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UW Westwide experimental hydrologic forecast system

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Presentation on theme: "UW Westwide experimental hydrologic forecast system"— Presentation transcript:

1 UW Westwide experimental hydrologic forecast system
Andy Wood and Dennis Lettenmaier Department of Civil and Environmental Engineering UW-NWS meeting on potential UW-RFC collaboration NOAA Pacific Marine Environmental Laboratory Seattle May 13, 2005

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

3 Forecast System Overview
Objective: To create a model-based testbed for evaluating potential sources of improvement in seasonal forecasts since inception of regression/ESP methods operational seasonal climate forecasts (model-based and otherwise) greater real-time availability of station data computing advances new satellite-based products (primarily snow cover) distributed, physical hydrologic modeling for macroscale regions

4 Forecast System Overview
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 25th Day, Month 0 1-2 years back LDAS/other real-time met. forcings for spin-up gap Hydrologic forecast simulation Month 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
Snowpack Initial Condition Soil Moisture Initial Condition

6 Forecast System Overview
sample validation of historic streamflow simulations

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

8 Forecast System Overview CPC-based SWE (% average) forecasts
SON DJF MAM JJA

9 Forecast System Overview CPC-based soil moisture (anomaly) forecasts
SON DJF MAM JJA

10 Forecast System Overview CPC-based runoff (anomaly) forecasts
SON DJF MAM JJA

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

12 Climate Forecasts: Operational Products

13 Background: W. US Forecast System
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 Hydrology Model NOAA NASA UW

14 Climate Forecasts: Scale Issues
Seattle

15 Approach: Bias Example
obs prcp GSM prcp obs temp GSM temp JULY Regional Bias: spatial example Sample GSM cell located over Ohio River basin important point(s): the first hurdle in making forecasts is to overcome the regional biases. this and the next slide show the bias -- first for one GSM cell, where you can see plotted the observed forcings vs. the GSM raw climatology forcings (for the climatology period ‘79-’99). biases are so large in temperature that a hydrologic model would be blown out of the water. this is from one set (May) of climatology & forecast ensembles back on the East Coast obs GSM

16 Approach: Bias Correction Scheme
from COOP observations from GSM climatological runs raw 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 ( ) for P and T are given

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

22 (1) (2) “Shaake Shuffle” “downscaling” we want to test (1) and (2):
Approach: CPC Seasonal Outlook Use climate division anomalies => model forcing ensembles (1) “Shaake Shuffle” (2) 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 “downscaling” 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

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

24 VIC model spinup methods: index stations estimating spin-up period inputs
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 sparse station network in real-time dense station network for model calibration 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.

25 VIC model spinup methods: index stations
Example for daily precipitation monthly gridded to 1/8 degree Index stn pcp pcp percentile 1/8 degree pcp disagg. to daily using interpolated daily fractions from index stations important point(s): the approach attempts to make use of forecast skill from 2 sources: better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models; the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling). Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management. 1/8 degree dense station monthly pcp distribution (N years for each 1/8 degree grid cell)

26 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

27 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 important point(s): the approach attempts to make use of forecast skill from 2 sources: better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models; the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling). Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

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

29 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

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

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

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

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
CPC estimates of seasonal precipitation and temperature March Only very dry hot

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

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

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: streamflow hydrographs
By Fall, slightly low flows were anticipated By winter, moderate deficits were forecasted

41 Results for Winter 2003-04: volume runoff forecasts
UPPER HUMBOLDT RIVER BASIN Streamflow Forecasts - May 1, 2003 <==== Drier === Future Conditions === Wetter ====> Forecast Pt ============ Chance of Exceeding * ===========    Forecast 90% 70% 50% (Most Prob) 30% 10% 30 Yr Avg    Period (1000AF) (% AVG.) MARY'S R nr Deeth, Nv APR-JUL 12.3       18.7       23       59       27       34       39       MAY-JUL 4.5       11.3       16.0       55       21       28       29       LAMOILLE CK nr Lamoille, Nv 13.7       17.4       20       67       26       30       11.6       15.4       18.0       64       24       N F HUMBOLDT R at Devils Gate 5.1       11.0       15.0       44       19.0       25       1.7       7.2       50       14.8       22      

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

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

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

45 Final Comments starting point…
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

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

47 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 mean OBS

48 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 mean OBS

49 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 Uncertainty Forecast 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 ESP addresses climate uncertainty, but the single model/calibration framework doesn’t address IC uncertainty -- ignoring calibration issues at moment – assuming reasonably well calibrated models can be further adjusted via bias-correction -- don’t forget issues to do w/ estimating inputs as an ensemble … hence importance of model & data errors also vary with lead time.

50 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 streamflow volume forecast period low high climate forecasts more important ICs more important ESP addresses climate uncertainty, but the single model/calibration framework doesn’t address IC uncertainty -- ignoring calibration issues at moment – assuming reasonably well calibrated models can be further adjusted via bias-correction -- don’t forget issues to do w/ estimating inputs as an ensemble

51 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 Hydrology Model NOAA NASA UW Seasonal Climate Forecast Data Sources

52 Expansion to multiple-model framework
Multiple Hydrologic Models CCA NOAA CAS OCN CPC Official Outlooks NWS HL-RMS SMLR CA Seasonal Forecast Model (SFM) VIC Hydrology Model NASA NSIPP-1 dynamical model others ESP weightings calibrated via retrospective analysis ENSO UW ENSO/PDO

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

54 misc

55 Approach: CPC Seasonal Outlook Use Downscaling Evaluation
Spatial Disaggregation transform CPC climate division retrospective timeseries ( ) 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 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.)

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

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

58 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

59 Results: CPC temp/precip w.r.t. UW obs dataset
based on

60 Results: CPC temp/precip w.r.t. UW obs dataset
based on

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