1. Land surface modeling for real-time hydrologic prediction and drought forecasting
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Meeting of the Americas 2008 Joint Assembly
Fort Lauderdale, FL
May 29, 2008

2. Introduction: LSM structure

3. Variable Infiltration Capacity (VIC) snow model schematic

4. Off-line testing and intercomparisons
Participating models in PILPS-2e (from Nijssen et al, 2003)

5. PILPS-2e model intercomparison wrt reproduction of mean seasonal distribution of streamflow

6. Relative bias in streamflow for N-LDAS models (from Mitchell et al, 2004)

7. Mean snow cover days over CONUS for four N-LDAS models (from Sheffield et al, 2003)

8. University of Washington West-wide Seasonal Hydrologic Forecast System
A real-time simulation test-bed for
climate forecast use in hydrology and water resources
satellite data assimilation
multi-model approaches
forecast verification
LDAS-era land surface models

9. Initial Conditions: Hydrologic Simulations
1-2 years back
start of month 0
end of mon 6-12
forecast ensemble(s)
model spin-up
initial conditions
climatology ensemble
NCDC met. station obs. up to 2-4 months from current
stations in west
LDAS/other real-time met. forcings for remaining spin-up
~ stations in west
climate forecast information
data sources
Forecast Products
streamflow
soil moisture
runoff
snowpack
derived products
e.g., reservoir system forecasts
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.
obs snow state information
(eg, SNOTEL)

10. Initial Conditions: estimating run-up conditions
Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate model
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.
sparse station network in real-time
dense station network for model calibration

11. Initial Conditions: snow state 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

12. Initial Conditions: Initial snow state 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.

13. Initial Conditions: Snow state assimilation
SWE state differences due to assimilation of SNOTEL/ASP observations, Feb. 25, 2004

14. Initial Conditions: final product
Snow Water Equivalent (SWE) and Soil Moisture

15. Comparison with RFC regression forecast for Columbia River at the Dalles
UW forecasts made on
25th of each month
RFC forecasts made
several times monthly:
1st, mid-month, late
(UW ESP unconditional forecasts shown) UW
RFC

16. Applications to drought monitoring
DM is widely used, but link to direct observations (e.g., of soil moisture) is weak – hence reliance on indirect methods, such as PDSI.
Need for reproducible basis for identifying drought-affected regions.
Land surface model representations of soil moisture (and runoff) offer an alternative means for estimating severity, frequency, duration, and variability of current droughts, and linking them to the climatology of observed droughts.

17. The challenge: Different land schemes have different soil moisture dynamics

18. Models VIC: Variable Infiltration Capacity Model (Liang et al. 1994)
CLM3.5: Community Land Model version 3.5
(Oleson et al. 2007)
NOAH LSM: NCEP, OSU, Air Force, Hydrol. research lab
(Mitchell et al. 1994, Chen and Mitchell 1996)
Catchment LSM: NASA NSIPP LSM
(Koster et al. 2000; Ducharne et al. 2000)
VIC-CLM: Hybrid of VIC and CLM 3.0
Grid-based Sacramento: Variation of NWS operational
(Burnash et al, 1973; Koren et al, 2004

19. Averaged soil moisture percentiles 1932-38

20. Spatial distribution of average (monthly) between-model correlations (of soil moisture percentiles)

21. e-folding time of soil moisture autocorrelation (months)

22. Multimodel results from UW real-time surface water monitor, 11/07 – 1/08

23. UW SWM for 5/21/08 (updated 5/20/08)

24. Drought recovery – the concept
Real-time applications!
Drought recovery probability described by soil moisture percentiles: (a) Current drought area (based on August 1933); and for different lead times, maps showing the probability (in each grid cell experiencing drought) that soil moisture percentiles will recover. (b) The grid cell-specific recovery probabilities are derived from real-time soil moisture simulations up to the current date, after which simulations are driven by ensemble climate forecasts based on a variety of sources -- e.g., ESP, climate index-conditioned ESP, and the CPC seasonal climate outlooks

25. Some obstacles and opportunities in hydrological application of climate information
The “one model” problem (multi-model ensemble is the solution; need to deal with weighting of ensembles)
Calibration at the basin scale (need to consider regional parameter estimation approaches; post-processing as an alternative to calibration)
The value of visualization
Opportunities to utilize non-traditional data (e.g. remote sensing)
So why does the U.S. not have a national hydrologic prediction strategy?