28 Jun 2021

28 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

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

Fanny Lehmann, Bramha Dutt Vishwakarma, and Jonathan Bamber Fanny Lehmann et al.
  • School of Geographical Sciences, University of Bristol

Abstract. 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 spatiotemporal 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 both in space and time. Despite these advances, closure of the water budget at 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 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 cyclostationary NSE. These 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 analyzing 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 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 the ERA5 Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of the Catchment Land Surface Model, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, to 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, the results of this study provide a roadmap for studying the water budget at the catchment scale.

Fanny Lehmann et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-279', Anonymous Referee #1, 27 Jul 2021
    • AC2: 'Reply on RC1', Fanny Lehmann, 09 Sep 2021
  • RC2: 'Comment on hess-2021-279', Christof Lorenz, 04 Aug 2021
    • AC1: 'Reply on RC2', Fanny Lehmann, 09 Sep 2021

Fanny Lehmann et al.

Model code and software

Water budget closure Fanny Lehmann

Fanny Lehmann et al.


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Short summary
Many data sources are available to evaluate components of the water cycle (precipitation, evapotranspiration, runoff, and terrestrial water storage). Despite this variety, it remains unclear how different combinations of datasets satisfy the conservation of mass. We conducted the most comprehensive analysis of water budget closure on a global scale. Our results can serve as a basis to select appropriate datasets for regional hydrological studies.