BASIN-3D: A brokering framework to integrate diverse environmental data. (February 2022)
- Record Type:
- Journal Article
- Title:
- BASIN-3D: A brokering framework to integrate diverse environmental data. (February 2022)
- Main Title:
- BASIN-3D: A brokering framework to integrate diverse environmental data
- Authors:
- Varadharajan, Charuleka
Hendrix, Valerie C.
Christianson, Danielle S.
Burrus, Madison
Wong, Catherine
Hubbard, Susan S.
Agarwal, Deborah A. - Abstract:
- Abstract: Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can beAbstract: Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies. Highlights: BASIN-3D is a Python-based software tool to integrate diverse, distributed environmental data based on on-demand queries. Synthesized timeseries at watershed to regional scales for two DOE applications. Extensible to diverse data types using concepts from OGC/ISO and FGDC standards. Applicable to environmental field and modeling studies requiring data integration. … (more)
- Is Part Of:
- Computers & geosciences. Volume 159(2022)
- Journal:
- Computers & geosciences
- Issue:
- Volume 159(2022)
- Issue Display:
- Volume 159, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 159
- Issue:
- 2022
- Issue Sort Value:
- 2022-0159-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-02
- Subjects:
- Data integration -- Multiscale diverse data -- Synthesis -- Environmental data
Environmental policy -- Periodicals
550.5 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00983004 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cageo.2021.105024 ↗
- Languages:
- English
- ISSNs:
- 0098-3004
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.695000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26848.xml