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.

中文翻译：

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从 SWOT 星载任务中检索河流基流

摘要 含水层对河流流量贡献的量化对于评估气候和人为压力对水资源可用性的影响至关重要。几种基流估计方法需要河流流量测量，这在大型流域的高时空分辨率下很难获得。未来的地表水和海洋地形 (SWOT) 卫星任务将为大型河流（> 50-100 m 宽）提供流量估计，即使在未测量的盆地中也是如此。这些估计的频率主要取决于纬度，在 21 天卫星周期中范围从零到十多个值。这项工作旨在回答以下问题：能否根据任务生命周期内的 SWOT 观测估计基流？Chapman 的一种基于水位图分离的算法 s 过滤器被开发用于自动估计区域尺度（>10 000 平方公里）河网中的基流。该算法被应用于塞纳河流域（75 000 平方公里，法国），使用水文-水文地质耦合模型在每日时间步长模拟的排放时间序列，以获得参考基流估计。然后使用以 SWOT 观察频率采样的放电时间序列强制执行相同的算法。对于每个周期至少观察一次的所有河段，以良好的准确度估计平均基流（相对偏差小于 8%）。基流的时间演变也得到了很好的检索，对于 96% 的网络长度，Nash-Sutcliffe 系数高于 0.7。对 SWOT 排放不确定性对基流估计影响的分析表明，偏差是排放误差中对基流误差贡献最大的成分。无论如何，当考虑 SWOT 排放采样和 SWOT 排放不确定性的综合影响时，基流估计的误差略小于排放的误差。这项工作为 SWOT 任务在全球水文分析和水循环闭合方面提供了新的潜力。