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Predicting Water Cycle Characteristics from Percolation Theory and Observational Data.
Hunt, Allen; Faybishenko, Boris; Ghanbarian, Behzad; Egli, Markus; Yu, Fang.
Affiliation
  • Hunt A; Department of Physics and Department of Earth & Environmental Sciences, Wright State University, 3640 Colonel Glenn Highway, Dayton, OH 45435, USA.
  • Faybishenko B; Energy Geosciences Division, E. O. Lawrence Berkeley National Laboratory, University of California, 1 Cyclotron Rd., Berkeley, CA 94720, USA.
  • Ghanbarian B; Porous Media Research Lab, Department of Geology, Kansas State University, Manhattan, KS 66506, USA.
  • Egli M; Department of Geography, University of Zürich, 8057 Zürich, Switzerland.
  • Yu F; Department of Forestry, Beihua University, 3999 Binjiangdong Road, Jilin 132013, China.
Int J Environ Res Public Health ; 17(3)2020 01 23.
Article in En | MEDLINE | ID: mdl-31979264
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Groundwater / Water Cycle Type of study: Guideline / Prognostic_studies / Qualitative_research / Risk_factors_studies Language: En Journal: Int J Environ Res Public Health Year: 2020 Document type: Article Affiliation country: United States Country of publication: Switzerland
Fulltext
Add to My VHL
Print
XML
PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Groundwater / Water Cycle Type of study: Guideline / Prognostic_studies / Qualitative_research / Risk_factors_studies Language: En Journal: Int J Environ Res Public Health Year: 2020 Document type: Article Affiliation country: United States Country of publication: Switzerland