Abstract:<p>Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and reservoir management but is challenging to predict at large scales. This study seeks to test the feasibility of deep-network-based models to predict SSC at basin outlets given basin-averaged forcings and basin-physiographic attributes as inputs and extract insights by interpreting the spatially-varying model performances. We trained long short-term memory (LSTM) deep networks either separately for each of … Show more
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