1. Assimilating Remotely Sensed Snow Observations into a Macroscale Hydrologic Model
Kostas Andreadis and Dennis Lettenmaier
UW-UBC Fall Hydrology Workshop Oct

2. Importance of Snow Snow plays key role in hydrologic cycle
As much as 90% of annual streamflow is snowmelt driven in the western US
In situ observations are unable to capture temporal and spatial variability of snow processes
Large-scale observation strategies focus on remote sensing

3. Satellite Remote Sensing of Snow
Snow Cover Extent (SCE)
Visible wavelength sensors (GOES, AVHRR, MODIS etc)
Cloud-free conditions required
Lack of any information about water storage
Snow Water Equivalent (SWE)
Passive microwave sensors (SSM/I, AMSR-E etc)
Coarse spatial resolution
Wet snow and metamorphism greatly affect signal

4. EOS-era Satellites
MODerate resolution Imaging Spectroradiometer (MODIS) was launched on board EOS-Terra in 1999
Provides a variety of land surface data, including snow cover extent
Advanced Microwave Scanning Radiometer (AMSR-E) is on board the EOS-Aqua satellite
Has been producing daily SWE datasets since February 2004

5. Model-based approaches for prediction of snow properties
Additional information about snow properties can be obtained by land surface hydrology models
Uncertainty in forcing data and/or model parameters
Nonlinearity and scale of modeled processes

6. Motivation Data assimilation offers the framework to :
optimally combine models and remote sensing observations
account for the limitations of both
Availability of improved remotely sensed data products (MODIS, AMSR-E) increases the potential for snow data assimilation

7. <Propagation Step>
Data assimilation
Initial State
Observation
Available?
Hydrologic
Model
<Propagation Step>
YES NO
Forcing Data
Assimilation
Algorithm
<Update Step>
Observation
Operator
Observation

8. Ensemble Kalman Filter
Propagation
Step
Analysis
Step
Propagation
Step
yαt
yfi,t
yfi,t+1
Time
tk-1 tk
tk+1

9. Variable Infiltration Capacity (VIC) Snow Model
Solves energy and water balance over grid cells
Subgrid variability in topography and vegetation (indirect representation of snow areal extent)
Two layer energy and mass balance model
Surface layer simulates energy exchanges with the atmosphere
Pack layer acts as storage and simulates deeper snowpacks
Accounts for processes like snow interception and densification

10. Experimental Design Study area selected: Snake River basin
Assimilation of MODIS SCE data for period
MODIS daily SCE 500 m spatial resolution
Assimilation of AMSR-E SWE data for 2004
AMSR-E daily SWE product dissagregated @ 12.5 km resolution

11. Model Implementation
VIC snow model hourly simulations at a spatial resolution of 1/8o
Ensemble of model states are generated by perturbing precipitation and temperature forcings
Precipitation perturbed by log-normally distributed spatially correlated random fields
Temperature range and daily mean are perturbed with Gaussian random fields

12. Snow Depletion Curve
We need a functional that relates model SWE with observed SCE
Developed by Anderson (1973) and used currently at NWS
Luce et al. 1999
Separate SDC for combinations of vegetation and elevation classes
VIC simulated SWE data and MODIS imagery used to calculate Wmax parameter
Gamma distributions fitted to infer the shape of the SDC

13. Snow Depletion Curve Vegetation classes: Forest Shrublands Grasslands
Elevation classes:
z < 1500 (m)
1500 < z < 2000 (m)
z > 2000 (m)
Figures show MODIS Snow Covered Area (SCA) versus simulated Snow Water Equivalent (SWE) in mm

14. MODIS Assimilation Results
Comparison of percentage agreement between MODIS and simulations
Days included based on a 50% cloud threshold

15. SWE Updating in MODIS SCE Assimilation
Snapshots of simulated SWE (with/without assimilation) and MODIS SCE
The enKF is able to consistently update SWE, by only using SCE information

16. MODIS Assimilation Results
Comparison with SNOTEL and COOP station snow observations
Stations with an elevation difference from the grid box, greater than 200 m were excluded (124 stations left from 257 originally)

17. MODIS Assimilation Results
Mean peak seasonal SWE
Filter estimated peak SWE values are closer to station values for 58 out of 66 available stations

18. SWE Validation
SWE Percentile Relative Mean Squared Error (RMSE) for all available stations for each winter season
enKF improved estimates for about half the stations
However, performance was worse for the rest of the stations

19. Mean of Snow Water Equivalent Differences from SNOTEL with Time

20. Mean SWE RMSE variability with elevation and time
Elevation zones are the same as those for the SDC
March 1st was taken as the separating date between periods

21. Snow Water Equivalent
SWE and SCE time series at West Yellowstone SNOTEL station (2008 m, VIC RMSE = 0.18 and enKF RMSE = 0.25)
enKF
SNOTEL
VIC
MODIS

22. Snow Water Equivalent
SWE and SCE time series at Beaver Reservoir SNOTEL station (1545 m, VIC RMSE = 0.25 and enKF RMSE = 0.19)
VIC
enKF
SNOTEL
MODIS

23. AMSR-E Assimilation Results
SWE Percentile RMSE as a function of maximum SNOTEL SWE
enKF RMSE was improved for 31 out of 66 available stations

24. Limitations
More accurate observation operator is required (e.g. use of independent SWE data to develop SDC)
Non-continuity of SCE (0 to 1) – Assimilation has no effect during full snow coverage
Modelling of both background and observation errors
Water balance errors imposed by temperature biases

25. Questions?