1. Comparisons of Simulation Results Using the NWS Hydrology Laboratory's Research Modeling System (HL-RMS) Hydrology Laboratory Office of Hydrologic Development National Weather Service/NOAA Ziya Zhang, Victor Koren, Seann Reed, Michael Smith, and David Wang HYDROLOGY LABORATORY, OHD/NWS/NOAA

2. What is HL-RMS? A Research Modeling System being developed by researchers in the Hydrology Laboratory, Office of Hydrologic Development, NWS/NOAA. Modeling framework for testing lumped, semi- distributed, and fully-distributed hydrologic modeling approaches.  

3. HYDROLOGY LABORATORY, OHD/NWS/NOAA HL-RMS Components Pre-simulation: Determines connectivity matrix Derives SAC parameters Derives routing parameters Simulation: Simulates rainfall/runoff for each grid (SAC,...) Conducts hillslope and channel routing Computes hydrographs  

4. HYDROLOGY LABORATORY, OHD/NWS/NOAA HL-RMS Capabilities Ingests NEXRAD Stage III xmrg data Uses lumped or distributed model parameters Uses lumped or distributed precipitation Computes hydrographs at any interior points Selection between lumped, semi-distributed, and fully distributed modes     

5. HYDROLOGY LABORATORY, OHD/NWS/NOAA HL-RMS Capabilities cont. Output Arc/Info grids for selected variables Flexible to modify gridded parameters Modular design to test other models Computational element: NEXRAD 4km HRAP grid    

6. HYDROLOGY LABORATORY, OHD/NWS/NOAA Test Basin Blue River Basin Oklahoma Missouri Kansas Arkansas Texas Area: 1233 km 2 Blue River Basin, OK

7. HYDROLOGY LABORATORY, OHD/NWS/NOAA Test Runs   Lumped parameters from manual calibration, lumped forcing Distributed parameters, distributed forcing Scaled distributed parameters, distributed forcing  Continuous Simulation from 1993 to 2000 Scale factor was based on comparing a calibrated value of a basin to an averaged value of gridded values of that basin

8. HYDROLOGY LABORATORY, OHD/NWS/NOAA Samples of Gridded Parameters

9. HYDROLOGY LABORATORY, OHD/NWS/NOAA Test Results Distributed Channel Runoff Channel Runoff (cms) November 7, 1996 22:00 outlet

10. HYDROLOGY LABORATORY, OHD/NWS/NOAA November 7, 1996 7:00 - 8:00 Test Results Rainfall & Surface Runoff

11. HYDROLOGY LABORATORY, OHD/NWS/NOAA Test Results Hydrographs @ Interior Points Basin Outlet A B C

12. HYDROLOGY LABORATORY, OHD/NWS/NOAA Basin Outlet A B C Test Results Hydrographs @ Interior Points

13. HYDROLOGY LABORATORY, OHD/NWS/NOAA outlet Total Precipitation (mm) An Event in 1994 Test Results Hydrograph Comparison

14. HYDROLOGY LABORATORY, OHD/NWS/NOAA outlet Total Precipitation (mm) An Event in 1995 Test Results Hydrograph Comparison

15. HYDROLOGY LABORATORY, OHD/NWS/NOAA outlet Total Precipitation (mm) An Event in 1996 Test Results Hydrograph Comparison

16. HYDROLOGY LABORATORY, OHD/NWS/NOAA An Event in 1997 outlet Total Precipitation (mm) Test Results Hydrograph Comparison

17. HYDROLOGY LABORATORY, OHD/NWS/NOAA outlet Total Precipitation (mm) An Event in 1998 Test Results Hydrograph Comparison

18. HYDROLOGY LABORATORY, OHD/NWS/NOAA outlet Total Precipitation (mm) An Event in 1999 Test Results Hydrograph Comparison

19. HYDROLOGY LABORATORY, OHD/NWS/NOAA Observations Simulations using HL-RMS in distributed mode without any calibration effort yields better results than calibrated lumped simulations at most times Distributed modeling yields much better results for events where rainfall has big variation across a basin Distributed simulation results are better when the SAC parameters are scaled based on calibrated parameters   

20. HYDROLOGY LABORATORY, OHD/NWS/NOAA Rainfall distribution has the biggest effect on differences between lumped and distributed simulations Derived gridded rainfall-runoff and routing parameters are reasonably good Use of only gridded parameters yields higher peaks in hydrographs Recession part of simulated hydrographs is off the most compared to observed discharges Observations cont.    

21. HYDROLOGY LABORATORY, OHD/NWS/NOAA Observations cont.  HL-RMS is a workable and a flexible research tool to study hydrologic processes, and to test the sensitivity of model parameters and input forcing on simulations