1. Hydrologic Considerations in Global Precipitation Mission Planning
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
With contributions from
Bart Nijssen
Eric Wood
Matthias Steiner
for presentation at
American Geophysical Union Spring Meeting
Washington, D.C.
May 28, 2002

2. GPM Objectives and Hydrological Relevance
Improve ongoing efforts to predict climate by providing near-global measurement of precipitation, its distribution, and physical processes.
Improve the accuracy of weather and precipitation forecasts through more accurate measurement of rain rates and latent heating.
Provide more frequent and complete sampling of the Earth's precipitation. This will provide better prediction of flood hazards and management of life-sustaining activities dependent upon fresh water

3. The need to better predict extreme floods
There are approximately 100 to 150 major floods per year, world-wide.
Many are of long duration (3 to 10 days) caused by large-scale weather systems (U.S. flood damages are in the $B 5-10 range annually, mostly from large scale flooding)
Many are in developing countries with poor meteorological data infrastructure.
Princeton University

4. Recent large floods Mississippi River 1993 ($B 20-30 damages)
Yangtze River 2001
Mekong River 2000
Mozambique 2001

5. Critical questions for hydrologic applications
What combination of the following control the usefulness of GPM for hydrologic prediction?
catchment size
Catchment hydrologic characteristics
precipitation structure (space-time variability)
forecast lead time
precipitation estimation error

6. Test Case 1: Alabama – Coosa – Tallapoosa (ACT) River Basin

8. Basin Location Alabama – Coosa – Tallapoosa (ACT)

9. Methodology Imposing Error on Precipitation
Precipitation over the ACT basin on day X:
“Truth”
Spatial Gaussian random field:
New precipitation fields
Uncorrelated
VIC
Correlated

10. Effect of Imposed Error on Predicted Discharge Spatially Uncorrelated Case
RMSE (%) 1 2 5 10 20 50
100
200
0.02
0.1
0.2
0.5
Upstream area (103 km2)
(r) = 0
CV=1.0
CV=0.5
CV=0.25
Precipitation
Discharge
CV=0.1

11. Effect of Imposed Error on Predicted Discharge Spatially Correlated Case
RMSE (%) 1 2 5 10 20 50
100
200
0.02
0.1
0.2
0.5
Upstream area (103 km2)
(r) = 1/e for r  50 km
CV=1.0
CV=0.5
CV=0.25
CV=0.1
Precipitation
Discharge

12. Predicted Discharge at Three Locations Spatially Uncorrelated and Correlated Case

13. Test Case 2: Ohio River Basin

14. Selected USGS Gauging Stations
Ohio Metropolis, IL
Cumberland River
Duck River
Ohio Louisville, KY
Levisa Fork
Green River
Scioto River
Wabash River
Princeton University

15. Ohio Valley Flood of 1997
Large scale meteorological event, with good records for a range of basins,
Produced extensive flooding across the region from Feb. 27 to Mar. 5, 1997,
Rainfall totals exceeded 300mm in some areas.
Princeton University

16. Rainfall Data Gauging station data Stage IV (WSR-88D+Gauge Data)
Daily ( ), gridded to 1/8th degree (about km)
Used to calibrate VIC hydrologic model over multiple years
Stage IV (WSR-88D+Gauge Data)
Hourly, gridded to 1/8th degree (about km)
Considered the most accurate and representative data set of the actual event
Used to produce the base-run simulation and to scale simulated GPM samples.
WSI Radar Reflectivity Data
15-minute instantaneous, resolution 2km, aggregated to 1/8th degree)
Used to simulate proposed 3-hour GPM sampling
Princeton University

17. GPM-Simulated Rainfall Realizations
Princeton University

18. GPM Simulations (Large Basins)
Ohio Metropolis, IL
(203,000 sq. km.)
Wabash River, KY
(28,635 sq. km.)
Base Case
Base Case
Princeton University

19. Some considerations in interpreting results of the test cases
Note that both case studies address role of sources of simulation error, not forecast error
In practice, GPM would provide an observational product that would define initial conditions for hydrologic forecasts (and which would/could be updated using other data sources, e.g., remotely sensed soil moisture, and river stage) precipitation
But, results of both studies suggest key role of catchment size, and GPM error characteristics at about the daily accumulation time scale

20. Other hydrologic considerations for GPM – the role of orography

21. GPCP Precipitation compared to PRISM Precipitation, Mississippi River basin

22. GPCP Precipitation compared to PRISM Precipitation, Columbia River basin

23. Implications of precipitation errors for hydrologic prediction, Columbia River basin
5000
10000
15000
20000
25000 m 3 /s J F M A S O N D
Mean monthly observed and simulated hydrographs for the Columbia river.
Observed flows
Run 1: GPCP precipitation
Run 2: PRISM precipitation

24. Conclusions (or perhaps “hypotheses”)
Hydrologic potential of GPM will be greatest in underdeveloped countries, with poor gage/radar networks
Potential is greatest for large area floods
Macroscale hydrology models can/will play an important role in providing ICs for flood forecasting based on GPM (and perhaps other R/S) data, and via updating/data assimilation, high quality streamflow forecasts
Importance of interaction of GPM error characteristics and catchment and forecast characteristics needs more attention