Calibration Data Research Articles

Coupling Different Road Traffic Noise Models with a Multilinear Regressive Model: A Measurements-Independent Technique for Urban Road Traffic Noise Prediction.

Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model-NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source-receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models' application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors.

Retrospective EPR dosimetry in glass and tooth enamel – The methodology and results of MUG group in RENEB 2021 inter-laboratory comparison

Electron Paramagnetic Resonance (EPR) is recognized method in retrospective dosimetry, based on detection of stable paramagnetic centres induced by radiation in biological materials, like enamel, bones, nails, or artificial materials, like glasses and other. EPR dosimetry in glass and enamel were included, in addition to several biological dosimetry tests, in the recent intercomparison project RENEB ILC 2021 as one of the physical dosimetry methods potentially applicable for dose assessment in radiation accidents. EPR group from Medical University of Gdansk (MUG) was one of participants in this intercomparison. The participants reconstructed doses in three blinded samples of watch glass, Gorilla Glass from phone screens and in enamel irradiated with 0 Gy, 1.2 Gy and 3.5 Gy (these doses were revealed after reporting the final results by all participants). For reconstruction of doses in the glass samples the respective calibration samples were provided by organizers of the ILC project. For dosimetry in enamel the participants used their own calibration data. The doses reconstructed by us in the blinded samples (in terms of kerma in air) were: in watch glass: −0.05 Gy, 1.03 Gy, 3.16 Gy, in Gorilla Glass: −0.03 Gy, 1.20 Gy, 3.44 Gy, in enamel: 0.1 Gy, 1.3 Gy, 3.1 Gy. The differences between the reconstructed and the nominal doses were small and the accuracy was sufficient for triage of irradiated people at the level of 1–2 Gy. The small sample mass of the enamel, suitable rather to Q-band EPR dosimetry and potentially available without whole tooth extraction, allowed to obtain satisfactory accuracy of EPR dosimetry also in the X-band measurements.

Online Self-Calibration of Cable-Driven Parallel Robots Using Covariance-Based Data Quality Assessment Metrics

Abstract This paper presents an algorithm to perform self-calibration of cable-driven parallel robots (CDPRs), where the CDPR's end-effector pose is estimated in conjunction with the calibration of biases in the CDPR's measurements. Two new metrics, known as the position dilution of precision (PDOP) and orientation dilution of precision (ODOP) are introduced as a means to quantify the quality of data collected with regards to self-calibration. These metrics are based on a covariance matrix that is computed online as part of the proposed self-calibration algorithm, which results in the PDOP and ODOP directly corresponding to the standard deviation of the position and orientation errors, respectively. These metrics are used to intuitively select which data points contribute to improved calibration, resulting in a computationally-efficient algorithm requiring few data points to maintain accurate calibration. Additionally, the PDOP and ODOP provide a means to assess when sufficient calibration data has been collected. Numerical results involving an inverse kinematic simulation with rigid cables and a dynamic simulation with flexible cables indicate that the proposed algorithm is capable of performing self-calibration in a computationally-efficient manner. Moreover, the simulation results indicate that the proposed PDOP and ODOP metrics result in smaller position and orientation errors when used to prune the data set compared to the observability indices found in the literature. Accuracy of the proposed algorithm is also confirmed through experiments when compared to ground-truth pose data.

Paleoclimate Proxies and the Benefits of Disunity

AbstractMeasuring the climates of the deep past requires the use of paleoclimate proxies. I describe two proxy data and measurement practices, regarding proxy calibration and proxy data infrastructure. I document how at least some data and measurement practices in paleoclimatology are disunified: these practices do not involve intercalibration or otherwise statistical combination of multiple proxy records, and metadata necessary for proxy data to be reused or intercompared is often not provided. I argue that, perhaps counterintuitively, this lack of standardization and unification of proxy data and measurements has several benefits, especially related to the management of error and uncertainty.

Improving new energy subsidy efficiency considering learning effect: A case study on wind power

Subsidy policies are instrumental in driving the development of new energy. However, the effective allocation of new energy subsidies over time is challenging given fiscal constraints. This study addresses this challenge by considering the learning effect associated with the new energy industry. A two-stage dynamic programming model is proposed to capture the investment decision-making process of companies under new energy subsidy policies and government subsidy setups. Theoretical findings suggest that company investment decisions in new energy are influenced by a guiding principle: The subsidy rate should be negatively correlated with the variation rate of production scale increment (VRPSI). We calibrate this investment decision principle using wind power data from 14 countries. According to this principle, excessive subsidy rates may result in a low VRPSI, thereby diminishing future investment profitability in the new energy industry and leading to subsidy inefficiency. Upon investigating the efficiency of annual subsidy allocation, we find that the subsidy rates were potentially set too high in 2014, 2016, and 2017. Furthermore, the government should exercise caution regarding an inefficient subsidy pattern whereby companies invest in new energy only when the subsidy rate exceeds a certain threshold, neglecting traditional power sources. It is crucial to note that although this study uses wind power industry data for calibration and simulation, the theoretical model can be broadly applied to other new energy industries and emerging industries with increasing marginal net profit.

Enhancing SSVEP Identification With Less Individual Calibration Data Using Periodically Repeated Component Analysis.

Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency. In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted in a self-collected 16-target dataset, a public 40-target dataset, and an online experiment. The proposed methods show significant performance improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a data length of 1 s and maximum average information transfer rates (ITR) of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two datasets, respectively. Averaged online accuracy of 94.00 ± 7.35% and ITR of 139.73±21.04 bits/min were achieved with 0.5-s calibration data per frequency. Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications in SSVEP-BCIs.

IMJENSE: Scan-Specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI.

Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an inverse problem relating the sparsely sampled k-space measurements to the desired MRI image. Despite the success of many existing reconstruction algorithms, it remains a challenge to reliably reconstruct a high-quality image from highly reduced k-space measurements. Recently, implicit neural representation has emerged as a powerful paradigm to exploit the internal information and the physics of partially acquired data to generate the desired object. In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms. With extremely limited calibration data, IMJENSE is more stable than supervised calibrationless and calibration-based deep-learning methods. Results show that IMJENSE robustly reconstructs the images acquired at 5× and 6× accelerations with only 4 or 8 calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and 19.5% undersampling rates. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.

The Identification of Acetic Acid-Ethanol Mixture Using Gas Sensor Array and Ensemble Regression

Identification of acetic acid-ethanol mixtures using a commercial gas sensor array equipped with ensemble regression has been carried out. The gas sensor analysis was simple, rapid, and fast since it did not require any sample preparation. A quantitative analysis of the acetic acid-ethanol mixture was carried out to determine the sensitivity and selectivity of the sensor in distinguishing the concentration of the acetic acid and ethanol mixture. This study focuses on the coefficient of determination of 80% of the calibration data set and recovery of 20% of the testing data set. The models showed excellent performance,specifically, the Bagging and Random Forest r2 for the ethanol calibration data reached 0.91 and 0.94, respectively. The corresponding ethanol test recoveries were 99.95% and 97.84%, indicating the robustness of the model in accurately predicting ethanol concentration. Acetic acid test recoveries were 100.56% and 101.38% with r2 of 0.89 and 0.93 for Bagging and Random Forest regression, respectively. Hence, the commercial gas sensor array equipped with ensemble regression can be applied to the quantification of the acetic acid – ethanol mixture and demonstrate opportunities for the practical use of this gas sensor array in analyzing real samples, i.e. human breath or environmental monitoring samples.

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (Rrs) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and Rrs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

Climate Change and its Impact on Water Requirement and Productivity of Bengal Gram Over Different Agro-climatic Zones of Tamil Nadu

Background: The AquaCrop model for bengal gram successfully matches the biomass and yield of reported crop data under various planting dates, according to results that have been validated. Further this study was carried out in different agroclimatic zones of Tamil Nadu to assess variations in yield and water requirement. Methods: In order to simulate realistic yields for the Bengal gram in response to planting date impacts in the Guziliamparai Block in the Dindugal District of Tamil Nadu, the study parameterized and verified performance of the AquaCrop model. Four field trials planted in D1 - 1st November, D2 - 15th November, D3 - 1st December, and D4 - 15th December provided the model calibration data. Result: Fruit yield and biomass had R2 values of 0.9 and 0.8, respectively, indicating strong agreement between observed and model-simulated data. Fruit output and biomass were found to have RMSE values of 0.4 and 0.2, respectively. The NRMSE values for fruit yield and biomass were determined to be 0.3 and 0.1, respectively. The AquaCrop model somewhat overestimated fruit production and biomass since the BIAS was less than 0.4 and 0.2 for yield and biomass, respectively. Fruit production and biomass showed less agreement between simulated and actual D levels (0.4 to 0.6). Across Tamil Nadu's ACZ, a 20C temperature increase decreased the yield by an average of 6%, with minimums of 2% and maximums of 13%. An rise in temperature of 3°C and 4°C, respectively, decreased the yield by 9% and 19% on average.

Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings in cold climates to reduce electricity demand during peak demand periods of the electric grid. To streamline the implementation of MPC, the proposed approach employs grey-box archetypes, a clustering of weather conditions to identify typical scenarios and a limited number of possible setpoint profiles. A data-driven grey-box approach is used to create archetype models for different thermal zones in a typical school building; this approach enables rapid development and requires much less calibration data than black-box models. A third-order resistance-capacitance thermal network for zones with convective heating and a fourth-order model for zones with radiant floor heating are developed and calibrated using measured data from an all-electric school building in Québec, Canada. The weather data are clustered into several categories, representing different weather conditions (6 clusters representing two ambient temperature ranges and three solar radiation ranges). The heuristic MPC strategy uses predefined optimal setpoint profiles for each cluster and weather prediction one day ahead to shift the building load from on-peak to off-peak hours. For each heuristic MPC scenario, the model runs a simulation using forecast weather data to quantify and activate energy flexibility in response to grid requirements. The developed MPC framework was implemented in the school used as the case study. Ten classrooms are investigated, with six using the MPC and four as a reference case with the reactive control system and default zone setpoint profiles. Results indicate that the school building can provide between 47% and 95% energy flexibility (load shifting relative to reference) during on-peak hours and up to 44% electricity cost reduction while satisfying acceptable temperature constraints. By implementing the proposed MPC, energy flexibility of 32 W/m2 of floor area for the zones with a convective heating system and 65 W/m2 of floor area for the zones with radiant heating can be achieved during a demand response event. The proposed strategy can be generalized and replicated in other school buildings.

Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions

Hidden within the Gaia satellite’s multiple data releases lies a valuable cache of dark companions. To facilitate the efficient and reliable detection of these companions via combined analyses involving the Gaia, Hipparcos, and Tycho-2 catalogs, we introduce an astrometric modeling framework. This method incorporates analytical least-square minimization and nonlinear parameter optimization techniques to a set of common calibration sources across the different space-based astrometric catalogs. This enables us to discern the error inflation, astrometric jitter, differential parallax zero-points, and frame rotation of various catalogs relative to Gaia Data Release 3 (DR3). Our findings yield the most precise Gaia DR2 calibration parameters to date, revealing notable dependencies on magnitude and color. Intriguingly, we identify submilliarcsecond frame rotation between Gaia DR1 and DR3, along with an estimated astrometric jitter of 2.16 mas for the revised Hipparcos catalog. In a thorough comparative analysis with previous studies, we offer recommendations on calibrating and utilizing different catalogs for companion detection. Furthermore, we provide a user-friendly pipeline (https://github.com/ruiyicheng/Download_HIP_Gaia_GOST) for catalog download and bias correction, enhancing accessibility and usability within the scientific community.

TELAMON: Effelsberg monitoring of AGN jets with very-high-energy astroparticle emission

Aims. We introduce the TELAMON program which is using the Effelsberg 100-m telescope to monitor the radio spectra of active galactic nuclei (AGN) under scrutiny in astroparticle physics, specifically TeV blazars and candidate neutrino-associated AGN. Here, we present and characterize our main sample of TeV-detected blazars. Methods. We analyzed the data sample from the first ∼2.5 yr of observations between August 2020 and February 2023 in the range from 14 GHz to 45 GHz. During this pilot phase, we observed all 59 TeV-detected blazars in the Northern Hemisphere (i.e., Dec > 0°) known at the time of observation. We discuss the basic data reduction and calibration procedures used for all TELAMON data and introduce a sub-band averaging method used to calculate average light curves for the sources in our sample. Results. The TeV-selected sources in our sample exhibit a median flux density of 0.12 Jy at 20 mm, 0.20 Jy at 14 mm, and 0.60 Jy at 7 mm. The spectrum for most of the sources is consistent with a flat radio spectrum and we found a median spectral index (S(ν)∝να) of α = −0.11. Our results on flux density and spectral index are consistent with previous studies of TeV-selected blazars. Compared to the GeV-selected F-GAMMA sample, TELAMON sources are significantly fainter in the radio band. This is consistent with the double-humped spectrum of blazars being shifted towards higher frequencies for TeV-emitters (in particular for high-synchrotron peaked BL Lac type objects), which results in a lower radio flux density. The spectral index distribution of our TeV-selected blazar sample is not significantly different from the GeV-selected F-GAMMA sample. Moreover, we present a strategy to track the light curve evolution of sources in our sample for future variability and correlation analysis.

Research on automotive scrap metal classification method using laser-induced breakdown spectroscopy and two-step clustering algorithm

In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing laser-induced breakdown spectroscopy (LIBS) and two-step clustering algorithm (K-means, hierarchical clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the multiplicative scatter correction to the spectral data for calibration, the initial clustering clusters were determined using the Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient. Then, the Kruskal–Wallis test was conducted on each cluster to check the significance. The clusters that failed the test were split and reclustered until all clusters met the significance criterion (α=0.05). The accuracy of the proposed method for classifying the collected automotive scrap metals reached 97.6%. This indicates the great potential of this method in the field of automotive scrap metal classification.

X‐Ray Fluorescence Determination of FeII/Fetotal Ratios in Sediments and Soils

The two most common oxidation states of iron can be determined by wavelength‐dispersive X‐ray fluorescence spectrometry relatively fast with an accuracy comparable to that of alternative methods. Main advantages for the analysis of sediments and soils are that organic matter does not interfere and solid samples can be analysed directly with minimal preparation effort. The limited availability of appropriate reference materials with certified data on their ferrous iron content turned out to be the major obstacle to implementing the method. For this reason and because no influence of the analysed geological materials on the intensity ratios of the closely adjacent Fe Kβ5 / Fe Kβ1,3 lines became evident, several rock types were selected in addition to sediments and soils to broaden and improve the calibration. Nevertheless, most reference data for calibration had to be determined by 57Fe Mössbauer spectroscopy. Plotting Fe Kβ5/Fe Kβ1,3 intensity ratios versus w FeO/w Fe2O3total ratios resulted in a correlation with R2 = 0.962 for the linear regression of all calibration data.

Norm Tweaking: High-Performance Low-Bit Quantization of Large Language Models

As the size of large language models (LLMs) continues to grow, model compression without sacrificing accuracy has become a crucial challenge for deployment. While some quantization methods, such as GPTQ, have made progress in achieving acceptable 4-bit weight-only quantization, attempts at lower-bit quantization often result in severe performance degradation. In this paper, we introduce a technique called norm tweaking, which can be used as a plugin in current PTQ methods to achieve high precision while being cost-efficient. Our approach is inspired by the observation that rectifying the quantized activation distribution to match its float counterpart can readily restore accuracy for LLMs. To achieve this, we carefully design a tweaking strategy that includes calibration data generation and channel-wise distance constraint to update the weights of normalization layers for better generalization. We conduct extensive experiments on various datasets using several open-sourced LLMs. Our method demonstrates significant improvements in both weight-only quantization and joint quantization of weights and activations, surpassing existing PTQ methods. On GLM-130B and OPT-66B, our method even achieves the same level of accuracy at 2-bit quantization as their float ones. Our simple and effective approach makes it more practical for real-world applications.

PTMQ: Post-training Multi-Bit Quantization of Neural Networks

The ability of model quantization with arbitrary bit-width to dynamically meet diverse bit-width requirements during runtime has attracted significant attention. Recent research has focused on optimizing large-scale training methods to achieve robust bit-width adaptation, which is a time-consuming process requiring hundreds of GPU hours. Furthermore, converting bit-widths requires recalculating statistical parameters of the norm layers, thereby impeding real-time switching of the bit-width. To overcome these challenges, we propose an efficient Post-Training Multi-bit Quantization (PTMQ) scheme that requires only a small amount of calibration data to perform block-wise reconstruction of multi-bit quantization errors. It eliminates the influence of statistical parameters by fusing norm layers, and supports real-time switching bit-widths in uniform quantization and mixed-precision quantization. To improve quantization accuracy and robustness, we propose a Multi-bit Feature Mixer technique (MFM) for fusing features of different bit-widths to enhance robustness across varying bit-widths. Moreover, we introduced the Group-wise Distillation Loss (GD-Loss) to enhance the correlation between different bit-width groups and further improve the overall performance of PTMQ. Extensive experiments demonstrate that PTMQ achieves comparable performance to existing state-of-the-art post-training quantization methods, while optimizing it speeds up by 100$\times$ compared to recent multi-bit quantization works. Code can be available at https://github.com/xuke225/PTMQ.

Time development of live-bed scour around an offshore-wind monopile under large current–wave ratio

Scour around a circular monopile in coastal regions has been investigated extensively over the past decades, but the time development of scour depth around an offshore-wind monopile under large current–wave ratio still lacks a predictive model. By considering the conservation of sand volume and adopting the conventional exponential law for temporal variation, a semi-empirical model, which has three parameters, i.e., an equilibrium scour depth, a shape coefficient and a scour characteristic time scale, is developed for predicting the time development of scour depth around a monopile under live-bed conditions of combined wave–current flows. A series of laboratory experiments was conducted in current-only and wave–current flows to obtain data for model calibration and validation. Experimental results indicate that adding weak waves on current accelerates scour development, which is successfully captured by the proposed model through using the far-field bottom shear stress as a key model input. The overall model inaccuracy is within 25%, and the model’s applicability is further confirmed by field measurements from an offshore wind farm in east China. This model can help to determine the timing of installing scour protection around offshore monopiles, especially for the circumstances with very strong local sediment transport (live-bed).

Coupling a Simple and Generic Membrane Fouling Model with Biological Dynamics: Application to the Modeling of an Anaerobic Membrane BioReactor (AnMBR).

A simple model is developed for membrane fouling, taking into account two main fouling phenomena: cake formation, due to attached solids on the membrane surface, and pore clogging, due to retained compounds inside the pores. The model is coupled with a simple anaerobic digestion model for describing the dynamics of an anaerobic membrane bioreactor (AnMBR). In simulations, we investigate its qualitative behavior: it is shown that the model exhibits satisfying properties in terms of a flux decrease due to membrane fouling. Comparing simulation and experimental data, the model is shown to predict quite well the dynamics of an AnMBR. The simulated flux best fits the experimental flux with a correlation coefficient r2=0.968 for the calibration data set and r2=0.938 for the validation data set. General discussions are given on possible control strategies to limit fouling and optimize the flux production. We show in simulations that these strategies allow one to increase the mean production flux to 33 L/(h·m2),whereas without control, it was 18 L/(h·m2).

Sensitivity of small river channels in the North Carolina Piedmont to change in streamflow conditions, Atlantic Multi-decadal Oscillation, and land use/land cover change

Twenty-four years (1999–2022) of channel change is assessed using specific-gage analysis based on the United States Geological Survey (USGS) rating curve calibration data from thirteen sites (drainage area < 500 km2) to understand the sensitivity of discharge-specific stage (DSS) or its inverse, stage-specific channel capacity (SSCC) to streamflow regimes, hydroclimatological history, and land use in small Piedmont watersheds of North Carolina, USA. We compare observed channel changes to common streamflow and flood metrics, Atlantic Multi-decadal Oscillation Index (AMOI), and land use/land cover data using regression and correlation techniques to suggest drivers of change and infer sensitivity. In addition to interannual variability, our results at most sites reveal three coherent periods of DSS/SSCC variation at the sub-decadal scale as well as multidecadal trend variation. The magnitudes and frequencies of these changes indicative of potential sensitivity vary substantially amongst sites. The trendlines from the DSS plots for the full length of record indicate that six sites showed increased specific stage while the remaining seven sites showed either reduced or a near-zero change in specific stage. Coefficients of determination relating specific stage and the index of AMO are low (R2 = 0.14—0.38) with both positive and negative relationships exhibited. Multiple regression, coefficients of determination relating specific stage and average monthly discharge are more variable, ranging from 0.18 to 0.94. Both positive and negative correlations (r ≤ 0.7) are found between specific stage and basin land use/land cover intensity indices, although few are significant at 95 % confidence interval. Both qualitative observations and correlation analyses support inferences that drought and persistent low mean discharge conditions tend to increase discharge-specific stage values (decreasing SSCC), by aggradation of the bed and/or lower banks. At our sites, this may be responsible for a sub-decadal trend of increasing specific stage between roughly 2013 and 2018, followed by ongoing reductions over the subsequent period.