1. INNOVATIVE SOLUTIONS for a safer, better world Capability of passive microwave and SNODAS SWE estimates for hydrologic predictions in selected U.S. watersheds Missouri Basin River Forecaster’s Meeting 28-29 January 2014 Carrie M. Vuyovich, PE ERDC/CRREL

2. Overview Background Distributed snow data available in the U.S. Previous study to evaluate Snow Water Equivalent (SWE) data Motivation for current study Water Budget Analysis Snowmelt Timing Comparison Conclusions 2011 Flood Demonstration

3. Distributed Snow Data in the U.S. NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC) SNODAS SWE estimates based on multi- sensor snow observations combined with energy balance snow model Hourly/Daily gridded SWE product for conterminous U.S. 1 km 2 resolution POR: October 2003 – Present Sources of Error: Uncertainty in forcing and observation data Gaps in available observation data Snow Water Equivalent 21 Feb 2006 (Cline 2008) NOHRSC Flight Lines (http://www.nohrsc.noaa.gov/)http://www.nohrsc.noaa.gov/

4. Distributed Snow Data in the U.S. Passive Microwave SWE SSM/I POR: July 1987 – Present Algorithm: SWE = C(T B,19 – T B,37 ) AMSR-E POR: June 2002 – July 2011 Algorithm accounts for forest cover, shallow/deep snow Sources of Error – Wet snow – Vegetation – Saturation depth – Topography – Snow metamorphosis 25x25 km resolution

5. Comparison of passive microwave and SNODAS SWE by HUC8  Conclusion: Best comparison in areas with < 20% forest cover with an average annual maximum SWE < 200 mm Vuyovich et al, (in press), Water Resources Research SNODAS - AMSR-ESNODAS - SSM/I R2R2

6. Comparison of passive microwave and SNODAS SWE by HUC8 SNODAS - AMSR- E SNODAS - SSM/I  Nash-Sutcliffe Efficiency measure:

7. Great Plains SWE estimates  Objective: Evaluate SWE estimates from the 3 datasets (SNODAS, AMSR-E and SSM/I) by comparison to water budget components in selected Great Plains basins. 1.Sheyenne River near Cooperstown, ND 2.Cannonball River at Breien, ND 3.Moreau River near Whitehorse, SD 4.Bad River near Ft. Pierre, SD 5.Cheyenne River at Spencer, WY 6.White River near Interior, SD 7.White River near Oacoma, SD 8.Ponca Creek near Verdel, NE 9.South Loup River at St. Michael, NE

8. Methods  Where the SWE was the max annual value  R, P and ET are the total volume measured through the spring melt period, typically March – June  GW is the loss to deep groundwater  ΔSM is the change in soil moisture from the beginning to end of the period Water Budget data Discharge: USGS daily streamflow records at basin outlet Precipitation: NOAA CPC model output, NCDC stations Evapotranspiration: NOAA CPC model output, NCDC stations Soil Moisture: NOAA CPC model output for soil moisture

9. Example Results Sheyenne River at Cooperstown, ND Ponca Creek at Verdel, NE

10. Results

11. Timing of snowmelt Timing of Spring runoff: typically corresponds to onset of snowmelt. Method: calculated timing difference between start of spring runoff and peak SWE Winter snowpack and spring runoff for the 2008-09 water year in the Moreau River basin, SD.

12. Results

13. Conclusion Passive microwave estimates of SWE are well-correlated to water budget components in the Great Plains region of the US. Potential use for satellite SWE estimates in water resource applications in the Plains.

14. 2011 Missouri River Flood 1May 1Apr 1Mar1Feb 1Jan US Army Corps of Engineers, 2011

15. 15 Passive Microwave signal observations

16. Questions?