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UW Experimental West-wide Seasonal Hydrologic Forecasting System Andy Wood and Dennis P. Lettenmaier Department of Civil and Environmental Engineering.

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Presentation on theme: "UW Experimental West-wide Seasonal Hydrologic Forecasting System Andy Wood and Dennis P. Lettenmaier Department of Civil and Environmental Engineering."— Presentation transcript:

1 UW Experimental West-wide Seasonal Hydrologic Forecasting System Andy Wood and Dennis P. Lettenmaier Department of Civil and Environmental Engineering for Workshop NWS/OHD January 27, 2005

2 Topics  forecasting system overview  climate forecasts  VIC model spin-up  index station approach  snotel assimilation  MODIS assimilation  selected results for winter 2003-04  final comments

3 Forecast System Overview

4 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 Forecast System Overview Soil Moisture Initial Condition Snowpack Initial Condition

6 Forecast System Overview sample validation of historic streamflow simulations

7 Forecast System Overview targeted statisticse.g., runoff volumes monthly hydrographs

8 Forecast System Overview CPC-based SWE (% average) forecasts JJASONDJFMAM

9 Forecast System Overview CPC-based soil moisture (anomaly) forecasts JJASONDJFMAM

10 Forecast System Overview CPC-based runoff (anomaly) forecasts JJASONDJFMAM

11 Topics  forecasting system overview  climate forecasts  VIC model spin-up  index station approach  snotel assimilation  MODIS assimilation  selected results for winter 2003-04  final comments

12 Climate Forecasts: Operational Products

13 Background: W. US Forecast System 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 Seasonal Climate Forecast Data Sources

14 Climate Forecasts: Scale Issues Seattle

15 Approach: Bias Example Sample GSM cell located over Ohio River basin obs prcp GSM prcp obs temp GSM temp JULY Regional Bias: spatial example obs GSM

16 Approach: Bias Correction Scheme from COOP observations from GSM climatological runsraw GSM forecast scenario bias-corrected forecast scenario month m

17 Climate Forecasts: forecast use challenges

18 Skill Assessment: Retrospective analysis tercile prediction skill of GSM ensemble forecast averages, JAN FCST local significance at a 0.05 level (based on a binomial model of success/failure, and assuming zero spatial and zero autocorellation, so that N=21 and p(success)=.33) masked for local significance

19 Background: CPC Seasonal Outlooks e.g., precipitation

20 Background: CPC Seasonal Outlook Use  spatial unit for raw forecasts is the Climate Division (102 for U.S.)  CDFs defined by 13 percentile values (0.025 - 0.975) for P and T are given

21 Background: CPC Seasonal Outlook Use probabilities => anomalies precipitation

22 Approach: CPC Seasonal Outlook Use climate division anomalies => model forcing ensembles we want to test (1) and (2):  testing (2) is easy, using CPC retrospective climate division dataset  testing (1) is more labor-intensive, less straightforward (2) “downscaling” (1) “Shaake Shuffle” CPC monthly climate division anomaly CDFs spatial / temporal disaggregation ensemble formation monthly climate division T & P ensembles daily 1/8 degree Prcp, Tmax and Tmin ensemble timeseries

23 Topics  forecasting system overview  climate forecasts  VIC model spin-up  index station approach  snotel assimilation  MODIS assimilation  selected results for winter 2003-04  final comments

24 VIC model spinup methods: originally, LDAS use

25 VIC model spinup methods: LDAS had problems in west

26 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 VIC model spinup methods: index stations 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.

27 VIC model spinup methods: index stations Example for daily precipitation Index stn pcppcp percentile gridded to 1/8 degree 1/8 degree dense station monthly pcp distribution (N years for each 1/8 degree grid cell) 1/8 degree pcp disagg. to daily using interpolated daily fractions from index stations monthly

28 VIC model spinup methods: 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

29 VIC model spinup methods: 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

30 VIC model spinup methods: SNOTEL assimilation April 25, 2004

31 VIC model spinup methods: snow cover (MODIS) assimilation (Snake R. trial) Snowcover BEFORE update Snowcover AFTER update MODIS update for April 1, 2004 Forecast snow added removed

32 Topics  forecasting system overview  climate forecasts  VIC model spin-up  index station approach  snotel assimilation  MODIS assimilation  selected results for winter 2003-04  final comments

33 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

34 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

35 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

36 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

37 Results for Winter 2003-04: initial conditions CPC estimates of seasonal precipitation and temperature very dryhot March Only

38 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

39 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

40 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

41 Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

42 Results for Winter 2003-04: streamflow hydrographs By Fall, slightly low flows were anticipated By winter, moderate deficits were forecasted

43 Results for Winter 2003-04: volume runoff forecasts 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

44 Results for Winter 2003-04: volume runoff 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

45 Results for Winter 2003-04: volume runoff 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

46 Results for Winter 2003-04 : volume forecasts for a sample of PNW locations

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52 Topics  forecasting system overview  climate forecasts  VIC model spin-up  index station approach  snotel assimilation  MODIS assimilation  selected results for winter 2003-04  final comments

53 Final Comments Ohio R. Basin / Corps of Engineers study, 1998  problems w/ climate model bias -> bias-correction approach  problems w/ real-time data availability -> retrospective study  problems w/ hydrology model calibration -> shrinking study domain  Corps operators interested, but busy, needed more proof starting point…

54 Final Comments west-wide expansion more forecast points more comprehensive outputs reorganized web-site more verification multi-model (land-surface in addition to climate) future plans…

55 Seasonal Hydrologic Forecast Uncertainty Single-IC ensemble forecast:  early in seasonal forecast season, climate ensemble spread is large  errors in forecast mainly due to climate forecast errors ensemble member ensemble mean OBS

56 Seasonal Hydrologic Forecast Uncertainty Single-IC ensemble forecast:  late in seasonal forecast season, climate ensemble is nearly deterministic  errors in forecast mainly due to IC errors ensemble member ensemble mean OBS

57 Seasonal Hydrologic Forecast Uncertainty Importance of uncertainty in ICs vs. climate vary with lead time … Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Forecast Uncertainty actual perfect data, model streamflow volume forecast period model + data uncertainty low high ICs low climate f’cast high ICs high climate f’cast low … hence importance of model & data errors also vary with lead time.

58 Expansion to multiple-model framework It should be possible to balance effort given to climate vs IC part of forecasts Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep N ensembles climate ensembles IC ensembles streamflow volume forecast period low high climate forecasts more important ICs more important

59 Expansion to multiple-model framework 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 Seasonal Climate Forecast Data Sources

60 Expansion to multiple-model framework 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 Multiple Hydrologic Models NWS HL-RMS others weightings calibrated via retrospective analysis

61 others Expansion to multiple-model framework ESP ENSO/PDO ENSO CPC Official Outlooks Seasonal Forecast Model (SFM) CAS OCN SMLR CCA CA NSIPP-1 dynamical model NOAA NASA UW Single Hydrologic Models, perturbed ICs VIC Hydrolog y Model perturbations calibrated via retrospective analysis

62 final comments by dennis

63 Approach: CPC Seasonal Outlook Use Downscaling Evaluation Question 1: Does hydrologic simulation driven by the downscaled forcings reproduce expected* streamflow mean and variability? *expected = simulated from 1/8 degree observed forcings (Maurer et al.) Spatial Disaggregation  transform CPC climate division retrospective timeseries (1960-99) into monthly anomaly timeseries (%P, delta T)  apply anomalies to 1/8 degree monthly P and T means (from UW COOP- based observed dataset of Maurer et al., 2001)  yields: 1/8 degree monthly P and T timeseries Temporal Disaggregation  daily weather generator creates daily P and T sequences for 1/8 degree grid  scale and shift sequences by month to reproduce monthly 1/8 degree P and T timeseries values

64 Results: CPC-based flow w.r.t. UW obs dataset Answer: YES, with help from bias-correction..........(but) mean std dev

65 Results: CPC-based flow w.r.t. UW obs dataset Additional examples show similar results Mean pretty well reproduced; variability improved mean std dev

66 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

67 Results: CPC temp/precip w.r.t. UW obs dataset based on 1960-99

68 Results: CPC temp/precip w.r.t. UW obs dataset based on 1960-99

69 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

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