Exploring Catchment Regionalization through the Eyes of HydroLSTM

Regionalization is an issue that hydrologists have been working on for decades. It is used, for example, when we transfer parameters from one calibrated model to another, or when we identify similarities between gauged to ungauged catchments. However, there is still no unified method that can successfully transfer parameters and identify similarities between different regions while accounting for differences in meteorological forcing, catchment attributes, and hydrological responses. Machine learning (ML) has shown promising results in the generalization of its results at temporal and spatial scales for streamflow prediction. This suggests that ML models have learned useful regionalization relationships that we could extract. This study explores how the HydroLSTM representation, a modification of traditional Long Short-Term Memory, can learn meaningful relationships between meteorological forcing and catchment attributes. One promising feature of the HydroLSTM representation is that the learned patterns can generate different hydrological responses across the US. These findings indicate that we can learn more about regionalization by studying ML models.