1. 1 Calibration of Watershed Models Why calibrate? –OFS: short term forecasts –ESP: no run time mods –Learn model and hydrology –Good training for forecasting Basic Methods –Manual/Expert – guided manual adjustment of parameters until simulated response agrees with observed. –Mathematical Optimization Driven by evaluation of an objective function- searches error surface for minimum point Not a substitute for manual calibration Purpose: to refine parameter estimates previously developed manually

2. 2 NWSRFS Components Historical Data Historical Data Analysis areal time series Model Calibration parameters, information CalibrationSystem (CS) Real-Time Observed and Forecast Data Operational Forecast System (OFS) Ensemble Streamflow Prediction (ESP) System Hydrologic and Hydraulic Models Hydromet Analysis observed and predicted values Hydrologic/ Hydraulic Models short term forecasts current states Statistical Analysis Probabilistic Predictions time window Interactive Forecast Program (IFP) Interactive Adjustments

3. 3 NWSRFS Programs MCP – Manual Calibration Program –Based completely on the operations table –Executes a single segment for a long period of time, usually years –Simulates a long period of record by executing the operations table one month at a time. ICP – Interactive Calibration Program –GUI for MCP

4. 4 NWS Hydrologic Modeling A B Operations Table Snow A SAC-SMA A UHG A Stage Q 1 Display 1 Rout 1->2 Snow B SAC-SMA B UHG B ADD/SUB 1+2 Stage-Q 2 Display 2 1 2

5. 5 Period of Record Calibration –Use at least a 10 year period east of Mississippi R. –In dryer West, may need longer period to obtain more events –Select period with low flow and high flow events –May be easier to identify some parameters in slightly wetter periods –For ESP, entire area to be run must have the same period of record Verification –Choose period with extreme lows and highs to check parameters.

6. 6 Automatic Optimization Program: OPT3 Search Algorithms –Pattern Search –Adaptive Random Search –Shuffled Complex Evolution Problems –One number to evaluate agreement at all flow ranges –Can lead to non-sensical values –Can lead to inconsistency across watersheds in a basin NEW Simplified Line Search

7. 7 Automatic Optimization, cont’d Problem Solutions –Mult-step Automatic Calibration Scheme (MACS) –Use of a-priori SAC parameter estimates to constrain the search space (Koren et al, 2002) and preserve natural variability –Simplified Line Search w/ a-prior parms.

8. 8 Automatic Optimization, cont’d MACS Procedure 1.Base flow: 1.Use LOG objective function 2.Optimize all SAC parameters 2.Fast Response 1.Fix base flow parameters from step 1. 2.Use DRMS objective function 3.Optimize fast response parameters 3.Base Flow 1.Fine tune base flow parameters 2.Use log objective function 4.Check Monthly Percent Bias 1.Optional 2.Manual, since OPT3 can’t optimize the ET-Demand curve

9. 9 Simplified Line Search vs Shuffled Complex Evolution 1) SLS needs less function evaluations, but it leads to similar result; 2) SLS stops much faster and closer to the start point (a priori parameters); 3) On some basins, SCE misses the nearest ‘best’ solution.

10. 10 Comparison of multi-scale criteria from SCE and SLS for calibration and validation periods Basin SCESLS 1234512345 Calibration GBHT2 14.114.013.814.313.814.314.414.014.614.1 GETT2 18.318.618.912.618.318.818.919.512.918.8 HBMT2 31.933.532.933.332.133.435.434.835.033.0 HNTT2 36.838.828.334.236.536.938.828.634.536.7 JTBT2 11.712.67.2115.713.212.612.27.1515.613.8 KNLT2 15.018.718.018.710.917.320.119.819.711.2 LYNT2 12.712.812.312.28.5412.713.012.412.38.69 MTPT2 38.041.741.340.038.037.941.541.340.137.9 Validation GBHT2 13.014.615.410.315.014.814.315.711.415.4 GETT2 14.29.733.5727.713.914.18.713.5026.213.0 HBMT2 29.927.925.121.535.927.034.627.625.247.1 HNTT2 33.34.8166.147.432.0 4.5166.344.132.0 JTBT2 12.44.3225.99.5926.24.963.7924.76.4717.6 KNLT2 31.94.3813.718.047.928.511.110.815.343.1 LYNT2 11.45.8911.011.436.911.84.9210.311.137.3 MTPT2 45.116.220.934.252.445.414.519.633.752.0

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