1. 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

2. Outline of this talk Introduction – research rationale
Framework and method of implementation
Selected Results for current winter
Conclusions and unsolved problems

3. 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

4. Framework: Forecast System Schematic
NCDC met. station obs. up to 2-4 months from current
local scale (1/8 degree) weather inputs
soil moisture
snowpack
Hydrologic model spin up
SNOTEL Update
streamflow, soil moisture,
snow water equivalent, runoff
25th Day, Month 0
1-2 years back
LDAS/other real-time met. forcings for spin-up gap
Hydrologic forecast simulation
Month
INITIAL STATE
SNOTEL
/ MODIS*
Update
ensemble forecasts
ESP traces (40)
CPC-based outlook (13)
NCEP GSM ensemble (20)
NSIPP-1 ensemble (9)
* experimental, not yet in real-time product

5. Framework: Hydrology Model

6. 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.

7. 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

8. 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
spatial weighting function
elevation
weighting
function
SNOTEL/ASP
VIC cell
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
important point(s):
the approach attempts to make use of forecast skill from 2 sources:
better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).
Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

9. Framework: Estimating Initial Conditions SWE state adjustment (using SNOTEL/ASP obs)
April 25, 2004

10. snow cover (MODIS) assimilation (Snake R. trial)
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

11. Framework: Downscaling Climate Model output NCEP GSM and NSIPP-1

12. 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

13. Framework: Downscaling CPC outlooks
spatial unit for raw forecasts is the Climate Division (102 for U.S.)
13 percentile values (from to 0.975) for P and T are given

14. 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

15. Framework: Streamflow Forecast Locations
California
Columbia R. basin
in development:
Colorado R.,
upper Rio Grande
Snake R. basin

16. Framework: Streamflow Forecast Products
monthly
hydrographs
targeted statistics
e.g., volume runoff forecast anomalies
raw ensemble data

17. Framework: Example of Spatial Forecasts

18. Results: Initial Conditions for Current Winter
Soil Moisture and Snow Water Equivalent (SWE)

19. 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

20. 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)
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