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Project goals

Identify and quantify return flows to the Truckee River and improve existing Reclamation DSS: Truckee Meadows Water Balance. Project goals. quantify known and unknown water balance components between USGS gages in the Truckee Meadows

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Project goals

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  1. Identify and quantify return flows to the Truckee River and improve existing Reclamation DSS:Truckee Meadows Water Balance

  2. Project goals • quantify known and unknown water balance components between USGS gages in the Truckee Meadows • evaluate current methods for predicting depletions in the Truckee Meadows • investigate methods to improve depletions forecasting, compatible with RiverWare

  3. node/gage *no model node corresponding with USGS gage at Tracy

  4. Water Balance Overall river behavior between Farad and Derby Dam Compare depletions behavior between reaches Quantify the fraction of depletions that are gaged Identify largest sources of uncertainty in depletions

  5. Available data and sources for Truckee River inflows and outflows through the Truckee Meadows. 8 Data Source: FWM = U.S. District Court Water Master; TMWRF = Truckee Meadows Water Reclamation Facility; NWIS = USGS National Water Information System; NDWR = Nevada Division of Water Resources

  6. Conclusions from water balance study • Monthly summary of known vs unknown depltions for each reach • Unknown depletions (seepage,ephemeral flow,etc) are within uncertainty in gaged flows • Fair Rating: 95 % of daily discharges within 15 % (Farad, Derby) • Good Rating: 95 % of daily discharges within 10 % (all others) • Excellent Rating: 95 % of daily discharges within 5 % • Largest source of uncertainty in depletions is TMWRF/Steamboat Creek inflow • High level of flow operations makes it difficult define relationships between Q, known depletions, unknown depletions, temperature

  7. Current FaradDerby Depletions Model • Truckee River depletions represented by a regression-based model that forecasts FaradDerby depletions based on previous day flow and depletions at Farad • Observed depletions calculated from measured flow FDdepletion = QFarad(t−1) − QDerby(t) • Estimated depletions calculated with regression developed using 1960-2003 data FDdepletion = A*month + B* Farad(t−1) + C*FDdepletion(t−1) + D Step 1

  8. Current FaradDerby Depletions Model Step 2 If necessary, depletions are adjusted to fall within historic depletion Step 3 Forecasts are blended using 5-day average Step 4 Depletions not accounted for by major diversions and returns are distributed as a fraction of total FaradDerby depletions

  9. Current FaradDerby Depletions Model FDdepletion = A*month + B* Farad(t−1) + C*FDdepletion(t−1) + D Coefficients and error analysis for 2004 Farad-Derby depletion regression equations. • Key points: • Model performs better than HSPF model developed by USGS (Kinematic wave eqn., PRMS, gage data, regression) • Previous day depletions drives model

  10. Difference between current Truckee depletions forecast and actual depletions • Key points: • depletions are typically overestimated (bias) • large Qlarge depletionlarger depletion forecast error

  11. Options for Improving Depletions Predictions in the Truckee Meadows • Keep current FaradDerby regression equations, developed using 1966 to 2004 data. Modify depletion redistribution within Truckee Meadows. • Replace current FaradDerby regression equations with FaradDerby regression equations developed using 1985 to 2007 data with extremes censored. Modify depletion redistribution within Truckee Meadows. • Replace FaradDerby regression equations and depletion redistribution with reach-by-reach regression equations developed using 1985 to 2007 data with censored extremes. • Reach-by-reach regression of unknown portion of depletions only. Incorporate real-time data into RiverWare forecasts.

  12. Recommendations Monitor Truckee River flow at Derby Dam • Derby Dam flow = Below Derby Gage + Canal @ Wadsworth+ transmission losses + Gilpin Spill • Improve combined TMWRF/Steamboat Creek discharge measurements. • The largest source of uncertainty in Truckee Meadows depletions accounting can be reduced with improved flow measurement. • Perform depletions modeling for each Truckee Meadows reach (regression on past data with extreme flow events censored) • rather than estimating FaradDerby depletions and back-calculating for nodes at Mogul, Reno, Sparks, and Vista. • Use of node by node regression equations presented in Option 3 will reduce error in depletions and flow forecasting at all Truckee Meadows nodes.

  13. Current work • Controls on magnitude of annual depletions • Reservoir operation data? • Estimate length of extreme flow event signature • Collaborate with Development of tools to esimate river transportation losses due to evaporation and seepage in the Truckee River to estimate depletions during drought

  14. Questions If we get off track on this depletion method, will the model be off track the entire year since it is based on previous day depletions? Whether we are able to make corrections to long term predictions depends on whether there is a useful depletions predictor that is related to conditions during previous years rather than a season-specific variable like March reservoir storage or snowpack. DRI needs to learn about reservoir operations: who should we contact? Will reduction in Floriston Rates under TROA have an impact on depletions and the proposed regression equations to be used in RiverWare? Because the model forecasts of depletions are overwhelmingly dependent on previous day depletions, we do not believe that a 100 cfs change in flow will affect the accuracy of regression equations.

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