Transient Time Series Research Articles

Designing a Double Filter-Encircled Wrappers Feature Selection Algorithm for Transient Stability Prediction Based on Transient Time Series Data

Designing a Double Filter-Encircled Wrappers Feature Selection Algorithm for Transient Stability Prediction Based on Transient Time Series Data

Dynamics of Delayed Neuroendocrine Systems and Their Reconstructions Using Sparse Identification and Reservoir Computing

Neuroendocrine system mainly consists of hypothalamus, anterior pituitary, and target organ. In this paper, a three-state-variable delayed Goodwin model with two Hill functions is considered, where the Hill functions with delays denote the hormonal feedback suppressions from target organ to hypothalamus and to anterior in the reproductive hormonal axis. The existence of Hopf bifurcation shows the circadian rhythms of neuroendocrine system. The direction and stability of Hopf bifurcation are also analyzed using the normal form theory and the center manifold theorem for functional differential equations. Furthermore, based on the sparse identification algorithm, it is verified that the transient time series generated from the delayed Goodwin model cannot be equivalently presented by ordinary differential equations from the viewpoint of data when considering that a library of candidates are at most cubic terms. The reason is because the solution space of delayed differential equations is of infinite dimensions. Finally, we report that reservoir computing can predict the periodic behaviors of the delayed Goodwin model accurately if the size of reservoir and the length of data used for training are large enough. The predicting performances are evaluated by the mean squared errors between the trajectories generated from the numerical simulations and the reservoir computing.

Machine learning model for transient exergy performance of a phase change material integrated-concentrated solar thermoelectric generator

Despite the merits of incorporating phase change materials in concentrating solar thermoelectric generating systems, the following research gaps still need to be filled to make the technology viable. Past research efforts focused on pulsed rectangular and sinusoidal heat flux forms which differ from actual transient weather data obtainable under field exposure. Additionally, a comprehensive exergy performance modeling of the system is yet to be presented in the literature, hence, there is no known thermodynamic implication of incorporating phase change materials in thermoelectric systems from a first and second law perspective. Finally, optimization insights obtained for designing these systems are based on inefficient, computationally expensive, and time-consuming numerical solvers or experimental methods which significantly hinder the ease at which useful design insights are obtained. This work aims to fill in these gaps by presenting the first-ever exergy performance investigation of a paraffin wax integrated thermoelectric generating system using data-driven surrogate machine learning models trained with numerically generated data computed from actual 12 h transient timeseries weather data collected every minute and averaged for a 10-year period (2010–2020). Results show that for an optical concentration of 10, the incorporated system with phase change material provides a 25%, 57%, 25%, and 56% improvement in the thermoelectric temperature difference, power generation, exergy efficiency, and thermodynamic irreversibility of the stand-alone system, respectively, without phase change material. Furthermore, the incorporated system generates electricity for 2 more hours into the night when solar irradiance is unavailable. Finally, the cheap surrogate machine learning model accurately predicts the thermodynamic performance of the stand-alone and incorporated systems with an almost zero error and almost unity coefficient of determination in just 5 s after the numerical method has taken 24 h to generate the data, indicating a 17,280-computation time saving.

Learning the dynamics of coupled oscillators from transients

Whereas the importance of transient dynamics to the functionality and management of complex systems has been increasingly recognized, most of the studies are based on models. Yet in realistic situations the mathematical models are often unknown and what is available are only measured time series. Meanwhile, many real-world systems are dynamically stable, in the sense that the systems return to their equilibria in a short time after perturbations. This increases further the difficulty of dynamics analysis, as many information of the system dynamics are lost once the system is settled onto the equilibrium states. The question we ask is: Given the transient time series of a complex dynamical system measured in the stable regime, can we infer from the data some properties of the system dynamics and make predictions, e.g., predicting the critical point where the equilibrium state becomes unstable? We show that for the typical transitions in system of coupled oscillators, including quorum sensing, amplitude death, and complete synchronization, this question can be addressed by the technique of reservoir computing in machine learning. More specifically, by the transient series acquired at several states in the stable regime, we demonstrate that the trained machine is able to predict accurately not only the transient behaviors of the system in the stable regime, but also the critical point where the stable state becomes unstable. Considering the ubiquitous existence of transient activities in natural and man-made systems, the findings may have broad applications.

Importance of Infiltration Rates for Fate and Transport of Benzene in High-Tiered Risk-Based Assessment Considering Korean Site-Specific Factors at Contaminated Sites

Widely used conservative approaches for risk-based assessments of the subsurface transport processes have been calculated using simple analytical equations or general default values. Higher-tier risk assessment of contaminated sites requires the numerical models or additional site-specific values for input parameters. Previous studies have focused on the development of sophisticated models fit into risk-based frameworks. Our study mainly aims to explore the applicability of site-specific parameters and to modify the risk-based fate and transport model according to the types of the site-specific parameters. To apply the modified fate and transport equation and the site-specific default infiltration range, this study assessed the source depletion, leachate concentrations, and exposure concentration of benzene, which is a representative organic contaminant. The numerical models consist of two continuous processes, the fate and transport of contaminants from (1) the soil to the groundwater table in the vadose zone and subsequently (2) from the groundwater table to exposure wells in the saturated zone. Spatially varied Korean domestic recharge data were successfully incorporated into site-specific infiltration parameters in the models. The numerical simulation results were expressed as transient time series of concentrations over time. Results presented the narrow range of predicted concentrations at the groundwater table when site-specific infiltration was applied, and the dilution–attenuation factors for the unsaturated zone (DAFunsat) were derived based on the prediction. When a contaminant travels to the longest path length of 10 m with a source depth of 1 m in the vadoze zone, the simulated DAFunsat ranged from 3 to 4. The highest DAFunsat simulation results are close to 1 when contaminants travel to a source depth of 5 m and the shortest path length of 1 m. In the saturated aquifer below the contaminated sites, the variation in exposure concentration with time at monitoring wells is detected differently depending on the depth of the saturated zone.

The Use of Multivariate Analysis for Accurate Control of the Oscillation Frequency of Homogeneous and Heterogeneous Systems

The control of the frequency of oscillation from experimental parameters is extremely important for a better understanding of how oscillatory systems work. Knowing this, much effort has been made to understand the effect of experimental parameters on it. Two-dimensional diagrams are constructed by varying each parameter individually, while the others are kept constant. However, as these oscillators have many control parameters, the use of multivariate statistical analysis is necessary for the accurate selection of the oscillation frequency. In addition, through this analysis it is possible to measure the possible synergistic combinations between the experimental parameters. Knowing this, in this work we performed a multivariate statistical analysis of homogeneous and heterogeneous oscillators. The homogeneous oscillator used is the Belousov-Zhabotinsky reaction while the heterogeneous system is an electrochemical deposition of Cu/Cu2OThe experiments of the homogeneous system (BZ reaction) were performed by a CSTR to keep the frequency of oscillation constant. For the experiments, two solutions were used, one of NaBrO3 and H2SO4 and the other of malonic acid (MA) and ferroin. These solutions were pumped by a peristaltic pump in a cuvette. The oscillations were monitored with a UV-visible spectrophotometer (λ=520nm). The experiments were conducted by a factorial design 23 with a central point in which the parameters analyzed were [NaBrO3], [MA] and temperature.The experiments of the heterogeneous system (Cu/Cu2O oscillatory deposition) were carried out in an electrochemical cell with three electrodes. These are a platinized-platinum foil as a counter electrode, a saturated calomel electrode as a reference electrode and a polycrystalline platinum sheet as a working electrode. The solution used was Cu(II) lactate (0.5mol L-1 + 2.50 mol L-1 of lactate) was prepared and was left under stirring for 4 days at 2000 RPM. The experiments were conducted according to Central Composite Design (CCD), with the axial distance set at α = 1. The factors were defined as pH, current density, and temperature.The factorial design 23 with the central point for the BZ reaction observed in Figure-1a shows that the dependence of the experimental parameters on the frequency is linear. We also see in Figure-1c that the parameter that most affected the oscillation frequency was [NaBrO3] followed by the temperature. Another important information that we observed in this graph is that there is a synergistic effect between [NaBrO3] and temperature, which is information that is not obtained through univariate analysis.The CDD used for the electrochemical system demonstrated a dependence on a wide combination of parameters, resulting in quadratic terms. This quadratic dependency can be seen in Figure-1b, d. We note that the parameter that most affects the frequency of oscillation is the current density followed by the pH. It is evident that the interaction effects demonstrated by this type of analysis contribute to the non-linearity of the system.The differences observed between the oscillators in which the most evident is the linearity of the oscillation frequency with the experimental parameters for the BZ reaction and the quadratic dependence on the oscillatory deposition of Cu/Cu2O can be attributed to the intrinsic nature of the oscillators. For the BZ reaction, the reaction rate constants are kept unchanged during oscillations. The oscillatory system, however, the rate constants are described by the Butler-Volmer kinetics and, therefore, exhibit a potential dependency. In addition, it is necessary to consider the mass transport processes present in the electrochemical system that is absent in the BZ reaction. It is also important to note that the electrochemical oscillators show a transient time series, even though we used the most stable region of the time series. All these characteristics justify the differences observed between the chemical and electrochemical oscillatory systems.The observed results can contribute, for example, in the synthesis of self-organized materials, in which an accurate selection of frequencies is necessary and, depending on their value, different physicochemical properties are obtained. In addition, the results demonstrate the importance of using multivariate statistical analysis due to the possibility of measuring the interactions between experimental parameters. Figure 1

Characteristic Analysis and Indexing of Multimachine Transient Stabilization Using Virtual Synchronous Generator Control

As distributed power sources via grid-connected inverters equipped with functions to support system stabilization are being rapidly introduced, individual systems are becoming more complex, making the quantification and evaluation of the stabilizing functions difficult. Therefore, to introduce distributed power sources and achieve stable system operation, a system should be reduced to a necessary but sufficient size in order to enable the quantification of its behavior supported by transient theory. In this study, a system in which multiple distributed power supplies equipped with virtual synchronous generator control are connected is contracted to a two-machine system: a main power supply and all other power supplies. The mechanical torque of each power supply is mathematically decomposed into inertia, damping, synchronization torques, and the governor effect. The system frequency deviations determined by these elements are quantitatively indexed using MATLAB/Simulink. The quantification index displayed in three-dimensioned graphs illustrates the relationships between the various equipment constants of the main power supply, the control variables of the grid-connected inverter control, and the transient time series. Moreover, a stability analysis is performed in both the time and frequency domains.

Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be regarded as a fundamental issue in the transient learning strategy. In fact, MDTFs extraction is raised to make a trade-off between paradoxically intertwined indices, namely the accuracy and processing time of TSP. To this end, we offer a bi-mode hybrid feature selection scheme called BMHFSS for extracting MDTFs in high dimensional transient multivariate time series (TMTS). First, we used the TMTS, which are effective features on TSA. Next, the trajectory-based filter-wrapper mode (TFWM) is applied on TMTS to surmount the curse of dimensionality in two phases. In the filter phase, statistical and intrinsic characteristics of the TMTS in the form of agglomerative hierarchical clustering (AHC) are measured, and relevant TMTS (RTMTS) is selected according to obtained weight. In the wrapper phase, the RTMTS is entered into the trihedral kernel-based approach, including both fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) to find the intersected most RTMTS (IMRTMTS). As a complementary step, the filter-wrapper scenario in point-based mode (PFWM) is conducted for selecting MDTFs per time series in IMRTMTS. Finally, the aggregated MDTFs (AMDTFs) are tested to verify their efficacy for TSP based on cross-validation. The results show that the proposed framework has prediction accuracy greater than 98 % and a processing time of 52.94 milliseconds for TSA.

Early Prediction of Lean Blowout from Chemiluminescence Time Series Data

ABSTRACT Lean combustion systems are usually employed in gas-turbine engines, particularly in land-based applications, to reduce NOx emission. These combustion systems are often susceptible to lean blowout (LBO), which can be detrimental for operation and productivity of gas-turbine engines. An abrupt decrease in the equivalence ratio during a throttling operation, which is often encountered in power plants due to sudden decrease in load demand and also in aircraft engines at the time of landing, may lead to an unexpected LBO. From this perspective, online data-driven algorithms are deemed necessary for early prediction of potential transitions to near-blowout conditions. This procedure would provide the human operator/active controller with an appropriate lead time to alter the operating conditions so that the system can be brought back to a desired stable condition. The paper emulates pertinent conditions of LBO on a laboratory-scale apparatus of swirl-stabilized dump combustor with transient time series of CH* chemiluminescence data, where the objective is early prediction of LBO. The underlying algorithms are constructed based on a well-known statistical learning tool, called Hidden Markov Modeling (HMM), which can be used in the setting of supervised learning to discern near-blowout time series data from stable data. Being solely data-driven, the proposed methodology is model-free; it has been shown to be numerically efficient as well as sensitive to regime changes when the combustion system moves toward or away from LBO.

Method of Voltage Sag Causes Based on Bidirectional LSTM and Attention Mechanism

The voltage sags’ cause recognition is the basis for formulating governance plans and clarifying liabilities for accidents. For voltage sag cause recognition methods based on physical characteristics, new challenges are presented in terms of accuracy, adaptability and algorithm efficiency. Deep learning is a method based on characterizing and learning data. The efficient mechanism of autonomous feature learning can effectively overcome the problems of information loss and generalization ability based on existing physical property methods. The long short-term memory network (LSTM) has the characteristics of memory and can better learn the data characteristics with time series characteristics. Bidirectional LSTM can consider historical information and future information compared with standard LSTM, and has more processing for time series data. While using the attention mechanism can highlight the key influencing factors in the time series and improve the recognition accuracy of the model. For the transient sag time series data, this paper proposes a multi-layer structure based on bidirectional LSTM and attention mechanism to classification recognition. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good anti-noise performance in the recognition of voltage sag causes, and can be reliably applied in practical engineering.

Design of Liquid Level and Generator Temperature Display System for EDG in Nuclear Power Station

At present, EDG used in nuclear power plants is relatively old. The main means of EDG condition monitoring is the on-site dashboard installed in the EDG factory, which shows the relevant thermal parameters of operation. The instrumentation equipped in the factory can only display the current value of the liquid level of the diesel engine and the temperature of the stator coil of the generator. It cannot view the historical data and has no function of data storage and transmission. At present, manual recording is carried out by means of periodic inspection, which cannot fully reflect the operation status of diesel engine, capture transient process and time series change process, and evaluate the health value of equipment. Moreover, the existing monitoring methods cannot detect abnormal conditions in advance before the serious failure of EDG. In this paper, the modification of the existing on-site instrument of EDG is discussed, and the intelligent display function of on-site liquid level and generator coil temperature is added, so that the device can be applied to the complex EDG working site.

In vivo photoprotection mechanisms observed from leaf spectral absorbance changes showing VIS\u2013NIR slow-induced conformational pigment bed changes

Regulated heat dissipation under excessive light comprises a complexity of mechanisms, whereby the supramolecular light-harvesting pigment–protein complex (LHC) shifts state from light harvesting towards heat dissipation, quenching the excess of photo-induced excitation energy in a non-photochemical way. Based on whole-leaf spectroscopy measuring upward and downward spectral radiance fluxes, we studied spectrally contiguous (hyperspectral) transient time series of absorbance A(λ,t) and passively induced chlorophyll fluorescence F(λ,t) dynamics of intact leaves in the visible and near-infrared wavelengths (VIS–NIR, 400–800 nm) after sudden strong natural-like illumination exposure. Besides light avoidance mechanism, we observed on absorbance signatures, calculated from simultaneous reflectance R(λ,t) and transmittance T(λ,t) measurements as A(λ,t) = 1 − R(λ,t) − T(λ,t), major dynamic events with specific onsets and kinetical behaviour. A consistent well-known fast carotenoid absorbance feature (500–570 nm) appears within the first seconds to minutes, seen from both the reflected (backscattered) and transmitted (forward scattered) radiance differences. Simultaneous fast Chl features are observed, either as an increased or decreased scattering behaviour during quick light adjustment consistent with re-organizations of the membrane. The carotenoid absorbance feature shows up simultaneously with a major F decrease and corresponds to the xanthophyll conversion, as quick response to the proton gradient build-up. After xanthophyll conversion (t = 3 min), a kinetically slower but major and smooth absorbance increase was occasionally observed from the transmitted radiance measurements as wide peaks in the green (~ 550 nm) and the near-infrared (~ 750 nm) wavelengths, involving no further F quenching. Surprisingly, in relation to the response to high light, this broad and consistent VIS–NIR feature indicates a slowly induced absorbance increase with a sigmoid kinetical behaviour. In analogy to sub-leaf-level observations, we suggest that this mechanism can be explained by a structure-induced low-energy-shifted energy redistribution involving both Car and Chl. These findings might pave the way towards a further non-invasive spectral investigation of antenna conformations and their relations with energy quenching at the intact leaf level, which is, in combination with F measurements, of a high importance for assessing plant photosynthesis in vivo and in addition from remote observations.

Detecting Hidden Units and Network Size from Perceptible Dynamics.

The number of units of a network dynamical system, its size, arguably constitutes its most fundamental property. Many units of a network, however, are typically experimentally inaccessible such that the network size is often unknown. Here we introduce a detection matrix that suitably arranges multiple transient time series from the subset of accessible units to detect network size via matching rank constraints. The proposed method is model-free, applicable across system types and interaction topologies, and applies to nonstationary dynamics near fixed points, as well as periodic and chaotic collective motion. Even if only a small minority of units is perceptible and for systems simultaneously exhibiting nonlinearities, heterogeneities, and noise, exact size detection is feasible. We illustrate applicability for a paradigmatic class of biochemical reaction networks.

Comparison of perturbation methods for rainfall and temperature data: case of a Belgian catchment

Analyses of hydrological impacts of climate change require appropriate methods for perturbing meteoric time-series to represent future climate conditions. Two readily available tools for perturbing rainfalls and temperatures are tested for a medium-sized catchment in Belgium. CCI-HYDR provides three scenarios, tailored for Belgium every decade until 2100. In contrast, KNMI-ADC tool provides 191 scenarios, at a regional level and for two horizons (near and far future). With its three contrasted scenarios of possible future climate conditions, CCI-HYDR is found suitable for forcing computationally intensive detailed hydrological models. With its broader spectrum of climate scenarios, KNMI-ADC tool is suitable for forcing multiple runs of fast conceptual hydrological models. As the two perturbation tools deliver stationary time-series, they are also compared to an alternate method producing transient time-series. Transient stochastic tools are particularly computationally demanding due to their stochastic nature, which is not optimal when combined with detailed distributed hydrological models.

Comparison of perturbation methods for rainfall and temperature data: case of a Belgian catchment

Analyses of hydrological impacts of climate change require appropriate methods for perturbing meteoric time-series to represent future climate conditions. Two readily available tools for perturbing rainfalls and temperatures are tested for a medium-sized catchment in Belgium. CCI-HYDR provides three scenarios, tailored for Belgium every decade until 2100. In contrast, KNMI-ADC tool provides 191 scenarios, at a regional level and for two horizons (near and far future). With its three contrasted scenarios of possible future climate conditions, CCI-HYDR is found suitable for forcing computationally intensive detailed hydrological models. With its broader spectrum of climate scenarios, KNMI-ADC tool is suitable for forcing multiple runs of fast conceptual hydrological models. As the two perturbation tools deliver stationary time-series, they are also compared to an alternate method producing transient time-series. Transient stochastic tools are particularly computationally demanding due to their stochastic nature, which is not optimal when combined with detailed distributed hydrological models.

Nitrogen leaching from natural ecosystems under global change: a modelling study

Abstract. To study global nitrogen (N) leaching from natural ecosystems under changing N deposition, climate, and atmospheric CO2, we performed a factorial model experiment for the period 1901–2006 with the N-enabled global terrestrial ecosystem model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator). In eight global simulations, we used either the true transient time series of N deposition, climate, and atmospheric CO2 as input or kept combinations of these drivers constant at initial values. The results show that N deposition is globally the strongest driver of simulated N leaching, individually causing an increase of 88 % by 1997–2006 relative to pre-industrial conditions. Climate change led globally to a 31 % increase in N leaching, but the size and direction of change varied among global regions: leaching generally increased in regions with high soil organic carbon storage and high initial N status, and decreased in regions with a positive trend in vegetation productivity or decreasing precipitation. Rising atmospheric CO2 generally caused decreased N leaching (33 % globally), with strongest effects in regions with high productivity and N availability. All drivers combined resulted in a rise of N leaching by 73 % with strongest increases in Europe, eastern North America and South-East Asia, where N deposition rates are highest. Decreases in N leaching were predicted for the Amazon and northern India. We further found that N loss by fire regionally is a large term in the N budget, associated with lower N leaching, particularly in semi-arid biomes. Predicted global N leaching from natural lands rose from 13.6 Tg N yr−1 in 1901–1911 to 18.5 Tg N yr−1 in 1997–2006, accounting for reductions of natural land cover. Ecosystem N status (quantified as the reduction of vegetation productivity due to N limitation) shows a similar positive temporal trend but large spatial variability. Interestingly, this variability is more strongly related to vegetation type than N input. Similarly, the relationship between N status and (relative) N leaching is highly variable due to confounding factors such as soil water fluxes, fire occurrence, and growing season length. Nevertheless, our results suggest that regions with very high N deposition rates are approaching a state of N saturation.

Detecting unstable periodic orbits and unstable quasiperiodic orbits in vibro-impact systems

In this paper, unstable dynamics is considered for the models of vibro-impact systems with linear differential equations coupled to an impact map. To provide a skeleton for the organization of chaotic attractors, we propose a method for detecting unstable periodic orbits embedded in chaotic attractors through a combination of unconstrained optimization technique and Poincaré map. Three numerical examples from different vibro-impact models demonstrate that the strategy can efficiently detect unstable periodic orbits in chaotic attractors. In order to explore the mechanism responsible for the creation of multi-dimensional tori attractors, we also present another method to detect unstable quasiperiodic orbits embedded multi-dimensional tori attractors by examining a specially transient time series. The upper bound and lower bound of the transient time series (in the Poincaré map) can be obtained by analyzing transient Lyapunov exponent and transient Lyapunov dimension. Some examples verify the effectiveness of the numerical algorithm.

A modified nonlinear POD method for order reduction based on transient time series

In this paper, a modified nonlinear proper orthogonal decomposition (POD) method based on transient time series on account of approximate inertial manifold method is proposed to reduce the order of the multiple degrees of freedom (DOFs) of a rotor system. A model of 23 DOFs rotor system comprising a pair of liquid-film bearing with pedestal looseness at one end is established by using the Newton’s second law. The multi-DOFs system is reduced to a two-DOFs model by using the modified POD method, which preserves the original dynamics behaviors. The comparison between the modified and the traditional POD method shows that the modified POD method is more effective especially in finding the bifurcation point and detecting the bifurcation diagrams and the mean square error of amplitudes curves. Finally, a relative error analysis is also carried out to evaluate the accuracy of the proposed order reduction method, indicating that the relative error is below 5 % excluding the interval between original bifurcation point and the shift of the reduced system.

A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging

Objective. Inferring the times of sequences of action potentials (APs) (spike trains) from neurophysiological data is a key problem in computational neuroscience. The detection of APs from two-photon imaging of calcium signals offers certain advantages over traditional electrophysiological approaches, as up to thousands of spatially and immunohistochemically defined neurons can be recorded simultaneously. However, due to noise, dye buffering and the limited sampling rates in common microscopy configurations, accurate detection of APs from calcium time series has proved to be a difficult problem. Approach. Here we introduce a novel approach to the problem making use of finite rate of innovation (FRI) theory (Vetterli et al 2002 IEEE Trans. Signal Process. 50 1417–28). For calcium transients well fit by a single exponential, the problem is reduced to reconstructing a stream of decaying exponentials. Signals made of a combination of exponentially decaying functions with different onset times are a subclass of FRI signals, for which much theory has recently been developed by the signal processing community. Main results. We demonstrate for the first time the use of FRI theory to retrieve the timing of APs from calcium transient time series. The final algorithm is fast, non-iterative and parallelizable. Spike inference can be performed in real-time for a population of neurons and does not require any training phase or learning to initialize parameters. Significance. The algorithm has been tested with both real data (obtained by simultaneous electrophysiology and multiphoton imaging of calcium signals in cerebellar Purkinje cell dendrites), and surrogate data, and outperforms several recently proposed methods for spike train inference from calcium imaging data.

Do transient storage parameters directly scale in longer, combined stream reaches? Reach length dependence of transient storage interpretations

Little work has been done to assess parameterizations and related interpretations (i.e., metrics of exchange) of transient storage modeling (TSM) over multiple spatial scales in streams. In this paper, we simulate conservative solute transport in a small mountain stream over combinations of five consecutive sub-reaches (38 m, 105 m, 281 m, 433 m, and 619 m below injection point) to (1) determine how optimized parameter estimates vary with reach length and reach combination, and (2) evaluate whether equally well-optimized simulations of solute transport in the channel result in varying interpretations of tracer exchange with the storage zone. Each simulated stream solute concentration breakthrough curve (BTC) showed consistently accurate fits to observations. However, our results indicate approximate equifinality (similar fits from different parameter sets) in the simulations of concentrations of stream tracer across individual sub-reaches and combined reaches leading to varying interpretations of transient storage exchange parameterization (i.e., variable optimized parameter estimates) and concentration time series of tracer in the storage zone. These results suggest strong reach-length dependence in simulated exchange. Based on stream BTCs alone, the TSM is useful in characterizing the influence of transient storage on in-stream solute transport, though it does not consistently reproduce storage zone dynamics. Characterization of the solute exchange between the stream and the storage zones remains problematic, and the effects of transient storage cannot be directly compared within overlapping stream reaches, an important consideration in designing and interpreting stream solute transport experiments.