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Dr. Longzhu Shen

GIS Modeller

Portrait of Dr. Longzhu Shen

GIS Modeller

Leibniz Institute of Freshwater Ecology and Inland Fisheries

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Interests

Hydrological modelling Machine learning algorithms in geo modelling Large-scale geocomputation

Education

  • PhD in Chemistry, 2013
    Carnegie Mellon University, Pittsburgh PA, USA
  • MSc in Computational Biology, 2006
    Beijing University of Technology, Beijing, China
  • BSc in Biochemistry, 2002
    Northwest University, Xi’an, China

Dr. Longzhu Shen is a GIS modeller at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB). Dr. Shen’s technical background was quantum chemistry with broad applications, especially in areas related to ecology, environment and public health. After Ph.D. training, he migrated further into machine learning oriented modellings and often crafted unique solutions via coupling the physical principles and data-driven approaches. He has built predictive models to estimate the distributions of nitrogen and phosphorus in their various forms in the streams around the globe.

Career

  • since Oct. 2020 - GIS modeller at Leibniz Institute of Freshwater Ecology and Inland Fisheries
  • 2019-2020 - Independent modelling consultant
  • 2017-2019 - Department of Zoology, University of Cambridge, UK (Postdoc)
  • 2014-2018 - School of the Environment, Yale University, USA (Postdoc)

Reviewer activities

  • Journal referee: Global Biogeochemical Cycles, Science of the Total Environment, Resources, Conservation & Recycling, Journal of Agriculture and Food Chemistry, Chemosphere, International Journal Polymer Science, Journal of Coordination Chemistry

Projects

  • Hydrography90m

Publications (3)

Long-term trends in crayfish invasions across European rivers (2023) · Science of the Total Environment, 867, 161537

DOI Authors & details

Hydrography90m: A new high-resolution global hydrographic dataset (2022) · Earth System Science Data, 14, 4525–4550

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Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework (2020) · Scientific Data, 7, 161

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© 2026 Sami Domisch. This work is licensed under CC BY NC ND 4.0

 
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