Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …

Recent advances in differentiable modeling, a genre of physics-informed machine learning
that trains neural networks (NNs) together with process-based equations, have shown …

Accurate hydrologic modeling is vital to characterizing how the terrestrial water cycle
responds to climate change. Pure deep learning (DL) models have been shown to …

Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and
reservoir management but is challenging to predict at large scales. It is unclear whether SSC …

Accurate hydrological modeling is vital to characterizing how the terrestrial water cycle
responds to climate change. Pure deep learning (DL) models have shown to outperform …

River bathymetry is critical for many aspects of water resources management. We propose
and demonstrate a bathymetry inversion method using a deep‐learning‐based surrogate for …

Predicting the response of hydrologic systems to modified driving forces beyond patterns
that have occurred in the past is of high importance for estimating climate change impacts or …

While deep learning (DL) models exhibit superior simulation accuracy over traditional
distributed hydrological models (DHMs), their main limitations lie in opacity and the absence …

Recently, differentiable modeling techniques have emerged as a promising approach to
bidirectionally integrating neural networks and hydrologic models, achieving performance …

Deep learning (DL) algorithms have previously demonstrated their effectiveness in
streamflow prediction. However, in hydrological time series modelling, the performance of …