Los Alamos National Laboratory

Los Alamos National Laboratory

Delivering science and technology to protect our nation and promote world stability
Find Expertise logo

Profile Pages

View homepages for scientists and researchers. Explore potential collaborations and project opportunities. Search the extensive range of capabilities by keyword to quickly find who and what you are looking for.


  • Computational Physics and Applied Mathematics
  • Numerical modeling
  • Statistics
  • Earth and Space Sciences
  • Geoscience
  • Hydrology
  • Remote sensing
  • Image analysis
  • Ecosystem dynamics
  • Information Science and Technology
  • Distributed parallel simulation capabilities
  • Machine Learning
  • Visualization
  • Science of Signatures - Remote and Standoff Sensing
  • Image analysis
  • High performance computing
  • Hyperspectral data processing algorithms and analysis tools
  • Earth and Space Sciences
  • Model calibration


My research focuses on using the interdisciplinary and ensemble geospatial and physical modeling approach for identifying and quantifying of multiscale spatiotemporal patterns in the hydrological and ecological processes. Specifically, I have been working on the connections between the variability of these patterns and changes in response mechanisms, under naturally and artificially driven factors and disturbance (e.g., change of landuse, terrain topography and climate conditions). An interdisciplinary understanding of the connections between geospatial information and physical processes can improve the reliability and predictability for current hydrological and ecological models, enable a deeper integration of large data sets (e.g., from high-resolution remote sensing or in-situ measurement), and provide important guidelines for water resources management.


2018 Purdue University, Ph.D. (Hydrology and Geospatial analysis), Earth, Atmospheric, and Planetary Sciences & Ecological Science and Engineering. Research Focus: Multiscale hydrologic/hydrodynamic processes modeling, spatiotemporal patterns and connectivity

2013 Purdue University, M.Sc. (Ecology and Geospatial analysis), Forestry and Natural Resources & Ecological Science and Engineering. Research Focus: Species distribution modeling and forecasting

2010 Beijing Forestry University, B.Sc. (Environmental Science)


LANL Positions

Current - 2019 Postdoctoral Research Associate, Earth and Environmental Sciences, Computational Earth Science, Los Alamos National Lab, Los Alamos, NM


Professional Societies

Urban and Regional Information Systems Association (URISA) 2012-present

American Association of Geographers (AAG) 2015-present

American Geophysical Union (AGU) 2018-present



2017 Cagiantas Fellowship, College of Science, Purdue University

2016 Henry Silver Graduate Scholarship and Darrel I. Leap Hydrogeology Graduate Research Fund, Department of Earth, Atmospheric and Planetary Sciences, Purdue University

2016 Lynn Fellowship, Ecological Science and Engineering graduate program, Purdue University

2014 Travel Award to ESRI User Conference, Purdue Graduate Student Government



Yu, F., Frankenberger, J., Ackerson, J., Reinhart, B. Potential suitability of subirrigation for field crops in the U.S. Midwest. Under Review at Transactions of the ASABE.

Yu, F., & Harbor, J. (2019). CSTAT+: A GPU-accelerated spatial pattern analysis algorithm for high-resolution 2D/3D hydrologic connectivity using array vectorization and convolutional neural network operators. Environmental Modeling & Software, 120: 104496. DOI: https://doi.org/10.1016/j.envsoft.2019.104496

Yu, F., & Harbor, J. (2019). The effects of topographic depressions on multiscale overland flow connectivity: a high resolution spatiotemporal pattern analysis approach based on connectivity statistics. Hydrological Processes, 33 (10), 1403-1419. DOI: https://doi.org/10.1002/hyp.13409

Zhao, X., Harbor, J., Engel, B., Theller, L., Yu, F., ... & Zhang, M. (2017). FEW: Analysis of food‐energy‐water nexus based on competitive uses of stream flows of Bei Chuan River in eastern Qing Hai‐Tibet Plateau, China. Environmental Progress & Sustainable Energy, 37(1), 62-72. DOI: https://doi.org/10.1002/ep.12764

Fei, S., & Yu, F. (2015). Quality of presence data determines species distribution model performance: a novel index to evaluate data quality. Landscape Ecology, 31(1), 31-42. DOI: https://doi.org/10.1007/s10980-015-0272-7