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Andy Wood and Dennis P. Lettenmaier

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Presentation on theme: "Andy Wood and Dennis P. Lettenmaier"— Presentation transcript:

1 Experimental real-time seasonal hydrologic forecasting for the western U.S.
Andy Wood and Dennis P. Lettenmaier Department of Civil and Environmental Engineering for Joint Session 5, Conf. on Hydrology 85th AMS Annual Meeting San Diego January 10, 2005

2 OUTLINE experimental western U.S. forecasting system
comments on hydrologic forecast uncertainty expansion to multiple hydrologic model framework conclusions

3 Experimental W. US Hydrologic Forecast System
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 Experimental W. US Hydrologic Forecast System
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 Experimental W. US Hydrologic Forecast System
Snowpack Initial Condition Soil Moisture Initial Condition

6 Experimental W. US Hydrologic Forecast System
monthly hydrographs targeted statistics e.g., runoff volumes

7 Seasonal Hydrologic Forecast Uncertainty
Simulation error aside, hydrologic forecast error can be largely attributed to either: error in ICs OR error in climate forecasts We tried contrasting 2 ensemble forecasts (retrospectively): (1) a “perfect” IC + an ensemble of climate forecasts (essentially, ESP) (2) ensemble of ICs + a “perfect” climate forecast (“reverse ESP”) We looked at monthly forecast metric: Relative Error (RE) = error (fcst-ens) error (IC-ens) Colorado R. basin Columbia R. basin

8 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

9 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

10 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.

11 Expansion to multiple-model framework
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

12 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

13 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

14 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

15 Expansion to multiple-model framework
Our current research with multi-model simulations is promising: 4 land surface models used to simulate arctic basin hydrology, 100 km resolution following linear combination approach of Krishnamurti et al., (2000) weighting calibration based on simulation of snow-covered area results for streamflow and other hydrologic variables evaluated multi-model errors are lower than single model errors, in most cases (work by Ted Bohn at U. of Washington)

16 Expansion to multiple-model framework
annual discharge predictions

17 Resulting research focus
Conclusions Current ensemble hydrologic forecasting schemes probably deal better with future climate uncertainty than IC uncertainty Multi-IC ensembles are important, particularly where IC-related errors outweigh climate forecast errors experimental western US forecasting system being expanded to include multi-model and perturbed IC ensembles Resulting research focus For more information:

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