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Calibration

Calibration. DOH Science Conference July 17, 2008. Mike Smith, Victor Koren, Zhengtao Cui, Seann Reed, Fekadu Moreda. Current Status. AWIPS DHM. HL-RDHM. DHM-TF. P& ET. P, T & ET. (Forecast). Auto Calibration. SNOW -17. rain. rain + melt. SAC-SMA. ICP. SAC-SMA, SAC-HT.

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Calibration

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

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

  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. 42 21 36 48 18 24 32 62 56 32 16 31 28 16 42 30 44 40 21 15 22 20 42 21 44 22 40 20 Calibration Approach Multiply each grid value by the same scalar factor. x 2 = Preserve Spatial Pattern of Parameters Example: I parameter out of N total model parameters th Calibrate distributed model by uniformly adjusting all grid values of each model parameter (i.e., multiply each parameter grid value by the same factor) 1. Manual: manually adjust the scalar factors to get desired hydrograph fit. 2. Auto: use auto - optimization techniques to adjust scalar factors .

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

  6. Multi-Scale Objective Function (MSOF) Emulates multi- time scale nature of manual calibration • 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 • mk = 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. =

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

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

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

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

  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

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