Geological map level of detail and its impact on our perception of dominant streamflow processes in large-sample hydrological studies

Large-sample hydrology (LSH) datasets have advanced hydrological research by enabling studies across a wide range of catchments. Yet, the impact of landscape attributes included in such datasets on their ability to inform perceptual understanding of catchment behaviour remains underexplored. Here we investigate how the level of detail in maps used to derive catchment-scale geological attributes influences their correlation with streamflow signatures. For this, we used a set of streamflow signatures, and climate and landscape attributes available from the recently released EStreams dataset, alongside geological attributes derived from three geology maps of varying levels of detail: global, continental, and regional. These maps are perceived to have increasing levels of accuracy and were reclassified into four permeability classes. In order to explore scale-dependent effects, we moved from breadth to depth, that is, from a broad continental scale with less detailed analyses to a finer sub-catchment setting with more detailed investigations. We found that the correlation between streamflow signatures and geology attributes generally increased when using more detailed geological maps, drastically changing the perception of the importance of geology in influencing catchment behaviour relative to other landscape properties. In the Moselle catchment, a global geology map with other catchment attributes (e.g., climate and soils) failed to capture regional variations in many streamflow signatures. Moving to the sub-catchment level, we observed that smaller, nested sub-catchments exhibited unique correlation patterns, particularly for the baseflow index, emphasizing the nuanced controls at finer scales. Overall, regional and continental maps generally captured geological details better than global maps. This was particularly evident in areas with heterogeneous rock types, where global maps often oversimplified rock classifications. These findings underscore the importance of region-specific characteristics, which become even more pronounced at local scales, and play a crucial role in detecting meaningful correlations. This has implications for hydrological regionalization and predictions in ungauged catchments, suggesting that integrating high-quality, region-specific geological data into LSH studies is essential for accurate predictions and deeper insights into dominant streamflow generation processes.