1. Columbia River Basin Hydrology and Water Resources
Dennis P. Lettenmaier
Alan F. Hamlet
Department of Civil and Environmental Engineering
University of Washington
March 7, 2002
Center for Study of the Earth System
Advisory Panel Presentation

2. Effects of the PDO and ENSO on Columbia River
Summer Streamflows
Cool
Cool
Warm
Warm

3. MODELING STRATEGY FOR CLIMATE FORECASTING AND ASSESSMENT

6. Retrospective Land Data Assimilation System (LDAS) simulations, Continental U.S., 1950-2000

7. Implementation Strategy
VIC model implemented for 15 sub-regions, with consistent forcings.
Surface forcing data:
Daily precipitation; maximum and minimum temperatures (from gauge measurements)
For hydrologic consistency, we divided the continental U.S. into 13 sub-areas, and placed the remainder into two areas for Canada and Mexico
Source data was as consistent as possible across all areas.
Model was developed as shown
Radiation, humidity parameterized from Tmax and Tmin
Wind (from NCEP/NCAR reanalysis)
Soil parameters: derived from Penn State State STATSGO in the U.S., FAO global soil map elsewhere.
Vegetation coverage from the University of Maryland 1-km Global Land Cover product (derived from AVHRR)

8. Validation with Observed Runoff
The VIC model is calibrated to observed or naturalized streamflow by adjusting:
the soil depth
baseflow parameters
infiltration capacity curve parameter
Routing from each computational cell to a stream measurement point is done with the method of Lohmann et al. (1996), which uses a time of concentration to the channel within each cell, and then routes the accumulated from in the stream.
Generally very good agreement of both peaks and low flows.
Natural flows for all basins except Arkansas River, which has significant withdrawals
This means VIC can be expected to be higher than the measured flows for the Arkansas R.
Within the 15 sub-areas, parameters are transferred from calibrated to uncalibrated areas.
Many additional incremental calibration points exist within each sub-basin.
Hydrographs of routed runoff show good correspondence with observed and naturalized flows.

9. Comparisons with Illinois Soil Moisture
19 observing stations are compared to the 17 1/8º modeled grid cells that contain the observation points.
Moisture Level
Moisture Flux
Soil moisture from Hollinger and Isard (1994) – unique data set in length and area covered.
Data from 19 sites throughout the state, with data from 1/1981-8/96
This is compared with the average for 800 VIC grid cells falling in the area
All soil moistures (both point measurements and grid cell modeled values) are adjusted by subtracting the minimum value that occurred during the simulation period, in order to remove the hydrologically inactive portion, analagous to a reservoir’s dead pool storage.
VIC appears to have a low bias most of the year
Monthly average fluxes are very well simulated by VIC
Persistence and inter-annual variability characteristics are also well simulated.
GIVEN THE SUCCESS AT REPRODUCING RUNOFF HYDROGRAPHS, WITH THE USE OF OBSERVED P (AND THE PHYSICAL REPRESENTATIONS OF SOIL MOISTURE AND RUNOFF PRODUCING PROCESSES) SUGGESTS THAT OTHER SURFACE FLUX AND STATE VARIABLES (ET & SOIL MOISTURE) ARE PROBABLY REASONABLE.
THIS GIVES US CONFIDENCE IN USING THESE FLUXES AS A BENCHMARK AGAINST WHICH TO COMPARE COUPLED MODELS
Variability
Persistence

10. Evaluation of Energy Forcings
Comparison with 4 SURFRAD Sites
3-minute observations aggregated to 3-hour
Average Diurnal Cycle is for June, July, August
Peak underestimated 3-15% at each site (avg. 10% for all sites)
Daily average within 10%, (avg. 2%)

11. Seasonal Soil Moisture Variation
Shown is seasonal variation of soil moisture.
Top plot is scaled by the total soil pore volume.
Bottom plot is scaled by its dynamic range for 50-years.
Spatial characterization of soil moisture can be represented many ways.
Top shows soil moiture as a fraction of the total pore volume in the soil column.
Bottom shows same as fraction of range (max-min soil moisture for 50+ year period)
Bottom characterization shows smoother fields, indicating less sensitive to parameterization of model, and may better reflect climatically driven soil column.
Recent investigations by investigators have shown that the range-standardized method better captures variability between stations, and therefore is preferred here (though it is dependent on period analyzed.

12. Soil Moisture - Active Range
50-Year Soil Moisture Range Scaled by Annual Precipitation
The previous slide demands a companion – showing the active soil moisture range across the domain.
This slide shows the dynamic range (max-min soil moisture over 50+years at each grid cell), scaled by the annual average precipitation.
This slide highlights the areas where the dynamic range of the soil column – analogous to the active storage in a reservoir – is large relative to the precipitation. This can be also seen as, in one case at least (drought), a comparison of the max disturbance to the restoring force.
Where this is large, anomalies of soil moisture (a dry soil column, for example) would be expected to persist for longer than in areas with low values. Persistence of, and therefore predictability based on, soil moisture would also be expected to be prevalent.
Scale indicates level of hydrologic interaction of soil column

13. Soil Moisture - Persistence
Persistence of soil moisture anomalies, based on the full 50+ year timeseries at each grid cell.
Persistence is generally seen where soil moisture interaction is high.
As anticipated from the previous slide, areas showing high soil moisture do indeed generally show persistence.
High latitude areas, where radiative forcing is low in winter, also show persistence.
Interpretation is somewhat difficult by lumping positive and negative anomaies together.

14. West-wide Streamflow Forecasting: Status Update
Dennis P. Lettenmaier
Andrew W. Wood
Department of Civil and Environmental Engineering
University of Washington
March 7, 2002
Center for Study of the Earth System
Advisory Panel Presentation

15. General Approach climate model forecast meteorological outputs
~1.9 degree resolution (T62)
monthly total P, avg T
Use 3 step approach: 1) statistical bias correction
2) downscaling
3) hydrologic simulation
 hydrologic (VIC) model inputs
streamflow, soil moisture,
snowpack,
runoff
1/8-1/4 degree resolution
daily P, Tmin, Tmax
important point(s):
the overall forecasting approach involves using forecast model (the global spectral model) T & P output at a coarse timestep & scale as hydrologic model input at a finer timestep and scale.
to make a hydrologic forecast, you need a transformation of the forecasts that first overcomes climate model bias and the scale differences, then simulates the water balance.
also, GSM is really run at very fine timestep (~5-15 minutes) but only the monthly anomalies are archived for our use. most of the signal is at the monthly scale, however, so this is acceptable.

16. Changes in Mean Temperature and Precipitation from GCMs
ColSim
Reservoir
Model
VIC
Hydrology Model

17. Quantile Mapping Bias Correction

18. One Way Coupling of GSM and VIC models
a b c.
TGSM
TOBS
a) bias correction:
climate model climatology  observed climatology
b) spatial interpolation:
GSM ( deg.)  VIC (1/8 deg)
c) temporal disaggregation (via resampling of observed patterns):
monthly  daily
important point(s):
this is our approach to turning GSM forecasts into VIC input: first, bias correction:
1. using the climatologies of the observed precip & temperature to define parallel distributions (for each month in the forecast), we translate each met. value in the GSM ensembles to a quantile value, then retrieve the met. variable value for that quantile from the observed distribution. (at the ends of the empirical distributions, we use fitted theoretical ones if needed).
then downscaling:
2. that was all done at the GSM scale. then we interpolate the anomalies to the to the VIC resolution (nothing fancy here).
then we impose the daily pattern by resampling the historic VIC forcings (P&T for each month taken from the same year to preserve correlations), and then scaling monthly avg. temp and shifting month. tot. P to reproduce the forecast anomalies. after all, when you sample at random, the daily pattern you get won’t have the monthly anomaly you need for the forecast signal.
the bias correction step is critical, as the next 2 plots will show.

19. Models: Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC
forecast ensembles available near beginning of each month, extend 6 months beginning in following month
each month:
210 ensemble members define GSM climatology for monthly Ptot & Tavg
20 ensemble members define GSM forecast
important point(s):
GSM forecasts take the form of monthly ensembles of length 6 months
we get them early in each month for a start date of the following month.
the climatology ensemble enables us to define the climate model bias and correct it
climatology ensembles run out 6 months just like the forecasts, but use observed rather than predicted tropical Pacific SSTs
also:
210 ensembles for GSM climatology are derived from observed SSTs in each year of the 21 year climatology period ( ) combined with 10 initial atmospheric conditions for each year
GSM is at T42 spatial resolution, but moving to T62 soon (resolution improvement of about 1/3)

20. GSM Regional Bias: a spatial example
Bias is removed at the monthly GSM-scale from the meteorological forecasts
(so 3rd column ~= 1st column)
important point(s):
here we see the biases from a spatial view -- note the large temperature bias in raw GSM forcing (for July), and in precip over the eastern edge of the Ohio Basin (as an example). In the third column of images, the biases have been removed by our method, so they should look like the first column (observed) – and they do.

21. Downscaling Test
Start with GSM-scale monthly observed met data for 21 years
Downscale into a daily VIC-scale timeseries
Force hydrology model to produce streamflow
Is observed streamflow reproduced?

22. GSM forecast and climatology ensembles
10 member climatology ensembles
(21 sets)
from 1979 SSTs
from 1980 SSTs
from 1981 SSTs
from 1999 SSTs
20 member forecast ensemble
from current SSTs

23. Simulations VIC model spin-up A B C Forecast Products streamflow
soil moisture
runoff
snowpack
VIC model spin-up
VIC forecast ensemble
climate forecast information (from GSM)
VIC climatology ensemble
1-2 years back
start of month 0
end of month 6
NCDC met. station obs. up to 2-4 months from current
LDAS/other met. forcings for remaining spin-up
data sources
A B C
important point(s):
after bias correcting and downscaling the climate model forecasts, the procedure for producing hydrologic forecasts is as follows:
we spin up the hydrologic model to the start of the forecast using observed met. data (from 2 sources: NCDC cooperator stations through 3-4 months before the start of the forecasts, then LDAS 1/8 degree gridded forcings thereafter).
The GSM forecasts comprise 2 sets of ensembles, one for climatology and one for the forecast. The climatology ensemble yields a distribution of the conditions we’ve seen over the period , while the forecast ensemble yields the distribution of the conditions we might see for the next 6 months. Although the climatology ensemble is nominally unbiased against a simulated climatology based on observed met. data (rather than bias-corrected, downscaled GSM met. forcings), we compare the forecast and GSM climatology so that any unforeseen biases (resulting, perhaps, from the downscaling method) occur in both climatology and forecast. Eventually this cautionary step may be eliminated, and we’ll compare directly to the simulated observed climatology.
at the end of the spin-up period and one month before month 1 (out of 6) of the forecasts, we save the hydrologic model state. The state is then used for initializing the forecast runs. Through the first month, the model runs on observed data to the last date possible, then switches to the forecast data. Usually, we process the observed forcings up through the 15th to 25th of this initialization month, then the forecast forcing data carries the run forward for the remaining days in the month, and throughout the following 6 month forecast period. Note, the state files used for the climatology runs correspond to the spin-up associated with the particular year (out of ) from which the climatology ensemble member is drawn.
the spin-up period captures the antecedent land surface hydrologic conditions for the forecast period: in the Columbia basin, the primary field of interest is snow water equivalent.
forecast products are spatial (distributed soil moisture, runoff, snowpack (swe), etc.), and spatial runoff + baseflow is routed to produce streamflow at specific points, the inflow nodes for a management model, perhaps.

24. CRB May 2001 forecast basin avg. soil moisture

25. CRB May 2001 Forecast Streamflow
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.

26. CRB May Forecast cumulative flow averages
important point(s):
plotted against the streamflow climatology used by other agencies, the forecast ensemble medians for average flow in the first 3 months of the forecast period (top) and over all six months (bottom) end up between the 1977 and 1988 runoff averages.
Given the uncertainties in the snow pack estimation used as initial conditions for the forecast runs, particularly the fact that our estimation is somewhat less dire than that of the NRCS (putting initial snowpack below their estimates for 1977), these results could be looked at as conservative – that is, 2001 runoff could actually be lower than we are showing here.
forecast
medians

27. Question for PLATIN – should there be parallel research and applications pathways?
Research questions have to do with role of land-atmosphere-ocean coupling in determining predictability of surface hydrology (especially precipitation and streamflow) in the Plata basin
Applications questions have to do with whether climate-based prediction methods offer potential improvements over currently used methods based on “traditional” stochastic hydrology (especially developed in Parana basin)

28. Research Pathway
Would consist primarily of coupled (and perhaps uncoupled) model and diagnostic studies, and supporting data analysis, designed to assess relative influence on surface hydrology of climate vs land surface initial conditions (and their evolution)
Data (observation) and support needs (other than $):
Precipitation (station and gridded products)
Steamflow
Other surface variables
Land cover
Soils
topogrpaphy

29. Applications Pathway
Would focus on S/I forecast development (esp. but not necessarily only hydrologic) and forecast diagnosis and evaluation, with aim of determining whether “climate based” (e.g., ensemble) forecasts offer improvements over “traditional” (based on stochastic methods) hydrologic forecasts
Support needs:
Global/regional ensemble forecast archive
Downscaling approaches to bridge gap between climate and hydrologic models
Forecast model climatologies
Observation climatology (per research pathway)
Runup surface obs (to real time) for surface variables – especially precipitation and temperature