Journal Articles

  • 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/10.1029/2022WR033808

  • 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, 35(11), 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

  • Wang, H., Liu, H., Wang, C., Bai, Y. and Fan, L., 2019. A study of industrial relative water use efficiency of Beijing: an application of data envelopment analysis. Water Policy, 21(2), pp.326-343. https://doi.org/10.2166/wp.2019.019

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

Selected Presentations

  • Liu, H., Clark, M., Tang, G., Knoben, W., Gharari, S., Freer, J., Arnal, L. and Casson, D., 2023. Sensitivity analysis of the SUMMA model on the global scale. EGU General Assembly 2023, Vienna, Austria.

  • Liu, H., 2023. Improving data uncertainty handling in hydrologic modeling and forecasting applications. Water Resources and Environmental Webinars 2023, University of Chile, Chile (online).

  • Liu, H., Clark, M.P., Tang, G., Knoben, W.J.M., Gharari, S., Freer, J., Arnal, L., Casson, D., 2022. A new spin on global sensitivity analysis: Sensitivity analysis of the SUMMA model on the global scale. AGU Fall Meeting 2022, Chicago, United States of America.

  • Liu, H., Gharari, S., Freer, J., Whitfield, P.H., Clark, M.P., Yeheyis, M., Yan, X., Pietroniro, A., Stadnyk, T. and Pomeroy, J.W., 2022. The anatomy and uncertainty of a Canadian national river gauging network. CWRA 2022 National Conference, Banff, Canada.

  • Liu, H., Clark, M.P., Freer, J., Tang, G., Knoben, W.J.M., Anrnal, L., Gharari, S., Casson, D., 2021. A new spin on global sensitivity analysis: sensitivity analysis of the global SUMMA model. AGU Fall Meeting 2021, United States of America (online).

  • Liu, H., Wood, A.W., Newman, A.J., Clark, M.P., 2020. Quantifying uncertainty in deterministic observed meteorological datasets: A case study applied to large-domain gridded NLDAS-2 daily precipitation and temperature fields. AGU Fall Meeting 2020, United States of America (online).

  • Liu, H., Wood, A.W., Broman, D., Bearup L., Lanini J., 2020. Assessing the influence of low-elevation snowmelt in streamflow forecasts in the Bighorn River basin. AMS Fall Meeting 2020, United States of America (online).

  • Liu, H., Brown, G., Craig, J., Tolson, B.A., Newman, A.J., Wood A., 2019. Discretization strategies for distributed models of mountainous watersheds. AGU Fall Meeting 2019, San Francisco, United States of America.

  • Liu, H., Tolson, B.A., 2019. Using calibration period ensembles of climate and flow data to optimize the characterization of hydrologic model prediction uncertainty. EGU General Assembly 2019, Vienna, Austria.

  • Liu, H., Tolson, B.A., 2018. Explicitly accounting for climate and flow data uncertainty in hydrologic model calibration. CGU, CSSS and CIG Joint Annual Meeting 2018, Niagara falls, Canada.

  • Liu, H., Thiboult, A., Tolson, B.A., Anctil, F., Mai, J., 2018. Efficient treatment of climate data uncertainty in ensemble Kalman filter based on an existing historical climate ensemble dataset. CGU, CSSS and CIG Joint Annual Meeting 2018, Niagara falls, Canada.

  • Liu, H., Tolson, B. A., 2017. An improved model calibration framework by incorporating data uncertainty. AGU Fall Meeting 2017, New Orleans, United States of America.