1. University of Washington Dept. of Civil and Environmental Engineering
Using CPC long lead climate outlooks for ensemble streamflow forecasting
Andy Wood and Dennis P. Lettenmaier
University of Washington
Dept. of Civil and Environmental Engineering
Session A24A
2006 Joint Meeting of the AGU
Baltimore, MD
May 23, 2006

2. Climate forecast importance: temporal variability
In Western US:
Jan – April forecasts of summer streamflow are critical for decision-making related to:
agriculture
environmental flows
hydropower
navigation
water supply
Western US Water Cycle
Climate Forecasts
Importance
Monthly Timestep

3. Climate forecast importance: spatial variability
Most basins east of the Sierras and Cascade Mtns
are heavily
influenced by
spring precipitation.
Water supply forecasts
there have unavoidably high uncertainty because
spring precipitation
is relatively unknown.
Wet spring
65%
Apr-Jun
Oct-Jun
PCP
Dry spring
15%
Courtesy of Tom Pagano, NRCS

4. Climate forecast importance: spatial variability
Example: Climate forecasts relatively unimportant by late Winter
Areas with dry spring ….
Summer flow forecast skill
Courtesy of Tom Pagano, NRCS
Wet spring
65%
Forecast Skill
Precip
Apr-Jun
Oct-Jun
Low High
Dry spring
15%

5. Climate forecast importance: spatial variability
Example: Climate Forecasts very important through Spring
Areas with wet spring ….
Summer flow forecast skill
Courtesy of Tom Pagano, NRCS
Wet spring
65%
Forecast Skill
Precip
Apr-Jun
Oct-Jun
Low High
Dry spring
15%

6. Background Current Practice for Western US Streamflow Forecasting
combine: (1) estimate of current hydrologic state
(2) forecast of historical climate…usually*
produce: streamflow forecast with uncertainty information
UPPER HUMBOLDT RIVER BASIN
Streamflow Forecasts - May 1, 2003
<==== Drier === Future Conditions === Wetter ====>
Forecast Pt
============ Chance of Exceeding * ===========
   Forecast
90%
70%
50% (Most Prob)
30%
10%
30 Yr Avg
   Period
(1000AF)
(% AVG.)
MARY'S R nr Deeth, Nv
APR-JUL
12.3      
18.7      
23      
59      
27      
34      
39      
MAY-JUL
4.5      
11.3      
16.0      
55      
21      
28      
29      

7. Research Objective
Current Practice for Western US Streamflow Forecasting
combine: (1) estimate of current hydrologic state
(2) forecast of historical climate CPC Outlook
produce: streamflow forecast with uncertainty information
ICs
Spin-up
Forecast
obs
recently observed
meteorological data
ensemble of met. data
to generate forecast
ESP-type forecast
method
hydrologic
state
We use a hydrologic model-based approach similar to the NWS River Forecast Center’s Ensemble Streamflow Prediction (ESP)

8. NWS Climate Prediction Center (CPC) Seasonal Outlooks
e.g., precipitation

9. CPC Seasonal Outlook Use
Challenge: Seasonal (3-month) probabilities must be converted to daily meteorological values at the scale of the hydrology model

10. CPC Seasonal Outlook Use
spatial unit for raw forecasts is the Climate Division (102 for U.S.)
CDFs defined by 13 percentile values ( ) for P and T, and μ and σ

11. Hydrologic Prediction using CPC Seasonal OutlooksCD
scale
CPC climate outlooks
variables:
mean temperature (Tavg)
total precipitation (Ptot)
scales:
102 climate division (CD) / US
overlapping 3-month timestep
information:
forecast (μ, σ) at each timestep
normal (μ, σ) at each timestep
disaggregate spatially
climate division unit
--- becomes ---
1/8 degree (~12-13 km)
disaggregate to a daily timestep
1/8 degree monthly Tavg and Ptot
--- becomes ---
1/8 degree daily Ptot, Tmin and Tmax
create Tavg & Ptot ensemble
forecasts (μ, σ) at each timestep/CD
generate seasonal ensemble data
disaggregate temporally
overlapping 3-month timestep
--- becomes ---
non-overlapping 1-month timestep
Use CPC forecasts as inputs to a hydrologic model to produce streamflow forecast ensembles
link Tavg & Ptot ensembles
Associate monthly variables
spatially & temporally

12. disaggregate temporally
overlapping 3-month timestep
--- becomes ---
non-overlapping 1-month timestep
Several methods of doing this work well but not perfectly.
Schneider et al., Weather & Forecasting (2005) – applied monthly/seasonal mean correction factors – approach being adopted by CPC
We are trying multiple linear regression: monthly values = f(seasonal values)

13. disaggregate temporally
overlapping 3-month timestep
--- becomes ---
non-overlapping 1-month timestep
Sample Results
ML regression approach appears to yield better variance, but is not markedly superior
ML regression approach
CPC approach
std dev
Schneider et al. (2005)

14. disaggregate temporally
overlapping 3-month timestep
--- becomes ---
non-overlapping 1-month timestep
Sample Results
ML regression approach
CPC approach
R = 0.80
Schneider et al. (2005)

15. link Tavg & Ptot ensembles
Associate monthly variables
spatially & temporally
Challenge:
Given monthly distributions for a climate variable, how do you associate the values in time to yield a single sequence of one variable? Of two variables?

16. link Tavg & Ptot ensembles
Associate monthly variables
spatially & temporally
Challenge:
Given monthly distributions in adjacent cells, how might sequences in one climate division be associated with those in another?

17. link Tavg & Ptot ensembles Clark et al., J. of Hydromet (2004)
Schaake Shuffle
link Tavg & Ptot ensembles
Associate monthly variables
spatially & temporally
Clark et al., J. of Hydromet (2004)

18. Spatial and Temporal Downscaling
disaggregate spatially
climate division unit
--- becomes ---
1/8 degree (~12-13 km)
Spatial sampling of anomalies within climate divisions
disaggregate to a daily timestep
1/8 degree monthly Tavg and Ptot
--- becomes ---
1/8 degree daily Ptot, Tmin and Tmax
Re-sampling of daily patterns
Scaling/shifting to reproduce CPC forecast anomalies
‘OBS’
Another new feature is that we’re now plotting up several analyses of snow observations, and these update on a daily basis. We’ve been automatically downloading the data for a long time for use in our assimilation, and the goal here was to show the west-wide conditions at a single glance, something that’s hard to find elsewhere.
Note, in addition to the NRCS snotel points, we also have the California DWR snow pillows, and the Env. Canada snow pillows in the Columbia R. drainage.
There are about 5 plots – some of which are for changes during the last week or two.
downscaled

19. University of Washington Forecast System Website project led by Dennis Lettenmaier funded by NOAA, NASA
Another new feature is that we’re now plotting up several analyses of snow observations, and these update on a daily basis. We’ve been automatically downloading the data for a long time for use in our assimilation, and the goal here was to show the west-wide conditions at a single glance, something that’s hard to find elsewhere.
Note, in addition to the NRCS snotel points, we also have the California DWR snow pillows, and the Env. Canada snow pillows in the Columbia R. drainage.
There are about 5 plots – some of which are for changes during the last week or two.

20. Streamflow Forecast Results: Westwide at a Glance
This bubble plot shows the streamflow outlook for summer runoff for about 90 locations in the domain. The anomalies are consistent with those shown in the spatial plots, with the lowest outlooks for the SW streams nearest that very low SM pattern we saw 2 slides back, and normal outlooks in the PNW.
Note, this west-at-a-glance display, with both mouse-overs that show various anomalies for the locations, and clickable points that launch more details, is something we have only recently added.

21. Streamflow Forecast Details
Clicking the stream flow forecast map also accesses current basin-averaged conditions
Streamflow Forecast Details
Flow location maps give access to monthly hydrograph plots, and also to raw forecast data.
In addition to the streamflow hydrographs that we’ve had for a while, the clickable streamflow map now brings up the current water year conditions for P,T,SM,SWE, RO – which are helpful in showing where we are with respect to climatology. These are averaged over the drainage basin contributing to streamflow at each location.

22. Streamflow Forecast Results: Spatial
Precip
Temp
SWE
Runoff
Soil Moisture
Apr-06
May-06
Jun-06

23. UW Real-time Daily Nowcast
SM, SWE
(RO)
½ degree VIC implementation
Free running since last June
Uses data feed from NOAA ACIS server
“Browsable” Archive, 1915-present
Another area of current research relates to the surface water monitor developed last year by A. Wood. This system, applied at coarse (1/2 degree) resolution over the entire CONUS, is completely automated (free-running) and updates every day. It’s just a prototype, demo project that have been unable to get funding to extend, and the main products are maps of current soil moisture & SWE, and an monthly archive that extends back to 1915, that also has SM & SWE maps.
Anyway, we are now adapting the daily update approach for use in the westwide forecast system, and should have the first basin (PNW) land surface conditions updating daily (at 1/8 degree) within the month. After that we’ll move on to other basins, and probably extend the 1/8 nowcast eastward to the Mississippi R.
We are currently migrating the CPC forecast approach to a national US implementation

24. http://www.hydro.washington.edu / forecast / westwide /
Conclusions
Our current approach for downscaling CPC seasonal outlooks is adequate from hydrologic perspective.
Simple temporal disaggregation approaches are sufficent, although it’s possible that slightly higher performance can be achieved via more elaborate disaggregation methods
Ensemble formation step bears further analysis at the monthly to seasonal time scale.
Translation of CPC outlooks to ensembles for hydrologic forecasting should not be an obstacle for their use.
For more information:
/ forecast / westwide /

25. Thank You