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Hydrology

Hongxiang Yan

Hongxiang Yan

Pacific Northwest National Laboratory
PO Box 999
Richland, WA 99352

Biography

Dr. Hongxiang Yan joined PNNL in 2017. Hongxiang is a hydrologist specializing in hydrologic modeling and forecasting, state-of-the-art data assimilation, uncertainty quantification, and climate change impact. He is also interested in Bayesian techniques and new developments in statistics to solve water problems. Aiming to advance hydrologic science through modeling climate-water-human interactions as a complex system to result in sustainable solutions.

Education and Credentials

  • Ph.D. (2016), Civil and Environmental Engineering, Portland State University, Portland, OR, USA
  • M.S. (2012), Civil Engineering, University of Arkansas, Fayetteville, AR, USA
  • B.E. (2010), Hydraulic Engineering, North China Electric Power University, Beijing, China

Affiliations and Professional Service

  • Member of the American Geophysical Union (AGU): 2012 to present
  • Member of the Environmental & Water Resources Institute (EWRI): 2012 to present
  • Member of the International Association of Hydrological Sciences (IAHS): 2013 to present
  • Member of the Hydrological Ensemble Prediction Experiment (HEPEX): 2014 to present

Awards and Recognitions

  • AGU-Water Resources Research Journal Cover Image, Volume 54 Issue 2 (2018)
  • Grand Prize Award, NASA-AGU Data Visualization and Storytelling Competition (2017)
  • Outstanding PHD Student Award, Department of Civil and Environmental Engineering, Portland State University (2016)
  • Student Educational Travel Award, Portland State University (2016)
  • Marie Brown Travel Award, Portland State University (2015)
  • Institute for Sustainable Solutions Travel Award, Portland State University (2014)

PNNL Publications

2019

  • Peng Y., Y. Shi, H. Yan, K. Chen, and J. Zhang. 2019. "Coincidence Risk Analysis of Floods using Multivariate Copulas: A Case Study of the Jinsha River and Min River, China." Journal of Hydrologic Engineering 24, no. 2:Article No. 05018030. PNNL-SA-127655. doi:10.1061/(ASCE)HE.1943-5584.0001744
  • Yan H., N. Sun, M.S. Wigmosta, R. Skaggs, L. Leung, A. Coleman, and Z. Hou. 2019. "Observed Spatiotemporal Changes in the Mechanisms of Extreme Water Available for Runoff in the Western United States." Geophysical Research Letters 46, no. 2:767-775. PNNL-SA-138215. doi:10.1029/2018GL080260

2018

  • Abbaszadeh P., H. Moradkhani, and H. Yan. 2018. "Enhancing Hydrologic Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo." Advances in Water Resources 111. PNNL-SA-125292. doi:10.1016/j.advwatres.2017.11.011
  • Yan H., N. Sun, M.S. Wigmosta, R. Skaggs, Z. Hou, and L. Leung. 2018. "Next-Generation Intensity-Duration-Frequency Curves for Hydrologic Design in Snow-Dominated Environments." Water Resources Research 54, no. 2:1093-1108. PNNL-SA-126849. doi:10.1002/2017WR021290

2017

  • Peng Y., K. Chen, H. Yan, and X. Yu. 2017. "Improving Flood Risk Analysis for Confluence Flooding Control Downstream Using Copula Monte Carlo Method." Journal of Hydrologic Engineering 22, no. 8:Article No. 04017018. PNNL-26189. doi:10.1061/(ASCE)HE.1943-5584.0001526
  • Yan H., H. Moradkhani, and M. Zarekarizi. 2017. "A Probabilistic Drought Forecasting Framework: A Combined Dynamical and Statistical Approach." Journal of Hydrology 548. PNNL-SA-123469. doi:10.1016/j.jhydrol.2017.03.004

Hydrology

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