Stream Monitoring Research Articles

Predicting lake chlorophyll from stream phosphorus concentrations

AbstractExcess nutrient loads from streams drive primary production in downstream lakes, and managing these loads is key to achieving desired conditions in lakes. However, quantifying nutrient loads requires intensive sampling of both nutrient concentrations and streamflow. Total phosphorus measurements collected during routine stream monitoring are broadly available, but these data are thought to provide little information on annual nutrient loads because they are typically collected during low, baseflow conditions. Here, we demonstrate that these routine phosphorus measurements are correlated with annual nutrient loads. We also show that the average of these routine measurements of stream phosphorus within a watershed predict the average lake chlorophyll concentration in that same watershed. These relationships can then be used to set targets for stream phosphorus concentrations to achieve desired conditions in lakes.

Citizen science promotes knowledge, skills and collective action to monitor and protect freshwater streams

Abstract Citizen science has great potential to foster ecological knowledge, skills and collective action for biodiversity conservation. To date, however, there are few experimental studies of behaviour change outcomes induced by citizen science. We assessed the effects of citizen science in ecological stream monitoring on participants' individual and collective behaviour and action intentions to protect streams beyond the immediate scope of the citizen science project. For this we applied a three‐wave, quasi‐experimental survey design with a citizen science treatment group (N = 213 participants of the project FLOW in Germany) and two control groups (waiting control group: N = 92, panel control group: N = 250). In addition, we analysed citizen science effects on potential predictors of freshwater conservation behaviour, such as knowledge, skills, awareness of stream stressors and group identification. Results of linear mixed‐model analysis showed that, compared to the control groups, citizen scientists significantly increased their objective stream‐related knowledge, their self‐reported stream monitoring skills and their awareness of stream stressors. These changes remained stable 6 weeks after the citizen science intervention. Notably, citizen scientists also reported increased identification with their citizen science project group and higher levels of collective and personal behaviour to protect streams at posttest (e.g. contacting local politicians with ideas for stream protection or using biodegradable detergents). Analysis revealed that increases in group identification and collective stream protection behaviour were significantly correlated. We could show that the citizen science intervention was particularly effective for participants with high intrinsic motivation and for those with low prior knowledge and experience in stream monitoring. Synthesis and applications. Citizen science can serve as a useful tool to promote ecological understanding, awareness and collective action to conserve freshwater streams. By fostering volunteer skills and community building, citizen science projects can empower participants with different levels of experience to engage in freshwater monitoring and protection. Decision makers should actively support approaches for citizen participation in freshwater monitoring, recognising that collective engagement of diverse community members is needed to finally meet the European Water Framework Directive's goal of restoring the good ecological status of freshwater ecosystems. Read the free Plain Language Summary for this article on the Journal blog.

Dynamic modeling and online monitoring of tensor data streams with application to passenger flow surveillance

Dynamic modeling and online monitoring of tensor data streams with application to passenger flow surveillance

Quantitative fecal pollution assessment with bacterial, viral, and molecular methods in small stream tributaries

Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under ‘wet’ and ‘dry’ conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.

Adaption of a traditional Māori fishing method for biomonitoring: using whakaweku for sampling benthic macroinvertebrates in streams

ABSTRACT Whakaweku are bundles of bracken fern (Pteridium esculentum) traditionally placed in lakes and rivers by Māori to harvest kōura (freshwater crayfish, Paranephrops spp.) in Aotearoa, New Zealand. Previous studies show they are effective for monitoring populations of kōura and toitoi (small eleotrid fish, Gobiomorphus spp.). We wanted to determine whether whakaweku are also effective for monitoring benthic macroinvertebrate communities in streams. We placed whakaweku in a hard-bottomed and a soft-bottomed reach of a rural stream in the Waikato, and took standard ‘kick-net’ samples from the same locations. We retrieved whakaweku after two and four weeks submerged. Whakaweku gave comparable results to kick-net sampling for common macroinvertebrate metrics (MCI, SQMCI, total richness and EPT* richness). Macroinvertebrate community composition was significantly different between the two methods, but most taxa were common to both. Macroinvertebrate metrics did not change significantly between two-week and four-week colonisation periods. A wider study would show whether whakaweku can distinguish between sites over a gradient of human impact. We recommend whakaweku in Māori cultural and community stream monitoring as it is inexpensive, simple to use, captures many macroinvertebrate taxa used in biomonitoring, works in different stream types and can be used to monitor kōura, toitoi and macroinvertebrates simultaneously.

Spatiotemporal Dynamics of Carbon and Nitrogen in Subtropical Urban Streams (Santo André, SP, Brazil)

Urban sprawl poses a significant threat to urban stream water quality due to impermeabilization, reduced vegetation cover, and the release of diffuse pollutants. This study evaluates water quality in seven catchments in Santo André, SP, considering seasonality. Nutrient concentrations and in situ measurements were taken during both dry and rainy seasons. Comparisons were made using Kruskal–Wallis and Mann–Whitney tests. Streams showed significant differences in relation to water quality parameters. The Carapetuba, Jundiaí, and Apiaí streams were most adversely affected, underscoring the need for urgent water quality intervention (water conductivity above 500 μS/cm, dissolved oxygen below 2 mg/L, total dissolved carbon above 50 mg/L, and total dissolved nitrogen above 25 mg/L). Significant differences were observed across seasons. The dry season showed elevated temperatures (above 25 °C) and increased total dissolved carbon (above 50 mg/L) and nitrogen concentrations (above 30 mg/L), indicating reduced dilution effects from rainfall and heightened organic contamination. Conversely, the wet season demonstrated lower nutrient concentrations, emphasizing seasonal dynamics. Sustained, long-term monitoring of urban streams in Santo André and the implementation of sewage collection and treatment in irregular settlements are recommended. These measures are essential to mitigate the adverse impacts of urban expansion on water quality.

Salt Belt Index (SBI): A biotic index for streams within the North American “salt belt,” with proposed baseline chloride thresholds

Road salt (commonly NaCl, CaCl2, and MgCl2) is widely used in the northern United States as a deicing agent for roadways and other byways. Millions of tons of road salt are used annually in the United States, resulting in drastic increases in freshwater salinity. This study aims to determine the chloride optima and tolerance ranges of macroinvertebrates using publicly accessible stream monitoring data from the US EPA. We assigned taxa region-specific tolerance values, which we then used to calculate the Salt Belt Index (SBI). In addition to the SBI, we determined new, region-specific, chronic Cl− thresholds, determined using threshold indicator taxa analysis (TITAN). Using generalized linear models, we found the SBI was highly accurate at estimating chloride concentration (mg/L Cl−) across the salt belt states. Macroinvertebrate community richness exhibited a significant negative relationship with increasing chloride concentrations. Newly proposed chloride thresholds, based on the richness-chloride relationship, were far lower than current thresholds. The SBI was able to differentiate between Low-, Medium-, and High-Impact sites, grouped based on proposed chloride thresholds. Based on our findings, it is clear current salinity thresholds are too high, and management practices should factor in regional variability, taxon-specific physiology, and historical instream chemistry when implementing salinity thresholds.

Advancing freshwater science with sensor data collected by community scientists

Autonomous sensor networks providing real‐time data are growing in popularity with community scientists due to instant availability of high‐frequency data. What role does this monitoring play in watershed assessment alongside agency‐run monitoring programs? How accessible, interoperable, and reusable are the data for other researchers? We compared a community science‐led stream monitoring network—EnviroDIY—in the Delaware River Basin, in which more than 50 watershed organizations have deployed more than 100 stations monitoring temperature, electric conductivity, depth, and sometimes turbidity, with the Basin's US Geological Survey (USGS) stream gauge network. The EnviroDIY network (n = 124) complemented the USGS network (n = 102) by monitoring sites with different watershed sizes and land‐use distributions. Although data were accessible and interoperable using a web data portal, community scientists had difficulty sharing metadata that would enable data reuse outside this project and they required support analyzing these large datasets to understand threats to watershed conditions. We address those needs here with a conceptual framework for interpreting data and communicating results.

Conditional heavy hitter monitoring and application of heterogeneous graph streams based on sketches

Graph streams offer valuable insights into real network evolution in the era of big data. Heavy hitters and conditional heavy hitters focus on identifying entities with significant network traffic, revealing network data characteristics. In this paper, we present two methods, BLSketch and MHLSketch, for identifying heavy hitters and conditional heavy hitters in heterogeneous graph streams. BLSketch uses the bidirectional mapper, Bimap, for efficient query response times while retaining candidate information. MHLSketch uses min heap to preserve a minimal set of candidates to minimize storage space, excelling in space efficiency. The two methods can be incorporated with any sketch based graph summarization method, showing excellent adaptability. Experimental results on four real-world heterogeneous datasets of varying scales with the largest dataset containing millions of edges demonstrate our methods significantly improve the quality of query answering, outperforming state-of-the-art methods, with precision close to 1 and average relative error close to 0. We further combine our methods with density clustering to introduce the SDC model, which efficiently identifies hot regions and key users in Location-Based Social Networks (LBSN). In experiments, SDC outperforms traditional clustering by reducing up to 93.1% of cold Points of Interest (POIs) and shows strong potential for practical applications.

A two-stage monitoring scheme for multiple high-dimensional data streams

With the rapid development of data acquisition and analysis technology, the online monitoring of data streams is particularly important in modern industries. This study proposes an efficient online monitoring scheme for multiple high-dimensional data streams. We first construct a point-wise process monitoring control chart based on statistical extreme value theory. Then we use the clustering method to roughly obtain reliable data streams and suspected abnormal data streams, within the sliding window range, further identify abnormal data streams one by one over time points, and estimate the potential change points of these abnormal streams. Finally, we conduct simulations and case studies on this method, and compare it with existing competitive methods to demonstrate its effectiveness.

Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams

Stream-observing cameras have recently been deployed in stream systems to monitor water depth dynamics. However, most existing image-based water depth monitoring methods require additional gauging equipment, extensive manual annotations, or complex manual calibration. In this paper, we propose the hierarchical model, a novel multi-modal and multi-scale deep learning framework for monitoring water depth in headwater streams with only a field camera capable of night vision and no additional equipment. In particular, the hierarchical model integrates long-term dynamic patterns extracted from large-scale meteorological data with short-term dynamic patterns extracted from small-scale stream image data to jointly monitor water depth at a fine-level temporal resolution. In order to overcome the issue of limited availability of images, we introduce a transfer learning strategy and incorporate more accurate long-term patterns that enable the hierarchical model to perform competitively even with a small number of images. We evaluate our method on a real-world headwater stream monitoring dataset from the West Brook study area in western Massachusetts, United States. Our extensive experiments demonstrate that the hierarchical model outperforms several state-of-the-art methods for water depth monitoring, and that more accurate long-term patterns can better guide the monitoring of short-term patterns with excellent flexibility and less computational cost. The mean absolute error of our hierarchical model achieves a remarkable level of 4.9cm at the study site with 0.89m average water depths, and only 12.5cm at more drastically varied site with 3.95m average depths.

Accumulation of phytotoxic metals and metalloids in vegetable continuum subjected to different irrigation streams: a threat towards nutritional security

The urban agro-ecosystem in developing countries plays a major role in uplifting the socioeconomic status of their inhabitants by confronting the challenges of improving food security and eradicating hunger. Food security and safety has become a serious global concern owing to accumulation of phytotoxic metals and metalloids in different food commodities subjected to different contaminated irrigation sources. The current study was undertaken to assess the concentrations of heavy metals and metalloid in ridge gourd (Luffa cylindrica L.), its planting soil medium and irrigation water sources used for crop production to determine the health risk. The results showed that the mean concentrations of different metals and metalloids in ground water samples i.e. Ni (0.14 mg L-1), Fe (1.29 mg L-1), Cu (0.10 mg L-1), Pb (0.55 mg L-1), As (0.10 mg L-1), Cr (2.38 mg L-1) and Cd (0.05 mg L-1) were over the permissible limits. Soil samples irrigated with ground water showed that concentrations of Cd (23.67 mg kg-1), Cr (26.66 mg kg-1) and As (45.97 mg kg-1) were above the permissible limits. Similarly, vegetable samples irrigated with ground water had a higher level of Cd (0.33 mg kg-1) than their prescribed safe limits. The bio-concentration factor ranged from 0.00 to 8.21 for all irrigation sources. The target hazard quotients (THQ) for Zn, Fe, and Cr were higher than the threshold value when ridge gourd was consumed, indicating a health concern. Similarly, hazard index values were 5.05, 28, 27, and 1.01, from groundwater, canal water, and sewage water, respectively. Target Cancer Risk (TCR) exposed Ni, Cr, Cd and As in canal water were sources of high cancer risk and Pb showed moderate cancer risk for the local community. These findings suggest that human intake of ridge gourd vegetable from the studied area may pose health hazards. Hence, frequent monitoring of different irrigation streams used in urban and semi-urban agriculture is strongly recommended to manage higher concentrations of phytotoxic metals and metalloids in crop produce and their associated health risks for humans.

Citizen science shows that small agricultural streams in Germany are in a poor ecological status

Agricultural pesticides, nutrients, and habitat degradation are major causes of insect declines in lowland streams. To effectively conserve and restore stream habitats, standardized stream monitoring data and societal support for freshwater protection are needed. Here, we sampled 137 small stream monitoring sites across Germany, 83 % of which were located in agricultural catchments, with >900 citizen scientists in 96 monitoring groups. Sampling was carried out according to Water Framework Directive standards as part of the citizen science freshwater monitoring program FLOW in spring and summer 2021, 2022 and 2023. The biological indicator SPEARpesticides was used to assess pesticide exposure and effects based on macroinvertebrate community composition. Overall, 58 % of the agricultural monitoring sites failed to achieve a good ecological status in terms of macroinvertebrate community composition and indicated high pesticide exposure (SPEARpesticides status class: 29 % “moderate”, 19 % “poor”, 11 % “bad”). The indicated pesticide pressure in streams was related to the proportion of arable land in the catchment areas (R2 = 0.23, p < 0.001). Also with regards to hydromorphology, monitoring results revealed that 65 % of the agricultural monitoring sites failed to reach a good status. The database produced by citizen science groups was characterized by a high degree of accuracy, as results obtained by citizen scientists and professionals were highly correlated for SPEARpesticides index (R2 = 0.79, p < 0.001) and hydromorphology index values (R2 = 0.72, p < 0.001). Such citizen-driven monitoring of the status of watercourses could play a crucial role in monitoring and implementing the objectives of the European Water Framework Directive, thus contributing to restoring and protecting freshwater ecosystems.

Captionomaly: A Deep Learning Toolbox for Anomaly Captioning in Social Surveillance Systems

Real-time video stream monitoring is gaining huge attention lately with an effort to fully automate this process. On the other hand, reporting can be a tedious task, requiring manual inspection of several hours of daily clippings. Errors are likely to occur because of the repetitive nature of the task causing mental strain on operators. There is a need for an automated system that is capable of real-time video stream monitoring in social systems and reporting them. In this article, we provide a tool aiming to automate the process of anomaly detection and reporting. We combine anomaly detection and video captioning models to create a pipeline for anomaly reporting in descriptive form. A new set of labels by creating descriptive captions for the videos collected from the UCF-Crime (University of Central Florida-Crime) dataset has been formulated. The anomaly detection model is trained on the UCF-Crime, and the captioning model is trained with the newly created labeled set UCF-Crime video description (UCFC-VD). The tool will be used for performing the combined task of anomaly detection and captioning. Automated anomaly captioning would be useful in the efficient reporting of video surveillance data in different social scenarios. Several testing and evaluation techniques were performed. Source code and dataset: https://github.com/Adit31/Captionomaly-Deep-Learning-Toolbox-for-Anomaly-Captioning.

Spectral Characterization of Battery Components from Li-Ion Battery Recycling Processes

Considering the increasing demand for Li-ion batteries, there is a need for sophisticated recycling strategies with both high recovery rates and low costs. Applying optical sensors for automating component detection is a very promising approach because of the non-contact, real-time process monitoring and the potential for complete digitization of mechanical sorting processes. In this work, mm-scale particles from shredded end-of-life Li-ion batteries are investigated by five different reflectance sensors, and a range from the visible to long-wave infrared is covered to determine the ideal detection window for major component identification as relevant input signals to sorting technologies. Based on the characterization, a spectral library including Al, Cu, separator foil, inlay foil, and plastic splinters was created, and the visible to near-infrared range (400–1000 nm) was identified as the most suitable spectral range to reliably discriminate between Al, Cu, and other battery components in the recycling material stream of interest. The evaluation of the different sensor types outlines that only imaging sensors meet the requirements of recycling stream monitoring and can deliver sufficient signal quality for subsequent mechanical sorting controls. Requirements for the setup parameters were discussed leading to the setup recommendation of a fast snapshot camera with a sufficiently high spectral resolution and signal-to-noise ratio.

Self-Starting Monitoring and Dynamic Sampling of High-Dimensional Data Streams

Self-Starting Monitoring and Dynamic Sampling of High-Dimensional Data Streams

Putting eDNA to the test: A field comparison of eDNA metabarcoding to established protocols for assessing biodiversity in Missouri's Ozark Highland streams

AbstractAquatic biodiversity monitoring to inform conservation and management efforts in‐stream systems has increasingly begun to incorporate environmental DNA (eDNA)‐based sampling methods. We conducted a comparison of eDNA metabarcoding to a traditional protocol of combined seining and electrofishing methods to assess fish biodiversity of wadeable stream sites in six separate drainages in the Ozark Highlands of Missouri (USA). The study further focused on the headwaters of the Meramec River, which included 11 sites and seasonal sampling (summer and winter). We compared estimates of diversity across sampling methods, assessed the influences of water flow (depth, velocity, and discharge) and season, and tested the effects of sampling method and site locality on fish assemblage composition. eDNA sampling detected approximately double the number of species compared to traditional methods, providing higher diversity estimates while maintaining the relative ranking of sites. eDNA detection probabilities were positively associated with stream depth and velocity and were generally higher in summer than in winter but not for all species. Estimated species richness was positively associated with discharge for both methods but the relationship was stronger with eDNA sampling. Assemblage differences between tributary and mainstem sites were attributable to a small number of species that were found predominantly in one stream size category or the other, indicating that eDNA was sensitive to within‐drainage assemblage structure relationships. We highlight improved species detection, a more comprehensive understanding of assemblage structural dynamics, and the potential ability to integrate data across sampling methods as important benefits that encourage the use of eDNA metabarcoding as a primary collection method in future stream biodiversity assessment and monitoring programs.

Image Based In-Process Powder Stream Monitoring during Laser Metal Deposition for the Measurement of the Gravimetric Powder Mass Flow Rate and the Detection of Deviations in the Powder Stream

In laser metal deposition, the quality of the applied clad as well as the additively manufactured component is directly linked to the precise powder feeding into the laser-induced melt pool. Despite the highly automated and closely monitored process, there is currently no industrially established in-process system for the powder stream monitoring. In this work, the powder is illuminated with a line laser in a defined horizontal plane to visualize the back-reflecting light to the camera. Optical filtering is needed to attenuate the process emissions, which is adjusted to the wavelength of the illumination laser. The powder stream is continuously monitored by observing grayscale values. The gravimetric measurements of the powder mass flow are correlated to the grayscale values to display the powder mass flow in absolute mass flow values from the image data. This is qualified by the detection of defined and temporarily introduced deviations in the powder stream.

Avaliando a suficiência taxonômica das comunidades de macroinvertebrados e seu potencial para distinguir os impactos do uso da terra em riachos de terras baixas no Uruguai

Abstract: Aim Macroinvertebrates are extensively employed as bioindicators for evaluating how stream ecosystems respond to alterations in physical and chemical habitats caused by human activity. Although family-taxonomic level is usually used to identify macroinvertebrates in biomonitoring programs, coarser taxonomic resolution may provide similar information. We aim to evaluate taxonomic sufficiency by comparing macroinvertebrate communities at the order and family levels to differentiate the effects of different land uses. Methods We conducted colonization experiments using artificial substrates in streams representing three land use types: urban (U, n=3), intensive agriculture - dairy production (AD, n=4), and extensive cattle ranching (CR, n=4). Results We obtained similar results using both taxonomic levels, which effectively distinguished the impacts of different land uses. In the sites examined, the majority of indicator groups exhibited only one family per order, with the exception of Ephemeroptera (Leptophlebiidae, Baetidae, and Caenidae). Notably, Baetidae and Caenidae were more prevalent in streams associated with extensive cattle ranching (CR). Conclusions Although this study provides a clear precedent, further investigation of these systems and other land uses (e.g., afforestation) is needed to validate this pattern. Coarser taxonomic assessment holds potential as a cost-effective method for stream monitoring and assessment in Uruguay.

Latent Autoregressive Student-t Prior Process Models to Assess Impact of Interventions in Time Series

With the advent of the “Internet of Things” (IoT), insurers are increasingly leveraging remote sensor technology in the development of novel insurance products and risk management programs. For example, Hartford Steam Boiler’s (HSB) IoT freeze loss program uses IoT temperature sensors to monitor indoor temperatures in locations at high risk of water-pipe burst (freeze loss) with the goal of reducing insurances losses via real-time monitoring of the temperature data streams. In the event these monitoring systems detect a potentially risky temperature environment, an alert is sent to the end-insured (business manager, tenant, maintenance staff, etc.), prompting them to take remedial action by raising temperatures. In the event that an alert is sent and freeze loss occurs, the firm is not liable for any damages incurred by the event. For the program to be effective, there must be a reliable method of verifying if customers took appropriate corrective action after receiving an alert. Due to the program’s scale, direct follow up via text or phone calls is not possible for every alert event. In addition, direct feedback from customers is not necessarily reliable. In this paper, we propose the use of a non-linear, auto-regressive time series model, coupled with the time series intervention analysis method known as causal impact, to directly evaluate whether or not a customer took action directly from IoT temperature streams. Our method offers several distinct advantages over other methods as it is (a) readily scalable with continued program growth, (b) entirely automated, and (c) inherently less biased than human labelers or direct customer response. We demonstrate the efficacy of our method using a sample of actual freeze alert events from the freeze loss program.