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Comparisons of Simulation Results Using the NWS Hydrology Laboratory's Research Modeling System

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

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Comparisons of Simulation Results Using the NWS Hydrology Laboratory's Research Modeling System

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  1. HYDROLOGY LABORATORY, OHD/NWS/NOAA Comparisons of Simulation Results Using the NWS Hydrology Laboratory's Research Modeling System (HL-RMS) Ziya Zhang, Victor Koren, Seann Reed, Michael Smith, and David Wang Hydrology Laboratory Office of Hydrologic Development National Weather Service/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. 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. 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. 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. Kansas Missouri Oklahoma Arkansas Blue River Basin Texas Test Basin Blue River Basin, OK Area: 1233 km2

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

  8. Samples of Gridded Parameters

  9. Test Results Distributed Channel Runoff Channel Runoff (cms) November 7, 1996 22:00 outlet

  10. Test Results Rainfall & Surface Runoff November 7, 1996 7:00 - 8:00

  11. Test Results Hydrographs @ Interior Points A B C Basin Outlet

  12. Test Results Hydrographs @ Interior Points A B C Basin Outlet

  13. Test Results Hydrograph Comparison Total Precipitation (mm) An Event in 1994 outlet

  14. Test Results Hydrograph Comparison Total Precipitation (mm) An Event in 1995 outlet

  15. Test Results Hydrograph Comparison Total Precipitation (mm) An Event in 1996 outlet

  16. Test Results Hydrograph Comparison Total Precipitation (mm) An Event in 1997 outlet

  17. Test Results Hydrograph Comparison Total Precipitation (mm) An Event in 1998 outlet

  18. Test Results Hydrograph Comparison Total Precipitation (mm) An Event in 1999 outlet

  19. 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. Observations cont.  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   

  21. 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

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