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.

Highlights

  • Total Water Storage (TWS) is a fundamental variable of the global hydrological cycle, representing the sum of all water storage components including water in rivers, lakes and reservoirs, wetlands, soil, and aquifers

  • While we do observe this to be the case for the general MC as well as selected sub-basins (UMM and Cauca), when comparing monthly time scale of TWS for the smaller basins, we find that the land surface models (LSM) WRR2 have a better agreement over global hydrological models (GHM) (Fig. 11)

  • The Gravity Recovery and Climate Experiment (GRACE) satellite dataset on the other hand, is a recent and powerful tool that provides independent and distributed observations of Total Water Storage (TWS) in river basins, giving insight into the water storage dynamics that would not be possible through conventional observations

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Summary

Introduction

Total Water Storage (TWS) is a fundamental variable of the global hydrological cycle, representing the sum of all water storage components including water in rivers, lakes and reservoirs, wetlands, soil, and aquifers. As an integrated measure of water availability, both surface and groundwater, the dynamic of TWS has significant implications for water resources management (Syed et al, 2008). For this reason, the monitoring of changes in TWS is critical for characterising water resources variability, and to improve the prediction of regional and global water cycles and interactions with the 35 Earth’s climate system (Famiglietti, 2004). Even many 40 traditional analyses have assumed that at longer timescales and over large regions, change in TWS can be approximated as zero This implies that in water balance studies it is common to ignore the long-term trends of TWS (Reager and Famiglietti, 2013)

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