BASIN-3D Data Integration for Selected ARM Data Field Campaign Report
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Varadharajan, Charuleka↗
Engineering topics
Christianson, Danielle
Publications and source records attributed to Christianson, Danielle.
AmeriFlux BASE Flux/Met Data QA/QC and Processing (AMF-BASE-QAQC) v1.0.0
The AmeriFlux BASE Flux/Met Data QA/QC and Processing (AMF-BASE-QAQC) code provides tools to review and prepare continuous flux/met data submitted to the AmeriFlux Management Project for publication as the AmeriFlux BASE data product. The code provides 3 core functionalities: Format QA/QC assesses submitted data files for compliance with the required submission format; Data QA/QC assesses the data quality; BASE Publish prepares the data for publication.
Christianson, Danielle↗
ESS-DIVE Reporting Format for Location Metadata
The ESS-DIVE location metadata reporting format provides instructions and templates for reporting a minimum set of metadata for discrete point locations in geographic space represented by x, y, and z coordinates. This format was created based on a need for earth and environmental science researchers to more consistently provide metadata about locations where they conduct studies. To create the format, we incorporated elements from ESS-DIVE’s community reporting formats as well as 12 additional data standards or other data resources (e.g., databases, data systems, or repositories). In the template, we ask researchers to indicate unique locations using Location IDs and indicate hierarchies of locations through parent location IDs. We also provide additional optional fields for researchers to indicate how they measured the point location and the date and time that the location was first used as a research siteThis dataset contains support documentation for the reporting format (README.md and instructions.md), a terminology guide (guide.md), a crosswalk indicating how this reporting format relates to existing standards and data resources (Location_metadata_crosswalk.csv), a data dictionary (dd.csv), file-level metadata (flmd.csv), and the location metadata templates in both CSV (Location_metadata_template.csv) and Excel formats (Location_metadata_template.xlsx).
54 ENVIRONMENTAL SCIENCES↗
E-Field_Log Metadata, BR-Ma2: Manaus, 2016 - 2017
This data package contains the Excel file "E-Field_Log_v1-1_BR-Ma2_20190725_20190910175444," which has metadata for sap velocity, leaf temperature, leaf gas exchange, and soil water content data packages. Each data package is listed in the below field "dataset references." All data was collected from the NGEE Tropics site in Manaus, Brazil (BR-Ma2), between 24 May 2016 and 16 March 2017. This dataset replaces the E-Field metadata file of two retired packages, NGT0019 and NGT0040. This metadata file includes site details, species information, equipment used, and more. For more information about the design and use of the E-Field_Log and accompanying metadata files, see Danielle Christianson, Charuleka Varadharajan, Brad Christoffersen, Matteo Detto, Boris Faybishenko, Val Hendrix, Kolby Jardine, Robinson Negron-Juarez, Bruno Gimenez, Gilberto Pastorello, Thomas Powell, Megha Sandesh, Jeffrey Warren, Brett Wolfe, Jeff Chambers, Lara Kueppers, Nate McDowell, Deb Agarwal(2018). FRAMES Metadata Reporting Templates for Ecohydrological Observations, version 1.1. NGEE Tropics Data Collection. Accessed at http://dx.doi.org/10.15486/ngt/1419956.
54 ENVIRONMENTAL SCIENCES↗
FLUXNET-CH 4 : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
Methane (CH 4 ) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH 4 emissions due to quasi-continuous and high-temporal-resolution CH 4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH 4 Version 1.0, the first open-source global dataset of CH 4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH 4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH 4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH 4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20°S to 20°N) the spring onset of elevated CH 4 emissions starts 3 d earlier, and the CH 4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH 4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH 4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH 4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH 4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH 4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH 4 peak). FLUXNET-CH 4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH 4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH 4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
54 ENVIRONMENTAL SCIENCES↗
Observational Capabilities to Capture Water Cycle Event Dynamics and Impacts in the Age of AI
This whitepaper is responsive to focal area (1) Data acquisition and assimilation enabled by machine learning (ML), Artificial Intelligence (AI), and advanced methods. Here we describe how Earth observations specific to water cycle disturbances can be collected in parallel with and integrated into future model development, and make use of the latest technologies other than AI/ ML such as 5G/satellite, edge computing, big data technologies, and cloud computing.
58 GEOSCIENCES↗
Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative’s Workshop and Follow-On Activities
Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.
54 ENVIRONMENTAL SCIENCES↗
A library of AI-assisted FAIR water cycle and related disturbance datasets to enable model training, parameterization and validation
This whitepaper is responsive to focal area Data acquisition and assimilation enabled by machine learning, AI, and advanced methods. Here we describe how FAIR (Findable, Accessible, Reusable, Interoperable) datasets related to water cycle extremes are essential for successful implementation of ML in Earth System and other models. We also describe how AI can be used to acquire and integrate water cycle data related to extreme events to create a library of FAIR datasets for training and evaluating algorithms.
58 GEOSCIENCES↗
Using Machine Learning to Develop a Predictive Understanding of the Impacts of Extreme Water Cycle Perturbations on River Water Quality
This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.
54 ENVIRONMENTAL SCIENCES↗
Introduction to Metadata and Ontologies: Everything You Always Wanted to Know About Metadata and Ontologies (But Were Afraid to Ask)
Metadata are contextual data about your experimental data. Metadata are the who, what, when, where, and why of these data. Metadata puts these data into context. In microbiome research, metadata includes information about the sample: when it was collected, where it was collected from, what kind of sample it is, and what were the properties of the environment or experimental condition from which the sample was taken. Information about sample processing is also metadata: methods used to extract and purify molecules (e.g., DNA) from the sample, type of DNA sequencing or other ’omics analyses done, and where the raw experimental data are located. This documentation includes information on the following topics: What are metadata? What kinds of metadata are there? Why are metadata important? Why should metadata be standardized? Why should metadata be machine readable? What is an ontology? Why are ontologies important? What are some examples of ontologies? The documentation also includes further resources on metadata guides and tools and tips for formatting metadata for maximum utility.
59 BASIC BIOLOGICAL SCIENCES↗