A multi‐scale study of the dominant catchment characteristics impacting low‐flow metrics. Issue 1 (10th January 2022)

• Record Type: Journal Article
• Authors: Floriancic, Marius G.; Spies, Daniel; van Meerveld, Ilja H. J.; Molnar, Peter
• Is Part Of: Hydrological processes. Volume 36:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-10
• Subjects: discharge -- hydrologic drought -- landscape properties -- machine‐learning -- recession -- spatial variation; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.14462
• Languages: English
• ISSNs: 0885-6087

Abstract

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