1. Calibration Mike Smith, Victor Koren, Zhengtao Cui, Seann Reed, Fekadu Moreda DOH Science Conference July 17, 2008

2. HL-RDHM SAC-SMA, SAC-HT Channel routing SNOW -17 P, T & ET surface runoff rain + melt Flows and state variables base flow Hillslope routing SAC-SMA Channel routing P& ET surface runoff rain Flows and state variables base flow Hillslope routing AWIPS DHM Mods Auto Calibration DHM-TF ForecastingCalibration (Forecast) ICP Current Status

3. Manual and Auto Calibration Adjustment of parameter scalar multipliers Use manual and auto adjustment as a strategy Start with hourly lumped calibration Model parameters optimized in auto calb: –SAC-SMA –Hillslope and channel routing –Snow-17 Search algorithms –Simple local search Objective function: Multi-scale Limited to headwater basins

4. 48 56 3262 42 3044 40 44 42 32 36 42 40 24 28 1631 21 1522 20 22 21 16 18 21 20 Multiply each grid value by the samescalar factor. x 2 = Calibrate distributed model byuniformlyadjusting all grid values of each model parameter (i.e., multiply each parameter grid value by the same factor) 1.Manual: manually adjust thescalarfactors to get desired hydrograph fit. 2.Auto: use auto-optimization techniques to adjust scalar factors. Example:I th parameter out of N total model parameters Calibration Approach Preserve Spatial Pattern of Parameters

5. HL-RDHM SAC-SMA, SAC-HT Channel routing SNOW -17 P, T & ET surface runoff rain + melt Flows and state variables base flow Hillslope routing Auto Calibration Execute these components in a loop to find the set of scalar multipliers that minimize the objective function

6. Multi-Scale Objective Function (MSOF) Minimize errors over hourly, daily, weekly, monthly intervals (k=1,2,3,4…n…user defined) q = flow averaged over time interval k n = number of flow intervals for averaging q m k = number of ordinates for each interval X = parameter set Weight: -Assumes uncertainty in simulated streamflow is proportional to the variability of the observed flow -Inversely proportional to the errors at the respective scales. Assume errors approximated by std. = Emulates multi- time scale nature of manual calibration

7. Average monthly flow Average weekly flow Average daily flow Hourly flow Calibration: MSOF Time Scales Multi-scale objective function represents different frequencies of streamflow and its use partially imitates manual calibration strategy

8. Before autocalibration of a priori parameters After autocalibration Observed Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2 Arithmetic Scale Auto Calibration: Case 1

9. Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2 Semi-Log Scale Auto Calibration: Case 1 Before autocalibration of a priori parameters After autocalibration Observed

10. Before autocalibration of a priori parameters After autocalibration Observed Auto Calibration: Case 2 Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2 Arithmetic Scale

11. HL-RDHM and ICP Display time series ICP modifications –Run MCP3 or HL-RDHM –Copy optimized parameters to HL- RDHM input file –Re-run HL-RDHM