How do geological map details influence hydrological model transferability to ungauged basins in large-sample studies?

Large-sample hydrology datasets have become increasingly popular in recent years, providing hydro-meteorological time series and catchment attributes for thousands of catchments worldwide. However, the role of such catchment attributes in informing model regionalization, and particularly the effect of their level of spatial detail on prediction in ungauged basins (PUB), remains poorly explored. This study addresses this gap by examining whether catchment attributes derived from geological maps of varying levels of detail improve model regionalization and, in turn, PUB, for both a bucket-type model and a data-driven Long Short-Term Memory (LSTM) model across 130 catchments in two independent basins: the Moselle (27 100 km²) and the Garonne (13 730 km²). We conducted five modeling experiments: a benchmark without geological information and four geology-informed configurations with increasing levels of detail (random, global, continental, and regional). A fold-based space–time cross-evaluation strategy was used to assess model performance on both time periods and catchments unseen during calibration. Performance was evaluated using a modified Nash–Sutcliffe Efficiency (NSE) and a set of streamflow signatures describing flow variability, storage, and regime behavior. Across both basins and model types, benchmark experiments yielded the lowest space–time performance, followed by the random experiment and the global geology experiment, while the experiments using continental and regional geology consistently resulted in higher NSE values. Improvements were strongest for the bucket-type model, with the most detailed geological attributes leading to consistent gains in median performance and robustness. Differences among experiments were more pronounced for streamflow signatures. For the bucket-type model, only the experiments adopting the continental or regional geology reproduced observed signatures with Spearman’s correlations exceeding 0.60, whereas the LSTM model already showed reasonable skill in the benchmark case but still benefited systematically from increasing geological detail. Together, these results demonstrate that incorporating detailed geological information can enhance streamflow representation and model transferability in PUB applications, and that the level of geological detail is a critical, yet often overlooked, factor in large-sample hydrology and regionalization studies.