Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements

Abstract

High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function.

Keywords: Catchment science; aquatic ecology; high-frequency; optical sensors; stream hydrochemistry; water quality monitoring.

Figure 1

Number of peer-reviewed journal articles containing search phrases “high-frequency” or “high-resolution” and “water quality” in the title, abstract, or keywords. Based on a Web of Science search in August 2022.

Figure 2

Six focus areas reviewed in the paper where high-frequency water quality monitoring contributes to significant scientific advancements within catchment science, stream hydrochemistry, and aquatic ecology and can lead to major improvements in freshwater quality. All icons were reproduced from https://icons8.com/icons/.

Figure 3

Six months of streamwater deuterium isotope ratios sampled at monthly, weekly, and 7-h frequencies (green, orange, and purple, respectively), compared to 7-h precipitation and streamflow water fluxes (light and dark blue, respectively) at Upper Hafren, Plynlimon, Wales (data of ref (19). Weekly sampling was simulated by resampling the high-frequency record every Wednesday at noon; monthly sampling was simulated by resampling at noon on the fourth Wednesday of each month.). Replicate analyses of a quality control standard (red) illustrate the noise level in the high-frequency measurements, showing that the fluctuations in the high-frequency isotope time series are mostly signal rather than noise. Coupling between hydrology and water quality dynamics is clearly visible in the high-frequency isotope record but is obscured by conventional weekly or monthly sampling.

Figure 4

Effect of sampling frequency on load estimation. Top graph shows time series of flow discharge (blue solid line) and total phosphorus (TP) concentrations at hourly (black solid line), daily (blue circles), and weekly (red squares) sampling intervals. Hourly TP concentrations follow directly variation in flow discharge indicating a close coupling between flow generation and TP mobilization and delivery. Depending on sampling routine (time of day and day of week), daily and weekly TP concentrations often do not capture the actual range of concentrations measured with hourly sampling. This is particularly visible during storm events. Bottom graph shows variation in apparent cumulative TP load depending on the sampling frequency. Actual load based on hourly measurements is shown as black solid line. We simulated 24 daily sampling routines (light blue) corresponding to sample collection every day at the same time, e.g., every day at 12 pm. We also simulated seven weekly sampling routines (light red) corresponding to sample collection every week on the same weekday at noon, e.g., every Monday at 12 pm. Median values for daily (dark blue, partly obscured by black line depicting actual loads) and weekly (dark red line) loads were also calculated. Relative errors compared to actual loads varied for daily sampling from −10% (12 pm) to 9% (4am) with a median value of −0.02% and for weekly sampling from −69% (Thursday) to 77% (Friday) with a median value of −16%. High-frequency data were derived from Bieroza et al.