New diagnostic estimates of variations in terrestrial water storage based on ERA-Interim data.

Terrestrial water storage is an important component of the climate system as it determines the partitioning of the water and energy fluxes at the land surface. Since in situ measurements are scarce, there is a strong need for alternative validation data sources. Here we present a new dataset of monthly basin-scale terrestrial water storage changes (TWSC) derived with the combined atmospheric-terrestrial water-balance approach, also referred to as basin-scale water-balance (BSWB) approach. It is diagnosed from observation-constrained reanalysis data for the atmospheric fields and observed streamflow. A previous BSWB dataset derived from the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis ERA-40 provided reasonable estimates of TWSC for mid-latitude basins. The here presented BSWB dataset is derived from the latest ECMWF reanalysis ERA-Interim, which is available in real time. The potential real-time availability of TWSC derived from ERA-Interim is of great relevance for several agricultural, hydrological, and climate applications. An uncertainty analysis of the derived TWSC highlights the importance of the atmospheric moisture flux convergence fields from the reanalysis for the resulting product uncertainty. We find that the BSWB estimates have a relative uncertainty of about 4-6% in basins larger than 1·5 × 10 km compared with the mean seasonal cycle. The ERA-Interim based dataset compares better with in situ soil moisture, snow depth, and groundwater measurements in the Ob basin and Illinois than the previous ERA-40 based dataset. We further compare the BSWB datasets to retrievals from the Gravity Recovery And Climate Experiment (GRACE), scatterometer data from the European Remote Sensing (ERS) satellites, and land surface model output from the second phase of the Global Soil Wetness Project (GSWP). We find a better correlation between ERS and the BSWB data than between GRACE and the BSWB data, which might be related to the relatively coarse resolution of GRACE. GSWP model output and the BSWB datasets compare well in most of the studied river basins. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]