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At least 505 records · Page 28Linked to original sources
USGS2011

Semiparametric bivariate zero-inflated Poisson models with application to studies of abundance for multiple species

Ecological studies involving counts of abundance, presence–absence or occupancy rates often produce data having a substantial proportion of zeros. Furthermore, these types of processes are typically multivariate and only adequately described by complex nonlinear relationships involving externally measured covariates. Ignoring these aspects of the data and implementing standard approaches can lead to models that fail to provide adequate scientific understanding of the underlying ecological processes, possibly resulting in a loss of inferential power. One method of dealing with data having excess zeros is to consider the class of univariate zero-inflated generalized linear models. However, this class of models fails to address the multivariate and nonlinear aspects associated with the data usually encountered in practice. Therefore, we propose a semiparametric bivariate zero-inflated Poisson model that takes into account both of these data attributes. The general modeling framework is hierarchical Bayes and is suitable for a broad range of applications. We demonstrate the effectiveness of our model through a motivating example on modeling catch per unit area for multiple species using data from the Missouri River Benthic Fishes Study, implemented by the United States Geological Survey.

Environmetrics↗
USGS2011

Inference for occupancy and occupancy dynamics

This chapter deals with the estimation of occupancy as a state variable to assess the status of, and track changes in, species distributions when sampling with camera traps. Much of the recent interest in occupancy estimation and modeling originated from the models developed by MacKenzie et al. (2002, 2003), although similar methods were developed independently (Azuma et al. 1990; Bayley and Petersen 2001; Nichols and Karanth, 2002; Tyre et al. 2003), all of which deal with species occurrence information and imperfect detection. Less than a decade after these publications, the modeling and estimation of species occurrence and occupancy dynamics have increased significantly. Special features of scientific journals have explored innovative uses of detection–nondetection data with occupancy models (Vojta 2005), and an entire volume has synthesized the use and application of occupancy estimation methods (MacKenzie et al. 2006). Reviews of the topical concepts, philosophical considerations, and various sampling designs that can be used for occupancy estimation are now readily available for a range of audiences (MacKenzie and Royle 2005; MacKenzie et al. 2006; Bailey et al. 2007; Royle and Dorazio 2008; Conroy and Carroll 2009; Kendall and White 2009; Hines et al. 2010; Link and Barker 2010). As a result, it would be pointless here to recast all that these publications have so eloquently articulated, but that said, a review of any scientific topic requires sufficient context and relevant background information, especially when relatively new methodologies and techniques such as occupancy estimation and camera traps are involved. This is especially critical in a digital age where new information is published at warp speed, making it increasingly difficult to stay abreast of theoretical advances and research developments.

Book chapter↗
USGS1974

Development and evaluation of a prototype global volcanic surveillance system utilizing the ERTS-1 satellite data collection system

The ERTS Data Collection System makes it feasible for the first time to monitor the level of activity at widely separated volcanoes and to relay these data rapidly to one central office for analysis. This capability opens a new era in volcanology where the hundreds of normally quiescent but potentially dangerous volcanoes near populated regions around the world can be economically and reliably monitored daily to warn when any one volcano is becoming active again. Before ERTS was launched only a few volcanoes in the world were monitored continuously because of the high cost of building and staffing volcano observatories. Yet it is known from data collected in this century, that while visible signs of pending eruptions may occur only minutes to days in advance, invisible but measurable signs may be detected days, weeks, months and even years before a major eruption. While prediction of specific eruptions is still an elusive goal, early warning of a reawakening of activity at quiescent volcanoes is now a distinct possibility. A prototype volcano surveillance system was established during the latter part of 1972 and early 1973 on 15 volcanoes in Alaska, Hawaii, Washington, California, Iceland, Guatemala, El Salvador, and Nicaragua. Nineteen seismic detectors that count four different sizes of earthquakes and six biaxial borehole tiltmeters that measure ground tilt with a resolution of 1 microradian have been installed. Data from these instruments are relayed through the ERTS satellite and through a teletype link to the U.S. Geological Survey Office in Menlo Park for rapid analysis. Only seismic and tilt data are collected because these have been shown in the past to indicate most reliably the level of volcanic activity and also because they can be measured relatively easily with available instrumentation. Experience during this project demonstrates the feasibility of building inexpensive, low power, reliable instruments that can be installed in remote locations and can be expected to run unattended for a few years. Comparison of the data from these new earthquake counters with data from nearby standard seismometers shows that the counters do normally indicate the level of seismic activity. During periods of high seismic background noise there may be a significant number of spurious counts but the existence and duration of such noisy periods are reliably indicated by other data collected by the earthquake counters. An eruption of Volcan Fuego in Guatemala was preceded by an order of magnitude increase in the number of seismic-event counts several days before. The tiltmeters operated stably in several different environments. A twenty-microradian collapse of the summit of Kilauea Volcano in Hawaii was observed on three tiltmeters. This initial experiment shows that now with the advent of inexpensive satellite telemetry it is both technologically and economically feasible to build a global volcano surveillance system. Several details in the design and deployment of appropriate low-power, inexpensive, and reliable instruments still need to be worked out. Work continues to evaluate the scientific feasibility of this system by collecting and analyzing data that clearly demonstrate the ability of this system to detect changes in volcanic activity.

Open-File Report↗
USGS2019

Data sharing in magnetotellurics

Here, we introduce the first openly available comprehensive database of magnetotelluric (MT) and related electromagnetic data that we developed and matured over the past decade, explain how to access the data, and describe the challenges that had to be overcome to make MT data sharing possible. The database is a helpful tool for MT scientists, and is widely used by the international scientific community in teaching, planning future surveys, and then to leverage the value of new data at the inversion and interpretation stage. It has also turned into a primary resource for researchers in space weather, and for power grid industry. MT impedance, a transfer function that relates geoelectric fields to geomagnetic fields at the Earth’s surface, provides a critical missing component for estimation of geomagnetically induced currents: quasi-direct currents caused by geomagnetic storms that can destroy valuable equipment and cause extended blackouts. We envision that this database will also be useful for researchers in other areas of geophysics, such as seismology, who would like to use real MT data for hands-on learning and joint inversion analysis.

Eos, Earth and Space Science News↗
USGS2022

Paleoclimatology and paleoceanography perspectives on integrated, coordinated, open, networked (ICON) science

This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1002/essoar.10508554.1 ) in the AGU section paleoclimatology and paleoceanography (P&P), and a discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, technology transfer and application, reproducibility, and data sharing and infrastructure; (Section 3) Local knowledge, global gain: improving interactions within the scientific community and with locals, indigenous communities, stakeholders, and the public; (Section 4) Field, experimental, remote sensing, and real-time data research and application. P&P projects can better include ICON principles by directly incorporating them into research proposals. A promising way to overcome the challenges of interdisciplinarity and integration is to foster networking, which will advance our research discipline through the application of ICON.

Earth and Space Science↗
USGS2019

Establishing high-frequency noise baselines to 100 Hz based on millions of power spectra from IRIS MUSTANG

Advances in seismic instrumentation have enabled data to be recorded at increasing sample rates. This has in turn created a need to establish higher-frequency baselines for assessing data quality, as the widely-used New High (NHNM) and Low Noise Models (NLNM) of Peterson (1993) do not extend to frequencies above 10 Hz. To provide a baseline for higher frequencies (10-100 Hz), we examine power spectral density probability density functions (PSDPDFs) for high-sample-rate stations available from the Incorporated Research Institutions for Seismology Data Services (IRIS DS) MUSTANG quality control system. We compute high-frequency high and low noise baselines by matching the appropriate composite PSDPDF percentile points to NHNM and NLNM power levels at overlapping frequencies (1-10 Hz) and then extending to higher frequencies (10-100 Hz) with piecewise linear fits to the matching PSDPDF percentile. We find that the Peterson NLNM remains an accurate representation of the lower bound of global ambient Earth noise since it is matched by only 0.1% of Global Seismographic Network (GSN) PSDs. We present high-frequency high and low noise baselines intended primarily for use by temporary networks targeting high-frequency signals (e.g. monitoring of aftershocks or induced seismicity) based on statistics of PSDPDFs from all publicly available high-sample-rate data. Most publicly-available high-sample-rate data is recorded by temporary deployments, and the experiment design and scientific targets of these deployments strongly influence the observed statistical distribution of high-frequency noise. We anticipate that the noise baselines presented here will be useful in automated quality control of high-sample-rate seismic data. However, we note that establishing a low noise model that accurately represents the lowest possible ambient Earth noise at frequencies up to 100 Hz will require additional continuous high-sample-rate data from high-quality permanent stations in low-noise environments.

Bulletin of the Seismological Society of America↗
USGS2021

Robust earthquake early warning at a fraction of the cost: ASTUTI Costa Rica

We show that a fixed smartphone network can provide robust Earthquake Early Warning for at least two orders of magnitude less cost than scientific-grade networks. Our software and cloud-based data architecture that we have constructed for the Alerta Sismica Temprana Utilizando Teléfonos Inteligentes (ASTUTI; Earthquake Early Warning Utilizing Smartphones) network in Costa Rica is easily scaled and exported. Implementation comprises provisioning and installing modern smartphones in judicious locations. Stand-up time for regionally operational networks can be on the order of days. We evaluated a non-parametric ground-motion detection and alerting strategy that would alert the entire Costa Rican population of any event with a ground motion detection threshold of 0.55–0.65 %g at four neighboring stations. During a 6-month evaluation period ASTUTI detected and alerted on five of 13 earthquakes with M w 4.8–5.3 that caused felt Modified Mercalli Intensity shaking levels of 4.3–6. The system did not produce any false alerts and the undetected events did not produce wide-spread or significant felt shaking. System latencies were less than or similar to scientific-grade latencies. Alerts for all five detected events would have reached the capital city, San Jose, before strong S -wave shaking. This would have afforded time for Drop Cover Hold On actions by most residents. Two of the five alerts were triggered by P -waves suggesting that smartphone-based networks could approach the fastest theoretical EEW performance, especially with future expected improvements in smartphone sensors and processing algorithms.

AGU Advances↗
USGS2017

Volcano Geodesy: Recent developments and future challenges

Ascent of magma through Earth's crust is normally associated with, among other effects, ground deformation and gravity changes. Geodesy is thus a valuable tool for monitoring and hazards assessment during volcanic unrest, and it provides valuable data for exploring the geometry and volume of magma plumbing systems. Recent decades have seen an explosion in the quality and quantity of volcano geodetic data. New datasets (some made possible by regional and global scientific initiatives), as well as new analysis methods and modeling practices, have resulted in important changes to our understanding of the geodetic characteristics of active volcanism and magmatic processes, from the scale of individual eruptive vents to global compilations of volcano deformation. Here, we describe some of the recent developments in volcano geodesy, both in terms of data and interpretive tools, and discuss the role of international initiatives in meeting future challenges for the field.

Journal of Volcanology and Geothermal Research↗
USGS2007

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

Fact Sheet↗
USGS2009

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

Fact Sheet↗
USGS2024

Evaporation from the interior of Lake Okeechobee—A large freshwater lake in Florida, 2013–16

In 2012, a platform at the approximate center of Lake Okeechobee in central Florida was instrumented to continuously measure evaporation with the Bowen-ratio energy-budget method as part of a long-term partnership between the South Florida Water Management District and the U.S. Geological Survey. The primary goal for the study was to quantify daily rates of open-water evaporation. A secondary goal was to assess differences in evaporation rates among alternate methods and determine if instrumentation and operational expenses associated with the Bowen-ratio method could be reduced. Mean annual evaporation from Lake Okeechobee for 2013–16 was about 1,825 millimeters per year. Annual evaporation from 2013 to 2016 was 1,760, 1,840, 1,810, and 1,890 millimeters per year, respectively. These evaporation rates are among the highest rates observed in Florida based on scientifically vetted methods such as evaporation pans, lysimeters, eddy-covariance, or Bowen-ratio methods. The high evaporation rates are largely a result of frequent clear-sky conditions over the interior of Lake Okeechobee, which allows solar radiation to reach the water surface and drive open-water evaporation. Cloud formation over the interior of Lake Okeechobee is suppressed because of a relatively large heat capacity for water that buffers convective fluxes of air that form clouds while rising and cooling. Estimated evaporation rates obtained using five alternative methods were compared to measured Bowen-ratio energy-budget daily, monthly, and annual evaporation: the Penman, Priestly-Taylor, Mass-Transfer, Simple, and Turc equations. All five methods performed relatively well (within 10 percent of the Bowen ratio annual totals). The Penman, Priestley-Taylor, and Mass-Transfer methods captured relatively large evaporation rates that occurred in the winter due to cold fronts, because these methods account for large wind speeds and vapor pressure deficits associated with the regional cold fronts. For operational implementation, the Simple, Mass-Transfer, or Turc methods are likely preferable because of their simplicity, limited data requirements, and improved accuracy for computing monthly and annual evaporation totals. The Turc equation computed monthly evaporation within 8 percent of the Bowen-ratio method, while requiring only air temperature and solar radiation data. The Simple equation achieved similar accuracy while requiring only solar radiation data.

Florida↗
USGS2017

The planetary data system

In the early 1980s, the Space Science Board (SSB) of the National Research Council was concerned about the poor and inconsistent treatment of scientific information returned from NASA’s space science missions. The SSB formed a panel [The Committee on Data Management and Computation (CODMAC)] to assess the situation and make recommendations to NASA for improvements. The CODMAC panel issued a report [1,2] that led to a number of actions, one of which was the convening of a Planetary Data Workshop in November 1983 [3]. The key findings of that workshop were that (1) important datasets were being irretrievably lost, and (2) the use of planetary data by the wider community is constrained by inaccessibility and a lack of commonality in format and documentation. The report further stated, “Most participants felt the present system (of data archiving and access) is inadequate and immediate changes are necessary to insure retention of and access to these and future datasets.”

Lunar and Planetary Information Bulletin↗
USGS2015

Community for Data Integration 2013 Annual Report

The U.S. Geological Survey (USGS) conducts earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups. However by 2009, it became apparent that many members of the council had an interest in developing data integration solutions and sharing expertise in a less formal grassroots perspective, thus transforming the “Council” into a “Community” for Data Integration (CDI). Today, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community. The CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques provide a forum for people doing work with data integration to come together to share ideas as well as learn new skills and techniques, and grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community. To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the Community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical 2 challenges, including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. Today the CDI continues to hold an annual RFP that create data management tools and practices, collaboration tools, and training in support of data integration and delivery.

Open-File Report↗
USGS2015

Community for Data Integration 2014 annual report

The U.S. Geological Survey (USGS) researches Earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of Earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups; however, by 2009 it became evident that many members of the Council for Data Integration had an interest in developing data integration solutions and sharing expertise in a less formal, grassroots manner, which transformed the Council into a Community for Data Integration (CDI). As of 2014, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community. The CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across the USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to do the following: • advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques, • provide a forum for people doing work with data integration to come together to share ideas and learn new skills and techniques, and • grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community. To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical challenges including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. As of 2014, the CDI continues to hold an annual RFP that creates data management tools and practices, collaboration tools, and training in support of data integration and delivery.

Open-File Report↗
USGS2011

An approach to regional wetland digital elevation model development using a differential global positioning system and a custom-built helicopter-based surveying system

Accurate topographic data are critical to restoration science and planning for the Everglades region of South Florida, USA. They are needed to monitor and simulate water level, water depth and hydroperiod and are used in scientific research on hydrologic and biologic processes. Because large wetland environments and data acquisition challenge conventional ground-based and remotely sensed data collection methods, the United States Geological Survey (USGS) adapted a classical data collection instrument to global positioning system (GPS) and geographic information system (GIS) technologies. Data acquired with this instrument were processed using geostatistics to yield sub-water level elevation values with centimetre accuracy (±15 cm). The developed database framework, modelling philosophy and metadata protocol allow for continued, collaborative model revision and expansion, given additional elevation or other ancillary data.

Florida↗
USGS2025

Leveraging an observed-data likelihood improves the use of machine learning labels in a Bayesian hierarchical model for bioacoustic data

Classification of massive datasets by machine learning (ML) algorithms is promising for many scientific domains, especially wildlife monitoring programs that rely on passive acoustic surveys for detecting species. However, treating ML-predicted class labels (e.g., species identity) as truth biases inferences of focal parameters within common modeling frameworks. One solution is to model the misclassification process explicitly using human-validated true-class labels for a subset of observations. Validation by experts can present a substantial bottleneck in otherwise efficient workflows that use ML predictions. Bioacoustics practitioners seek guidance on both the quantity and process for selecting ML-labeled data to validate by an expert. We derive an alternative model formulation that jointly models human-validated and ML-predicted class labels with an observed-data likelihood (ODL) and use empirically informed simulations motivated by a real-data application to explore different probability designs for selecting class labels for validation. Simulation results suggest that with smaller validation sets the ODL formulation increases computational speed and reduces estimation error compared to a default MCMC data augmentation routine. Our methodology is transferable to applications that treat predictions from classification algorithms as the response variable of interest.

Annals of Applied Statistics↗
USGS2016

Missouri River Scaphirhynchus albus (pallid sturgeon) effects analysis—Integrative report 2016

The Missouri River Pallid Sturgeon Effects Analysis was designed to carry out three components of an assessment of how Missouri River management has affected, and will affect, population dynamics of endangered Scaphirhynchus albus (pallid sturgeon): (1) collection of reliable scientific information, (2) critical assessment and synthesis of available data and analyses, and (3) analysis of the effects of actions on listed species and their habitats. This report is a synthesis of the three components emphasizing development of lines of evidence relating potential future management actions to pallid sturgeon population dynamics. We address 21 working management hypotheses that emerged from an expert opinion-based filtering process. The ability to quantify linkages from abiotic changes to pallid sturgeon population dynamics is compromised by fundamental information gaps. Although a substantial foundation of pallid sturgeon science has been developed during the past 20 years, our efforts attempt to push beyond that understanding to provide predictions of how future management actions may affect pallid sturgeon responses. For some of the 21 hypotheses, lines of evidence are limited to theoretical deduction, inference from sparse empirical datasets, or expert opinion. Useful simulation models have been developed to predict the effects of management actions on survival of drifting pallid sturgeon free embryos in the Yellowstone and Upper Missouri River complex (hereafter referred to as the “upper river”), and to assess the effects of flow and channel reconfigurations on habitat availability in the Lower Missouri River, tributaries, and Mississippi River downstream of Gavins Point Dam (hereafter referred to as the “lower river”). A population model also has been developed that can be used to assess sensitivity of the population to survival of specific life stages, assess some hypotheses related to stocking decisions, and explore a limited number of management scenarios. Consideration of lines of evidence for each of the 21 hypotheses includes a discussion of how the degree of uncertainty and risk associated with each hypothesis may guide science and implementation strategies. Implementation strategies include full implementation in the field, limited implementations as field-scale experiments, or (in the case of greatest uncertainty) implementation as learning actions, including research and opportunistic experiments or field-based gradient studies. Given the substantive uncertainties associated with pallid sturgeon population dynamics and the need to continually assimilate and assess new information, we proposed that an Effects Analysis-like process should be considered an integral part of ongoing Missouri River adaptive management.

Missouri↗
USGS2020

Using the Lomb-Scargle method for wave statistics from gappy time series

Sandwich Town Neck Beach in Sandwich, MA, has experienced substantial erosion and has been the subject of efforts by the town and private landowners to limit the sand loss. Erosion has been particularly dramatic in the past five years with the loss of dwellings. Sandwich's nourishment efforts presented a unique opportunity for scientists at the U.S. Geological Survey Woods Hole Coastal and Marine Science Center to monitor beach morphology and to test new technologies and techniques such as geo-referenced drone imaging. Two bottom lander deployments were performed in Cape Cod Bay at a location that was key to model the fate of waves at Sandwich Town Neck Beach and to support the study of beach morphological evolution. The study period was after the town nourished the beach and during a time when several intense winter storms reshaped the beach and removed much of the nourished sand. A TRDI Workhorse Sentinel V ADCP was used for both deployments. For wave bursts, the instruments collected 2048 samples at 2 Hz every hour. The first deployment during the winter of 2016 returned good quality data. The second deployment during the following winter had gaps throughout the time series from a wiring problem in the external battery pack. The timing of the gaps was random, the duration approximately 100 s. While most of the bursts started at the top of each hour, many had 1-3 gaps within. Time series data with random gaps are problematic for computing spectral density, and thus, wave statistics. This kind of situation is familiar in other scientific disciplines such as astrophysics [1], where techniques exist to find stationary signals in sparse data. One of these methods is the Lomb-Scargle technique for computing periodograms. The most useful feature of the Lomb-Scargle (LS) method is that it allows the spectral analysis of incomplete records, without having to manipulate the record to extrapolate from or replace missing data. We compared the effectiveness of LS against common methods of averaging Fourier transforms such as a simple un-windowed Fast Fourier transform (FFT), Welch's method, and TRDI's Wavesmon software; methods that are commonly used in oceanography for non-gappy data. Synthetic data series that have been artificially modified to introduce gaps were used to evaluate the performance of each method. The LS approach was able to recover spectral density even with several 100-s gaps present. The method was applied here to the gappy and non-gappy data from both Sandwich deployments, and wave statistics were obtained and compared to the wave-buoy data. LS was used to process data that contains gaps that was rejected by Wavesmon, which was approximately 39% of the dataset. Significant wave height and peak period from LS compared well with buoy data. Mean period computed on gappy data using LS produced values biased low, compared with other methods when gaps were filled with the mean value. The LS technique has potential to uncover low-frequency signals such as infragravity waves from gappy records where the non-gappy segments are not long enough to resolve them. It has potential to unlock new information from older data sets.

Massachusetts↗