Stream Monitoring Research Articles

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning

Stream temperatures are influenced by the amount of solar insolation they receive. Increasing stream temperatures associated with climate warming pose detrimental health risks to freshwater ecosystems. In British Columbia (BC), Canada, timber harvesting along forested streams is managed using riparian buffer zones of varying widths and designations. Within buffer zones, depending on distance from the stream, selective thinning may be permitted or harvest may be forbidden. In this study, we used airborne laser scanning (ALS) point cloud data acquired via a remotely piloted aircraft system (RPAS) to derive forest canopy characteristics that were then used to estimate daily incoming summer and fall solar insolation for five stream reaches in coastal conifer-dominated temperate forests in Vancouver Island, BC, Canada. We then examined empirical relationships between estimated insolation and actual instream temperature measurements. Based on these empirical relationships, the potential effects of timber harvest on instream temperatures were simulated by comparing scenarios of different riparian forest harvest intensities. Our results indicated that modeled solar insolation explained 43–90 % of the variation in observed stream reach temperatures, and furthermore, when a single cold-water stream reach was excluded explained an overall 81 % of variation. Simulated harvesting scenarios generally projected increases in maximum stream reach temperatures 1–2 °C in summer and early fall months. However, in a full clearcut scenario (i.e. where all trees were removed), maximum stream reach temperatures increased as much as 5.8 °C. Our results emphasize the importance of retaining riparian vegetation for the maintenance of habitable temperatures for freshwater-reliant fish with thermal restrictions. In addition, we demonstrate the feasibility of RPAS-based monitoring of stream reach shading and canopy cover, enabling detailed assessment of environmental stressors faced by fish populations under climate warming.

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.

Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams.

Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.

Continuous manufacturing of pharmaceutical products: A density-insensitive near infrared method for the in-line monitoring of continuous powder streams

Near infrared (NIR) spectroscopy is a valuable analytical technique for monitoring chemical composition of powder blends in continuous pharmaceutical processes. However, the variation in density captured by NIR during spectral collection of dynamic powder streams at different flow rates often reduces the performance and robustness of NIR models. To overcome this challenge, quantitative NIR measurements are commonly collected across all potential manufacturing conditions, including multiple flow rates to account for the physical variations. The utility of this approach is limited by the considerable quantity of resources required to run and analyze an extensive calibration design at variable flow rates in a continuous manufacturing (CM) process. It is hypothesized that the primary variation introduced to NIR spectra from changing flow rates is a change in the density of the powder from which NIR spectra are collected. In this work, powder stream density was used as an efficient surrogate for flow rate in developing a quantitative NIR method with enhanced robustness against process rate variation. A density design space of two process parameters was generated to determine the conditions required to encompass the apparent density and spectral variance from increases in process rate. This apparent density variance was included in calibration at a constant low flow rate to enable the development of a density-insensitive NIR quantitative model with limited consumption of materials. The density-insensitive NIR model demonstrated comparable prediction performance and flow rate robustness to a traditional NIR model including flow rate variation (“gold standard” model) when applied to monitoring drug content in continuous runs at varying flow rates. The proposed platform for the development of in-line density-insensitive NIR methods is expected to facilitate robust analytical model performance across variable continuous manufacturing production scales while improving the material efficiency over traditional robust modeling approaches for calibration development.

An Assessment of In-stream Sampling Activity of Macroinvertebrates and Stream Channelization in Dug Run, Allen County, Ohio, USA

Macroinvertebrates are good indicators of stream quality. Changes in populations of sensitive macroinvertebrates help to show stressors to the stream. Student sampling of a section of Dug Run in northwestern Ohio has occurred since 2015. This work has been to identify how changes on the campus including construction, tree removal, and channelization may be impacting stream macroinvertebrates. Student sampling, however, also causes disturbances that may negatively impact macroinvertebrate populations. A break in student sampling—due first to the use of an adjacent off-site location in 2019 and then to COVID-19 beginning in 2020—was expected to impact the number of mayfly nymphs and caddisfly larvae captured, both considered sensitive macroinvertebrates in the stream. To measure the impact of channelization in Dug Run, the study area was split into a channelized reach, an upstream reach, and a downstream reach. Stream habitat was also studied in each reach with macroinvertebrates collected from riffles, undercuts, and pools. After a break in sampling, caddisfly larvae increased initially but have declined in the 2 following years, while mayfly nymphs increased in the last 2 years of the study. No significant differences were found in stream quality monitoring (SQM) index scores between the channelized reach compared to upstream and downstream reaches (H = 4.15; p = 0.126). There was a significant difference in taxa richness among pools, riffles, and undercuts (H = 14.09; p < 0.001). A significant difference was also found in the moderately sensitive macroinvertebrates captured in riffles between the channelized, upstream, and downstream reaches (H = 6.82; p = 0.033). A break in sampling resulted in an initial increase in mayfly nymph and caddisfly larvae samples, but it appears a variety of factors may be responsible for the numbers captured. The channelized reach had higher numbers of scuds and crayfish in riffles among the 3 reaches, which may be the result of a change in their distribution related to lack of undercuts. Both scuds and crayfishwere found in significantly greater abundance in undercuts compared to pools and riffles.

An adaptive approach for online monitoring of large-scale data streams

In this article, we propose an adaptive top-r method to monitor large-scale data streams where the change may affect a set of unknown data streams at some unknown time. Motivated by parallel and distributed computing, we propose to develop global monitoring schemes by parallel running local detection procedures and then use the Benjamin–Hochberg false discovery rate control procedure to estimate the number of changed data streams adaptively. Our approach is illustrated in two concrete examples: one is a homogeneous case when all data streams are independent and identically distributed with the same known pre-change and post-change distributions. The other is when all data are normally distributed, and the mean shifts are unknown and can be positive or negative. Theoretically, we show that when the pre-change and post-change distributions are completely specified, our proposed method can estimate the number of changed data streams for both the pre-change and post-change status. Moreover, we perform simulations and two case studies to show its detection efficiency.

Characterization of optical emissions during laser metal deposition for the implementation of an in-process powder stream monitoring

In laser metal deposition (LMD), the powder is fed into the laser-induced melt pool using different powder nozzles for the purpose of additive manufacturing and the generation of wear and corrosion protection coatings. So far, there are no industrially established in-process monitoring systems for the powder stream but mainly measuring systems that examine the powder stream propagation offline and without the processing laser. A challenge in implementing an image-based in-process monitoring system is the process illumination for the distinction of the powder particles from the background radiation caused by the processing laser and the melt pool. To overcome this challenge, filtering is needed to attenuate the process emissions and simultaneously brighten the powder stream. Therefore, this work focuses on generating a continuous high contrast between the powder and the background. The powder particles are illuminated by a light source mounted laterally to the powder stream in the horizontal plane below the nozzle opening to make the reflecting powder particles visible to the camera. The optical process emissions were characterized during LMD with respect to the influence of an increasing laser power, which was presented in correlation to the increasing process emissions. The evaluation of the spectrograms has made it possible, due to the adapted illumination and filtering, to ensure a constantly high contrast between the process emissions and the powder so that online monitoring of the powder stream was implemented successfully during the LMD process despite the active processing laser.

Adaptive Sampling and Quick Anomaly Detection in Large Networks

The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies.

An EWMA chart for high dimensional process with multi-class out-of-control information via random forest learning

ABSTRACT Modern manufacturing and quality monitoring involve multi-class out-of-control (OOC) information from the training sample. It is essential to use such information during online monitoring of data streams from complex processes. In this paper, a monitoring framework is designed by combining the random forest technique with the exponentially weighted moving average method for monitoring complex processes with multi-class OOC information. To be specific, a process surveillance technique in the form of a control chart is proposed based on the probability that the online data is classified as an in-control (IC) sample, and the control chart triggers an alarm when the probability is lower than the control limit. Our numerical findings based on the Monte–Carlo simulation show that the proposed control chart performs more effectively than its competitors under various distributions and data types, especially for high-dimensional cases when multi-class OOC information is known in advance. Moreover, the proposed method is illustrated with an application using the data related to the hard disk manufacturing processes.

Evaluating the potential of digital twin derived from UAV imagery in vegetation monitoring of downtown streams

Evaluating the potential of digital twin derived from UAV imagery in vegetation monitoring of downtown streams

Linking the uptake of best management practices on dairy farms to catchment water quality improvement over a 20-year period

Intensive land use, such as dairying, can impair water quality. Although many guidelines exist on how to mitigate the loss of dairy-associated contaminants from land to water through best management practices (BMPs), few datasets exist on the success of implementation on-farm. Five dairy-dominated catchments (from 598 to 2480 ha) in New Zealand were studied from 2001 to 2020. The first period, from 2001 to 2010, involved comprehensive “extension” advice to farmers consisting of workshops, stream water quality and flow monitoring, farm practice surveys, and identified solutions to address site-specific contaminant losses. In the second period (2011−2020), termed “post-extension”, only water quality monitoring and farm practice surveys were continued. Of the water quality contaminants (including dissolved reactive phosphorus (DRP), total phosphorus (TP), ammoniacal-nitrogen, nitrate-nitrite-nitrogen [NNN], suspended sediment and E. coli), 83 % of water quality trend directions were either improving (n = 16) or showed no change (n = 9) during the extension period. Over the 20-year dataset, which included the post-extension period, 20 out of 30 contaminant-catchment combinations (67 %) were improving, but nine were degrading, dominated by NNN (n = 4), DRP (n = 2) and E. coli (n = 2). Abrupt decreases in contaminant concentrations, were correlated with on-farm practice changes mainly associated with transition from direct discharge of farm dairy shed effluent to waterways to land application, and the capture of effluent from off-paddock facilities (like stand off or feed pads). Best management practices reduced phosphorus (P) forms, E. coli and sediment concentrations. Increase in NNN concentrations was caused by transitioning from flood to spray irrigation and a commensurate increase in cow numbers and NNN leaching. These data indicate that extension advice and on-farm practice change have helped to improve overall water quality over time. Nevertheless, recent regulatory threshold values for some contaminant concentrations are not being met, meaning that more actions are required, over and above the BMPs implemented.

Use of multivariate analysis to identify phytoplankton bioindicators of stream water quality in the monomodal equatorial agroecological zone of Cameroon.

The aquatic ecosystem is compromised by many contaminants that may reduce ecosystem functions and severely affect human health. This study aimed at determining suitable phytoplankton bioindicators of water quality for biomonitoring of freshwater streams in the monomodal agroecological zone of Cameroon. Water physicochemical and hydrological parameters, together with phytoplankton abundance and diversity, were measured from June 2016 to May 2017 along the Benoe Stream. Principal component analysis and redundancy analysis were used to determine phytoplankton spatial and temporal distribution and identify indicator species. The Shannon-Wiener diversity and Pielou's evenness indices indicated a clean to mildly polluted stream with a diverse phytoplankton community consisting of 84 genera belonging to 51 families that was dominated by the Bacillariophyta (64%), followed by Chlorophyta (13%) and Cyanophyta (10%). The total dissolved solids, electrical conductivity, stream water velocity, and discharge were the most important stream characteristics affecting the abundance of the dominant phytoplankton genera. Seasonal variations in the stream characteristics as well as spatial community distribution along an urban-small-scale farming - large-scale farming gradient were unveiled and their influence on the phytoplankton relative abundances. Increased abundance of Synedra ulna was indicative of low TDS and EC, which was the contrary for Gyrosigma baltium dominance. High Pleurosira laevis abundance was associated with the urban zone while high Diatoma sp. and Oscillatoria sp. abundances were related to the large-scale farming zone of the stream. These phytoplankton species have good potential for use as bioindicators for stream water quality monitoring in the monomodal agroecological zone.

Together is Better: Heavy Hitters Quantile Estimation

Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of particular interest, as they often capture the user's utility. For example, if a video connection has high tail (e.g., 99'th percentile) latency, the perceived quality will suffer, even if the average and median latencies are low. In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, value) tuples, and we wish to track the quantiles for each ID. Existing quantile sketches are designed for a plain number stream (i.e., containing just a value). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand. We first present a couple of simple and effective algorithms that serve as baselines, a sampling approach and a sketching approach. Then, we present SQUAD, an algorithm that combines sampling and sketching while improving the asymptotic space complexity. Intuitively, SQUAD uses a background sampling process to capture the behaviour of the quantiles of an item before it is allocated with a sketch, thereby allowing us to use fewer samples and sketches. The algorithms are rigorously analyzed, and we demonstrate SQUAD's superiority using extensive~simulations on real-world traces.

A slurry approach to identify nutrient critical source areas from subtropical catchment erosion

Targeting catchment nutrient critical source areas (CSAs) (areas contributing most of the nutrients in a catchment) is an efficient way to prioritize remediation sites for reducing nutrient runoff to waterways. We tested if the soil slurry approach – with particle sizes and sediment concentrations representative of those in streams during high rainfall events – can be used to identify potential CSAs within individual land use types, examine fire impacts, and identify the contribution of leaf litter in topsoil to nutrient export in subtropical catchments. We first confirmed the slurry approach met the prerequisite to identify CSAs with relatively higher nutrient contribution (not absolute load estimation) by comparing the slurry sampling with stream nutrient monitoring data. We validated that: 1) differences in slurry total nitrogen to phosphorus mass ratios from different land uses were consistent with stream monitoring data; and 2) our estimated nutrient export contribution from agricultural land, via the slurry approach, was comparable to that derived from monitoring data. Additionally, we found nutrient concentrations in slurries differed across soil types and management practice within individual land uses, correlating with nutrient concentrations in fine particles. These results indicate the slurry approach can be used to identify potential small-scale CSAs. Slurry results from burnt soils were also comparable to other studies showing increased levels of dissolved nutrient loss and higher nitrogen than phosphorus loss, than non-burnt soils. The slurry method also showed the contribution of leaf litter to slurry nutrient concentrations from topsoil was greater for dissolved nutrients than particulate nutrients, indicating different forms of nutrients need to be considered for impacts of vegetation. Our study reveals that the slurry method can be used to identify potential small-scale CSAs within the same land use from erosion and can account for impacts of vegetation and bushfires, providing timely information to guide catchment restoration actions.

Identifying the recharge and salinization mechanisms of the Shekastian saline spring in southern Iran.

To identify the recharge, and the salinization mechanisms of Shekastian saline spring, appearing via thin limestone layers on the Shekastian stream bed, southern Iran, a comprehensive study was conducted using multi-tracing approach. Hydrochemical tracing indicated that the halite dissolution is the main salinity source for Shekastian spring. Similar to surface waters, spring salinity is amplified by evaporation during dry season, indicating that recharge comes from surface waters. The temperature of the spring water varies hourly, which is another sign that surface waters recharge the spring. Applying the discharge tracing method at two low-discharge times in two consecutive years by precise longitudinal discharge monitoring of the Shekastian stream above and below the spring site revealed that the main source of recharge for the Shekastian saline spring is water escape via thin limestone layers, located on a stream bed above the spring site. Results of isotope tracing indicated that Shekastian saline spring is recharged from evaporated surface water, subjecting to CO2 gas along subsurface flow path of recharging water. Geologic/geomorphologic evidences, supported by results of hydrochemical tracing, revealed that halite dissolution of Gachsaran evaporite formation by spring recharging water is the main salinization source for the Shekastian saline spring. To avoid salinization of Shekastian stream by Shekastian saline spring, draining the spring recharging water at downstream vicinity of spring recharging stream by building an underground interceptor drainage system is suggested, in which resulted in flow stopping the spring.

Outdoor River Activities: Relations with Geological Background and Extreme Events in the Perspective of Geoeducation

As for sport climbing, also for outdoor activities such as rafting and canyoning, a strong connection with geofeatures on a long and short time scale exists. For investigating this, three river segments were selected in the Sesia hydrographic basin (Sesia Val Grande UNESCO Global Geopark, Northwest Italy). Here, commercial rafting and canyoning activities are popular, and important geological features are present (Sesia Supervolcano, Insubric Line). The segments were investigated using partly IDRAIM (system for stream hydromorphological assessment, analysis, and monitoring). Bedrock features, confinement, sinuosity, bankfull bottom, morphological units, and steepness were characterized. The segments were hence divided in several reaches. Correlations on the long time scale were found mainly between the geology and bankfull bottom configuration, while sinuosity allowed us to highlight the possible structural control on the genesis of gorges. Moreover, the analysis of changes in a short time scale along the river segments after the extreme meteorological event occurred on 2–3 October 2020, highlighted a rapids difficulty variation, channels diversion, and woody–rocky debris accumulation in the bankfull, deeply influencing river activities. Through the proposed approach, new frontiers in the outreach of geosciences could be opened, especially in virtue of the several Sustainable Development Goals that could be achieved through river outdoor activities, among the others new employment opportunities for local operators.

A Robust Regime Shift Change Detection Algorithm for Water-Flow Dynamics

Stream and river monitoring have an influential role in agriculture, the fishing industry, land surveillance, the oil and gas industry, etc. Recognizing sudden changes in the behavior of streamflow could also provide tremendous insight for decision-making and administration purposes. The primary purpose of this study is to offer a new robust Regime Shift Change Detection (RSCD) algorithm which can identify periods and regime changes without any assumptions regarding the length of these periods. A regime shift algorithm using two different refined method approaches is proposed in this article. The RSCD with Relative Difference (RSCD-RD) and RSCD with Growth Rate (RSCD-GR) are the two main specializations of this regime shift algorithm. We compared these two specializations on train and test datasets and commented on the advantages and each specialization. RSCD-GR and RSCD-RD were equally effective in detecting regime changes when thresholds were pinpointed for each station and season. However, RSCD-RD outperformed RSCD-GR when general thresholds were used for cold and warm months. A strength of RSCD-GR is the ability to investigate newly observed data separately, while RSCD-RD may require re-investigation of historical data in some cases. A regime change was detected in the monthly streamflow data of the Athabasca River at Athabasca (07BE001) in May 2007, while no such change was observed in the monthly streamflow data of the Athabasca River below Fort McMurray (07DA001). The discrepancy could be attributed to factors such as the clarity of the river water from Saskatchewan or the utilization of industrial water. Additional investigation might be required to determine the underlying causes.

Double Sampling Adaptive Thresholding LASSO Variability Chart for Phase II Monitoring of High-Dimensional Data Streams

In many manufacturing or service systems, the product quality and/or process outcome is described in terms of a large number of quality characteristics. Since the use of large sample sizes in such systems imposes significant costs on the manufacturer, the sample size is usually considered smaller than the process dimension. In this condition, which is referred to high-dimensionality, the sample covariance matrix is not a positive semi-definite matrix. Moreover, another issue to establish variability control charts under high-dimensional setting is their speed to detect the process disturbances under both diagonal and off-diagonal shift patterns. To solve these drawbacks, in this study, a double sampling-based adaptive thresholding LASSO control chart called DSATL is developed to monitor the variability matrix of high-dimensional processes under sparsity assumption. The run length distribution of the DSATL chart under seven out-of-control patterns is evaluated taking a different number of quality characteristics into account. The simulation results show that the detectability of the DSATL chart outperforms the classical adaptive thresholding LASSO one under various changes in the process covariance matrix. The results also confirm that the sensitivity of the DSATL chart for real-time detection of covariance matrix anomalies improves as the process dimension increases.

Water temperature dynamics in a headwater forest stream: Contrasting climatic, anthropic and geological conditions create thermal mosaic of aquatic habitats.

The thermal regime of streams is a relevant driver of their ecological functioning. As this regime is presently submitted to numerous alterations (among others, impoundments, and climate change), it seems important to study both their effects and potential recovery from the latter. Thus, we investigated the surface and hyporheic water temperature along a small headwater stream with contrasting environmental contexts: forest landscape, open grassland landscape without riparian vegetation, several artificial run-of-the-river impoundments and one discharge point of a by-pass impoundment. The main objectives were to study the influence of these contrasting contexts on surface and subsurface water temperature at a local scale. Contrasting contexts were supposed to create effects on both surface and hyporheic thermal regimes at a local scale. Differences of thermal regimes between surface and hyporheos were expected, as well as between geological contexts. Sensors located at multiple stations allowed monitoring of stream and hyporheos temperature along the stream, while comparison with adjacent reference stream allowed for surface water thermal regime benchmark. Impoundments and landscapes significantly influenced stream thermal regime at a local scale (impoundments created up to +3.7°C temperature increase in average). Their effect on hyporheos thermal regime was less marked than the ones generated by solar radiation or geological features. Hyporheos thermal regime varies from stream one by temperature dynamics delay (up to 18h) and decrease (up to -7°C between surface and hyporheos temperature in average). These coupled effects create a mosaic of thermal habitats, which could be used for river biodiversity preservation and restoration.

Online monitoring of high-dimensional asynchronous and heterogeneous data streams for shifts in location and scale

Recent advancement of sensor technology has made it possible to monitor high-dimensional data streams in various manufacturing systems for quality improvement. However, existing monitoring schemes commonly assume that all data streams have the same sampling interval. This assumption does not always hold in practice, which poses new and unique challenges for multivariate statistical process control. In this paper, we propose a generic nonparametric monitoring framework to online monitor high-dimensional asynchronous and heterogeneous data streams, where sampling intervals of data streams are different from each other, and measurements of each data stream follow arbitrary distributions. In particular, we first propose a quantile-based nonparametric framework to monitor each data stream locally for possible shifts in both location and scale. Then, for unsampled measurements due to different sampling intervals, a compensation strategy based on the Bayesian approach is introduced. Furthermore, we develop a global monitoring scheme using the sum of top- r local statistics, which can quickly detect a wide range of possible shifts in all directions. Simulations and case studies are conducted to evaluate the performance and demonstrate the superiority of the proposed method.