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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Computational provenance in hydrologic science: a snow mapping example.

Jeff Dozier1, James Frew

  • 1Donald Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106-5131, USA. dozier@brern.ucsb.edu

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|December 18, 2008
PubMed
Summary
This summary is machine-generated.

We developed the Earth System Science Server (ES3) to automatically track the processing history of scientific data. This system enhances the reliability and reproducibility of hydrologic science research, particularly for snow cover area estimations.

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Area of Science:

  • Earth Science
  • Hydrologic Science
  • Remote Sensing

Background:

  • Computational provenance is crucial for documenting computer-based scientific research.
  • Accurate documentation ensures research integrity and reproducibility.
  • Existing methods for tracking computational history can be cumbersome.

Purpose of the Study:

  • To develop a system for automatically capturing and managing computational provenance.
  • To support hydrologic science investigations, specifically for daily fractional snow-covered area (SCA) data.
  • To enhance the transparency and reliability of scientific data processing.

Main Methods:

  • Utilized NASA's Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance data in seven wavelengths.
  • Estimated daily fractional snow-covered area (SCA) at 500 m pixel resolution.
  • Developed the Earth System Science Server (ES3) for data management and provenance tracking.
  • Implemented interpolation and smoothing techniques to address data gaps and errors in MODIS SCA products.
  • Modeled provenance as relationships between processes and their input/output files within ES3.

Main Results:

  • Successfully produced daily fractional snow-covered area (SCA) products from MODIS data.
  • ES3 demonstrated unique capabilities for automatic and transparent provenance acquisition.
  • The ES3 system effectively managed data and computational history for evolving algorithms.
  • Transparent acquisition was achieved without requiring scientists to use specific languages or schemas.

Conclusions:

  • The Earth System Science Server (ES3) provides a robust solution for managing computational provenance in data-intensive Earth science.
  • ES3 enhances the reproducibility and trustworthiness of scientific research, particularly in hydrologic science.
  • The system's ability to handle complex, multi-component algorithms makes it highly valuable for scientific data processing.