Evaluation of the Global Land Data Assimilation System using global river discharge data and a source-to-sink routing scheme

Advanced land surface models (LSMs) offer detailed estimates of distributed hydrological fluxes and storages. These estimates are extremely valuable for studies of climate and water resources, but they are difficult to verify as field measurements of soil moisture, evapotranspiration, and surface and subsurface runoff are sparse in most regions. In contrast, river discharge is a hydrologic flux that is recorded regularly and with good accuracy for many of the world's major rivers. These measurements of discharge spatially integrate all upstream hydrological processes. As such, they can be used to evaluate distributed LSMs, but only if the simulated runoff is properly routed through the river basins. In this study, a rapid, computationally efficient source-to-sink (STS) routing scheme is presented that generates estimates of river discharge at gauge locations based on gridded runoff output. We applied the scheme as a postprocessor to archived output of the Global Land Data Assimilation System (GLDAS). GLDAS integrates satellite and ground-based data within multiple offline LSMs to produce fields of land surface states and fluxes. The application of the STS routing scheme allows for evaluation of GLDAS products in regions that lack distributed in situ hydrological measurements. We found that the four LSMs included in GLDAS yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. The choice of atmospheric forcing data set also had a significant influence on the accuracy of simulated discharge.