1. Hydrologic ensemble prediction - applications to streamflow and drought
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
And
University of Washington
American Geophysical Union Fall Meeting
San Francisco
December 19, 2008

2. Outline of this talk
Why ensemble prediction, and why multimodel approaches in general?
Some examples – western U.S. and ConUS hydrologic and drought nowcasting and forecasting
Can we really do better than the best model in terms of forecasting, and if not, is there a rationale for multimodel ensemble prediction?

3. UW West-wide forecast system – current domain and streamflow forecast points
~250 forecast points, including ~15 in Mexico
Forecast models/methods include CPC “official” forecasts, ESP, and stratified ESP
Forecasts for 6-12 month lead issued twice monthly (winter), monthly otherwise

4. UW West-wide forecast system soil moisture nowcast (12/14/08)
Daily updates, 24 hour lag effective ~2 pm Pacific
Based on ~2000 index stations, adjusted to long-term (1915 – present) climatology

5. Princeton University drought monitoring and prediction system
~weekly nowcast update, eastern U.S. domain
Uses NLDAS forcings
Focus on (soil moisture) drought nowcast and forecast
Forecasts based on Bayesian MME merging of GFS and ESP

6. UW West-wide forecast system – current domain and streamflow forecast points
~250 forecast points, including ~15 in Mexico
Forecast models/methods include CPC “official” forecasts, ESP, and stratified ESP
Forecasts for 6-12 month lead issued twice monthly (winter), monthly otherwise

7. UW National Surface Water Monitor
½ degree spatial resolution
Updates daily (same lag as west-wide system)
Same index station approach as west-wide system
Climatology 1915-present

8. UW Multi-model monitor
Same approach as VIC-based SWM
Models include VIC, Noah, CLM, Sac

9. Cumulative Probability, Cumulative Probability,
Multi-model Ensemble
Model i
soil moisture
For each model, re-express current soil moisture as percentile of climatology for this day of year
100
Model i
Cumulative Probability, 50
800
Soil Moisture (mm) %
Average all models’ percentiles
= 1/N Σ (i=1 to N) percentile i
Model i
percentile
100
Multi-Model
Cumulative Probability, 50
800
Soil Moisture (mm) %
Multi-Model
percentile
Multi-model ensemble result is the percentile of the average of model percentiles
This procedure occurs separately for each grid cell

10. Soil Moisture Percentiles w.r.t. 1920-2003
VIC
CLM
SAC
NOAH
ENSEMBLE
US Drought Monitor

11. July Comparison of ESP Drought Prediction with USDM and Nowcast
Initialized on June 28, 2008
1-month lead soil moisture percentile
VIC
1-month lead predicted 3 month accumulated runoff percentile
July 29, 2008
VIC based 1 month lead soil moisture and runoff percentiles prediction, initialized on June is compared with USDM portal as displayed on July 29 and Multi-model based actual nowcast made on the same date. At that time drought forecast used to be made only using VIC, which is why we are comparing VIC based forecast with the multi-model based nowcast ( which compares well USDM). Multi-model based drought forecast has been set up and should be operational very soon.
VIC
Multi-Model

12. August Comparison of ESP Drought Prediction with USDM and Nowcast
Initialized on June 28, 2008
2-month lead soil moisture percentile
VIC
August 29, 2008
2-month lead predicted 3 month accumulated runoff percentile
Yet again comparing VIC based 2 month lead soil moisture and runoff percentiles prediction, initialized on June with USDM portal as displayed on August 29 and Multi-model based actual nowcast made on the same date.
Multi-Model
VIC

13. Multi-model ensemble vs. bias correction
Multi-model ensembles have been used in many contexts to reduce prediction errors
Hydrologic forecasting poses some unique challenges:
Strong seasonal variations
Common model forcings
The role of bias

14. Models Basins Multi-model Pilot Study Salmon River Feather River
VIC
Physically-based soil layers
Energy balance
2-layer snow pack
NOAH
Single-layer snow pack
Sacramento/SNOW17 (SAC)
Conceptually-based soil storages
No energy balance
Degree-day snow melt scheme
No explicit vegetation
Potential Evapotranspiration computed by NOAH is an input
Basins
Salmon River
Feather River
Colorado River at Grand Junction
Focus on Colorado for now

15. Test 1: Retrospective simulation 1951-2005,
Performance over entire period
Four cases:
Raw model outputs, constant ensemble weights
Constant bias correction, constant ensemble weights
Raw model outputs, monthly ensemble weights
Monthly bias correction, monthly ensemble weights
Monthly Bias correction makes individual models competitive with ensemble 1 2 3 4 1 2 3 4

16. Examining the hydrographs: reduction of model independence
monthly bias correction removes differences in model timing of peak flows (e.g. circle “1”)
monthly bias correction removes systematic bias shared by all models (e.g. circle “2”) a b 2 2 1 1

17. Test 2: ESP “forecasts”, 1951-2005, monthly performance
Monthly bias correction yielded substantial improvements in CP, due to reduction of RMSE.
Little change in R.
Individual models with bias correction outperformed both (uncorrected) individual and multimodel ensemble.
Bias corrected multimodel ensemble yielded little or no improvement in CP or R in most forecast month/lead time combinations relative to best model.
Months for which error correlation among models was low (circles 1-3) corresponded to largest improvements in multimodel ensemble relative to individual models -- summer months (circle 4) is an exception.

18. Conclusions
Multimodel ensembles have been very popular with users, e.g. of SWM (NCEP/CPC has a similar initiative)
Improvements in hydrologic forecast accuracy are not demonstrable, at least in part because our models have common forcings, and forecasts tend to be highly correlated (especially after bias removal)
There nonetheless remains a rationale (e.g., robustness) for multimodel ensemble forecasting, even if the multimodel forecast is comparable to the best model