experimental seasonal hydrologic forecasting for the western u.s
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Experimental seasonal hydrologic forecasting for the Western U.S. Dennis P. Lettenmaier Andrew W. Wood, Alan F. Hamlet Climate Impacts Group University of Washington Climate Prediction Applications Workshop Tallahassee, FL March 10, 2004. Project Domain / Website. - PowerPoint PPT PresentationTRANSCRIPT
Experimental seasonal hydrologic forecasting for the Western U.S.
Dennis P. LettenmaierAndrew W. Wood,
Alan F. Hamlet
Climate Impacts GroupUniversity of Washington
Climate Prediction Applications WorkshopTallahassee, FL
March 10, 2004
Project Domain / Website
Forecasting System Evolution
(Funding from: NASA NSIPP; OGP/ARCS; OGP GAPP), NASA Applications)
1998-9
2000
2001
2003
2004
Ohio R. basin w/ COE: First tried climate model-based seasonal forecasts on experimental (retrospective) basis
Eastern US: First attempted real-time seasonal forecasts during drought condition in southeastern states -- results published in: Wood et al. (2001), JGR
Columbia R. basin: Implemented approach during the PNW drought, again using climate model based approach
2002 Western US: Retrospective analysis of forecasts over larger domain (for one climate model and for ESP)
Columbia R. basin: New funding for “pseudo-operational” implementation for western US; began with pilot project in CRB
Western US: expanded to western U.S domain for real-time forecasts; working to improve and evaluate methods each forecast cycle
1. Downscaling2. VIC hydrologic
simulations
UW Experimental West-wide hydrologic prediction system
ESP as baseline fcst
Real-timeEnsemble Forecasts
Ensemble Hindcasts(for bias-correction
and preliminaryskill assessment) West-wide forecast products
streamflowsoil moisture, snowpack
tailored to application sectorsfire, power, recreation
* ESPextended streamflow prediction(unconditional climate forecastsrun from current hydrologic state)
climate model output(NCEP, NSIPP)
CPC official forecasts (in progress)
Challenge: Climate Model Forecast Use
bias-correcting…
then downscaling…
CRB domain,June precip
Overview: Bias Correction
specific to calendar monthand climate model grid cell
numerous methods of downscaling and bias correction exist
the relatively simple one we’ve settled on requires a sufficient retrospective climate model climatology, e.g., NCEP: hindcast ensemble climatology, 21 years X 10 member NSIPP-1: AMIP run climatology, > 50 years, 9 member
Overview: VIC Hydrologic Model
Overview: Hydrologic Simulations
Forecast Productsstreamflow soil moisture
runoffsnowpack
derived productse.g., reservoir system
forecasts
model spin-upforecast ensemble(s)
climate forecast
information
climatology ensemble
1-2 years back start of month 0 end of mon 6-12
NCDC met. station obs. up to 2-4
months from current
2000-3000 stations in western US
LDAS/other real-time met. forcings for remaining
spin-up~300-400
stations in western US
data sources
obs snow state information
initi
al
cond
ition
s
Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate model
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.
Challenge: Hydrologic State Initialization
dense station network for model calibration sparse station network in real-time
Overview: Initial snow state assimilation
Problem sparse station spinup 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
Overview: Initial snow state assimilation
SWE state differences due to assimilation of SNOTEL/ASP observations, Feb. 25, 2004
Overview: Initial conditionsSnow Water Equivalent (SWE) and Soil Moisture
Overview: Streamflow Forecast Locations
Overview: Spatial Forecastsmonthly values, anomalies and percentiles of: precip, temp, SWE, soil moisture, runoffgive streamflow forecasts greater context
SWE soil moisture
Overview: Streamflow Forecastshydrographs targeted statistics
raw ensemble data
Challenge: Balancing IC effort with forecast effort
Columbia R. Basin (CRB)
Rio Grande R. Basin (RGB)
RMSE (perfect IC, uncertain fcst)
RMSE (perfect fcst, uncertain IC)RE =
www.hydro.washington.edu/Lettenmaier/Projects/fcst/
Some obstacles and opportunities in hydrological application of
climate information• The “one model” problem
• Calibration and basin scale (post-processing as an alternative to calibration)
• The value of visualization
• Opportunities to utilize non-traditional data (e.g. remote sensing)