Abstract. A new algorithm to estimate river discharges from altimetry measurements only is designed. A first estimation is obtained by an artificial neural network trained from the altimetry large scale water surface measurements plus drainage area information. The combination of this purely data-based estimation and a dedicated algebraic flow model provides a first physically-consistent estimation. The latter is next employed as the first guess of an advanced variational data assimilation formulation. The final estimation is highly accurate for rivers presenting features within the learning partition; for rivers far outside the learning partition, the space-time variations of discharge remain accurately approximated however the global estimation presents a potential bias. Indeed, it is shown that if the estimation is based on the hydrodynamics models only, the inverse problem may be well-defined but up to a bias only (the bias scales the global estimation). This bias is removed thanks to the ANN but for rivers in the learning partition only. For rivers outside the learning partition, any mean value (eg. annual, seasonal) enables to remove the bias. Finally, the present hybrid and hierarchical inversion strategy seems to provide much more accurate estimations compared to the state-of-the-art for the considered 29 heterogeneous river portions.

中文翻译：

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混合神经网络–变异数据同化算法，可从类似SWOT的数据中推断河流流量

摘要。设计了一种仅根据高程测量来估算河流流量的新算法。第一次估算是通过人工神经网络获得的，该人工神经网络是根据测高仪大规模水面测量结果加上排水面积信息而训练的。这种纯粹基于数据的估计与专用代数流模型的组合提供了第一个物理上一致的估计。接下来，将后者用作高级变异数据同化公式的第一个猜测。对于在学习分区内呈现特征的河流，最终估算值非常准确；对于远离学习分区的河流，流量的时空变化仍可精确估算，但总体估算存在潜在偏差。实际上，已经表明，如果估算仅基于流体动力学模型，逆问题可能是定义明确的，但最多只能是一个偏差（该偏差可缩放全局估计）。借助ANN消除了这种偏见，但仅针对学习分区中的河流。对于学习分区以外的河流，任何平均值（例如，年度，季节性）都可以消除偏差。最后，与现有技术相比，对于考虑的29条异质河段，本混合和分层反演策略似乎提供了更为准确的估计。