Andy Wood, Alan Hamlet and Dennis P. Lettenmaier University of Washington

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Mokelumne River Pardee & Camanche Delta Outflow Delta Calaveras River Shasta Trinity Whiskeytown Trinity River New Hogan Clear Creek Hydrologic Forecasting Simulations Stanislaus River San Joaquin River San Luis Oroville (SWP) Feb 1 Feather River New Melones Sacramento River Dam Power Plant River Transfer Dam Power Plant River/Canal Transfer Tuolumne & Merced Rivers American River Folsom Eastman, Hensley, & Millerton Delta New Don Pedro & McClure January Forecast start of month 0 end of mon 6-12 1-2 years back forecast ensemble(s) model spin-up climatology ensemble NCDC met. station obs. up to 2-4 months from current LDAS/other real-time met. forcings for remaining spin-up climate forecast information data sources Forecast Products streamflow soil moisture runoff snowpack derived products snow state information April Forecast Downscaling Climate Model Output Step 1: Statistical Bias Correction At the climate model scale (1.8-2.5 deg), use a quantile to quantile mapping from climate model climatology to observed historical climatology, for precipitation & temperature separately, e.g., July Forecast Initial Snow Water Equivalent Feb 1, 2003 Mar 1, 2003 Apr 1, 2003 Dec 28, 2002 Jan 15, 2003 Step 2: Downscaling • climate model spatial scale  VIC (1/8-1/4 deg) • simple inverse distance interpolation of precip & temp anomalies October Forecast • climate model temporal scale (monthly)  VIC (daily) • conditional resampling of historic record • imposition of daily signal for precip & temp (same month) • rescaling of precip & shift of Tmin, Tmax according to forecast anomaly • Method described in Wood et al. (2002) Columbia River Sacramento River Simulated System Storage (acre-ft) Simulated System Storage (acre-ft) Colorado River San Joaquin River computer disk failure halted UW forecasts A west-wide seasonal to interannual hydrologic forecast system Andy Wood, Alan Hamlet and Dennis P. Lettenmaier University of Washington • A retrospective forecast skill analysis for the NCEP seasonal forecasts over the entire western U.S. domain was undertaken to ascertain the value of the climate model forecasts, relative to the ESP forecast and climatological forecasts baselines. In general, the GSM retrospective forecasts did not improve upon the skill of the ESP streamflow forecasts; however, in years when strong ENSO anomalies were present in the forecast initiation month, the GSM-based forecasts yielded skill increases in California and the Columbia River basin, but lower forecast skill (relative to ESP) in the Colorado and the upper Rio Grande River basins. • We have implemented the Variable Infiltration Capacity (VIC) macroscale hydrology model over the western U.S. at 1/8 degree spatial resolution for experimental ensemble hydrologic prediction at lead times up to six months. • We have implemented the Variable Infiltration Capacity (VIC) macroscale Climate forecast ensembles are presently taken from the NCEP Global Spectral Model (GSM) and the NASA NSIPP model, and will eventually be expanded to incorporate more models in a multi-model ensemble. • As a benchmark, we also use the VIC model to produce parallel forecasts via the well-known Extended Streamflow Prediction (ESP) method. The ESP forecasts are further composited to provide ENSO and PDO conditioned ensembles, which past work has shown can considerably reduce seasonal forecast error variance. Retrospective skill assessment for forecasts of basin averages of hydrologic and climate variables NOTE: Skill Score = 1 - RMSE(GSM) / RMSE(baseline) where baseline is either: CLIM = unconditional climatology ESP = ESP-derived forecast Shown below are the skill scores for GSM-based forecasts over 1979-1999, relative to two forecast baselines (CLIM and ESP), for all years (top 2 sets) and for a strong ENSO composite (abs(Nino 3.4) > 1). Strong ENSO Composite: GSM wrt ESP GSM wrt CLIM GSM wrt ESP January Forecast Models NCEP GSM forecasts VIC Hydrologic Model (Liang et al., 1994) • T62 (~1.9 degree) resolution • 6 month forecast duration • each month, ensemble product has: • 20 forecast members • 210 rolling climatology members (derived from 10 initial atmos. condition perturbations for each year of a 21 year climatology period) • we use monthly total precip & average temperature NSIPP forecasts October Forecast • 2 x 2.5 degree (lat x lon) resolution • 7 month forecast duration • 9-member forecast ensembles • fixed 50 year climatology based on 9 continous AMIP runs *numbered locations were used for retrospective streamflow forecasting analysis (results not shown) (April and July forecasts not shown; also, streamflow forecasts not shown) • Our initial model domain is the Pacific Northwest. Initial testing in real-time began with bi-monthly updates starting at the end of December, 2002, and ran through April 2003. • Upgrades to the modeling system during the test period included: a) the development of a simple method for assimilating snow water equivalent observations at the start of the forecast, and b) a modification of the surface forcing estimation immediately prior to the forecast start using a set of real-time index stations in lieu of the Land Data Assimilation System (LDAS) real-time forcings. • We also describe the development of a set of reservoir system models for the western U.S., and their implementation within the system to produce ensemble forecasts of reservoir system storages, operations and releases. Real-time Hydrologic Forecasting for Columbia River Basin in Winter 2003 Forecast Approach upgrades Reservoir system forecasts Use of real-time SWE observations (right) (from the 600+ station USDA/NRCS SNOTEL network and several ASP stations in BC, Canada, run by Environment Canada) to adjust snow state at the forecast start date (left) (spin-up met. data improvements method not shown) 6-Month Ensemble Forecasts of System Storage for the Columbia River Basin Using VIC Streamflow Forecasts and the ColSim Reservoir Model Initialized by Observed Reservoir Elevations (~ Feb 1, 2001) Initial hydrologic condition estimates Streamflow hydrograph forecasts (example from February 1) Ongoing Work blue/red are storage boundaries green is ensemble mean thick red is historical average black: init. cond. with normal climate • implementing remainder of western U.S. domain with real-time forecasts to recommence in Sept. • working on alternative spin-up meteorology approaches • expanding products to include spatial fields (snow, soil moisture), wider reservoir system coverage • improving web site (http://www.ce.washington.edu/pub/HYDRO/aww/w_fcst/w_fcst.htm) • developing a downscaling approach for official forecasts from NCEP and other centers • pursuing linkages to NRCS and NWS streamflow forecasting operations groups Streamflow volume forecast comparison with NRCS official forecasts References Wood, A.W., E.P. Maurer, A. Kumar and D.P. Lettenmaier, 2002. Long Range Experimental Hydrologic Forecasting for the Eastern U.S., J. Geophys. Res., 107(D20). Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428, 1994.