Strategy for Interactive Calibration of the Sacramento Model Using NWSRFS

1. Strategy for Interactive Calibration of the Sacramento Model Using NWSRFS Interactive Calibration Program (ICP) NWS Calibration Workshop, LMRFC March, 2009 slide - 1

2. General Considerations when using ICP • Change duration/scale depending on flow • components/parameters being examined • Remove large errors in parameter values whenever • detected • Periodically return to previous steps to recheck results • Primary statistics to periodically examine: • annual bias • seasonal bias • flow interval bias • Remain flexible • Think! Ask questions before you view the simulation • What do I expect to see? • What secondary effects could I see? • Why didn’t I see what I expected? NWS Calibration Workshop, LMRFC March, 2009 slide - 2

3. NWS Calibration Workshop, LMRFC March, 2009 slide - 3

4. NWS Calibration Workshop, LMRFC March, 2009 slide - 4

5. NWS Calibration Workshop, LMRFC March, 2009 slide - 5

6. Sacramento Model Calibration Strategy • Start with a priori parameters • 1. Remove large errors, especially volume • 2. Adjust low flow parameters to get a • reasonable baseflow simulation • 3. Adjust tension water capacities • 4. Adjust parameters that primarily affect storm runoff • 5. Final adjustments to improve seasonal • and flow interval bias patterns NWS Calibration Workshop, LMRFC March, 2009 slide - 6

7. Remove large errors, timing of snowmelt runoff. form of precipitation Adjust major parameters MFMAX, MFMIN SCF UADJ SI Snow-17 Calibration Strategy NWS Calibration Workshop, LMRFC March, 2009 slide - 7

8. Each parameter is designed to represent a specific portion of the hydrograph under certain moisture conditions Concentrate on having each parameter serve its primary function rather than overall goodness of fit. Sacramento Model Calibration NWS Calibration Workshop, LMRFC March, 2009 slide - 8

9. Check water balance – annual bias should be less than 10-20% Check for large timing errors, most common: Storm runoff/baseflow ration in error (raise or lower entire percolation curve) Amount of surface runoff incorrect (adjust UZFWM) Improper channel response function for Sacramento Model (remove interflow from channel response unit hydrograph) Sacramento Model Calibration Strategy Step 1 – Remove Large Errors NWS Calibration Workshop, LMRFC March, 2009 slide - 9

10. Identify primary baseflow component Adjust key parameters: LZPK, LZSK, LZFPM, LZFSM, PFREE May need to adjust ZPERC and REXP Determine if riparian evaporation exists and determine general magnitude of RIVA (then set RIVA back to zero) Sacramento Model Calibration StrategyStep 2 – Reasonable baseflow simulation NWS Calibration Workshop, LMRFC March, 2009 slide - 10

11. Determine UZTWM and LZTWM based on periods when maximum soil moisture deficits occur. While examining UZTWM, check and adjust value of PCTIM Sacramento Model Calibration StrategyStep 3 – Tension water capacities NWS Calibration Workshop, LMRFC March, 2009 slide - 11

12. Get proper division between surface runoff and interflow by changing UZFWM Adjust UZK to get correct timing of interflow. Refine percolation curve over large range of LZDEFR values Primarily adjust ZPERC and REXP Use ICP percolation analysis feature Determine if ADIMP is needed. If so, determine proper value. Sacramento Model Calibration StrategyStep 4 – Storm runoff simulation NWS Calibration Workshop, LMRFC March, 2009 slide - 12

13. Determine value of RIVA if riparian evaporation exists Adjust ET-Demand values to improve seasonal bias pattern (alter by changing monthly PE adjustment curve). Refine timing of peaks by modifying channel response (unit hydrograph) Raise or lower percolation curve to improve flow interval bias pattern by changing LZFSM and LZFPM by the same ratio. Sacramento Model Calibration StrategyStep 5 – Final Adjustments NWS Calibration Workshop, LMRFC March, 2009 slide - 13

14. Keep parameter values the same, except when the hydrograph from one sub-area can be isolated (then can modify parameters for the sub-area influencing that response). Relationships can be established between certain values based on soils, vegetation, etc, (then maintain that ration (ratio, diff) as parameter values are adjusted. Parameter Relationships when Watershed Divided into Sub-Areas NWS Calibration Workshop, LMRFC March, 2009 slide - 14

15. Calibration of HL-RDHM NWS Calibration Workshop, LMRFC March, 2009 slide - 15

16. Use of scalar multipliers (assumed to be uniform over a basin) greatly reduces the number of parameters to be calibrated. This assumes the spatial distribution of a-priori parameters is realistic. Parameters from 1-hour, lumped model calibrations can be a good starting point. Lumped model parameters, if derived at the 1 hour time scale, can be used to derive initial scalar multipliers, i.e. multiplier = [lumped model parameter]/[basin average of gridded a-priori parameter values] Scalar multipliers are adjusted using similar strategies and objectives to those for lumped calibration Both manual and a combination of automatic and manual calibration on scalar multipliers have proven effective Calibration of SAC Parameters with Scalar Multipliers NWS Calibration Workshop, LMRFC March, 2009 slide - 16

17. Comparison Between Calibration Steps for Distributed and Lumped Modeling Distributed Lumped Distributed Lumped NWS Calibration Workshop, LMRFC March, 2009 slide - 17

18. Follow similar strategies to those used for lumped calibration except make changes to multipliers, e.g. from Anderson (2002): “Remove large errors Obtain reasonable simulation of baseflow Adjust major snow model parameters, if snow is included\ Adjust tension water capacities Adjust parameters that primarily affect storm runoff Make final parameter adjustments” Manual Headwater Calibration Can still use PLOT-TS and STAT-QME • Stat-Q event statistics summarize how well you do on bias, peaks, timing, and RMSE, etc over any # of selected events. • R scripts assist with routing parameter adjustment. See HL-RDHM User Manual for a detailed example. NWS Calibration Workshop, LMRFC March, 2009 slide - 18

19. 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 NWS Calibration Workshop, LMRFC March, 2009 slide - 19

20. Multi-Scale Objective Function (MSOF) Emulates multi-scale nature of manual calibration • Minimize errors over hourly, daily, weekly, monthly intervals (k=1,2,3,4…n) • q = flow averaged over time interval k • n = number of flow intervals for averaging • mk = number of ordinates for each interval • X = parameter set -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. Weight = NWS Calibration Workshop, LMRFC March, 2009 slide - 20

21. 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 NWS Calibration Workshop, LMRFC March, 2009 slide - 21

22. Start with best a-priori or scaled lumped parameters Run automatic calibration Make manual adjustments (particularly for routing parameters) to get the preferred storm event shapes Possible Strategy NWS Calibration Workshop, LMRFC March, 2009 slide - 22

23. NWS Calibration Workshop, LMRFC March, 2009 slide - 23