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Seasonal outlooks for hydrology and streamflow in the western U.S. Andy Wood, Alan Hamlet and Dennis P. Lettenmaier Department of Civil and Environmental.

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Presentation on theme: "Seasonal outlooks for hydrology and streamflow in the western U.S. Andy Wood, Alan Hamlet and Dennis P. Lettenmaier Department of Civil and Environmental."— Presentation transcript:

1 Seasonal outlooks for hydrology and streamflow in the western U.S. Andy Wood, Alan Hamlet and Dennis P. Lettenmaier Department of Civil and Environmental Engineering for UW Climate Impacts Group Water Workshop Portland, OR September 22, 2004

2 Topics  Background on forecasting system  Selected Results for winter 2003-04  Current forecasts  Final Comments

3 Background

4 Background: Forecast System Schematic NCDC met. station obs. up to 2-4 months from current local scale (1/8 degree) weather inputs soil moisture snowpack Hydrologic model spin up SNOTEL Update streamflow, soil moisture, snow water equivalent, runoff 25 th Day, Month 0 1-2 years back LDAS/other real-time met. forcings for spin-up gap Hydrologic forecast simulation Month 6 - 12 INITIAL STATE SNOTEL / MODIS* Update ensemble forecasts ESP traces (40) CPC-based outlook (13) NCEP GSM ensemble (20) NSIPP-1 ensemble (9) * experimental, not yet in real-time product

5 Seasonal Climate Forecast Data Sources ESP ENSO/PDO ENSO CPC Official Outlooks Seasonal Forecast Model (SFM) CAS OCN SMLR CCA CA NSIPP-1 dynamical model VIC Hydrolog y Model NOAA NASA UW

6 CPC outlooks: probabilities => anomalies => ensembles precipitation

7 CPC outlooks: probabilities => anomalies => ensembles temperature

8 Background: Hydrology Model

9 Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate and run model in most of spin-up period Background : Estimating Initial Conditions estimating spin-up period inputs dense station network for model calibration sparse station network in real-time Solution: use interpolated monthly index station precip. percentiles and temperature anomalies to extract values from higher quality retrospective forcing data -- then disaggregate using daily index station signal.

10 Background : Estimating Initial Conditions SNOTEL assimilation Problem sparse station spin-up period incurs some systematic errors, but snow state estimation is critical Solution use SWE anomaly observations (from the 600+ station USDA/NRCS SNOTEL network and a dozen ASP stations in BC, Canada) to adjust snow state at the forecast start date

11 Background: Estimating Initial Conditions SNOTEL 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 means, combined with VIC-simulated SWE adjustment specific to each VIC snow band

12 Background: Estimating Initial Conditions SWE state adjustment (using SNOTEL/ASP obs) April 25, 2004

13 Background : Streamflow Forecast Locations in development: Colorado R., Upper Rio Grande Columbia R. basin California Snake R. basin

14 Topics  Background on forecasting system  Selected Results for winter 2003-04  Current forecasts  Final Comments

15 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

16 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

17 Initial Conditions for Winter 2003-04 CPC estimates of seasonal precipitation and temperature normal to wet near normal temperatures Dec-Jan-Feb

18 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

19 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

20 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

21 Initial Conditions for Winter 2003-04 CPC estimates of seasonal precipitation and temperature normal to drygenerally warm Mar-Apr-May

22 Initial Conditions for Winter 2003-04 CPC estimates of seasonal precipitation and temperature very dryhot March Only

23 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

24 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

25 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

26 Initial Conditions for Winter 2003-04 CPC estimates of seasonal precipitation and temperature normal to wetslightly warm Jun-Jul-Aug

27 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

28 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

29 Initial Conditions for Winter 2003-04 Soil Moisture and Snow Water Equivalent (SWE)

30 Winter 2003-04: PNW streamflow By Fall, slightly low flows were anticipated By winter, moderate deficits were forecasted

31 Aside: volume forecast format UPPER HUMBOLDT RIVER BASIN Streamflow Forecasts - May 1, 2003 Forecast Pt============ Chance of Exceeding * =========== Forecast90%70%50% (Most Prob)30%10%30 Yr Avg Period(1000AF) (% AVG.)(1000AF) MARY'S R nr Deeth, Nv APR-JUL12.3 18.7 23 59 27 34 39 MAY-JUL4.5 11.3 16.0 55 21 28 29 LAMOILLE CK nr Lamoille, Nv APR-JUL13.7 17.4 20 67 23 26 30 MAY-JUL11.6 15.4 18.0 64 21 24 28 N F HUMBOLDT R at Devils Gate APR-JUL5.1 11.0 15.0 44 19.0 25 34 MAY-JUL1.7 7.2 11.0 50 14.8 20 22

32 Aside: what is a “normal” year in “most-probable % of average” terms? PNW CALI medianmean medianmean runoff histogram

33 Winter 2003-04: seasonal volume forecasts Comparison with RFC forecast for Columbia River at the Dalles, OR UW forecasts made on 25 th of each month RFC forecasts made several times monthly: 1 st, mid-month, late (UW’s ESP unconditional and CPC forecasts shown) UW RFC

34 Winter 2003-04: seasonal volume forecasts Comparison with RFC forecast for Sacramento River near Redding, CA UW forecasts made on 25 th of each month RFC forecasts made on 1 st of month (UW’s ESP unconditional forecasts shown) UW RFC

35 Last winter: volume forecasts for a sample of PNW locations

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45 Topics  Background on forecasting system  Selected Results for winter 2003-04  Current forecasts  Final Comments

46 December Winter Climate Forecasts Dominate Hydrologic State Variables Dominate JuneMarch Range =16.7% of ensemble summer mean April 1 SWE (mm)

47 CPC-based climate forecasts

48 CPC-based SWE (% average) forecasts JJASONDJFMAM

49 CPC-based soil moisture (anomaly) forecasts JJASONDJFMAM

50 CPC-based runoff (anomaly) forecasts JJASONDJFMAM

51 PNW monthly flow forecasts

52

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54 Current volume forecast summary CanadaWA-OR-IDCalifornia

55 Topics  Background on forecasting system  Selected Results for winter 2003-04  Current forecasts  Final Comments

56 For more information: www.hydro.washington.edu/Lettenmaier/Projects/fcst/ Final Comments  2005 will resemble 2004, but flow deficits will be smaller  forecast system is for research, not operations  strengths of western U.S. forecast system may lie at boundaries of current forecasting approach strengths  longer lead times  use of non-traditional inputs  visualization  disaggregation  diagnosis  apparent skill cannot be determined from one season alone

57 misc slides

58 Framework: Estimating Initial Conditions snow cover (MODIS) assimilation (Snake R. trial) Snowcover BEFORE update Snowcover AFTER update MODIS update for April 1, 2004 Forecast snow added removed

59 Framework: Downscaling Climate Model output NCEP GSM and NSIPP-1

60 Framework: Bias-correcting Climate Model output  numerous methods of downscaling and/or bias correction exist  the relatively simple one we’ve settled on requires a sufficient retrospective climate model climatology, e.g.,  NCEP: hindcast ensemble climatology, 21 years X 10 member  NSIPP-1: AMIP run climatology, > 50 years, 9 member specific to calendar month and climate model grid cell

61 Framework: Downscaling CPC outlooks  spatial unit for raw forecasts is the Climate Division (102 for U.S.)  13 percentile values (from 0.025 to 0.975) for P and T are given

62 Framework: Downscaling CPC outlooks downscaling uses Shaake Shuffle (Clark et al., J. of Hydrometeorology, Feb. 2004) to assemble monthly forecast timeseries from CPC percentile values

63 Results: Initial Conditions for Current Winter Soil Moisture and Snow Water Equivalent (SWE)

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