Continuous Flow Data Research Articles

Staged SWAT calibration with bias-corrected precipitation product for enhancing flow data continuity in tributaries of the Mekong River

Accurate and continuous flow data are crucial for effective water management in large river basins worldwide. However, these catchments often face challenges regarding data continuity in the mainstream and their tributaries. This study proposes a methodological framework for enhancing flow data continuity that uses a staged calibration scheme of Soil and Water Assessment Tool (SWAT) modeling with an appropriate satellite precipitation product (SPP) for each watershed. This framework is successfully applied in the Mun-Chi River Basin, the largest tributary of the Lower Mekong River, over a 20-year period at 34 stations. The staged calibration involves partitioning the catchment into smaller sub-catchments, guided by the flow monitoring stations and flow discontinuity structures such as dams. The sequential calibration from upstream to downstream partitions the overall model calibration challenge into smaller problems and contributes to a more efficient and accurate calibration and validation. Alternative SPPs were considered to overcome monitoring gaps. Their bias was corrected using quantile mapping, and their performance was evaluated with flow simulations using the SWAT model. The assessment indicates that the CMORPH-CRT product, with a spatial resolution of 0.25°, demonstrates good suitability for hydrological modeling of the Mun-Chi River Basin. The proposed methodological framework provides a continuous time series of flow discharge at multiple stations within the watershed, offering valuable insights for sustainable water resource management strategies in river systems under changing climate and land use conditions, and supporting future studies on environmental issues.

Advancing Discharge Ratings: A Novel Approach Based on Observed and Derivable GIS Factors in Alluvial Systems

Depth–discharge rating is required at gauged and ungauged locations for hydrologic modeling of alluvial systems to evaluate streamflow and manage regional water resources. Spanning low to high-flow conditions, manual field measurements are used to develop discharge ratings at gauged locations, producing continuous flow data from automated water depth measurements. The discharge rating is dependent on channel geometry, stream slope, vegetation, roughness coefficient, sediment load, and bank stability. To construct discharge ratings for many locations within larger model domains (hundreds to thousands of km2), intensive GIS and manual (spreadsheet) data manipulation are often required. In this analysis, available USGS gauging stations and readily available GIS coverages were used to learn and implement a novel method to characterize the depth–discharge relationships for hydrologic modeling of larger or complex areas using commonly available data and normalization techniques. The improved procedure simply uses drainage area, channel slope, and channel width, readily derivable GIS data, to develop discharge ratings for gauged and ungauged sections. The discharge rating curves for 70 USGS streamflow gauges were reproduced using the procedure. Then, the produced and observed discharge rating curves were compared to evaluate the accuracy of the method. In the analysis of streamflow depth predictions, the average Root Mean Squared Error was recorded at approximately 0.38 m (≈1.24 ft), with an interquartile range between 0.21 m and 0.49 m. The Mean Error remained centered around 0 m, with interquartile values ranging from −0.24 m to 0.24 m.

Effects of stream drying, season, and distance to refuge on macroinvertebrate community structure in an arid intermittent stream basin

AbstractIntermittent streams are globally ubiquitous and represent a large percentage of stream networks. As climate change in many arid regions increases the frequency and intensity of drying disturbances, it is important to understand how aquatic biota will respond to such disturbances and how it would impact aquatic biodiversity. To address these topics, we sampled 10 stream reaches in the Sycamore Creek basin, an arid‐land stream in central Arizona (USA), with reach‐scale flow regimes ranging from perennial to highly intermittent. We sampled aquatic macroinvertebrates during 4 seasons to explore seasonal variability in community structure through flowing and drying phases. We also collected continuous flow data with remote data loggers to explore the impacts of intermittency and distance to perennial refuges on species richness, taxonomic composition and trait composition. Overall, richness was lower at intermittent reaches than perennial reaches, and richness values increased linearly as flow duration increased. We found no relationship between richness and distance to the nearest perennial refuge. Community assemblages differed significantly by season but were not distinct between perennial and intermittent reaches. Trait composition was also distinct between seasons and flow regimes, with traits such as a lack of diapause, longer life span and predatory feeding behaviours being indicators for perennial reaches. As climate change alters natural flow regimes, understanding the responses of macroinvertebrate community structure to drying disturbances in arid‐land streams can provide insight on aquatic community responses to climate change at larger scales.

Using Continuous Flow Data to Predict the Course of Air Leaks After Lung Lobectomy.

Assessments of air leaks are usually performed subjectively, precluding the use of air leaks as an evaluation factor. We aimed to identify objective parameters as predictive factors for prolonged air leak (PAL) and air leak cessation (ALC) from air flow data produced by a digital drainage system. Flow data records of 352 patients who underwent lung lobectomy were reviewed, and flow data at designated intervals (1, 2, and 3 hours postoperatively [POH] and 3 times a day thereafter [06:00, 13:00, 19:00]) were extracted. ALC was defined by flow less than 20 mL/min over 12 hours, and PAL was defined as ALC after 5 days. Cumulative incidence curves were obtained using Kaplan-Meier estimates of time to ALC. Cox regression analysis was performed to determine the effects of variables on the rate of ALC. The incidence of PAL was 18.2% (64/352). Receiver operating characteristic curve analysis showed cut-off values of 180 mL/min for the flow at 3 POH and 73.3 mL/min for the flow on postoperative day 1; the sensitivity and specificity of these values were 88.9% and 82.5%, respectively. The rates of ALC by Kaplan-Meier analysis were 56.8% at 48 POH and 65.6% at 72 POH. Multivariate Cox regression analysis revealed that the flow at 3 POH (≤80 mL/min), operation time (≤220 minutes), and right middle lobectomy independently predicted ALC. Air flow measured by a digital drainage system is a useful predictor of PAL and ALC and may help optimize the hospital course.

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network.

As the aggravation of road congestion leads to frequent traffic crashes, it is necessary to relieve traffic pressure through traffic flow prediction. As well, the traffic flow of the target road section to be predicted is also closely related to the adjacent road sections. Therefore, in this paper, a prediction method based on the combination of multiple linear regression and Long-Short-Term Memory (MLR-LSTM) is proposed, which uses the incomplete traffic flow data in the past period of time of the target prediction section and the continuous and complete traffic flow data in the past period of time of each adjacent section to jointly predict the traffic flow changes of the target section in a short time. The accurate prediction of future traffic flow changes can be solved based on the model supposed when the traffic flow data of the target road section is partially missing in the past period of time. The accuracy of the prediction results is the same as that of the current mainstream prediction results based on continuous and non-missing target link flow data. Meanwhile, there is a small-scale improvement when the data time interval is short enough. In the case of frequent maintenance of cameras in actual traffic sections, the proposed prediction method is more feasible and can be widely used.

Event mean concentration and first flush from residential catchments in different climate zones

Most studies on EMC (Event mean concentration) and first flush are reported as local studies; however variations of EMC and first flush across catchments in different climate zones has not been studied. This research collected continuous flow and discrete water quality data and rainfall measurements from 17 catchments, EMC and rainfall data from 14 catchments, and an additional dataset where only average EMC values are reported (19 catchments). The data are from residential sites across temperate, tropical, dry, and continental climate zones and include water quality parameters in particulate (total suspended solids), mixed (total nitrogen and total phosphorus) and dissolved (orthophosphate and ammonium nitrogen) forms. Our study shows that EMC differs significantly between climate zones. The average EMC is highest in dry followed by continental and temperate, with lowest in the tropical zone. Pearson's correlation analysis revealed that the rainfall depth is negatively correlated with EMC for particulate and mixed form parameters for the tropical, temperate and dry zones, but positively correlated for the continental zone. The discrete time-series data from the 17 catchments were used to evaluate first flush and it was found that catchments in the tropics exhibit stronger first flush than temperate zone catchments, for all the water quality parameters with particulate showing a stronger first flush compared to dissolved forms. Based on the distribution of the data, new limits for very strong, strong, moderate, and very weak to no first flush are suggested for TSS for different climate zones. The new limits were quantified by fitting the function L^=V^bwhere L^and V^are the normalized cumulative runoff load and volume, respectively. For catchments in the tropics, this corresponds to b < 0.47, 0.6 > b > 0.47, 0.76 > b > 0.6 and b > 0.76, respectively. For the temperate zone, b < 0.5, 0.67 > b > 0.5, 0.85 > b > 0.67 and b > 0.85, are appropriate. From a design perspective, the FF20 concept defined as the load corresponding to 20% runoff volume, is often used. The ranges FF20 < 0.27, 0.36 > FF20 > 0.27, 0.45 > FF20 > 0.36 and FF20 > 0.45 and FF20 < 0.24, 0.31 > FF20 > 0.24, 0.31 > FF20 > 0.39 and FF20 > 0.39 are proposed for tropical and temperate catchments, respectively. Other limits for TP, TN, OP and NH4-N are also suggested. This is the first study of its kind and an expended dataset especially for continental and dry regions is needed to further validate the findings.

Macroinvertebrates at the source: flow duration and seasonality drive biodiversity and trait composition in rheocrene springs of the Western Allegheny Plateau, USA.

Documenting flow regimes and the ecology of source headwater streams has gained considerable attention for scientific and regulatory purposes. These streams do not appear on standard maps, and local physiographic and climatologic conditions can control their origins. We investigated macroinvertebrate assemblages seasonally and in relation to flow duration, catchment and habitat variables within 14 source headwaters (< 1 ha) in the Western Allegheny Plateau over a 19-month period. We classified 6 perennial (P) and 8 intermittent (I) streams directly with continuous flow data loggers. Several biological and trait-based metrics could distinguish flow class, but few instream physical measures could. Macroinvertebrate metrics and assemblage dispersion varied seasonally and responded significantly along a gradient of total flow duration. Separate indicator species analyses generated 22 genera and 15 families with significant affinities to P streams. Richness of P-indicator taxa was also strongly correlated with flow duration gradients, and we estimated a total flow duration changepoint at 77% (3 indicator families) followed by a sharp increase in richness. Two rapid field-based flow duration methods (NC Stream Identification index and OH Headwater Habitat Evaluation index) could distinguish upstream ephemeral reaches from P and I reaches, but misclassified P as I more frequently. Our findings highlight that diverse coldwater macroinvertebrate assemblages inhabited extremely small, low-discharge springs in the region, and responded with flow duration. These source headwater habitats are susceptible to human disturbance and should be monitored as is routinely done in larger lotic systems.

Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison

Short-term traffic flow prediction plays a key role of Intelligent Transportation System (ITS), which supports traffic planning, traffic management and control, roadway safety evaluation, energy consumption estimation, etc. The widely deployed traffic sensors provide us numerous and continuous traffic flow data, which may contain outlier samples due to expected sensor failures. The primary objective of the study was to evaluate the use of various smoothing models for cleaning anomaly in traffic flow data, which were further processed to predict short term traffic flow evolution with artificial neural network. The wavelet filter, moving average model, and Butterworth filter were carefully tested to smooth the collected loop detector data. Then, the artificial neural network was introduced to predict traffic flow at different time spans, which were quantitatively analyzed with commonly-used evaluation metrics. The findings of the study provide us efficient and accurate denoising approaches for short term traffic flow prediction.

Policy Innovation and Governance for Irrigation Sustainability in the Arid, Saline San Joaquin River Basin

This paper provides a chronology and overview of events and policy initiatives aimed at addressing irrigation sustainability issues in the San Joaquin River Basin (SJRB) of California. Although the SJRB was selected in this case study, many of the same resource management issues are being played out in arid, agricultural regions around the world. The first part of this paper provides an introduction to some of the early issues impacting the expansion of irrigated agriculture primarily on the west side of the San Joaquin Valley and the policy and capital investments that were used to address salinity impairments to the use of the San Joaquin River (SJR) as an irrigation water supply. Irrigated agriculture requires large quantities of water if it is to be sustained, as well as supply water of adequate quality for the crop being grown. The second part of the paper addresses these supply issues and a period of excessive groundwater pumping that resulted in widespread land subsidence. A joint federal and state policy response that resulted in the facilities to import Delta water provided a remedy that lasted almost 50 years until the Sustainable Groundwater Management Act of 2014 was passed in the legislature to address a recurrence of the same issue. The paper describes the current state of basin-scale simulation modeling that many areas, including California, are using to craft a future sustainable groundwater resource management policy. The third section of the paper deals with unique water quality issues that arose in connection with the selenium crisis at Kesterson Reservoir and the significant threats to irrigation sustainability on the west side of the San Joaquin Valley that followed. The eventual policy response to this crisis was incremental, spanning two decades of University of California-led research programs focused on finding permanent solutions to the salt and selenium contamination problems constraining irrigated agriculture, primarily on the west side. Arid-zone agricultural drainage-induced water quality problems are becoming more ubiquitous worldwide. One policy approach that found traction in California is an innovative variant on the traditional Total Maximum Daily Load (TMDL) approach to salinity regulation, which has features in common with a scheme in Australia’s Hunter River Basin. The paper describes the real-time salinity management (RTSM) concept, which is geared to improving coordination of west side agricultural and wetland exports of salt load with east side tributary reservoir release flows to improve compliance with river salinity objectives. RTSM is a concept that requires access to continuous flow and electrical conductivity data from sensor networks located along the San Joaquin River and its major tributaries and a simulation model-based decision support designed to make salt load assimilative capacity forecasts. Web-based information dissemination and data sharing innovations are described with an emphasis on experience with stakeholder engagement and participation. The last decade has seen wide-scale, global deployment of similar technologies for enhancing irrigation agriculture productivity and protecting environmental resources.

Streaming Big Data Analytics- Current Status, Challenges and Connection of unbounded data Processing platforms

A strategy of examining immense dimensions of structured, un-structured, Semi-Structured data sets is referred as Big data Analytics. Streaming Big Data refers to data generated continuously from number of data sources like Internet-of-Things (IoT) devices, mobile applications, Embedded Sensors, web clicks and many more are needed to be store, processed and analyzed in a tiny interval of time in order to extract meaningful insights and take proper decisions in a timely fashion as the necessity arises. However analyzing streaming big data (continuous flow or unbounded data) is a very challenging problem. Continuous data streams have become essential prerequisite for numerous industrial and scientific applications, the current existing technology Hadoop-MapReduce is not appropriate for stream processing of big data. This paper discusses the challenges and benefits of streaming big data along with its architecture, and focuses on different open source streaming processing platforms that are existed to process the huge data at a high speed

Modeling fine particle dynamics in gravel-bedded streams: Storage and re-suspension of fine particles

Fine particles or sediments have various effects on water quality and aquatic ecosystems. Thus, understanding the dynamics of these fine particles between water body and stream bed is an important issue in sediment research. Previous studies and analysis of empirical data suggest that fine particles are stored in the sediment bed in the low flow regime, where flow rate is smaller than the critical flow rate that mobilizes the sediment bed. These fine particles are re-suspended during flood events when the flow rate becomes larger than the critical flow rate that mobilizes bed material. The transition from pattern recognition to process analysis required incorporation of the dominant processes controlling fine particle dynamics within gravel-bedded streams into a model. The process analysis was performed using continuous flow and turbidity data at two locations on the Russian River in California to test process descriptions and then calibrate a quantitative model to represent those processes. The resulting process model coupled fine particle retention within the sediment bed by filtration and sedimentation with the release of accumulated fine particles in response to flood events. Model parameters, such as the critical flow rate required for initiating sediment bed fluidization, the maximum fine particle storage capacity within the sediment bed, and background particle concentration for the watershed, were estimated from the monitoring data. Model calibration optimized the filtration and the sediment bed fluidization parameters over two or three years of data. Overall, the difference between modeled and observed fine particle mass released from the sediment bed was within 20% of the measured mass.

Tools for managing hydrologic alteration on a regional scale: Estimating changes in flow characteristics at ungauged sites

Abstract Hydrologic alteration is a predominant stressor for biological resources in streams. This stress is further aggravated by competing human and ecological demands for limited water resources. Understanding flow–ecology relationships and establishing relevant and implementable flow targets are essential to protect biological communities. Estimating degree of ecologically relevant hydrologic alteration depends on the availability of long‐term flow data at sites with biological information. However, measured flow data are seldom available at sufficient density to support large‐scale analyses of the biological effects of hydrologic alterations. The ability to accurately simulate flows and estimate flow metrics at many ungauged locations across a broad geographical area remains a fundamental challenge. We address this challenge by applying a novel technique to simulate flow regimes at any stream reach of interest by first developing an ensemble of regionally calibrated and validated hydrological models, and then using a selection tool to match the “best‐fit” model to ungauged stream reaches. An ensemble of 26 HEC‐HMS rainfall–runoff models were calibrated to represent the range of catchment conditions in the southern California region. We developed current and historical flow regimes and a suite of flow metrics at 572 ungauged sites in southern California with bioassessment monitoring data. The flow metrics represent hydrograph characteristics of magnitude, timing, frequency, duration and variability. The flow metrics were estimated under three precipitation conditions—dry, wet and average. In addition, we estimated aggregated flow metrics for (dry + wet + average) condition. Hydrologic alteration was estimated as the deviation between the modelled current and historical flow metrics. Approximately 79% of the region shows some degree of hydrologic alteration, and approximately 40% of the sites are estimated to be severely altered. Magnitude metrics tend to increase in response to urban and agricultural land uses, whereas the timing and duration metrics are mostly unchanged. This mechanistic modelling approach demonstrates the feasibility of estimating flow alterations for ungauged catchments with relative ease of transferability over a broad geographical region. The continuous granular flow data allow for computation and consideration of metrics that may be applicable to a variety of ecological endpoints and consideration of a range of management trade‐offs.

Viscosity-adjusted estimation of pressure head and pump flow with quasi-pulsatile modulation of rotary blood pump for a total artificial heart

Estimation of pressure and flow has been an important subject for developing implantable artificial hearts. To realize real-time viscosity-adjusted estimation of pressure head and pump flow for a total artificial heart, we propose the table estimation method with quasi-pulsatile modulation of rotary blood pump in which systolic high flow and diastolic low flow phased are generated. The table estimation method utilizes three kinds of tables: viscosity, pressure and flow tables. Viscosity is estimated from the characteristic that differential value in motor speed between systolic and diastolic phases varies depending on viscosity. Potential of this estimation method was investigated using mock circulation system. Glycerin solution diluted with salty water was used to adjust viscosity of fluid. In verification of this method using continuous flow data, fairly good estimation could be possible when differential pulse width modulation (PWM) value of the motor between systolic and diastolic phases was high. In estimation under quasi-pulsatile condition, inertia correction was provided and fairly good estimation was possible when the differential PWM value was high, which was not different from the verification results using continuous flow data. In the experiment of real-time estimation applying moving average method to the estimated viscosity, fair estimation could be possible when the differential PWM value was high, showing that real-time viscosity-adjusted estimation of pressure head and pump flow would be possible with this novel estimation method when the differential PWM value would be set high.

Noninvasive measurement of cerebrospinal fluid flow using an ultrasonic transit time flow sensor: a preliminary study.

OBJECT Mechanical failure-which is the primary cause of CSF shunt malfunction-is not readily diagnosed, and the specific reasons for mechanical failure are not easily discerned. Prior attempts to measure CSF flow noninvasively have lacked the ability to either quantitatively or qualitatively obtain data. To address these needs, this preliminary study evaluates an ultrasonic transit time flow sensor in pediatric and adult patients with external ventricular drains (EVDs). One goal was to confirm the stated accuracy of the sensor in a clinical setting. A second goal was to observe the sensor's capability to record real-time continuous CSF flow. The final goal was to observe recordings during instances of flow blockage or lack of flow in order to determine the sensor's ability to identify these changes. METHODS A total of 5 pediatric and 11 adult patients who had received EVDs for the treatment of hydrocephalus were studied in a hospital setting. The primary EVD was connected to a secondary study EVD that contained a fluid-filled pressure transducer and an in-line transit time flow sensor. Comparisons were made between the weight of the drainage bag and the flow measured via the sensor in order to confirm its accuracy. Data from the pressure transducer and the flow sensor were recorded continuously at 100 Hz for a period of 24 hours by a data acquisition system, while the hourly CSF flow into the drip chamber was recorded manually. Changes in the patient's neurological status and their time points were noted. RESULTS The flow sensor demonstrated a proven accuracy of ± 15% or ± 2 ml/hr. The flow sensor allowed real-time continuous flow waveform data recordings. Dynamic analysis of CSF flow waveforms allowed the calculation of the pressure-volume index. Lastly, the sensor was able to diagnose a blocked catheter and distinguish between the blockage and lack of flow. CONCLUSIONS The Transonic flow sensor accurately measures CSF output within ± 15% or ± 2 ml/hr, diagnoses the blockage or lack of flow, and records real-time continuous flow data in patients with EVDs. Calculations of a wide variety of diagnostic parameters can be made from the waveform recordings, including resistance and compliance of the ventricular catheters and the compliance of the brain. The sensor's clinical applications may be of particular importance to the noninvasive diagnosis of shunt malfunctions with the development of an implantable device.

Network Flow Awareness System for E-Science Collaborative Application

Recently big science data transfer for globally collaborative research on high performance network in High Energy Physics, Climate Change, Bio-Genome and so on, is needed for large-scale parallel processing based on GRID, correlation analyzing observation big data and reprocessing experimental raw data. However it is not easy to monitor the big elephant flow data, extremely large continuous flow and mice flow data, very little internet flow, at the same time on big pipe network owing to performance issue in capturing and analyzing with more than 10G full line rate and also there isn’t intuitive visualization system of monitoring flow data for network operator and user. In this paper, we design and implement a network flow awareness system based on ARGUS framework and 3D visualization technology that supports not only high performance collection of more rich flow information than traditional net flow data and intuitive visualization of the elephant flow data with research project information for efficiently monitoring e-Science elephant flow data.

Evaluation of Steelhead Passage Flows Using Hydraulic Modeling on an Unregulated Coastal California River

Passage and habitat connectivity flows for steelhead Oncorhynchus mykiss through depth sensitive natural, low gradient, critical riffle sites were investigated in the unregulated Big Sur River, California. The River2D two-dimensional hydraulic habitat model, along with quantitative passage metrics and species-specific and lifestage-specific depth criteria, were used to evaluate and compare predicted fish passage flows with flows derived by a traditional empirical critical riffle fish passage method. Passage flows were also compared with historical unimpaired natural hydrology patterns to assess the frequency and duration of suitable passage flows under the naturally variable flow regimes characteristic of Central California coastal rivers. A strong relationship (r 2 = 0.93) was observed between flows predicted by hydraulic modeling and flows identified by the empirical critical riffle method. River2D provided validation that the flows derived using the traditional critical riffle methodology provided for contiguous passable pathways of suitable hydraulic (depth and velocity) conditions through complex cobble-dominated riffle sites. Furthermore, steelhead passage flows were spatially and temporally consistent between lagoon and upstream riffles for adults, and were generally indicative of a river system in equilibrium with a naturally variable flow regime and associated intact ecological processes. An analysis of 25years of continuous flow data indicated sufficient flows for upstream passage by young-of-year and juvenile steelhead were produced between 37% and 100% and between 1% and 95% of the time, respectively. September and October are the most challenging months for natural flows to meet young-of-year and juvenile passage and habitat connectivity flows. Careful consideration of seasonal and interannual flow variability dynamics, therefore, are critical components of an effective flow management strategy for the maintenance and protection of passage and habitat connectivity flows between lagoon and upriver habitats. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

An assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue, Ireland

This paper presents an assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue in Ireland. The rivers, located in the South of Ireland, are underlain by sandstone, limestones and mudstones, and the catchments are primarily agricultural. A comprehensive database of suspended sediment data is not available for rivers in Ireland. For such situations, it is common to estimate suspended sediment concentrations from the flow rate using the suspended sediment rating curve approach. These rating curves are most commonly constructed by applying linear regression to the logarithms of flow and suspended sediment concentration or by applying a power curve to normal data. Both methods are assessed in this paper for the Rivers Bandon and Owenabue.Turbidity-based suspended sediment loads are presented for each river based on continuous (15min) flow data and the use of turbidity as a surrogate for suspended sediment concentration is investigated. A database of paired flow rate and suspended sediment concentration values, collected between the years 2004 and 2011, is used to generate rating curves for each river. From these, suspended sediment load estimates using the rating curve approach are estimated and compared to the turbidity based loads for each river. Loads are also estimated using stage and seasonally separated rating curves and daily flow data, for comparison purposes.The most accurate load estimate on the River Bandon is found using a stage separated power curve, while the most accurate load estimate on the River Owenabue is found using a general power curve. Maximum full monthly errors of −76% to +63% are found on the River Bandon with errors of −65% to +359% found on the River Owenabue. The average monthly error on the River Bandon is −12% with an average error of +87% on the River Owenabue. The use of daily flow data in the load estimation process does not result in a significant loss of accuracy on either river. Historic load estimates (with a 95% confidence interval) were hindcast from the flow record and average annual loads of 7253±673tonnes on the River Bandon and 1935±325tonnes on the River Owenabue were estimated to be passing the gauging stations.

Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data

Currently, assessment of broiler (meat) chicken welfare relies largely on labour-intensive or post-mortem measures of welfare. We here describe a method for continuously and robustly monitoring the welfare of living birds while husbandry changes are still possible. We detail the application of Bayesian modelling to motion data derived from the output of cameras placed in commercial broiler houses. We show that the forecasts produced by the model can be used to accurately assess certain key aspects of the future health and welfare of a flock. The difference between healthy flocks and less-healthy ones becomes predictable days or even weeks before clinical symptoms become apparent. Hockburn (damaged leg skin, usually only seen in birds of two weeks or older) can be well predicted in flocks of only 1-2 days of age, using this approach. Our model combines optical flow descriptors of bird motion with robust multivariate forecasting and provides a sparse, efficient model with sparsity-inducing priors to achieve maximum predictive power with the minimum number of key variables.

River flux uncertainties predicted by hydrological variability and riverine material behaviour

AbstractData on riverine fluxes are essential for calculating element cycles (carbon, nutrients, pollutants) and erosion rates from regional to global scales. At most water‐quality stations throughout the world, riverine fluxes are calculated from continuous flow data (q) and discrete concentration data (C), the latter being the main cause of sometimes large uncertainties. This article offers a comprehensive approach for predicting the magnitude of these uncertainties for water‐quality stations in medium to large basins (drainage basin area &gt; 1000 km²) based on the commonly used discharge‐weighted method. Uncertainty levels – biases and imprecisions – for sampling intervals of 3 to 60 days are correlated first through a nomograph with a flux variability indicator, the quantity of riverine material discharged in 2% of time (M2%). In turn, M2% is estimated from the combination of a hydrological reactivity index, W2% (the cumulative flow volume discharged during the upper 2% of highest daily flow) and the truncated b50sup exponent, quantifying the concentration versus discharge relationship for the upper half of flow values (C = a q b50sup, for q &gt; q50, where q50 is the median flow): M2% = W2% + 27.6b50sup. W2% can be calculated from continuous flow measurements, and the b50sup indicator can be calculated from infrequent sampling, which makes it possible to predict a priori the level of uncertainty at any station, for any type of riverine material either concentrated (b50sup &gt; 0) or diluted (b50sup &gt; 0) with flow. A large data base of daily surveys, 125 station variables of suspended particulate matter (SPM), total dissolved solids (TDS) and dissolved and particulate nutrients, was used to determine uncertainties from simulated discrete surveys and to establish relationships between indicators. Results show, for example, that for the same relatively reactive basin (W2% &gt; 25%), calculated fluxes from monthly sampling would yield uncertainties approaching ±100% for SPM (b50sup &gt; 1.4) fluxes and ±10% for TDS (b50sup = −0.2). The application to the nitrate survey of the river Seine shows significant trends for the 1972–2009 records. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Detecting unstable periodic orbits from continuous chaotic dynamical systems by dynamical transformation method

Detecting unstable periodic orbits (UPOs) from chaotic dynamic systems is a challenging problem. For a large number of complex systems, we can collect some experimental time series data but cannot find theoretical models to describe them. Thus, detecting unstable periodic orbits from experimental data can help us understand the chaotic properties of physical phenomenon without using theoretical models. We, in this paper, first use the dynamical transformation (DT) algorithm to detect unstable periodic orbits from chaotic systems, and find that the original DT algorithm can detect the UPOs from the time series of chaotic discrete map, but it is infeasible for the time series from continuous chaotic flow. In this regard, we then propose an improved DT algorithm that is based on the Poincare section method to detect the UPOs from continuous chaotic flow. In particular, we transform the continuous flow data into discrete map time series in terms of Poincare section, and then detect unstable periodic orbits from the transformed discrete map time series. In addition, we take Rössler and Lorenz chaotic systems as examples to demonstrate the effectiveness of our proposed method.