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[Display omitted] • Five discharge datasets have median correlation coefficients > 0.6 with the observations. • Overestimation of monthly mean flow is more common than underestimation. • GRFR showed the best estimate at around 40% of the locations. • None of the models provides good estimates for rivers and streams in arid regions. High-resolution global flow discharge datasets are critical to understand the terrestrial water cycle and river ecosystem services. Recently, a number of global hydrological models have been developed to capture global river flow dynamics at locally relevant scales. However, the relative accuracy of these simulated flow discharges is largely unknown. In this paper, using the largest observed streamflow datasets from > 26,000 gauging stations, we evaluated the performance of monthly and sub-monthly flow variation (1980–2014) generated from five major global hydrological models, Catchment-based Macro-scale Floodplain model (CaMa-Flood), Global Flood Awareness System (GloFAS-reanalysis), Global Reach-level Flood Reanalysis (GRFR), PCRaster GLOBal Water Balance model (PCR-GLOBWB), and Water Global Assessment and Prognosis (WaterGAP). Significant spatial heterogeneity in model performance was identified. We found that estimates by all five discharge datasets have a relatively high correlation with the observations (medians > 0.6). But none of them showed adequate correlations between simulated and observed flows for systems in Southwest or Central U.S.A., South Africa, or South Australia. Across all models, overestimation of monthly mean flow was more common than underestimation and high flows at a sub-monthly scale were more accurately characterized than low flows. Performance of all models was poor in Africa and Oceania. No single model performed universally best at all gauging stations, but GRFR showed the best estimate at around 40 % of the locations. None of the models provided satisfying estimates for rivers and streams in arid regions. We also found a significant negative effect of flow regulation on the predictive accuracy of all models. Our study highlights the importance of thorough evaluation of global discharge datasets before their local and regional application and provides guidance for future enhancement of global hydrological models. [ABSTRACT FROM AUTHOR]

It has been shown that the changes in global terrestrial water storage (TWS) are strongly linked to the teleconnections (TCs) that induce large-scale climate variations. However, the contributions of the different TCs to global changes of TWS and its components (water storage components, WSCs) remain undetermined. To fill this gap, we systematically assess the relationships between six major ocean-related TCs and different WSCs derived from the Gravity Recovery and Climate Experiment (GRACE) mission and hydrological models under different timescales. Additionally, the interrelationships of the TCs are also analyzed via the independent component analysis for further investigation. The results allow an improved understanding of the hydrometeorological process controlling WSC changes. Specifically, the annual timescale analysis can constrain high-frequency noises and retain the informative fluctuations of WSC residuals. ENSO and AMO are found to be the two most dominant TCs controlling the variations of WSCs globally. TWS and groundwater storage (GWS) are the two WSCs most correlated with the dominant TCs. The WSCs at shallow depths, which are largely affected by strongly hysteretic controls of TCs, are more closely linked to the TCs with many high-frequency components that tend to have weak hysteresis on WSCs. As for the interrelationships of TCs, the independent component, which is highly correlated with all six TCs, has a predominant influence on WSCs. [ABSTRACT FROM AUTHOR]