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1. Drivers of Dissolved Organic Carbon Mobilization From Forested Headwater Catchments: A Multi Scaled Approach

   https://doi.org/10.3389/frwa.2021.578608  

   Adler, Thomas ; Underwood, Kristen L. ; Rizzo, Donna M. ; Harpold, Adrian ; Sterle, Gary ; Li, Li ; Wen, Hang ; Stinson, Lindsey ; Bristol, Caitlin ; Stewart, Bryn ; et al ( July 2021 , Frontiers in Water)

   Understanding and predicting catchment responses to a regional disturbance is difficult because catchments are spatially heterogeneous systems that exhibit unique moderating characteristics. Changes in precipitation composition in the Northeastern U.S. is one prominent example, where reduction in wet and dry deposition is hypothesized to have caused increased dissolved organic carbon (DOC) export from many northern hemisphere forested catchments; however, findings from different locations contradict each other. Using shifts in acid deposition as a test case, we illustrate an iterative “process and pattern” approach to investigate the role of catchment characteristics in modulating the steam DOC response. We use a novel dataset that integrates regional and catchment-scale atmospheric deposition data, catchment characteristics and co-located stream Q and stream chemistry data. We use these data to investigate opportunities and limitations of a pattern-to-process approach where we explore regional patterns of reduced acid deposition, catchment characteristics and stream DOC response and specific soil processes at select locations. For pattern investigation, we quantify long-term trends of flow-adjusted DOC concentrations in stream water, along with wet deposition trends in sulfate, for USGS headwater catchments using Seasonal Kendall tests and then compare trend results to catchment attributes. Our investigation of climatic, topographic, and hydrologic catchment attributesmore »vs. directionality of DOC trends suggests soil depth and catchment connectivity as possible modulating factors for DOC concentrations. This informed our process-to-pattern investigation, in which we experimentally simulated increased and decreased acid deposition on soil cores from catchments of contrasting long-term DOC response [Sleepers River Research Watershed (SRRW) for long-term increases in DOC and the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) for long-term decreases in DOC]. SRRW soils generally released more DOC than SSHCZO soils and losses into recovery solutions were higher. Scanning electron microscope imaging indicates a significant DOC contribution from destabilizing soil aggregates mostly from hydrologically disconnected landscape positions. Results from this work illustrate the value of an iterative process and pattern approach to understand catchment-scale response to regional disturbance and suggest opportunities for further investigations.« less
2. Sources of uncertainty in estimating stream solute export from headwater catchments at three sites

   https://doi.org/10.1002/hyp.10265  

   Yanai, Ruth D. ; Tokuchi, Naoko ; Campbell, John L. ; Green, Mark B. ; Matsuzaki, Eiji ; Laseter, Stephanie N. ; Brown, Cindi L. ; Bailey, Amey S. ; Lyons, Pilar ; Levine, Carrie R. ; et al ( August 2014 , Hydrological Processes)

   Uncertainty in the estimation of hydrologic export of solutes has never been fully evaluated at the scale of a small‐watershed ecosystem. We used data from the Gomadansan Experimental Forest, Japan, Hubbard Brook Experimental Forest, USA, and Coweeta Hydrologic Laboratory, USA, to evaluate many sources of uncertainty, including the precision and accuracy of measurements, selection of models, and spatial and temporal variation. Uncertainty in the analysis of stream chemistry samples was generally small but could be large in relative terms for solutes near detection limits, as is common for ammonium and phosphate in forested catchments. Instantaneous flow deviated from the theoretical curve relating height to discharge by up to 10% at Hubbard Brook, but the resulting corrections to the theoretical curve generally amounted to <0.5% of annual flows. Calibrations were limited to low flows; uncertainties at high flows were not evaluated because of the difficulties in performing calibrations during events. However, high flows likely contribute more uncertainty to annual flows because of the greater volume of water that is exported during these events. Uncertainty in catchment area was as much as 5%, based on a comparison of digital elevation maps with ground surveys. Three different interpolation methods are used atmore »the three sites to combine periodic chemistry samples with streamflow to calculate fluxes. The three methods differed by <5% in annual export calculations for calcium, but up to 12% for nitrate exports, when applied to a stream at Hubbard Brook for 1997–2008; nitrate has higher weekly variation at this site. Natural variation was larger than most other sources of uncertainty. Specifically, coefficients of variation across streams or across years, within site, for runoff and weighted annual concentrations of calcium, magnesium, potassium, sodium, sulphate, chloride, and silicate ranged from 5 to 50% and were even higher for nitrate. Uncertainty analysis can be used to guide efforts to improve confidence in estimated stream fluxes and also to optimize design of monitoring programmes. © 2014 The Authors.Hydrological Processespublished John Wiley & Sons, Ltd.

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3. Rainfall, runoff and shallow groundwater response in a mixed‐use, agro‐forested watershed of the northeast, USA

   https://doi.org/10.1002/hyp.14312  

   Gootman, Kaylyn S. ; Hubbart, Jason A. ( August 2021 , Hydrological Processes)

   Stream and shallow groundwater responses to rainfall are characterized by high spatial variability, but hydrologic response variability across small, agro‐forested sub‐catchments remains poorly understood. Conceivably, improved understanding in this regard will result in agricultural practices that more effectively limit nutrient runoff, erosion, and pollutant transport. Terrestrial hydrologic response approaches can provide valuable information on stream‐aquifer connectivity in these mixed‐use watersheds. A study was implemented, including eight stream and co‐located shallow groundwater monitoring sites, in a small sub‐catchment of the Chesapeake Bay watershed in the Northeast, USA to advance this ongoing need. During the study period, 100 precipitation‐receiving days (i.e., 24‐hour periods, midnight to midnight) were observed. On average, the groundwater table responded more to precipitation than stream stage (level change of 0.03 vs. 0.01 m and rainfall‐normalized level change estimate of 3.81 vs. 3.37). Median stream stage responses, groundwater table responses, and response ratios were significantly different between sub‐catchments (n = 8;p < 0.001). Study area average precipitation thresholds for runoff and shallow groundwater flow were 2.8 and 0.6 cm, respectively. Individual sub‐catchment thresholds ranged from 0.5 to 2.8 cm for runoff and 0.2 to 1.3 cm for shallow groundwater flow. Normalized response lag times between the stream and shallow groundwater ranged from −0.50 to 3.90 s·cm⁻¹, indicatingmore »that stormflow in one stream section was regulated by groundwater flow during the period of study. The observed differences in hydrologic responses to precipitation advance future modelling efforts by providing examples of how terrestrial groundwater response methods can be used to investigate sub‐catchment spatial variability in stream‐aquifer gradients with co‐located shallow groundwater and stream stage data. Additionally, results demonstrate asynchronous stream and shallow groundwater responses on precipitation‐receiving days, which may hold important implications for modelling hydrologic and biogeochemical fate and transport processes in small, agro‐forested catchments.

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4. Landscape heterogeneity drives contrasting concentration–discharge relationships in shale headwater catchments

   https://doi.org/10.5194/hess-19-3333-2015  

   Herndon, E. M. ; Dere, A. L. ; Sullivan, P. L. ; Norris, D. ; Reynolds, B. ; Brantley, S. L. ( January 2015 , Hydrology and Earth System Sciences)

   Abstract. Solute concentrations in stream water vary with discharge in patterns that record complex feedbacks between hydrologic and biogeochemical processes. In a comparison of three shale-underlain headwater catchments located in Pennsylvania, USA (the forested Shale Hills Critical Zone Observatory), and Wales, UK (the peatland-dominated Upper Hafren and forest-dominated Upper Hore catchments in the Plynlimon forest), dissimilar concentration–discharge (C–Q) behaviors are best explained by contrasting landscape distributions of soil solution chemistry – especially dissolved organic carbon (DOC) – that have been established by patterns of vegetation and soil organic matter (SOM). Specifically, elements that are concentrated in organic-rich soils due to biotic cycling (Mn, Ca, K) or that form strong complexes with DOC (Fe, Al) are spatially heterogeneous in pore waters because organic matter is heterogeneously distributed across the catchments. These solutes exhibit non-chemostatic behavior in the streams, and solute concentrations either decrease (Shale Hills) or increase (Plynlimon) with increasing discharge. In contrast, solutes that are concentrated in soil minerals and form only weak complexes with DOC (Na, Mg, Si) are spatially homogeneous in pore waters across each catchment. These solutes are chemostatic in that their stream concentrations vary little with stream discharge, likely because these solutes are released quickly frommore »exchange sites in the soils during rainfall events. Furthermore, concentration–discharge relationships of non-chemostatic solutes changed following tree harvest in the Upper Hore catchment in Plynlimon, while no changes were observed for chemostatic solutes, underscoring the role of vegetation in regulating the concentrations of certain elements in the stream. These results indicate that differences in the hydrologic connectivity of organic-rich soils to the stream drive differences in concentration behavior between catchments. As such, in catchments where SOM is dominantly in lowlands (e.g., Shale Hills), we infer that non-chemostatic elements associated with organic matter are released to the stream early during rainfall events, whereas in catchments where SOM is dominantly in uplands (e.g., Plynlimon), these non-chemostatic elements are released later during rainfall events. The distribution of SOM across the landscape is thus a key component for predictive models of solute transport in headwater catchments.

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5. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEGmore »channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less