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Improving Seasonal Forecasting in the Snake River Basin, Idaho

Improving Seasonal Forecasting in the Snake River Basin, Idaho. University of Washington – University of British Columbia Fall Hydrology Workshop Oct 3, 2003 Marketa McGuire With contributions from: Alan Hamlet, Andy Wood, Kostas Andreadis, Dennis Lettenmaier. Objectives.

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Improving Seasonal Forecasting in the Snake River Basin, Idaho

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  1. Improving Seasonal Forecasting in the Snake River Basin, Idaho University of Washington – University of British Columbia Fall Hydrology Workshop Oct 3, 2003 Marketa McGuire With contributions from: Alan Hamlet, Andy Wood, Kostas Andreadis, Dennis Lettenmaier

  2. Objectives • To evaluate the impact of remote sensing data for improved estimates of initial snow conditions in streamflow forecasting • To evaluate the impact of streamflow forecast products for improved water resources operations

  3. Motivation • In regions like the Snake River basin, where spring and summer streamflows are dominated by snow-melt, it is important to know accurately the extent of snow in the initial condition of a streamflow forecast • Previous work by Maurer et al (2003) suggests that MODIS remotely sensed snowcover has the potential to improve hydrological modeling and prediction in the Snake River basin • Current meteorological station data are provisional • Result: estimates of forecast initial conditions have large uncertainty

  4. Forecasting Approach using MODIS Updating Initial Conditions: soil moisture, snowpack Ensemble Forecast: streamflow, soil moisture, snowpack, runoff Hydrologic model spin up Hydrologic simulation local scale weather inputs NCDC met. station obs. up to 2-4 months from current LDAS/other real-time met. forcings for remaining spin-up MODIS Update 1-2 years back 25th Day of Month 0 End of Month 6 - 12

  5. Variable Infiltration Capacity (VIC) Model Snake River 1/8° Resolution Routing Flow Network

  6. VIC SWEApril 4, 2000(1/8 degree) MODIS Snowcover April 4, 2000(500 m) Land Snow (SWE >= 5mm) Snow Clouds No Data/No Decision/Saturated Land (within Snake River Basin)

  7. Updating VIC Snow State • Current Version of VIC model: • Snow model runs on a 3 hours timestep • Each grid cell has up to 5 elevation bands • Each elevation band either has snow (coverage = 1) or does not (coverage = 0) • VIC model with updated snowcover (2 options): • Apply MODIS snowcover uniformly over elevation bands based on some threshold fraction of snowcover • Utilize VIC model version 4.1 that incorporates fractional snowcover (in testing phase Fall ’03)

  8. MODIS Fractional Snowcover • Idaho National Environmental and Engineering Laboratory (INEEL) is processing snow cover fractions for VIC model grid cells • Have obtained all Daily Snow cover tiles available for Snake Basin from February 2000 to present • Have automatic subscription with the NSIDC to obtain all newly processed scenes, with lag time of 2-3 days • Working toward fully automating the prototype algorithm to provide near real-time snow cover fractions for the Snake River Basin test application

  9. Strategy for Evaluating the Impact of MODIS • Conduct a sensitivity analysis to determine the importance of snow in the Snake River basin • Analysis based on discrepency between MODIS snowcover and VIC snowcover • Compare streamflow forecasts, with and without MODIS updating, beginning at various dates throughout the winter • Hypothesis: Updating will be more valuable for a streamflow forecast in early winter and spring when snow cover tends to change rapidly • Compare streamflow forecasts, with and without MODIS updating, to streamflow forecasts produced by the NRCS for a subset of basins within the Snake River Basin

  10. Objectives • To evaluate the impact of remote sensing data for improved estimates of initial snow conditions in streamflow forecasting • To evaluate the impact of streamflow forecast products for improved water resources operations

  11. Update Forecast Initial Conditions using Remote Sensing Update Forecast Initial Conditions with Current Reservoir Storages Modeling Approach to Evaluate Operations Observed Meteorological Time Series Precip, temp, wind, etc. Streamflow Forecasts Reservoir Forecasts Hydrology Model (VIC) Water Resources Operations Model (SnakeSim) Storage, reliability, spill, energy Streamflow at 21 locations MODIS: fraction of snowcover

  12. Sept 1, 1960 . . . Sept 1, 1961 . . . Sept 1, 1962 . . . Sept 1, 1963 . . . VIC Hydrologic Model Standardized Initial Conditions Observed Meteorological Time Series or Climate Model Output Ensemble Streamflow Prediction Ensembles Streamflow Ensemble

  13. Sample Streamflow Forecast Jackson Lake Inflows www.hydro.washington.edu/Lettenmaier/Projects/fcst/index.htm

  14. Jackson Lake 1997 WY 1992 WY 1977 WY

  15. Jackson Lake 1997 WY 1962 WY 1961 WY

  16. Linking Streamflow Forecasts to SnakeSim SnakeSim Water Resources Operations Model Ensemble Streamflow Forecast Bias Correction Demand Scenarios Initial Reservoir Contents from USACE or USBR Storage Ensemble

  17. SnakeSim Operations Model Overview • Developed by Nathan VanRheenen, UW • Stella modeling environment • Simulation for 1950 - 1992 • 21 Inflow Nodes, utilizing: • Historic naturalized flows • Routed flows from VIC model • 18 Reservoirs Modeled • 13.3 MAF Total Storage (16.4 BCM) • Simulation of Snake River Plain Aquifer • Historic Demand Scenarios

  18. SnakeSim Operations Model Assumptions • Current levels of operation adhering to IDWR, BOR, COE rules for reservoir storage and releases • Instream targets for fish, water quality, and hydropower production • Surface water diversions grouped by river reach • Groundwater response curves are linear and based on University of Idaho algorithms • 1980 groundwater pumping curves and irrigation areas

  19. Portion of Domain in Storage Forecast

  20. Active Reservoir Storage (kaf) Green = ensemble mean System Storage Forecast from SnakeSim: Jackson Lake Palisades Island Park Ririe American Falls Lake Walcott 11 ENSO neutral years Random historic demand scenarios Full Pool Full Pool

  21. Strategy for Evaluating the Use of Streamflow Forecasts in Water Management • Do similar comparisons as with streamflow forecasts • Sensitivity analysis to determine importance of snow • Compare operations, using streamflow forecasts with and without MODIS updated snow cover, beginning at various dates throughout the winter • Other

  22. Summary: Current Status • We produced first set of streamflow forecasts for Sept 1, 2003 for 21 locations in the Snake River basin • Snake River basin streamflow forecasts, updated every month, will now be available on the web as part of the UW S/I Hydrologic Forecast System (www.hydro.washington.edu/Lettenmaier/Projects/fcst/index.htm) • Acquisition of near real-time MODIS fractional snowcover for the Snake Basin is in progress • Testing of VIC 4.1 planned for Fall 2003 (utilizes fractional snowcover for each elevation band)

  23. Questions ?

  24. Bias Correction Objectives: Raw Bias Corrected Result: Bias corrected hydrologic simulations are quite consistent with observed streamflows in absolute value and climate change signals are translated without significant distortion.

  25. Quantile-Based Bias Correction (Wood et al. 2002) VIC Input = 19000 Bias Corrected Output = 10000

  26. Jackson Lake 1997 WY 1979 WY 1982 WY 1994 WY

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