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

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Presentation transcript:

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

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

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

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

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

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

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

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

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

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

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