Record Length Research Articles

Climate benchmarks and input parameters representing locations in 68 countries for a stochastic weather generator, CLIGEN

Abstract. This dataset contains input parameters for 12 703 locations around the world to parameterize a stochastic weather generator called CLIGEN. The parameters are essentially monthly statistics relating to daily precipitation, temperature, and solar radiation. The dataset is separated into three sub-datasets differentiated by having monthly statistics determined from 30-, 20-, and 10-year record lengths. Input parameters related to precipitation were calculated primarily from the NOAA GHCN-Daily network. The remaining input parameters were calculated from various sources including global meteorological and land-surface models that are informed by remote sensing and other methods. The new CLIGEN dataset includes inputs for locations in the US, which were compared to a selection of stations from an existing US CLIGEN dataset representing 2648 locations. This validation showed reasonable agreement between the two datasets, with the majority of parameters showing less than 20 % discrepancy relative to the existing dataset. For the three new datasets, differentiated by the minimum record lengths used for calculations, the validation showed only a small increase in discrepancy going towards shorter record lengths, such that the average discrepancy for all parameters was greater by 5 % for the 10-year dataset. The new CLIGEN dataset has the potential to improve the spatial coverage of analysis for a variety of CLIGEN applications and reduce the effort needed in preparing climate inputs. The dataset is available at the National Agriculture Library Data Commons website at https://data.nal.usda.gov/dataset/international-climate-benchmarks-and-input-parameters-stochastic-weather-generator-cligen (last access: 20 November 2020) and https://doi.org/10.15482/USDA.ADC/1518706 (Fullhart et al., 2020a).

Sub-Basalt Imaging Reveals Deeper Plays Offshore India

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30279, “Revealing Deeper Plays, Offshore Kutch, India: A Success Story of Sub-Basalt Imaging,” by S.K. Biswal, N.N.B. Naidu, and S. Basu, ONGC, et al., prepared for the 2020 Offshore Technology Conference Asia, originally scheduled to be held in Kuala Lumpur, 2-6 November. The paper has not been peer reviewed. Copyright 2020 Offshore Technology Conference. Reproduced by permission. The Deccan Traps volcanic province of India is considered one of the largest basalt-covered regions in the world but is essentially unexplored because of the limitations of conventional marine streamer P-wave seismic acquisition in imaging structures both intrabasalt and sub-basalt. In the complete paper, the authors demonstrate that, even with legacy marine streamer surveys, an appropriate work flow of combining suitable advanced technologies can help to overcome the long-standing challenges of sub-basalt imaging. The reprocessed data show clear uplift in sub-basalt imaging, and inversion results validate the quality of the new data in relation to the well logs. Introduction The Kutch offshore basin is characterized by the presence of the Deccan Traps, a large igneous province of up to 2000-m-thick basalt lava flows. These lava flows have hindered successful imaging of sub-basalt Mesozoic sediments for hydrocarbon exploration. To date, no single technique has been found to produce considerable improvements in deeper image quality. The solution lies in an appropriate combination of advanced technologies. The project consists of three legacy data sets acquired in 2004, 2010, and 2014 in the shallow-water area (water depth ranges from 25 to 50 m). Two of the surveys were shot in the north/south direction with six streamers having 100-m separation, 25-m shot spacing, 12.5-m receiver spacing, and 6-second record length. The third survey was acquired oblique to these with a similar acquisition geometry; however, it featured sparser 25-m receiver spacing and 8- second record length. These surveys were matched and merged before migration to ensure a seamless image across the surveys in the post-migration domain. A tailored processing work flow improved existing data quality significantly and provided new insights into the sub-basalt geology, thereby opening a new play to exploration and production. Challenges and Work Flow Sub-basalt imaging challenges include transmission losses, scattering, complex wave kinematics, prevalent multiples, interference effects, and variable illumination caused by high and variable acoustic impedance of thick heterogeneous basalt layers. The tertiary sedimentary sequences overlying the Deccan Trap consist predominantly of carbonates, shale, and fine-grained clastic sediments, accompanied by channels and nearly vertical faulting. The geological complexities from the water bottom to the base of the basalt present a substantial geophysical challenge to successful deeper imaging and require an appropriate work flow to mitigate them. Broadband processing, including de-ghosting, can increase the signal-to-noise ratio across the broad range of frequencies in the seismic bandwidth and can enhance the lower frequencies required to achieve enhanced imaging at sub-basalt targets. Demultiple methods can reduce the presence of surface-related and interbed multiples that prohibit reliable interpretation of Mesozoic sediment; imaging methods can focus the recorded data when used in conjunction with an accurate Earth model that captures the velocity complexities of carbonates, shale, basalt, channel, and faults.

Hunting by the Stroke: How Foraging Drives Diving Behavior and Locomotion of East-Greenland Narwhals (Monodon monoceros)

Deep diving air-breathing species by necessity must balance submergence time and level of exercise during breath-holding: a low activity level preserves oxygen stores and allows longer duration submergence whereas high activity levels consume oxygen quickly and shorten submergence time. In this study, we combined high-resolution multi sensor animal-borne tag data to investigate diving behavior and locomotion styles of the narwhal (Monodon monoceros) (n= 13, mean record length 91 h)–a deep diving Arctic species. Narwhals in this study dove down to >800 m but despite the deep diving abilities, one-third of the dives (33%) were shallow (>100 m) and short in duration (<5 min). Narwhals utilized energy saving measures such as prolonged gliding during descent with increasing target depth but stroked actively throughout the ascent indicating excess oxygen storages. Foraging behavior, as detected by the presence of buzzes, was a key factor influencing dive depth and spinning behavior—the rolling movement of the animal along its longitudinal axes. Narwhals in East Greenland utilized two foraging strategies, while transiting and while stationary, with different target depths and buzzing rates. The first targeted deep-dwelling, possibly solitary prey items and the latter, more schooling prey closer to the surface. The buzzing rate during stationary foraging was on average twice as high as during transiting foraging. Spinning was an integrated part of narwhal swimming behavior but the amount of spinning was correlated with foraging behavior. The odds for spinning during all dive phases were 2–3 times higher during foraging than non-foraging. Due to the spinning behavior, stroking rate might be better suited for estimating energy consumption in narwhals than ODBA (overall dynamic body acceleration). The narwhal is considered as one of the most sensitive species to climate change–the results from this study can act as a baseline essential for evaluating changes in the behavior and energy usage of narwhals caused by stressors evolving in the Arctic.

Sea-Level Acceleration: Analysis of the World's High-Quality Tide Gauges

Houston, J.R., 2021. Sea-level acceleration: Analysis of the world's high-quality tide gauges. Journal of Coastal Research, 37(2), 272–279. Coconut Creek (Florida), ISSN 0749-0208.Coastal sea-level acceleration is analyzed using all of the world's high-quality tide gauge recordings with lengths of at least 75 years that extend through 2017–19. Earlier studies have demonstrated that tide gauge recordings of at least 75 years in length are required to reduce the effects of multidecadal variations on acceleration. There are 149 tide gauge records that meet the criteria. Mean and median sea-level accelerations based on these gauges were 0.0128 ± 0.0064 mm/y2 and 0.0126 ± 0.0080 mm/y2, respectively, both at the statistically significant 95% confidence level. The mean acceleration is larger than that of earlier studies that analyzed fewer gauges or considered record lengths shorter than 75 years.

The zone of influence: matching sea level variability from coastal altimetry and tide gauges for vertical land motion estimation

Abstract. Vertical land motion (VLM) at the coast is a substantial contributor to relative sea level change. In this work, we present a refined method for its determination, which is based on the combination of absolute satellite altimetry (SAT) sea level measurements and relative sea level changes recorded by tide gauges (TGs). These measurements complement VLM estimates from the GNSS (Global Navigation Satellite System) by increasing their spatial coverage. Trend estimates from the SAT and TG combination are particularly sensitive to the quality and resolution of applied altimetry data as well as to the coupling procedure of altimetry and TGs. Hence, a multi-mission, dedicated coastal along-track altimetry dataset is coupled with high-frequency TG measurements at 58 stations. To improve the coupling procedure, a so-called “zone of influence” (ZOI) is defined, which confines coherent zones of sea level variability on the basis of relative levels of comparability between TG and altimetry observations. Selecting 20 % of the most representative absolute sea level observations in a 300 km radius around the TGs results in the best VLM estimates in terms of accuracy and uncertainty. At this threshold, VLMSAT-TG estimates have median formal uncertainties of 0.58 mm yr−1. Validation against GNSS VLM estimates yields a root mean square (rmsΔVLM) of VLMSAT-TG and VLMGNSS differences of 1.28 mm yr−1, demonstrating the level of accuracy of our approach. Compared to a reference 250 km radius selection, the 300 km zone of influence improves trend accuracies by 15 % and uncertainties by 35 %. With increasing record lengths, the spatial scales of the coherency in coastal sea level trends increase. Therefore, the relevance of the ZOI for improving VLMSAT-TG accuracy decreases. Further individual zone of influence adaptations offer the prospect of bringing the accuracy of the estimates below 1 mm yr−1.

An Evaluation of Remotely Sensed and In Situ Data Sufficiency for SGMA‐Scale Groundwater Studies in the Central Valley, California

AbstractCalifornia’s Central Valley aquifer is a critical freshwater resource for the state, providing drinking water to 6.5 million residents and irrigation water for more than half of the nation’s produce. Increasing demands have led to a steady decline of aquifer water levels, leading to irreversible compaction and land subsidence. The Sustainable Groundwater Management Act of California (SGMA), instituted in 2014, requires high‐priority regions to establish sustainability plans by 2020–2022 that mitigate groundwater depletion and land subsidence. Many regions within the Central Valley lack groundwater monitoring wells or continuous well data, rendering in situ monitoring challenging. When combined with other data, National Aeronautics and Space Administration’s Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On missions provide valuable information about groundwater storage changes at a subbasin scale. Interferometric Synthetic Aperture Radar measurements can map land subsidence, and Global Positioning System can be used to estimate crustal uplift. Yet, all of these measurements have differences in resolution, coverage, discretization, and record length. Here we assess the various datasets that are potentially useful for Central Valley subbasin groundwater monitoring and provide an analysis of gaps and other issues in regard to their suitability for SGMA‐related analysis. Finally, we offer the next steps and recommendations on data treatment and integration.

Effects of acquisition device, sampling rate, and record length on kinocardiography during position-induced haemodynamic changes

BackgroundKinocardiography (KCG) is a promising new technique used to monitor cardiac mechanical function remotely. KCG is based on ballistocardiography (BCG) and seismocardiography (SCG), and measures 12 degrees-of-freedom (DOF) of body motion produced by myocardial contraction and blood flow through the cardiac chambers and major vessels.ResultsThe integral of kinetic energy ( iK) obtained from the linear and rotational SCG/BCG signals was computed over each dimension over the cardiac cycle, and used as a marker of cardiac mechanical function. We tested the hypotheses that KCG metrics can be acquired using different sensors, and at 50 Hz. We also tested the effect of record length on the ensemble average on which the metrics were computed. Twelve healthy males were tested in the supine, head-down tilt, and head-up tilt positions to expand the haemodynamic states on which the validation was performed.ConclusionsKCG metrics computed on 50 Hz and 1 kHz SCG/BCG signals were very similar. Most of the metrics were highly similar when computed on different sensors, and with less than 5% of error when computed on record length longer than 60 s. These results suggest that KCG may be a robust and non-invasive method to monitor cardiac inotropic activity.Trial registration Clinicaltrials.gov, NCT03107351. Registered 11 April 2017, https://clinicaltrials.gov/ct2/show/NCT03107351?term=NCT03107351&draw=2&rank=1.

Estimating Gestational Age From Maternal-Fetal Heart Rate Coupling Parameters

Maternal and fetal heartbeat couplings are evident throughout fetal development. Most of the published work, however, did not consider maternal physiological factors such as Heart Rate Variability (HRV), and did not investigate the interrelationships of maternal-fetal coupling parameters. The aims of this study are to investigate whether: 1) maternal-fetal Heart Rate (HR) coupling ( $\lambda $ -based) parameters are associated with fetal development, and 2) fetal gold standard Gestational Age (GA) can be estimated using maternal-fetal HR coupling and variability of various recording lengths. The study considered Electrocardiogram (ECG) signals from 60 healthy pregnant women with no records of fetal abnormalities. HRV and $\lambda $ parameters at various Maternal:Fetal coupling ratios were calculated, and stepwise regression was utilized to create generalized linear regression models considering various lengths of recorded signals (1 and 5 min) to produce a robust estimate of fetal age. Cross-validation performances were evaluated by the mean square root of the average of squared errors (mRMSE) between age values estimated by the proposed models and gold standard GA identified by Crown-Rump Length (CRL). Effect of Fetal Behavioral States (FBSes) on proposed models with different recording lengths was considered to examine the highly nonstationary nature of signals. We found that HR coupling strength for a specific ratio is not constant throughout gestation. Results showed that ratios of 2:3 and 2:4 were common between the proposed models. The value of $\lambda $ [2:3] was found to be positively correlated with GA, while $\lambda $ [2:4] had a negative correlation. Compared with gold standard GA identified by CRL, the proposed regression model resulted in mRMSE of 2.67 and 3.69 weeks for the recordings of 5 and 1 min, respectively. However, when FBS was considered, both models produced lower estimation errors. Fetal GA can be more reliably estimated by a multivariate model incorporating fetal and maternal HR coupling and HRV parameters using 5 min of ECG signals.

Relationship between acoustic indices, length of recordings and processing time: a methodological test

Ecoacoustic approaches have the potential to provide rapid biodiversity assessments and avoid costly fieldwork. Their use in biodiversity studies for improving management and conservation of natural landscapes has grown considerably in recent years. Standardised methods for sampling acoustic information that deliver reliable and consistent results within and between ecosystems are still lacking. Sampling frequency and duration are particularly important considerations because shorter, intermittent recordings mean recorder batteries last longer and data processing is less computationally intensive, but a smaller proportion of the available soundscape is sampled. Here, we compare acoustic indices and processing time for subsamples of increasing duration clipped from 94 one-hour recordings, to test how different acoustic indices behave, in order to identify the minimum sample length required. Our results suggest that short recordings distributed across the survey period accurately represent acoustic patterns, while optimizing data collection and processing. ACI and H are the most stable indices, showing an ideal sampling schedule of ten 1-minute samples in an hour. Although ADI, AEI and NDSI well represent acoustic patterns under the same sampling schedule, these are more robust under continuous recording formats. Such targeted subsampling could greatly reduce data storage and computational power requirements in large-scale and long-term projects.

Advancing PPG Signal Quality and Know-How Through Knowledge Translation-From Experts to Student and Researcher.

Objective: Despite the vast number of photoplethysmography (PPG) research publications and growing demands for such sensing in Digital and Wearable Health platforms, there appears little published on signal quality expectations for morphological pulse analysis. Aim: to determine a consensus regarding the minimum number of undistorted i.e., diagnostic quality pulses required, as well as a threshold proportion of noisy beats for recording rejection.Approach: Questionnaire distributed to international fellow researchers in skin contact PPG measurements on signal quality expectations and associated factors concerning recording length, expected artifact-free pulses (“diagnostic quality”) in a trace, proportion of trace having artifact to justify excluding/repeating measurements, minimum beats required, and number of respiratory cycles.Main Results: 18 (of 26) PPG researchers responded. Modal range estimates considered a 2-min recording time as target for morphological analysis. Respondents expected a recording to have 86–95% of diagnostic quality pulses, at least 11–20 sequential pulses of diagnostic quality and advocated a 26–50% noise threshold for recording rejection. There were broader responses found for the required number of undistorted beats (although a modal range of 51–60 beats for both finger and toe sites was indicated).Significance: For morphological PPG pulse wave analysis recording acceptability was indicated if <50% of beats have artifact and preferably that a minimum of 50 non-distorted PPG pulses are present (with at least 11–20 sequential) to be of diagnostic quality. Estimates from this knowledge transfer exercise should help inform students and researchers as a guide in standards development for PPG study design.

Disturbance detection in landsat time series is influenced by tree mortality agent and severity, not by prior disturbance

Landsat time series (LTS) and associated change detection algorithms are useful for monitoring the effects of global change on Earth's ecosystems. Because LTS algorithms can be easily applied across broad areas, they are commonly used to map changes in forest structure due to wildfire, insect attack, and other important drivers of tree mortality. But factors such as initial forest density, tree mortality agent, and disturbance severity (i.e., percent tree mortality) influence patterns of surface reflectance and may influence the accuracy of LTS algorithms. And while LTS algorithms are widely used in areas with a history of multiple disturbance events during the Landsat record, the effectiveness of LTS algorithms in these conditions is not well understood. We compared products from the LTS algorithm LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) with a unique field dataset from a landscape heavily influenced by both wildfire and spruce beetles (Dendroctonus rufipennis) since c. 2000. We also compared LandTrendr to other common methods of mapping fire- and spruce beetle-affected areas. We found that LandTrendr more accurately detected wildfire than spruce beetle-induced tree mortality, and both mortality agents were more easily detected when they occurred at high severity. Surprisingly, prior spruce beetle outbreaks did not influence the detectability of subsequent wildfire. Compared to alternative disturbance mapping approaches, LandTrendr predicted a c. 40% lower area affected by wildfire or spruce beetle outbreaks. Our findings indicate that disturbance type- and severity-specific differences in omission error may have broad implications for disturbance mapping efforts that utilize Landsat data. Gradual, low-severity disturbances (e.g., background tree mortality and non-stand replacing disturbance) are pervasive in forest ecosystems, yet they can be difficult to detect using automated LTS algorithms. Whenever possible, methods to account for these biases should be incorporated in LTS-based mapping efforts, including the use of multispectral ensembles and ancillary spatial data to refine predictions. However, our findings also indicate that LTS algorithms appear to be robust in areas with multiple disturbance events, which is important because these areas will increase as new acquisitions extend the length of the Landsat record.

A method for simulating risk profiles of wheat yield in data-sparse conditions

AbstractProcess-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

Geomorphic and Sedimentary Effects of Modern Climate Change: Current and Anticipated Future Conditions in the Western United States

AbstractHydroclimatic changes associated with global warming over the past 50 years have been documented widely, but physical landscape responses are poorly understood thus far. Detecting sedimentary and geomorphic signals of modern climate change presents challenges owing to short record lengths, difficulty resolving signals in stochastic natural systems, influences of land use and tectonic activity, long‐lasting effects of individual extreme events, and variable connectivity in sediment‐routing systems. We review existing literature to investigate the nature and extent of sedimentary and geomorphic responses to modern climate change, focusing on the western United States, a region with generally high relief and high sediment yield likely to be sensitive to climatic forcing. Based on fundamental geomorphic theory and empirical evidence from other regions, we anticipate climate‐driven changes to slope stability, watershed sediment yields, fluvial morphology, and aeolian sediment mobilization in the western United States. We find evidence for recent climate‐driven changes to slope stability and increased aeolian dune and dust activity, whereas changes in sediment yields and fluvial morphology have been linked more commonly to nonclimatic drivers thus far. Detecting effects of climate change will require better understanding how landscape response scales with disturbance, how lag times and hysteresis operate within sedimentary systems, and how to distinguish the relative influence and feedbacks of superimposed disturbances. The ability to constrain geomorphic and sedimentary response to rapidly progressing climate change has widespread implications for human health and safety, infrastructure, water security, economics, and ecosystem resilience.

Extreme wave events and sampling variability

Wave field data are affected not only by the accuracy of instruments recording them but also by sampling variability, an uncertainty due to the limited number of observations. For stationary meteorological conditions, due to the randomness of the sea surface elevation, wave parameters derived from a temporal or spatial wave record will depend on which part of a wave record is used in an analysis as well as on the length of a wave record and/or size of the investigated ocean area. This study demonstrates, using numerical simulations, challenges that sampling variability brings in the interpretation of nonlinear wave characteristics of the surface elevation when single 20- or 30-min field wave records are used in an analysis. As examples, we use sea states in which rogue waves were observed in the North Sea and investigate them using linear, second-, and third-order numerical simulations. The third-order wave data are simulated by a numerical solver based on the higher order spectral method (HOSM) which includes the leading order nonlinear dynamical effects, accounting for the effect of modulational instability. Wave steepness, the maximum wave crest, skewness, and kurtosis are investigated in unidirectional and directional wave fields. The study shows that having a single 20- or 30-min wave record may make it difficult to determine on the degree of wave field nonlinearity and the accuracy of derived wave parameters, as well as to evaluate the validity of wave models. Both single-point temporal and stereo-video camera data are discussed. We demonstrate that numerical simulations represent important supporting tools for wave field measurements.

What Metadata Matters?: Correlation of Metadata Elements with Click-Through Rates for E-Books and Streaming Video in the Academic Library Catalog

ABSTRACT The relationship between metadata and a user’s ability to effectively locate and select a given resource has been a primary interest of library and information professionals. Important questions about how metadata can best meet emerging search behaviors require continuous and engaged analysis. This paper describes such an analysis of click-through rates for e-book and streaming video resources, attempting to correlate use rates with the presence and character of metadata elements in their associated records. We found no strong correlation between the length of a metadata record, or the presence of individual fields, and higher use rates. A discussion of the implications of these findings for further research is presented.

Full-field real-time characterization of creeping solitons dynamics in a mode-locked fiber laser.

Creeping solitons, which belong to the class of pulsating solitons, can be meaningful for fundamental physics owing to their fruitful nonlinear dynamics. Their characteristics in mode-locked lasers have been studied theoretically, but it is difficult to experimentally observe evolution dynamics in real time. Here, we have experimentally observed the temporal and spectral evolution dynamics of creeping solitons in a passively mode-locked fiber laser by employing time-lens and dispersive Fourier transform technique. With the aid of Raman amplification, the measured recording length of the time lens in the asynchronous mode could be substantially improved. Temporal soliton snaking motion and spectral breathing dynamics are experimentally obtained, confirming intrinsic feature of pulsation dynamics. These results display how single-shot measurements can offer new insights into ultrafast transient dynamics in nonlinear optics.

Interactions of Key Hematological Parameters with Red Cell Distribution Width (RDW) Are Associated with Incidence of Thromboembolic Events (TEs) in Polycythemia Vera (PV) Patients: A Machine Learning Study (PV-AIM)

Interactions of Key Hematological Parameters with Red Cell Distribution Width (RDW) Are Associated with Incidence of Thromboembolic Events (TEs) in Polycythemia Vera (PV) Patients: A Machine Learning Study (PV-AIM)

A Regional Frequency Analysis of Tide Gauges to Assess Pacific Coast Flood Risk

A regional frequency analysis (RFA) of tide gauge (TG) data fit with a Generalized Pareto Distribution (GPD) is used to estimate contemporary extreme sea level (ESL) probabilities and the risk of a damaging flood along Pacific Basin coastlines. Methods to localize and spatially granulate the regional ESL (sub-annual to 500-yr) probabilities and their uncertainties are presented to help planners of often-remote Pacific Basin communities assess (ocean) flood risk of various threshold severities under current and future sea levels. Downscaling methods include use of local TG observations of various record lengths (e.g., 1-19+ years), and if no in-situ data exist, tide range information. Low-probability RFA ESLs localized at TG locations are higher than other recent assessments and generally more precise (narrower confidence intervals). This is due to increased rare-event sampling as measured by numerous TGs regionally. For example, the 100-yr ESLs (1% annual chance event) are 0.15 m and 0.25 higher (median at-site difference) than a single-TG based analysis that is closely aligned to those supporting recent Intergovernmental Panel on Climate Change (IPCC) assessments and a third-generation global tide and surge model, respectively. Height thresholds for damaging flood levels along Pacific Basin coastlines are proposed. These floods vary between about 0.6-1.2 m or more above the average highest tide and are associated with warning levels of the U.S. National Oceanic and Atmospheric Administration (NOAA). The risk of a damaging flood assessed by the RFA ESL probabilities under contemporary sea levels have about a (median) 20 to 25-yr return interval (4-5% annual chance) for TG locations along Pacific coastlines. Considering localized sea level rise projections of the IPCC associated with a global rise of about 0.5 m by 2100 under a reduced emissions scenario, damaging floods are projected to occur annually by 2055 and >10 times/year by 2100 at the majority of TG locations.

An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia

Diabetes is a chronic metabolic disease that causes blood glucose (BG) concentration to make dangerous excursions outside its physiological range. Measuring the fraction of time spent by BG outside this range, and, specifically, the time-below-range (TBR), is a clinically common way to quantify the effectiveness of therapies. TBR is estimated from data recorded by continuous glucose monitoring (CGM) sensors, but the duration of CGM recording guaranteeing a reliable indicator is under debate in the literature. Here we framed the problem as random variable estimation problem and studied the convergence of the estimator, deriving a formula that links the TBR estimation error variance with the CGM recording length. Validation is performed on CGM data of 148 subjects with type-1-diabetes. First, we show the ability of the formula to predict the uncertainty of the TBR estimate in a single patient, using patient-specific parameters; then, we prove its applicability on population data, without the need of parameters individualization. The approach can be straightforwardly extended to other similar metrics, such as time-in-range and time-above-range, widely adopted by clinicians. This strengthens its potential utility in diabetes research, e.g., in the design of those clinical trials where minimal CGM monitoring duration is crucial in cost-effectiveness terms.

Approaching 80 years of snow water equivalent information by merging different data streams

Merging multiple data streams together can improve the overall length of record and achieve the number of observations required for robust statistical analysis. We merge complementary information from different data streams with a regression-based approach to estimate the 1 April snow water equivalent (SWE) volume over Sierra Nevada, USA. We more than double the length of available data-driven SWE volume records by leveraging in-situ snow depth observations from longer-length snow course records and SWE volumes from a shorter-length snow reanalysis. With the resulting data-driven merged time series (1940–2018), we conduct frequency analysis to estimate return periods and associated uncertainty, which can inform decisions about the water supply, drought response, and flood control. We show that the shorter (~30-year) reanalysis results in an underestimation of the 100-year return period by ~25 years (relative to the ~80-year merged dataset). Drought and flood risk and water resources planning can be substantially affected if return periods of SWE, which are closely related to potential flooding in spring and water availability in summer, are misrepresented.