Invited keynote for the 2017 Marine GIS User Group meeting held Thursday, May 25th at Stanford’s Hopkins Marine Station, 120 Ocean View Blvd., Pacific Grove, CA. The main web site for this user group is walrus.wr.usgs.gov/MontereyBayMarineGIS. The event page for the talk: https://hopkinsmarinestation.stanford.edu/events/dawn-wright-oregon-state-university-new-public-private-partnership-global-ocean
Ecological Marine Units: A 3-D Mapping of the Ocean Based on NOAA’s World Oce...Dawn Wright
This webinar to the Ecosystem Based Management Tools Network, May 17, 2017, reported progress on the Ecological Marine Units (EMU) project, a new undertaking commissioned by the Group on Earth Observations, to develop a standardized and practical global ecosystems classification and map for the oceans. The EMU is comprised of a global point mesh framework, created from 52,487,233 points from the NOAA World Ocean Atlas. Each point has x, y, z, as well as six attributes of chemical and physical oceanographic structure (temperature, salinity, dissolved oxygen, nitrate, silicate, phosphate) that are likely drivers of many ecosystem responses. We identify and map 37 environmentally distinct 3D regions (candidate ‘ecosystems’) within the water column. These units can be attributed according to their productivity, direction and velocity of currents, species abundance, global seafloor geomorphology, and more. A series of data products for open access will share the 3D point mesh and EMU clusters at the surface, bottom, and within the water column, as well as 2D and 3D web apps for exploration of the EMUs and the original World Ocean Atlas data. This webinar provided an overview of the EMU project and cover recent developments and future plans for the EMUs. Webinar recording at https://www.openchannels.org/webinars/2017/ecological-marine-units-3-d-mapping-ocean-based-noaas-world-ocean-atlas
HLEG thematic workshop on measuring economic, social and environmental resili...StatsCommunications
HLEG thematic workshop on Measuring economic, social and environmental resilience, 25-26 November 2015, Rome, Italy, More information at: http://oe.cd/StrategicForum2015
Ecological Marine Units: A 3-D Mapping of the Ocean Based on NOAA’s World Oce...Dawn Wright
This webinar to the Ecosystem Based Management Tools Network, May 17, 2017, reported progress on the Ecological Marine Units (EMU) project, a new undertaking commissioned by the Group on Earth Observations, to develop a standardized and practical global ecosystems classification and map for the oceans. The EMU is comprised of a global point mesh framework, created from 52,487,233 points from the NOAA World Ocean Atlas. Each point has x, y, z, as well as six attributes of chemical and physical oceanographic structure (temperature, salinity, dissolved oxygen, nitrate, silicate, phosphate) that are likely drivers of many ecosystem responses. We identify and map 37 environmentally distinct 3D regions (candidate ‘ecosystems’) within the water column. These units can be attributed according to their productivity, direction and velocity of currents, species abundance, global seafloor geomorphology, and more. A series of data products for open access will share the 3D point mesh and EMU clusters at the surface, bottom, and within the water column, as well as 2D and 3D web apps for exploration of the EMUs and the original World Ocean Atlas data. This webinar provided an overview of the EMU project and cover recent developments and future plans for the EMUs. Webinar recording at https://www.openchannels.org/webinars/2017/ecological-marine-units-3-d-mapping-ocean-based-noaas-world-ocean-atlas
HLEG thematic workshop on measuring economic, social and environmental resili...StatsCommunications
HLEG thematic workshop on Measuring economic, social and environmental resilience, 25-26 November 2015, Rome, Italy, More information at: http://oe.cd/StrategicForum2015
UNCERTAINTY OF HYDROLOGIC EVENTS UNDER SOUTH DAKOTA’S CHANGING CONDITIONS: A ...Boris Shmagin
Widespread flooding across South Dakota in 2011 has spurred a new look at the institutional, regulatory, and mathematical models used to manage the Upper Missouri River Basin as it affects all aspects of life in South Dakota. An SD EPSCoR planning grant was awarded to a team of local, national and international
researchers, who produced a strategy to create a research infrastructure with the goal of developing conceptual and mathematical models to understand and describe the uncertainty of hydrological events (HE) across South Dakota. The strategy involves two main tasks: 1) planning for study of the uncertainty of HE in the Upper Missouri Basin (Shmagin, B. 2011. Missouri River watershed: the object for hydrological study and uncertainty of models. Available from Nature
Precedings at <http: />. [Accessed Oct 3, 2012].)
and 2) developing concepts for communicating uncertainty of HE for wider use outside the professional community. The plan brings together a variety of disciplines, and outlines the development of an artificial intelligence approach to analyzing the interaction of HE, engineering installations and social systems in South Dakota.
The focus of study is the system hydrological researcher – mathematical modeler
– stakeholder, and the process considered is the interaction of knowledge with uncertainty in application to HE. Uncertainty in HE will be defined using concepts broader than hydrology (such as statistical learning) and linked to the concerns of all social, cultural and economic sectors in South Dakota.
Considering this system of interacting participants allows focusing on the principal stages in tackling uncertainty, from developing the research task and obtaining the hydrological results to communication between researcher and stakeholder. Mathematical models are the universal language in scientific research
and will be adapted to bring the results to stakeholders. Three mathematical
approaches to modeling HE and impacts to South Dakota will be considered: 1) distributed system interactions, 2) statistical learning and 3) cellular automata.
Specific concepts of uncertainty for modeling watersheds and describing the time-space variability of water cycles and budget for regional hydrologic study were developed. These concepts include remotely sensed data use, scale and influence of drainage and irrigation on the groundwater regime and hydrology of wetlands and lakes in the Missouri River Valley and Prairie Pothole Region. Additional necessary concepts concern risk assessment and HE interaction with the sociology and economy (e.g., types and scales of regionalization of the physical and human environment), and the design of interactive simulation models (e.g., cartographic presentation and simplified educational modeling after A. Voinov [2008]. of HE in the natural landscapes and industrial/changed conditions in South Dakota.
Biodiversity conservation in fragmented landscapesMarco Pautasso
Biodiversity conservation in fragmented landscapes, the importance of habitat and landscape connectivity, resilience to abrupt climate changes, roadless areas, protected areas
UNCERTAINTY OF HYDROLOGIC EVENTS UNDER SOUTH DAKOTA’S CHANGING CONDITIONS: A ...Boris Shmagin
Widespread flooding across South Dakota in 2011 has spurred a new look at the institutional, regulatory, and mathematical models used to manage the Upper Missouri River Basin as it affects all aspects of life in South Dakota. An SD EPSCoR planning grant was awarded to a team of local, national and international
researchers, who produced a strategy to create a research infrastructure with the goal of developing conceptual and mathematical models to understand and describe the uncertainty of hydrological events (HE) across South Dakota. The strategy involves two main tasks: 1) planning for study of the uncertainty of HE in the Upper Missouri Basin (Shmagin, B. 2011. Missouri River watershed: the object for hydrological study and uncertainty of models. Available from Nature
Precedings at <http: />. [Accessed Oct 3, 2012].)
and 2) developing concepts for communicating uncertainty of HE for wider use outside the professional community. The plan brings together a variety of disciplines, and outlines the development of an artificial intelligence approach to analyzing the interaction of HE, engineering installations and social systems in South Dakota.
The focus of study is the system hydrological researcher – mathematical modeler
– stakeholder, and the process considered is the interaction of knowledge with uncertainty in application to HE. Uncertainty in HE will be defined using concepts broader than hydrology (such as statistical learning) and linked to the concerns of all social, cultural and economic sectors in South Dakota.
Considering this system of interacting participants allows focusing on the principal stages in tackling uncertainty, from developing the research task and obtaining the hydrological results to communication between researcher and stakeholder. Mathematical models are the universal language in scientific research
and will be adapted to bring the results to stakeholders. Three mathematical
approaches to modeling HE and impacts to South Dakota will be considered: 1) distributed system interactions, 2) statistical learning and 3) cellular automata.
Specific concepts of uncertainty for modeling watersheds and describing the time-space variability of water cycles and budget for regional hydrologic study were developed. These concepts include remotely sensed data use, scale and influence of drainage and irrigation on the groundwater regime and hydrology of wetlands and lakes in the Missouri River Valley and Prairie Pothole Region. Additional necessary concepts concern risk assessment and HE interaction with the sociology and economy (e.g., types and scales of regionalization of the physical and human environment), and the design of interactive simulation models (e.g., cartographic presentation and simplified educational modeling after A. Voinov [2008]. of HE in the natural landscapes and industrial/changed conditions in South Dakota.
Biodiversity conservation in fragmented landscapesMarco Pautasso
Biodiversity conservation in fragmented landscapes, the importance of habitat and landscape connectivity, resilience to abrupt climate changes, roadless areas, protected areas
Dealing with heterogeneous data to improve our knowledge of biodiversity dynamics and ecosystem function: perspectives from synthesis projects: presented by Orlane Anneville for GEISHA (Global evaluation of the impacts of storms on freshwater habitat and structure of phytoplankton assemblages) at the sfécologie conference 2018.
for more information on the group: http://www.cesab.org/index.php/fr/projets-en-cours/projets-2015/138-geisha
The report summarizes the science and the impacts of climate change on the United States, now and in the future. It focuses on climate change impacts in different regions of the U.S. and on various aspects of society and the economy such as energy, water, agriculture, and health. It’s also a report written in plain language, with the goal of better informing public and private decision making at all levels.
In addition to discussing the impacts of climate change in the U.S., the report also highlights the choices we face in response to human-induced climate change
Geologic Map of EuropaHow Will Climate ChangeA ect the .docxhanneloremccaffery
Geologic Map of Europa
How Will Climate Change
A� ect the United States?
Tracking River Flows
from Space
VOL. 99 • NO. 1 • JAN 2018
BRIDGING BETWEEN
DATA AND
SCIENCE
honors.agu.org
2018
HONORS
Recognize a colleague, mentor, peer or student
for their achievements and contributions
to the Earth and space sciences.
Nominations Open 15 January
• Union Medals • Union Fellowship • Union Prizes
• Union Awards • Sections Awards and Lectures
Earth & Space Science News Contents
Earth & Space Science News Eos.org // 1
JANUARY 2018
VOLUME 99, ISSUE 1
Giovanni: The Bridge Between
Data and Science
A Web- based tool provides a way to access, visualize, and explore many of NASA’s
Earth science data sets.
COVER
24
12 To Understand Future Solar Activity, One Has
to Know the Past
Short-term funding strategies present
serious problems for programs like solar
activity studies, where observations and
analysis span decades or longer.
OPINION
How Will Climate Change
Affect the United States
in Decades to Come?
A new U.S. government report shows
that climate is changing and that human
activities will lead to many more changes.
These changes will affect sea levels,
hurricane frequency, wildfires, and more.
FEATURE
18
Tracking River Flows
from Space
Satellite observations, combined
with algorithms borrowed from river
engineering, could fill large gaps in our
knowledge of global river flows where field
data are lacking.
PROJECT UPDATE
32
January 2018
Contents
Editor in Chief
Barbara T. Richman: AGU, Washington, D. C., USA; eos_ [email protected]
Christina M. S. Cohen
California Institute
of Technology, Pasadena,
Calif., USA;
[email protected] .caltech.edu
José D. Fuentes
Department of Meteorology,
Pennsylvania State
University, University
Park, Pa., USA;
[email protected]
Wendy S. Gordon
Ecologia Consulting,
Austin, Texas, USA;
[email protected]
.com
David Halpern
Jet Propulsion Laboratory,
Pasadena, Calif., USA;
[email protected]
.com
Carol A. Stein
Department of Earth and
Environmental Sciences,
University of Illinois at
Chicago, Chicago, Ill.,
USA; [email protected]
Editors
Editorial Advisory Board
Mark G. Flanner, Atmospheric Sciences
Nicola J. Fox, Space Physics
and Aeronomy
Peter Fox, Earth and Space Science
Informatics
Steve Frolking, Biogeosciences
Edward J. Garnero, Study of the
Earth’s Deep Interior
Michael N. Gooseff, Hydrology
Brian C. Gunter, Geodesy
Kristine C. Harper, History of Geophysics
Sarah M. Hörst, Planetary Sciences
Susan E. Hough, Natural Hazards
Emily R. Johnson, Volcanology,
Geochemistry, and Petrology
Keith D. Koper, Seismology
Robert E. Kopp, Geomagnetism
and Paleomagnetism
John W. Lane, Near-Surface Geophysics
Jian Lin, Tectonophysics
Figen Mekik, Paleoceanography
and Paleoclimatology
Jerry L. Miller, Ocean Sciences
Thomas H. Painter, Cryosphere Sciences
Philip J. Rasch, Global Environmental
Change
Eric M. Riggs, Education
Adrian Tuck, No ...
2010 Science Framework Overview by
Mary Wroten, Science Specialist
Office of Curriculum and Instruction
P.O. Box 771
Jackson, MS 39205-0771
601-359-2586
mwroten@mde.k12.ms.us
Estuaries, long recognized for their local importance, form collectively an important global ecosystem, sensitive to both climate change and local pressures. This has been recognized by a 2013 U.S. workshop, which issued a set of recommendations directed at building worldwide capacity and collaborations to address estuaries as a global ecosystem. The workshop recognized that modern observation and modeling technology is poised to play a key role in advancing the scientific understanding of estuaries, and identified the need to map the resulting understanding of individual estuaries into a common global framework. An international partnership has since emerged, driven by the increasingly recognized need to advance estuarine observation, modeling, science and science translation worldwide. Anchoring the partnership is a belief that there are important commonalities across estuaries that, if explored, will prove synergistic and transformation towards understanding and sustainable management of all estuaries. On behalf of this emerging international partnership, we describe here steps that are being taken to develop Our Global Estuary. Integral to these efforts are: (a) the organization of regular international workshops, to build a common vision and global capacity and collaborative networks—the first of these workshops planned for Chennai, India; (b) the creation of a pilot project, Our Virtual Global Estuary, where a common modeling and analysis framework, supported by and supporting local observations, will be progressively put in place for estuaries across the world—with an initial set identified in Brazil, China, Portugal, Spain, and United States, and additional estuaries under consideration; and (b) exploration of synergies with global organizations (such as the Partnership for Ocean Global Observations) and global-scale programs and initiatives (such as Blue Planet), to further contextualize the role of estuaries in the earth’s sustainability.
Ease Leads to Exposure, Exposure Leads to AdoptionDawn Wright
Federation of Earth Science Information Partners (ESIP) ESIP 2019 Summer Meeting - Day 2 Plenary – Tacoma, WA, July 17, 2019 In thinking about my remarks this morning, it occurred to me that there is very little that I could say to an outstanding community such as yours that you haven’t already heard. I do know, however, that one of the hallmarks of your community is your wonderful ethic of sharing, of giving, and your deep understanding that the more you GIVE in this community, the more you RECEIVE, and all toward better science for everyone, and better actions to literally save our planet.
Toward this end, I want to share some remarks under a fun Star Wars theme, which I hope you’ll enjoy. These thoughts are about getting people to actually USE the resources that we’ve worked so very hard to build, and thus is in keeping with your conference theme: “Data to Action: Increasing the Use and Value of Earth Science Data and Information.”
Slide design based on the Powerpoint slide template of Joshua D. Clarke, foxgguy2001.deviantart.com, Annville, KY
Data for the Blue Future: New Collaborations for ProgressDawn Wright
2020 UN World Data Forum Ocean Data Panel -- Data on the world’s oceans is vital to protect the environment, track and manage changes to the marine ecosystem, ensure access to coastal resources, and sustainably grow the “Blue Economy.” Climate scientists have noted that ocean data is as important as atmospheric and satellite data in understanding, predicting, and mitigating climate change. But despite its importance, ocean data has not yet been collected, shared, analyzed, and applied in all the ways that can achieve its full benefit.
Many converging factors provide the opportunity to make a quantum leap in the use of this global data resource. UN SDG Goal 14 highlighted the need to improve the health of the oceans and to collect data to track progress. In the United States, the National Oceanic and Atmospheric Administration (NOAA) and private-sector partners are making NOAA’s vast ocean data resources available and computable in the cloud. Simultaneously, established industries, emerging start-ups, academic and research institutions, and other non-government entities are collecting more data than ever about the ocean through active and autonomous measurements.
This session will be a “participatory panel” that presents case studies of ocean data’s application, describes current data sources and collaborations, and invites participants to help envision new collaborative models for improving ocean data collection, sharing, and analysis. The panelists, representing diverse organizations, will give lightning talks to describe existing data resources and examples of ocean data’s application, including the use of AI and machine learning for data management and for managing ocean resources. They will also describe collaborative programs that bring diverse stakeholders together to support the use of ocean data, such as NOAA’s Big Data Project and the Global Ocean Observing System.
AGU Sharing Science - Social Media TipsDawn Wright
Part of an American Geophysical Union (AGU) Sharing Science webinar on May 8, 2019, with yours truly and Scripps doctoral student Tashiana Osborne sharing our science communication, science policy and social media outreach tips in advance of World Oceans Day. The webinar is also on at http://ow.ly/4KVH50u5gUI.
AGU is one of the world's largest scientific societies with a membership of 60,000+. The AGU Sharing Science Program regularly runs webinars pertaining to science communication such as scicomm via storytelling, social media, multimedia, etc. Additionally, this year is AGU’s 100 year anniversary. As part of their centennial celebration, AGU is highlighting some national and international science days, among them World Oceans Day. In advance of World Oceans Day, AGU asked Dawn and Tashiana to describe to our scientific peers how we share our science as oceanographers, women in science, and effective science communicators.
The Perils and Promise of Environmental Data ScienceDawn Wright
Keynote address delivered in April 2019 to the Yale School of Forestry & Environmental Studies, during their annual research conference. "The mission of the Annual F&ES Research Conference is to provide a forum for research degree students and postdocs to share their original work with the F&ES community, as well as with the broader Yale and New Haven communities. After the success of last year's partnership with Yale Pathways to Science, we will again open conference attendance to local high school students and host events emphasizing research communication. Our aim is for the conference to facilitate interdisciplinary communication and collaboration both within the School and beyond the walls of Kroon."
Discovery, Technology, Hope: Colorado College Roberts SymposiumDawn Wright
A plenary talk within the Harold Roberts Endowed Symposium at Colorado College on the theme "Beyond Climate Change: The Earth in the Anthropocene." The symposium (in May 2019) featured commentary on the lithosphere, biosphere, cryosphere, and Dawn's treatment of the hydrosphere (including #SDGs), all with an eye toward developing a public (and student) understanding of the breadth of environmental issues facing humanity.
Marie Tharp, Giants of Tectonophysics Session, American Geophysical UnionDawn Wright
Invited paper U22A-02, Giants of Tectonophysics Session, American Geophysical Union Fall Meeting.
In honor of AGU's centennial, this session profiles breakthrough discoveries by leading contributors to the study of plate tectonics, geodynamics, earthquakes and faulting, and rock mechanics over the past century. Biographers and colleagues of these leading lights presented the stories of these giants to give us an understanding about how they worked, how they acquired their unique insight, what conflicts they faced in presenting their ideas, and what it was like to collaborate with or debate them.
By the latter half of the 20th century the technologies behind SONAR, marine gravity, and marine magnetics had advanced to the point that the complexities of the ocean floor and beneath could be unraveled in unprecedented detail. Hence, scientists would finally be able to provide conclusive evidence for plate tectonics by way of plausible, proven physical mechanisms. But it took a young woman with an unusual background in geology, mathematics, and art to use that info to posit one of the most fundamental proofs of continental drift: a rift valley caused by the faulting of seafloor spreading. She did this while a researcher at Columbia University, in the lab of the iconic Maurice “Doc” Ewing, founder of the Lamont Geological Observatory. Along with geologist Bruce Heezen, she began the first systematic, comprehensive attempt to map the entire ocean floor. Heezen collected the data at sea, while Tharp developed a truly unique process for translating millions of these ocean-sounding records into a single drawing. During this process, she discovered the rift valley of the Mid-Atlantic Ridge, which Heezen at first discounted, holding incorrectly to his expanding Earth theory. Tharp's name was absent from the 1956 scientific paper that released this discovery to the world, and she was not given proper recognition for this and many other accomplishments until decades later. In 1968, she finally had the opportunity to go to sea, and performed the first ever shipboard processing and plotting of bathymetric data. During this time, Tharp and Heezen also formed a successful partnership with Austrian landscape painter Heinrich Berann to produce several panoramas of the ocean floor, leading to some of the most widely recognized and beloved images in all of modern Earth science. Indeed, as her first, long-time employer, Doc Ewing, invented the field of marine geophysics, Tharp invented the field of marine cartography. Her story in words, data, and maps is a story that must continue be told for the future of science as well as the past. It is a remarkable testament to persistence, conviction, and courageous innovation.
EarthX Exhibition and EarthX Ocean Conference, Dallas, Texas, April 20-22, 2018
EarthX, the largest Earth Day celebration in the world! Multiplying Environmental Awareness. Visit earthx.org to discover the expo, conference and film festival for creating positive solutions for the Earth
Toward Easy Export of Imagery Products and Feature Classes as Training Data f...Dawn Wright
American Association of Geographers (AAG) 2018 Symposium on Artificial Intelligence and Deep Learning in Geospatial Research
Whether to train a Deep Learning (DL) model to find objects of interest such as cars or solar panels in satellite or aerial images, or to classify such images into different categories of land-use, or other such tasks, a common starting point is always labeled ground truth or training data. From an industry perspective, an organization such as ESRI has a large user base of roughly 350,000 agencies, universities, non-profits, and other partners, with most of them maintaining and permanently updating their own GIS data. But how to allow this treasure trove of data to be effectively and appropriately used for training new DL models? This talk will provide an overview of new tools to export GIS data from multiple sources into popular DL formats such as KITTI or PASCAL_VOC. These can then be directly used as input to DL frameworks such as Microsoft CNTK or Google TensorFlow in order to train DL models. For example, NAIP images and building footprints of an entire county can be exported as a sequence of equally sized image chips plus one meta data file per image chip containing the bounding boxes around all buildings in KITTI format. From this data a DL model can be trained that detects buildings. The hope is that this new suite of tools will make it easier for DL researchers and students at all levels (from undergraduate to doctoral and beyond) to access existing GIS data and to use them for training new DL models.
Integrated GIS/Machine-Learning Workflows - Seagrass Use CaseDawn Wright
Integrated GIS/Machine-Learning Workflows
for Modeling Spatiotemporal Variations in Potential Seagrass Habitats within a Changing Climate, European Geosciences Union General Assembly Paper ESSI4.3 – EGU2018-10081, Vienna, Austria, April 2018.
Coastal marine plant habitats are impacted by changes in ocean conditions and the resulting changes in plant
populations can produce positive climate feedbacks which exasperate warming. (Waycott et al., 2009). One such
example is seagrasses, marine plants that can sequester vast amounts of carbon. When compared to tropical
terrestrial forests, seagrasses can store up to 100 times more CO2 at a rate that is 12 times faster (Mcleod et al.,
2011). Understanding the future of an important biologic carbon sink such as seagrass can shed some light into
future carbon balance. Modeling the relationships between seagrass occurrence and ocean conditions, current and
future, can aid in quantifying the impacts on future carbon balance. In this work, we use an integrated GIS and
machine learning approach to build a data-driven model of seagrass presence-absence in a changing climate. We
quantify the relationships between observed seagrass occurrence and ocean conditions. This relationship allows us
to delineate patterns in current ocean conditions that promote favorable seagrass habitats.We pose this relationship
as a binary classification problem and utilize Random Forest to establish a relationship for seagrass occurrence.
This relationship is projected into the future under changing ocean conditions. We use deep-learning methods,
recurrent neural networks, to forecast ocean conditions as the oceans get warmer and use these conditions in
conjunction with the Random Forest model to predict the abundance of future seagrass habitats. We integrate
multiple data sources including fine-scale seagrass data from MarineCadastre.gov and the recently available,
globally extensive publicly available Ecological Marine Units (EMU) dataset. In addition, we use global ocean
models from NOAA to calibrate our ocean forecasts. Our analysis includes a sensitivity study which investigates
the vulnerability of seagrass to changes in specific ocean variables. We use the proposed model to provide an
upper bound of the amount of carbon that can be stored in seagrasses as ocean conditions change. Finally, we
use a Getis-Ord Gi* statistic within a space-time window to quantify the temporal changes in potential seagrass
habitats.
Keynote address for 20th Society of Conservation GIS (SCGIS) Meeting, Asilomar Conference Center near Monterey, CA, July 17, 2017. Full notes available at http://esriurl.com/scgis17.
A Dark Side to Data-Centric Geography? Where are the Reward Systems?Dawn Wright
Introduction to a panel session at the 2016 American Association of Geographers Annual Meeting in San Francisco. The panel was part of the AAG Symposium on Human Dynamics Research and discussed an apparent disconnect in academia where skills in research computing and programming are still not properly rewarded.
In geography as in other disciplines, our ability to collect, process, visualize, and interpret datasets of unprecedented size and detail is helping to push the boundaries of our knowledge. This stands to affect just about every aspect of geography. Other disciplines are now discussing how best to promote a (new) culture where achievements in computational or “data science” are rewarded. This includes the emerging fields of computational social science and digital humanities. But where does geography fall within this “landscape?”
Should we not seek to train and reward a new breed of geographer, broadly skilled in computing and coding, as well as in the careful management and analysis of very large datasets? What of the reward structure for the geography faculty member or postdoc who brings these skills to the classroom, while also releasing his/her own scientific code, workflows and datasets? In many geography departments these activities do not fall within the traditional modes of writing, publishing, or even grantsmanship. Hence they may not translate to academic career advancement.
By not properly rewarding these activities, are we unwittingly driving a host of promising researchers away from the academic community?
Full notes on the session at http://dusk.geo.orst.edu/aag16-darkside.html
Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...Dawn Wright
Invited talk for 2016 AGU Fall Meeting Session IN12A Big Data Analytics I
Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These “feature geo analytics” tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical “big” data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in ~1 minute. The approach is “hybrid” in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.
Department of Geography and Geoinformation Science Seminar, George Mason University, Falls Church, VA, September 2015.
Increasingly, GIS is part of the collaboration between computer scientists, information scientists, and domain scientists to solve complex scientific questions. Successfully addressing scientific problems, such as informing regional decision- and policy-making for coastal zone management and marine spatial planning, requires integrative and innovative approaches to analyzing, modeling, and developing extensive and diverse data sets. The current chaotic distribution of available data sets, lack of documentation about them, and lack of easy-to-use access tools and computer modeling and analysis codes are still major obstacles for scientists and educators alike. Contributing solutions to these problems is part of an emerging science agenda at Esri for a range of environmental, conservation, climate and ocean sciences that will be discussed. The talk will highlight some recent projects in progress, including a new global map of ecological land units, new tools to support multidimensional scientific data, continued work on an ocean basemap, and more.
Latest Developments in Oceanographic Applications of GIS, including Near-real...Dawn Wright
Invited presentation for Schmidt Ocean Institute Research Planning Workshop on Transforming Seagoing Science with Robotic Platforms, Innovative Software Engineering, and Data Analytics, August 2015
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Ecological Marine Units: A New Public-Private Partnership for the Global Ocean
1. Ecological Marine Units:
A New Public-Private Partnership for the Global Ocean
Monterey Bay GIS User Group
Stanford U. Hopkins Marine Station
Pacific Grove, CA, May 25, 2017
Dawn Wright, Chief Scientist, Environmental Systems Research Institute (aka Esri)
Affiliated Professor, Oregon State University
Roger Sayre, USGS Senior Scientist for Ecosystems, Climate and Land Use Change
Sean Breyer, Esri ArcGIS Content Program Manager
2. GEOSS Task EC-01-C1 (2014) / GI-14 GECO (2016)
Global Ecosystem Classification and Mapping
• Develop a standardized, robust, and practical global ecosystems
classification and map for the planet’s terrestrial, freshwater,
and marine ecosystems.
• Dr. Roger Sayre, USGS, Task Lead
• Esri is a partner, engaged in producing and hosting the content
• Secretary Sally Jewell at the GEO 2015 Plenary in Mexico City:
“The US Geological Survey and Esri will develop a new map of
standardized global marine ecosystems”
3. Terrestrial Effort: Ecological Land Units (ELUs)
Land Cover
Lithology
Landform
Bioclimate Example: Warm Wet Plains on Metamorphic Rock
with Mostly Deciduous Forest
48,872 Combinations (Facets)
3,923 Unique Land Units/Colors
www.aag.org/global_ecosystems
esriurl.com/elu
esriurl.com/ecotapestry
esriurl.com/landscape
4. Ecological Marine Units (EMUs)
Who wants one?
GEO & GEOSS (GEO BON MBON, GECO)
Global Ocean Refuge System (GLORES)
IUCN, WWF, CI, Mission Blue Sylvia Earle
Alliance
FAO and ICES
OOI and IOOS/GOOS
Essential Ocean Variables community (e.g.,
World Climate Research Program)
Researchers
Educators
Local agencies who want the global context
Natl science agencies
Editors of textbooks
Why?
• Ecosystem Health, Resilience, Ecosystem Goods & Services;
Ecosystem Services Valuation
• Nature Conservation Reporting
• Conservation planning
• Ecosystem Classification
• Ecosystem Based Management
• Fisheries Management
• Marine Data Management
• Indicating Species Distributions
• Explaining and Understanding Nature
• Risk Reduction
• Context: Local related to Global
• System Connectivity
5. EMUs:
• cover all the ocean
• are 3D
• are based on best available data
• are independent of political,
social and economic influence
• Promote further understanding of
how the environment structures
biodiversity (including fisheries,
threatened species, etc.)
How is this different from what exists?
Graphic courtesy of Mark Costello et al., U. of Auckland, New Zealand
6. Based on NOAA’s World Ocean Atlas 2013 v. 2
Nitrate
Silicate
Phosphate
Temperature*
Salinity
Dissolved Oxygen
e.g., *Locarnini, R.A., A.V. Mishonov, J.I. Antonov, T.P. Boyer, H.E. Garcia, O.K. Baranova, and others. 2013. World Ocean Atlas 2013
version 2 (WOA13 V2), Volume 1: Temperature. In: NOAA National Centers for Environmental Information S. Levitus, ed, and A.
Mishonov, technical ed, NOAA Atlas NESDIS 73, doi:10.7289/V55X26VD, www.nodc.noaa.gov/OC5/woa13/
0 m
5500 m 100 m
100 m
5 m
10 m
25 m
7. EMU 3D Point Mesh Framework
UnitTop
SurfaceArea
5500 m
100 m
100 m
5 m
10 m
25 m
0 m
-5500 m
Feature Attributes
Depth_Level
Temperature
Salinity
Dissolved Oxygen
Nitrate
Silicate
Phosphate
MODIS Ocean Color
PointID
QuarterID
UnitTop (m)
UnitMiddle (m)
UnitBottom (m)
Thickness (m)
ThicknessPos (m)
EMUID
EMU Name
GeomorphologyBase
GeomorphologyFeatures
SurfaceArea
Volume, SpecialCases
0 m
UnitBottom
Unit Middle
Thickness
Volume
World Ocean Atlas EMUPoints
• K-means statistical clustering
• Backwards stepwise discriminant analysis
• Pseudo F-statistic 37 clusters
• Canonical discriminant analysis
8. EMU 13 Summary
Technical Name:
• Bathypelagic
• Very Cold
• Euhaline
• Hypoxic
• High Nitrate
• Medium Phosphate
• High Silicate
Common Name:
• Deep
• Very Cold
• Normal Salinity
• Low Oxygen
• High Nitrate
• Medium Phosphate
• High Silicate
9. EMU 13 Summary
Technical Name:
• Bathypelagic
• Very Cold
• Euhaline
• Hypoxic
• High Nitrate
• Medium Phosphate
• High Silicate
Common Name:
• Deep
• Very Cold
• Normal Salinity
• Low Oxygen
• High Nitrate
• Medium Phosphate
• High Silicate
12. Do Our Depth Findings Support
Traditional Ocean Zonation Concepts?
Figure courtesy of Paul R. Pinet, Invitation to Oceanography, 5th ed., Jones and Bartlett Publishers
13. Paper for peer-reviewed journal Oceanography
Full Title:
A Three-Dimensional Mapping of the Ocean Based on Environmental Data
Short title:
A 3D Mapping of the Global Oceans
Author List
Roger G. Sayre1, Dawn J. Wright2, Sean P. Breyer2, Kevin A. Butler2, Keith Van Graafeiland2, Mark J. Costello3,
Peter T. Harris4, Kathleen L. Goodin5, John M. Guinotte6, Zeenatul Basher1, Maria T. Kavanaugh7, Patrick N.
Halpin8, Mark E. Monaco9, Noel Cressie10, Peter Aniello2, Charles E. Frye2, and Drew Stephens2.
1Land Change Science Program, United States Geological Survey, Reston, Virginia, United States of America
2Esri, Redlands, California, United States of America
3Institute of Marine Science, University of Auckland, Auckland, New Zealand
4GRID-Arendal, Arendal, Norway
5NatureServe, Arlington, Virginia, United States of America
6United States Fish and Wildlife Service, Denver, Colorado, United States of America
7Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, United States of America
8Nicholas School of the Environment, Duke University, Durham, North Carolina, United States of America
9National Ocean Service, National Oceanic and Atmospheric Administration, Silver Spring, Maryland, United States of America
10National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, Australia
23. Next Stages
ADDITIONAL DATA
On the Surface
OBIS
More ocean color
In Water Column
**Seasonal TEMPORAL WOA data
Particulate Organic Carbon
OBIS
On the Seafloor
Reef/Vents features
Sediment sizes
**In Your Own Study Area with higher-rez data
TOOLS
Viewer Tools
3D Web Viewer
3D Cross Section (Fence)*
Analysis Tools
Compare Multiple Locations
Multidimensional Range Slider
3D Kriging
3D Geo Enrichment
24. Next Set of Global Ecosystem Maps: ECUs and EFUs
Ecological Coastal Units (ECUs) Ecological Freshwater Units (EFUs)
The work to produce the map and data was commissioned by the Group on Earth Observations, a mini “United Nations” of sorts consisting of almost 100 nations collaborating to build the Global Earth Observation System of Systems (GEOSS) in 9 Societal Benefit Areas (Agriculture, Biodiversity, Climate, Disasters, Ecosystems, Energy, Health, Water, and Weather). The global ecosystem mapping task, as defined here, is a key program within the GEO Biodiversity Observation Network (GEO BON) and the GEO Ecosystems Initiative (GEO ECO).
One impt thing to mention is that the ELUs were released and launched as an earlier contribution to the President's Climate Data Initiative.
The ELUs are now on that list of Climate Data Initiative (CDI) resources, and of course are registered on data.gov. Now the EMUs should be considered a similar contribution but for the marine environment. Since Fabien was and is apparently still engaged with the CDI, this is a major hook into White House interest.
EMU is now under the new GEO Global Ecosystems initiative (GECO) arising from the GEO 2016 Transitional Workplan. The former Ecosystems Societal Benefit Area and the former Biodiversity Societal Benefit Area have been combined into a new Biodiversity and Ecosystems Sustainability SBA.
The GECO is a new task, and it has four pieces to it related to 1) the European Horizon 2020 ECOPOTENTIAL project, 2) the H2020 SWOS (Satellite-based Wetlands Observation System) project, 3) global EMUs, and 4) global EFUs.
So why do we need a global ecosystem map anyway? Such a map, and more importantly, the data, will provide scientific support for planning and management, and enable understanding of impacts to ecosystems from climate change and other disturbances. The map and data should also prove useful as an ecologically meaningful spatial accounting framework for assessments of the economic and social values of ecosystem goods and services.
Should aid in REPEATABLE landscape mgmt - a platform for geo-accounting (instead of reducing so much by national boundaries, we are using real ecological units)
A standard repeatable accounting framework
A global view of environmental diversity
Ecosystems defined by humans for humans as opposed to ecosystem HEALTH, a healthy ecosystem vs a service that the ecosystem provides - the next level to resilient ecosystems rather than ecosystem services
Research goal in future? what are the indicators that if merged together in a better way would provide better services; one can still be SICK and provide services
Example – indicators may be relative to the status of the fish stock but not indicators as to how the ecosystem is working.
Specific needs include:
Assessments of Economic and Social Value of Ecosystem Goods and Services
Biodiversity Conservation Planning
Analysis of Climate Change Impacts to Ecosystems (and other impacts e.g. fire, invasive species, land use, etc.)
Resource Management
Research
Bioclimate, Landform, and Lithology = Drivers of Ecological Character (physical setting)
Land Cover = Response to the Physical Setting
We found 48,872 unique combinations aggregated to 3923 ELUs. In 2015 106,959 unique combos thanks to the updated land forms and land cover, 2010 epoch, Global Land Cover, v. 1.4
Bioclimate, Landform, and Lithology = Drivers of Ecological Character (physical setting)
Land Cover = Response to the Physical Setting
Bioclimates - Global Environmental Stratification (GEnS), U. of Edinburgh - 50 year avg of temp/precip from met stations throughout world
30 arc sec raster, down-sampled to 250-m raster
Landforms – USGS – 250-m raster, derived from GMTED2010
Surficial Lithology - Global Lithological Map (GLiM), Hamburg University, Vector Polygons converted to 250-m raster
Land Cover - GlobCover, 2009, European Space Agency - MARIS satellite, 300 m rez resampled to 250 m
Version 2 recently released in 2015 with updated land cover, 2010 epoch, Global Land Cover, v. 1.4
Only layer that we had an option: GlobCover 2009, GlobeLand30 or MDA’s NaturalVue
GlobCover 2009 offered a richer, more flexible classification, which is compatible with USGS NLCD
NaturalVue was too old.
Both had significant quality issues relative to broad audience acceptance
Today, there are more options. Globeland30 continues to be improved. MDA has produced BaseVue
How did we make the map? Again, we define ecosystems as distinct physical environments and their associated vegetation, so we map ecosystems by first mapping, and then combining in a GIS, global bioclimates, global landforms, global geology, and global land cover.
Characterize the principle ecological land components of the terrestrial surface of the earth in a micro-scale, bottom-up, hierarchical classification process.
Subdivide the land surface of the earth into macro-scale physiographic (geomorphological) areas in a top-down, hierarchical regionalization process.
Combine the physiographic regionalization process with the ecological classification process to develop a hierarchical, ecophysiographic segmentation of the planet.
Weightings of 4 layers: 3, 3, 2, 1
Instead of reflecting JUST researchers perceptions and local experiences, our EMUs provide quantifiable definitions for these such as epipelagic, mesopelagic, bathypelagic, etc.
Where do we get the best “physical setting” for the ocean, which will in turn drives its ecological character? WOA is probably the best available set of “objectively analyzed climatologies” for the major physical parameters of the world’s oceans (interpolated mean fields at standard depth levels).
From NOAA NCEI (formerly NODC), http://www.nodc.noaa.gov/OC5/woa13/
SPATIALLY
WOA 2013 at finest rez of ¼ degree (27 km at equator) for all variables save for nutrients at 1 km (subsampled nutrients so there is a slight source of error there)
¼ deg horiz and vertical, 102 depth zones ranging in thickness from 5 m at surface to 500 m in deep ocean
TEMPORALLY
WOA 2013 has 5 or 6 decadal averages
- 1 point in our mesh is the avg of a 57-year period, so it’s an average of an average of the prominent mean over 50 years
- trying to conceptualize regions as long-term historical average, possibly stable
WOA has seasonal averages – we are not dealing with those – we assume that these are already part of the annual/decadal
but this is the next logical step, to do clustering on monthly avgs as part of a later study; once we understand the decadal we can apply to quarterly/seasonal intervals
NOAA administrator Kathryn Sullivan likens this to a “christmas tree” that we ALL can hang ornaments on now. In GIS-speak this means additional Feature Attributes
Step 1 - Build 3-D framework (point mesh), where we extracted the World Ocean Atlas data into a global point mesh framework created from 52,487,233 points, each with at least 6 WOA attributes
Step 2 - Attribute mesh points with 6 WOA physical/chemical parameters, in addition to the x, y, and z coordinates (more attributes possible)
Step 3 – Used k-means statistical clustering algorithm to identify physically distinct, relatively homogenous, volumetric regions in the water column (EMUs). Backwards stepwise discriminant analysis to determine if all of six variable contributed significantly to the clustering – all six were significant. pseudo F-statistic gave us the optimum # of clusters at 37. Then used canonical discriminant analysis to verify that all 37 clusters were significantly different from one another and they were.
Compare/combine surface-occurring EMUs with other sea surface partitioning efforts using ocean color, etc. (e.g., Longhurst, Oliver and Andrew, MBON, Seascapes, etc.)
Compare/combine bottom-occurring EMUs with seafloor physiographic regions and features, etc. (e.g., Harris et al.)
Assess relationship between physically distinct regions and biotic distributions (e.g., OBIS Biogeographic Realms, etc.), and maybe combine to incorporate biotic dimension into the EMUs
[In the weeds: A globally comprehensive subset (25,000 points) of all points was used for the determination of the optimum cluster number using the pseudo F-statistics, yielding an optimum of 37 clusters. For the approach, the approximately 52 million global points were then clustered in a series of sequential iterations where the number of clusters requested ranged from 5 to 500, increasing the cluster number by ten for each successive iteration.]
Example summary of an EMU. There is one for each of the 37
You can download this from your seat right now, but especially for you young people, I hope you’ll stay pay attention during the remainder of my talk!
Most oceanography and marine biology textbooks include diagrams dividing the oceans into depth zones. Although depth boundaries for these regions are largely arbitrary and can vary from text to text, they are meant to describe the ecological variation that is correlated with depth. It is possible that sea temperature and physiographic features (e.g. abyssal plain) create distinct zones differing in environmental conditions with depth, and these conditions may be reflected in different ecological communities. However, this has never been objectively tested using data at a global scale
most existing marine maps and zonation systems are derived from supervised classification and thus biased by the experiences and perspective of their authors and the availability of observations in particular regions
This article now appears in Oceanography, 30(1): 90-103, 2017.
HORIZONTALLY on the LEFT
VERTICALLY on the RIGHT
ON THE LEFT: 37 mutually exclusive EMU clusters (shown with ELUs) representing the maximum global horizontal dimensions of the clusters AT SELECTED DEPTHS AND in different colors.
While a total of 37 EMUs were statistically determined, a number of them are small, localized, and shallow, and are not discernible in these depth-layer maps. Black indicates regions shallower than the depth at that layer. Major hydrographic features like Northern and Southern Hemisphere gyre systems and coastal upwelling-based westward flow of water from western continental margins are evident, particularly at shallower depths (upper left and right panels). Colors reflect mean EMU temperatures, with pink colors representing warmer EMUs and blue colors representing colder EMUs.
ON THE RIGHT: Vertical profile area graph with depth on Y-axis and cell count for each Cluster/area it covers on X-axis. This graph shows the cluster variety at the top of the water column and through the water column we can see how each Cluster either slowly disappears with depth or in some cases deep water clusters become more dominant. It also help illustrate how in some cases the cluster is spread across the CMECS depth terms and we may need a better data-driven depth name for the clusters. Interesting too that there are apparent depths where groups of clusters end -100 to -200m and -500 to -700m and -1400 to -1600m.
Our diagram illustrates that there is no simple clear-cut HORIZONTAL boundary for water attributes – an overlay of depth distribution on it will also be informative
The two-dimensional global area (km2) at any depth is shown for the 22 EMUs that comprise 99% of the ocean volume. The horizontal boundary lines separating the depth zone classes are as described in the Coastal and Marine Ecosystem Classification Standard (CMECS), the Federal Geographic Data Committee (FGDC) standard for the United States (FGDC, 2012). The EMU number labels are placed at the median unit-middle depth for each EMU. Although the EMUs are not uniformly distributed into the CMECS depth zones, strong vertical separation is evident, with many small EMUs in the upper water column and fewer larger EMUs in the middle and lower water columns. Pink colors indicate warmer EMUs, and blue colors indicate colder EMUs.
How do we best visualize something that is really continuous and in 3D? One way is to conceptualize the data as columnar stacks of cells whose centroids define the point mesh
In live presentation there was a VIDEO flythrough that took us up the US west coast, stopping at Monterey Canyon.
As we zoom in, cylinders will pop up, representing data points from NOAA’s World Ocean Atlas, 52 million observations over a span of 50 years about the primary physical and chemical characteristics of the oceans at 105 depth levels: in other words, the key variables that enable life throughout the ocean such as salinity, temperature, dissolved oxygen, phosphate, nitrate, silicate.
This is actually a continuous grid of data at the surface and continuous volumes at depth but we are representing the units as columns so that you can see sideways better into the layers at depth.
One major point is that nutrient and oxygen distributions in particular not only shape but ARE SHAPED by biological processes (physicochemical).
This information will be hugely significant biologically, to be able to see that over a global expanse, where it thins out, where it mixes with other water masses. This is a global framework.
Will soon start time slicing into monthly averages, OBIS has not been added to this yet, but that is in progress.
It will be exciting to be able to continually populate and improve this with data from any cruise or expedition as we go forward in time. NOAA administrator Kathryn Sullivan likens this to a christmas tree that we ALL can hang ornaments on now, and over time really come to a richer understanding of our ocean, while also helping us to understand what’s the next science data or target we should go after to make this more useful, especially for MPA designation or evaluation and CMSP.
From the main EMU web site.
Dissolved oxygen column, south of Tasmania
We integrated global ocean currents directly into the Ecological Marine Units point mesh. The data came to us courtesy of Bernard Barnier <bernard.barnier@univ-grenoble-alpes.fr> and his colleagues in the DRAKKAR consortium, a scientific and technical coordination between French research teams (LGGE-Grenoble, LPO-Brest, LOCEAN-Paris), MERCATOR-ocean, NOC Southampton, IFM-Geomar Kiel, and other teams in Europe and Canada. They have designed, assessed, and distributed high-resolution global ocean/sea-ice numerical simulations based on the NEMO platform as performed over long periods (five decades or more). The ocean currents were produced by their eddy-permitting ORCA12 model, thus far the highest resolution global realistic model of the DRAKKAR hierarchy (Barnier et al., 2006, Drakkar Group, 2014). The grid size of ORCA12 is 9.25 km at the Equator, 4 km on average in the Arctic, and up to 1.8 km in the Ross and Weddell seas. The model uses 46 vertical levels with a vertical grid spacing finer near the surface (6 m) and increasing with depth to 250 m at the bottom. The model simulation which produced the present ocean currents is part of a series of high resolution model hindcasts aimed at representing the ocean circulation from the 1960s to present (DRAKKAR Group, 2014). It was driven by the DFS4.4 atmospheric forcing that combines satellite observations with ERA40 and ERA interim analytics into a single 6-hourly forcing set for the period 1958-2012 (Dussin et al., 2016), using the methodology developed by Brodeau et al. (2010). The currents within the EMUs are the climatological mean for the period 2000-2012.
The original 1/12° resolution currents were interpolated onto the 1/4° standard resolution grid that is the basis for our EMUs.
References:
Barnier, B., G. Madec, T. Penduff, J.M. Molines, A.M. Treguier, J. Le Sommer, A. Beckmann, A. Biastoch, C. Böning, J. Dengg, C. Derval, E. Durand, S. Gulev, E. Remy, C. Talandier, S. Theetten, M. Maltrud, J. McClean, B. De Cuevas 2006: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy permitting resolution. Ocean Dynamics, 56, 543-567, DOI: 10.1007/s10236-006-0082-1.
DRAKKAR Group (, B. Barnier, A.T. Blaker, A. Biastoch, C.W. Böning, A. Coward, J. Deshayes, A. Duchez, J. Hirschi, J. Le Sommer, G. Madec, G. Maze, J. M. Molines, A. New, T. Penduff, M. Scheinert, C. Talandier, A.M. Treguier), 2014: DRAKKAR: Developing high resolution ocean components for European Earth system models. CLIVAR Exchanges Newsletter, No.64 (Vol 19, No.1).
Madec,G., 2008: NEMO ocean engine, Note du Pole de modelisation, Institut Pierre-Simon Laplace (IPSL), France, No 27 ISSN,1288–1619.
Brodeau, L., B. Barnier, A.M. Treguier, T. Penduff, S. Gulev, 2010: An ERA40-based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88-104, doi: 10.1016/j. ocemod.2009.10.005.
Dussin, R., B.Barnier and L. Brodeau, 2016. The making of Drakkar forcing set DFS5.DRAKKAR/MyOcean Report 01-04-16, LGGE, Grenoble, France.
Akima, H., 1970: A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures. J. ACM, 17 (4), 589-602, doi: 10.1145/321607.321609.
From the main EMU web site.
We can use 3D analysis to create vertical fences as a way of interpolating the water column. In this example points were measurements of oil in seawater after an oil spill with concentrations of the pollutant from the surface down to a certain depth interpolated into a ”fence” or curtain.”
This tool is now free, open and available for download at esriurl.com/3dfence.
The Parallel Fences option of the tool can generate sets of parallel fences in directions that are related to either longitudes, latitudes, or depths. The Interactive Fences tool can generate fences based on lines digitized on the map
GIS deep dive: a free ArcGIS ToolBox called “3D Fences” cuts slices through 3D point data and applies empirical bayesian kriging analysis to the slices (including error surfaces). Internally the tool uses Empirical Bayesian Kriging (EBK) to interpolate values between samples and then converts the EBK output to points for display. The tools provide an option to output either EBK Prediction or EBK Prediction Standard Error as well as options to control minimum fence dimensions, sample points and interpolation resolution. Motivated and curious python savvy users can easily change the interpolation method employed by the tools if input data warrant use of a different method or a Geostatistical Analyst license is unavailable
Fence diagrams from EMU data across the Puerto Rico Trench (salinity). This is from a demo and not yet released to the public, but coming soon.
To stay apprised of progress and to interact with the project team further, I strongly encourage you to join the Ecological Marine Units discussion group, a discussion group of the GeoNet community. If not already familiar with the GeoNet community, there are helpful guidelines at https://geonet.esri.com/docs/DOC-9165-what-is-geonet .
Regarding additional data to be added to EMUs, POC may be useful more as a validation of the clustering rather than as input for wholesale reclustering (POC data are scattered, hard to obtain from Lutz or to compile from NASA, hard to recalculate for entire global water column)
We plan on holding a kickoff meeting for the ECU component on October 31st as a sidebar meeting within the next Esri Ocean GIS Forum in Redlands – so bookmark http://www.esri.com/events/ocean .
See the new EMU story map at http://esriurl.com/emustory