Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington
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Dennis P. Lettenmaier
Department of Civil and Environmental EngineeringUniversity of Washington
GEOSS Workshop XXXIII: Using Earth
Observations for Water Management
San Francisco
December 18, 2009
Advances in seasonal hydrologic prediction
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Talk Outline1) Background
2) The University of Washington west-wide seasonal hydrologic forecast system
3) Current and recent research -- assimilation of satellite data
4) Is there hydrologically useful skill in climate forecasts?
5) Concluding thoughts
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1. Background: The importance of Seasonal Hydrologic Forecasting
water management hydropower
irrigationflood controlwater supply
fisheriesrecreationnavigation
water quality
Aug Dec Apr
Res
ervo
ir S
tora
ge
Aug
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Sn
ow w
ater
con
ten
t on
Ap
ril 1
April to August runoff
McLean, D.A., 1948 Western Snow Conf.
SNOTEL Network
Application of statistical methods to seasonal hydrologic prediction in the western U.S.
PNW
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Overview: ESP Hydrologic prediction strategy
ESP data flow
The ESP “spider web”
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2. The University of Washington west-wide seasonal hydrologic forecast system
6-month ESP streamflow forecasts for western U.S. and Mexico effective 12/7/09
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UW Seasonal HydrologicForecast System Website
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Forecast System Initial State information
Soil MoistureSimulated Initial
Condition
SnowpackSimulated Initial Condition
Observed SWE
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Streamflow Forecast Details
Flow location maps give access to monthly hydrograph plots, and also to raw forecast data.
Clicking the stream flow forecast map also accesses current basin-averaged conditions
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Streamflow Forecast Results: Westwide at a Glance
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3a: Current and recent research: Snow data assimilation
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NCDC met. station obs. up to 2-4 months from current
local scale weather inputs
Initial Conditions: soil moisture,snowpack
Hydrologic model spin up
MODIS Update
Ensemble Forecast:streamflow, soil moisture, snowpack, runoff
25th Day of Month 01-2 years back
LDAS/other real-time met.
forcings for remaining
spin-up
Hydrologic simulation
End of Month 6 - 12
MODIS updating of snow covered area
Snowcover before MODIS update Snowcover after MODIS update
Change in Snowcover as a Result of MODIS Update for April 1, 2004 Forecast
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Unadjusted vs adjusted forecast errors, 2001-2003, for reservoir inflow volumes (left plot) and
reservoir storage (right)
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4: Is there hydrologically relevant skill in climate forcings
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Wood et al 2005: Retrospective Assessment: Results using GSM
General finding is that NCEP GSM climate forecasts do not add to skill of ESP forecasts, except…
April GSM forecast with respect to climatology (left) and to ESP (right)
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Wood et al 2005: Retrospective results for ENSO years
October GSM forecast w.r.t ESP: unconditional (left) and strong-ENSO (right)
Summary: During strong ENSO events, for some river basins (California, Pacific Northwest) runoff forecasts improved with strong-ENSO composite; but Colorado River, upper Rio Grande River basin RO forecasts worsened.
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Reverse ESP vs ESP – typical results for the western U.S.
Columbia R. Basin
Rio Grande R. Basin
ICs more impt
fcst more impt
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DEMETER forecast evaluation
• VIC model long-term (1960-99) simulations at ½ degree spatial resolution assumed to be truth
• DEMETER reforecasts with ECMWF seasonal forecast model for 6 month lead, forecasts made on Feb 1, May 1, Aug 1, Nov 1 1960-99
• 9 forecast ensembles on each date
• Forecast forcings (precipitation and temperature) downscaled and bias corrected using Wood et al approach (also incorporated in UW West-wide system)
• On each forecast date, 9 ensemble members also resampled at random from 1960-99 to form ESP ensemble
• Forecast skill evaluated using Cp for unrouted runoff
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Test sites
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Missouri River at Fort Benton
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Snake River at Milne
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Concluding thoughts
• Hydrologic prediction skill at S/I lead times comes mostly from initial conditions.
• Hence more focus on data assimilation, and its implications for hydrologic forecast skill, needs more attention.
• The role of model error in hydrologic predictions needs more focus – how do we best weight land models in multimodel ensemble?
• Do hydrologists (and the land data assimilation community) need to expend more effort on hydrologic forecasting?
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Streamflow forecast skill, observed streamflow simulated (left panel) and forecasted (right two) using model soil moisture and SWE; MAMJ streamflow conditioned on January 1 model conditions
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7) Multimodel approaches
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UW Multi-model monitor
•Same approach as VIC-based SWM
•Models include VIC, Noah, CLM, Sac
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The challenge: Different land schemes have different soil moisture dynamics
Model simulated soil moisture at cell(40.25N, 112.25W)
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Areas for spatially averaged soil moisture percentiles
Box sizes are 5 x 5 degrees
NW
NENE
SWSWSE
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NW
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SE
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Soil Moisture Percentiles w.r.t. 1920-20032008-07-01
CLM
SAC NOAH
ENSEMBLE
VIC
US Drought Monitor
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Summary• West-wide forecast system and SW Monitor are
templates for exploration of new forecasting methods
• Methods perform well in the U.S., where surface obs are relatively abundant.
• However, ongoing work illustrates the potential for using similar methods in areas where in situ obs are sparse, using e.g. remotely sensed precipitation, and/or weather prediction model analysis fields.
• New remote sensing data sources (e.g. SWOT) offer tremendous opportunities for extension of these methods to the underdeveloped world.