Transcript CUAHSI Hydrologic Information System and its role in

Real-time experimental seasonal
hydrologic forecasting for the western U.S.
A. Wood, A.F. Hamlet, M. McGuire, S. Babu and Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
for
Session H22B
Adv. Methods for Probabilistic Hydrometeorologic Forecasting II
2004 Joint Assembly: CGU, AGU, SEG and EEGS
Montreal, Canada
May 18
Outline of this talk
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Introduction – research rationale
Framework and method of implementation
Selected Results for current winter
Conclusions and unsolved problems
Introduction: Research Rationale
Are current seasonal hydrologic forecasts all that they can be?
How can ongoing research on land-atmosphere interactions
help to improve seasonal streamflow forecasts in the western U.S.?
Potential sources of improvement since inception of
regression/ESP methods:
• operational seasonal climate forecasts (model-based and
otherwise)
• greater availability of station data
• computing
• new satellite-based products (primarily snow cover)
• distributed, physical hydrologic modeling for macroscale
regions
Framework: Forecast System Schematic
local scale (1/8 degree)
weather inputs
Hydrologic
model spin up
NCDC met.
station obs.
up to 2-4
months from
current
1-2 years back
LDAS/other
real-time
met. forcings
for spin-up
gap
soil moisture
snowpack
INITIAL
STATE
streamflow, soil moisture,
snow water equivalent, runoff
Hydrologic forecast
simulation
ensemble forecasts
SNOTEL
SNOTEL
/ MODIS*
Update
Update
25th Day, Month 0
ESP traces (40)
CPC-based outlook (13)
NCEP GSM ensemble (20)
NSIPP-1 ensemble (9)
Month 6 - 12
* experimental, not yet in real-time product
Framework: Hydrology Model
Framework: Estimating Initial Conditions
estimating spin-up period inputs
Problem: met. data availability in 3 months prior to forecast has only a tenth of
long term stations used to calibrate and run model in most of spin-up period
sparse station network in real-time
dense station network for model calibration
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.
Framework: Estimating Initial Conditions
SNOTEL assimilation
Problem
sparse station spin-up period 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
Framework: Estimating Initial Conditions
SNOTEL assimilation
Assimilation Method
• weight station OBS’ influence over VIC cell based on distance and
elevation difference
• number of stations influencing a given cell depends on specified
influence distances
• distances “fit”: OBS
weighting increased
throughout season
• OBS anomalies applied to
VIC long term means,
combined with VIC-simulated
SWE
• adjustment specific to each
VIC snow band
spatial weighting function
elevation
weighting
function
SNOTEL/ASP
VIC cell
Framework: Estimating Initial Conditions
SWE state adjustment (using SNOTEL/ASP obs)
April 25, 2004
Framework: Estimating Initial Conditions
snow cover (MODIS) assimilation (Snake R. trial)
Snowcover
BEFORE
update
Snowcover
AFTER
update
MODIS update for April 1, 2004 Forecast
snow
added
removed
Framework: Downscaling Climate Model output
NCEP GSM and NSIPP-1
Framework: Bias-correcting Climate Model output
 numerous methods of downscaling and/or 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
specific to calendar month
and climate model grid cell
Framework: Downscaling CPC outlooks
 spatial unit for raw forecasts is the Climate Division (102 for U.S.)
 13 percentile values (from 0.025 to 0.975) for P and T are given
Framework: Downscaling CPC outlooks
downscaling uses Shaake Shuffle (Clark et al., J. of Hydrometeorology, Feb.
2004) to assemble monthly forecast timeseries from CPC percentile values
Framework: Streamflow Forecast Locations
California
Columbia R. basin
in development:
Colorado R.,
upper Rio Grande
Snake R.
basin
Framework:
Streamflow
Forecast
Products
monthly
hydrographs
targeted statistics
e.g., volume runoff
forecast anomalies
raw ensemble data
Framework: Example of Spatial Forecasts
Results: Initial Conditions for Current Winter
Soil Moisture and Snow Water Equivalent (SWE)
Results: current seasonal volume forecasts
Comparison with RFC regression forecast for
Columbia River at the Dalles, OR
UW forecasts made
on 25th of each
month
RFC forecasts made
several times
monthly:
1st, mid-month, late
(UW’s
ESP unconditional
and
CPC forecasts
shown)
UW
RFC
Final Comments
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)
For more information:
www.hydro.washington.edu/Lettenmaier/Projects/fcst/