Turbidity‐discharge hysteresis in a meso‐scale catchment: The importance of intermediate scale events

Abstract In‐situ sensors for riverine water quality monitoring are a powerful tool to describe temporal variations when efficient and informative analyses are applied to the large quantities of data collected. Concentration‐discharge hysteresis patterns observed during storm events give insights into headwater catchment processes. However, the applicability of this approach to larger catchments is less well known. Here, we evaluate the potential for high‐frequency turbidity‐discharge (Q) hysteresis patterns to give insights into processes operating in a meso‐scale (722 km 2 ) northern mixed land use catchment. As existing event identification methods did not work, we developed a new, objective method based on hydrograph characteristics and identified 76 events for further analysis. Qualitative event analysis identified three recurring patterns. Events with low mean Q (≤ 2 m 3 /s) often showed short‐term, quasi‐periodic turbidity variation, to a large extent disconnected from Q variation. High max Q events (≥15 m 3 /s) were often associated with spring flood or snowmelt, and showed a disconnection between turbidity and Q. Intermediate Q events (mean Q: 2–11 m 3 /s) were the most informative when applying hysteresis indexes, since changes in turbidity and Q were actually connected. Hysteresis indexes could be calculated on a subset of 60 events, which showed heterogeneous responses: 38% had a clockwise response, 12% anticlockwise, 12% figure eight (clockwise–anticlockwise), 10% reverse figure eight (anticlockwise–clockwise) and 28% showed a complex response. Clockwise hysteresis responses were associated with the wetter winter and spring seasons. Generally, changes in Q and turbidity were small during anticlockwise hysteresis events. Precipitation often influenced figure‐eight patterns, while complex patterns often occurred during summer low flows. Analysis of intermediate Q events can improve process understanding of meso‐scale catchments and possibly aid in choosing appropriate management actions for targeting a specific observed pattern.