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

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

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

Same as previous, but for entire Colorado basin

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

Snow Water Equivalent (SWE) measurement – old and new

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

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

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

RCEW Ridge Site (visual courtesy Danny Marks)

RCEW Grove Site (visual courtesy Danny Marks)

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

Visual courtesy John Pomeroy

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

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)

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

Introduction: Forecasting with the VIC Model

Variable Infiltration Capacity (VIC) snow model schematic

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

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 2000-3000 stations in west LDAS/other real-time met. forcings for remaining spin-up ~300-400 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 1979-1999, 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 1979-1999) 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)

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

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

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

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

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