1. Integration of SNODAS Data Products and the PRMS Model – An Evaluation of Streamflow Simulation and Forecasting Capabilities George Leavesley 1, Don Cline 2, Tom Carroll 2, Lauren Hay 1, and Roland Viger 1 1 USGS, Denver, CO; 2 NOHRSC, Chanhassen, MN

2. Focus Issue The distribution of point precipitation measurements for streamflow simulation and forecasting. Concerns: Spatial and temporal availability and variability Measurement error and missing data Ungauged basins …

3. Meteorological Variable Forecast Methodologies - Historic data as analog for the future Ensemble Streamflow Prediction (ESP) - -Synthetic time-series Weather Generator - Atmospheric model output Dynamical Downscaling Statistical Downscaling

4. Ensemble Streamflow Prediction Using history as an analog for the future Simulate to today Predict future using historic data Probability of exceedence NOAA USGS BOR

5. ESP Forecast Error Sources Uncorrected Climate Data Corrected Climate Data

6. Hunter Creek nr Aspen, Colorado Hunter Midway No Name Gage Trans-mountain Diversion Points HRUs Forecasting at Internal Nodes

7. Precipitation Interpolation Methods Inverse distance weighting Kriging Multiple linear regression Climatological multiple linear regression Locally weighted polynomial k nearest neighbor …

8. XYZ Distribution

9. San Juan Basin Observation Stations 37 XYZ Spatial Redistribution of Precip and Temperature 1. Develop Multiple Linear Regression (MLR) equations (in XYZ) for PRCP, TMAX, and TMIN by month using all appropriate regional observation stations.

10. XYZ Spatial Redistribution 2. Daily mean PRCP, TMAX, and TMIN computed for a subset of stations (3) determined by the Exhaustive Search analysis to be best stations 3. Daily station means from (2) used with monthly MLR xyz relations to estimate daily PRCP, TMAX, and TMIN on each HRU according to the XYZ of each HRU Precip and temp stations

11. Z PRCP 2. PRCP mru = slope*Z mru + intercept where PRCP mru is PRCP for your modeling response unit Z mru is mean elevation of your modeling response unit x One predictor (Z) example for distributing daily PRCP from a set of stations: 1. 1.For each day solve for y-intercept intercept = PRCP sta - slope*Z sta where PRCP sta is mean station PRCP and Z sta is mean station elevation slope is monthly value from MLRs Plot mean station elevation (Z) vs. mean station PRCP Slope from monthly MLR used to find the y-intercept XYZ Methodology

12. Predicted and Measured Streamflow Animas Basin, CO 1990 - 2005 PREDICTEDMEASURED

13. 2005 ESP Forecast Forecast Period 4/3 – 9/30 Made 4/2/2005 All historic years Only el nino years Observed 2005

14. Animas Basin Snow-covered Area Year 2000 Simulated Measured (MODIS Satellite) Error Range <= 0.1

15. PRMS SNODAS swe (in) PRMS SNODAS PRMS and SNODAS Basin Average Snowpack Water Equivalent (SWE)

16. Models

17. Ground-based Snow Data METAR, SNOTEL, CADWR, HADS, NWS Coop, etc. Airborne Snow Water Equivalent Satellite Snow Cover Data GOES, AVHRR, SSM/I, MODIS NEXRAD Radar Data Numerical Weather Model Data Eta, RUC2, MAPS NOHRSC Database Management System Data ingest, quality control, pre-processing Data and Product Archive NOHRSC Snow Data Assimilation System Energy-and-mass-balance snow modeling and observed snow data assimilation Product Generation and Distribution Elements: Daily National Snow Analyses: (water equivalent, snow depth, temperature, sublimation, condensation, snow melt) Formats: Interactive map, time-series plots, text discussions, alphanumeric and gridded products Distribution: NOHRSC Web Site, AWIPS, direct FTP, NSIDC, NCDC NOHRSC Operations

18. NSA Product Generation Interactive Maps Digital Data Discussions NSA Product Generation Interactive Maps Digital Data Discussions Temperature Relative Humidity Wind Speed Solar Radiation Atmos. Radiation Precipitation Precipitation Type Hourly Input Gridded Data (1 km) Hourly Input Gridded Data (1 km) Soils Properties Land Use/Cover Forest Properties Static Gridded Data (1 km) Static Gridded Data (1 km) Snow Energy and Mass Balance Model Blowing Snow Model Radiative Transfer Model State Variables for Multiple Vertical Snow & Soil Layers Snow Water Equivalent Snow Depth Snow Temperature Liquid Water Content Snow Sublimation Snow Melt State Variables for Multiple Vertical Snow & Soil Layers Snow Water Equivalent Snow Depth Snow Temperature Liquid Water Content Snow Sublimation Snow Melt NOHRSC Snow Modeling Framework 1 1 Data Assimilation 2 3 Snow Observations Snow Water Equivalent Snow Depth Snow Cover Snow Observations Snow Water Equivalent Snow Depth Snow Cover

19. NOHRSC Snow Model Physics

20. National Snow Analyses (NSA) High-resolution Daily and Hourly Gridded Snow Data Sets of Fused Model and Observations Snow Water Equivalent Snow Density Snow Sfc. Temperature Snow Avg. Temperature Snow Melt Sublimation Snow Wetness Local Information (1 km 2 ) Continental U.S. Information Snow Depth Archived at NCDC, NSIDC, and NDFD (soon) Data Products Interactive Maps Time Series Plots Text Discussions Snow Information Products

21. PRMS

22. PRMS Snowpack Energy Balance Components

23. Animas River Basin, CO

24. Animas Basin SWE - 2004 SNODAS PRMS April 1 May 1 (in.)

25. Animas Basin SWE - 2005 SNODAS PRMS (in.) April 1 May 1

26. SWE_diff = SNODAS - PRMS SWE Difference on Selected HRUs

27. PRMS OBS Q (cfs) Animas PRMS OBS PRMS SNODAS melt (in) PRMS SNODAS PRMS SNODAS swe (in) PRMS SNODAS

28. PRMS OBS No Update Selected Update Daily Update Update PRMS SWE with SNODAS SWE Animas Basin

29. East Fork Carson Basin, CA

30. Predicted and Measured Streamflow East Fork Carson Basin, CA 1990 - 2005 PREDICTEDMEASURED

31. SNODAS PRMS (in.) East Fork Carson SWE - 2004 April 1 May 1

32. East Fork Carson SWE - 2005 SNODAS PRMS April 1 May 1 (in.)

33. Q (cfs) PRMS OBS East F. Carson PRMS SNODAS melt (in) swe (in) PRMSSNODAS

34. No Update March 1 Update April 1 Update Update PRMS SWE with SNODAS SWE East Fork Carson Basin

35. Skykomish Basin, WA

36. (in.) April 1 May 1 SNODASPRMS Skykomish Basin SWE - 2004

37. (in.) April 1 May 1 Skykomish Basin SWE - 2005 SNODASPRMS

38. Q (cfs) PRMS OBS Skykomish melt (in) PRMS SNODAS swe (in) PRMS SNODAS

39. A work in progress (Sample of 2 basins). Remotely sensed measures of SCA are valuable, but the combined products of SCA and SWE from SNODAS provide a needed extra dimension for modeling. Similar mean daily melt rates in PRMS and SNODAS can result from different spatial HRU melt rates. Update of PRMS SWE may be possible when distributional patterns of SNODAS SWE are similar. DISCUSSION AND CONCLUSIONS

40. The weaknesses of a climatological multiple linear regression precipitation distribution method was demonstrated Work is continuing to identify the most robust precipitation distribution methods for different climatic and physiographic regions and will build on the SNODAS product. DISCUSSION AND CONCLUSIONS

41. Working with the NRCS and NWS to develop a Modular Modeling System forecasting toolbox using MMS/OMS and PRMS

42. TOOL PITCH Parameterizer (GIS Weasel) DISCUSSION AND CONCLUSIONS

43. TOOL PITCH Parameterizer (GIS Weasel) Downsizer DISCUSSION AND CONCLUSIONS

44. TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator DISCUSSION AND CONCLUSIONS

45. TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca) DISCUSSION AND CONCLUSIONS

46. TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca) Visualizer DISCUSSION AND CONCLUSIONS

47. TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca) Visualizer Analyzer DISCUSSION AND CONCLUSIONS Statistical and graphical sensitivity and uncertainty analysis tools

48. TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca) Visualizer Analyzer Terminator DISCUSSION AND CONCLUSIONS