1. The potential for long-lead drought forecasting in the North American Monsoon region
Dennis P. Lettenmaier
Chunmei Zhu
Department of Civil and Environmental Engineering
University of Washington
Workshop on
Managing Water Resources Under Conditions of High
Climatic Variability in the US-Mexico Border Region
January 13, 2003
La Paz, BCS, Mexico

2. Modeling Domain Domain is North America between 25º and 53º N
Resolution 1/8º
77,000 grid cells through domain (56,000 in Continental U.S.)
Model developed for 15 sub-regions
Model forcing data ( ) derived from observations
Run at a 3-hour time step
The domain is that studies by the Land Data Assimilation Project – North America, a NASA/NOAA/other instituion joint project which studies the possibility of improving land surface representation for initializing coupled climate models
For hydrologic consistency, we divided the continental U.S. into 13 sub-areas, and placed the remainder into two areas for Canada and Mexico – I coordinated the work of many people were involved in separate simulations
Calibration on sub-areas was performed on infiltration parameter, baseflow curve parameters, and to some degree, soil depth.
50+ years of simulation, coinciding with length of the Precipitation record, took 3 months to run on several machines, produced a terabyte of output.

3. Hydrologic Model VIC Model Features: Developed over 10 years
Energy and water budget closure at each time step
Multiple vegetation classes in each cell
Sub-grid elevation band definition (for snow)
Subgrid infiltration/runoff variability
3 soil layers used
Non-linear baseflow generation
VIC has been developed over the past 10 years at Princeton and UW.
The VIC Model has been regionally (river basins like the Delaware and Potomac to larger ones like the Missouri and Columbia) and globally.
It is designed to represent the land surface at scales from 1/8 degree (10-12 km) to 2 degrees (~200km)
Performs full energy and water budget solution at time steps from hourly to daily.
Distinguishing features are statistical sub-grid variability of vegetation, infiltration capacity, and elevation/temperature/snowfall.

4. Gridding Temperature and Precipitation Data
Precipitation and Temperature from gauge observations gridded to 1/8o
Avg. Station density:
1) Gridding:
For each 1/8o grid cell, search radius set to include min. of 4 Co-op stations with data
Inverse square distance weighting
Min. and max daily temperatures lapsed from stations to grid cell (-6.5oC/km)
2) Radiation, humidity parameterized from Tmax and Tmin
3) Precipitation adjusted to account for the recorded time-of-observation at each Co-op station
4) Precipitation scaled to match long term climatology, to account for orographic and aspect effects in areas with sparse data coverage.
PRISM mean for :
PRISM monthly average data at 1/24o (about 4 km) aggregated to 1/8o
Scaling factors derived by month, applied to daily data
Accounts for orientation and orography, and reduces bias due to observation point locations
No adjustment is made for gauge undercatch, which would mean underestimation in snow-dominated areas.
Lack of rescaling in Canada could also mean underestimation on precipitation in Canadian Rockies, since orographic effect would be underestimated due to lower station density, esp. in mountains.
Most recent months (1/1999 – 7/2000) uses less reliable data from global, gridded data sets: 1-degree merged gauge/satellite precipitation and reanalysis temperatures.
Area
Km2/station
U.S.
Canada
2500
Mexico
6000
Within the U.S.:
Precipitation adjusted for time-of-observation
Precipitation re-scaled to match PRISM mean for

5. Derived Meteorological Variables
Certain meteorological variables not well observed
Use parameterizations to derive them from better know variables (Tmin, Tmax, P)
Humidity (Vapor Pressure)
MTCLIM - Tdew estimated from Tmin (with aridity index based on Pannual and Rsolar)
Downward Solar Radiation
transmissivity estimated from Tdew, Tmax – Tmin
Downward Longwave Radiation
estimated from Taverage, humidity, atm. transmissivity
Wind
daily wind speed from NCEP/NCAR reanalysis
One approach to dealing with estimating poorly observed meteorlogical data is to estimate them from better known variables.
Dew Point: Kimball, et al. (1997)
Downward Solar: Thornton and Running (1999)
Longwave: Bras (1990)
Wind: Kalnay et al. (1996)

6. Model Simulation for 50-Years
Using VIC model, simulation run for 50 years at 3-hour time step
Input
Time series of spatial data
One terabyte of output archived

7. Evaluation of Energy Parameterization
Comparison with 4 SURFRAD Sites
Obs
VIC
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%)
Now I have a terabyte of model output, covering the entire domain for 50 years. This is a better resource than has been avaialble for past studies, which have generally relied on climate division data (344 of them in the US), so we have better resolution and a longer period.
The lack of observations that motivated the simulation also limits the validation possibilities. Here we compare at 4 sites providing the highest quality (gold standard as far as quality controlled) radiative observations
Here we validate the incoming shortwave radiation, which uses a cloudiness estimation in the model derived from Tmax and Tmin, and the Net long+shortwave radiation, which includes the model parameterization of surface temperature.
Results show good agreement with daily average, at within 10% over all stations and withon 2% averaged over all stations. Some underestimation of the Peak radiation is seen, varying between 3-15%.
Shortwave is paraterization of cloud cover
Red lines include shortwave and longwave, so land surface temperature is included.

8. Comparison of Simulated and Observed Runoff
Daily grid cell runoff routed to edge of grid cell
Daily grid cell outflow routed through river network to observation point.
Monthly hydrographs of routed daily flows can be compared to observations
To validate with streamflow, the runoff from each grid cell is routed to the outlet, then aggregated through the stream network to the basin outlet point where streamflow is measured.
Shorter simulation captures non-linear response of the land surface to surface forcing

9. 6 Sample Hydrographs Good agreement of Seasonal cycle Low Flows
Peak Flows
Model
Obs.
This shows a blow-up of 6 of the hydrographs.
Resulting monthly hydrographs, aggregated from daily flows, show good agreement of seasonal cycle, peak flows, and baseflow dominated periods
Many additional incremental calibration points exist within each sub-basin.

10. Comparison 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
Variability
Illinois Soil moisture data unique data set in length and area covered.
Data for top 1 meter is compared with VIC
Moisutre flux is the most important variable, since it is the component of the water budget, and is well simulated
Variability is the C.V. (std.dev/mean) of monthly simulated soil moistures. Model slightly underestimates interannual variability, but has good seasonal cycle.
Persistence is measured as the autocorrelation of soil moisture anomalies, and represents the how rapidly an anomaly is dissipated – this is the characteristic exploited for predictability. Is also reasonably well simulated.
Now we have built and validated the datasets needed to run VIC, and we have spatial and tmporally complete and consistent data sets of all land surface water (and energy) variables over 50 years, a terabyte of output.
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
Remember the persistence characteristics
Obs.
Model
Persistence

11. Predictability due to Soil Moisture
Widespread predictability at 0 lead (1½ month)
Little predictability in zones where winter runoff is high
For summer runoff, significant predictability up to 3 seasons
Predictability at greater than 1 season only in west.
Some prediction of JJA runoff the previous Sept 1 in the west!
What is evident from this is that the coincidence of runoff magnitude and predictability may be a better gauge of the importance of these predictabilities.

12. Hypotheses for Teleconnection between Sea Surface Temperature and Soil Moisture
Teleconnection between SST and Precipitation
The SST anomalies in the eastern Pacific Ocean in the antecedent winter were often linked to summer monsoon rainfall anomalies in thesouthwestern United States and northwestern Mexico. (Carleton, A.M., D.A. Carpenter, 1990, J.Climate, 3, )
Feedback between Soil Moisture and Precipitation
Soil moisture responds to Asian and Africa Monsoon precipitation variability and also affects the precipitation. (H.Douville, F.Chaauvin
and H. Broqua, 2001, J. Climate, 14, )
Correlation between SST and Soil Moisture on the Canadian Prairies
Correlation analysis indicates that late spring/early summer North Pacific sea-surface temperature influences autumn soil moisture. (V. Wittrock and E.A. Ripley, 1999, Intern. J. Climatology. )

13. Exploratory Work on Teleconnection between SST and Soil Moisture Study Domain and Datasets
Sea surface temperature: Extended Reconstruction of Global Sea Surface Temperature data set based on COADS data. ( ) developed by T.M. Smith and R.W. Reynolds, NCDC. The original data resolution is 2ºlongitude, 2 º latitude. It was interpolated into 0.5 º resolution (The ocean domain is chosen according to the Bin Yu and J.M. Wallace’s paper, 2000, J. Climate, 13, )
Soil Moisture: VIC retrospective land surface dataset ( ). The original data with 1/8 degree resolution is aggregated into 0.5 º resolution.

14. Maximum Positive Correlation Coefficient
SST has significant positive correlation coefficient with soil moisture in most areas even with lead time more than 9 months.
Southwestern United States shows higher correlation Coefficient (greater than 0.6) with SST than Mexico region.
June shows larger area with higher coefficient than other months.

15. Maximum Negative Correlation Coefficient
SST tends to have positive correlation relationship with soil moisture than negative correlation.

16. Maximum Positive Correlation
Most sea surface cells with maximum positive correlation are found in 20~30 N and 10S sea surface region.

17. Maximum Positive Correlation

18. Maximum Negative Correlation
Most negative correlation is found in gulf of Mexico sea surface region.

19. Maximum Negative Correlation

20. Sea Surface Temperature Monthly First and Second PC
SST first and Second PC altogether can explain about 80% variance

21. Predictability of Soil Moisture by SST First and Second PC
Southwestern US area shows highest predictability (the highest variance explained is about 0.45)

22. June soil moisture predicted by SST First and Second PCs
Last December SST first and second PCs can explain 44.1% variance of June soil moisture
LDAS Data
Predicted

23. June soil moisture predicted by SST First and Second PCs
January SST can explain 32.0% variance of June soil moisture
LDAS Data
Predicted

24. Predictability of Soil Moisture by Precipitation
Winter precipitation in the Southwestern US influences summer soil moisture especially for June and July.

25. Predictability of Soil Moisture by Precipitation and SST PCs
The predictability increases by introducing precipitation. In some cells in the U.S. Southwest, as much as 70% of the variance can be explained.

26. Predictability of Soil Moisture by Persistence
Soil moisture shows significant persistence even in at 6-month lead time especially for June soil moisture.
Mexican part of the domain also shows high persistence for June soil moisture

27. June Soil Moisture Predictability by Persistence and SST PCs
The highest variance explained is more than 90%. For June, over 40% of the variance is explained over most of the study domain, including Mexico.

28. June soil moisture predicted by Persistence and SST PCs
LDAS Data
Predicted
Last December can explain 66.7% June soil moisture

29. SST and Persistence
Persistence
Introducing SST PCs benefits long-time lead predictability (of June soil moisture), but no significant benefits for less than 6-month lead time predictability.

30. Conclusions All of this is exploratory, but …
“Some” potential for long-lead forecasting of soil moisture over NAMS region