1. Hydrologic issues in the measurement of snowfall
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
International Workshop on Satellite Snow Measurement
Steamboat Springs, CO
Apr 1, 2008

2. Global land areas where runoff is dominated by snowmelt (from Barnett et al, 2005)

3. 20-yr average SWE, runoff, and accumulated Precipitation for 25% of area of Colorado River producing the most runoff

4. Same as previous, but for entire Colorado basin

5. Mean SWE, P < 0o C, and total P, four sites from Alaska to CO

6. Snow Water Equivalent (SWE) measurement – old and new

7. Partial snow coverage – Reynolds Creek Experimental Watershed (photo courtesy Danny Marks)

8. The existing network of real-time SWE observations in the western U. S
The existing network of real-time SWE observations in the western U.S. and Canada: NRCS/SNOTEL; ASP in Canada (Columbia basin only shown); DWR in CA

9. Cooperative observer precipitation network, northwestern U.S.
Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate model
Solution: use interpolated monthly index station precip percentiles and temperature anomalies to extract values from higher quality retrospective forcing data -- then disaggregate using daily index station signal.
sparse station network in real-time
dense station network for model calibration

10. RCEW Ridge Site (visual courtesy Danny Marks)

11. RCEW Grove Site (visual courtesy Danny Marks)

12. WMO Intercomparison Study Results
Catch Efficiency vs Wind for the 4 most widely used gauges

13. Visual courtesy John Pomeroy

14. Investigation of forest canopy effects on snow accumulation and melt
Measurement of Canopy Processes via two 25 m2 weighing lysimeters (shown here) and additional lysimeters in an adjacent clear-cut.
Direct measurement of snow interception

15. Calibration of an energy balance model of canopy effects on snow accumulation and melt to the weighing lysimeter data. (Model was tested against two additional years of data)

16. Energy balance over mountain snowpack, San Juan Mountains, CO, Spring 2005
from Bales et al, WRR, 2006

17. Introduction: Forecasting with the VIC Model

18. Variable Infiltration Capacity (VIC) snow model schematic

19. Initial Conditions: estimating run-up conditions
Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate model
Solution: use interpolated monthly index station precip percentiles and temperature anomalies to extract values from higher quality retrospective forcing data -- then disaggregate using daily index station signal.
sparse station network in real-time
dense station network for model calibration

20. Initial Conditions: Hydrologic Simulations
1-2 years back
start of month 0
end of mon 6-12
forecast ensemble(s)
model spin-up
initial conditions
climatology ensemble
NCDC met. station obs. up to 2-4 months from current
stations in west
LDAS/other real-time met. forcings for remaining spin-up
~ stations in west
climate forecast information
data sources
Forecast Products
streamflow
soil moisture
runoff
snowpack
derived products
e.g., reservoir system forecasts
important point(s):
after bias correcting and downscaling the climate model forecasts, the procedure for producing hydrologic forecasts is as follows:
we spin up the hydrologic model to the start of the forecast using observed met. data (from 2 sources: NCDC cooperator stations through 3-4 months before the start of the forecasts, then LDAS 1/8 degree gridded forcings thereafter).
The GSM forecasts comprise 2 sets of ensembles, one for climatology and one for the forecast. The climatology ensemble yields a distribution of the conditions we’ve seen over the period , while the forecast ensemble yields the distribution of the conditions we might see for the next 6 months. Although the climatology ensemble is nominally unbiased against a simulated climatology based on observed met. data (rather than bias-corrected, downscaled GSM met. forcings), we compare the forecast and GSM climatology so that any unforeseen biases (resulting, perhaps, from the downscaling method) occur in both climatology and forecast. Eventually this cautionary step may be eliminated, and we’ll compare directly to the simulated observed climatology.
at the end of the spin-up period and one month before month 1 (out of 6) of the forecasts, we save the hydrologic model state. The state is then used for initializing the forecast runs. Through the first month, the model runs on observed data to the last date possible, then switches to the forecast data. Usually, we process the observed forcings up through the 15th to 25th of this initialization month, then the forecast forcing data carries the run forward for the remaining days in the month, and throughout the following 6 month forecast period. Note, the state files used for the climatology runs correspond to the spin-up associated with the particular year (out of ) from which the climatology ensemble member is drawn.
the spin-up period captures the antecedent land surface hydrologic conditions for the forecast period: in the Columbia basin, the primary field of interest is snow water equivalent.
forecast products are spatial (distributed soil moisture, runoff, snowpack (swe), etc.), and spatial runoff + baseflow is routed to produce streamflow at specific points, the inflow nodes for a management model, perhaps.
obs snow state information
(eg, SNOTEL)

22. Initial Conditions: MODIS-updated initial conditions
MODIS: Moderate-Resolution Imaging Spectroradiometer (on satellite TERRA)
Update of SWE using MODIS

23. SnowSTAR2002 transect snow pit locations (data courtesy Matt Sturm and Glen Liston)

24. Simulated (SNTHRM) and observed profiles of temperature, density, and grain size, site A12F

25. Simulated (SNTHRM) and observed profiles of temperature, density, and grain size, site IC10

27. Some general thoughts
“Satellite snow measurement” implies large spatial coverage (and probably relatively coarse spatial resolution)
Current status is mostly measurement at points (precipitation gauges, snow courses, etc)
Models in general have outpaced the data to drive them
Snow measurement problems in non-mountainous areas may differ somewhat from mountainous areas
From a hydrologic standpoint, now clear that it makes much difference whether we have measurements of snowfall, or accumulated snow on the ground (SWE) – so long as the data are better than what we have now!
There are multiple options for satellite snow “measurement”, for instance:
Satellite retrieval of snowfall rates
Satellite retrieval of snow water equivalent (SWE) and/or snow covered area
Analysis fields (for snowfall or SWE) from numerical weather prediction models