Abstract
Abstract. Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1∘ at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling–Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions to the identification of hydroclimatic variability and change and as raw input for post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview (last access: 28 June 2020) with the following DOI: https://doi.org/10.24381/cds.a4fdd6b9 (C3S, 2019).
Highlights
A key challenge in hydrology is estimating past, present, and future hydrological conditions in rivers around the world
The aim of this paper is to describe and evaluate the Global Flood Awareness System (GloFAS)-ERA5 global river discharge reanalysis dataset launched on 5 November 2019
These months correspond with a lower Southern Hemisphere correlation (Fig. 9b) and a higher proportion of stations with large positive variability ratios (i.e. GloFAS-ERA5 has higher variability than observed river discharge)
Summary
A key challenge in hydrology is estimating past, present, and future hydrological conditions in rivers around the world This is largely due to severe temporal and spatial gaps in the global river discharge observing network. Fekete et al, 2002; Döll et al, 2003; Qian et al, 2006; Sperna Weiland et al, 2010; Reichle et al, 2011; Yamazaki et al, 2011; Beck et al, 2017; Ghiggi et al, 2019; Lin et al, 2019) While these datasets can be used to understand past variability and change in the terrestrial hydrological cycle, they are currently not produced in an operational environment in near real time and so cannot be used for monitoring current global river conditions or providing initial conditions to hydrometeorological forecasting systems
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