Finding similarities between model parameters across different catchments has proved to be challenging, especially for ungauged catchments. Existing approaches struggle due to catchment heterogeneity and non-linear dynamics. In particular, attempts to correlate catchment attributes with hydrological responses have failed due to interdependencies among variables and consequent equifinality.

Machine Learning (ML), particularly Long Short-Term Memory (LSTM) approach, has demonstrated strong predictive and spatial regionalization performance. However, understanding the nature of the regionalization relationships remains difficult. This study proposes a novel approach to partially decouple the representation learning of (a) catchment dynamics by using the HydroLSTM architecture and (b) spatial regionalization relationships by using a Random Forest(RF) clustering approach to learn the relationships between catchment attributes and the dynamics.

This coupled approach, called Regional HydroLSTM, generates a representation of “potential streamflow” using a single cell-state, while the output gate corrects it given the temporal context of the hydrologic regime. RF clusters mediate the relationship between catchment attributes and dynamics, allowing the identification of spatially consistent hydrological regions, thereby providing insight into the factors driving spatial and temporal hydrological variability.

Results suggest that combining the two complementary architectures can enhance the interpretability of regional machine learning models in hydrology, offering a new perspective on the ”catchment classification” problem and potentially advancing streamflow prediction in ungauged basins. We conclude that an improved understanding of the underlying nature of hydrologic systems can be achieved by careful design of ML architectures to target the specific things we are seeking to learn from the data.