Preprints
https://doi.org/10.5194/hess-2021-323
https://doi.org/10.5194/hess-2021-323

  30 Jul 2021

30 Jul 2021

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

Benchmarking global hydrological and land surface models against GRACE in a medium-size tropical basin

Silvana Bolaños Chavarría1,2, Micha Werner3, and Juan Fernando Salazar1 Silvana Bolaños Chavarría et al.
  • 1Grupo de Ingeniería y Gestión Ambiental (GIGA), Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia
  • 2Grupo de Investigación en Ingeniería Sostenible (GIS), Facultad de Ingeniería, Politécnico Colombiano Jaime Isaza Cadavid, Medellín, Colombia
  • 3Water Science and Engineering Department, IHE Delft Institute for Water Education, Delft, the Netherlands

Abstract. The increasing reliance on global models to address climate and human stresses on hydrology and water resources underlines the necessity for assessing the reliability of these models. In river basins where availability of gauging information from terrestrial networks is poor, models are increasingly proving to be a powerful tool to support hydrological studies and water resources assessments. However, the lack of in-situ data hampers rigorous performance assessment, particularly in tropical basins where discordance between global models is considerable. Remotely sensed data of the terrestrial water storage obtained from the GRACE satellite mission can, however, provide independent data against which the performance of such global models can be evaluated. Here we assess the reliability of six global hydrological models (GHM) and four land surface models (LSM) available at two resolutions. We compare Total Water Storage (TWS)'s modelled dynamics with TWS derived from GRACE data over the Magdalena-Cauca basin in Colombia, a medium-sized tropical basin with a comparatively well-developed gauging network. We benchmark monthly TWS changes from each model against GRACE data for 2002–2014, evaluating monthly variability, seasonality, and long-term trends. TWS changes are evaluated at basin level, as well as for selected sub-basins with decreasing basin size. We find that the models poorly represent TWS for the monthly series, but they improve in representing seasonality and long-term trends. The high-resolution GHM W3RA model forced by the Multi-Source Weighted Ensemble Precipitation (MSWEP) is most consistent at providing the best performance at almost all basin scales, with higher-resolution models generally outperforming lower-resolution counterparts. This is, however, not the case for all models. Results highlight the importance of basin scale in the representation of TWS by the models, as with decreasing basin area, we note a commensurate decrease in the model performance. A marked reduction in performance is found for basins smaller than 60,000 km2. Although uncertainties in the GRACE measurement increase for smaller catchments, the models are clearly challenged in representing the complex hydrological processes of this tropical basin, as well as human influences. We conclude that GRACE provides a valuable dataset to benchmark global simulations of TWS change, in particular for those models with explicit representation of the internal dynamics of hydrological stocks, offering useful information for the continued improvement of large-scale hydrological and land-surface models of the global terrestrial water cycle, including in tropical basins.

Silvana Bolaños Chavarría et al.

Status: open (until 13 Oct 2021)

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Silvana Bolaños Chavarría et al.

Silvana Bolaños Chavarría et al.

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Short summary
Using total water storage (TWS) from GRACE satellites, we assess the reliability of global hydrological and land surface models over a medium-sized tropical basin with a well-developed gauging network. We find the models poorly represent TWS for the monthly series, but they improve in representing seasonality and long-term trends. We conclude that GRACE provides a valuable dataset to benchmark global simulations of TWS change, offering a useful tool to improve global models in tropical basins.