A multi?scale study of the dominant catchment characteristics impacting low?flow metrics

Low flows can impact water use and instream ecology. Therefore, reliable predictions of low-flow metrics are crucial. In this study, we assess which catchment characteristics (climate, topography, geology landcover) explain the spatial variability at two different scales: regional scale small headwater scale. For regional-scale analysis, calculated mean 7-day annual minimum flow (qmin), that is exceeded 95% year (q95), master recession constant (C) for 280 independent gauging stations across Swiss Plateau Alps 2000–2018 period. We assessed relation between 44 three based on correlation analysis a random forest model. Low-flow magnitudes were positively correlated with fraction area covered by sandstone bedrock or alluvium, has slope 10° 30°. Across Alps, slopes 30° 60°, glacial deposits debris cover. There was good agreement observations regression model top 11 both regions: 80% catchments 60% Alpine catchments, could predict within an error 30%. The residuals model, however, varied short distances, suggesting local affect metrics. local-scale conducted 1-day snapshot field campaigns in 16 during periods 2015 2016. measurements these sub-catchments also showed areas storage-to-river connectivity had above average magnitudes. Including knowledge may help to improve predictions, not all useful descriptors larger scales.