GEO BluePlanet - NOAA CoastWatch - ESRI Coastal Eutrophication Index in support of Sustainable Development Goal 14.1.1

Timestamp: 44819.6683347106 Email Address: [email protected] Name: Merrie Beth Neely Affiliation: Global Science and Technology Program Office/Division: NESDIS/STAR/SOCD/NOAA CoastWatch Position Title: research scientist (contractor) Title of use case: GEO BluePlanet - NOAA CoastWatch - ESRI Coastal Eutrophication Index in support of Sustainable Development Goal 14.1.1 Authors or Creators: Neely, M and Lance, V Affiliations of Authors or Creators: GST, Inc. (Neely) and NOAA Federal (Lance) Contributors: Smail, E. and Ramachandran, S Affiliation of Contributors: GEO BluePlanet (Smail) and RIVA (Ramachandran) Description: UN Environment requested assistance developing two satellite-based ocean color indicators of coastal eutrophication. This global product covers the EEZs for all coastal nations, enabling reporting toward meeting national benchmarks for SDG 14.1.1. Keywords: coastal eutrophication, satellite, ocean color, SDG Start date of use case: 44348 End date of this use case: Is this use case ongoing? : Yes Use case URL : https://chlorophyll-esrioceans.hub.arcgis.com/apps/EsriOceans::sdg-14-1-1a-coastal-eutrophication-reporting/explore Data source URL: https://chlorophyll-esrioceans.hub.arcgis.com/apps/EsriOceans::sdg-14-1-1a-coastal-eutrophication-reporting/explore Image:https://drive.google.com/open?id=1PTFNVg0ZgKEyQ-qY7KcoV40cO0_4B3_8 Ocean Region: Global Oceans Sea: Large Marine Ecosystem Area: Country: Other Geography: Used by any country with a coastline. Format Type: Narrative description, Report, publications are pending Data Type:Data Service, GIS Raster, GIS Vector, REST API, Web Service Primary Use: Research, Education, Resource Management, Weather/Climate, Environmental Management User Type: Private Individual, Government Professional, Industry Professional, NGO/Non-Profit Professional, Academic Data Type: Biological, Geospatial Ocean Observing System (OOS) Variable: Phytoplankton biomass and diversity, Ocean color Information Type: Remote sense data Other Format Data: Published Date: Publisher Name: Publisher City: Publisher State : Publisher Country: Publisher/Distributor URL: Publication URL: DOI: Industries which benefit: Aquaculture, Marine Research and Education, Marine Related Professional and Technical Services, National Defense and Public Safety, Living resources (not specified) - check this box and elaborate in “Other” box below, SAV, Coral, Benefits to ecosystems: Ecosystem Health, Biodiversity Ecosystem Services: Fisheries (commercial or recreational, Aquaculture Ecosystem Regulation and Maintenance Services: Cultural Ecosystem Services: Direct, in-situ and outdoor interactions with living systems that depend on presence in the environmental setting, Spiritual, symbolic and other interactions with natural environment, Other biotic characteristics that have a non-use value Are benefits documented?: Unknown/Don\u27t Know Are the benefits documented by: Are the benefits quantified?: Unknown/Don\u27t Know Are the quantified benefits reported as monetary values?: Unknown/Don’t Know Other Benefits: Data Service, GIS Raster, GIS Vector, REST API, Web Servic

Critical Use of NOAA CoastWatch Great Lakes Node Remote Sensing of Sea Ice for USCG mission planning

Timestamp: 44823.3273319444 Email Address: [email protected] Name: Merrie Beth Neely Affiliation: Global Science and Technology, Inc. Program Office/Division: NESDIS/STAR/SOCD/NOAA CoastWatch Position Title: Research Scientist (contractor) Title of use case: Critical Use of NOAA CoastWatch Great Lakes Node Remote Sensing of Sea Ice for USCG mission planning Authors or Creators: Neely, M.B. and Lance, V. Affiliations of Authors or Creators: GST, Inc. (Neely) and NOAA Federal (Lance) Contributors: VanderWoude, A and Liu, S. Affiliation of Contributors: NOAA Federal (VanderWoude), CIGLR - University of Michigan (Liu) Description: USCG uses NOAA CoastWatch-supplied true-color imagery, the RADARSAT ice classification, and ice extent imagery when selecting their ice breaking route. This NOAA-supplied service is critical for the USCG and impacts the shipping traffic in the Great Lakes. Keywords: Great Lakes, sea ice, shipping, transport, commercial, remote sensing Start date of use case: 43647 End date of this use case: Is this use case ongoing? : Yes Use case URL : Data source URL: https://coastwatch.glerl.noaa.gov/ice.html Image: Ocean Region: Great Lakes Sea: Large Marine Ecosystem Area: Country: Other Geography: Format Type: Narrative description Data Type:Tabular Data, Data Service, GIS Raster, GIS Vector, REST API, Web Service Primary Use: Public Safety/Law Enforcement, Weather/Climate, Commercial Fishing, Commercial Shipping, Operations User Type: Government Professional, Industry Professional Data Type: Physical, Geospatial, Safety Ocean Observing System (OOS) Variable: Sea ice, Sea surface ice, Ocean color Information Type: In situ data, Remote sense data, Model output Other Format Data: Published Date: Publisher Name: Publisher City: Publisher State : Publisher Country: Publisher/Distributor URL: Publication URL: DOI: Industries which benefit: National Defense and Public Safety Benefits to ecosystems: Ecosystem Services: Regulation and Maintenance Services, Fisheries (commercial or recreational Ecosystem Regulation and Maintenance Services: Cultural Ecosystem Services: Are benefits documented?: Are the benefits documented by: Are the benefits quantified?: Unknown/Don\u27t Know Are the quantified benefits reported as monetary values?: Other Benefits: Tabular Data, Data Service, GIS Raster, GIS Vector, REST API, Web Servic

OOI Biogeochemical Sensor Data: Best Practices and User Guide. Version 1.0.0.

The OOI Biogeochemical Sensor Data Best Practices and User Guide is intended to provide current and prospective users of data generated by biogeochemical sensors deployed on the Ocean Observatories Initiative (OOI) arrays with the information and guidance needed for them to ensure that the data is science-ready. This guide is aimed at researchers with an interest or some experience in ocean biogeochemical processes. We expect that users of this guide will have some background in oceanography, however we do not assume any prior experience working with biogeochemical sensors or their data. While initially envisioned as a “cookbook” for end users seeking to work with OOI biogeochemical (BGC) sensor data, our Working Group and Beta Testers realized that the processing required to meet the specific needs of all end users across a wide range of potential scientific applications and combinations of OOI BGC data from different sensors and platforms couldn’t be synthesized into a single “recipe”. We therefore provide here the background information and principles needed for the end user to successfully identify and understand all the available “ingredients” (data), the types of “cooking” (end user processing) that are recommended to prepare them, and a few sample “recipes” (worked examples) to support end users in developing their own “recipes” consistent with the best practices presented here. This is not intended to be an exhaustive guide to each of these sensors, but rather a synthesis of the key information to support OOI BGC sensor data users in preparing science-ready data products. In instances when more in-depth information might be helpful, references and links have been provided both within each chapter and in the Appendix

Integrating inland and coastal water quality data for actionable knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.Additional co-authors: Anders Knudby, Camille Minaudo, Nima Pahlevan, Ils Reusen, Kevin C. Rose, John Schalles and Maria Tzortzio

Benthic Microalgae and Nutrient Flux in Florida Bay, USA

The objective of this study was to address the relationship between benthic microalgal communities and the phosphate nutrient dynamics of Florida Bay sediments and how they relate to benthic and water column primary production. In situ phosphate (P) flux between the sediment and the water column was measured in three regions of Florida Bay. Differences in the ratio of inorganic to organic phosphate flux were found between the three regions in relation to the amount of phosphate measured in the water column. Based upon the average sediment flux in my study, more than 1600 metric tons of P would be supplied by the sediment per year in Florida Bay. Based upon my measurements, dissolved nutrient flux from the sediment can be an important contribution to pelagic phytoplankton blooms in Florida Bay, accounting for 6.5 - 41% of demand and TDN accounts for 100% of the N demand. My findings were similar to others for both benthic nutrient flux and benthic microalgal chlorophyll a concentration. Benthic microalgae in Florida Bay contribute 700 kg Chl a per day to the system. Mesocosm experiments demonstrated that benthic microalgae and water column phytoplankton can respond differently to changes in nutrient availability. The dissolved nutrient in least supply in the water column does not necessarily correspond to the limiting nutrient for benthic microalgae. ³³P acted as a tracer between sediment and water column dissolved P pools. The presence of benthic microalgae enhanced the transport of ³³P to the water column as compared to simple Fickian diffusion. This was supported by the positive flux of dissolved P from the sediment to the water column pools in control treatments with a living benthic microalgal layer. Primary production by benthic microalgae were measured using dissolved O2 evolution and PAM fluorometry. Primary production for BMA habitat in Florida Bay was between 400 and 800 tons of C per day, based upon O2 production and PAM fluorometry, respectively

Florida\u27s Black Water Event

In January 2002, fishermen first noticed dark, discolored water in the southeastern Gulf of Mexico near Florida’s Marquesas Islands, which they called “black water.” The accumulated evidence suggests the dark water was caused by a series of algal blooms, from red tide to diatoms, which were supported by both marine and estuarine sources of nutrients. The passage of fewer fronts during the winter of 2001–2002, combined with local circulation patterns and heavy rainfall, contributed to the formation of this expansive bloom that persisted for many months

Florida\u27s Black Water Event

In January 2002, fishermen first noticed dark, discolored water in the southeastern Gulf of Mexico near Florida’s Marquesas Islands, which they called “black water.” The accumulated evidence suggests the dark water was caused by a series of algal blooms, from red tide to diatoms, which were supported by both marine and estuarine sources of nutrients. The passage of fewer fronts during the winter of 2001–2002, combined with local circulation patterns and heavy rainfall, contributed to the formation of this expansive bloom that persisted for many months

Integrating inland and coastal water quality data for actionable knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.Mathematical Physic

Integrating inland and coastal water quality data for actionable knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision makin