Abstract

The quantification of aquifer contribution to river discharge is of primary importance to evaluate the impact of climatic and anthropogenic stresses on the availability of water resources. Several baseflow estimation methods require river discharge measurements, which can be difficult to obtain at high spatio-temporal resolution for large basins. The future Surface Water and Ocean Topography (SWOT) satellite mission will provide discharge estimations for large rivers (>50–100 m wide) even in ungauged basins. The frequency of these estimations depends mainly on latitude and ranges from zero to more than ten values in the 21-day satellite cycle. This work aims at answering the following question: can baseflow be estimated from SWOT observations during the mission lifetime? An algorithm based on hydrograph separation by Chapman's filter was developed to automatically estimate the baseflow in a river network at regional scale (>10 000 km2). The algorithm was applied to the Seine river basin (75 000 km2, France) using the discharge time series simulated at daily time step by a coupled hydrological-hydrogeological model to obtain the reference baseflow estimations. The same algorithm is then forced with discharge time series sampled at SWOT observation frequency. The average baseflow is estimated with good accuracy for all the reaches which are observed at least once per cycle (relative bias less than 8%). The time evolution of baseflow is also rather well retrieved, with a Nash-Sutcliffe coefficient above 0.7 for 96% of the network length. An analysis of the effect of SWOT discharge uncertainties on baseflow estimation shows that bias is the component of discharge error that most contributes to the error on baseflow. Anyway, when the combined effect of SWOT discharge sampling and SWOT discharge uncertainties is considered, the error on baseflow estimates is slightly smaller than that on discharge. This work provides new potential for the SWOT mission in terms of global hydrological analysis and water cycle closure.

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