G‐RUN ENSEMBLE: A Multi‐Forcing Observation‐Based Global Runoff Reanalysis

G Ghiggi,S I Seneviratne,V Humphrey,L Gudmundsson

doi:10.1029/2020wr028787

Abstract

AbstractRiver discharge is an Essential Climate Variable (ECV) and is one of the best monitored components of the terrestrial water cycle. Nonetheless, gauging stations are distributed unevenly around the world, leaving many white spaces on global freshwater resources maps. Here, we use a machine learning algorithm and historical weather data to upscale sparse in situ river discharge measurements. We provide a global reanalysis of monthly runoff rates for periods covering decades to the past century at a resolution of 0.5° (about 55 km), and with up to 525 ensemble members based on 21 different atmospheric forcing data sets. This global runoff reconstruction, named Global RUNoff ENSEMBLE (G‐RUN ENSEMBLE), is evaluated using independent observations from large river basins and benchmarked against other publicly available runoff data sets over the period 1981–2010. The accuracy of the data set is evaluated on observed river flow from basins not used for model calibration and is found to compare favorably against state‐of‐the‐art global hydrological model simulations. The G‐RUN ENSEMBLE estimates the global mean runoff volume to range between 3.2 × 104 and 3.8 × 104 km3 yr−1. This publicly available data set (https://doi.org/10.6084/m9.figshare.12794075) has a wide range of applications, including regional water resources assessments, climate change attribution studies, hydro‐climatic process studies as well as the evaluation, calibration and refinement of global hydrological models.

Highlights

River discharge is listed as an Essential Climate Variable (ECV) by the World Meteorological Organization (WMO) (Bojinski et al, 2014) and is one of the best monitored variables of the terrestrial water cycle
This study builds on the established methodology presented by Gudmundsson and Seneviratne (2015) and Ghiggi et al (2019) and derives monthly runoff estimates from an ensemble of atmospheric forcing data
The resulting multi-forcing ensemble of runoff reconstructions, termed G-RUN ENSEMBLE, allows us to quantify the uncertainty associated to model input data

Summary

Introduction

River discharge is listed as an Essential Climate Variable (ECV) by the World Meteorological Organization (WMO) (Bojinski et al, 2014) and is one of the best monitored variables of the terrestrial water cycle. Human-induced climate change affects the hydrological cycle and the availability of water resources (Gudmundsson et al, 2021; Gudmundsson, Seneviratne, & Zhang, 2017; Padrón et al, 2020). Global hydrological models (GHMs) provide gridded estimates of the various water balance components, several model evaluation studies did highlight large discrepancies between simulations, in situ observations and remote-sensing estimates of evapotranspiration (Miralles et al, 2016; Mueller et al, 2013; Wartenburger et al, 2018), terrestrial water storage (Humphrey & Gudmundsson, 2019; Humphrey, Gudmundsson, & Seneviratne, 2017; Scanlon et al, 2018) and runoff

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