=
Combined Model and Observations say :
X+ = 6 + (2/(2+1)) x (4 – 6)
Our Analysis is X+ = 4.66
Analysis variance : 2a= 0.66
Analysis Variance
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EnKF Example: Snow Assimilation
• NWS SNOW-17 model
• Generated cross validated ensemble forcing
• Used cross validated observation ‘estimates’
• Withholding experiments
• Accounted for filter divergence
• Assimilation shown to produce better results
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EnKF Example: Snow Assimilation
Interpolated SWE Mean & Std. Dev
Model
Truth
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White without Red = B.L.U.E
• SWE contains red (time correlated) noise• Only want to use “new” information• Example – assimilate @ same timestep • Filter Divergence = potential problem
Slater & Clark, 2006
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Summary• Many snow measurement techniques
• Depth versus water equivalent• Key consideration is station representativeness
• Snow is an important source of hydrological predictability• Need good models• Need capability to assimilate available observations
• Including satellite observations of snow extent (Clark et al., 2006)
• Snow modelling methods• Energy balance models limited by intensive data requirements• Temperature index models can work well• Important to account for spatial variability of snow within a model element
• Snow data assimilation• Important to use observations to constrain models, so as to capitalize on increases in hydrological
predictability possible through knowledge of snow
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The End(thank you)