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      GRUN: an observation-based global gridded runoff dataset from 1902 to 2014

      , , ,
      Earth System Science Data
      Copernicus GmbH

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          Abstract

          Abstract. Freshwater resources are of high societal relevance, and understanding their past variability is vital to water management in the context of ongoing climate change. This study introduces a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. In situ streamflow observations are used to train a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from an atmospheric reanalysis. The accuracy of this reconstruction is assessed with cross-validation and compared with an independent set of discharge observations for large river basins. The presented dataset agrees on average better with the streamflow observations than an ensemble of 13 state-of-the art global hydrological model runoff simulations. We estimate a global long-term mean runoff of 38 452 km3 yr−1 in agreement with previous assessments. The temporal coverage of the reconstruction offers an unprecedented view on large-scale features of runoff variability in regions with limited data coverage, making it an ideal candidate for large-scale hydro-climatic process studies, water resource assessments, and evaluating and refining existing hydrological models. The paper closes with example applications fostering the understanding of global freshwater dynamics, interannual variability, drought propagation and the response of runoff to atmospheric teleconnections. The GRUN dataset is available at https://doi.org/10.6084/m9.figshare.9228176 (Ghiggi et al., 2019).

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          Rainfall on Earth is most intense in the intertropical convergence zone (ITCZ), a narrow belt of clouds centred on average around six degrees north of the Equator. The mean position of the ITCZ north of the Equator arises primarily because the Atlantic Ocean transports energy northward across the Equator, rendering the Northern Hemisphere warmer than the Southern Hemisphere. On seasonal and longer timescales, the ITCZ migrates, typically towards a warming hemisphere but with exceptions, such as during El Niño events. An emerging framework links the ITCZ to the atmospheric energy balance and may account for ITCZ variations on timescales from years to geological epochs.
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            Changes in Climate Extremes and their Impacts on the Natural Physical Environment

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              A global hydrological model for deriving water availability indicators: model tuning and validation

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                Author and article information

                Journal
                Earth System Science Data
                Earth Syst. Sci. Data
                Copernicus GmbH
                1866-3516
                2019
                November 13 2019
                : 11
                : 4
                : 1655-1674
                Article
                10.5194/essd-11-1655-2019
                307eee4a-1a84-4b20-a705-1685439592fe
                © 2019

                https://creativecommons.org/licenses/by/4.0/

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