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We continued our exploration of 2019's CDI funded projects in April's monthly meeting with presentations on the Climate Scenarios Toolbox, developing cloud computing capability for camera image velocity gaging, and integrating environmental DNA (eDNA) data into the USGS Nonindigenous Aquatic Species database. 

For more information, questions and answers from the presentation, and a recording of the meeting, please visit the CDI wiki. 

Collection of photos of people collaborating around climate scenarios and adaptation planning graphs.

Open-source and open-workflow Climate Scenarios Toolbox for adaptation planning 

Aparna Bamzai-Dodson, USGS, presented on the Climate Scenarios Toolbox (now renamed to the Climate Futures Toolbox!), an open-source tool that helps users formulate future climate scenarios for adaption planning. Scenario planning is a way to consider the range of possible outcomes by using projections based on climate data to develop usually 3-5 plausible divergent future scenarios (ex: hot and dry; moderately hot with no precipitation change; and warm and wet). Resource managers and scientists can use these scenarios to help predict the effects of climate change and attempt to select appropriate adaptation strategies. However, climate projection data can be difficult to work with in areas of discovery, access, and usage, involving multiple global climate model repositories, downscaling techniques, and file formats. The Climate Futures Toolbox aims to take the pain out of working with climate data. 

The creators of the Toolbox wanted a way to make working with climate data easier by lowering the barrier to entry, automating common tasks, and reducing the potential for errors. The Climate Futures Toolbox uses a seamless R code workflow to ingest historic and projected climate data and generate summary statistics and customizable graphics. Users are able to contribute open code to the Toolbox as well, building on its existing capabilities and empowering a larger user community. The Climate Futures Toolbox was created in collaboration with University of Colorado-Boulder's Earth Lab, the U.S. Fish and Wildlife Service, and the National Park Service. 

CDI members are encourage to become engaged in the Toolbox by installing and using it, providing feedback on issues, and contributing code to the package. Since April's monthly meeting, the project has developed and undergone renaming, so this is a rapidly evolving endeavor. 

Workflow for getting streamflow data into a cloud computing system.

Develop Cloud Computing Capability at Streamgages using Amazon Web Services GreenGrass IoT Framework for Camera Image Velocity Gaging 

Frank Engel at the USGS Texas Water Science Center presented next on a CDI project involving non-contact stream gaging within a cloud computing framework. 

Measuring stream flow is an important aspect of USGS' work in the Water Mission Area, and stream gaging, a way to measure water quantity, is a technique with which many scientists are familiar. However, it is sometimes difficult to obtain measurements with traditional stream gaging, like at times of flooding, or when measurement points are unsafe or unreachable. Additionally, post flood measurement methods can often be expensive and not as accurate. 

To get around these issues, scientists have developed non-contact methods with which to measure water quantity. For example, cameras are utilized to view a flooding river, which can produce a velocity measurement after processing and other analysis steps. This is a complicated method and requires many steps and extensive training. Thus, the goal of this project is to make this process work automatically utilizing cloud computing and IoT. 

The first step required building a cloud infrastructure, with the help of Cloud Hosting Solutions (CHS). This involves connecting the edge computing (camera and raspberry PI footage of a stream) to an Amazon Web Services (AWS) IoT system and depositing camera footage and derivative products into a S3 bucket. The code for this portion of the product is in a preliminary GitLab repository that is projected to be published as a part of the long-term project. The team is also still working toward building the infrastructure through to data serving and dissemination. 

Other successes accomplished with this project so far include auto-provisioning (transmitting location and metadata) of edge computing systems to the cloud; establishing global actions (data is transmitted to the cloud framework and can roll into automated processing, like extracting video into frames); and building automated time-lapse computation. 

Engel and the project team have taken away a couple lessons from their experience with this project: first, cloud computing knowledge takes a lot of work and time to acquire, and second, in the short term, It can be difficult to establish a scope that encompasses the needs and wants of all stakeholders. 

List of steps taken in integrating eDNA data into the Nonindigenous Aquatic Species Database

Establishing standards and integrating environmental DNA (eDNA) data into the USGS Nonindigenous Aquatic Species database 

Jason Ferrante with the Wetland and Aquatic Research Center discussed his team's project on establishing standards for eDNA data in the USGS Nonindigenous Aquatic Species database (NAS). 

eDNA is genetic material released by an organism into its environment, such as skin, blood, saliva, feces. By collecting water, soil, and air samples, scientists can detect the presence of a species with eDNA. Ferrante's project aims to combine the traditional specimen sightings already available in the NAS with eDNA detections for a more complete distribution record and improved response time to new invasions. 

There is currently a need for an open, centralized eDNA database. eDNA data is currently scattered among manuscripts and reports, and thus not easily retrievable via web searches. Additionally, there are no databases dedicated to Aquatic Invasive Species (AIS), which are the species of interest for this project. A centralized, national AIS viewer will allow vetting and integration of data from federal, academic, and other sources, increase data accessibility, and improve coordination of research and management activities. 

In order to successfully create a centralized AIS viewer, community standards need to be established so that data can be checked for quality and validity, especially within the FAIR data framework (Findable, Accessible, Interoperable, and Reusable). To establish community standards and successfully integrate eDNA into NAS, the project team accomplished several objectives: 

1) Experimental Standards 

  • Collating best standards and practices for sampling design and collection, laboratory processing, and data analysis, in an eDNA literature review. 

2) Stakeholder Backing 

  • Gathered a group of five other prominent/active eDNA researchers within DOI to discuss standards and vetting process 
  • Teleconferences to gain consensus 
  • Plan to produce a white paper 

3) Integration into NAS 

  • Pre-submission form about eDNA scientists' design and methodology in order to vet data 
  • Prototype web viewer (see meeting recording for more; must be logged into CDI wiki) 

Some challenges faced during the project included gaining consensus on the questions for the pre-submission form; staying organized and in communication; and meeting the needs of managers and researchers. Ferrante and the project team would love to follow up with CDI for help developing new tools which use eDNA data across databases to inform management; and providing feedback on an upcoming manuscript about the project's process. 

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