Remotely Sensed Terrestrial Water Balance of the Nile Basin

Satellite-derived estimates of precipitation, evapotranspiration (ET), and water storage have transformed our understanding of hydrological dynamics at the basin scale. At the same time, the profusion of satellite and model-generated hydrological estimates has demonstrated that there is still considerable uncertainty associated with the quantification of hydrologic states and fluxes at basin scale: the use of different combinations of data products can lead to dramatically different conclusions regarding water balance partitioning as well as variability and trends in water storage. Including multiple independent products in studies of basin-scale hydrology makes it possible to evaluate known uncertainties in satellite estimates of each component of the hydrological cycle, to assess the influence of these uncertainties on our ability to address hydrological questions of interest, and to identify critical data needs for future satellite missions. Here we present results of a basin-scale water balance analyses of the Nile River basin over the time period of 2007-2010. Multiple satellite-derived and model-based precipitation, ET, and terrestrial water storage products are included in order to characterize absolute and relative uncertainties for each variable of the terrestrial water balance equation. Monthly runoff values are estimated as the residual of the basin water balance. These runoff values are then compared with historical river gauge data to assess the utility of each data combination for estimating river flow and flow variability. Tested products include: the Tropical Rainfall Measurement Mission (TRMM) Multisensor Precipitation Analysis and Climate Prediction Center operational Africa Rainfall Estimates (RFE 2) for precipitation; the Atmospheric-Land Exchange Inverse (ALEXI) model and offline Land Surface Models (LSMs) for ET; and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly, merged thermal and microwave derived soil moisture estimates and offline LSMs for basin-scale water storage.