1. Preliminary Applications of the HL-RDHM within the Colorado Basin River Forecast Center Ed Clark, Hydrologist Presented July 26 th, 2007 as part of the WR Field Hydrology Seminar Series

2. Discussion Outline 1.Distributed Model background information. 2.Parameterization and Setup. 3.Applications and Preliminary Results.

3. NWS Distributed Model(s) RDHM: Research Distributed Hydrologic Model Object oriented with SAC-SMA, Snow17, API, Frozen Ground, Channel Shape, and Routing Curve sub-routines DHM: NWSRFS Operation Requires RDHM to setup parameter grids. Limited to SAC-SMA model and routing via the connectivity file. Delivered to the RFC’s in build 8.2. Output: Grids -- Any parameters states Time Series: Basin Average (any parameter) and Outlet Discharge. Output: Discharge time-series for display within NWSRFS

4. Model Structure Divides the basin of interest into ~4 km 2 grids. (HRAP) Requires gridded Surface Temperature and Precipitation (xmrg’s from MPE). Runs modules the existing models Snow17, Sacramento Soil Moisture Accounting Model (SAC-SMA) to generate runoff. Runoff is routed by solving the Kinematic Wave Equations.

5. Model Concept Precip Surface Temperature Snow17 SAC-SMA Water content in Soil tanks Rain +melt Runoff RDHM RoutingDischarge Hydrograph Snow Pack Water Contents at Depth FRZ (Sac-HT)

6. Channel and Hill-slope routing Real HRAP Cell Hillslope model Cell-to-cell channel routing From Yu Zhang, OHD

7. Control and Calibration Input card is passed to the model. Defines location of data, simulation period, and calibration parameters. Parameters grids can be calibrated by multiplying the by a scalar. >1 <1 Gridded Parameter

8. Model Parameterization Snow17 MFMax and MFMin based on DEM analysis, provided by OHD. SAC-SMA grids provided by OHD and based on STATSGO data. Routing grids are generated from USGS field measurements by defining a relationship between channel shape and discharge. Other grids written out from the calibrated lumped model.

9. Evolution to a Fully Distributed Model Lumped Model. Extensively calibrated for the CBRFC with a great deal of forecaster skill. Lumped parameter values distributed by elevation zone. Used to check the mechanics of the model, and increase forecast skill with high intensity, convective events. Fully Distributed. Parameters based on Statsgo data, calibrated by applying a scalar multiplier, High Spatial Variability past Future Sac_LZTWM

10. Application/Demonstration Basins Comparison w/ RTi SNOWDAS. Snow 17 Investigation Demonstration of gridded soil moisture “norms” and QPE driven small basin hydrograph. [Santa Cruz and San Pedro basins.]

11. Distributed Snow Model Theoretically, a distributed model will provide a better model to real world relationship of snow covered area eliminating some error in simulation. Preliminary investigation sought to match current lumped model skill and check mechanics of the model.

12. Animas, nr Durango –CBRFC Lumped Sac-SMA Grids Run: WY 1975-2000 Scalars set to -1

13. Application of Downstream Calibration to Upstream points of Interest

14. Observations and areas of future study A-priori MFMAX slightly low resulting in delayed of snowmelt. ET grids require further analysis - grid values from the lumped model. More rigorous forcing are required: Use Auto- Daily-QC (Mountain-Mapper) to generate 30 years of 6-hr xmrgs from station records. Operationally created xmrg’s can’t be used until MPE includes 24 hour data (SNOTEL.)

15. Rapid surface-runoff and near surface soil moisture Does spatially distributing convective precipitation help improve our ability to simulate hydrographs in arid portions of the Southwest? Can the gridded Sac-SMA model be used to qualify the current conditions of near- surface soil moisture?

16. QPE driven small basin hydrograph – flash flooding quantification. MPE xmrgs and scaled (calibrated lumped to a-priori basin mean) OHD a-priori sac-sma grids

18. “Qualified” Soil Moisture Generate historical daily average states from the lumped model calibration. Compare the current contents to this day’s average conditions. Where are we today compared to where we are “usually?”

19. Soil Moisture Percent of Normal Upper Zone Lower Zone July 1, 2006

20. Soil Moisture Percent of Normal Upper Zone Lower Zone July 25, 2006

21. Soil Moisture Percent of Normal Upper Zone Lower Zone July 26, 2006

22. Soil Moisture Percent of Normal Upper Zone Lower Zone July 27, 2006

23. Soil Moisture Percent of Normal Upper Zone Lower Zone July 28, 2006

24. Soil Moisture Percent of Normal Upper Zone Lower Zone July 29, 2006

25. Soil Moisture Percent of Normal Upper Zone Lower Zone July 30, 2006

26. Soil Moisture Percent of Normal Upper Zone Lower Zone July 31, 2006

27. Soil Moisture Percent of Normal Upper Zone Lower Zone August 01, 2006

28. Soil Moisture Percent of Normal Upper Zone Lower Zone August 02, 2006

29. Soil Moisture Percent of Normal Upper Zone Lower Zone August 03, 2006

30. Soil Moisture Percent of Normal Upper Zone Lower Zone July 18, 2007

31. Contents at Depth Utilizes Victor Koren’s Sacramento Heat Transfer (SAC-HT) model. Calculates frozen and liquid contents in a set of computational layers at specified depths. Outputs as outlet time-series or individual grids.

32. Prototype Plot: Lower Santa Cruz

33. Continued Study Increase the number of calibrated basins. Increase the region over which soil moisture modeling is computed. Work with our customers (WFO’s, regional scientists and land managers) to better publish soil moisture simulations and observations.

34. Questions? Available at: http://www.cbrfc.noaa.gov/present/present2007.cgi