GloFAS-ERA5 operational global river discharge reanalysis 1979-present

Abstract

<p>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, known as a reanalysis. While the current generation of NWP 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/) by coupling surface and sub-surface runoff from the HTESSEL land surface model used within ECMWF’s latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model.</p><p>This work presents the new 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 days behind real time). The reanalysis was evaluated against a global network of 1801 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 large scale hydrology applications ranging from monitoring global flood and drought conditions, understanding hydroclimatic variability and change, initialising hydrological forecasts, and as raw input to post-processing and machine learning methods that can add further value.</p><p><strong>Data availibility:</strong> The dataset is openly available from the Copernicus Climate Change Service (C3S) Climate Data Store (C3S): https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview and further details and the evaluation of the dataset can be found in the accompanying data description paper: </p><p><strong>Data paper:</strong> Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-232, 2020.</p>

Characteristics of Propagation From Meteorological Drought to Hydrological Drought in the Pearl River Basin

“…Although station observations are regarded to be relatively reliable at the point where a meteorological station locates, the density of the meteorological station network is generally not high enough to fully describe the spatial distributions of meteorological variables (Shi et al., 2020). From the perspective of spatial coverage, grid‐based data sets (Harrigan, et al., 2020; Mishra, et al., 2011) can be considered as a viable substitute or supplement for station observations when conducting drought analysis. Therefore, the precipitation and runoff data provided by European Centre for Medium‐Range Weather Forecasts (ECMWF) are used in this study, which are from 1981 to 2019 with a spatial resolution of 0.1°.…”

Evaluation of the Drivers Responsible for Flooding in Africa

“…CHIRPS represent a good option to estimate rainfall for different regions of Africa based on results from previous studies (Dinku et al., 2018; Harrison et al., 2019; Satgé et al., 2020). Moreover, the recently developed ERA5 reanalysis has been commonly used for large scale studies including in Africa (Ficchì & Stephens, 2019; Harrigan et al., 2020). For both datasets, we extracted the daily and 5‐day annual maximum rainfall (the 5‐day annual maximum precipitation is computed from a running sum of daily precipitation, with the date of the selected 5‐day accumulation period corresponding to the last day of the accumulation window).…”

“…(2018) analyzed damaging flood events in sub‐Saharan Africa reported in the NatCatSERVICE insurance database; they found that these flood events were more strongly related to the seasonal negative anomalies of the standardized precipitation evapotranspiration index (SPEI) than to the 7‐day precipitation totals before the flood events. In terms of flood seasonality in Africa, Ficchì and Stephens (2019) evaluated the influence of large‐scale climate variability on flood timing using GLOFAS‐ERA5 (Harrigan et al., 2020) runoff reanalysis and 65 time series of observed river discharge. They found that both the Indian Ocean Dipole and El Niño–Southern Oscillation have a significant influence on the seasonality of flooding, which depends on the positive/negative phases of these indices.…”

Regional analysis of multivariate compound coastal flooding potential around Europe and environs: sensitivity analysis and spatial patterns

“…Of note is that although these databases are not available on a common grid and there are differences in their spatial resolution, they are all derived from the latest European Centre of Medium-Range Weather Forecasts (ECMWF) global atmospheric reanalysis (ERA5; Hersbach et al, 2020), or from hindcasts forced by this atmospheric reanalysis. The new database GloFAS-ERA (Harrigan et al, 2020) is used for the first time to characterize river discharge when studying compound flooding potential. We do not account for pluvial flooding directly, as pluvial flooding is a much smallerscale process.…”

“…River discharge time series were extracted from the Global Flood Awareness System (GloFAS)-ERA5 reanalysis (Harrigan et al, 2020). This reanalysis is a global gridded dataset (excluding Antarctica), with a horizontal resolution of 0.1 • at a daily time step and with a 40-year-long duration starting 1 January 1979.…”