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

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?

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

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

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

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

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

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

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, 1916-2004 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, 1916-2004 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

Soil Moisture Percentiles w.r.t. 1920-2003 2008-07-01 VIC CLM SAC NOAH ENSEMBLE US Drought Monitor

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 28-2008 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

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 28-2008 with USDM portal as displayed on August 29 and Multi-model based actual nowcast made on the same date. Multi-Model VIC

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

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

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

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

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.

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