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Istituto sull'inquinamento atmosferico

Torna all'elenco Contributi in rivista anno 2015

Contributo in rivista

Tipo: Abstract in rivista

Titolo: Brokering as a framework for hydrological model repeatability

Anno di pubblicazione: 2015

Formato: Elettronico

Autori: Daniel Fuka (1), Amy Collick (2), Charlotte MacAlister (3), Aaron Braeckel (4), Dawn Wright (5), Siri Jodha Khalsa (6), Enrico Boldrini (7), and Zachary Easton (1)

Affiliazioni autori: (1) Virginia Tech, USA, (2) USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, PA, USA, (3) International Research Development Center, Ottawa, ON, Canada, (4) National Center for Atmospheric Research, Research Applications Laboratory, Boulder, USA, (5) ESRI , Redlands, CA, USA, (6) National Snow and Ice Data Center, Boulder, CO, USA, (7) National Research Council of Italy (CNR), Institute on Atmospheric Pollution Research (IIA), Sesto Fiorentino (FI), Italy

Autori CNR:

  • ENRICO BOLDRINI

Lingua: inglese

Abstract: Data brokering aims to provide those in the the sciences with quick and repeatable access to data that represents physical, biological, and chemical characteristics; specifically to accelerate scientific discovery. Environmental models are useful tools to understand the behavior of hydrological systems. Unfortunately, parameterization of these hydrological models requires many different data, from different sources, and from different disciplines (e.g., atmospheric, geoscience, ecology). In basin scale hydrological modeling, the traditional procedure for model initialization starts with obtaining elevation models, land-use characterizations, soils maps, and weather data. It is often the researcher's past experience with these datasets that determines which datasets will be used in a study, and often newer, or more suitable data products will exist. An added complexity is that various science communities have differing data formats, storage protocols, and manipulation methods, which makes use by a non native user exceedingly difficult and time consuming. We demonstrate data brokering as a means to address several of these challenges. We present two test case scenarios in which researchers attempt to reproduce hydrological model results using 1) general internet based data gathering techniques, and 2) a scientific data brokering interface. We show that data brokering can increase the efficiency with which data are obtained, models are initialized, and results are analyzed. As an added benefit, it appears brokering can significantly increase the repeatability of a given study.

Pagine totali: 1

Rivista:

Geophysical research abstracts Copernicus GmbH
Paese di pubblicazione: Germania
Lingua: inglese
ISSN: 1607-7962

Stato della pubblicazione: Published version

Parole chiave:

  • brokering
  • hydrology
  • model
  • repeatability
  • GI-suite
  • GI-cat

URL: http://meetingorganizer.copernicus.org/EGU2015/EGU2015-7897-1.pdf

Altre informazioni: Data brokering aims to provide those in the the sciences with quick and repeatable access to data that represents physical, biological, and chemical characteristics; specifically to accelerate scientific discovery. Environmental models are useful tools to understand the behavior of hydrological systems. Unfortunately, parameterization of these hydrological models requires many different data, from different sources, and from different disciplines (e.g., atmospheric, geoscience, ecology). In basin scale hydrological modeling, the traditional procedure for model initialization starts with obtaining elevation models, land-use characterizations, soils maps, and weather data. It is often the researcher's past experience with these datasets that determines which datasets will be used in a study, and often newer, or more suitable data products will exist. An added complexity is that various science communities have differing data formats, storage protocols, and manipulation methods, which makes use by a non native user exceedingly difficult and time consuming. We demonstrate data brokering as a means to address several of these challenges. We present two test case scenarios in which researchers attempt to reproduce hydrological model results using 1) general internet based data gathering techniques, and 2) a scientific data brokering interface. We show that data brokering can increase the efficiency with which data are obtained, models are initialized, and results are analyzed. As an added benefit, it appears brokering can significantly increase the repeatability of a given study.

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