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