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JCS Input to OHD Response to Core Project Review

JCS Input to OHD Response to Core Project Review. Response Topics. Hydrologic Application of Intra-Seasonal Atmospheric Forecasts Shows need for seamless weather/climate forecast systems Land surface memory increases forecast lead time Removal of Bias from Hydrologic Forecast Products

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JCS Input to OHD Response to Core Project Review

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  1. JCS Input to OHD Response to Core Project Review

  2. Response Topics • Hydrologic Application of Intra-Seasonal Atmospheric Forecasts • Shows need for seamless weather/climate forecast systems • Land surface memory increases forecast lead time • Removal of Bias from Hydrologic Forecast Products • Hydrologic ensemble forecasts must be adjusted for “bias” • If hydrologic forecasts are made with “reliable/unbiased” weather and climate ensemble forcing, a simple procedure can be used to adjust hydrologic ensemble forecast products without additional calibration using hydrologic hindcasts • Compensating for Effects of “Unknown” Upstream Regulation (e.g. Diversions and Reservoir Operations) • The procedure we developed for bias and reliability adjustment to hydrologic ensemble forecast products may also compensate for some anthropogenic effects not accounted for by hydrologic forecast models

  3. Hydrologic Application of Intra-Seasonal Atmospheric Forecasts • CNRFC forecast points • NFDC1 – North Fork American River (N. Sierra: rain and snow) • CREC1 – Smith river at Crescent City (N. California Coast: rain only) • Comparison of Prediction Skill in: • Simulation using observed forcing • Prediction using climatological forcing • Prediction using 14 days of ensemble mean GFS forcing

  4. CNRFC Ensemble Prototype Smith River Mad River Salmon River Van Duzen River American River (11 basins) Navarro River

  5. NFDC1 – Prediction Skill in Forecasts and Historical Simulations(correlation between model prediction and observations)

  6. CREC1 – Prediction Skill in Forecasts and Historical Simulations(correlation between model prediction and observations)

  7. Analysis of Intra-seasonal Hydrologic Prediction Results • Hydrologic forecast skill derives from 2 sources: skill in prediction of future forcing and information in knowledge of initial conditions • Most of the skill in intra-seasonal atmospheric forecasts is in the beginning of the forecast period • It is essential in hydrologic forecasting to account for the time-scale dependent skill in atmospheric forecasts because skill in predicting

  8. Removal of Bias from Hydrologic Forecast Products • NWS hydrologic forecast models are calibrated to reduce bias and increase skill • But significant climatological biases may exist at some times of the year and for some forecast products • Uncertainty in initial conditions, hydrologic model response and model parameters is not accounted for in present ensemble forecasts • Un-accounted-for uncertainty in hydrologic ensemble forecasts varies seasonally and is space and time-scale dependent

  9. Removal of Bias from Hydrologic Forecast Products (Continued) • Ultimately, we would like to adjust ensemble streamflow traces to climatological bias and assure reliable probability forecasts for all forecast products made at any time of the year. • Because hydrologic models are highly non-linear and hydrologic uncertainty is space and time-scale dependent we do not know how to make direct adjustments to hydrologic ensemble member traces. • But it may be possible to make reliable adjustments to ensemble member “products” derived from the ensemble member traces.

  10. June 15: 30-day Calibration

  11. June 15: 30-day Forecasts

  12. NFDC1 – March 1530-day GFS-Based Ensemble Forecasts

  13. NFDC1 – March 15 ForecastsCumulative Rank Histograms

  14. Compensating for Effects of “Unknown” Upstream Regulation (e.g. Diversions and Reservoir Operations)

  15. Russian River • Total Area 3465 km2. • Elevation 17m - 1245m. • 2 Flood Control Reservoirs • 3 Local Areas. • 3 Official Flood Forecast Points. • Floods Nearly Every Year. • 3 Major Floods in Past 40 Years.

  16. LAMC1(Lake Mendocino, CA)

  17. LAMC1 – Schematic of Possible Post Processor Diversion from Eel Basin Estimated Natural Flow COE Estimated Inflow Gaged Outflow Basin Model Of Natural Flow Post-Processor To Adjust to Observed Inflow Reservoir Operations Model Post-Processor To Adjust to Observed Outflow

  18. Climatologies of Measured Inflow and Modeled Natural Flow (December – June)

  19. Inflow – March 15

  20. Outflow March 15

  21. GLDA3(Lake Powell Monthly Inflow) EPG Post-Processor Test Results

  22. June Calibration

  23. Some Challenges • Alternative ways to evaluate Post-Processor integral to relax calibration bivariate normality assumption that I used to get started? • Can we adjust individual ESP traces (preserving temporal scale-dependent uncertainty) by using a cascade approach to apply the product-based postprocessor together with the “Schaake Shuffle”? • Multi-model applications?

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