Real-time seasonal hydrologic forecasting for the Western U.S.: Recent Progress Andrew Wood University of Washington JISAO weekly seminar Seattle, WAJune 10, 2003

Topics 1.Overview of Approach a)climate model based forecasts b)ESP (and ESP-composite) forecasts 2.Current Columbia River basin forecasts a)spin-up approach b)snow initialization 3.Retrospective West-wide forecast skill analysis 4.Next Steps

Overview: Background Previously, demonstrated an approach for combining seasonal climate model forecasts with hydrologic simulation to create hydrologic forecasts Climate Model: NCEP Global Spectral Model (GSM), for Hydrology Model: VIC (at 1/8 or 1/4 degree resolution) Real-time experimental applications: East Coast (Spring/Summer 2000, November 1997) (paper: Wood et al. (2001), JGR) Columbia R. basin (Spring/Summer 2001) Alternate applications: DOE Parallel Climate Model (PCM) climate change analyses for CRB, California and Colorado; as well as a downscaling method comparison (papers: Wood et al. (2003) and 3 others, Climatic Change) Currently, supported by several funding sources to implement seasonal forecast approach for the Western U.S., in real-time, “pseudo-operationally”. Support: NASA NSIPP IRI/ARCS NOAA GCIP/GAPP

Overview: Project Goals Implement the hydrologic forecasting approach over the western U.S. domain. Move from aperiodic experimental forecasts to quasi-operational forecast products. Calibrate streamflow forecast points throughout the domain, and identify potentially associated uses and users. Use the NCEP GSM retrospective ensemble climatology to assess streamflow forecast accuracy For routine implementation over the large domain, automate various processing steps through enhancements to existing software. Identify and evaluate potential real time data sources for use in hydrology model initialization (spin-up), a critical factor for forecast accuracy. Standardize ongoing retrospective efforts to verify recent forecasts and diagnose prediction accuracy and identify sources of error. Enhance our existing web site for disseminating forecasts and forecast retrospective evaluation results, and will forge links to interested operating agencies. Investigate approaches to making the forecast results available to these communities other than water management.

Overview: VIC Simulations Forecast Products streamflow soil moisture runoff snowpack derived products model spin-up forecast ensemble(s) climate forecast information climatology ensemble 1-2 years back start of month 0end of mon 6-12 NCDC met. station obs. up to 2-4 months from current LDAS/other real-time met. forcings for remaining spin-up data sources snow state information

Overview: Hydrologic Forecast Approach climate forecast ~2-3 degree resolution (T42-T62) monthly total P, avg T Use 2 step approach:1) statistical bias correction 2) downscaling  hydrologic model inputs  streamflow, soil moisture, snowpack, runoff, derived products 1/8-1/4 degree resolution daily P, Tmin, Tmax climate model ensemble outputs Extended (“Ensemble) Streamflow Prediction (ESP) 1/8-1/4 degree resolution daily P, Tmin, Tmax No bias-correction or downscaling needed Met. traces can be composited before/after ensemble is run to represent ENSO, PDO conditions, etc.

Overview: Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC forecast ensembles available near beginning of each month, extend 6 months beginning in following month each month: 210 ensemble members define GSM climatology for monthly Ptot & Tavg 20 ensemble members define GSM forecast

Overview: VIC Hydrologic Model

Overview: Bias Correction

Overview: climate model forecast processing sequence a) bias correction: climate model climatology  observed climatology b) spatial interpolation: e.g., GSM ( deg.)  VIC (1/8 deg) c) temporal disaggregation (via resampling of observed patterns): monthly  daily a. b. c. T GSM T OBS

Current Forecasts Updates Dec 28, 2002 Jan 15, 2003 Feb 1 Feb 15 Mar 1 Mar 16 Apr 1 ESP ESP, GSM ESP ESP, GSM ESP ESP, GSM

Current Forecasts: Spin-up approach Problem: For most recent months, meteorological data availability is poor 1-2 years before start date:dense station network (~1000), forcings consistent with those used in model calibration, etc. (mostly COOP stations) months prior to start:coarse reporting network (~150 stns) density lower in Canada. (some COOP stns) Solutions: > LDAS 1/8 degree real-time forcings from NOAA/NASA > Index station method: combine coarse network signals with dense station climatology

Current Forecasts: Spin-up approach, Index Stn Method 1. interpolate daily anomalies from index stations over domain 2. apply the result to daily averages taken from the dense station-derived climatology

Current Forecasts: Spin-up approach, Index Stn Method Example for daily precipitation Index stn pcppcp anomaly gridded to 1/8 degree 1/8 degree dense station daily average 1/8 degree pcp

Current Forecasts: Initial snow state assimilation Problem: index station method incurs some systematic errors, but snow state estimation is critical Solution: use SWE observations (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

Current Forecasts: Initial snow state assimilation Assimilation Method weight station obs.’ influence over VIC cell based on distance and elevation difference number of stations influencing a given cell depends on specified influence distances spatial weighting function elevation weighting function SNOTEL/ASP VIC cell distances “fit”: obs. weighting increased throughout season obs. anomalies applied to vic long term, combined with vic simulated SWE adjustment specific to each VIC snow bands

Current Forecasts: Initial Conditions Jan 15, 2003Dec 28, 2002 Feb 1, 2003 Mar 1, 2003Apr 1, 2003 This past winter, alarmingly low December snowpacks mostly recovered by April, although some locations are still well off their long term averages

Current forecasts: streamflow (ESP) Apr 1 Jan 1 Feb 1Mar 1

Current forecasts: streamflow (GSM) Apr 1 Feb 1 Mar 1 GSM forecasts were somewhat similar to the ENSO/PDO composite forecasts, relative to unconditional ESP: slightly greater spring flows, slightly drier summer … and yet…

Current Forecasts: GSM verifications GSM forecasts this winter were muddled. e.g., February forecast:

Current Forecasts: Statistics A limited set of statistics were calculated for the forecasts this spring, partly for comparison with official streamflow forecasts from NWS/NRCS. One example: Forecast flow anomaly (percent) at three percentiles, for 2003 APR-SEP average flow percentile # NAME MICAA REVEL ARROW DUNCA LIBBY CORRA HHORS COLFA KERRR WANET CHIEF PRIES DWORS ICEHA DALLE

Current forecasts: UW/NRCS comparison UW results to date are comparable to the official streamflow forecasts of the National Resources Conservation Service (NRCS) streamflow forecast group (one location shown). (the best estimate to date is given by the NRCS May 1 forecast) computer disk failure halted UW forecasts

Current Forecasts: Results ensemble median flow

Retrospective Assessment Objective: Quantify skill of GSM- based forecasts for western U.S. Used GSM climatology ensemble as forecasts Four start dates (JAN,APR,JUL,OCT) Five basins/sub-domains 20 streamflow locations Compared results to 2 baselines: climatology ensemble (CLIM) unconditional ESP ensemble Assessed significance of results with Monte Carlo experiments Results calculated for: monthly basin-wide averages of P, T, RO, SM, SWE Average flow in forecast months 1-3 and 1-6

Retrospective Assessment: GSM forecast and climatology ensembles 20 member forecast ensemble from 1979 SSTs from 1980 SSTs from 1981 SSTs from 1999 SSTs from current SSTs (21 sets)10 member climatology ensembles

Retrospective Assessment: Results General finding is that NCEP GSM climate forecasts do not add to skill of ESP forecasts, except… April GSM forecast with respect to climatology (left) and to ESP (right)

Retrospective Assessment: Results October GSM forecast w.r.t ESP: unconditional (left) and strong-ENSO (right) during strong ENSO events, for some river basins: California, Pacific Northwest RO forecasts improved with strong-ENSO composite; but Colorado River, upper Rio Grande River basin RO forecasts worsened.

Retrospective Assessment: Results Additional findings: P/T skill doesn’t guarantee RO skill RO forecast skill doesn’t automatically result in streamflow forecast skill for the statistics evaluated thus far (e.g., 3 and 6 month flow averages)

Next Steps  Add NSIPP forecasts (retrospectively) to climate model forecast set (and web site)  More complete evaluation of results and comparison with official NRCS/NWS products  Increasing contact with NRCS/NWS forecasting groups  Improved spin-up process  Development of hydrologic ensembles from official NCEP seasonal forecasts

Next Steps: Official forecast product  Consensus forecasts based on a number of tools: CANONICAL CORRELATION ANALYSIS COMPOSITE ANALYSIS OPTIMAL CLIMATE NORMALS METHOD CONSTRUCTED ANALOG ON SOIL MOISTURE SCREENING MULTIPLE LINEAR REGRESSION  Released as Probability of Exceedence (POE) curves/tables by Climate Division, separately for precipitation and temperature

Next Steps: Official forecast product  Consensus forecasts based on a number of tools: CANONICAL CORRELATION ANALYSIS COMPOSITE ANALYSIS OPTIMAL CLIMATE NORMALS METHOD CONSTRUCTED ANALOG ON SOIL MOISTURE SCREENING MULTIPLE LINEAR REGRESSION

Problem: how to create meteorological ensembles of [P,T]? Solution: “Shaake Shuffle” approach (Martyn Clark et al., JHM, submitted) Pull out P,T values that span forecast distribution, then associate them using randomly sampled rank structure from appropriate historical distributions Next Steps: Official forecast product A. Ranked ensemble output Ens # Stn 1Ens # Stn 2Ens # Stn 3 (5)7.5(2)6.3(9)12.4 (7)8.3(9)7.2(3)13.5 (3)8.8(4)7.5(4)14.2 (6)9.7(3)7.9(7)14.5 (10)10.1(7)8.6(2)15.6 (9)10.3(1)9.3(6)15.9 (2)11.2(6)11.8(10)16.3 (4)11.9(10)12.2(1)17.6 (8)12.5(5)13.5(5)18.3 (1)15.3(8)17.7(8)23.9 B. Randomly selected historical observations Ens # DateStn 1 Stn 2 Stn 3 18 th Jan th Jan th Jan nd Jan th Jan th Jan th Jan th Jan th Jan th Jan

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Bias: Developing a Correction 20 member forecast ensemble from 1979 SSTs from 1980 SSTs from 1981 SSTs from 1999 SSTs from current SSTs (21 sets)10 member climatology ensembles

Bias: Developing a Correction GSM Observed July Tavg, for 1 GSM cell 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. * for each month, each GSM grid cell and variable *

Bias-Correction: Spatial Perspective shown 1 month, 1 variable (T), 1 ens-member raw GSM outputbias-corrected

Bias: Spatial Perspective express as anomaly bias-corrected

Downscaling: step 1 is interpolation (bias corrected) anomalyanomaly at VIC scale

Downscaling: step 2 adds spatial VIC-scale variability to smooth anomaly field mean fields anomaly note: month m, m = 1-6 ens e, e = 1-20 VIC-scale monthly forecast

Lastly, temporal disaggregation… VIC-scale monthly forecast

Lastly, temporal disaggregation… VIC-scale monthly forecast

Downscaling Test 1.Start with GSM-scale monthly observed met data for 21 years 2.Downscale into a daily VIC-scale timeseries 3.Force hydrology model to produce streamflow 4.Is observed streamflow reproduced?

GSM climatology: use #2 sample: 21 member climatology ensemble from 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. (21 sets)

GSM climatology: use #2 sample: 21 member climatology ensemble from 1979 SSTs etc. from 1999 SSTs 10 member climatology ens. (21 sets) 20 member forecast ens.