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

Optimising multi-site sensor networks in lowland permeable catchments for comprehensive water quality monitoring and nitrogen mass balancing during baseflow conditions

High-frequency data are essential to elucidate intricate fluvial water quality dynamics, but current understanding is often limited by measurements taken solely at the catchment outlet. In densely populated and agriculturally intensive lowland permeable catchments, such as Chalk streams in southern England, the spatial heterogeneity of processes driving solute mobilisation, transport, and fate can only be unravelled through monitoring at high spatio-temporal resolutions. In this study, we deployed a network of in-situ sensors in the lower section (ca. 18 km third-order reach) of a chalk stream during baseflow conditions to address this limitation. We focused particularly on reach-scale processing of reactive nitrogen (N), using a mass-balance approach based on high-frequency measurements, to quantify the relative importance of different sources (springs, sewage effluent) and sinks (microbiological processes in the river). Continuous in-situ measurements revealed important event-based influences that grab sampling would likely fail to capture, such as rainfall disturbance of metabolic activity and polluted discharges from combined sewer overflows. Mass balances showed the majority of fluvial nitrate load originates in the first half (ca. 8 km) of the study reach, where it increased by a factor of 2.8 from 324 kg d-1 to 914 kg d-1. This was mainly attributed to a sewage treatment discharge (37% of accreted load), and chalk spring discharges (55%) carrying loads primarily from agricultural inputs. Nitrate assimilation (overall for the study reach 80 mg m-2 d-1) by autotrophs was estimated to be the main retention pathway but accounted for only 2.6% ± 0.6% of total loading to the stream reach. Despite the short study duration (5 weeks) and extreme low-flow conditions, we concluded that the river has limited capacity to attenuate gross N loads, causing detrimental ecological impacts to the downstream marine conservation area. Our findings underscore the management imperative of reducing catchment N loading to nitrate-enriched streams as the most effective way of controlling N exports and restoring the removal efficacy of natural stream ecosystems. Our novel mass-balance sensor-network approach is effective at quantifying the relative importance of solute sources and sinks in heterogenous catchments. But, to maximise the data value of multi-station networks, we recommend recording measurements over long durations and in unison with other sampling strategies e.g. tracer studies, terrestrial and ecological monitoring, sediment-core sampling, and longitudinal profiling.

Inorganic nitrogen and phytoplankton dynamics in a subtropical reservoir during low-flow winter periods: Implications for nitrogen management

The dynamics of nutrient cycling in inland waters, particularly during algal blooms, play a critical role in shaping aquatic ecosystems. However, the interaction between phytoplankton and inorganic nitrogen during low-flow periods remains poorly understood. This study presents high-frequency monitoring of ammonium (NH4-N), nitrate (NO3-N), nitrite (NO2-N) and phytoplankton communities in a subtropical river-type reservoir during the low-flow winter period, characterized by blooms dominated by cryptophytes and green algae. Our results revealed that NH4-N concentrations exhibited a diurnal pattern of decreasing during the day and increasing during the night, which was negatively correlated with total algal biomass at the intraday fluctuation scale (coefficient = -0.378, p = 0.028), indicating strong algal uptake of ammonium during daytime. NO3-N and NO2-N concentrations, however, did not show clear diurnal co-varied patterns with algae. On the day-to-day scale, the external nitrogen inputs resulting from rainfall contributed to the changes, particularly after extended dry periods. We observed low NH4-N concentrations and total algal biomass during the end of algal bloom. However, 3-4 days later ammonium thrived, followed by another algal bloom. Algal bloom occurrences caused large diurnal fluctuations in reservoir NH4-N concentrations (daily differences >5 μmol L-1), resulting the maximum nighttime NH4-N flux reaching up to five times the minimum daytime flux. Our study highlights the advantages of high-frequency synchronous monitoring of nutrient-algae dynamics to understand their interactions, and the importance of ammonium control on preventing algal blooms during low-flow winter periods.

Establishing performance criteria for evaluating watershed-scale sediment and nutrient models at fine temporal scales

Watershed water quality models are mathematical tools used to simulate processes related to water, sediment, and nutrients. These models provide a framework that can be used to inform decision-making and the allocation of resources for watershed management. Therefore, it is critical to answer the question "when is a model good enough?" Established performance evaluation criteria, or thresholds for what is considered a 'good' model, provide common benchmarks against which model performance can be compared. Since the publication of prior meta-analyses on this topic, developments in the last decade necessitate further investigation, such as the advancement in high performance computing, the proliferation of aquatic sensors, and the development of machine learning algorithms. We surveyed the literature for quantitative model performance measures, including the Nash-Sutcliffe efficiency (NSE), with a particular focus on process-based models operating at fine temporal scales as their performance evaluation criteria are presently underdeveloped. The synthesis dataset was used to assess the influence of temporal resolution (sub-daily, daily, and monthly), calibration duration (< 3 years, 3 to 8 years, and > 8 years), and constituent target units (concentration, load, and yield) on model performance. The synthesis dataset includes 229 model applications, from which we use bootstrapping and personal modeling experience to establish sub-daily and daily performance evaluation criteria for flow, sediment, total nutrient, and dissolved nutrient models. For daily model evaluation, the NSE for sediment, total nutrient, and dissolved nutrient models should exceed 0.45, 0.30, and 0.35, respectively, for 'satisfactory' performance. Model performance generally improved when transitioning from short (< 3 years) to medium (3 to 8 years) calibration durations, but no additional gain was observed with longer (> 8 years) calibration. Dissolved nutrient models calibrated to load (e.g., kg/s) out-performed those calibrated to concentration (e.g., mg/L), whereas selection of target units was not significant for sediment and total nutrient models. We recommend the use of concentration rather than load as a water quality modeling target, as load may be biased by strong flow model performance whereas concentration provides a flow-independent measure of performance. Although the performance criteria developed herein are based on process-based models, they may be useful in assessing machine learning model performance. We demonstrate one such assessment on a recent deep learning model of daily nitrate prediction across the United States. The guidance presented here is intended to be used alongside, rather than to replace, the experience and modeling judgement of engineers and scientist who work to maintain our collective water resources.

Best practice in high-frequency water quality monitoring for improved management and assessment; a novel decision workflow

The use of high-frequency water quality monitoring has increased over several decades. This has mostly been motivated by curiosity-driven research and has significantly improved our understanding of hydrochemical processes. Despite these scientific successes and the growth in sensor technology, the large-scale uptake of high-frequency water quality monitoring by water managers is hampered by a lack of comprehensive practical guidelines. Low-frequency hydrochemical data are still routinely used to review environmental policies but are prone to missing important event-driven processes. With a changing climate where such event-driven processes are more likely to occur and have a greater impact, the adoption of high-frequency water quality monitoring is becoming more pressing. To prepare regulators and environmental and hydrological agencies for these new challenges, this paper reviews international best practice in high-frequency data provision. As a result, we summarise the added value of high-frequency water quality monitoring, describe international best practices for sensors and analysers in the field, and evaluate the experience with high-frequency data cleaning. We propose a decision workflow that includes considerations of monitoring data needs, sensor choice, maintenance and calibration, and structured data processing. The workflow fills an important knowledge-exchange gap between research and statutory surveillance for future high-frequency water quality sensor uptake by practitioners and agencies.

Improving monitoring of dissolved organic matter from the wastewater treatment plant to the receiving environment: A new high-frequency in situ fluorescence sensor capable of analyzing 29 pairs of Ex/Em wavelengths

A high-frequency, in situ fluorescence probe, called Fluocopée®, has been developed in order to better monitor variations in both the quality and quantity of dissolved organic matter within various aquatic environments (e.g. wastewater, receiving environments) thanks to a wide choice of 29 measured Excitation/Emission wavelength pairs. This advance pave the way to new measurement possibilities in comparison with existing probes, which are usually only able to measure 1-4 fluorophores. The qualification tests of the Fluocopée® probe indicate a high level of accuracy for the measurements of tyrosine, tryptophan and humic acids solutions. Good repeatability and reproducibility are also observed. For the first time, this tool has been deployed in an urban watershed (Bougival, Seine River, downstream of Paris) and in the settled effluent from a wastewater treatment plant (Seine aval, Achères, France). This new high-frequency in situ probe offers great application potential, including organic matter quality and quantity monitoring at drinking and wastewater treatment plants (treatment optimization) and in continental and marine waters (the fate of organic matter in biogeochemical cycles).

Evaluating multiannual sedimentary nutrient retention in agricultural two-stage channels

The two-stage channel (TSC) design with a vegetated man-made floodplain has been recommended as an alternative to conventional re-dredging for managing suspended sediment (SS) and nutrient loads in agricultural streams. However, there are currently uncertainties surrounding the efficiency of TSCs, since mass balances covering the whole annual hydrograph and including different periods of the channel life cycle are lacking. This paper aims to improve understanding of the medium-term morphological development and sedimentary nutrient retention when a dredged, trapezoidal-shaped channel is converted into a TSC, using a mass balance estimate of nutrient and carbon retention from immediately after excavation until the establishment of approximate biogeochemical equilibrium retention. We developed a framework allowing estimation of the sedimentary net retention of phosphorus (P), nitrogen (N) and carbon (C) considering the differences in the initial and mature biogeochemical conditions in topsoil between different channel parts. Further, we conducted repeated elevation surveys and analyses of vertical sedimentary elemental chemistry over a 9-year period to apply the framework at a pilot site in Southern Finland. The pilot TSC floodplain significantly retained SS and nutrients while the low-flow channel did not suffer from siltation, supporting the hypothesized enhanced self-cleansing capacity of TSCs compared to trapezoidal cross-sections. Because of the flushing of the earlier bed deposits, there was net release of SS, P, and N over the first 9 years in the entire TSC system. Depending on the element and channel part considered, physical deposition constituted 13‒79% of the net retention on the newly exposed, excavated surfaces, while the remainder could be attributed to biogeochemical retention, enriching the topsoil in nutrients and carbon. The developed framework is highly suitable to assess the medium-term sedimentary nutrient retention in TSC systems. As a novelty, the framework improves the reliability of the retention efficiency evaluation compared to the typically used snapshot water quality sampling and allows prioritizing the required sediment coring at further TSC sites. The results allow heterogeneities in the process rates to be quantified and potential inefficiencies in nutrient retention due to channel design and morphology to be identified.

Ardulake temperature profiler: An open-source, low-cost, automated monitoring system to unravel the mixing behavior of lakes

Understanding the thermal classification of lakes based on mixing regimes is fundamental in limnology. Although this classification has traditionally been considered well-established, recent studies highlight variations in the mixing behaviors of ponds and shallow lakes. This paper introduces the Ardulake temperature profiler, an innovative, simple, and autonomous high-frequency temperature monitoring system designed for shallow to moderately deep lakes (3.5 to 10 m). Utilizing Arduino technology and GPRS telemetry, the system is cost-effective, with electronic components and sensors costing approximately USD 250 and buoy construction and deployment around USD 1000. The Ardulake enables real-time environmental temperature monitoring and data storage on an online platform for subsequent analysis and visualization. The collected data supports ecosystem research and the numerical modeling of thermal behavior in lakes. Key strengths of the system include low production and maintenance costs, replicability, and customization capabilities. Challenges, such as interference from animal activity, are addressed with recommended preventive measures tailored to specific fauna. Overall, the Ardulake temperature profiler offers a practical tool for advancing limnological research, with potential for modification to various environmental monitoring objectives.

Corroboration of arsenic variation over the Indian Peninsula through standardized precipitation evapotranspiration indices and groundwater level fluctuations: Water quantity indicators for water quality prediction

The contamination of groundwater with arsenic (As) as a result of geo-morphological and hydrogeochemical factors has been the subject of comprehensive research. However, there has been limited exploration of the spread of As under the influence of dynamic elements such as floods, droughts, and rapidly declining groundwater levels. Moreover, the utilization of rapidly changing natural forces, including hydroclimatic extremes and declining groundwater levels, in conjunction with standard climate indices such as the Standard Precipitation Index (SPI) and the Standard Precipitation Evapotranspiration Index (SPEI), for the purpose of elucidating As distribution has been minimal. Accordingly, this study specifically addresses these water quantity indicators, along with Gravity Recovery and Climate Experiment (GRACE) derived groundwater levels, to expound on As contamination at a Pan-Indian scale. Significant correlations were delineated between SPI, SPEI, GRACE-derived groundwater levels, and arsenic concentrations. Clustering results unveiled the grouping of states according to agro-climatic zones, thereby underscoring the similarities in water quantity dynamics across the Indian peninsula. The study additionally computed the Saturation Index (SI) for aragonite and deliberated on the potential future saturation of this pivotal mineral. The primary contribution of this study lies in the successful demonstration of a methodology for prognosticating As distribution based on available precipitation and climatic indices, groundwater withdrawal, and the geological prospects of agroclimatic zones. The insights derived from the analysis of SPI, GRACE data, and As concentrations furnish valuable input for water resource management vis-à-vis strategies for mitigating As contamination.

Interpretation of river water quality data is strongly controlled by measurement time and frequency

Water quality monitoring at high temporal frequency provides a detailed picture of environmental stressors and ecosystem response, which is essential to protect and restore lake and river health. An effective monitoring network requires knowledge on optimal monitoring frequency and data variability. Here, high-frequency hydrochemical datasets (dissolved oxygen, pH, electrical conductivity, turbidity, water temperature, total reactive phosphorus, total phosphorus and nitrate) from six UK catchments were analysed to 1) understand the lowest measurement frequency needed to fully capture the variation in the datasets; and 2) investigate bias caused by sampling at different times of the day. The study found that reducing the measurement frequency increasingly changed the interpretation of the data by altering the calculated median and data range. From 45 individual parameter-catchment combinations (six to eight parameters in six catchments), four-hourly data captured most of the hourly range (>90 %) for 37 combinations, whilst 41 had limited impact on the median (<0.5 % change). Twelve-hourly and daily data captured >90 % of the range with limited impact on the median in approximately half of the combinations, whereas weekly and monthly data captured this in <6 combinations. Generally, reducing sampling frequency had most impact on the median for parameters showing strong diurnal cycles, whilst parameters showing rapid responses to extreme flow conditions had most impact on the range. Diurnal cycles resulted in year-round intra-daily variation in most of the parameters, apart from nutrient concentrations, where daily variation depended on both seasonal flow patterns and anthropogenic influences. To design an optimised monitoring programme, key catchment characteristics and required data resolution for the monitoring purpose should be considered. Ideally a pilot study with high-frequency monitoring, at least four-hourly, should be used to determine the minimum frequency regime needed to capture temporal behaviours in the intended focus water quality parameters by revealing their biogeochemical response patterns.

SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters

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.

High-intensity rainfall following drought triggers extreme nutrient concentrations in a small agricultural catchment

The profound influence of climate change on the hydrological cycle raises concerns about its potential impacts on water quality, particularly in agricultural catchments. Here, we analysed 200 storm events monitored for nitrate and total phosphorus (TP) at sub-hourly intervals from 2016 to 2023 in the Kervidy-Naizin catchment (north-western France). Using Extreme Value theory, we identified storm events with extreme concentrations and compared their hydroclimatic characteristics to those of non-extreme events. We hypothesised that extreme concentration events occurred under extreme hydroclimatic conditions, which are projected to become more frequent in the future. The extreme events identified showed dilution patterns for nitrate, with concentrations decreasing by up to 41 %, and accretion patterns for TP, with concentrations increasing by up to 1400 % compared to non-extreme events. Hydroclimatic conditions during extreme concentration events were characterised by high rainfall intensities and low antecedent discharge, but no particular conditions for mean discharge. During non-extreme events, nitrate concentration-discharge relationships exhibited primarily clockwise hysteresis, whereas TP displayed an equal mix of clockwise and anticlockwise loops. In contrast, extreme events showed more anticlockwise hysteresis for nitrate and weak hysteresis for TP. We interpreted these dynamics and their hydroclimatic controls as the result of infiltration-excess overland flow diluting nitrate-rich groundwater and exporting large amounts of TP during intensive rainfall events following droughts, while groundwater fluctuations in the riparian zone and streambed remobilization control nutrient exports during non-extreme events. Given the increasing frequency and intensity of hydroclimatic extremes, such retrospective analyses can provide valuable insights into future nutrient dynamics in streams draining agricultural catchments.

Trade-offs between nitrogen and phosphorus removal with floodplain remediation in agricultural streams

To improve water quality and reduce instream erosion, floodplain remediation along agricultural streams can provide multiple ecosystem services through biogeochemical and fluvial processes. During floodplain inundation, longer water residence time and periodic anoxic conditions can lead to increased nitrogen (N) removal through denitrification but also mobilization of phosphorus (P), impeding overall water quality improvements. To investigate the capacity for N and P processing in remediated streams, we measured potential denitrification and nitrous oxide production and yields together with potential P desorption and P fractions in floodplain and stream sediments in ten catchments in Sweden. Sediment P desorption was measured as equilibrium P concentration, using P isotherm incubations. Denitrification rates were measured with the acetylene inhibition method. Sediment nutrient process rates were combined with hydrochemical monitoring along remediated streams and their paired upstream control reaches of trapezoidal shape to determine the impact of floodplains on water quality. The correlation between floodplain denitrification rates and P desorption (r = 0.53, p = 0.02) revealed a trade-off between soluble reactive P (SRP) and nitrate removal, driven by stream water connectivity to floodplains. Nitrous oxide production was not affected by differences in P processing, but nitrous oxide yields decreased with higher denitrification and P desorption. The release of SRP from floodplains (0.03 ± 0.41 mg P kg-1 day-1) was significantly lower than from trapezoidal stream banks (0.38 ± 0.37 mg P kg-1 day-1), predicted by long-term SRP concentrations in stream water and floodplain inundation frequency. The overall impact of SRP release from floodplains on stream SRP concentrations in remediated reaches was limited. However, the remediated reaches showing increased stream SRP concentrations were also frequently inundated and had higher labile P content and coarse soil texture in floodplain sediments. To fully realize the potential for water quality improvements with constructed floodplains in agricultural streams, the promotion of denitrification through increased inundation should be balanced against the risk of P release from sediments, particularly in streams with high SRP inputs.

Divergent drivers of the spatial variation in greenhouse gas concentrations and fluxes along the Rhine River and the Mittelland Canal in Germany

Lotic ecosystems are sources of greenhouse gases (GHGs) to the atmosphere, but their emissions are uncertain due to longitudinal GHG heterogeneities associated with point source pollution from anthropogenic activities. In this study, we quantified summer concentrations and fluxes of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and dinitrogen (N2), as well as several water quality parameters along the Rhine River and the Mittelland Canal, two critical inland waterways in Germany. Our main objectives were to compare GHG concentrations and fluxes along the two ecosystems and to determine the main driving factors responsible for their longitudinal GHG heterogeneities. The results indicated that the two ecosystems were sources of GHG fluxes to the atmosphere, with the Mittelland Canal being a hotspot for CH4 and N2O fluxes. We also found significant longitudinal GHG flux discontinuities along the mainstems of both ecosystems, which were mainly driven by divergent drivers. Along the Mittelland Canal, peak CO2 and CH4 fluxes coincided with point pollution sources such as a joining river tributary or the presence of harbors, while harbors and in-situ biogeochemical processes such as methanogenesis and respiration mainly explained CH4 and CO2 hotspots along the Rhine River. In contrast to CO2 and CH4 fluxes, N2O longitudinal trends along the two lotic ecosystems were better predicted by in-situ parameters such as chlorophyll-a concentrations and N2 fluxes. Based on a positive relationship with N2 fluxes, we hypothesized that in-situ denitrification was driving N2O hotspots in the Canal, while a negative relationship with N2 in the Rhine River suggested that coupled biological N2 fixation and nitrification accounted for N2O hotspots. These findings stress the need to include N2 flux estimates in GHG studies, as it can potentially improve our understanding of whether nitrogen is fixed through N2 fixation or lost through denitrification.

Recognizing Agricultural Headwaters as Critical Ecosystems

Agricultural headwaters are positioned at the interface between terrestrial and aquatic ecosystems and, therefore, at the margins of scientific disciplines. They are deemed devoid of biodiversity and too polluted by ecologists, overlooked by hydrologists, and are perceived as a nuisance by landowners and water authorities. While agricultural streams are widespread and represent a major habitat in terms of stream length, they remain understudied and thereby undervalued. Agricultural headwater streams are significantly modified and polluted but at the same time are the critical linkages among land, air, and water ecosystems. They exhibit the largest variation in streamflow, water quality, and greenhouse gas emission with cascading effects on the entire stream networks, yet they are underrepresented in monitoring, remediation, and restoration. Therefore, we call for more intense efforts to characterize and understand the inherent variability and sensitivity of these ecosystems to global change drivers through scientific and regulatory monitoring and to improve their ecosystem conditions and functions through purposeful and evidence-based remediation.

Biological-based techniques for real-time water-quality studies: Assessment of non-invasive (swimming consistency and respiration) and toxicity (antioxidants) biomarkers of zebrafish

Swimming consistency and respiration of fish are recognized as the non-invasive stress biomarkers. Their alterations could directly indicate the presence of pollutants in the water ecosystem. Since these biomarkers are a routine process for fish, it is difficult to monitor their activity manually. For this reason, experts employ engineering technologies to create sensors that can monitor the regular activities of fish. Knowing the importance of these non-invasive stress biomarkers, we developed online biological behavior monitoring system-OBBMS and online biological respiratory response monitoring system-OBRRMS to monitor real-time swimming consistency and respiratory response of fish, respectively. We continuously monitored the swimming consistency and respiration (OCR, CER and RQ) of zebrafish (control and atrazine-treatments) for 7 days using our homemade real-time biological response monitoring systems. Furthermore, we analyzed oxidative stress indicators (SOD, CAT and POD) within the vital tissues (gills, brain and muscle) of zebrafish during stipulated sampling periods. The differences in the swimming consistency and respiratory rate of zebrafish between the control and atrazine treatments could be precisely differentiated on the real-time datasets of OBBMS and OBRRMS. The zebrafish exposed to atrazine toxin showed a concentration-dependent effect (hypoactivity). The OCR and CER were increased in the atrazine treated zebrafish. Both Treatment I and II received a negative response for RQ. Atrazine toxicity let to a rise in the levels of SOD, CAT and POD in the vital tissues of zebrafish. The continuous acquisition of fish signals is achieved which is one of the main merits of our OBBMS and OBRRMS. Additionally, no special data processing was done, the real-time data sets were directly used on statistical tools and the differences between the factors (groups, photoperiods, exposure periods and their interactions) were identified precisely. Hence, our OBBMS and OBRRMS could be a promising tool for biological response-based real-time water quality monitoring studies.

Routine stream monitoring data enables the unravelling of hydrological pathways and transfers of agricultural contaminants through catchments

Catchment-scale understanding of water and contaminant fluxes through all pathways is essential to address land use and climate change impacts on freshwater. However, few options exist to obtain this understanding for the many catchments worldwide for which streamflow and low-frequency water chemistry, but little other data exists. We applied the Bayesian chemistry-assisted hydrograph separation and load partitioning model (BACH) to 47 catchments with widely differing characteristics. As BACH relies on concentration differences between pathways, chemodynamic behaviour of a water constituent indicates its likely suitability as tracer. Typical tracers (e.g. silica, chloride) were unavailable, but Electrical Conductivity and a few monitored nutrients proved chemodynamic in most catchments. Using one of two tracer combinations (Total Nitrogen + Electrical Conductivity, Total Nitrogen + Total Phosphorus) allowed in 85 % of the catchments to estimate streamflow contributions by near-surface (NS), shallow groundwater (SGW), and deep groundwater (DGW) pathways and pathway-specific tracer concentrations and yields with acceptable confidence. In 46 catchments, at least two pathways contributed ≥20 % of the streamflow, and all three ≥20 % in 12 catchments, cautioning against the notion of a single 'dominant' pathway. In contrast to hydrometric hydrograph separation, BACH allows differentiation between 'young' (NS + SGW) and 'old' (DGW) water, which is crucial for the understanding of pollution in catchments with strong temporal gradients in land use intensity. Consistent with generally increasing land use intensity, and groundwater denitrification occurring in some catchments, Total Nitrogen (TN) concentrations were in most catchments higher in NS and SGW compared to DGW. In most catchments, the greatest fraction of the TN yield was conveyed by SGW (≈ 40-90 %). Exceptions were wet and hilly catchments under bush, where the NS transferred most of the very low yields, and three young volcanic catchments where the DGW transferred the majority of the yield due to particularly high DGW flow contributions.

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.

Monitoring to detect changes in water quality to meet policy objectives

Detecting change in water quality is key to providing evidence of progress towards meeting water quality objectives. A key measure for detecting change is statistical power. Here we calculate statistical power for all regularly (monthly) monitored streams in New Zealand to test the effectiveness of monitoring for policy that aims to decrease contaminant (phosphorus and nitrogen species, E. coli and visual clarity) concentrations to threshold levels in 5 or 20 years. While > 95% of all monitored sites had sufficient power and samples to detect change in nutrients and clarity over 20 years, on average, sampling frequency would have to double to detect changes in E. coli. Furthermore, to detect changes in 5 years, sampling for clarity, dissolved reactive phosphorus and E. coli would have to increase up to fivefold. The cost of sampling was predicted to increase 5.3 and 4.1 times for 5 and 20 years, respectively. A national model of statistical power was used to demonstrate that a similar number of samples (and cost) would be required for any new monitoring sites. Our work suggests that demonstrating the outcomes of implementing policy for water quality improvement may not occur without a step change in investment into monitoring systems. Emerging sampling technologies have potential to reduce the cost, but existing monitoring networks may also have to be rationalised to provide evidence that water quality is meeting objectives. Our study has important implications for investment decisions involving balancing the need for intensively sampled sites where changes in water quality occur rapidly versus other sites which provide long-term time series.

IR-based device to acquire real-time online heart ECG signals of fish (Cyprinus carpio) to evaluate the water quality

Water quality monitoring is a challenging task due to continuous pollution. The rapid development of engineering technologies has paved the way for the development of efficient and convenient computer-based online continuous water-quality assessment techniques. Techniques based on biological-responses are gaining attention, worldwide. Different biosensors have been developed in recent years to monitor real-time biological responses to evaluate water-quality. The survival and function of various organs of the organism depends on the cardiac system. Alterations in the cardiac system could signify the occurrence/initiation of stress in the organism. We developed a real-time online cardiac function assessment system-OCFAS to acquire fish ECG-signals. We obtained P-wave, R-wave, T-wave, PR-intervals, QT-intervals and QRS-complex continuously, which did not affect the normal activities of carp. We exposed Cyprinus carpio to different concentrations (National Environmental Quality Standards) of ammonia for 48 h. Our OCFAS has precisely acquired the required ECG-signals. A real-time dataset reveals sensitivity to ammonia in carp ECG-indexes. Compared with the control group the P-wave, R-wave and T-wave were weaker in ammonia-treated groups. In contrast, the PR-intervals, QT-intervals and QRS-complex were prolonged in the ammonia-treatment groups. The self-organizing map signifies that the PR-intervals, the QRS-complex and the QT-intervals are consistent with environmental stress. Linear regression analysis also quantitatively signifies that the PR interval has the highest R2 value and the lowest SSE-value, followed by the QRS complex and the QT interval. A concentration-related effect was observed in the ammonia treated groups. The integrated biomarker response (IBRv2) index was used to determine the overall stress of ammonia on carp heart ECG-indexes. IBRv2 also supports the real-time response of carp to ammonia stress. Ammonia levels in the aquaculture and water environment require special attention to avoid its adverse effects on the health of aquatic biota. Our study emphasizes the importance of online real-time fish ECG for water-quality assessment.

Glacial Erosion Drives High Summer Mercury Exports from the Yukon River, Canada

Mercury concentrations and yields in the Yukon River are the highest of the world's six largest panarctic drainages. Permafrost thaw has been implicated as the main driver of these high values. Alternative sources include mercury released from glacial melt and erosion, atmospheric mercury pollution, or surface mining. To determine the summer source and speciation of mercury across the Yukon River basin within Canada, we sampled water from 12 tributaries and the mainstem during July 2021. The total (unfiltered) mercury concentration in the glacier-fed White River was 57 ng/L, >10 times higher than all other sampled tributaries. The White River's high total mercury concentrations were driven by suspended sediment and persisted ∼300 km downstream of glacierized headwaters. Total mercury concentrations were lowest (typically <2 ng/L) in tributaries downstream of still-water landscape features (e.g., lakes and settling ponds), suggesting these features are effective sinks for sediment-bound mercury. Low total mercury concentrations (∼2 ng/L) were also observed in five tributaries across diverse thawing permafrost landscapes. These results suggest that glacial erosion and meltwater transport, not permafrost, drive enhanced exports of mercury with suspended sediment. Mercury exports may decline as glacial watersheds pass peak water. Other factors, including mercury released from permafrost thaw, are minor components at present.

Large-stream nitrate retention patterns shift during droughts: Seasonal to sub-daily insights from high-frequency data-model fusion

High-frequency nitrate-N (NO3--N) data are increasingly available, while accurate assessments of in-stream NO3--N retention in large streams and rivers require a better capture of complex river hydrodynamic conditions. This study demonstrates a fusion framework between high-frequency water quality data and hydrological transport models, that (1) captures river hydraulics and their impacts on solute signal propagation through river hydrodynamic modeling, and (2) infers in-stream retention as the differences between conservatively traced and reactively observed NO3--N signals. Using this framework, continuous 15-min estimates of NO3--N retention were derived in a 6th-order reach of the lower Bode River (27.4 km, central Germany), using long-term sensor monitoring data during a period of normal flow from 2015 to 2017 and a period of drought from 2018 to 2020. The unique NO3--N retention estimates, together with metabolic characteristics, revealed insightful seasonal patterns (from high net autotrophic removal in late-spring to lower rates, to net heterotrophic release during autumn) and drought-induced variations of those patterns (reduced levels of net removal and autotrophic nitrate removal largely buffered by heterotrophic release processes, including organic matter mineralization). Four clusters of diel removal patterns were identified, potentially representing changes in dominant NO3--N retention processes according to seasonal and hydrological conditions. For example, dominance of autotrophic NO3--N retention extended more widely across seasons during the drought years. Such cross-scale patterns and changes under droughts are likely co-determined by catchment and river environments (e.g., river primary production, dissolved organic carbon availability and its quality), which resulted in more complex responses to the sequential droughts. Inferences derived from this novel data-model fusion provide new insights into NO3- dynamics and ecosystem function of large streams, as well as their responses to climate variability. Moreover, this framework can be flexibly transferred across sites and scales, thereby complementing high-frequency monitoring to identify in-stream retention processes and to inform river management.