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Zhi Li

Government of the United States of America - Pacific Northwest National Laboratory

901 D Street

370 L'Enfant Promenade, S.W.

Washington, DC 20024-2115

United States

SCHOLARLY PAPERS

2

DOWNLOADS

123

TOTAL CITATIONS

1

Scholarly Papers (2)

1.

A Hydrogeophysical Framework to Assess Infiltration During a Simulated Ecosystem-Scale Flooding Experiment

Number of pages: 52 Posted: 07 Jun 2023
University of Toledo, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Smithsonian Environmental Research Center, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, University of Toledo, Smithsonian Environmental Research Center, Government of the United States of America - Pacific Northwest National Laboratory, Smithsonian Environmental Research Center, Government of the United States of America - Pacific Northwest National Laboratory, affiliation not provided to SSRN, Government of the United States of America - Pacific Northwest National Laboratory, affiliation not provided to SSRN and University of Toledo
Downloads 74 (844,901)

Abstract:

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Simulated flooding, Soil saturation, Time-lapse resistivity, ATS-based flow model

2.

Impact of Topography and Climate on Post-Fire Vegetation Recovery Across Different Burn Severity and Land Cover Types Through Machine Learning

Number of pages: 21 Posted: 29 Feb 2024
Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, University of Dayton and Government of the United States of America - Pacific Northwest National Laboratory
Downloads 49 (1,074,648)
Citation 1

Abstract:

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Wildfire, Remote sensing, Vegetation recovery, Machine learning