How well are we able to close the water budget at the global scale?

The water budget equation describes the exchange of water between the land, ocean, and atmosphere. Being able to adequately close the water budget gives confidence in our ability to model and/or observe the spatio-temporal variations in the water cycle and its components. Due to advances in observation techniques, satellite sensors, and modelling, a number of data products are available that represent the components of water budget in both space and time. Despite these advances, closure of the water budget at the global scale has been elusive. In this study, we attempt to close the global water budget using precipitation, evapotranspiration, and runoff data at the catchment scale. The large number of recent state-of-the-art datasets provides a new evaluation of well-used datasets. These estimates are compared to terrestrial water storage (TWS) changes as measured by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. We investigated 189 river basins covering more than 90 % of the continental land area. TWS changes derived from the water balance equation were compared against GRACE data using two metrics: the Nash–Sutcliffe efficiency (NSE) and the cyclostationary NSE. These metrics were used to assess the performance of more than 1600 combinations of the various datasets considered. We found a positive NSE and cyclostationary NSE in 99 % and 62 % of the basins examined respectively. This means that TWS changes reconstructed from the water balance equation were more accurate than the long-term (NSE) and monthly (cyclostationary NSE) mean of GRACE time series in the corresponding basins. By analysing different combinations of the datasets that make up the water balance, we identified data products that performed well in certain regions based on, for example, climatic zone. We identified that some of the good results were obtained due to the cancellation of errors in poor estimates of water budget components. Therefore, we used coefficients of variation to determine the relative quality of a data product, which helped us to identify bad combinations giving us good results. In general, water budget components from ERA5-Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of CLSM, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, with its predecessor and other datasets available. Thus, the nature of the catchment dynamics and balance between components affects the optimum combination of datasets. For regional studies, the combination of datasets that provides the most realistic TWS for a basin will depend on its climatic conditions and factors that cannot be determined a priori. We believe that the results of this study provide a road map for studying the water budget at catchment scale