  Time-series modeling has shown great promise in recent studies using the
latest deep learning algorithms such as LSTM (Long Short-Term Memory). These
studies primarily focused on watershed-scale rainfall-runoff modeling or
streamflow forecasting, but the majority of them only considered a single
watershed as a unit. Although this simplification is very effective, it does
not take into account spatial information, which could result in significant
errors in large watersheds. Several studies investigated the use of GNN (Graph
Neural Networks) for data integration by decomposing a large watershed into
multiple sub-watersheds, but each sub-watershed is still treated as a whole,
and the geoinformation contained within the watershed is not fully utilized. In
this paper, we propose the GNRRM (Graph Neural Rainfall-Runoff Model), a novel
deep learning model that makes full use of spatial information from
high-resolution precipitation data, including flow direction and geographic
information. When compared to baseline models, GNRRM has less over-fitting and
significantly improves model performance. Our findings support the importance
of hydrological data in deep learning-based rainfall-runoff modeling, and we
encourage researchers to include more domain knowledge in their models.
