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.

N‐loads from subsurface, drains, and groundwater‐fed surface (bypass) flows via two riparian zones (crop field and wetland) to a second order stream were investigated by sampling of shallow and deep groundwater on both sides and monthly measurements of bypass flows from springs, drains, and stream including water quality (nitrate). A push‐pull test in the crop field gave a first‐order denitrification rate (0.23 day^–1). Reactive transport modeling evaluated observations of water chemistry and denitrification processes in the groundwater below the crop field showing that nitrate was completely removed by denitrification with pyrite in the aquifer (model rates of 0.6–2.5 mmol NO₃ L⁻¹ yr⁻¹). A drain in the crop field routed ∼10% (bypass) of the regional groundwater inflow to the stream. Buffer efficiency was high at 90%. The wetland on the other side of the stream hosts several locations of nitrate‐rich bypass flow from springs, predominantly through a non‐maintained drainage system of drainpipes and ditches with bypass flow accounting for 59% of the regional flow input. Nitrate was completely removed in groundwater by denitrification with dissolved organic matter in shallow groundwater. The regional inflow and N load to the wetland is amongst the highest recorded and data shows that the N load to the stream is very high. The buffer efficiency ranged from 45% to 83% depending on if all bypass flows contributed to the stream or only the two with visible outflow. A conceptual model for nitrate removal efficiency as a function of Damköhler number and percent bypass flow is proposed.

The growing use of sensors in fresh waters for water quality measurements generates an increasingly large amount of data that requires quality assurance (QA)/quality control (QC) before the results can be exploited. Such a process is often resource-intensive and may not be consistent across users and sensors. SentemQC (QA-QC of high temporal resolution sensor data) is a cost-efficient, and open-source Python approach developed to ensure the quality of sensor data by performing data QA and QC on large volumes of high-frequency (HF) sensor data. The SentemQC method is computationally efficient and features a six-step user-friendly setup for anomaly detection. The method marks anomalies in data using five moving windows. These windows connect each data point to neighboring points, including those further away in the moving window. As a result, the method can mark not only individual outliers but also clusters of anomalies. Our analysis shows that the method is robust for detecting anomalies in HF sensor data from multiple water quality sensors measuring nitrate, turbidity, oxygen, and pH. The sensors were installed in three different freshwater ecosystems (two streams and one lake) and experimental lake mesocosms. Sensor data from the stream stations yielded anomaly percentages of 0.1%, 0.1%, and 0.2%, which were lower than the anomaly percentages of 0.5%, 0.6%, and 0.8% for the sensors in Lake and mesocosms, respectively. While the sensors in this study contained relatively few anomalies (<2%), they may represent a best-case scenario in terms of use and maintenance. SentemQC allows the user to include the individual sensor uncertainty/accuracy when performing QA-QC. However, SentemQC cannot function independently. Additional QA-QC steps are crucial, including calibration of the sensor data to correct for zero offsets and implementation of gap-filling methods prior to the use of the sensor data for determination of final real-time concentrations and load calculations.

We investigated the utility of using synchronous measurements to create nitrogen (N) emission and retention maps of agricultural areas. Total N (TN) emissions from agricultural areas in three different Danish pilot catchments (1800–3737 ha) and within sub-catchments (100–1200 ha) were determined by a source apportionment approach. Intensive daily (main gauging stations) and fortnightly (synchronous stations) monitoring of discharge, TN, and nitrate-N (NO₃-N) concentrations was conducted for two years. The groundwater N retention was calculated as the difference between a model-calculated NO₃-N leaching from agricultural fields and the calculated agricultural N emission. The average annual N leaching and N emission in the three catchments amounted to 68, 48, and 58 kg N/ha and 6, 30, and 40 kg N/ha, respectively. The N retention in groundwater in the three catchments, calculated based on either TN or NO₃-N emissions, amounted to 26 and 44%, 44 and 57%, and 93 and 97%, respectively, with large variations within two of the main catchments. From this study, we conclude that synchronous measurements in streams provide a good opportunity for developing local N emission and N retention maps. However, NO₃-N should be used when dealing with N retention calculation at the finer resolution scale of 100–300 ha catchments.

The growing use of sensors in fresh waters for water quality measurements generates an increasingly large amount of data that requires quality assurance (QA)/quality control (QC) before the results can be exploited. Such a process is often resource-intensive and may not be consistent across users and sensors. SentemQC (QC of high temporal resolution sensor data) is a cost-efficient, and open-source Python approach developed to ensure the quality of sensor data by performing data QC on large volumes of high-frequency (HF) sensor data. The SentemQC method is computationally efficient and features a six-step user-friendly setup for anomaly detection. The method marks anomalies in data using five moving windows. These windows connect each data point to neighboring points, including those further away in the moving window. As a result, the method can mark not only individual outliers but also clusters of anomalies. Our analysis shows that the method is robust for detecting anomalies in HF sensor data from multiple water quality sensors measuring nitrate, turbidity, oxygen, and pH. The sensors were installed in three different freshwater ecosystems (two streams and one lake) and experimental lake mesocosms. The explored sensor data revealed anomaly percentages ranging from 0.1% to 0.8% of the cleaned datasets by SentemQC. While the sensors in this study contained relatively few anomalies (<2%), they may represent a best-case scenario in terms of use and maintenance. SentemQC allows the user to include the individual sensor uncertainty/accuracy when performing QC. However, SentemQC cannot function independently. Additional QA-QC steps are crucial, including calibration of the sensor data to correct for zero offsets and implementation of gap-filling methods prior to the use of the sensor data for determination of final real-time concentrations and load calculations.

N-loads from subsurface, drains, and groundwater-fed surface (bypass) flows via two riparian zones (crop field and wetland) to a second order stream were investigated by sampling of shallow and deep groundwater on both sides and monthly measurements of flows from springs, drains, and stream including water quality (nitrate). A push-pull test in the crop field gave estimates of first-order denitrification rate (0.23 day ^–1 ). Reactive transport modelling evaluated observations of water chemistry and denitrification processes in the groundwater below the crop field showing that nitrate was completely removed by denitrification with pyrite in the aquifer (model rates of 0.6–2.5 mmol NO ₃ L ⁻¹ yr ⁻¹ ). A drain in the crop field routed approximately 10% (bypass) of the regional groundwater inflow to the stream. Buffer efficiency was high at 90%. The wetland on the other side of the stream hosts several locations of focused nitrate-rich groundwater-fed spring discharge, predominantly through a non-maintained drainage system of drainpipes and ditches with bypass accounting for 59% of the regional flow input. Nitrate was completely removed in groundwater by denitrification with dissolved organic matter in shallow groundwater. The regional inflow and N load to the wetland is amongst the highest recorded and data shows that the N load to the stream is very high. The buffer efficiency ranged from 45–83% depending on if all springs contributed to the stream or only the two with visible outflow. A conceptual model for nitrate removal efficiency as a function of Damköhler number and percent bypass flow is proposed.