Publications

Evaluation of precipitation products for global hydrological prediction

Citation

Voisin, N., Wood, A.W., Lettenmaier, D.P.  2008. Evaluation of precipitation products for global hydrological prediction. Journal of Hydrometeorology 9(3):388-407.


Abstract

Accurate precipitation data are critical for hydrologic prediction, yet outside the developed world in situ networks are so sparse as to make alternative methods of precipitation estimation essential. Several such alternative precipitation products that would be adequate to drive hydrologic prediction models at regional and global scales are evaluated. As a benchmark, a gridded station-based dataset is used, which is compared with the global 40-yr ECMWF Re-Analysis (ERA-40), and a satellite-based dataset [i.e., the Global Precipitation Climatology Project One-Degree Daily (GPCP 1DD)]. Each dataset, with a common set of other meteorological forcings aside from precipitation, was used to force the Variable Infiltration Capacity (VIC) macroscale hydrology model globally for the 1997-99 period for which the three datasets overlapped. The three precipitation datasets and simulated hydrological variables (i.e., soil moisture, runoff, evapotranspiration, and snow water equivalent) are compared in terms of the implied water balances of the continents, and for prediction of streamflow for nine large river basins.

The evaluations are in general agreement with previous but more local evaluations of precipitation products and water balances: the precipitation datasets agree reasonably on the seasonality but less on monthly anomalies. Furthermore, the largest differences in precipitation are in mountainous regions and regions where in situ networks are sparse (such as Africa). Derived runoff is highly sensitive to differences in precipitation forcings. At a global level, all three simulations result in water budgets that are within the range of other water balance climatologies. Although uncertainties in the three datasets preclude an evaluation of which one has the lowest errors, overall ERA-40 is preferred because of its agreement with the station-based dataset in locations where the station density is high, its periodic availability, and its temporal resolution.