Abstract
<jats:p>Abstract. Global river discharge observations are critical for climate and hydrological research, used for water resource management, risk mitigation, infrastructure and environment conservation, among many other areas. However, existing observations are limited in availability, accuracy and spatial extent, with uneven geographical distribution and high levels of uncertainty often recorded. The NASA SWOT mission provides the opportunity to fill this gap, providing water level observations for all rivers exceeding 100 m in width worldwide. Combined with the daily discharge model GRADES-hydroDL, we present a statistical method to produce a set of global virtual gauging stations with the same temporal resolution as the satellite observations. The high level of accuracy that the SWOT water surface elevation observations allows us to improve the dynamics of discharge estimation over the input discharge model. For our validation gauges, where we compared modelled discharge to observed values, 66 % of Pearson R values are larger than 0.9, showing the method’s skill at replicating temporal patterns in recorded flow. The median Root Mean Square Error, RMSE, of the input discharge set is 36.3 m3 s−1 larger than that of our proposed method, and the NSE 0.3 lower. However, our method fails to improve the bias; the median absolute normalised bias is 0.10 (10 %) higher in our model than the input model, indicating that while the inclusion of Earth Observation can greatly improve the discharge time series dynamics, the trade-off is an increased bias in the distribution. Nevertheless, as the record of SWOT observations increases in length, these results should improve and the margins of bias difference decrease. The result is a globally applicable reanalysis dataset of discharge, which will complement existing physically based models and ground observations, and can be widely used within global hydrological models.</jats:p>