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

Emma E Lannergård,Martyn N Futter,Jens Fölster

doi:10.1002/hyp.14435

Emma E Lannergård, Martyn N Futter + Show 1 more

Open Access

https://doi.org/10.1002/hyp.14435

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Abstract

AbstractIn‐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 km2) 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 m3/s) often showed short‐term, quasi‐periodic turbidity variation, to a large extent disconnected from Q variation. High max Q events (≥15 m3/s) were often associated with spring flood or snowmelt, and showed a disconnection between turbidity and Q. Intermediate Q events (mean Q: 2–11 m3/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.

Highlights

Successful management of surface water quality is dependent on adequate and appropriate monitoring (Fölster et al, 2014)
The spring season was related to maximum snowmelt and Highfrequency Q (HFQ), while the winter season was correlated to mean snow depth, accumulated snowfall, Q 10 days before the event (Qd-10), accumulated hydrologically effective rainfall as well as minimum turbidity concentration during the events
Efficient and informative data analysis of HF data are needed for the use of in-situ sensors in, for example, in national monitoring programmes

Summary

| INTRODUCTION

Successful management of surface water quality is dependent on adequate and appropriate monitoring (Fölster et al, 2014). The C-Q mismatch in time can be further analysed and different hysteresis patterns could, for example, give insight into dominant hydrological pathways during flow events (Evans & Davies, 1998; Rose et al, 2018), contributing source areas or source limitations (Outram et al, 2014; Williams, 1989). Hysteresis analysis has been used to understand processes and mechanisms under varying environmental conditions and land use (Bowes et al, 2005; Eder et al, 2010; Haddadchi & Hicks, 2020a; Lana-Renault et al, 2011; Lawler et al, 2006; Rose et al, 2018; Sherriff et al, 2016), and is potentially a way to convert HF data into insights supporting better management of surface water quality (e.g., Wenng et al, 2021). The final ordination based on the 20 selected variables was compared to the original full ordination using a Procrustes analysis, which indicated only small differences between the two ordinations

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| DISCUSSION

Findings

| CONCLUSIONS