Hongli Liu, PhD

Area of Study / Keywords

hydrology machine learning climate change

About

• PhD, Civil Engineering, Collaborative Water Program, University of Waterloo, 2019
• MSc, Environmental Science, Beijing Normal University, 2014
• BSc, Geography, Shandong Normal University, 2011

Research

Our Computational Hydrology Group (Website) aims to advance process-based hydrologic modeling, and its application in extreme event predictions (e.g., floods and droughts) and impact assessment under climate change. 

Recent Publications

• Liu, H., Clark, M.P., Gharari, S., Sheikholeslami, R., Freer, J., Knoben, W.J., Marsh, C.B. and Papalexiou, S.M., 2024. An improved copula‐based framework for efficient global sensitivity analysis. Water Resources Research, 60(1), p.e2022WR033808. https://doi.org/10.1029/2022WR033808
• Tang, G., Clark, M.P., Knoben, W.J., Liu, H., Gharari, S., Arnal, L., Beck, H.E., Wood, A.W., Newman, A.J. and Papalexiou, S.M., 2023. The impact of meteorological forcing uncertainty on hydrological modeling: A global analysis of cryosphere basins. Water Resources Research, p.e2022WR033767.  https://doi.org/10.1029/2022WR033767
• Liu, H., Wood, A.W., Newman, A.J. and Clark, M.P., 2022. Ensemble dressing of meteorological fields: using spatial regression to estimate uncertainty in deterministic gridded meteorological datasets. Journal of Hydrometeorology, 23(10), pp.1525-1543. https://doi.org/10.1175/JHM-D-21-0176.1
• Liu, H., Tolson, B.A., Newman, A.J. and Wood, A.W., 2021. Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models. Hydrological Processes, p.e14410.  https://doi.org/10.1002/hyp.14410
• Han, M., Mai, J., Tolson, B.A., Craig, J.R., Gaborit, É., Liu, H. and Lee, K., 2020. Subwatershed-based lake and river routing products for hydrologic and land surface models applied over Canada. Canadian Water Resources Journal, 45(3), pp.237-251. https://doi.org/10.1080/07011784.2020.1772116
• Liu, H., Thiboult, A., Tolson, B., Anctil, F. and Mai, J., 2019. Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset. Journal of Hydrology, 568, pp.985-996. https://doi.org/10.1016/j.jhydrol.2018.11.047
• Liu, H., Tolson, B.A., Craig, J.R. and Shafii, M., 2016. A priori discretization error metrics for distributed hydrologic modeling applications. Journal of Hydrology, 543, pp.873-891. https://doi.org/10.1016/j.jhydrol.2016.11.008

Open-source Datasets and Toolboxes

• pyVISCOUS global sensitivity analysis toolbox. link
• Parameter estimation toolbox for the Structure for Unifying Multiple Modeling Alternatives (SUMMA). link
• Ensemble Dressing of North American Land Data Assimilation version 2 (EDN2). NCAR Research Data Archive. link
• Watershed discretization toolbox. link
  

CIV E 321 - Principles of Environmental Modeling and Risk

Introduction modeling environmental processes to predict the movement of water and fate of contaminants in the hydrologic cycle. Principles of mass transfer, conservation of mass, environmental transformations, nutrient enrichment and depletion are developed. Introduction to storm events, rainfall, runoff, stream discharge and stormwater management. Applications of modeling results to the quantification of risk using examples from hydrology, water pollution and health protection and development of environmental regulations. Prerequisite: CIV E 221. Corequisite: CIV E 330.

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